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Diagnosis of headwater sediment dynamics in Nepal’s middle mountains: implications for land management Carver, Martin 1997

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D I A G N O S I S O F H E A D W A T E R S E D I M E N T D Y N A M I C S I N N E P A L ' S M I D D L E M O U N T A I N S : I M P L I C A T I O N S F O R L A N D M A N A G E M E N T by Martin Carver B.Sc, University of Manitoba, 1984 M.A.Sc., University of Waterloo, 1988 A T H E S I S S U B M I T T E D I N P A R T I A L F U L F I L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F D O C T O R O F P H I L O S O P H Y I N T H E F A C U L T Y O F G R A D U A T E S T U D I E S (Interdisciplinary Studies in Resource Management Science) We accept this thesis as conforming to the required standard The University of British Columbia June, 1997 ® Martin Carver, 1997 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. The University of British Columbia Vancouver, Canada Date DE-6 (2/88) 11 Diagnosis of Headwater Sediment Dynamics in Nepal's Middle Mountains: Implications for Land Management Abstract An evaluation of headwater erosion and sediment dynamics was carried out to assess the health of the Middle Mountain agricultural system in Nepal. Controversial statements predicting this system's imminent demise and identifying Middle Mountain farming practices as major contributors to downstream sedimentation and flooding have long been promoted and have suggested the following research hypothesis: soil and sediment dynamics and the indigenous management techniques within headwater Middle Mountains basins do not indicate a deterioration in the health of the agricultural system. Three questions were addressed in this research. What are the main controls on normal-regime erosion? How effective is the system of indigenous management at modifying sediment dynamics? What do headwater sediment budgets (erosion, storage, and yield) reveal about the health of the agricultural system? Answers to these questions are suggested and development initiatives proposed. Intensive monitoring was carried out during 1992-1994 within nested basins ranging in size from 72 to 11 141 ha. Variation of storm-period variables in time and space was assessed using five recording rain gauges and a network of up to fifty 24-hour gauges. Surface erosion was measured from five erosion plots on steep bari (rainfed cultivated land). Suspended sediment behaviour was examined through event sampling at seven hydrometric stations. Basin sediment yield was determined for three of these nested basins. Sediment storage was assessed using accumulation pins in khet fields (irrigated cultivated land), khet canals, and bari ditches and through erosion and channel surveys. An annual average of 77 storms were identified over the three-year period with 3.5% of these delivering more than 30 mm total rainfall and a peak 10-minute rainfall intensity of more than 50 mm/h. About 1/3 of all storms regardless of magnitude occurred during the pre-monsoon season. Pre-monsoon and monsoon storms delivered equivalent high-intensity short-term rainfall disputing the hypothesis that it is a higher rainfall intensity in the pre-monsoon season which causes an elevated sediment regime Ill during that season. Total storm rainfall was significantly higher during the monsoon season whereas the period without rain before a storm begins was longer for pre-monsoon storms. The source of suspended sediment was found to vary with season and spatial scale. During the pre-monsoon season, surface erosion from bari was severe when high-intensity rain fell on bare ground. Indigenous farming practices were found to be effective at limiting surface erosion except during the pre-monsoon season when targeted intervention may be useful. During the pre-monsoon season, nutrient loss from headwater basins due to sediment export was at its highest. Severely degraded land remained bare throughout the rainy season, producing sediment at an elevated rate and in relation to total rainfall. The onset of the monsoon season reduced this bari source markedly due to the complete development of a vegetative cover under conventional management. The pre-monsoon-season surface-erosion mechanism of sediment production was replaced with scale-dependent mechanisms resulting from the higher total rainfall of monsoon-season storms. Within the steep terraced hillslopes, the capacity of runoff ditches was more often exceeded resulting in episodic-regime rilling, gullying, and in some instances, terrace failure. When sufficiently heavy and widespread, monsoon storm rainfall led also to stream discharge high enough to damage riparian areas and the system of irrigation dams. The farmers alter the sediment regimes profoundly and their management activities reduce soil loss collectively over all spatial scales. Sediment budgets reveal that a significant component of the sediment produced in the study basin (5.3 km2) was recaptured (35% to 50%) because of these indigenous farming practices. Objective calibration of indigenous knowledge showed it to be well founded but inconsistent. Farmers practise techniques which are well adapted to this environment reflecting their stated receptiveness to innovation and outside support. The detailed measurements show that the important controls on erosion are variable temporally and spatially over scales too small to be considered by conventional monitoring programs in these environments. Spatial differences in rainfall delivery, hysteresis effects, variability in land-surface response, and management activities conspire to yield sediment dynamics which are difficult or impossible iv to quantify with typical limited monitoring. Site-specific opportunities for investigation should be exploited and a high degree of uncertainty be anticipated. Management recommendations focus on two topics. An improved vegetative cover during the pre-monsoon season is required to reduce soil erosion during that period. Greater retention of these nutrient-rich soils would directly benefit the upland farmer. Rehabilitation of degraded lands and the halting or reversing of further degradation would benefit all farmers by providing a greater land base for biomass production especially in light of an increasing population. Both strategies would benefit hydropower developments by limiting reservoir sedimentation. Above all, proposed changes should enhance - not undermine - indigenous management. Current soil dynamics may be sustainable but it is unlikely that they can remain so in the future under the increased landuse intensification that may be necessary with projected population increases unless support is provided strategically from outside sources. Working with the farmers to develop techniques to improve their ability to recapture previously-eroded soil is a useful area of applied research. The high degree of skill and adaptability of the farmers within this environment suggest that carefully designed intervention which targets vulnerable aspects of the agricultural system while not undermining the present methods have a reasonable likelihood for success. V Diagnosis of Headwater Sediment Dynamics in Nepal's Middle Mountains: Implications for Land Management TABLE OF CONTENTS Abstract u Table of Contents v List of Figures * x List of Tables x i v List of Abbreviations x v ' u List of Symbols x * x Acknowledgements x x * Foreword x x n PART I Biophysical Analyses l 1. General Introduction 1 1.1 Problem statement 1 1.2 Research context 2 1.3 Goals and Objectives 5 2. Study Area 8 2.1 Jhikhu River basin 8 2.1.1 Location and physiography 8 2.1.2 Farming system and landuse 10 2.1.3 Regional climate 11 2.1.4 Local climate 12 2.2 Study catchments 19 3. Methods 2 6 3.1 Field methods 26 3.1.1 Climate 26 3.1.2 Stream measurements 31 3.1.3 Erosion plots 32 3.1.4 Soil properties 33 3.1.5 Soil movement 34 3.1.6 Mapping 36 3.1.7 Interviews and questionnaires 38 3.2 Laboratory methods 39 3.2.1 Stream sediment samples 39 3.2.2 Stream water samples 40 3.2.3 Soil samples 40 3.3 Data Synthesis 4 1 3.3.1 Stage-discharge relations 41 3.3.2 Automated data 42 3.3.3 Tipping-bucket rainfall data 42 3.3.4 Geographic Information System 43 vi 4. Characterising and Monitoring Monsoonal Rainfall for Studying Erosion and Sedimentation 45 4.1 Introduction 45 4.2 Research background 47 4.2.1 Patterns of rainfall delivery 47 4.2.2 The Asian monsoon . 51 4.3 Definitions 54 4.3.1 Storm 54 4.3.2 Storm-period variables 57 4.4 Storm-period variables 58 4.4.1 Distributions 59 4.4.2 Seasonal distributions 62 4.4.3 Event ranking 64 4.5 Spatial variation 68 4.5.1 Elevation 68 4.5.2 Storm cell 71 4.6 Integration 82 4.6.1 Classification . . 82 4.6.2 Storm frequency 85 4.7 Summary and conclusions 86 4.7.1 Summary of quantitative findings 87 4.7.2 Conclusions 90 5. Diagnosing Headwater Controls on Erosion and Sediment Transport 92 5.1 Introduction 92 5.2 Research background 93 5.2.1 Behaviour of fine-sediment erosion and transport 93 5.2.2 Quantitative Himalayan data 103 5.3 Surface erosion on cultivated rainfed uplands 107 5.3.1 Controlling factors 108 5.3.2 Erosion plots: annual regimes 108 5.3.3 Erosion plots: seasonal regimes 110 5.3.4 Erosion plots: event regimes 114 5.4 Stream sediment regimes 119 5.4.1 Controlling factors • • 120 5.4.2 Seasonal regimes 120 5.5 Landuse signatures 134 5.5.1 Surface degradation: comparison of two basins 136 5.5.2 Surface degradation: within Lower Andheri basin 139 5.5.3 Sediment storage by water diversion for irrigation 146 5.6 Conclusions 151 VII 6. Signatures of Erosional Sources and Sediment-Transport Behaviour in the Physical and Chemical Character and the Patterns of Movement of Suspended Sediments 153 6.1 Introduction 153 6.2 Research background 153 6.2.1 Hysteresis 154 6.2.2 Particle-size behaviour 158 6.2.3 Fingerprinting 164 6.3 Hysteresis 168 6.3.1 Single events 169 6.3.2 Multiple events 177 6.3.3 Implications 180 6.4 Entrainment and transport behaviour by particle-size class 181 6.4.1 Controlling factors 181 6.4.2 Seasonal regimes 184 6.4.3 Hysteresis 202 6.5 Fingerprints of suspended sediment 208 6.5.1 Sediment properties 208 6.5.2 P-Q relations 212 6.5.3 Implications 224 6.6 Conclusions 228 PART II Management and Implications 231 7. The Influence of Indigenous Management on Sediment Dynamics in the Middle Mountains of Nepal 231 7.1 Introduction 231 7.2 Research background 231 7.2.1 Indigenous knowledge 232 7.2.2 Indigenous management 235 7.3 Environmental perceptions and system of soil classification 236 7.3.1 Farmer attitudes and perceptions 237 7.3.2 Soil classification 240 7.3.3 Significance 245 7.4 Techniques of water management and erosion control 246 7.4.1 Description of some techniques observed in the Jhikhu basin 246 7.4.2 Irrigated lands 249 7.4.3 Rainfed lands 253 7.4.4 Significance 255 7.5 Implications of quantitative study for indigenous management 255 7.6 Conclusions • 256 viii 8. Sediment Budgets: Implications for Landuse Management 259 8.1 Introduction 259 8.2 Research background 259 8.2.1 The sediment-budget technique 260 8.2.2 Sediment-yield calculation methods 266 8.2.3 Case studies 269 8.3 Sediment sources and pathways 274 8.4 Components of sediment budget 277 8.4.1 Normal-regime behaviour 277 8.4.2 Episodic sediment production 289 8.5 Sediment budgets in space and time 295 8.5.1 Detailed basin sediment budgets 296 8.5.2 Basin sediment production and delivery across temporal scales 298 8.5.3 Three-year sediment production and yield across spatial scales 304 8.6 Implications for nutrient loss 309 8.7 Conclusions and recommendations 311 8.7.1 Conclusions 311 8.7.2 Recommendations 313 9. General Discussion, Conclusions, and Recommendations 316 9.1 Conclusions 316 9.2 General discussion 319 9.3 Recommendations 324 9.3.1 Farming system 324 9.3.2 Monitoring 326 9.3.3 Further research 327 9.4 Postscript 328 Appendices 329 Al. Photographs of the Study Area 329 A2. Descriptive Rainfall Statistics of Study Area 331 A3. Location and Performance Information of Rain Gauges 334 A3.1 Location 334 A3.2 Performance characteristics 336 A4. Questionnaires, Interviews, and Site Description for Indigenous Knowledge 339 A4.1 Perceptions, attitudes, and approaches 339 A4.2 Soil classification (1992) 339 A4.3 A/ier-accumulation management 340 A4.4 Soil classification (1992) 341 A4.5 5ari-erosion management 342 A4.6 Irrigation-dam management 343 A4.7 Site descriptions of soils described by farmers (1992 & 193 interviews) . . 344 A5. Stage-Discharge Relations 345 A6. 1992 Survey of Mass Wasting 350 Literature Cited 353 ix LIST OF FIGURES Figure 2.1 Location of the Jhikhu River basin within Nepal 9 Figure 2.2 Mean, maximum, and minimum monthly rainfall at (a) Kathmandu airport (HMG Records 1968-1990) and (b) Panchkhal (HMG Records 1978-1994;, means based on 1978-1985 and 1988-1994) 13 Figure 2.3 Mean, maximum, and minimum monthly rainfall at (a) Baluwa (1992-1994; means based on 1993-1994), (b) Bela (1990-1994), and (c) Dhulikhel (1990-1994) 15 Figure 2.4 Mean monthly rainfall (a) and maximum 24-hour rainfall (b) at Kathmandu Airport (1968-1990), Panchkhal (1978-1985 and 1988-1994), Baluwa (1993-1994), Bela (1990-1994), and Dhulikhel (1990-1994) 16 Figure 2.5 Mean-monthly and extreme-monthly maximum/minimum temperatures at (a) three low-elevation sites (Panchkhal, 1978-1994; Baluwa, 1993-1994; Bhimsenthan, 1993-1994) and (b) three high-elevation sites (Kathmandu airport 1968-1990;, Bela, 1990-1994; Bhetwaltok, 1993-1994) 18 Figure 2.6 Locations of the nested catchments, the hydrometric stations, and the erosion plots 20 Figure 2.7 Elevational profiles of the study streams (vertical scale is exaggerated by 10 times) 23 Figure 3.1 Lay-out of monitoring network for rainfall and temperature within the Jhikhu River basin 27 Figure 4.1 Number of storms in relation to (a) generalised minimum-time-without rain, and (b) specific minimum-time-without-rain at the five study locations 56 Figure 4.2 Cumulative frequency distributions of storm-period variables (1992-1994) at the five study locations: (a) maximum 10-minute intensity (110), (b) maximum 60-minute intensity 0«>)> (c) total rainfall (RTOT)> (d) duration (TDUR), (e) start of maximum 10-minute rainfall intensity (T10), (f) start of maximum 60-minute rainfall intensity (T10), and (g) period without rain before storm start (S) 60 Figure 4.3 Ranking of largest monitored storms in upland and lowland of Andheri basin by (a) I10 and (b) RT 65 Figure 4.4 Ranking of monitored floods at (a) Lower Andheri, (b) Kukhuri, and (c) Jhikhu stations 67 Figure 4.5 Effect of elevation on rainy-season rainfall at eight monitoring stations distributed across the Jhikhu River basin (100 km2; 1992-1994): (a) June-September, (b) June, (c) July, (d) August, and (e) September . . 69 Figure 4.6 Effect of elevation on rainy-season rainfall at 20 monitoring stations located on the Andheri basin hillslope (10 km2; 1992-1994): (a) June-September, (b) June, (c) July, (d) August, (e) September 70 X Figure 4.7 Effect of elevation on total rainfall at 18 monitoring stations located on the south-facing hillslope (10 km2; 1992): (a) July - only 7 data available, (b) August, and (c) September 72 Figure 4.8 Rainfall isolines for storm rainfall over Andheri basin for (a) an upland event during the transition season which was also the heaviest event of the study period and (b) a basin event typical of the monsoon season 77 Figure 4.9 Rainfall isolines for storm rainfall over Andheri basin for upland events during the (a) pre-monsoon season and (b) monsoon season 78 Figure 4.10 Rainfall isolines for storm rainfall over Andheri basin for lowland events during the (a) pre-monsoon season and (b) monsoon season 79 Figure 4.11 Number of rainfall monitoring sites required in relation to the relative error for a range in coefficient of variation 81 Figure 5.1 Soil loss from the erosion plots on an event basis, 1992-1994 112 Figure 5.2 Average maximum maize height (a) and maize leaf length (b) at all erosion plots in 1994 113 Figure 5.3 The effect of maximum 10-minute rainfall intensity (J10) on soil loss at all erosion plots, seasonally stratified for all events of 1992-1994 115 Figure 5.4 The effect of maximum 10-minute rainfall intensity flio) on event runoff coefficient ( C R ) at all erosion plots, seasonally stratified for all events of 1992-1994 117 Figure 5.5 Relation between runoff coefficient ( C R ) and event soil loss at all erosion plots, seasonally stratified for all events of 1992-1994 118 Figure 5.6 Sediment rating curves for monitored hydrometric stations based on entire data set (1992-1994): (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) Dhap, and (e) Jhikhu 121 Figure 5.7 Seasonally-stratified sediment rating curves based on all entire data set: (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) Dhap, and (e) Jhikhu 126 Figure 5.8 Seasonal variation in a, and bf with basin area ( C = A Q B ) 128 Figure 5.9 Seasonal sediment rating curves overlain for all study basins showing both the expected functional relations and envelopes representing confidence limits to these relations (at 90%): a) monsoon regime b) pre-monsoon regime 131 Figure 5.10 Suspended sediment data of the transition season and the seasonal regression regimes based on the entire data set: (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) Dhap, and (e) Jhikhu 133 Figure 5.11 Seasonally-stratified suspended-sediment data for the mid-reach Andheri River station based on 1993-1994 data: (a) mid-reach #1 (11), and (b) mid-reach #2 (12) . . . . 135 xi Figure 5.12 Seasonal sediment rating curves for Lower Andheri and Dhap basins overlaid for comparison 137 Figure 5.13 Sediment-phosphorus rating curves stratified by rainfall location and colour for Lower Andheri basin (2) based on (a) rainfall differences only, and (b) both rainfall differences and their relative upland-lowland sediment contributions 141 Figure 5.14 Locations of the rain gauges within and near the Andheri basin overlain on the red soil map 143 Figure 5.15 Sediment rating curves for Lower Andheri station (2) including sediments from only lowland and upland events (1992-1994) 144 Figure 5.16 Map of Andheri basin showing all irrigation diversion dams on the Kukhuri and Andheri rivers 148 Figure 6.1 C-Q relations for events within Kukhuri basin (station 10) illustrating single-value behaviour 171 Figure 6.2 C-Q Relations for events within Andheri Lower basin (2) and Dhap basin (3) illustrating CCW hysteretic behaviour 173 Figure 6.3 C-Q relations for events within Jhikhu basin (station 1) illustrating CW hysteresis behaviour 175 Figure 6.4 CO behaviours: (a)-(c) step CO C-Q behaviour at Jhikhu and Lower Andheri stations, and (d) CO behaviour at Kukhuri station 176 Figure 6.5 Functional relations for rising and falling limbs contrasted within the pre-monsoon and monsoon seasons 179 Figure 6.6 Comparison of pre-monsoon and monsoon sediment rating curves for four particle-size classes at Kukhuri station (10) based on functional analysis 187 Figure 6.7 Comparison of pre-monsoon and monsoon sediment rating curves for four particle-size classes at Upper Andheri station (9) based on functional analysis 188 Figure 6.8 Comparison of pre-monsoon and monsoon sediment rating curves for four particle-size classes at Lower Andheri station (2) based on functional analysis 189 Figure 6.9 Comparison of pre-monsoon and monsoon sediment rating curves for four particle-size classes at Dhap station (3) based on functional analysis 190 Figure 6.10 Comparison of pre-monsoon and monsoon sediment rating curves for four particle-size classes at Jhikhu station (1) based on functional analysis 191 Figure 6.11 Seasonal contrasts of functional relations (b5 and aj) for suspended coarse- and fine-sand fractions 195 X I I Figure 6.12 Seasonal contrasts of functional relations (bf and af) for suspended silt and clay fractions 196 Figure 6.13 Seasonal functional relations for suspended coarse sand contrasted by basin area 197 Figure 6.14 Seasonal functional relations for suspended fine sand contrasted by basin area . . 198 Figure 6.15 Seasonal functional relations for suspended silt contrasted by basin area 200 Figure 6.16 Seasonal functional relations for suspended clay contrasted by basin area 201 Figure 6.17 Seasonal functional relations for Kukhuri basin for four particle-size classes . . . 203 Figure 6.18 Seasonal functional relations for Upper Andheri basin for four particle-size classes4203 Figure 6.19 Seasonal functional relations for Lower Andheri basin for four particle-size classes 205 Figure 6.20 Seasonal functional relations for Dhap basin for four particle-size classes 206 Figure 6.21 Seasonal functional relations for Jhikhu basin for four particle-size classes . . . . 207 Figure 6.22 C-Q graphs by particle size for pre-monsoon and transition season events at four stations 209 Figure 6.23 C-Q graphs by particle size for monsoon-season events at four stations 210 Figure 6.24 Soil colour and available-phosphorus maps for Andheri basin 213 Figure 6.25 Soil colour map for Jhikhu basin 214 Figure 6.26 Sediment rating curve stratified by colour and sediment-phosphorus rating curve stratified by both colour and season for Kukhuri basin (10) 215 Figure 6.27 Sediment rating curve stratified by colour and sediment-phosphorus rating curve stratified by colour and season for Upper Andheri basin (9) 216 Figure 6.28 Sediment and sediment-phosphorus rating curves stratified by colour and season for Lower Andheri basin (2) 217 Figure 6.29 Rainfall distribution over Andheri basin for an upland and a lowland pre-monsoon event, overlaid on the soil colour map 220 Figure 6.30 C-Q (a) and P-Q (b) graphs stratified by rainfall location (upland and lowland) and sediment colour (red and brown) for Lower Andheri basin (2) 221 Figure 6.31 Sediment and sediment-phosphorus rating curves stratified by season and colour for Dhap basin (3) 223 Figure 6.32 Sediment and sediment-phosphorus rating curves stratified by season and colour for Andheri Mid #2 basin (12) 224 X l l l Figure 6.33 Sediment and sediment-phosphorus rating curves stratified by season and colour for Andheri mid #1 basin (11) 225 Figure 6.34 Sediment and sediment-phosphorus rating curves stratified by season and colour for Jhikhu basin (1) 226 Figure 7.1 Nutrient enrichment in khet fields due to annual deposition 257 Figure 8.1 Sediment routing within the Andheri River basin including sources and opportunities for storage 275 Figure 8.2 Sediment yield at Lower Andheri in relation to average rainfall for lowland events and corrected for included production and storage from non-degraded land 281 Figure 8.3 Annual fine-sediment yield and fine-sediment delivery ratio in relation to scale during 1992-1994 308 Figure Al.l Terraced agriculture in the study area: (a) rainfed (pari) and (b) irrigated (khet) . 329 Figure A1.2 Reach of the Lower Andheri River during mid-monsoon at (a) high flow and (b) low flow 330 Figure A3.1 Catch ratios (Custom/Tipping-bucket) at Sites 1, 2, 3, and 4 335 Figure A3.2 Catch ratios (Custom/Tipping-bucket & Custom/HMG) at Sites 5, 6, and 7 . . . . 336 Figure A5.1 Stage-discharge relationship at station 1, Jhikhu River at Bhendabaribesi 343 Figure A5.2 Stage-discharge relationship at station 2, Lower Andheri River 344 Figure A5.3 Stage-discharge relationship at station 3, Dhap River at Shree Rampati 345 Figure A5.4 Stage-discharge relationship at station 9, Upper Andheri River 346 Figure A5.5 Stage-discharge relationship at station 10, Kukhuri River at Andheri River . . . . 347 XIV LIST OF TABLES Table 2.1 Slope, aspect, and elevation of the Jhikhu River basin (from 1:20 000 mapping) . . 10 Table 2.2 Landuse of the Jhikhu River basin in 1990 11 Table 2.3 Descriptive temperature statistics from the six climate stations 19 Table 2.4 Topography of the six study catchments based on 1994 1:5 000 mapping. Data for the Dhap catchment are taken from 1990 mapping at 1:20 000 scale 21 Table 2.5 1990 landuse distribution of study sub-catchments within the Andheri basin, mapped at 1:20 000 scale. (Total area based on 1994 1:5 000 photogrammetric map.) . . . 22 Table 2.6 Slope, order, and bankfull discharge of the study streams at each hydrometric statior22 Table 3.1 Comparison of catch ratios of the rain gauges at seven test sites 29 Table 3.2 Installation information of temperature measurements 31 Table 3.3 Characteristics of erosion plots in the study 33 Table 3.4 Topics examined for farmer indigenous knowledge 38 Table 3.5 Tolerances used to parse automated data 43 Table 4.1 Published storm definitions 49 Table 4.2 Frequency of occurrence of four storm-period variables in three classes 61 Table 4.3 Spatial variation within a storm cell and distribution of events according to lowland, upland, and basin area-events for storms of RT ^ 3mm and RT ^ 10mm 74 Table 4.4 Seasonal distributions of spatial variation within a storm cell and distribution of events according to lowland, upland, and basin area-events for storms of RT ^ 10 mm total rain 76 Table 4.5 Definitions of minor, intermediate, and major storm classes in relation to total rainfall and peak 10-minute rainfall intensity 83 Table 4.6 Distribution of storm events in three storm classes (minor, intermediate, major) at five sites 83 Table 4.6 Distribution of storm events in three storm classes (minor, intermediate, major) at five sites 84 Table 4.8 Number of storms in each year (1992-1994) at each site as given by recorded data 86 Table 4.9 Expected average seasonal storm frequency by class (minor, intermediate, major) within the study area 86 Table 5.1 Surface erosion rates as determined by field studies using erosion plots within or near the Middle Mountains 104 XV Table 5.2 Surface erosion rates as determined by field studies using check dams and hydrometric stations 105 Table 5.3 Annual rate of soil loss (tonnes/ha) from all plots, 1992-1994 108 Table 5.4 Surface-soil characteristics of erosion plots 109 Table 5.5 Percentage of each plot's annual erosion occurring in the pre-monsoon, transition, and monsoon season, 1992-1994 110 Table 5.6 The percentage of the total annual erosion at each plot which occurred in the two most-damaging events of each year, 1992-1994 114 Table 5.7 Sediment-rating-curve relations based on seasonally-stratified data using log-linear regression (1992-1994, assuming Q known without error) excluding data from the transition season 124 Table 5.8 Sediment rating-curve relations derived using functional analysis 127 Table 5.9 Annual and seasonal sediment-rating-curve relations for Lower Andheri station based on lowland/upland data using log-linear regression 145 Table 5.10 Contributing areas and number of irrigation dams for Kukhuri and Lower Andheri hydrometric stations (stations 10 and 2 respectively) 147 Table 5.11 Flow-ratio comparison of 16 individual floods for Kukhuri (station 10) and Lower Andheri (station 2) hydrometric stations 149 Table 5.12 Sediment accumulation in irrigated fields measured using pegs 151 Table 6.1 Theoretical models of primary hysteretic behaviour 155 Table 6.2 Classes of sediment behaviour observed in C-Q graphs 170 Table 6.3 Seasonal sediment-rating-curve relations derived using log-linear regression (1992-1994, Q known without error) for rising and falling hydrograph limbs 177 Table 6.4 Seasonal sediment-rating-curve relations derived using functional analysis for rising and falling hydrograph limbs 178 Table 6.5 Dominant hysteresis indicated by net behaviour of suspended sediment of rising and falling hydrograph limbs 180 Table 6.6 Number of samples analysed for particle-size distribution by station and season . 182 Table 6.7 Pre-monsoon sediment-rating-curve relations for coarse sand, fine sand, silt, and clay at stations 1, 2, 3, 9, and 10 using log-linear regression (1992-1994, Q known without error) excluding data from the transition season 185 Table 6.8 Monsoon sediment-rating-curve relations for coarse sand, fine sand, silt, and clay at stations 1, 2, 3, 9, and 10 using log-linear regression (1992-1994, Q known without error) excluding data from the transition season 186 XVI Table 6.9 Pre-monsoon sediment-rating-curve relations derived using functional analysis for coarse sand, fine sand, silt, and clay at stations 1, 2, 3, 9, and 10 excluding data from the transition season 192 Table 6.10 Monsoon sediment-rating-curve relations derived using functional analysis for coarse sand, fine sand, silt, and clay at stations 1, 2, 3, 9, and 10 excluding data from the transition season 193 Table 6.11 Annual coarse and fine-sand ratings for Kukhuri basin based on data from entire rainy season 194 Table 6.12 Number of sediment samples analyses for colour and phosphorus by station and season 211 Table 6.13 Mean sediment phosphorus content by station and season 227 Table 7.1 Farmer descriptions of selected soils (1993) 241 Table 7.2. Definitions of primary terms of indigenous soil classification system 243 Table 7.3 Distribution of terms used by farmers to name specific soils (1992) 244 Table 7.4 Summary of quantitative results from farmer interviews about the irrigation and khet system 250 Table 7.5 iton-management data based on farmers interviews and detailed measurements . 253 Table 8.1 Annual and seasonal rates of normal-regime sediment production (t/ha) from surface erosion determined for bari, shrub, forest, and grassland 280 Table 8.2 Sediment production by surface erosion from gullied lowlands (tonnes) calculated using an empirical relation derived from lowland rainfall events (limits represent 95% confidence) 282 Table 8.3 Annual and seasonal sediment yield at Kukhuri (10), Lower Andheri (2), and Jhikhu (1) hydrometric stations (limits represent 95% confidence) 284 Table 8.4 Average annual soil accumulation measured annually (1992-1994) in khet fields . 286 Table 8.5 Estimated annual rates of accumulation (cm/yr) and total annual sediment storage by basin (tonnes) in the khet fields (ha), khet canals (m), and bari ditches (m) . . . . 287 Table 8.6 Seasonal storage (tonnes) by basin resulting from sediment deposition within khet, khet canals, and bari ditches 288 Table 8.7 The 10 high-flow events at Kukhuri station with the highest sediment yield during 1992-1994 292 Table 8.8 The 14 high-flow events at Andheri station with the highest sediment yield during 1992-1994 294 XVII Table 8.9 Seasonal and annual sediment budget components (production, storage, yield) in tonnes in 1992 for Kukhuri and Lower Andheri basins 297 Table 8.10 Seasonal and annual sediment budget components (production, storage, yield) in tonnes in 1993 for Kukhuri and Lower Andheri basins 298 Table 8.11 Seasonal and annual sediment budget components (production, storage, yield) in tonnes in 1994 for Kukhuri and Lower Andheri basins 299 Table 8.12 Seasonal and annual sediment production, yield, and overall percentage delivery for the Kukhuri basin (1992-1994) 300 Table 8.13 Seasonal and annual sediment production, yield, and overall percentage delivery for the Lower Andheri basin (1992-1994) 301 Table 8.14 Seasonal and annual sediment yield from the Jhikhu basin (1992-1994) 303 Table 8.15 Seasonal and annual sediment yield as percentage of seasonal and annual totals respectively for individual and selected groups of events for all spatial scales . . . 305 Table 8.16 Average seasonal sediment budgets across all spatial scales 306 Table 8.17 Sediment yield at nested hydrometric stations for single events 310 Table A2.1 Average monthly, maximum monthly, minimum monthly, and maximum 24-hour rainfall (mm) at Baluwa, 900 m (1992-1994, averages based on 1993-1994) . . . 331 Table A2.2 Average monthly, maximum monthly, minimum monthly, and maximum 24-hour rainfall (mm) at Kathmandu Airport, 1336 m (1968-1986) 332 Table A2.3 Average monthly, maximum monthly, minimum monthly, and maximum 24-hour rainfall (mm) at Bela, 1211 m (1990-1994) 332 Table A2.4 Average monthly, maximum monthly, minimum monthly, and maximum 24-hour rainfall (mm) at Dhulikhel, 1500 m (1990-1994) 333 Table A2.5 Average monthly, maximum monthly, minimum monthly, and maximum 24-hour rainfall at Panchkhal, 865 m (1978-1994, averages based on 1978-1985 and 1988-1994) 333 Table A3.1 Rain-gauge summary information including gauge number, elevation, and location 334 Table A4.1 Summary of site data for 11 soils selected and described by farmers in 1993 . . . 344 Table A4.2 Summary of site data for 16 soils selected and described by farmers in 1992 . . . 344 Table A6.1 Summary of sediment production and delivery of episodic erosion attributed to inadequate runoff management from the event of July 10, 1992 351 Table A6.2 Summary of sediment production and delivery of episodic erosion attributed to streambank erosion due to high-flow conditions from the event of July 10, 1992 352 XV111 LIST OF ABBREVIATIONS ASCE American Society of Civil Engineers DHM Department of Hydrology and Meteorology DSCWM Department of Soil Conservation and Watershed Management FAO Food and Agricultural Organisation HMG His Majesty's Government SDR sediment delivery ratio USLE Universal Soil Loss Equation xix LIST OF SYMBOLS X ratio of the error variances (E^/EQ2) a coefficient of the power-law relation C=aQb b exponent of the power-law relation C=aQb C total suspended sediment concentration, g/1 (cover factor in USLE) CR event runoff coefficient (erosion plots) = the percentage of incoming storm rainfall which runs off the plot CF correction factor for calculating stream discharge CV coefficient of variation E realised measurement error E C 2 error variance of C E Q 2 error variance of Q F falling limb GH gauge height (cm) I10 maximum 10-minute storm rainfall intensity (mm/h) J-eo maximum 60-minute storm rainfall intensity (mm/h) K soil erodibility factor (USLE) L slope length factor (USLE) M monsoon season n sample size N number of storms P pre-monsoon season (management factor in USLE) Q stream flow rate (m3/s) R falling limb (rainfall factor in USLE) R2 correlation coefficient XX RMHM minimum total event rainfall to be considered a storm (mm). R T total storm rainfall (mm), s sample standard error S time without rain between storms, h (slope steepness factor in USLE) sr standard error of the estimate SmN minimum time without rain to declare new storm (h) t student's t-distribution T transition season T 1 0 timing of I10 relative to the storm start (h) Tgo timing of relative to the storm start (h) T D U R total storm duration (h). V flow velocity (m/s) x sample mean COMMON SUBSCRIPTS exp expected value (based on marginal regression) f result from functional analysis meas measured value min minimum max maximum r result from marginal regression surface measured at the surface T total xxi ACKNOWLEDGEMENTS I would like to express my thanks to the International Development Research Centre for its direct support of this research through a Young Canadian Researcher's Award to me and their ongoing support of the Mountain Resources Management Project of which this research has been a part. I would also like to thank the Natural Sciences and Engineering Research Council of Canada for supporting me financially during 1990-1992. In addition, the International Centre for Integrated Mountain Development QCIMOD, Kathmandu) and the Integrated Survey Section (His Majesty's Government, Kathmandu) both provided invaluable assistance and administrative support during my field seasons in Nepal during 1991-1994. The dedication of Dr. Hans Schreier, my research supervisor, in collaboration with Mr. Pravakar B. Shah (ICIMOD) made this work possible. Without their tenacity in keeping the larger project alive, this research would not have occurred. I would also like to thank my entire Supervisory Committee - Dr. Hans Schreier, Dr. Mike Church, Dr. Les Lavkulich, Dr. Tim Ballard, and Dr. Hamish Kimmins - for giving direction especially during the earlier years of this work. An interdisciplinary research project like this is rarely achieved without the support and participation of many people. A dedicated team of individuals in Nepal under the direction of Mr. Pravakar B. Shah assured the completion of this research. The tireless enthusiasm of Mr. Gopal Nakarmi and the calm thoroughness of Mr. Bhuban Shrestha were instrumental in motivating field staff and making the field work successful. My stay in the field was made much easier and more enjoyable by the collaboration of Mr. A. Raj Pathak whose good humour and capacity for field work is well known throughout Nepal. Many farmers within the study area participated directly in this research through data collection. I am grateful to them and would like to mention in particular Mr. Dipak Bhetwal, Mr. Uddav Pathak, Mr. Hirinath Bhetwal, Mr. Prem Lama, and Mr. Sudarsan Accharya and their families for their partnership and the high quality of their work. XXll Technical support in both Canada and Nepal has been invaluable. Claire Dat, Kathi Hofmann, and SiPing Tu worked diligently with irreplaceable sediments in the UBC Soil Science and Geography laboratories. Technical support from Sandra Brown, Yao Cui, and Wayne Tamagi in the Geographic Information System Laboratory (UBC) is also gratefully acknowledged. Andrew Faulkner's suggestions on database management improved the efficiencies of the analyses. I have benefitted greatly from discussions with many other scientists. Mr. Brian Carson kindly shared with me his experience and reflections from his years of work in Nepal. Dr. Johannes Ries and Petra Schweizer provided hospitality and inspiration on the mechanics of carrying out the field work required for this study. Similarly, Ms. Susanne Wymann and Dr. Jemuel Perino shared their experiences of carrying out research in Nepal. Discussions with Dr. Marwan Hassan, Dr. Judy Haschenburger, and Dr. Tony Kozak at UBC were always very helpful. Thanks also to my office friend, Kathy Cook, who shared with me her critical thinking. A special appreciation is extended to Dr. Mike Church who influenced profoundly the study design and data analysis in this research and whose level of involvement in this work, for me, matched that of a co-supervisor. His ability to marry the highest standard of scientific endeavour with the realities of applied research will have a long-lasting effect on my future research activities and scientific interpretations. And most of all, thank you to Kathi for accepting these years of research and for being there at their conclusion. FOREWORD The findings reported in this document are based on extensive field and laboratory data. The original data are available on diskette by contacting the author through the Institute for Resources and Environment, University of British Columbia, 436E-2206 East Mall, Vancouver, B.C., CANADA V6T 1Z3 (or via email at ire@unixg.ubc.ca). 1 PART I Biophysical Analyses 1. General Introduction 1.1 Problem Statement Nepal's dramatic mountainous relief, frequently incompetent bedrock, rapid tectonic uplift, and warm monsoonal climate all indicate that weathering and erosion have long been intense and an important aspect of life in Nepal for a very long time. More recentiy, with expanding populations both in Nepal and in downstream neighbouring regions, there has been increasing alarm about steepland cultivation and other agricultural landuse practices in the mountainous regions due to the presumed acceleration of erosion. Concern about stream sediment levels and their potential both for endangering the viability of upland farming and for yielding negative consequences downstream have led to many strong statements about sediment dynamics on these southern slopes of the Himalaya (World Bank 1979; Eckholm 1975). Unfortunately, when the bases of these statements are examined, little or no quantitative scientific data are available to substantiate the statements made. A clearer picture of the fertility and landuse dynamics of these headwater catchments is emerging. Detailed measurements in the Jhikhu River basin show that although there have been significant increases in both forest cover and cultivated land from 1972 to 1990 (Schreier et al. 1994), the fertility of the agricultural land (Wymann 1993) and the quality of the forests (Schmidt 1992) are declining. We do not, however, have equivalent quantitative measurements of erosion and sediment transport even though tolerable rates of surface erosion are an essential underpinning to a sustainable agricultural system. No matter how good the economy might be, and no matter how many inputs can be brought in from the outside, if erosion is excessive, widespread site degradation will put the entire system into decline. It is clear that rates of erosion in the steepland agricultural areas of Nepal are high. What is far less clear is what these rates are over various spatial and temporal scales, whether these rates are acceptable within the framework of a sustainable agricultural system, and how effective the indigenous population is at controlling erosion. By looking in detail at sediment dynamics in low-order Middle Mountains catchments of varying size, this study tries to answer some of these questions. This study is also relevant to a debate which has persisted for the past two decades concerning the influence of landuse practices in the uplands on flooding in the distant lowlands. Eckholm (1975), Myers (1986) and others have promoted the notion that deforestation in the upland agricultural areas of Nepal is causing massive stream sedimentation and consequent devastating floods in the lowlands of Bangladesh and that there will be no forests left in Nepal by the turn of the century. These forecasts of Himalayan environmental catastrophe greatly influenced development activity until the mid-1980s. Many other authors and researchers (e.g., Gilmour 1988; Ives and Messerli 1989; Lauterburg 1993) have tried to show that the linkages are mythical and statements of imminent forest and agricultural demise are unfounded. No one study can answer all of the questions posed and suggested above. In this study, the focus is on headwater catchments. The steep topography and intense rainfall in these areas present a challenging environment for agriculture. These natural factors, combined with the heavily manipulated agricultural lands, render these headwater areas vulnerable to severe erosion. Because of population increase, the headwater areas are under tremendous pressures from landuse intensification. Thus, important baseline information must be established if we hope to improve the diagnosis of this agricultural system to enhance its future viability. 1.2 Research Context Ives and Messerli (1989) have summarised others' hypotheses of this region's imminent catastrophe calling it the Theory of Himalayan Environmental Degradation. In the theory, it is assumed that population growth is the root cause of all environmental degradation in the Himalayan 3 region. The mountain farmer is seen as an ignorant accomplice in a vicious cycle of resource extraction and environmental demise. The upland degradation is soon followed by widespread flooding and sedimentation causing further disruption and demise for a far greater population. Though based on economic indicators and real data describing landuse change, these statements ignored scientific understanding about highland-lowland linkages. Until the late 1970s, there was an absence of any quantitative measurements of erosion and sediment transport in the Middle Mountains. From the late 1970s through the late 1980s, a number of independent studies (Kandel 1978; Mulder 1978; Laban 1978; Upadhaya et al. 1991; DSCWM 1991; Sherchan et al. 1991) attempted to examine erosion quantitatively in the southern Himalaya and in the Middle Mountains. These initial attempts to assess erosion in the Middle Mountains, reviewed later in Chapter 5, emphasise surface-erosion estimates over fixed spatial and temporal scales. Spatial scales varied from an erosion-plot scale (10 to 100 m2) to specific catchment scales (10 to 1000 km2) with little or no intermediate resolution. Similarly, the results were generally presented as annual rates with little or no temporal resolution. There was a growing awareness in the 1980s that the Theory of Himalayan Environmental Degradation should be challenged on the basis of its inadequate data and fundamental misunderstandings about both the behaviour of highland-lowland systems and the role of the people living within the area. This recognition was formalised at the Mohonk Mountain Conference in 1985 which set the stage for integrated studies of landuse, erosion, and management. Thompson and Warburton (1985) put forth the thesis that uncertainty in the Himalaya is so extreme that uncertainty itself contains the problem and therefore standard scientific approaches to understanding and addressing the Theory of Himalayan Environmental Degradation cannot succeed. In the 1990s, several long-term integrated studies were initiated, incorporating measurements of surface erosion and mass wasting with related precipitation and landuse parameters (Perino 1993; Ries 1994; Overseas Development Agency 1995). These studies are attempting to reach more 4 meaningful conclusions about catchment-scale processes. For instance, Perino (1993) described a paired-catchment study evaluating the consequences of improved farming methods on total basin sediment output. This study integrated agrometeorologic variables with comprehensive sediment and water measurements for basins of about two hectares. Ries (1994) measured rainfall input, surface erosion, and stream suspended-sediment yield in the High Mountains for low, medium, and high landuse intensity. Overseas Development Agency (1995) described results from three years of research looking at the relations amongst soil erosion, water quality, landuse change, management practices, and in-stream fauna. The conclusions of these studies generally point to a strong, indigenous agricultural system helping to sustain - not undermine - food production. The above comments point out how limited the database is on erosion and sedimentation in Nepal. In comparison, we have a reasonable understanding of many catchment-scale processes within small temperate-region basins. However, the characteristics of these Middle Mountain catchments are quite distinct from their temperate counterparts. In particular, aspects such as the steep topography, heavy precipitation concentrated within a few months, soil types, and high level of human manipulation over every part of the landscape are likely to create very different outcomes than have been observed elsewhere. This study tries to fill this gap through an intensive monitoring program over a range of spatial scales from the plot scale (0.01 ha) through basin spatial scales of 100 to 500 to 10 000 ha in size and covering temporal scales from a single flood event (over 300 individual floods are examined) to seasonal and annual timescales during a three-year period. A range of related biophysical measurements are made to describe sediment dynamics within these headwater catchments. The findings of this study are assembled to produce a diagnostic evaluation of the health of these headwater agricultural systems with respect to soil erosion. Ehrenfeld (1992) described ecosystem health as a bridging concept: Health is an idea that transcends scientific definition...it contains values that are not 5 amenable to scientific methods of exploration... Health is a bridging concept connecting two worlds: it is not operational in science if you try to pin it down, yet it can be helpful in communicating with non-scientists. Equally important, if used with care in ecology, it can enrich scientific thought with the values and judgments that make science a valid human endeavour. (Ehrenfeld 1992) The overall diagnosis is non-scientific but draws heavily from the scientific findings. It is put forth because there is an urgent need for a diagnosis. It is this need which was the genetic influence for the research. 1.3 Goals and Objectives Spatial and temporal scales impose a number of constraints on research of this kind. The Jhikhu River basin has been chosen for a case study because it is typical of the Middle Mountains with respect to climate, physiography, landuse, and soil type and is representative of the level of landuse intensity likely to affect most other basins in the region in the near future. Further, there already exists a large inventory of physiographic data in a GIS for this basin (Shah et al. 1994). In applying well-established scientific concepts over manageable spatial and temporal scales in low-order basins of the Middle Mountains of Nepal, this thesis sets out to accomplish four central goals, each with specific research objectives: 1) Diagnose causes of sediment dynamics • Identify the sources of suspended sediment in Middle Mountain streams. • Evaluate the importance of topography, rainfall, and landuse (and other management practices) in shaping the sediment regime over relevant spatial and temporal scales. Research questions: • What are the significant sediment sources? • Do seasonal changes in rainfall regime (intensity, duration, frequency, spatial variation), surface condition, and management cause important changes in the sediment regime? • What is the extent of seasonal changes in sediment loss? 6 2) Assess the efficacy of indigenous management techniques • Document all important indigenous soil-management approaches related to erosion and sedimentation. • Evaluate the influence of these techniques on sediment dynamics. Research questions: • Are local farmers effective in modifying sediment dynamics? Do their activities reduce or increase basin sediment loss? • To what extent are the farmers able to favourably influence the erosive fate of their soils? At what point does their management regime become ineffective? 3) Determine sediment and nutrient budgets • For the three-year period of study, construct sediment budgets over specific spatial (plot, sub-basins, basin) and temporal (event, season, year) scales to determine the relative importance of sediment sources and sediment storage to basin yield. • Using phosphorus as a representative limiting nutrient, examine its relative redistribution within sub-basins and net loss from sub-basins; identify the landuse(s) which provide the dominant phosphorus contributions to basin phosphorus loss. Research questions: • How does the spatial scale of a basin affect its sediment output? • What proportion of the annual basin sediment loss is accounted for in the biggest floods? • Can predictive relations be developed for sediment output? • What is the significance of surface degradation to basin budgets? • To what extent are nutrients redistributed within (but not "lost from") sub-basins? 4) Prescribe soil/sediment management recommendations • If appropriate, suggest management recommendations to improve the effectiveness of present farming techniques. • Provide a statement clarifying the contribution of headwater areas to downstream concentrations of suspended sediment. • Provide a statement of sustainability regarding the current management regime with respect to erosion. Research questions: • To what extent are the soils redistributed rather than lost in this highland-lowland system? • Are there feasible options available to the farmers to reduce their risk of soil loss? 7 The completion of these four research objectives will make it possible to assess the following hypothesis: soil and sediment dynamics and the indigenous management techniques within headwater Middle Mountains basins do not themselves indicate a deterioration of the health of the agricultural system. The thesis comprises two parts - one, Biophysical Analyses and two, Management and Implications. The first part focuses on quantitative field measurements and their detailed analyses. Chapter 2 provides a description of the study area and Chapter 3 gives a summary of methods used in the entire study. Chapters 4 through 6 address specific biophysical concerns associated with the causes of sediment dynamics laid out as the first goal. Specifically, Chapter 4 presents the precipitation regime of the study area, developing appropriate temporal and spatial scales for relating the precipitation regime to the sediment regime. Chapter 5 examines the many interacting factors involved in sediment transport in the study area - for example, rainfall characteristics, surface cover, soil characteristics, management, and topography. Chapter 6 uses sediment properties (particle-size distribution, colour, and phosphorus content) to look at some fundamental dynamics associated with suspended-sediment transport. These insights have implications for the causes of observed sediment regimes presented in Chapter 5. The second part is concerned with management and the implications of the findings presented in Part 1. Chapter 7 evaluates the effectiveness of indigenous management techniques. Chapter 8 presents sediment budgets based on the relations developed earlier in Chapters 5 and 6. Finally, Chapter 9 addresses integration and overall conclusions and suggests recommendations for management and further research. 8 2. Study Area 2.1 Jhikhu River basin 2.1.1 Location and physiography The Jhikhu River basin is located 35 km east of Kathmandu in Nepal's Middle Mountains as shown in Figure 2.1 and illustrated in Appendix Al. It has an area of 111 km2 and elevations ranging from 800 to 2030 m. The topography within the basin, presented quantitatively in Table 2.1, includes large areas of steep land. Landuse is dominated by subsistence agriculture though, with recent political changes, a market economy is rapidly developing. The population in this basin was 32 956 in 1990, growing at a rate of 2.9% per annum (Shrestha and Brown 1995). The Arniko highway, a major east-west corridor in Nepal, passes through the watershed providing good access to Kathmandu and the Tibetan border. The geology of the Jhikhu River basin consists of sedimentary rocks which have been affected by low- and high-grade metamorphism (Dongol 1991). These metasediments include phyllite, quartzite, schists and mica-schists. The geomorphological development of this region has been influenced by tectonic uplift leading to over-steepened slopes that are prone to instability (Saijo 1991). The highly dissected landscape contains ridge slopes formed by soil creep and the weathering of bedrock, erosional slopes formed by landslides, and landforms originating from sediment deposition. The ridge slopes remain active and are the most vulnerable to gully and sheet/rill erosion. Large terraces exist above the Jhikhu River valley bottom, created in the Quaternary Period when this drainage was tectonically dammed (Dongol 1991). Red soils are dominantly Ultisols and some Alfisols whereas the non-red soils are dominantly Entisols and Inceptisols and some Alfisols. The Ultisols are the oldest soils in the study area; they dominate the terraces above the main valley bottom, though they can be found up to elevations as high as 1400 m. These red soils have a generally lower soil fertility than do the non-red soils. 10 Table 2.1 Slope, aspect, and elevation of the Jhikhu River basin (from 1:20 000 mapping). Class Percentage of Basin Area Slope (•) 0-10 43.4 10-20 24.6 20-30 21.1 £>30 10.9 Aspect Flat 16.3 NE, N, NW 34.0 E 12.8 SE, S, SW 29.7 W 7.2 Elevation (m) 800-999 39.6 1000-1199 27.7 >1200 32.7 Note: slope does not consider terracing. The highly dissected landscape signifies a dense natural drainage network. Tributaries of the Jhikhu River are steep, confined, boulder-bed channels. The Jhikhu River, in contrast, is a meandering sand-bed river in some of its lower reaches with a slope of about 0.1" (0.2 %). 2.1.2 Farming system and landuse Table 2.2 shows the landuse breakdown in the Jhikhu River basin for 1990. The landscape is dominated by cultivation on slopes of up to 40'. Annually, up to three crops are grown on the irrigated khet fields and two crops on the rainfed bari land. Rice is the dominant crop on the khet land, though recently cash crops such as tomatoes and peppers have risen in importance. Maize continues to dominate on the upland rainfed fields, with millet and wheat grown during the dry season. Legumes are typically intercropped on many fields. The agricultural system has traditionally relied on the forests for nutrient inputs. Forests provide about 40% of the feed for livestock (Carson 1992) whose dung is incorporated into compost, 11 Table 2.2 Landuse of the Jhikhu River basin in 1990. Landuse Percentage of .Basin Area khet 15.4 bari 39.1 forest 30.2 shrub 8.4 grassland 4.2 other 2.7 becoming the prime source of added nutrients to the cultivated fields. Over the past 20 years, synthetic fertilizers have gained in prominence as landuse pressures have increased (Chitrakar 1990). In the past few years, the cost of these amendments has risen dramatically, placing the cash-poor farmers in a difficult situation. Further, much of the forest land is in a degraded condition with crown cover below 10% and little or no surface cover. Landuse mapping over the past 50 years shows a steady conversion of land into cultivation (Schmidt 1992). Schreier et al. (1994) showed that between 1947 and 1981, this conversion was at the expense of forest cover. More recent mapping (1972-1990) documents an increase in forest cover and agricultural land at the expense of both shrub land and grassland. Schreier et al. (1994) explained that these changes in forest cover reflect widespread deforestation earlier this century followed more recently by both afforestation associated with plantations during the past 15 years and increased tree planting on private land (Gilmour 1991). 2.1.3 Regional climate The climate of Nepal is strongly influenced by the southwest monsoon across the Indian Subcontinent and by its own abrupt relief. The seasonal reversal of winds associated with the South Asian Monsoon brings warm humid air to much of northern India and southern Nepal during the summer months. Nepal rises from the Indo-Gangetic Plain to the highest mountains on Earth within 12 only 150 km. These two factors combine to yield annual precipitation of over five metres in parts of the country and cold deserts on some leeward mountainsides (Department of Hydrology and Meteorology 1992). Unfortunately, these regional variations are not well described because of the lack of monitoring stations in the country's difficult mountainous regions (Ramanathan 1981). The Middle Mountains physiographic region (see Figure 2.1) does not experience the extremes of climate found in much of the rest of Nepal. At about 25' north latitude, the climate of this physiographic region varies from warm temperate on high mountain ridges to subtropical in the low valleys. Locally, climatic patterns can vary strongly due to elevation and aspect. Air temperatures span O'C to 40"C annually (Kenting Earth Sciences Ltd. 1986). 2.1.4 Local climate In the Jhikhu River basin, the rainy season starts normally in the beginning of June, ending by the end of September. Precipitation in this basin is in the form of rainfall only and varies typically between 900 and 1600 mm annually. Up to 90% of the annual rainfall falls during the monsoon period with a distinct prolonged period of drought throughout most of the rest of the year. The local climate of the Jhikhu River basin can be characterised using data from several stations covering a range of elevations. Sources include a long-term monitoring station run by His Majesty's Government (HMG) and found within the valley lowland at 865 m, other long-term HMG climate stations found in the region of the Jhikhu basin, and stations which have been maintained by the present study for three to five years at several points within the study area. The HMG data are used to describe long-term trends in climate in this region. The data from within the Jhikhu basin provide the best picture of the variation in weather occurring within the study site. Rainfall One of the longest records of precipitation measurement available near the study basin is that 13 Figure 2.2 Mean, maximum, and minimum monthly rainfall at (a) Kathmandu airport (HMG Records 1968-1990) and (b) Panchkhal (HMG Records 1978-1994;, means based on 1978-1985 and 1988-1994). 700 J F M A M J J A S O N D (a) Kathmandu Airport 1336 m 1968-1990 Mean annual rainfall (1968-1990) = 1408.2 mm J F M A M J J A S O N D (b) Panchkhal 865 m 1978-1994 Mean annual rainfall (1978-1985 & 1990-1994) = 1251.0 mm Mean Maximum Minimum 14 of the Kathmandu airport. At this station, data from 1968 to 1990 are currently available in published government records (Department of Hydrology and Meteorology 1992 etc.). These precipitation measurements are summarised in Figure 2.2a. An HMG climate station at Panchkhal is located within the study area itself with data available from 1978-1994 (Department of Hydrology and Meteorology 1992 etc.). These data are summarised in Figure 2.2b. Together these data sets provide a long-term indication of averages and extremes expected in the study site. The Panchkhal station is a valley-bottom station indicative of the subtropical lowland areas while the station at the Kathmandu airport is indicative of the more-temperate regions. Both figures show that average monthly precipitation peaks in July at about 300-350 mm per month. Interestingly, the maximum monthly precipitation peaks in June at both locations. In each case, this peak is from different events, each of unusual magnitude for the month: in 1971 at the Kathmandu airport and in 1978 in Panchkhal. In fact, while the one station experienced very high rainfall, the other experienced a rainfall of average magnitude yet the two stations are only 30 km apart, within the same Middle Mountain physiographic region. Figures 2.3 provides summary statistics of precipitation data gathered by the present study at three points within the Jhikhu River basin. The Baluwa station (1992-1994) is within the Andheri River catchment at 900 m, 4 km from the HMG Panchkhal station (also in the valley bottom). The Bela station (1990-1994) is at 1211 m, in the Andheri catchment and also in the upland Kukhuri catchment. The Dhulikhel station (1990-1994) is at 1500 m. The locations of these stations are indicated on Figure 3.1. As indicated in Figure 2.4a, across these five sites, the average monthly precipitation does not vary greatly. Though the periods of record differ, there appears to be a tendency for the peak monthly rainfall to be higher at the Dhulikhel and Baluwa locations. Comparing the longer-term records (Figure 2.2) with the records from this study, one can see an obvious difference in the lack of a peak monthly rainfall in June for Baluwa, Bela, and Dhulikhel. 15 Figure 2.3 Mean, maximum, and minimum monthly rainfall at (a) Baluwa (1992-1994; means based on 1993-1994), (b) Bela (1990-1994), and (c) Dhulikhel (1990-1994). Ol 1 700 r -600 -500 -400 -300 -200 -100 h 0 (a) Baluwa 900 m 1992-1994 Mean annual (1990-1994) = 1152.6 mm r n i i i i i i r F M A M J J A S O N D (b) Bela 1211 m 1990-1994 700 600 -500 -400 -300 -200 F-100 0 Mean annual (1990-1994) = 1352.9 mm I I I I I I I I I F M A M J J A S O N D 1 i o 700 600 h 500 -400 -300 f-200 100 0 (c) Dhulikhel 1500 m 1989-1994 Mean annual (1990-1994) = 1689.0 mm m 1 1 1 1 1 1 1 F M A M J J A S O N D Mean Maximum — - — Minimum 16 Figure 2.4 Mean monthly rainfall (a) and maximum 24-hour rainfall (b) at Kathmandu Airport (1968-1990), Panchkhal (1978-1985 and 1988-1994), Baluwa (1993-1994), Bela (1990-1994), and Dhulikhel (1990-1994). J F M A M J J A S O N D J F M A M J J A S O N D (a) Mean monthly rainfall (b) Maximum 24-hour rainfall Kathmandu Airport — Panchkhal Dhulikhel Bela Achharya Tol 17 Presumably, this suggests that heavy June rainfall can occur but it is infrequent and has not happened in the Jhikhu River basin during this period of study (1990-1994). Figure 2.4b contrasts the maximum 24-hour rainfall measured at all five sites during the periods of record. Though the mean precipitation in June is lower than in July on average at all sites, this figure reveals a tendency for the highest 24-hour precipitation to occur in June. These comparisons hint at patterns which will be more fully examined in Chapter 4. A striking result in Figure 2.4b is the heavy post-monsoon rainfall measured on October 20, 1987 at the Kathmandu Airport. Finally, all of these descriptive rainfall statistics are summarised in tabular form in Appendix A2. Temperature Temperature data for the three valley-bottom climate stations are presented in Figure 2.5. Figure 2.5a shows the mean maximum and mean minimum temperatures and Figure 2.5b shows the extreme maximum and minimum temperatures. Though the data are taken from different periods, they provide a consistent characterisation of the valley-bottom temperature regime: April through September have mean monthly temperatures well above 30°C, temperatures frequently exceed 35*C and, during the monitored periods, the minimum has rarely gone below freezing. Figures 2.5c and 2.5d present the equivalent temperature data for the two high-elevation sites and provide the mean result from the Kathmandu airport for comparison. The mean maximum monthly temperature at the high-elevation sites is several degrees cooler than at the valley-bottom sites. During the indicated periods, the temperature rarely exceeded 35'C and went below freezing especially at the Kathmandu station. Summary statistics taken from these monthly results are given in Table 2.3. Not unexpectedly, elevation influences the mean annual temperature. The high-elevation sites show a slightly lower average mean annual temperature than the valley-bottom sites. However, the lower elevation sites 18 Figure 2.5 Mean-monthly and extreme-monthly maximum/minimum temperatures at (a) three low-elevation sites (Panchkhal, 1978-1994; Baluwa, 1993-1994; Bhimsenthan, 1993-1994) and (b) three high-elevation sites (Kathmandu airport 1968-1990;, Bela, 1990-1994; Bhetwaltok, 1993-1994). (a) Low-elevation sites (b) Low-elevation sites J F M A M J J A S O N D J F M A M J J A S O N D 40 (c) High-elevation sites mean-monthly max/min Bela — - Bhetwaltok I I I I I I I I I I J F M A M J J A S O N D 40 (d) High-elevation sites extreme-monthly max/min -10 — — — Bela — — Bhetwaltok I I I I I I I I I I J F M A M J J A S O N D 19 Table 2.3 Descriptive temperature statistics from the six climate stations. Site Elev. (m) Mean Annual Mean Daily Max Mean Daily Min Extreme Daily Max Extreme Daily Min Period of Data Low Elevation Panchkhal (flat) 865 21.2 28.1 14.2 39.5 -0.2 (1989) 78-94 Baluwa (north-facing) 865 21.0 28.8 13.1 38.0 1.0 93-94 Bhimsenthan (south-facing) 895 21.2 28.0 14.4 37.0 2.0 93-94 High Elevation Kathmandu Airport 1336 17.9 n/a n/a 34.0 -3.5 (1978) 68-86 Bela (north-facing) 1254 20.6 26.2 14.9 35.0 3.0 90-94 Bhetwaltok (south-facing) 1300 20.3 26.1 14.5 39.0 (Jun 9 92) 3.0 93-94 appear to have lower minima providing evidence for inversions. The annual extremes and mean monthly temperatures are comparable at all sites. There does not appear to be a large effect on temperature due to aspect at these sites. 2.2 Study catchments A total of six smaller catchments and sub-catchments in the lower regions of the Jhikhu River basin are the focus of this study. Other catchments have been examined as part of the larger study not included in this thesis. Some of these catchments are nested as illustrated in Figure 2.6. The topography of these six study catchments is contrasted in Table 2.4. Two sources of topographic data are used in this study's analyses. A topographic map at 1:20 000 scale was produced in 1990 for the entire basin and a detailed digital topographic map at 1:5 000 scale was produced in 1994 for a cross-20 21 Table 2.4 Topography of the six study catchments based on 1994 1:5 000 mapping. Data for the Dhap catchment are taken from 1990 mapping at 1:20 000 scale. Dhap Andheri Andheri Mid #1 Andheri Mid #2 Upper Andheri Kukhuri St. No. 3 2 11 12 9 10 Area (ha) 558 532 401 299 178 72 Slope f) 0-10 73.2% 19.2% 16.6% 11.2% 9.5% 10.3% 10-20 20.2 25.4 24.4 22.7 22.0 21.9 20-30 5.9 38.2 39.2 43.1 42.9 43.6 >30 0.8 17.2 19.8 23 25.6 24.2 Elev. (m) 800-1000 98.3% 24.8% 15.2% 0.0% 0.0% 0.0% 1000-1200 1.7 34.9 31.8 28.9 14.3 27.7 >1200 0.0 40.3 53.0 71.1 85.7 72.3 Aspect Flat 35.4% 6.3% 5.2% 3.4% 3.2% 3.2% N, NE, NW 9.8 53.1 53.5 56.3 63.8 51.1 E 7.3 9.54 9.1 9.6 12.9 0.9 S, SE, SW 36.4 15.8 17.1 14.7 6.6 22.3 W 11.1 15.3 15.1 16.0 13.5 22.5 . Note: numbers represent percentage of total area. section of the Jhikhu River valley, containing the Andheri River study catchments. All digital topographic data have been incorporated into a Geographic Information System to produce quantitative information on elevation, aspect, and slope (see section 3.3.4). The results for the Jhikhu basin appeared earlier in Table 2.1. The landuse of these six catchments is presented in Table 2.5. Mapping was carried out in 1990 at 1:20 000 scale for the entire Jhikhu River basin and these results appeared in Table 2.2. Table 2.6 summarises the features of these streams in terms of channel slope and stream order at the hydrometric station (Leopold et al. 1964) and their estimated bankfull discharge (Dunne and Leopold 1978). The streams in these catchments are contrasted in profile in Figure 2.7. 22 Table 2.5 1990 landuse distribution of study sub-catchments within the Andheri basin, mapped at 1:20 000 scale. (Total area based on 1994 1:5 000 photogrammetric map.) Dhap Andheri Lower Andheri Mid #1 Andheri Mid #2 Andheri Upper Kukhuri St. No. 3 2 11 12 9 10 Area (ha) 558 532 401 299 178 72 khet 24.6% 6.8% 8.7% 8.4% 8.9% 8.2% bari 36.8 33.1 41.1 51.3 53.8 55.4 forest 20.9 32.3 24.4 22.0 22.5 17.9 shrub 3.8 14.6 11.3 7.2 4.3 6.1 grassland 7.6 8.0 9.0 7.4 6.9 8.3 other 6.3 5.3 5.4 3.7 3.7 4.1 Note: numbers refer to percentage of total area. Table 2.6 Slope, order, and bankfull discharge of the study streams at each hydrometric station. Station Order Slope C) Bankfull Discharge (m3/s) 1 6 0.3 85 2 4 1.9 25 3 5 1.0 20 9 3 5.1 13 10 2 6.6 5 11 3 2.1 unknown 12 3 4.8 unknown Andheri catchment The Andheri catchment is characterised by intensively-managed cultivated uplands and degraded, often-gullied, red soils in the lowland. It is north-facing with a moderate temperature regime. Station 2 is situated at 850 m near the Andheri River's confluence with the Jhikhu River. 23 Figure 2.7 Elevational profiles of the study streams (vertical scale is exaggerated by 10 times). 1750 20 15 10 5 0 Horizontal Distance (km) from Jhikhu Station (1) Note: numbers denote hydrometric stations 24 Kukhuri catchment The Kukhuri River catchment forms part of the headwater area of the Andheri catchment. This catchment is dominated by well-managed rainfed cultivated steeplands with only a small red-soil component. The Kukhuri River is monitored at 1060 m at station 10, 40 m upstream of its confluence with the Andheri River. Upper Andheri catchment Along with the Kukhuri River catchment, this catchment forms the headwaters of the Andheri River. Though the characteristics of these catchments are generally similar, the Kukhuri catchment is proportionately steeper and its irrigated land is consequently of lower productivity. A relict landslide (occupying about 1 % of the basin) along with some smaller pockets of mildly-degraded red soils are located in the northwestern portion of the catchment. This upland section of the Andheri River is monitored at Station 9 at 1060 m about 200 m upstream of its confluence with the Kukhuri River (station 10). Mid-Reach Andheri catchments Stations 11 and 12 are hydrometric stations located between the headwater area (stations 9/10) and the mouth (station 2) of the Andheri River. These monitoring positions better isolate the transition in this basin between the steep, intensively-managed headwaters and the gullied, red-soil, partly-abandoned lowland. Specifically, station 12 (elevation 1020 m) includes all of the Upper Andheri and Kukhuri sub-catchments along with a portion of degraded forest along the Andheri River. Station 11 (elevation 880 m) includes all of the station 12 drainage as well as extensive forest and degraded shrub land and a small amount of the gullied lowland. These are referred to as the Andheri Mid #1 (station 11) and Andheri Mid #2 (station 12) catchments. Dhap catchment The Dhap River catchment, largely flat and south-facing, provides a strong contrast to the characteristics of the catchments within the Andheri catchment. Dominated by red soils, its land is 25 often in a degraded condition. Its drainage is composed of two distinct units - one in a steep location near the river's confluence with the Jhikhu River and the other flatter and more distant. This affects hydrological behaviour at its mouth accordingly. This river is monitored at station 3 at 825 m, 675 m upstream of its confluence with the Jhikhu River. 26 3. Methods Methods used in the study are described according to field and laboratory approaches. A selection of procedures used for data synthesis is presented in the chapter's final section. 3.1 Field methods 3.1.1 Climate Climate measurements carried out in this study consist of rainfall and temperature using both manual and automated instruments. Rainfall Figure 3.1 shows the locations of all rainfall monitoring stations in the study area. At these locations, a mix of storage and recording gauges has been in place from three to seventeen years with most records being of a three- to five-year duration. Appendix A3 provides a summary of the gauges, including gauge number, location, and elevation. Most gauges are located between 850 and 1350 m elevation. Measurement of rainfall intensity has been made at five sites for three years using recording rain gauges (tipping buckets - Middleton and Spilhaus 1953) complete with pulse data loggers. Rainfall resolution is 1.0 mm at the two monitoring sites within the Andheri Basin (Baluwa and Bela) and 0.25 mm at the other three sites (Bhetwaltok, Bhimsenthan, and Kamidanda). The tipping buckets were installed on a permanent table, one metre from ground level, and have a circular opening of eight inches (324.3 cm2). Generally, they were visited monthly, though during the rainy season this frequency was increased. Files were converted into ASCII format and manipulated as described in section 3.3.3. The calibration for each tipping bucket was checked twice annually. Sampling frequency varied from 2 to 10 minutes in the earlier period of monitoring (depending on the resolution of the tipping mechanism) and was fixed throughout at 2 minutes for subsequent monitoring. Figure 3.1 27 Lay-out of monitoring network for rainfall and temperature within the Jhikhu River basin. Climate Monitoring Network — Major Rivers • 24-hour Rain Gauge A Tipping Bucket (includes a 24-hr rain gauge) O Air (manual, max/min) O Air/Soil (automated) (§) Manual & Automated both present 28 To assure data continuity, a 24-hour rain collector was installed nearby each tipping bucket. Occasionally, the automated gauge record is incomplete as a result of data logger malfunction; in these instances, the supplementary gauge is used to complete the data record (though the short-term rainfall-intensity result clearly is lost). Two types of storage gauges were installed for measuring rainfall over 24 hours and were visited daily at 7:00 am local time. The first is of the type used by the Department of Hydrology and Meteorology of HMG. It has an opening of eight inches (324.3 cm2) which stands 1.5 m from the surface. The rain flows directly into a cylinder calibrated for the opening. Four of these rain gauges have been in place in the Jhikhu River basin during the study period. The second rain collector was custom designed using off-the-shelf ABS (hardened plastic). This collector was produced inexpensively so that over 50 could be installed in the field. Its opening is four inches (81.1 cm2) and it sits 55 cm from ground level. This compares well with the standard gauge of the Atmospheric Environment Service (of Canada) which has a 64.5-cm2 opening, sitting 30.5 cm off the ground (Storr and Ferguson 1972). The custom gauge has a straight 5-inch-long entranceway, tapered at the bottom, and a storage capacity equal to a total rainfall of 250 mm (double that of the AES standard gauge). The rainfall reading (mm) is made by measuring the volume of water in the storage bottle (ml) and then applying a conversion factor of 0.1233 to account for the size of the collector's opening. The catches from these three gauge types have been compared at seven locations to test for consistency across gauges as shown in Table 3.1. The catch ratio for each measurement at each test site has been calculated for all storms and the results appear in Appendix A3. The well-known negative bias associated with wind exposure of a gauge (Brown and Peck 1962) should be responsible for most of this variability. It is to be expected that the custom gauge best represents rainfall because of its smaller opening and its installation closer to the ground. Keller (1972) pointed out that a rain gauge in a network in uniform terrain with the highest catch should be the gauge least affected by 29 wind. McGuinness and Vaughan (1969) observed a seasonal change in rainfall and snowfall catch and determined that it was in fact a wind effect due to seasonal wind regimes. They also found a large difference in the ability of different instruments to cope with the wind effect. The design of the custom gauge minimises wind bias which serves to increase the catch of the custom gauges relative to the two "standard" gauges. The averages and distributions of all catch ratios at each site are given in Table 3.1 and suggest that the standard gauges underpredict rainfall by an average of 20.5% (0.258/1.258) using the six comparisons which fall in the tight range of 1.23-1.29 having an average of 1.258. (A catch ratio of 1.258 means that 0.258 mm out of each 1.258 mm is not measured by the standard instruments; this represents an underprediction of 20.5% and can be corrected using a multiplier adjustment of 1.258 on the rainfall measured by the standard instruments.) As a result of this finding, all measurements from the standard gauges have been increased by 25.8% from that measured. Table 3.1 Comparison of catch ratios of the rain gauges at seven test sites. Test Location Gauges Installed Catch Ratio Custom/Tipping Bucket Custom/HMG N X a N X a 1 Baluwa 111 1.28 0.18 2 Bela (1) 58 1.00 0.083 3 Bhimsenthan 78 1.23 0.17 4 Kami dan da 95 1.44 0.13 5 Bhetwal tok 82 1.24 0.30 104 1.29 0.24 6 Dhulikhel 70 1.27 0.21 7 Bela (2) 93 1.24 0.19 It is expected that the wind bias is variable depending on wind speed. Measurements of wind speed within the study area are unavailable. However, observations during the study period suggest that only infrequently is wind speed significant during heavy rain. As a result, the effect of wind 30 speed is neglected in adjusting rainfall records. Further examination of Table 3.1 indicates that the catch ratios at Sites 2 and 4 are very different from the 1.258 average. Site 4 (catch ratio of 1.44), and the tipping bucket in particular at this site, is exposed to a higher degree of wind than at the other sites and as a result the catch ratio at this site is higher than the average catch ratio at the other sites. At Site 2, the custom rain gauge is installed on a small ridge which effectively raises its point of installation above the ground. As a result, both the custom and standard gauges experience a similar wind bias at Site 2 and the catch ratio is unity. These two anomalies are not used to calculate the average catch ratio. The adjustment is applied to every measurement of the tipping bucket regardless of rainfall intensity. The bias associated with the moderate- and high-intensity rainfall dominates the aggregate bias, here called the catch ratio. Since moderate- and high-intensity rainfall are of the greatest interest in this study, it is reasonable to adjust all rainfall intensities consistently by the overall catch ratio. Temperature Air temperature was monitored using a combination of manual and recording devices. These sites are shown in Figure 3.1 and are summarised in Table 3.2. Temperature was measured manually at a total of six locations. In each case, the max-min thermometer was installed in a standard Stevenson's screen (Middleton and Spilhaus, 1953). Five of these were maintained by this study and provide a record of the daily maximum and minimum air temperatures during the past four years. The sixth site was maintained by HMG and provides a daily record of maximum and minimum temperature covering 17 years. Automated temperature monitoring, recording every 10 minutes, was begun in 1992 at three sites as shown in Figure 3.1. In these cases, both air and soil temperature (depth of 20 cm) are measured. These sites were chosen to reflect the characteristics of north-facing, south-facing, and valley-bottom microclimates as summarised in Table 3.2. 31 Table 3.2 Installation information of temperature measurements. Location Aspect Elev. (m) Installation Date Type Baluwa Flat 865 May 29, 1992 air; manual Baluwa Flat 865 Jun 12, 1992 air/soil; automated Panchkhal Flat 865 Nov 1970 air; manual Bhimsenthan Flat 895 May 30, 1992 air; manual Bhetwaltok South 1300 Jun 6, 1992 air; manual Bhetwaltok South 1300 May 27, 1993 air/soil; automated Bela North 1255 Jan 1, 1990 air; manual Bela North 1255 Jun 12, 1992 air/soil; automated Dhulikhel North 1545 Jun 1, 1989 air; manual Note: Automated measurements are sampled at 10-minute intervals. 3.1.2 Stream measurements Flow measurements or suspended-sediment sampling, or both, were carried out at seven hydrometric stations during 1992-1994 as shown earlier in Figure 2.6. These measurements were challenging because the floods are generally of short duration and most occur at night following afternoon and evening convectional showers. A monitoring team was trained for each station to improve measurement success. Bridges were constructed at five of these cross-sections (stations 1, 2, 3, 9, and 10) to take discharge measurements at high flow. Flow measurements were made using Price AA current meters. At low flow, readings were taken by wading in the stream and recording the flow velocity at frequent lateral intervals (Buchanan and Somers 1969). Due to the very brief nature of high-flow conditions and to prevent damage to the meter, flow measurements of the surface were taken and estimates made of the average velocities at three lateral positions in the stream cross-section. Following an approach modified from Hoyt (1912), a conversion factor was applied to the flow velocity of the surface. Rating curves have been constructed for each station using the three years of flow measurements and are discussed later in Section 3.3.1. Event and annual hydrographs have been constructed using both automated and manual 32 records of gauge height (stage). Manual records consist of daily (7:00 am) measurements throughout the years 1992-1994 and flood measurements when possible. Manual measurements form the entire record of stage for stations 3, 11, and 12. Automated readings provide the large majority of the flow record at the other four stations and are augmented when necessary with the manual record. At each of stations 1, 2, 9, and 10, an automated monitoring system was installed in 1992. The equipment consisted of a pressure transducer installed near the gauge, a data logger, and a 12-volt power supply. A two-minute sampling interval was selected during most of the study period. Samples of suspended sediments were taken during flood events at all stations. A DH-48 depth-integrating sediment sampler (Guy and Norman 1970) was used at stations 1, 2, 3, 9, and 10 while dipping-bottle samples were taken at stations 11 and 12. When safe to do so, the rivers were waded in order to sample the thalweg. For safety reasons, sampling at higher flows was carried out at a position at or near the bank. These streams are steep and well-mixed when in flood, permitting this varied approach. Sampling frequency was changed according to station and flood size. In the steeper catchments such as stations 9, 10, and 12, as many samples as possible were taken - generally up to ten samples per flood. At stations 1, 2, 3, and 11, between 5 and 15 samples were taken per flood. At Station 1, as many as 30 samples are available for some floods. 3.1.3 Erosion plots Five erosion plots were built in 1991 and then monitored throughout 1992-1994. Some features of the plots are summarised in Table 3.3 and their locations are indicated in Figure 2.6. Each plot contained two terraces connected by a steep terrace riser. In each case, the cropping practice was the same: maize during the rainy season and either millet or wheat during the dry period. The farmers were encouraged to manage the plots in the same way that they managed their other fields. Each plot was on red soils because it is widely accepted that red soils are more difficult to manage and erode at 33 Table 3.3 Characteristics of erosion plots in the study. Plot No. Elev Aspect Hillslope Angle Area Upper Terrace Slope/Length Terrace Riser Height Lower Terrace Slope/Length USDA Soil Type (m) C) (m2) (Vm) (m) (Vm) 1 1240 NNE 30 70 18/4.3 1.6 18/5.0 Haplustalf 2 1240 NNW 27 68 23/10 0.8 23/3 Rhodustalf 3 1240 NNW 27 64 23/9 0.8 23/4.2 Rhodustalf 4 1305 sw 22 108 12/6 1.0 11/6 Rhodustalf (degraded) 5 1260 ssw 25 100 8/10 5.0 15/9 Ustochrept (truncated Rhodustalf) a greater rate than non-red soils (Turton et al. 1994). Erosion and runoff were measured on an event basis. Three drums were located in series at the outlet of each plot. The soil and water mixture in the drums was sampled after each erosion-causing storm event. The methodology used to achieve this evolved during the study period. For each drum, the total height of soil and water was recorded. The water column was sampled and drained off to leave behind the eroded soil which had settled to the bottom of the drum. The height of this soil was also recorded and this material sampled. Using these measurements and the samples, and knowing the dimensions of the drums, total erosion and total runoff were calculated for each storm event. 3.1.4 Soil properties In the field, a selection of soil properties was measured including compactness and infiltration rate. In addition, a complete profile description was recorded at 20 sites. 34 A penetrometer was used to estimate surface-soil compactness. The instrument was used at ten locations within a field and the results averaged for the entire field. The penetrometer was inserted into the surface using roughly the same force for the surface (0-15 cm) and the subsurface (15-40 cm) soils. The value noted was the highest value reached while the penetrometer was being pushed downward in each vertical section. Soil moisture content and bulk density were measured concurrently in most cases. The penetrometer measurements provide a relative index of the compactness of agricultural fields in the study area. This index can be easily qualitatively calibrated to other quantitative measures of compaction. Infiltration rates of surface soils were measured using double-ring infiltrometers following Booker Agriculture International Limited (1984). For each measurement, three sets of infiltrometers were installed and monitored simultaneously. The test was run for 4-6 hours, though in clay-rich soils this often had to be extended to up to 10-12 hours. Soil profile descriptions were recorded at all sites where infiltration was measured. At each site, the depth and type of each horizon down to the parent material was noted. For each horizon, the following soil parameters were determined (Booker Agriculture International Limited 1984): • colour • texture (of the fraction < 2 mm in size) • structure • consistency • porosity • presence of roots • horizon transition • coarse-fragment content (percentage of sample > 2 mm in size) • moisture content In addition, the landuse, physiographic position and topography of the site were recorded. The indigenous soil classification was also determined for the surface horizon (see Chapter 7). 3.1.5 Soil movement A collection of techniques was employed to provide spatial estimates of soil movement. 35 Erosion and accumulation pins were installed to indicate change in soil level at specific spatial locations. Surveys provided information on the spatial variation of erosion and deposition across an area. Bamboo pegs were used in khet, bari, and in gullies to assess rates of soil loss and accumulation. In the khet where they were used to measure soil accumulation, between 4 and 12 accumulation pins were installed in each individual field. In the first year, pins were installed at entranceways and exits to the fields to estimate deposition and scouring occurring there and to relate these areas to the average field values. It was found that these edge effects can be significant but are generally confined to the perimeter of the field due to the complex routing water takes in the fields. Therefore in subsequent years, pins were placed in only the centre of khet fields. For each pin, an average of all four sides was taken of the soil height relative to the notch showing the original soil level. In the bari, soil pins were used to estimate soil accumulation in permanent and temporary drainage ditches. In each case, three pins were installed - at the beginning, the middle, and at the end of the ditch length. All pins were placed in the lateral centre of the ditch. These pins were also measured on all four sides. Finally, in gullied land, pegs were installed near active gullies to estimate rate of change of gully dimensions. For each monitored gully, about five to ten pins were installed and maintained as permanent control points. They were installed flush with soil level to make them invisible to passers-by. Detailed records were kept of the relative locations to facilitate quick relocation of the pins, to have many lines from which to measure the gully sides, and to cope with pins lost to gully growth. Survey techniques were used on several occasions to record the spatial variation of soil erosion and deposition. Surveys of surface erosion and mass wasting were carried out after several major events during 1992-1994. At its most-detailed level, the entire basin was examined on foot for evidence of erosion and deposition. For each example found, its location was noted on the aerial 36 photograph and the following characteristics were recorded: • type of erosion • cause • volume of material moved (estimate) • description of eroded material • connection (if any) to other observed erosion Where deposition was observed, the volume and type of material involved were noted. Later in the study, surveys of stream deposition were carried out to quantify changes in sediment storage within the bed of the Andheri and Kukhuri Rivers. Rather than attempting a complete inventory over time of bed storage, locations were chosen to monitor bed stability and changes in bed storage over time. The following observations and measurements were made at these points of reference: • photographs • rate of sand-bar accretion/loss using bamboo pegs • description of sediment sizes present • grab sample of fine sediment (for colour) This information was used to evaluate the extent of storage within the bed of the Andheri and Kukhuri Rivers. 3.1.6 Mapping Topography, resource attributes, and landuse have been mapped variously at 1:50 000 (1947), 1:20 000 (1972, 1990), and 1:5 000 (1994) within the Jhikhu River basin. For this study, the 1:20 000 topographic base map with 50-metre contours is used for overall assessments within the Jhikhu basin and the 1:5 000 topographic base map with 10-metre contours is used for detailed evaluations within the Andheri basin. The 1990 landuse was mapped using photo interpretation and follow-up field verification (Schmidt 1992). In 1994, the 1:5 000 map was developed from the 1990 map using analytical photogrammetry techniques. The landuse was updated by field checking to yield a 1994 landuse map for use with this 1:5 000 topographic map. Both maps were converted for use in a Terrasoft Geographic Information System. 37 Landuse mapping used in this study follows six categories (Schmidt 1992): bari cultivated rainfed khet cultivated irrigated forest trees with a crown cover of more than 10% and a height greater than 2 m shrub trees less than 2 m in height or with a crown cover of 1-10% if greater than 2 m grassland uncultivated with less than 1 % crown cover other outcrop; built-up areas. Forests in the Middle Mountains are heavily coppiced and harvested but still maintained as "forests" hence the low value of 10% crown cover to distinguish forest and shrub classes. Note that forest, shrub, and grassland definitions are not tied to litter or surface cover. A new legend is developed in this study to evaluate land degradation. Its three classes are: moderately degraded - little or no vegetation with minor gullies; or - forests with less than 25% crown cover and no understorey vegetation; or - shrub with crown cover less than 25% severely degraded - heavily gullied with little or no vegetation productive - active bari; active khet - built-up areas - forests with more than 25% crown cover or with good understorey vegetation - shrub with crown cover greater than 25%. The 25% crown cover value distinguishing forests between the productive and moderately degraded classes recognises that many forests in the study area are in a moderately degraded state (i.e., 10-25% crown cover). 38 3.1.7 Interviews and Questionnaires Throughout the period of fieldwork, constant contact was made with the local farmers to improve understanding of their knowledge about their environment. Of particular interest were the techniques they employ to manage their agricultural landscape and the rationale on which these approaches are based. A great deal of this fact-finding was informal and completely unstructured. However, several attempts were made to formalise this part of the study using interviews and questionnaires. A complete list of the interviews which were carried out is provided in Table 3.4. Table 3.4 Topics examined for farmer indigenous knowledge. Year Interview Type Subject No. 1991 unstructured Perceptions, attitudes, and approaches 5 1992 semi-structured Soil classification 12 1992/3 structured £7ief-accumulation management 32 1993 structured Soil classification 11 1993 structured fiari-erosion management 21 1993 structured Irrigation-dam management 32 Interviews were unstructured, semi-structured, and structured. Unstructured interviews were carried out only in the first year of the study to improve overall understanding. Semi-structured interviews were used to pursue a more narrowly-defined subject and to allow the farmer to reveal as much of her or his knowledge as possible. Structured interviews were used to evaluate farmer approaches for specific subjects after unstructured and semi-structured techniques were exhausted. These enquiries consisted of questions which could be answered quantitatively. Questions were written into formal questionnaires so that the results could be compiled easily on a spreadsheet. Questionnaires were brief, taking at most ten minutes to complete. Typically, they were translated to the farmer by a Nepali staff member who also recorded the farmer's response. An example of each questionnaire appears in Appendix A4. 39 3.2 Laboratory methods 3.2.1 Stream Sediment Samples Analysis of stream suspended sediment was carried out in the field headquarters and at the University of British Columbia. Before filtering, both the conductivity and the pH were measured using a Hanna HI 9025 meter. The samples were filtered (generally overnight) through a pre-weighed Whatman 40 filter (medium - 0.008-mm mesh). Some clay-rich samples with a high amorphous content were filtered twice. Each filter paper was air dried and then sent to Kathmandu from the field where it was oven-dried and weighed. Further analyses of the suspended sediment were carried out at the University of British Columbia. The Munsell colour designation of all samples was noted first before any destructive analyses took place. Chemical analyses carried out on these sediments were limited to phosphorus, carbon, and pH due to the small sample sizes generally available. Phosphorus was measured as orthophosphate with a Lachat instrument. Total carbon was determined with the Leco Analyser (Lavkulich 1978). pH was measured using a Radiometer Copenhagen PHM62 Standard pH Meter. A sub-sample of no more than 3 g of sediment was taken manually from a thoroughly mixed sample. Sediment particle-size distribution was determined by first dry-sieving this sub-sample to 0.250 mm. In each case, the sediment was separated into the following size classes: 1.4-2.0 (mm) 1.0 - 1.4 (mm) 0.710 - 1.000 (mm) 0.500 - 0.710 (mm) 0.355 - 0.500 (mm) 0.250 - 0.355 (mm) The size distribution of the remaining sample (though not less than 1.5 g) was determined using a Sedigraph 5100 Particle Size Analysis System. The two sets of results were reconciled to the original sample using a spreadsheet. 40 3.2.2 Stream water samples Stream water samples were analysed in Kathmandu in the Water Laboratory operated by the Department of Hydrology and Meteorology (HMG). Conductivity, pH, sediment concentration, and dissolved sodium, potassium, calcium, magnesium and orthophosphate contents were measured. Conductivity and pH were measured in the same way as they were in our field laboratory. Sediment concentration was virtually nil because filtering was normally carried out in the field before sending the sample for water analysis. Dissolved phosphorus was measured as orthophosphate. Sodium and potassium were analysed using a flame photometer. Calcium and magnesium were measured by titration. 3.2.3 Soil samples Soil analyses were carried out on soils from upland bari, surface and cumulic soils from irrigated khet, and soil horizons samples taken from soil pits dug nearby where measurements of infiltration capacity were taken. Depending on the type of soil sample, a variety of physical and chemical parameters was determined. Soil chemical analyses included pH, total carbon, available phosphate, exchangeable cations, cation exchange capacity, and base saturation (Lavkulich 1978). The Leco Analyser gave total carbon for a 0.5-g sub-sample through complete combustion. Available phosphorus was measured as orthophosphate using a Lachat Autoanalyser. Exchangeable cations (Ca, Mg, K, and Na) and cation exchange capacity were determined using ammonium acetate extraction and atomic adsorption spectrophotometry. Physical parameters included particle-size analysis and colour. Particle-size distribution was determined using a hydrometer and Stokes' Law (Lavkulich 1978). The coarse fragment content was calculated from dry sieving to 2 mm. Colour was determined according to the Munsell colour system. 41 3.3 Data synthesis To prepare the data for detailed analysis, some basic assimilation procedures were used. Four important examples are: • Development of stage-discharge relations • Parsing of automated data. • Reconstruction of rainfall record from tipping-bucket record. • Use of a geographic information system to integrate spatial data 3.3.1 Stage-discharge relations Stage-discharge relations have been developed at five hydrometric stations: Jhikhu River below Bhendabaribesi (station 1), Lower Andheri River (station 2), Dhap River at Shree Rampati (station 3), Upper Andheri River above Kukhuri River (station 9), and Kukhuri River at Andheri River (station 10). For each point on the stage-discharge relation, individual velocity measurements are combined to yield an estimate of total flow at the recorded gauge height. Total flow is determined by first measuring flow velocity and depth at a selection of lateral points in the stream cross-section. At each lateral point, at least one velocity measurement is taken at a vertical position chosen to represent the average velocity over the entire depth at that lateral position as described by Buchanan and Somers (1969). If the lateral slice is less than one metre in depth, a vertical distance of 0.6 of the depth below the surface is acceptable. If the depth is greater than one metre, two measurements are recommended - one each at 0.2 and 0.8 of the total depth. Where the 0.6 or 0.2/0.8 method has been successfully carried out, the entire flowrate is determined by addition knowing the area of each slice (using stream depth and the amount of the stream width represented by the measurement at that lateral position). As explained in subsection 3.1.2, many flow measurements cannot be carried out in this standard way. Instead, a flow measurement is made at the stream surface which is at a higher point in the flow than the 0.6 and 0.2/0.8 methods suggest. In these instances, a correction factor (CF, modified from Hoyt 1912 based on comparison of surface and 0.6 measurements taken at medium 42 flows) has been applied to the calculated velocity (VtUT(aa.) as follows: V = CF x V,u r f a c e where depth < 1 m CF = 1 depth ^ 1 m CF = 1 - (depth/20) (depth does not approach 20 m) Once the flows are calculated and summed for each measurement, the stage-discharge pairs are assembled in an x-y plot. Linear regression using a logarithmic transform is used to develop relationships of discharge as a function of gauge height. The five stage-discharge relations which are used in this study are provided in Appendix A5. 3.3.2 Automated data Automated data from the twelve data loggers in this study were parsed using in-house algorithms. Typically, long periods pass with little or no change in data reading. This applies to river stage, rainfall, and air/soil temperature. Data parsing follows the general principle of retaining only the first and last readings within an allowable, user-defined tolerance. Thus, when a gap in the parsed record exists (other than data gaps due to equipment error etc. which were appropriately flagged), it is known that the "missing" data fall between the values bounding them subject to the tolerance applied by the parsing algorithm. Table 3.5 shows the tolerances used to parse all automated data. The value of zero for the rainfall data indicates that only non-zero readings were retained and therefore the rainfall record can be reconstructed with absolutely no loss of information. In contrast, the stage and temperature record suffer a small loss in resolution according to the magnitude of the tolerance. The tolerances used in both cases were far smaller than any value which would threaten the integrity of the database. 3.3.3 Tipping-bucket rainfall data Any tipping-bucket rain gauge records total rainfall during small time periods continuously. No information is retained over timescales smaller than the basic time period of integration. At high 43 Table 3.5 Tolerances used to parse automated data. Data Type Tolerance Stage, station 1 Stage, station 2 6.6 cm 3.0-4.4 cm Stage, stations 9 and 10 Rainfall 0 mm 2.6 cm Air temperature Soil temperature 0.5'C 0.8'C Note: In each case, if the change in reading is less than or equal to the tolerance, then the subsequent reading is not retained. rainfall rates, this is essentially of no concern but at low rates, especially those falling below the rate of one tip/period, the loss of information can be important. For instance, if one tip is recorded every hour for three hours, did each tip's rainfall occur during one period? two periods? or during some combination of periods up to each entire hour? The tipping bucket does not provide this information so some assumptions must be made to deal with these low-rainfall rates. The assumptions used in this study are as follows: 1) Single tips are averaged over all preceding sample periods (within the same storm) for which no rainfall was recorded. This value is also imposed upon a preceding single tip if it begins the storm record. 2) If more than one tip occurs in a tipping period then the tips are not averaged over preceding tip periods regardless of the number of tips recorded in these earlier periods. 3.3.4 Geographic information system The larger study has integrated all spatial biophysical and socioeconomic data into a Terrasoft Geographic Information System (GIS). The GIS has also been used to derive a digital terrain model for the basins under study. The present study uses this GIS and its database for three applications: 44 1) Overlay techniques Land and landuse attributes are overlaid to determine spatial relationships. For example, the soil colour map is overlaid with the digital terrain model to reveal the elevation of red soils. Also, rainfall isohyets are overlaid on the soil colour map to determine the soil colour where rain is heaviest. 2) Area computation Areas are computed for various physiographic and landuse classes. For example, the area is computed within a basin which is in specific bands of elevation, aspect, and slope. 3) Point data A variety of point data are determined from the GIS. For example, stream profiles are developed using point data along stream courses. 45 4.0 Monitoring monsoonal rainfall for studying erosion and sediment transport 4.1 Introduction In Chapter 2, patterns of local and regional climate were characterised descriptively with a focus on rainfall over monthly and annual timescales. However, rainfall delivery is highly variable in time and space and, in the Middle Mountains of Nepal, these variabilities have the potential to affect profoundly erosion and sediment transport. Patterns of rainfall behaviour important to erosion and sediment transport are therefore evaluated here to address the significance of rainfall to the overall diagnosis of basin sediment dynamics over different spatial and temporal scales. A storm classification is developed for use in the erosion analysis. In the study area, erosion and sediment transport can respond quickly to changes in rainfall delivery. The landscape and its surface form a highly heterogenous mountainous environment, resulting from both very steep topography and a high degree of site-specific human manipulation. Surface condition changes both temporally and spatially in response to management. Variability in both rainfall delivery and erosion susceptibility interact to yield highly complex basin sediment dynamics. Rainfall monitoring is a sampling exercise which must pay careful attention to regime variability to be successful in its characterisation. Decades of research examining the mechanics of rainfall measurement have yielded a good understanding of how to sample rainfall effectively at a point. In contrast, understanding how to sample for temporal and spatial variabilities is grossly inadequate, especially in mountainous environments. Logistics, cost, and challenges in measurement all contribute to the paucity of studies. Some specific questions addressed by this chapter include: Temporal Variability a) How do storm-period variables (especially storm intensity, total rainfall, duration, and burst timing) vary between storms? 46 b) Do these storm-period variables change seasonally? c) Does the pattern of spatial variability in storm rainfall change seasonally? d) Does storm frequency change seasonally? e) How frequently must rainfall be measured to adequately assess storm-period variability? Spatial Variability a) How much rain is actually falling over the basin? b) Where is the rain falling? c) How accurately does the gauge network monitor rainfall? d) Are there predictable local and elevational changes in rainfall delivery? This study pursues answers to these questions using recording and storage rain-gauge data gathered over a variety of spatial and temporal scales. Recording rain gauges have been in place for three years at five different sites (see Figure 3.1). Four of these sites are located to provide results for a 2x2 matrix of high and low elevation and south and north aspect. The fifth gauge is located at high elevation and north aspect, far removed from the sites of the other four. About 25 storage gauges provide spatial resolution of 24-hour rainfall on each of two adjacent hillslopes during the rainy season. The monitoring network on the south-facing hillslope was in place for 1992 only while the north-facing network has been in place during each rainy season of 1992-1994. On each hillslope, the network is divided into a high- and low-elevation cluster each of at least 10 gauges. Eight storage gauges have been in place since 1992 providing uninterrupted measurements of 24-hour rainfall at locations throughout the Jhikhu River basin. After discussing findings from other research which are useful to this study, three analyses are presented. Analysis of temporal variability focuses on changes in storm-period variables during a storm and across seasons. Spatial variation regionally with elevation and locally within a storm cell and across a hillslope form the focus of the following section. Findings from these two analyses are assembled in the last section to yield a classification approach to addressing the rainfall parameter in 47 studies of erosion and sedimentation. 4.2 Research background Though there has been a great deal of study about the mechanics of precipitation, there remain large knowledge gaps in topographic patterns of rainfall delivery, especially in the Himalaya. The extent of variability inherent in systems of mountain precipitation, the difficulty of gathering data - including automated data - in the Himalaya, and the common problem of adequate monitoring to address spatial variation are the most common causes for these gaps in understanding. After presenting a review of rainfall behaviour emphasising rainfall delivery at the ground surface, findings from Himalayan studies useful to the present work are highlighted to calibrate the understanding developed in other regions and to provide a context for this study's data and results. 4.2.1 Patterns of rainfall delivery Spatial patterns Although a wide variety of parameters affects rainfall delivery and its spatial distribution as measured at the ground surface, it is the effect of elevation which has received perhaps the greatest interest. Henry (1919) outlined the early studies (of the 19th century), discussing examples from around the world of changes in precipitation with elevation. He pointed out that there is typically a zone of maximum precipitation - generally below 1000 m in the tropics and between 1400 m and 1500 m in the temperate regions. He recognised that for mountains to cause an increase in precipitation, their axis cannot be parallel to the direction of the winds bringing the storm rainfall and that the steeper the slope, the greater the rate of change of precipitation. Givone and Meignien (1990) recognised this relation in establishing an array of 14 recording gauges perpendicular to the mountain range under study. They examined the detailed meteorological features of the rainfall before studying the influence of topography on it. They found that instability triggered at the footslope of the 48 mountain yielded the heaviest hourly totals at low elevation with the heaviest daily totals occurring at the top of the range. Henderson (1993) examined elevational effects on storm total-rainfall in the Southern Alps of New Zealand and found the maximum rainfall to occur at 10 to 20 km upwind of the elevational maximum. These findings underscore the importance of carefully selecting the storm-period variables under consideration and the range of elevation for which extrapolation is acceptable. Smith (1989) described the four most likely mechanisms of orographic precipitation: smooth, forced ascent; seeder-feeder mechanism; diurnally-forced convection; and triggered convection by forced ascent or blocking. A common aspect is that precipitation enhancement occurs on the upwind slope and can be so reliable as to be used as a crude indicator of regional wind direction. Unfortunately, despite significant study of precipitation change with elevation, comparatively little is understood about the specific mechanisms involved. He suggested that this lack of understanding must change if we are to better predict spatial variability in mountainous terrain. The rainfall-elevation effect, however, is far from alone in shaping spatial patterns of rainfall delivery. Spreen (1947) realised this and included slope, exposure, and orientation as independent variables important to rainfall spatial variability. Spreen's ability to predict precipitation locally increased dramatically when these other factors were also considered (R2=55% elevation only; R2=94% all factors). Importantly, he found that in steep mountainous terrain, these non-elevational factors dominate locally. Sinclair (1993) blamed this lack of "orographic resolution" on the inability of elevationally-driven approaches to predict rainfall spatial variation in an extreme event. These studies have one thing in common: they examine spatial variability over large scales (> 100 km2). In the Middle Mountains, and certainly in many other mountainous areas of the world, though the average rainfall over many storms may vary spatially in a predictable way, individual events may not. This temporal variability over a fixed spatial scale is a topic rarely discussed in the literature. There are few examinations of the behaviour of individual cells of limited spatial extent (1 to 10 km2) in mountainous terrain. For instance, Paturel and Chocat (1993) used measurements from 49 a network with average density of one gauge per 20 km2 by asserting that this figure corresponds to the smallest convective cell likely to have an influence on the drainage network. They noted and did hot explain, however, that a high degree of spatial heterogeneity resulted in their rainfall data. Temporal patterns Another common approach to examining dynamics of storm rainfall uses descriptive statistics of myriad storm-period variables. There appear to be as many analyses of storm-period variables as there are authors. For instance as shown in Table 4.1, the definition of a storm (when it is given) is rarely the same from author to author. Minimum total rainfall (R) and time-without-rain (S) requirements are the dominant criteria. How these are applied appears to depend on the nature of available data. For instance, Reid et al. (1981) had 24-hour rain data and established the definition of storm on that basis. Tropeano (1991) discussed 280 events in detail but did not present his storm definition. These studies tend to define a storm to accommodate the data available and do not evaluate the appropriateness of the definition chosen. Depending on the goal of the analysis, there exists the potential of introducing an error at a fundamental level. Table 4.1 Published storm definitions. Author(s) Huff (1967) Collinge and Jamieson (1968) Reid et al. (1981) Tropeano (1991) Farmer and Fletcher (1972) Patural and Chocat (1993) Overseas Development Agency (1995) Storm Definition S 2> 6h RT ^ threshold from network RT ;> 25 mm anywhere in basin RT ^ 0.5 mm in 24 hours with a day of RT < 0.5mm on each side None provided S > 1 h and RT ^ 2.5 mm S ^ 4h S > 1 h Note: RT = total storm rainfall (m) S = length of time without rain before and after a storm (h) 50 Having established the definition of a storm, there are many approaches to analysing the storm-period variables. Herschfield (1962) presented extensive analyses of storm-period variables and Huff (1967) pointed out the importance of tailoring such analyses to a specific application with specific spatial and temporal scales of concern. Unfortunately, after analysing 261 storms using a quartile-separation approach, Huff (1967) determined that quartile separation does not vary consistently with storm type. The results of these analyses uncover some interesting temporal dynamics in rainfall behaviour at a point. Farmer and Fletcher (1972) examined convectional, high-elevation burst-rainfall behaviour in Utah finding that over half of storms yielded more than 50% of the storm rainfall in a 10-minute burst. Also, in most storms the period of heaviest rainfall occurred in the first quartile. They used their burst information to define storms for planning purposes in soil erosion and flood protection. Their study illustrates that relevant rainfall statistics ultimately depend upon precipitation mechanisms, in this case semi-desert to desert convectional showers driven by extensive surface heating. Bryant (1991) documented an apparent worldwide maximum envelope of rainfall intensity for any given timescale. Presumably such a relation could help develop an appreciation for the relative severity of different mountain precipitation regimes. Bryant also emphasises the lack of rainfall data over short timescales of less than 5 minutes. Given the many hurdles to adequately measuring storm rainfall distribution in both time and space including the instrumentation concerns reviewed in section 3.1.1, it is not surprising that many researchers have turned to rainfall models for their rain data (e.g., Rodriguez-Iturbe and Eagleson 1987). Another approach to estimate rainfall in mountainous terrain is to use streamflow data distributed back over the catchment and corrected for evapotranspiration (Danard 1971; Anderson 1972; Ishihara and Ikebuchi 1972; Dingman 1981; Griffiths 1981). Unfortunately, it is for flood prediction in mountainous terrain that storm characterisation is most needed (e.g., Collinge and Jamieson 1968; Dingman 1981). 51 4.2.2 The Asian Monsoon The Asian monsoon which brings the rainfall examined in this study occurs because of the seasonal reversal of winds across the Indian subcontinent. Webster (1987) identified the three fundamental driving mechanisms behind the planetary monsoon: 1) the differential heating of the land and ocean creating the pressure gradient which drives the winds from high pressure to low pressure; 2) the rotation of the Earth which introduces a swirl to the winds; and 3) moist processes that store, redistribute, and selectively release the solar energy arriving over most of the tropics and subtropics. The Asian monsoon is notable for its immense extent and the penetration of its influence beyond tropical latitudes (Barry and Chorley 1992). The potential magnitude of rainfall in this region and both spatial and temporal variability of the monsoon are important to the present study and are discussed in this section in relation to synoptic-scale precipitation mechanisms. The Asian monsoon is known for its ability to deliver huge quantities of rainfall. In developing the concept of an envelope of maximum possible rainfall in relation to total measurement time (Bryant 1991), the data for all time periods greater than one week came from Asian monsoon measurements in Cherrapunji India. The highest 24-hour rainfall ever recorded in the Ganges basin was 823 mm (at Nagina in Uttar Pradesh), in the Brahmaputra basin it was 1036 mm (at Cherrapunji) and in Nepal it was 539.5 mm at Tistung (Dhital et al. 1993). The mechanisms responsible for these extreme rates of rainfall delivery include cyclonic storms near the Bay of Bengal, various types of low-pressure (widespread across the Subcontinent), and orographic mechanisms due to complex topography and, at times, extreme relief (Takahashi and Arakawa 1981). The variety of large-scale effects and the contrasts in topography over this vast region result in considerable spatial variation in rainfall delivery across the Subcontinent. Because the coastal cyclonic storms generally do not adequately penetrate the Subcontinent to 52 reach the Middle Mountains near Kathmandu, it is the monsoon depressions (areas of moderate low-pressure covering tens to hundreds of thousands of square kilometres) originating near the Bay of Bengal which bring most of the precipitation to the study area during the period of the southwest monsoon. The area is prone to thunderstorms (Takahashi and Arakawa 1981), likely triggered by the suite of orographic effects resulting from the area's complex topography. The Middle Mountains possess adequate relief (in contrast to the Siwalik and Mahabharat Ranges to the southwest) to cause smooth forced ascent and to trigger conditional and convective instabilities. Also, convection is triggered by upslope winds and the considerable diurnal heating of slopes (Barry and Chorley 1992). Undoubtedly, these effects may all be present in addition to topographically-created preferred pathways. Superimposed on the large-scale regional spatial variability in mountain precipitation are significant local variations. For example, Upadhyay and Bahadur (1982) studied rain gauge data in the Dehang catchment in the Brahmaputra basin and found that correlation between adjacent stations became negligible at about 35 km. Dittman (1970, in Domroes 1979) found that there was "no systematic connection" between the daily totals of rainfall for five gauges - located in a mixture of valley bottoms and ridge tops - within a few kilometres of each other in the elevation range of 1860-2130 m near Jiri, Nepal. LRMP (1984) suggested that rainfall intensity is higher at lower elevations. Despite the extent of these important local differences, they are poorly researched (Bruijnzeel and Bremmer 1989) and, unfortunately, the overall density of rain gauges is also low - in Nepal, 1 in 490 km2 in 1984 (Department of Hydrology and Meteorology 1984). The World Meteorological Organisation recommends that in mountainous regions of tropical zones rainfall monitoring stations be installed in altitude zones of 500 m per zone and with a minimum density of 1 station for 100-250 km2 (World Meteorological Organisation 1981). Given the emphasis on valley-bottom gauge sites in Nepal and the other factors mentioned above, there is considerable uncertainty about actual precipitation rates at ungauged mountainous locations. 53 Orography causes total precipitation generally to increase because of the relation between temperature and saturation vapour pressure. But since less moisture reaches still higher (and colder) elevations, precipitation declines (Upadhyay and Bahadur 1982). This behaviour has been observed in, for example, the Canadian Coast Range (Loukas 1994). In the eastern part of the Ganges-Brahmaputra basin, a decrease in rainfall with elevation has also been observed (Bruijnzeel and Bremmer 1989). In his work in the High Mountains, Ries (1991) found rainfall to be inversely proportional to elevation between 2000 and 3300 m. Rainfall intensities were found to be greater in the valleys than on the hillslopes. Unfortunately, these authors do not report how well gauge catch biases were controlled in their studies. Although the Asian monsoon demonstrates considerable predictability, temporal variability can be significant. Das (1987) illustrated the annual arrival date of monsoon rains across the Indian subcontinent. Near Kathmandu where the study area of the present research is located, the initial monsoon burst typically comes in the second week of June. This start date is defined by an increase in the moisture content of the atmosphere and a sustained increase in rainfall (Das, 1987). Although 80% of annual precipitation falls during the monsoonal months of June, July, August, and September, there is a variable pulse alternating between active and break periods (Barry and Chorley 1992). This wet-season variability in monsoonal rainfall delivery is similar to the widely-recognised variability in tropical rainfall (Jackson 1989). Although breaks in the Asian monsoon are most common in August and September and last on average five days, they may occur at any time during the summer and can last as long as five weeks. Rainfall delivery in the Middle Mountains shows a nocturnal maximum. Winkler, Skeeter, and Yamamoto (1988) found this for hourly precipitation in the United States, particularly at high intensities. This behaviour is likely related to regular diurnal cooling of convective showers (Shaw 1972). 54 4.3 Definitions How storm-period variables are defined strongly influences how effectively they can be used in this study's diagnosis of basin sediment dynamics. The storm and seven storm-period variables are defined in this section. In each case, the choice of spatial and temporal scales is guided by the application to erosion and sediment transport of interest here (100 m2 to 10 km2 in particular). 4.3.1 Storm In quantitatively representing storm rainfall-intensity data, the continuous record of rainfall is first divided into distinct events. After storms have been delineated, storm-period variables can be quantitatively extracted. Two steps are applied in defining a storm. In the first step, a minimum "time-without-rain" or storm separation time (S^ N) is used and is applied to the beginning and end of a period of rain. Because storm events will later be related to flood events, this approach is convenient because it creates a storm characterisation which parallels that of the floods. In the second criterion, "storms" of inconsequence for sediment dynamics are filtered out through a minimum total-rain constraint (RMJN). At what value should be set? Two practical considerations narrow the range initially: • avoid combining storm rainfall arising from distinctly separate rainfall events and • avoid resolution effects of the tipping bucket rain gauge. The first of these suggests that SmN should be less than the time it takes for a synoptic scale event to come and go - no less than about half a day and generally much more. To assess the quantitative extent of the resolution effect, we divide the poorest resolution (1.258 mm - adjusted) by the lowest intensity of consequence for erosion in a storm event, about 10 mm/h. With these numbers, we expect 7.5 minutes to occur between measurements by the instrument even under continuous rain. If we make SM,n less than 7.5 minutes, we will create separate storm events as a result of this resolution limit. 55 To fix this variable's timescale within this "operating range", consider the variation in the number of storms (N) as a function of SM1N. As illustrated in Figure 4.1a, as SmN becomes large, N becomes small and eventually reaches unity. Conversely, as Smvi becomes small, N will grow large up to a maximum related to the length of the record and the propensity for and the frequency of rainfall in the specific location. The continuous data for all five recording gauges in this study have been subjected to this sensitivity analysis and the normalised results are presented in Figure 4.1b for the range 8^ ,^= 0.1 to 12 hours. This graph confirms the intuitive trend illustrated in Figure 4.1a. Additionally, between SmN= 1 and 2 hours, a major change develops in N. In other words, as Smu is reduced from 12 to 2 hours, little change in total number of storms results, but when the criterion is varied further to 1 hour, a large increase occurs. This is the lumping together of separate flood-causing convective bursts which we set out to avoid. We can get further help in choosing Smti within this range by also considering the flood events which result from these storms. Ideally, SMIN should be chosen to avoid unnecessary splitting and lumping so that all the rainfall that contributes to a certain flood event gets accounted for in an individual storm. In these headwater areas, it takes typically only a few hours for a flood to rise and return to a small percentage of its peak flow. If is significantly less than these few hours, then undesirable storm splitting will occur. Specifically, in this case, it is best to use the highest value in the range being considered, namely SMIN = 2 hours. Having divided the continuous rainfall record into discrete events, a minimum-size criterion (Rvnn) is applied to the results. Storms of less than 3 mm are excluded from further analysis because they are unimportant to erosion and in addition, the measurement of single-tip and double-tip storms can be unfavourably affected by resolution limitations of the instrumentation. The combined application of these criteria yields, typically, 80 storm events per year at each of the five sites. It is these storms which are the subject of the analyses in the following sections. 56 Figure 4.1 Number of storms in relation to (a) generalised minimum-time-without rain, and (b) specific minimum-time-without-rain at the five study locations. Nmax o o N= 1 S = 0 S-> large Minimum time-without-rain (S) O CO o 1.0 0.8 0.6 0.4 0.2 Storm number normalised as a proportion of the number of storms at 0.1 h. High Elevation Low Elevation Tipping Bucket Resolution •»—•«—- Coarse Fine *"™" ""","»» «» Ss jn 1 0 2 4 6 8 10 12 Storm Separation (hr) 57 4.3.2 Storm-per iod variables Appropriate storm characterisation underpins any examination of the effect of rainfall on erosion and sediment transport. Storm-period variables must be extracted from the rainfall record so that rainfall aspects most germane to erosion will be quantitatively and comparatively expressed. For example, it is known (Hudson 1981) that rainfall intensity plays an important role in initiating surface erosion, but what is the best expression of this storm-period variable? Which timescale(s) is most appropriate? Short-term rainfall intensity and total rainfall are the two rainfall characteristics which most strongly shape the character of the erosion process (see Chapter 5). High-intensity bursts are effective at initiating surface erosion at the plot scale as a result of the kinetic energy they possess. Antecedent moisture conditions (influenced by prior rain activity) can also greatly affect the ability of these bursts to cause surface erosion. Large rainfall volumes (usually over a long period) shift the emphasis within the sediment budget from surface erosion at the plot scale to slumping, streambank erosion and bed scouring (often farther downstream) due to large, channelised flows. Several storm-period variables are proposed to address surface erosion. A characteristic of tropical and subtropical rainfall is heavy burst rainfall lasting only a few minutes. Several minutes of rainfall at high intensity can cause damaging surface erosion on sloping agricultural fields. In choosing a timescale to characterise this intensity, the resolution of the recording rain gauge is the major constraint in accurately quantifying the short-term rainfall intensity. In this study, most measurements of rainfall intensity are for two minutes, though 5-minute and 10-minute intervals have also been used. If this short-term intensity timescale is set smaller than ten minutes, the intensity parameter cannot be calculated for all the storms. Further, if it is set too close to the resolution of the instrument, then the intensity parameter will be undesirably affected by how the instrument randomly measures the event. For these reasons, the maximum rain that falls in a ten-minute period of a storm 58 (I10) is measured for each storm. The timing of the peak 10-minute burst (T10) and antecedent moisture conditions can both greatly influence the erosivity of the burst. For this reason, two other storm-period variables are considered: T 1 0 The start time of the peak 10-minute rainfall intensity relative to storm start S The period flir) before a storm since the end of the previous storm ("period-without-rain") Together, these variables provide a good relative indication of the antecedent moisture conditions as I10 begins. Several storm-period variables are defined to address the effect of large amounts of storm rainfall. Since total storm rainfall (RTOT) and duration (TDUR) are frequently cited in other studies these are also considered in this analysis. In these steep headwater catchments, the flood-causing rain of a storm often does not occur over the duration of the storm (sometimes it is delayed as long as ten hours). It appears that the bulk of the rainfall of most storms occurs over a period of about one hour. What timescale best represents this characteristic? As the value becomes larger, more storms will not be characterised because of inadequate duration. As the value is made smaller, it will not properly represent the total-flood-causing-rainfall concept. One hour is chosen and 1^ , is defined as the maximum amount of rainfall occurring in a 60-minute period of the storm. Consistent with T1 0, Tw is defined as the start time of the peak 60-minute rainfall intensity. 4.4 Storm-period variables This section describes the temporal variability of rainfall by examining storm-period variables in the study area and assessing the seasonal change in the distribution of their characteristics. To make the storm classification relevant to the observed geomorphic thresholds for erosion over contrasting spatial scales, findings from Chapters 5 and 8 are brought in to the quantitative discussion 59 which follows. 4.4.1 Distributions Seven storm-period variables - total rainfall (RT0T), event duration (TDUR), maximum 10-minute (r10) and 60-minute (1^ ) rainfall intensities, within-storm timing of 10-minute (T10) and 60-minute (Tgo) peak intensities, and the rain-free period before each storm (S) - have been determined for all the events at the five recording rain gauges. Distributions of these variables are discussed on both annual and seasonal bases. Cumulative frequency diagrams are used to examine gross trends and similarities. Occurrence frequencies by class are examined for statistical differences. Class intervals for frequency distributions follow Conrad and Pollak (1950). Figure 4.2 shows the cumulative frequency distributions of the seven studied storm-period variables. These cumulative plots exaggerate similarities and highlight only large differences. All these plots indicate a preponderance of storms at the low-value end of the distributions. This is consistent with the field observation that the majority of storms is not flood-producing. Figures 4.2a and 4.2b show the cumulative frequencies of I10 and 1^ , in terms of percentage of total occurrences for each of the five recording rain gauges within the Jhikhu River basin (1992-1994). Figure 4.2a reveals a consistent pattern across all gauges: about 75% of storms have a maximum 10-minute rainfall intensity of less than 30 mm/hr. In Chapter 5, this value is found to represent a threshold for initiation of significant surface erosion. Only 10% of all storms exceed 50 mm/hr, a minimum threshold (of I10) determined in Chapter 8 to be required for significant basin soil loss to occur (see also section 4.6). 1^ , exhibits the same coherent pattern, the values being correspondingly lower. RT 0 T (Fig 4.2c) shows more variability between sites with 75% of all storms delivering less than 15 mm and only 10% of all storms bringing more than 25 mm. Although storm duration is often cited in studies describing storm characteristics, the distributions of T D U R given in Figure 4.2d reveal the inadequacy of this storm-period variable for this 60 Figure 4.2 cr1 S u 100 80 60 40 20 0 Cumulative frequency distributions of storm-period variables (1992-1994) at the five study locations: (a) maximum 10-minute intensity (I10), (b) maximum 60-minute intensity (!«))> (c) total rainfall (RTOT), (d) duration (T D U R), (e) start of maximum 10-minute rainfall intensity (T10), (f) start of maximum 60-minute rainfall intensity (Tw)> and (g) period without rain before storm start (S) - /AS 1, I 30 I 50 I , I , 0 20 40 60 80 100 Max. 10-Min Intensity (mm/h) s u Max. 60-Minute Intensity (mm/h) 0 1 2 3 4 0 1 2 3 4 Time of Max. 10-Min. Intensity (h) Time of Max. 60-Min. Intensity (h) 0 20 40 60 80 100 Period Without Rain Before Storm Start (h) 61 purpose: the result is sensitive to the resolution of the instrumentation. Whereas T 1 0 (Fig 4.2e) is also sensitive to gauge resolution, T^ is not because the effect of gauge resolution is lost over the longer period. Figures 4.2e and 4.2f show that burst rainfall occurs very soon after the beginning of a storm. For instance, in half of all storms the 10-minute and 60-minute peak intensities begin in the first 15 minutes of the storm. Figure 4.2g shows how many hours without rain precede storms. About 50% of all storms follow only 12 hours without rain. A further 25% follow 12-24 hours without rain. The final 25% can follow long periods without rain, often of many days. This timescale has important implications for surface erosion, especially if the storm rainfall arrives abruptly on dry ground. For the variables not sensitive to the measuring instrument, Table 4.2 summarises the ranges of the variables in the three classes: 0-75%, 75-90%, 90-100%. These distributions provide an overview of the nature of storms in the study area, one which will be useful in section 4.6 when a storm classification system is described and applied to these data. Table 4.2 Frequency of occurrence of four storm-period variables in three classes. Percentage RT 0 T 1.0 S Occurrence (mm) (mm/h) (mm/h) (h) (h) First 75% 0-15 0-30 0-7.5 0-0.5 0-17 Next 15% 15-25 30-50 7.5-15 0.5-1 17-34 Last 10% £25 >50 £15 £1 £34 The distributions shown cumulatively in Figure 4.2 were tested for differences using a Chi-Squared Goodness of Fit analysis for the five storm-period variables not sensitive to the measuring instrument. Occurrence frequencies were classified using a five-class system with the class boundaries established so that each class contained about 20% of all occurrences. For each of the five variables, three tests were carried out. In the first, the distributions at all five sites were tested in a combined 5 x 5 test with 24 degrees of freedom. In the other two, occurrences from the high- and low-elevation 62 sites were pooled to form two groups which were tested in a combined 5x2 test with 9 degrees of freedom. Only RT 0 T showed a significant difference (0.90 < P < 0.95). Aspect appeared to drive this difference (0.95 < P < 0.975) though no consistent trend between the two distributions pooled by elevation was observed. 4.4 .2 Seasonal distributions Figures 2.3 and 2.4 demonstrated that averages and extremes of total monthly rainfall vary during the year suggesting that storm characteristics might change seasonally. The data on storm-period variables are used to determine quantitatively how storm characteristics change seasonally. The majority of the annual rainfall occurs during the months of June through September with a distinct dry season from November to April. During May and June, the rains first start to arrive while the land surface is desiccated and the cultivated fields generally bare. This period is known as the pre-monsoon season. During the months of July, August, and September, vegetative cover is well developed - this season is termed the monsoon season. The date of onset of the monsoon season is variable but in this study, the end of the pre-monsoon season is determined to be June 30, followed by a transition season of 19 days' duration (from July 1-19) at the end of which the "monsoon" season begins. The choice of these dates is made on the basis of stream suspended-sediment regimes (see section 5.4.2) so in a sense, this is to some degree a "land-surface response" monsoon definition rather than a strictly hydrometeorologic one. For consistency, the same seasonal definitions are used to characterise both the rainfall and sediment regimes. Before continuing, it had to be decided whether the 19-day transition season -designed to facilitate the analysis of sediment dynamics - will be included with the pre-monsoon season or with the monsoon season. A sensitivity analysis using three options was evaluated for demarcating the end of the pre-monsoon and the beginning of the monsoon season: (midnight) June 30, July 10, and July 20. 63 Initially, data from the two gauges on the north-facing hillslope were used to assess the sensitivity of all the variables to changes in the date separating the seasons. I i 0 was the most important to surface erosion and therefore was used as the key indicator in examining the data from all five sites. The seasonal difference in I 1 0 was the greatest using June 30. As the separation date advanced, the differences tend to decline at all five sites suggesting a dilution of real seasonal differences as "monsoon" storms of July 1-19 are added to the "pre-monsoon" storm population. Hence, in this seasonal analysis, the pre-monsoon season ends on June 30 (midnight) and the monsoon season begins on July 1. A second set of Chi-Squared Goodness of Fit analyses containing two parts is carried out on these seasonal storm data. In the first, the analyses carried out for the combined data are repeated for the separate seasonal groups. These tests examine differences within seasons. In the second, differences between seasons are examined by pooling the data within each season in different ways to yield five tests for each variable: all sites (24 degrees of freedom), high elevation, low elevation, north aspect, and south aspect (9 degrees of freedom). To limit the accumulation of Type I Error, only differences limited to 0.5% shouldbe considered significant [(1-0.005)25 = 0.882] though all differences within 10% are discussed. The within-season analyses revealed no important differences within either season. The test using data pooled by elevation resulted in a difference in Tm at 90% though no consistent cause or result of this difference was evident. Differences between seasons were the most statistically important of all the results. Of the differences observed, the results for S were the strongest. The high-elevation and north-aspect groupings showed differences of 99% and 97.5% respectively while the result for the seasonal contrast for the south aspect pool was 90%. Consistently, S was higher in storms of the pre-monsoon season. This is an important difference because a longer period without rain before the start of a storm suggests drier conditions and can point to more erodible surface soils. 64 The test results suggest that T«, comes earlier in pre-monsoon storms: differences in south-facing pooled data were significant at 97.5% and in low-elevation pooled data at 90%. Early arrival of a storm's heavy rain should increase its erosivity. If combined with a longer pre-storm dry period, the effect can be important. Consistent with the non-seasonal analyses, some differences were found in R x o x. Not surprisingly, RT 0 X for the monsoon storms was shifted toward higher values. All distributions showed this characteristic. The seasonal contrast using all five sites was significant at 90% and it was at the south-facing sites where this effect was the greatest (99.5%). In summary, the similarities between seasons far outweigh the differences. Storm characteristics in the study area are conducive to surface erosion as heavy rain can fall over dry ground throughout the year. The seasonal contrasts suggest that the pre-monsoon storm regime is somewhat more liable to generate surface erosion, exacerbating a situation where the surface soil is already vulnerable to erosion due to its bare condition. 4.4.3 Event ranking Rainfall Figure 4.3 illustrates the ranking of the largest monitored storms within the Andheri basin by Rx and I10. Though the previous section showed that storm-period variables are not significantly different between the upland and lowland, these distributions reveal them to be slightly larger in magnitude at the upland recording rain gauge location. These distributions also reveal that there is little correspondence between the upland and lowland areas for the rankings of the largest events -only two events appear in the top five for both the upland and lowland areas. Only the July 10, 1992 event appears in all categories, suggesting that it is a significant event of the study period. Though the three-year record is inadequate to justify the development of intensity-duration-frequency relations, these rankings are useful to illustrate the relative magnitude of the major 65 Figure 4.3 Ranking of largest monitored storms in upland and lowland of Andheri basin by (a) I10 and (b) RT. co »—i i o X a a • l -H -4—> o 140 120 100 80 |- A (a) Ranking of max. storm 10-minute intensity »•* (>30mm/h) 60 h 40 h 20 R a n k L o w l a n d U p l a n d 1 June 15, 1994 June 9, 1992 2 Sept 12, 1992 J u l y 10, 1992 3 Ju ly 24, 1992 M a y 27, 1993 4 June 5 , 1993 June 2 2 , 1 9 9 2 5 Ju ly 10, 1992 Ju ly 18, 1994 0 10 20 30 40 50 60 70 • Upland Rank • Lowland 110 I4 (b) Ranking of storm total rainfall 9 0 I" (>10mm) 70 b 50 30 10 R a n k L o w l a n d U p l a n d 1 Ju ly 24, 1992 Ju ly 10 ,1992 2 Ju ly 21 , 1992 A u g . 10 ,1993 3 A u g . 10 ,1993 M a y 27, 1993 4 Sept. 9 , 1 9 9 2 A u g . 2 4 , 1 9 9 3 5 July 10, 1992 Ju ly 30, 1992 June 9 , 1 9 9 2 0 20 T 40 60 Rank 80 100 66 monitored storm events. The upland experienced two or three events noticeably heavier (with respect to both R x o x and I10) than all the other events at both the upland and lowland stations. Overseas Development Agency (1995) report a maximum measured rainfall intensity of 144 mm/hr (for one minute) from a three-year monitoring study at several nearby locations. High-flow A ranking of high-flow peaks at Khukuri, Lower Andheri, and Jhikhu stations is presented in Figure 4.4. The continuous nature of the data from Khukuri and Jhikhu suggests that these distributions represent events with low return period of perhaps less than three years. The result for Lower Andheri stands in stark contrast suggesting that the return period of the flood event of July 10, 1992 is significantly higher than that of the other events during the study period. The local farmers indicated that a flood event like that of July 10, 1992 had not been experienced in over ten years. Though it is difficult to provide a confident estimate of this event's return period, it may be reasonable to consider that it was a ten-year flood event. This event was the highest measured at all three scales providing further confidence in this estimate. With analysis of the subsequent three years' data, this estimate can be improved. Though the five biggest events at the Khukuri and Lower Andheri stations exhibit some consistency, the rankings of the high-flow events at these two stations shows no relation to that of Jhikhu (except for the July 10, 1992 event). These changes in ranking with scale hint at spatial variability in the rainfall-runoff process. In addition, a comparison of the flood rankings at Khukuri/Andheri to the rainfall rankings provided in Figure 4.3 suggests that it is the large total-rainfall events of the upland that are most able to shape the high-ranking events of Lower Andheri station. Rainfall is an important control on the flood regime but it is clearly not the only important control. These comparisons will be useful in Chapter 8 where individual events are examined further. 67 Figure 4.4 Ranking of monitored floods at (a) Lower Andheri, (b) Kukhuri, and (c) Jhikhu stations. 140 120 -100 -80 -60 40 20 0 (a) Lower Andheri (2) (b) Lower Andheri (2), partial Rank Date 1 Jury 10,1992 2 July 30, 1992 3 May 27,1993 4 Sept. 3,1994 5 Aug. 28, 1994 i | ^ r " p » i » M | M " | H " | | 0 10 20 30 40 50 60 Rank (c) Kukhuri (10) 5 4 3 2 1 Rank Date A. 1 July 10, 1992 2 May 27,1993 • 3 June 9, 1992 — 4 July 30,1992 5 Aug. 3, 1992 • • 0 10 20 30 40 50 60 Rank 120 100 h 80 60 40 20 0 0 (d) Jhikhu (1) — A Rank Date 1 Jury 10,1992 2 Aug. 8,1994 3 Aug. 10,1993 4,5 Sept. 3,1994 July 9, 1994 i r 20 40 60 80 100 120 Rank 68 4.5 Spatial variation The effect of elevation over three different scales (1, 10, and 100 km2) and local variation within individual storm cells (1 km2) are examined using 24-hour rainfall measurements. The 100-km2 analysis (the Jhikhu River basin) uses data from long-term climate stations. The 10-km2 (hillslope) and 1-km2 (storm cell) analyses use data from the detailed wet-season rain-gauge network (see section 3.1.1). 4.5.1 Elevation Over the spatial scale of the Jhikhu basin (100 km2), how does elevation influence rainfall? Rainy-season (June-September) data from eight long-term monitoring stations within the basin are presented in Figure 4.5a. The graph suggests that total June-September rainfall increases with elevation, perhaps at a rate of about 0.5 mm/m. These data are also plotted monthly in Figures 4.5b through 4.5e: the same trend is evident in each month. The data are too few to justify further quantitative analysis. Data from the detailed north-facing study basin in Figure 4.6a show that on the Andheri hillslope (5 km2), June-September rainfall declines with elevation, driven by the behaviour during June and September. The two groups, separated elevationally at 1150 m, were tested using the Mann-Whitney U-Test. In June and September, the rainfall in these two groups is significantly different (September: 95%, P=0.014; June: 99.9%, P=0.0001). Reporting on rainfall dynamics in the Likhu basin near Kathmandu, Overseas Development Agency (1995) found that the areas which are the first to receive a system are also the areas with the higher rainfall. This is generally the case on this hillslope: systems arrive in the Jhikhu River valley and move up into the Andheri sub-basin. The shoulder months also exhibit a tighter pattern suggesting that wind effects may be minimised during this period. The variability in rainfall measured over the hillslope during July and August may be attributed largely to the negative bias of wind. The high measurements at Gauge 19 (1182 m) situated 69 Figure 4.5 1500 Effect of elevation on rainy-season rainfall at eight monitoring stations distributed across the Jhikhu River basin (100 km2; 1992-1994): (a) June-September, (b) June, (c) July, (d) August, and (e) September. (a) June-September 3 • Study Data o HMG data Note: HMG data are uncorrected. 800 1000 1200 1400 1600 Elevation (m) (b) June (c) July c2 200 100 3 300 800 1000 1200 1400 1600 Elevation (m) 800 1000 1200 1400 1600 Elevation (m) 500 400 -*3 300 po 2 200 h (d) August 100 500 (e) September £ 200 1-800 1000 1200 1400 1600 Elevation (m) 800 1000 1200 1400 1600 Elevation (m) 70 Figure 4.6 1500 Effect of elevation on rainy-season rainfall at 20 monitoring stations located on the Andheri basin hillslope (10 km2; 1992-1994): (a) June-September, (b) June, (c) July, (d) August, (e) September. (a) June-September 1 500 400 -300 -<2 200 h 1000 1200 1400 1600 Elevation (m) (b) June 100 800 1000 1200 1400 1600 Elevation (m) 500 (d) August 500 400 -S 300 r (2 200 h (c) July 100 800 1000 1200 1400 1600 Elevation (m) 500 g 400 -9 300 -3 200 -(e) September 100 800 1000 1200 1400 1600 Elevation (m) 800 1000 1200 1400 1600 Elevation (m) 71 at the break in slope below the Kukhuri sub-basin (10) suggests that there may be a mid-slope maximum - perhaps due to a modest exhaustion effect - but the data are too few to confirm or refute this hypothesis. Data are available from 1992 for the south-facing hillslope and are presented monthly in Figures 4.7a through 4.7c. The July data are too few to infer any trend in rainfall with elevation on the hillslope. In contrast, data for August reveal a strong increase with elevation, one which experiences no exhaustion upslope. This contrast to the north-facing hillslope could be due to the abrupt nature of the south-facing slope. These findings have important implications for rainfall monitoring in these mountainous regions. Spatially, measured rainfall can vary by 50% on a 5-km2 hillslope reflecting the combination of real spatial variability of rainfall and the variability due to gauge performance as affected by wind patterns. Real rainfall spatial variability is dependent on the elevation of the slope and on topographic peculiarities of the area especially in relation to prevailing patterns of the region whereas the variability due to gauge performance is undoubtedly affected by seasonal changes in wind strength, a well known control on rainfall catch - unstudied here. 4.5.2 Storm cell The rain-gauge clusters located on the Andheri hillslope are used to examine the variability in rainfall delivery within a storm cell (1 km2). The monthly changes in total rainfall revealed in the last section suggest that elevation may affect within-storm spatial variability; because the elevation of these clusters is contrasting, the extent of influence of elevation can also be investigated. The 24-hour rain-gauge network, in place for three rainy seasons on the Andheri hillslope, has yielded hundreds of days of rainfall data, each day's data comprising simultaneous measurements at up to 30 locations. The gauge locations form two clusters. One cluster is over steep, intensively-managed agricultural land in the headwater Kukhuri basin. The other cluster is over the lower reaches 72 Figure 4.7 Effect of elevation on total rainfall at 18 monitoring stations located on the south-facing hillslope (10 km2; 1992): (a) July - only 7 data available, (b) August, and (c) September. (a) July 500 400 r 3 300 c2 200 (b) August 100 800 1000 1200 1400 1600 Elevation (m) 800 1000 1200 1400 1600 Elevation (m) 500 400 h 3 300 r 200 h (c) September 100 800 1000 1200 1400 1600 Elevation (m) 73 of the Andheri basin where bare and heavily-gullied, red soils prevail. To examine the variation of rainfall within a storm cell, only the daily 24-hour measurements which derive from individual storm events are considered. Initially, following the storm definition developed in section 4.3.1, only storms with an average of at least 3 mm of rainfall (from either the upland or lowland rain-gauge cluster) are used. To remove the data for which more than one storm contributes, the detailed record from the tipping bucket within each rain-gauge cluster is used. Eight discrete 24-hour periods contain two distinct events (none contains three); the rainfall for these events cannot be known. In addition, storm rainfall cannot be known for the five events which straddle the measurement time (7:00 am) and are followed immediately by a 24-hour period with a storm (or storms). After removing other data, 90 events remain for consideration. For each of these 90 storm events, eleven well-distributed gauges are chosen from each cluster. The mean rainfall and its variance are determined from these data for each event in each cluster individually and for the two clusters combined. These descriptive statistics are then tested for significant difference. Both parametric (t-test, 95%) and non-parametric (Mann Whitney U Test) tests are applied due to the uncertainty of the underlying distribution. In most cases, the two tests agree. When the result from one test is at a lower confidence limit, then the result from the other test is used to rule on the difference. In one case only did the two tests outright disagree: for this data pair, it is assumed that no significant difference exists. Using the results from the statistical tests, the data are divided into three groups. Basin events occur where there is no significant difference between the data from the two clusters. Where significant differences are found, the events are termed as either upland or lowland depending on which has a greater mean rainfall. Finally, the coefficient of variation is calculated for each event. The average of the coefficients of variation for all events within each of the three event-area classes (basin, upland, lowland) is presented in Table 4.3. Also, for all rainfall over each area (the upland, the lowland, and the entire basin) the average coefficient of variation is determined regardless of the 74 Table 4.3 Spatial variation within a storm cell and distribution of events according to lowland, upland, and basin area-events for storms of RT £ 3mm and RT £ 10mm. Areal Extent Events Included Average Coefficient of Variation Number of Events Percentage of Total Events RT £ 3 mm ; 90 events low-elevation lowland 0.283 22 24% cluster only high-elevation upland 0.480 26 29 cluster only entire basin basin 0.459 47 low-elevation all 0.445 88 100 cluster only high-elevation all 0.426 90 100 cluster only entire basin all 0.594 90 100 RT > 10 mm; 63 events low-elevation lowland 0.244 20 32% cluster only high-elevation upland 0.307 14 22 cluster only entire basin basin 0.328 29 46 low-elevation all 0.338 63 100 cluster only high-elevation all 0.332 63 100 cluster only entire basin all 0.459 63 100 si** The events with > 3 mm include those with £ 10 mm. Two upland events yielded no rain in the lowland hence CV cannot be calculated. event designation (as "upland", "lowland", and "basin"). The lowland events (CV=0.28) show less variability than do their upland counterparts (CV=0.48). The upland cluster sits in steep topography and the resulting variability is evidenced in Table 4.3, this despite the fact that the gauges of the lowland cluster are more widely dispersed than those in the upland. The variability of the basin events (CV=0.46) is similar to that of the upland cluster. Not surprisingly, rainfall for all the events over each area type shows generally more variability than do the area events when considered on their own. About half of these events provide basin coverage with the other half split between the upland and the lowland. Though it was not possible to select this set of storm events to be representative of 75 the entire record, there are 90 events and as such it may be reasonable to assume that the double-event (and triple-event) days which were removed have the same distribution. The entire set of events considered above includes many events of low RT (though greater than 3 mm). Events with RT < 10 mm over both the upland and lowland areas are rarely flood-producing and are therefore of limited interest in this study. These events are removed from the data set, the summary statistics are recalculated, and the results appear in the bottom half of Table 4.3. The spatial variability within a cluster is reduced when only the larger events (RT ^ 10 mm) are considered. Variability in the upland remains higher than in the lowland. For each area type, the variability is always higher if the entire data subset is included rather than exclusively the local events. Note also that there are far fewer large upland events than large lowland events. By subtraction, there are proportionately far more small (3 to 10 mm) upland events (15) than there are equivalent lowland events (2). The previous section described changes in monthly rainfall on the north-facing hillslope in terms of elevational differences and possible exhaustion and wind-bias effects. Though the exhaustion mechanism may be present and the wind bias mechanism is certainly present, it is clear from the findings of this section that 22% of high-rainfall events (RT ^ 10 mm) bring a significantly lower rainfall to the lowland hence hillslope exhaustion is not an appropriate explanation for all storm events. Convection is strongly "cellular" in this region at times bringing rainfall to one area but not to the other (50% of cases) and at other times, bringing rainfall to the entire hillslope, perhaps with an exhaustion effect. The mechanism for this elevational difference remains open to speculation. Do hillslope exhaustion effects operate during most storms and cause there to be less rainfall over the upland? Does the local, convective nature of showers in this region mean that storms arriving via distinct topographic pathways bring characteristic levels of rainfall? In Table 4.4, the large (RT ^ 10 mm) events are stratified according to season. Basin events, though common during the monsoon season (49%), occur infrequently during the pre-monsoon 76 Table 4.4 Seasonal distributions of spatial variation within a storm cell and distribution of events according to lowland, upland, and basin area-events for storms of RT £ 10 mm total rain. Areal Extent Events Included Coefficient of Variation Number of Events Percentage of Total Events Pre-monsoon season; 9 events low-elevation cluster only lowland 0.282 5 45% high-elevation cluster only upland 0.252 4 37 entire basin basin 0.346 2 18 low-elevation cluster only all 0.366 11 100 high-elevation cluster only all 0.328 11 100 entire basin all 0.585 11 100 Monsoon season; 45 events low-elevation cluster only lowland 0.230 15 29% high-elevation cluster only upland 0.330 10 19 entire basin basin 0.326 27 52 low-elevation cluster only all 0.331 52 100 high-elevation cluster only all 0.331 52 100 entire basin all 0.433 52 100 season. In other words, the pre-monsoon season shows a greater propensity for local rain. However, the spatial variability of the local pre-monsoon events is equivalent to those of the monsoon season. Figures 4.8 through 4.10 illustrate the rainfall delivery over the Andheri basin hillslope for representative basin, upland, and lowland storms. Figure 4.8a occurred on July 10, 1992 (transition season) and Figure 4.8b shows a basin event which is typical of the monsoon season. Although it was an upland event, it brought heavy rainfall to the entire basin to be the most significant event of the study period. Figures 4.9 and 4.10 contrast upland and lowland events of the pre-monsoon and monsoon seasons. Upland storms like the one presented in Figure 4.9a are infrequent but when they 77 Figure 4.8 Rainfall isolines for storm rainfall over Andheri basin for (a) an upland event during the transition season which was also the heaviest event of the study period and (b) a basin event typical of the monsoon season. (a) July 10, 1992 Lowland U p l a n d 500 1000 1500 2000 Isoline values in mm. (b) August 25, 1993 Lowland Upland 78 Figure 4.9 Rainfall isolines for storm rainfall over Andheri basin for upland events during the (a) pre-monsoon season and (b) monsoon season. (a) June 9, 1992 \ \ / •• \ \ .\ ^ • . \ \ \ \ • L o w l a n d U p l a n d 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 I s o l i n e v a l u e s i n m m . (b) August 3, 1992 L o w l a n d U p l a n d 79 Figure 4.10 Rainfall isolines for storm rainfall over Andheri basin for lowland events during the (a) pre-monsoon season and (b) monsoon season. (a) June 6, 1993 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 I s o l i n e v a l u e s i n m m . (b) July 21, 1992 L o w l a n d U p l a n d 80 occur, they are a major threat to upland soil erosion. Lowland pre-monsoon storms (Figure 4.10a) appear to be more frequent in occurrence. An important application of these data involves the determination of how accurately point measurements represent the areal average. This is especially important when attempting to construct basin water budgets. To investigate this representativeness, the following equation E = (t) (s) ( n r 5 (Cochran 1977) relating realised error (E) to the standard error (s) and size (n) of the sample is used (t is the appropriate value from student's t-distribution). This equation is solved for the relative or acceptable error (AE=E/x, where x is the sample mean) to give n = (t)2 (CV)2 (AE)"2 where CV is the coefficient of variation (s/x) and t is a function of n. This equation is solved iteratively to yield the results which appear in Figure 4.11. Figure 4.11 combined with Table 4.4 indicates the number of rainfall monitoring sites required to achieve a specified level of relative error for upland, lowland, and basin events of the pre-monsoon and monsoon seasons. For example, with a coefficient of variation of 38% (monsoon season) and 56% (pre-monsoon season), in the order of 100 monitoring sites are required to measure basin rainfall (for storms of RT £ 10 mm) to a confidence of within 10%. If the gauge requirement were limited to, say, 10 sites a level of error of 30% would result. The coefficient of variation of the lowland (0.23 to 0.28) and upland (0.154 to 0.196) events is smaller so fewer gauges are required for the same level of relative error: 10% requires 10-40 monitoring sites, 20% requires 5-20, 30% requires 3-6, and 50% requires 2-4, depending on which area/season combination is under consideration. A 10% level of error appears unworkable for most applications due to the excessive monitoring requirements - a level of error of 30% appears to be the most practical, if acceptable in the management or research application. Certainly, if only one gauge is installed in the basin, the data will essentially tell nothing reliable about storm rainfall delivery within a basin. This result casts Figure 4.11 Number of rainfall monitoring sites required in relation to the relative error for a in coefficient of variation. 200 0 10 20 30 40 50 Acceptable Error (%) 82 serious concerns on the value of data from thinly-spread (in valleys) HMG gauges, for example. 4.6 Integration The previous sections examined separately spatial and temporal variability in rainfall delivery as measured throughout the study site. This section attempts to integrate these findings to serve two purposes. The first provides guidelines for those attempting to measure rainfall in the Middle Mountains. The second establishes a quantitative reference framework to be used in subsequent chapters in the diagnosis of erosion. 4.6.1 Classification A storm classification system is proposed based on RT and Ij0 because these are the storm-period variables most important to surface erosion (plot scale, on-site) and mass wasting (larger scales, off-site). Findings from later chapters are combined with insights from section 4.4.1 to yield thresholds. In Chapter 5, a 30 mm/h threshold is observed for significant surface erosion at the plot scale (similar to Hudson (1981) who described a 25 mm/h threshold). In Chapter 8, it is shown that serious basin-scale sediment output corresponds roughly to storm events with Ij0 exceeding 50 mm/h and RT ;> 30 mm. Events with RT < 10 mm rainfall yield insignificant basin sediment output. Table 4.5 illustrates the class thresholds of each of these parameters and how their combinations are used to derive three storm classes: minor, intermediate, and major. For all five recording rain gauges, storm-period variables have been analysed to determine storm type for every storm and the resulting distributions appear in Table 4.6. Despite the changes in elevation and aspect and the differences in data gaps from gauge to gauge, there is a striking consistency in the five distributions. A large majority (77%) of all events is minor (either Rx < 10 mm or I10 < 30 mm/h), of little consequence for flood generation. One in five events is intermediate and likely causes floods. The erosional consequences of these events focus on either surface erosion 83 Table 4.5 Definitions of minor, intermediate, and major storm classes in relation to total rainfall and peak 10-minute rainfall intensity. Peak 10-minute Rainfall Intensity (mm/h) 0-30 30-50 £50 Total Rainfall 3-10 Minor 10-30 Intermediate (mm) £30 Major Table 4.6 Distribution of storm events in three storm classes (minor, intermediate, major) at five sites. Site Total Minor Intermediate Major North facing; 211 75.4 20.9 3.8 high elevation (159) (44) (8) North facing; 211 76.8 19.0 4.3 low elevation (162) (40) (9) South facing; 200 78.5 19.0 2.5 high elevation (157) (38) (5) South facing; 173 78.0 18.5 3.5 low elevation (135) (32) (6) Outside detailed 229 75.1 21.4 3.5 study area (172) (49) (8) Overall average n/a 76.8% 19.7% 3.5% (if I10 £ 50 mm/h) or on slumping and stream bank/bed erosion (if RT £ 50 mm) but not both. The major events are erosive in terms of both surface erosion and mass wasting as a result of their high intensity and high volume. These events are infrequent, occurring in about 3% of all storms (1 in 33) Table 4.7 presents the seasonal distribution of storm class at all sites. On average, 38% of all storms occur in the pre-monsoon period whereas 62% occur during the monsoon season. The natural variability around these trends is potentially enhanced by the unequal distribution of data gaps at the sites. Though the storm class distribution within each season varies from site to site, it is remarkably 84 Table 4.7 Seasonal distribution of storm events in three storm classes (minor, intermediate, major) at five sites for 1992-1994 data. Site Percentage of All Percentage of Season's Events Occurring Within Events Occurring Each Class Within Each Season P M Pre-Monsoon (P) Monsoon (M) Min Int Maj Min Int Maj North facing; 30.3 69.7 76.9 20.9 2.2 74.2 20.8 5.0 high elevation (64) (147) (45) (15) (4) (117) (25) (5) North facing; 43.1 56.9 70.3 23.4 6.3 79.6 17.0 3.4 low elevation (91) (120) (70) (19) (2) (89) (25) (6) South facing; 42.0 58.0 83.3 16.7 0.0 75.0 20.7 4.3 high elevation (84) (116) (70) (14) (0) (87) (24) (5) South facing; 31.2 68.8 72.2 22.2 5.6 80.7 16.8 2.5 low elevation (54) (119) (39) (12) (3) (96) (20) (3) Outside detailed 41.9 58.1 72.9 24.0 3.1 76.7 19.5 3.8 study area(high elev) (96) (133) (70) (23) (3) (102) (26) (5) Overall, High Elev. 38.1 61.9 75.8 21.3 2.9 77.3 18.9 3.8 Overall, Low Elev. 37.8 62.2 75.2 21.4 3.4 77.4 18.8 3.8 Overall 37.7 62.3 75.1 21.4 3.5 77.2 19.0 3.8 consistent. In fact, the integrated average of all sites yields essentially the same distribution for each season - 76.8% minor, 19.7% intermediate, 3.5% major (and obviously the same as the non-seasonal distribution). Observed differences in the storm class distributions were tested for significance using a Chi-Squared Goodness of Fit test. Annual and seasonal distributions were tested by site, elevation, and aspect and each test showed no significant difference (90%). In addition, seasonal differences in these categories were found not to be significant. The significant differences in storm-period variables found in section 4.4.1 are insufficient to persist after storm classification. It is the temporal change in storm characteristics at a site that provides the important difference geomorphically. The minor events rarely cause a change in streamflow important to erosion. The intermediate events are significant but tend to be easily managed. It is the major events, because of their combined high intensity and high 85 volume which are seriously damaging. While these differences are statistically nonsignificant, the small differences in measured occurrence of major events may be important if they persist at other times. Due to their importance in initiating erosion, a slight systematic departure in frequency of occurrence of major events may make some sites more prone to erosion. For instance, at the low-elevation site, 6.3% of pre-monsoon storms, 3.4% of monsoon storms, and 4.3% of all storms are major storms in contrast with the 3.5% average overall. If important, these concerns would need consideration in light of total storm frequency (Table 4.8). 4.6.2 Storm Frequency In the previous section, conclusions were reached about overall and seasonal distributions of events within the study site. This section seeks to extend those findings by examining the annual and seasonal frequency of occurrence of storms within the basin during the three-year study period. Table 4.8 presents the total number of storm events at each site for each year (1992-1994) as recorded by the tipping buckets. These figures are not exact averages due to the presence of data gaps of variable extent at most sites. Because these data gaps are generally small, the estimates form reasonable approximations, completed using adjacent 24-hour measurements and inference from the nearest recording gauge (with data). Sites had between 68 and 87 events per year with most sites having between 70 and 80. The overall mean is 77.5 events/year. In Table 4.9, the seasonal distributions by storm class (from Table 4.7) are applied to the average annual event frequency (77.5) to yield expected annual storm frequencies in the study area. On average, of the 77.5 events which occur each year, 48.0 of them occur in the monsoon season while the remaining 29.5 occur in the pre-monsoon season. Only 17.8 of these are non-minor; of the major events, one will occur in the pre-monsoon season of almost every year while one or two should occur each year during the monsoon season. We also expect almost ten intermediate storms during the 86 Table 4.8 Number of storms in each year (1992-1994) at each site as given by recorded data. Site Total Annual Number of Storms 1992 1993 1994 Mean North facing; high elevation 71 87 79 79.0 North facing; low elevation 71 68 72 70.3 South facing; high elevation 70 74 78 74.0 South facing; low elevation 72 83 78 77.7 Outside detailed study area; high elevation 85 87 87 86.3 Overall mean 73.8 79.8 78.8 77.5 Table 4.9 Expected average seasonal storm frequency by class (minor, within the study area. intermediate, major) Minor Intermediate Major Total Pre-monsoon 21.9 season 6.2 1.0 29.2 Monsoon 37.2 season 9.2 1.8 48.3 Both seasons 59.0 combined 15.7 2.8 77.5 Note: Frequencies of occurrence for combined seasons are based on an averaged distribution, hence do not identically equal the sum of the two seasonal distributions presented. monsoon season and about six intermediate events during the pre-monsoon season. 4.7 Summary and conclusions The specific quantitative findings from this chapter are listed in this section followed by the 87 main conclusions derived from these detailed results. 4.7.1 Summary of quantitative findings The findings from this chapter can be grouped around four topics: temporal variation, spatial variation, storm classification and frequency, and guidelines for monitoring and analysis. In making these observations, a storm is defined as delivering at least 3 mm of rainfall and being separated by at least two hours from other rainfall. Temporal variation 1) Storm-period variables • Peak 10-minute and 60-minute intensities begin within the first 15 minutes in half of all storms. • In 25% of all storms: • the peak 10-minute rainfall intensity exceeds 30 mm/hr; • more than 15 mm total rainfall is delivered; and • more than 24 hours without rain precedes the storms. 2) Seasonal effects • Storm characteristics are similar between the pre-monsoon and monsoon seasons: they deliver both high-volume and high-intensity rainfall capable of causing severe erosion. • In comparison with monsoon storms, pre-monsoon storms: • deliver less total rainfall; • are shorter in duration; • occur after longer periods without rain; • show a delayed occurrence of peak rainfall. Spatial variation Spatial variation was assessed as a function of elevation and storm cell variability in terms of total rainfall (over specific time periods) and in terms of storm-period variables: 88 3) Storm-period variables • Systematic differences in storm-period variables are not evident between elevation and aspect; differences observed relate to narrow combinations of season and storm characteristic. 4) Elevation • Over the scale of 100 km2, wet-season rainfall (June-September) increases with elevation; the trend is insensitive to season. • Over the scale of a hillslope (10 km2) rainfall variation shows a trend to increase with elevation though marked exceptions are observed: • total pre-monsoon-season rainfall decreases with elevation; • total monsoon-season rainfall shows a midslope (1050-1150 m) maximum. It is not clear the extent to which these elevation differences are due to hillslope exhaustion and due to the local "cellular" nature of convection. • Over a hillslope (10 km2), total variation in rainfall can exceed 50% in a month; a large proportion of this variance may be due to increased wind bias in measurement over complex topography. 5) Storm cell • About half of all hillslope storm events deliver significantly different total rainfall within contrasting 1-km2 upland and lowland subregions; • Hillslope-wide events are uncommon during the pre-monsoon season; • Low-rainfall events (3 mm < Rx < 10 mm) are highly variable especially in the mountainous, high-elevation terrain where CV=0.48; • Lowland and upland high-rainfall events (Rx £ 10mm) demonstrate reduced variability. • High-rainfall "area" events (isolated over either the lowland or upland) occur more frequently at low elevation than at high elevation and together constitute more than half the total number of hillslope events. 89 Storm Classification and Frequency 6) Storm classification • A matrix classification using I,,, and R T provides a convenient basis for classification because its basis is compatible with the rainfall-induced variation in character of erosion: • Minor events deliver R T < 10 mm or have I10 < 30 mm/h; • Major events deliver R x ^ 50 mm and have I i 0 ^ 50 mm/h; and • Intermediate events form the remainder of the storms. 7) Storm distributions • Annually, 76.8% of all storms are minor events; 19.7% are intermediate events; and 3.5% are major events. • Storm-class distributions are insensitive to aspect, elevation, and season. • Of the wet-season storms, 37.7% occur during the pre-monsoon season and 62.3% occur during the monsoon season. 8) Storm classification and frequency • Annually, an average of 77.5 storms were recorded across all five sites: 59.0 minor events, 15.7 intermediate events, and 2.8 major events on average across all sites. • Only 2.8 major storm events are expected annually (1.0 pre-monsoon; 1.8 monsoon). Guidelines for monitoring and analysis 9) Field Instrumentation • When measuring rainfall intensity for erosion studies in the Middle Mountains, use a minimum resolution of 0.25 mm and a sampling frequency not exceeding 2 minutes; maintain these constant during the monitoring period. • To measure mean storm rainfall over a fourth-order tributary basin, data from one gauge is inadequate; in the 5.3-km2 study basin, 2 to 4 gauges are needed to limit the allowable error to 50% and about eight gauges are required for a maximum allowable error of 25%. 90 • Maintain duplicate instruments at many sites, especially when different types of instrumentation are used and breakdown can cause gaps. 10) Analysis • A storm definition based on a two-hour minimum period without rain respects the inherent characteristics of rainfall in the Middle Mountains and Rmfi = 3 mm eliminates gauge resolution effects. • I10 and Iw best reflect rainfall characteristics affecting surface erosion and mass wasting respectively (provided the sampling frequency is ^ 5 minutes). • S and Teo provide useful relative indices of antecedent moisture conditions. 4.7.2 Conclusions These findings suggest this chapter's three principal conclusions: • Seasonal storm characteristics Storms of the pre-monsoon and monsoon seasons deliver equivalent high-intensity, high-volume rainfall capable of causing severe erosion. However, in comparison with monsoon storms, pre-monsoon storms deliver less total rainfall, are shorter in duration, occur after longer periods without rain, and show a delayed occurrence of peak maximum 60-minute rainfall intensity. Spatially, pre-monsoon storm cells are smaller than monsoon storms. These conclusions suggest that the erosivity of storm rainfall in these two seasons should be equivalent but the timing of pre-monsoon storms may enhance the relative erodibility of the land surface during this season's significant storms in contrast with the monsoon season. • Spatial variability Over a hillslope (10 km2), variation in total rainfall often exceeds 50% in a month. This variability is driven by a combination of wind bias (due to instrument limitations and complex topography), local storm-cell structure, and hillslope exhaustion. This conclusion raises severe 91 concerns about the usefulness of rainfall data derived from single gauges, notably when these gauges are not within the catchment of concern. The data may provide useful information regarding broad regional trends but they do not provide any reliable information on synoptic-scale flood-producing storm rainfall. • Storm distribution Under a three-class storm classification system designed for this study of erosion and sediment transport and using class divisions based on maximum 10-minute rainfall intensity and total rainfall there is no significant difference in the distributions of storm classes between site, aspect, and elevation within either season. Annually, an average of 77.5 storms was recorded across all five sites. Of these, an average of 2.8 major events (more than 50 mm/hr maximum 10-minute intensity and greater than 30 mm total rainfall) occurs: 1.0 event during the pre-monsoon season and 1.8 events during the monsoon season. 92 5. Diagnosing headwaters controls on erosion and sediment transport 5.1 Introduction Factors which shape the erosion regimes of the study catchments are examined diagnostically to evaluate their relative effects on sediment dynamics. A wide assortment of geomorphologic, hydrologic, and agricultural techniques is available for measuring rates of erosion over widely-contrasting spatial and temporal scales. Data from erosion plots are used here to evaluate the effect of individual storms at the plot (100 m2) scale during all seasons. The sediment-rating-curve technique is used to examine the combined effects of all erosion processes operating within the catchments during individual flood events over various spatial and temporal scales. By contrasting the behaviours of catchments of different character and over different seasons, dominant causes are inferred. Erosion-pin measurements and erosion surveys provide corroboration of the findings. The diagnosis provides a foundation for the sediment-budget analysis in Chapter 8. Rainfall is obviously the precursor to erosion in this region. Rainfall intensity patterns of the pre-monsoon season are strikingly similar to those of the monsoon season. However, pre-monsoon storms tend to be of shorter duration and have longer dry periods between them than do monsoon storms. More rainfall occurs at lower elevations in the study area yet peak rainfall intensity tends to be somewhat higher at higher elevation. These distinct patterns evident in the rainfall regime will be examined more closely for their relation to observed patterns of sediment dynamics. Having established how rainfall behaves in the study area, it is now possible to attempt a diagnosis using erosion and sediment-transport data from this study. The evaluation begins with an examination of surface erosion from cultivated fields at the plot scale. Findings are tested and conclusions extended by examining stream suspended-sediment regimes over contrasting spatial scales and basin character. 93 5.2 Research background A wide variety of methods has been used to study patterns of fine-sediment transport, including erosion plots (Mutchler et al. 1988), stream hydrometric stations (Gregory and Walling 1973), soil-profile development (Harden et al. 1979), tracers (Bovis 1982), erosion pins, and erosion surveys (ASCE 1975). Each approach focuses on specific processes and spatial and temporal scales. Erosion plots and stream hydrometric stations are the two techniques used in this chapter so a review of these two tools forms the focus of this section. Himalayan studies of direct relevance to this study are also reviewed. 5.2.1 Behaviour of fine-sediment erosion and transport Erosion plots Erosion-plot soil-erosion research has a long history in agriculture and the Universal Soil Loss Equation (USLE) represents the most-comprehensive application of this work (Wischmeier and Smith 1978). This equation predicts average annual sheet and rill erosion and was developed using data from over 10 000 plot-years' data from standard plots of 72.6 ft (22.1 m) in length and 9% slope throughout the United States Midwest (Wischmeier 1976). It represents the major controls on plot-scale erosion using six parameters: rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), cover and management (C,P). The Universal Soil Loss Equation (USLE) is useful in that it explicitly recognises all the controls on soil loss operating at the plot scale. For conditions within the range of parameters studied, it provides a convenient and accurate assessment of soil erosion for management. Several attempts have been made to improve upon the USLE. The Revised Universal Soil Loss Equation, RUSLE, (Renard et al. 1991) uses modified USLE factors and computerised algorithms. Unfortunately, like the USLE, outside of the conditions for which the equation was developed, it is often not useful. Increasing computer capabilities have led to the development of 94 complex mathematical predictors which model the successive detachment, transport, and deposition processes within small catchments. The Water Erosion Prediction Project (WEPP) model (Laflen et al. 1991; Risse et al. 1995) is the modern replacement to the USLE, building on earlier attempts like the Chemicals, Runoff, and Erosion for Agricultural Management Systems (CREAMS) model (Foster 1988). These mathematical models are limited in their application in developing countries because of their high data requirements. Despite the limitations, researchers have attempted to extend use of the USLE to other regions. The Soil Loss Estimator for Southern Africa (SLEMSA) was developed in Zimbabwe based on the USLE model (Wendelaar 1978 in Elwell 1984). Narayana et al. (1983) used the USLE and river, reservoir, and soil-loss data to estimate an average annual rate of erosion for India as 16.4 tonnes/ha. They used work by Babu et al. (1978) in developing an R-factor map of India based on climate records. Low (1967) proposed an estimator of soil loss for developing countries based on easily-obtained mean terrain attributes. Elwell (1984) stresses that countries which attempt to determine local USLE factors face an onerous task and often lose interest in prediction techniques believing that development costs are well beyond their resources. In situations where a quantitative prediction of soil erosion is not directly necessary, erosion plots can be efficiently used to diagnose patterns of sediment production over small spatial scales (< 1 ha). For example, Lai (1982) studied the effect of terraces on sediment production and delivery in small basins (4 ha) in Nigeria and found that while terraces are effective at reducing the delivery of soil from the plot, they do not substantially reduce soil detachment. Young and Onstad (1982) looked at the effect of soil characteristics on soil erosion and found that the degree of aggregation, aggregate stability, and the soil clay content most strongly influenced sheet erosion. Pandey et al. (1983) and Pathak et al. (1984) used plots to assess hydrological aspects of forested and nonforested slopes in the Kumaun Himalaya in India. Pinczes (1982) studied sloping, Hungarian vineyards, finding that erosion rates correlated well to runoff and rainfall intensity for low-intensity rain events and correlated poorly 95 for high-intensity events. Richter and Kertesz (1987) discovered a strongly seasonal pattern of erosion from plots in Germany and Hungary. Hudson (1981) explained the importance of the kinetic energy of rainfall in determining soil loss from plots. In developing an alternative to the USLE R-factor for use in Africa, he had found that 25 mm/hr represents a threshold of kinetic energy between erosive and non-erosive rain. He pointed out that others went on to modify this concept proposing various combinations of total rainfall, rainfall intensity, and the energy of the storm (Elwell and Stocking 1975; Lal 1976; Morgan 1977). These studies focus attention on specific erosive storms rather than total annual rainfall at a plot. Plot-basin scale linkage To address soil loss over larger spatial scales, the concept of a "sediment delivery ratio" has been developed (Glymph, 1954; Maner 1958; Roehl 1962) and applied to erosion data extrapolated spatially over a basin to link the plot to the basin scales. Walling (1983) defined the sediment delivery ratio (SDR) as the ratio of the sediment delivered at the catchment outlet to the gross erosion within the basin on an annualised areal basis. Unfortunately, the SDR is very hard to predict and depends on the sediment sources, the drainage network, relief, slope, soil characteristics, vegetation, and landuse (Walling 1983; Ichim 1990). The SDR concept is a black-box model which lumps all spatial and temporal variabilities into a single number. Spatial heterogeneities have led to distributed application of the delivery-ratio concept often in conjunction with the USLE. Burns (1979 in Walling 1983) focuses on individual sources and on identifying their separate delivery potentials. With adequate spatial resolution and a thorough diagnosis of the delivery potential of important sediment sources, the SDR concept could accommodate spatial heterogeneity. The alluvial erosion and storage component of the basin sediment budget is not generally included in the calculation of "gross erosion" (Piest et al 1975; Walling 1983) and forms the key 96 weakness of the delivery-ratio concept. Church and Slaymaker (1989) examined landscapes in British Columbia (Canada) responding to glacial disturbance and found an increase in specific sediment delivery downstream due to stream channel bed and bank erosion. Though this has been termed a controversy (Bull et al. 1995), it is simply the alluvial component of the basin sediment budget operating over very long temporal scales. This relaxation time of disturbance is also evident over shorter timescales as sediment wedges cascade through a drainage system over decades as a result of anthropogenic disturbance associated with logging activities (Roberts and Church, 1986). Equivalently, it can also occur over short timescales such as the single event. The problem is further complicated by the size distribution of transported sediment (Walling and Moorehead 1989) and the existence of geomorphic thresholds (Schumm 1977). Considering sediment as a homogeneous material is to create another black box akin to the one associated with spatial lumping of the delivery-ratio concept. This will be considered in detail in Chapter 6. Extreme events occur even for a system in equilibrium and can greatly affect the storage term. The relative importance of extreme versus frequent geomorphic events has been well discussed (Wolman and Miller 1960) and will be addressed in Chapter 8. The difficulties associated with linking plot-scale measurements to basin sediment yield are clear. An effective approach uses multiple methods covering a wide variety of spatial and temporal scales. In this study, stream sediment sampling to determine stream sediment yield is the other dominant technique used to assess erosion. Stream sediment yield Patterns of suspended-sediment transport in streams have long been studied to determine average areal denudation rates for spatial scales that are impractical for the erosion-plot technique (> 1 to 10 ha). The larger spatial scale expands the range of controls under consideration. Campbell and Bauder (1940) popularised the concept of a sediment rating curve to determine the characteristic suspended-sediment concentration for any stream discharge. They identified the straight-line 97 logarithmic relation between suspended load and stream discharge, C=aQb where C is suspended-sediment concentration, Q is stream discharge, b is the exponent of the relation (or "slope" on logarithmic coordinates) and a is the coefficient (or y-intercept on logarithmic coordinates). Walling (1974) pointed out that b is normally between 1.0 and 2.0, though smaller values have been reported (e.g., Loughran et al. 1986 found b = 0.68 for a 170-ha basin in New South Wales). This relation has been exploited extensively for determining total fine-sediment output from basins (e.g., Johnson 1942; Porterfield 1972; Singh and Durgunoglu 1992). Due to the distributed, stochastic nature of sediment production and delivery, the technique of regressing C on Q remains a common approach to estimating basin sediment yield (Shen and Li 1976). The sediment-rating curve represents the net effect of the interaction of sediment availability (supply) and its movement through a basin (transport) (van Sickle and Beschta 1983). Most basins studied in the literature are supply-limited resulting in suspended-sediment concentrations which often range across two orders of magnitude (Walling 1977a) - the combined event, seasonal, and spatial variation prevented Brown and Krygier (1971) from reaching general conclusions. Analysis of variance and multiple regression are frequently used to identify dominant controls (e.g., Walling 1974, McPherson 1975, Griffiths 1981). Unfortunately, these methods rest on the appropriate choice of independent variables and require that each variable be measurable and expressed quantitatively; in addition, the variables can be confounded (e.g., precipitation and discharge). Typical reported high values of annual suspended sediment yield range between 100 and 1 000 fknr 2'yr 1 for small basins (1 to 10 km2) (e.g., Nordin 1963; Griffiths 1979; Doty et al. 1981; Tropeano 1991). Tropeano (1991) also reported 5 200 t • km"2 • yr"1 for a 0.75 km2 basin in northwestern Italy. Church et al. (1989) reported almost 20 000 t • km"2 • yr"1 for sub-basins (0.2 to 400 km2) in China's Middle Yellow River basin. Effective use of the rating-curve technique requires that an adequate number of events be sampled, covering a wide range of the controls on sediment yield. In small, especially mountainous 98 basins, flow and suspended-sediment can change rapidly making measurement difficult, increasing error (Walling 1977b), whereas in larger basins more controls can be operating increasing the variability of the net suspended-sediment response. Measurement error and variability in response must be considered when using sediment rating curves to calculate stream event, seasonal, and annual sediment yields (Walling 1977a; Walling and Webb 1981) as discussed in Chapter 8. These concerns are particularly problematic in Nepal (Rakoczi 1985). A variety of mechanisms controls sediment availability and its transport or retention in a basin. To improve the accuracy of the relation, rating curves can be stratified according to these controls. Ultimately, the relation is established by five primary controls - geology, climate, hydrology, topography, and management (adapted from Griffiths 1981). However, to stratify results effectively, measurable parameters important to the controlling mechanisms are the most useful. Effective controls on suspended-sediment rating curves can be grouped into four categories: Hydrology • stream discharge • storm-period variables (peak intensity, total rainfall) Surface response • topography • soil characteristics • surface cover • antecedent soil-moisture conditions Scale • spatial/temporal variability in rainfall/runoff • travel time • antecedent flood history Management • modification of surface soil/cover • structures • water diversion These controls interact to affect the mobilisation, transport, and deposition of sediment, yielding a spatially- and temporally-heterogenous relation. Measurement difficulties and high variability can make it difficult to isolate the effects of some of these controls. Two common approaches are to 99 stratify by hydrograph stage (rising versus falling limb) and by season. Frequently, a much smaller number of controls change with either season or stage and by examining these stratified rating curves, information can be gained about the importance of certain controls. Most of the literature reviewed in the following discussion uses stratification by season to examine, through inference, the operation of the above controls. Hysteretic effects complicate analysis of sediment rating curves by desynchronising changes in sediment and discharge. Though this decoupling introduces difficulties in developing an accurate predictive relation, it also provides new information about upstream sediment dynamics which can assist in evaluating the controls. In this discussion, the general importance of hysteresis is indicated when it arises - a complete discussion of the mechanics and consequences of hysteresis for sediment rating curves is given in Chapter 6. Discharge is generally observed to be the strongest single control on suspended sediment. Higher flows possess a greater ability to carry sediment, especially the larger size classes. Discharge can also induce a change in riparian sediment supply. Sidle and Campbell (1985) suggested that high flows in a gravel-bed stream break the surface armour allowing the fines to be flushed out before re-armouring again at or near the peak flow. Paustian and Beschta (1979) studying forested basins in the Oregon Coast Range found that over 30% of the winter sediment output of gravel-bed streams was stored in the bed. Discharge-induced supply contributes to the well-known clockwise hysteretic behaviour described in Chapter 6. At higher flows, the stream is also able to access sediment supplies not frequentiy available. Sidle and Campbell (1985) observed a steeper sediment-discharge relation at higher flows - that is, a stronger relation of C to Q. These in-stream supply dynamics compelled Van Sickle and Beschta (1983) to propose the partitioning of supply among several compartments accessed at different levels of streamflow. Some authors prefer to use rainfall instead of discharge as the independent variable. Griffiths (1981) derived regional relations for large basins in South Island New Zealand concluding that mean 100 rainfall is the best predictor of suspended sediment output (6 basins of 4-100 km2, 27 basins of 100-1680 km2). He reached similar conclusions for North Island basins (Griffiths 1982). Griffiths (1981) noted the dominance of precipitation found in other climatic regions (semiarid, arid, subhumid, tropical). Walling (1974) used multivariate analysis to assess the effect on suspended sediment on an event basis of three rainfall parameters (15-minute intensity, total rainfall, storm kinetic energy) and found them to have a strong relation. Unfortunately, because discharge and precipitation are confounded in small basins, it is difficult with this approach to determine the relative importance of storm-period variables to suspended-sediment output. Burt (1989) stressed that although topography can be a strong control on suspended sediment, it is in the sub-basin scale where it is most important. In the headwaters, topography influences how fast runoff is concentrated into a powerful stream. In very small basins (scale depends on the climate), there is inadequate flow to be competent in transport. In very large basins, tributary inflows are desynchronised reducing the effect of topography. It is in the mid-sized tributary basins where topography can exercise the greatest effect on the flow regime. Tropeano (1991) echoed these ideas pointing out that basin lag time is also a function of basin shape and dimensions - lag time is therefore considerably shorter in headwater basins. Surface condition - expressed through surface cover and soil characteristics - is important in determining the quantity of sediment available for transport. Because most systems are supply-limited (van Sickle and Beschta 1983), this effect can be large. Tropeano (1991) found suspended sediment output from a 6.8-km2 basin in Northern Italy to be greatest in the summer resulting from recently-ploughed fields. Walling (1974) also attributed a seasonal change in response in a British basin to a decrease in vegetative protection during winter. With bare ground, storm-period variables - especially short-term versus long-term rainfall intensity - become important to sediment production. Infiltration rate and soil moisture conditions affect runoff and its ability to entrain material. Surfaces with low permeability - e.g., built environments - encourage shorter times of concentration 101 resulting in higher peak flows and greater sediment production. Wood (1977) pointed out that long periods without erosion can cause fines to be in greater supply due to weathering and biological activity. He also suggested that desiccated surfaces may favour drying and crumbling which cause fines to be more readily entrained during the onset of precipitation. Conversely, fines may be harder to entrain when wet, especially if the surface is hydrophobic or clay-rich. Findings of Chapter 4 indicated that antecedent moisture conditions of surface soils during the pre-monsoon season should be drier than those of the monsoon season - the difference may contribute to seasonal erodibility of cultivated soils in the study basins. Spatial- and temporal-scale interactions modify the sediment response measured at a point. Scale influence has already been mentioned in terms of headwater hydrology. Variability in rainfall input and a heterogeneous surface response add further spatial-scale influences to the sediment-discharge relation Q?orterfield 1972). This effect is lost in very small and very large basins (scale depending on the climate). Studies often find that total output is dominated by sediment production from a limited area of the basin (e.g., Tropeano 1991 found that 30-50% of total tributary input came from 8% of the land area, dominated by badlands). Also, Sidle and Campbell (1985) pointed out that the sediment response of small basins can be dominated by one source such as a landslide (e.g., Brown and Krygier 1971). Antecedent flood history can modify the supply regime. Many authors have noted seasonal, inter-event, and intra-event exhaustion patterns (Colby 1964; Arnborg et al. 1967; Walling and Teed 1971; Wood 1977; Beschta 1978; Walling and Webb 1982). Beschta (1978) observed the seasonal shift after the annual peak flow had occurred. Walling and Webb (1982) found sediment output to increase strongly with "recovery period" (defined as the time between successive events). Wood (1977) found that sediment exhaustion within individual events depends upon the length and severity of the event. Arnborg et al. (1967 in Wood 1977) suggested that the entrainment of sediment deposited on the bed during the recession stage of a previous flood also results in this differential 102 supply regime. In large basins, the different travel times of water and sediment can modify the sediment-discharge relation. This spatial-scale effect results in a counterclockwise hysteresis loop, in contrast to the clockwise loop commonly found from the effects of supply exhaustion. Watershed management can reduce or enhance the effects of most of the controls described above. Anderson (1981) found that landuse variables accounted for 30% of the variance in sediment rating curves for 61 basins in California. Management comes in many forms; the two of most interest to the present study are structures and modification of surface conditions. Roads are one of the most familiar modifications made to a managed watershed. In British Columbia and the USA Pacific Northwest, the increase in sediment production resulting from construction of forest access roads is well documented (Brown and Krygier 1971; Megahan and Kidd 1972; Beschta 1978; Reid et al. 1981; Reid and Dunne 1984; Anderson and Potts 1987). Other structures such as reservoirs and diversion dams can also strongly modify the downstream sediment-discharge relation. In agricultural basins, perhaps the greatest effect from management is the surface modification brought about by cultivation and other agricultural activities. Loughran (1986) studied a 1.7-km2 managed basin in New South Wales, Australia (forestry/agriculture) and found that 93% of sediment output derived from vineyards (60% of the area) and only 7% came from the forested land (30% of the area). Using erosion plots, he was able to attribute the difference to the absence of a surface cover on cultivated fields. Though output from sloping agricultural fields can be high, it is important to remember that typically only one-third to one-half of the amount eroded from the surface actually leaves the basin (Walling 1983). For example, Imeson (1974) examined an 18.9-km2 basin in England and found that because only a third of the soil eroded from the bare agricultural fields actually left the basin, the river channels were an important sediment source accounting for over 35% of the total basin output. Presumably, in steepland agricultural basins like those in Nepal, the effect of surface-103 cover modification is pronounced. This is discussed further in the following section. 5.2.2 Quantitative Himalayan data Himalayan soil-erosion research has evolved from brief and superficial assessments of the dominant causes of erosion, to measurements of the rate of erosion over limited spatial and temporal scales, to the current focus on combined measurements of precipitation and landuse variables important to erosion and sediment transport. Several good overviews exist (Carson 1985, Ramsay 1986; Bruijnzeel and Bremmer 1989). This review focuses on studies specifically designed to measure erosion rates in the Middle Mountains and other research designed to explain the causes. Early research on erosion in the Himalaya sought to explain the causes of observed erosion using inference from very limited data. In particular, research was focused on determining whether erosion was natural in origin or caused primarily by farming activities. Laban (1977) estimated erosion due to mass movements by counting landslides from an aircraft and noting their association with land use. His results led him to conclude that about 75% were from natural causes and 25% were induced by human activity. Using a geomorphological analysis initiated as a result of catastrophic rainfall during October 1968 (500 mm in 24 hours), Starkel (1972) estimated surface lowering in the Darjeeling Himalaya to be of the order of 60 to 70 t-ha"1 - yr"1 over the prior century. Mass movements resulting from extreme rainfall events dominated this rate of surface denudation, and there was not a direct relation between the amount of mass wasting on forested versus nonforested slopes. According to Carson (1985), both Sastray and Narayana (1984) and Winiger (1983) found that terracing and related farming practices have a stabilising influence on steep slopes as long as farming is economic; otherwise, slope degradation can occur at a greatly accelerated rate until natural rates resume. Attempts at measuring rates of erosion in the Himalayan region have been based largely on erosion plots and stream sediment yields as summarised in Tables 5.1 and 5.2 respectively. The 104 Table 5.1 Surface erosion rates as determined by field studies using erosion plots within or near the Middle Mountains. Source Details of Study Scale (m2) Erosion Rate t • ha"1 • yr"1 Erosion Plots Laban (1978) Middle Mountains grassland overused grassland seriously eroded, gullied n/a 10-20 20-50 200-500 Mulder (1978) Kathmandu Valley densely forested well-managed pasture steep, overgrazed 10 0.34 9 35 DSCWM (1991) Shivapuri terraced; cultivated; mulched/nonmulched; steep 15 6-32 Sherchan et al. (1991) Pakhribas cultivated terrace; (various treatments) 18 18-35 Upadhaya et al. (1991) Kulekhani terraced; cultivated; 5% and 10% slopes; 90 0.8-7 Ries (1994) High Mountains traditional cultivations 14 1-9 Overseas Development Agency (1995) traditional cultivations 76-536 3-13 degraded shrub 25-95 6-22 degraded forest 71-85 0-19 grassland 30-69 < 1 - based on extrapolations from partial sampling during 1992/3. Note: Bruijnzeel and Bremmer (1989) summarise other early Himalayan measurements. results from erosion plots indicate that at the spatial scale of less than 100 m2, annual erosion rates vary on cultivated land and on non-gullied shrub/forest land from 1 to 35 t/ha. The low annual rates reported by Upadhaya et al. (1991) at the DSCWM Kulekhani site (most fall between 1 and 3 t/ha) may be due to measurement discontinuities and the gentle slopes of the plots. Laban (1978) summarised his measurements and a variety of earlier field measurements concluding that 10 to 20 t/ha was a reasonable estimate of annual surface erosion for well-managed cultivated or grazing land, rising to 20 to 50 t/ha if overused and increasing locally to 200 to 500 t/ha if seriously eroded and 105 Table 5.2 Surface erosion rates as determined by field studies using check dams and hydrometric stations. Source Details of Study Scale Erosion Rate (km2) t • ha-1 • yr1 Accumulation behind Check Dams (all sites within Middle Mountains) Laban (1978) overgrazed grassland 0.13-0.25 22 gullied, overgrazed grassland 29 - parallel-dipping phyllitic schists - 30% trap efficiency - 70% SDR Laban (1978) overgrazed scrubland 0.18 43 severely gullied 0.11-0.19 125-570 - weakly consolidated granites and migmatites - 50% trap efficiency -100% SDR Laban (1978) degraded, gullied forest 0.11-0.15 63-420 - Mahabarhat Lekh; very steep - metamorphic/sedimentary rocks Basin Sediment Yield Ries (1994) High Mountains: Bamti 0.08 13 Chhukarpo Low 0.24 30 Chhukarpo Middle 2.7 7.5 Chhukarpo Middle 3.7 3.7 Surma 5.7 4 Kandel (1978) Middle Mountains 6-585 3-46 6 basins; mix of forested, (excluding pre-cultivated, degraded land monsoon) Sharma (1977) Bagmati 585 46 Trisuli 4110 19 Karnali 42 890 51 Ramsay (1986) some major Nepalese rivers > 5000 10-70 Williams (1977) Tamur 5770 38 in Carson (1985) Aran 34 525 7.6 Sunkosi 18 985 21 Saptakosi 59 280 15 Note: Some values based on conversion of 1 mm =13 t/ha 106 gullied. Most denudation estimates from suspended-sediment data are for large rivers (5 000 to 50 000 km2) and generally range from 10 to 70 t-ha"1 'yr1. Agricultural erosion rates averaged over larger scales typically result in lower rates due to deposition within the basin. At the large scales assessed in the studies which are summarised in Table 5.2, myriad sediment sources - especially those associated with riparian erosion (Bruijnzeel and Bremmer 1989) - are active making extrapolation to field-scale erosion extremely difficult and uncertain (Ramsay 1986). The sediment sources which contribute to the denudation rates calculated using river measurements are different than those under far narrower consideration in the plot studies. In the High Mountains, Ries (1994) measured annual sediment yield from small basins (0.1 to 6 km2) of 4 to 30 t/ha; these rates may be more representative of those expected for agricultural basins however this work was carried out in a physiographic region very different to the Middle Mountains. Many of these studies, unfortunately, are based on significant assumptions in extrapolating their results through space and time. For instance, although many researchers have contended that surface erosion rates are highest in the pre-monsoon period (Overseas Development Agency 1995; Ramsay 1986; Bruijnzeel and Bremmer 1989), data are rarely available to defend this assertion. Impat (1981) found that soil loss was greatest at the beginning of the measurement period in June despite precipitation peaking in August (in Ramsay 1986). Overseas Development Agency (1995) reported that 45-60% of measured soil loss occurred in May (part of the pre-monsoon period) but did not provide details on differences between monsoon and pre-monsoon sampling coverage. The spatial scales of most of these measurements are either very large (> 5 000 km2) or very small (< 1 km2) -processes and sediment sources important in the intermediate scales are rarely evaluated. Several studies have recently tried to integrate specific measurements of surface erosion with precipitation and landuse measurables over contrasting spatial and temporal scales to reach more meaningful conclusions about catchment-scale processes. Ries (1994) (Table 5.2) provided perhaps the 107 best example: field-scale erosion rates are contrasted with basin sediment yields and rainfall delivery in the High Mountains over consistent spatial and temporal scales. Perino (1993) documented a paired-catchment approach for basins of 2 hectares in size within the Phewa Tal and Kulekhani basins in the Middle Mountains. The complete capture of all runoff (including bedload) in a large structure with detailed hydrometeorologic measurements is combined with manipulation for conservation practices. The low basin output of the first year (1992) - less than 1 t/ha - was attributed to exceptionally low rainfall in the study area. Overseas Development Agency (1995) (Table 5.1) provided detailed measurements of surface cover and rainfall storm-period variables and concluded that the lack of surface cover in May of the pre-monsoon season is a strong control on field-scale soil loss; unfortunately, they failed to relate this to suspended sediment dynamics at larger basin scales. Despite these recent catchment-scale research efforts, there remain few quantitative data describing erosion in the Middle Mountains over spatial and temporal scales of importance to farming activities and their relation to downstream sediment transport. Specific parameters crucial to understanding the erosion process remain conspicuously absent. These include high-flow sediment samples, rainfall intensity and distribution measurements, and the measurements of sediment storage to address within-catchment variability. Further, measurements remain dominated by monsoon observations, ignoring the pre-monsoon season when it is widely believed transport rates are the highest. 5.3 Surface erosion on cultivated rainfed uplands Five erosion plots have been monitored throughout 1992-1994 on steep, high-elevation, rainfed, cultivated fields. In this section, the factors which shape surface erosion at this scale are examined. Event, annual and seasonal erosion rates are determined. Event analyses provide insight into the mechanics of the surface-erosion process and strengthen the conclusions from the integrated annual and seasonal analyses. 108 5.3.1 Controlling factors The factors which determine surface erosion from the erosion plots include topography (slope length and steepness), soil characteristics, storm-period variables, and management. In the Jhikhu River basin, management is pervasive, greatly influencing all controlling factors except rainfall characteristics. For instance, management affects the rate of surface erosion through cropping practices and soil characteristics such as the organic-matter content. The topography of the cultivated fields is greatly modified by management through the construction of terraces. The Middle Mountain farmers who cultivate the steeplands have developed a long-standing tradition of terraced agriculture which works to minimise the negative effects for cultivation of the steep topography. 5.3.2 Erosion plots: annual regimes Table 5.3 presents a summary of the annual rate of soil loss from each of the five erosion plots during 1992-1994. Management practices were held constant across the plots and were characteristic of prevailing management on the rainfed cultivated fields (section 3.1.3). Similarly, the slope of the plots (22'-30°) is typical of much of these cultivated uplands. The results suggest a tremendous range of surface-erosion in these upland fields from almost none to rates exceeding 40 t'ha"1 •yr"1. Table 5.3 Annual rate of soil loss (tonnes/ha) from all plots, 1992-1994. Plot 1992 1993 1994 1 18 4.1 42 2 23 34 6.4 3 38 37 6.9 4 0.1 0.2 2.9 5 0.1 0.3 2.6 109 The significant plot-to-plot variation in soil erosion is largely a reflection of the huge variation in soil properties across the five plots. In particular, infiltration rate as affected by the texture of the surface soil is a controlling influence on the nature of storm runoff and therefore on the rate of erosion. Soil loss from plots 1, 2, and 3 is over an order of magnitude greater than that from plots 4 and 5 (see also Figure 5.3). Table 5.4 contrasts coarse-fragment content, fine-fraction texture, and infiltration rate for the five plots. Plots 1,2, and 3 have a much greater tendency for overland flow as a result of the fine surface texture and the consequent reduction in surface infiltrability. In contrast, the surface (and subsurface) of plots 4 and 5 are highly porous, resulting in infrequent overland flow. Although there are differences in clay content of the surface soils between the plots, it is the much larger difference in coarse-fragment content which is the first order effect and sets apart the erosion rates at the two sets of plots. Plots 1 and 2/3 also differ in their responses largely due to their surface soil characteristics. (Plots 2 and 3 have the same physical characteristics because they are adjacent - see Chapter 3.) Generally, Plot 1 yields less erosion than Plots 2/3. This may be due to the inherent erodibility of Table 5.4 Surface-soil characteristics of erosion plots. Plot No. Surface Sub-surface Surface Horizon (#1) Horizon (#2) Infiltration Rate (about 0-15 cm) (about 15-50 cm) Coarse Fragment Content (%) Texture of Fine Fraction (S/Si/C) Coarse Fragment Content (%) Texture of Fine Fraction (S/Si/C) Initial (10 min) cm/hr Final (>4hr) 1 1.7 44/35/21 1.1 40/32/28 42 32 2 4.2 37/34/29 3.4 37/33/30 47 16 3 4.2 37/34/29 3.4 37/33/30 47 16 4 45.8 52/37/11 69.3 53/36/11 68 20 5 67.3 39/42/19 39.8 34/40/26 94 51 Fine fraction consists of particles < 2 mm in size. Coarse fragments include large gravels. S=sand; Si-silt; C=clay. 110 these two soils: plots 2/3 contain red soils which are highly weathered and of low carbon content (Shah et al. 1994) and lack aggregate stability (though direct measurements are unavailable). In contrast, the soil of Plot 1 is better aggregated to resist surface erosion. Also, infiltrability is somewhat higher in Plot 1 than in Plots 2/3 (see Table 5.4) which might further discourage overland flow in Plot 1. The reversal in erosion rates between 1993 and 1994 at these plots was driven largely by storm patterns (not by soil properties) especially in relation to surface cover. Table 5.4 also shows a large inter-annual variability of erosion from each plot of an order of magnitude. This variability can be investigated further by examining seasonal changes in erosion rate, the subject of the next section. 5.3.3 Erosion plots: seasonal regimes Within each plot there is also a large intra-annual variability as shown in Figure 5.1. Table 5.5 shows the percentage of each plot's annual erosion which occurs during the pre-monsoon, transition, and monsoon seasons. In most years and at every plot, more than half of the annual erosion occurs in the pre-monsoon season. In fact, often over 80% occurs in the combined pre-monsoon and transition seasons, yet only 45% of the annual precipitation occurs during this period. The presence of swelling clays in Plot 1 might explain why a greater proportion of the annual erosion Table 5.5 Percentage of each plot's annual erosion occurring in the pre-monsoon, transition, and monsoon season, 1992-1994. Plot No. pre-monsoon season transition season monsoon season 92 93 94 92 93 94 92 93 94 1 1 15 31 64 10 57 35 75 12 2 82 100 98 17 0 1 1 0 1 3 60 100 96 39 0 1 1 0 3 4 37 62 100 50 6 0 13 30 0 5 23 74 100 8 4 0 69 22 0 I l l from this plot occurs during the monsoon season (see the end of section 5.3.4). When the rains first start to arrive in the late spring, in the pre-monsoon season, the land surface is desiccated and the upland cultivated fields bare and vulnerable to erosion. By mid-July, the monsoon season is well under way, normally bringing regular rainfall. The first few weeks of July show erosion-regime behaviour which intergrades between that of the pre-monsoon and monsoon seasons and, in this study, is called the transition season (see section 5.4.2 for more details). The major seasonal change in surface condition which occurs at the plot is a change in surface cover as weed growth and the summer crop develop. In 1994, measurements were made of average maize stalk height and leaf length to document the seasonal development of the crop within the five erosion plots. The results shown in Figure 5.2 indicate a close correspondence between these basins' erosion regimes and the development of surface cover in the plots especially when compared to each plot's chronological erosion history (Figure 5.1). The farmers also frequently intercrop providing a further protection of the soil from intense rainfall. Changes in vegetative cover also explain the large inter-annual variation in soil loss at a given plot. Surface cover does not develop immediately but requires rainfall to get started and then takes several weeks to be complete. If damaging rains occur when the surface cover is only partially complete - which is common - then significant losses are likely at the plot in a single event. If the fields have recently been weeded (typically once in either June or July) then further losses can result. Subsequent rains falling on the same ground with a strong vegetative cover (monsoon season) often cause almost negligible surface erosion. As a result, loss from single events dominates annual soil loss. Table 5.6 shows the percentage of each plot's annual soil loss that occurred in the two most-damaging events at each plot and their dates of occurrence. At all plots and in almost all years, over 50% of the annual total occurs in only two events. At plots 2 and 3, the rate is over 80%. These two events typically occur during the pre-monsoon and transition seasons. 112 Figure 5.1 Soil loss from the erosion plots on an event basis, 1992-1994. 40 Note: Scale on ordinate for plots 1 through 3 differs from that of plots 4 and 5. Seasons D = Dry P = Pre-monsoon T = Transition M = Monsoon M D Plotl 10 -1 -0.1 -0.01 0.001 + +*1 + M 1992 1993 1994 40 IT 10 H 1 1 ^ ^ 0.01 0.001 £ 1992 .2 40 3 O i -o.Ol -0.001 — D O a 1992 \ 0.1 0.01 0.001 D 1992 " M D Plot 2 M "TT 1993 1994 T T I + M D Plot 3 + + + M D 1993 1994 M D Plot 4 + + + + --M D + + "M" p m + 4« + + + + ~W 1993 1994 o.i -o.oi -o.ooi D 1992 + " M D Plot 5 P T p + + + •-"ST IIIIIII 1993 1994 1995 113 Figure 5.2 Average maximum maize height (a) and maize leaf length (b) at all erosion plots in 1994. May Jun Jul Aug Sep Oct 114 Table 5.6 The percentage of the total annual erosion at each plot which occurred in the two most-damaging events of each year, 1992-1994. Plot No. 1992 1993 1994 1 55 50 44 (10/7; 15/8) (21/7;22/7) (9/7;2/7) 2 96 88 87 (10/6; 10/7) (17/5;27/5) (9/5;30/5) 3 78 91 82 (10/6; 10/7) (17/5;27/5) (9/5;30/5) 4 69 40 60 (9/7; 10/6) (21/4; 10/8) (19/6; 15/6) 5 60 57 67 (21/7; 11/6) (21/4; 10/8) (15/6;17/6) Note: Dates (day/month) represent when the two events occurred. 5.3.4 Erosion plots: event regimes In Chapter 4, it was shown that rainfall characteristics of storms in the pre-monsoon season differ - but not greatly - from those of the monsoon and transition seasons. In section 5.3.2, it was suggested that soil texture strongly influences the likelihood of runoff to occur (and hence surface erosion) in any season. It was determined that under conditions when storm runoff occurs, it is vegetative cover that is the dominant seasonal control on surface erosion, with other factors such as soil aggregate stability exercising a non-seasonal control. But to what extent do differences in storm-period variables, and particularly rainfall intensity, contribute to the observed seasonally-variable surface erosion at the plots? Figure 5.3 shows the influence of peak 10-minute storm rainfall intensity on soil loss at the five erosion plots for each event of the three-year period. The seasonal differences observed in the last section are reinforced here and the effect of rainfall intensity is revealed. There appears to be a threshold of about 30 mm/hr below which storms are non-erosive at the plot scale. Above this value, significant surface erosion can occur in the pre-monsoon and transition seasons, increasing with peak rainfall intensity. A threshold of this order was described by Hudson (1981) based on African data. 115 Figure 5.3 The effect of maximum 10-minute rainfall intensity (I10) on soil loss at all erosion plots, seasonally stratified for all events of 1992-1994. 25 ~} I d § 2 0 0 (a) Erosion Plot 1 O A O ~ l — 0 40 80 120 160 Max 10-Min Intensity (mm/h) 25 -j § 2 0 ~_ CA t*> . r- -I I1" "3 o (b) Erosion Plot 2 O o jpea i • 0 40 80 120 160 Max 10-Min Intensity (mm/h) 1.0 §0 .8 -CA o0.6 H § 0 . 4 H lo.2 H rS 0.0 (d) Erosion Plot 4 O O o 0 40 80 120 160 Max 10-Min Intensity (mm/h) o Pre-Monsoon A Transition • Monsoon Dashed line indicates 30 mm/h. 25 § 2 0 -_ CA CA ., s 5 0 (c) Erosion Plot 3 O IO o o T 0 40 80 120 160 Max 10-Min Intensity (mm/h) 1.0 §0.8 -CA o0.6 H i—J §0.4 H §0.2 (e) Erosion Plot 5 O - D O O o.o JJamflBmaui. • fjnn i A | • 11 • 0 40 80 120 160 Max 10-Min Intensity (mm/h) 116 High-intensity rainfall clearly occurs at all plots in all seasons yet it is rarely of concern at this scale during the monsoon season. This observation is consistent with the conclusion that vegetative cover is a dominant seasonal control on surface erosion at the plot scale. Above the threshold, a relation between I10 and soil loss is of limited use because of the lack of data with respect to the tremendous variability in soil loss resulting from the timing of heavy rainfall relative to the development of a vegetative cover. Consistent with the limited spatial scale of the erosion plots and the resulting minor extent of channelised flows within the plots, soil loss does not correlate with rainfall intensity over significantly longer periods than that of I10 (including RT0T)-Figure 5.4 relates event runoff coefficient (CR) to the corresponding I10. CR is defined as the percentage of incoming event rainfall which runs off the plot. It tends to be the highest during the pre-monsoon season (declining in value through the transition season) at all plots except Plot 1. With saturated soils often prevailing in the monsoon season, one might expect higher CR during this period but the opposite is generally observed. This behaviour might be due to a change in dominant runoff mechanism with season for significant erosion events. Figure 5.5 shows seasonal variation in the influence of CR on the soil loss at the five erosion plots. The greater CR, the more likely are overland flow and surface erosion to occur. There appears to be a threshold in CR below which erosion does not occur, though it is inconsistent across the plots. Above some threshold of between 5 and 10% (depending on soil characteristics), erosion can be serious in the pre-monsoon and transition seasons. Permeability also influences the number of events that occur at each plot and is reflected in CR. About half as many runoff events occur at Plots 4 and 5 as occur at Plot 1, with Plots 2 and 3 intermediate to these. This is a direct result of the coarse-fragment texture of the surface soils, influencing the propensity for erosion to be able to occur on a given high-elevation cultivated field. Although within-plot observations during rain events are few in number, the presence or absence of rills combined with an examination of the pattern of rainfall for specific events suggests 117 Figure 5.4 The effect of maximum 10-minute rainfall intensity (I10) on event runoff coefficient (CR) at all erosion plots, seasonally stratified for all events of 1992-1994. (a) Erosion Plotl 0 40 80 120 160 Max 10-Min Intensity (mm/h) 0 40 80 120 160 Max 10-Min Intensity (mm/h) S10 -J£ [ T i l l 0 50 100 150 Max 10-Min Intensity (mm/h) o Pre-Monsoon A Transition • Monsoon Runoff is higher than indicated for solid symbols because drums overflowed. 0 40 80 120 160 Max 10-Min Intensity (mm/h) 40 (e) Erosion Plot 5 0 50 100 150 Max 10-Min Intensity (mm/h) 118 Figure 5.5 Relation between runoff coefficient (CR) and event soil loss at all erosion plots, seasonally stratified for all events of 1992-1994. 25 -3 § 2 0 I10 > 5 0 25 § 2 0 110 S 5 0 (a) Erosion Plotl o 1 — r 0 10 20 30 40 Event Runoff (%) (b) Erosion f Plot 2 p o 1 ° • o T 1 1 — 1 0 10 20 30 40 Event Runoff (%) 0 10 20 30 40 50 60 70 Event Runoff (%) o Pre-Monsoon A Transition • Monsoon Runoff is higher than indicated for darkened symbols because drums overflowed. Dashed line indicates 10 %. 25 § 2 0 60 I10 > 5 0 * (c) Erosion ° Plot 3 ~. A o — A o - o D 0 10 20 30 40 Event Runoff (%) 0 10 20 30 40 50 60 70 Event Runoff (%) 119 runoff mechanisms for different storm types. Those storms which bring heavy rainfall on dry ground within the first few minutes (during the pre-monsoon season in particular - see Chapter 4) resulted in Hortonian overland flow. Storms bringing high total rainfall initiated saturation overland flow in those instances when a rate of erosion was measurable; brief periods of Hortonian overland flow when the rainfall intensity was sufficiently high may also have been possible during those storms. In Chapter 4, it was concluded that the most significant seasonal differences in rainfall regimes involved total storm rainfall, the period of time before a storm without rain, and the within-storm timing of storm rainfall: pre-monsoon storms are more likely to bring heavy rain on dry ground than are their monsoon counterparts. This is very important to surface erosion and may be a major factor contributing to the high values of CR during the pre-monsoon season. There are several possible explanations for the more-frequent runoff at Plot 1 as compared to the other plots. The farmer who monitors this plot tended to sample all the small (minor) events which were not sampled at Plots 2 and 3 because they are insignificant in the soil-loss budget. It is also possible that the soil in Plot 1 possesses a greater aggregate stability and can sustain greater overland flow before being entrained. This is consistent with farmers' comments on this soil using the indigenous classification; it is a clayey soil with high productivity and different behaviour as discussed further in Chapter 7. A final possibility is that the soil of Plot 1 may contain swelling clays which increase cohesion, discouraging entrainment. This is an area of potential research on these cultivated steeplands. If so, this could explain why Plot 1 shows its highest percentage runoff during the monsoon season (when the soil is frequently saturated) instead of during the pre-monsoon season as it occurs at the other four plots. 5.4 Stream sediment regimes The findings of the last section are expanded by examining sediment regimes in the streams of the detailed study basins especially those nested around erosion plots 2 and 3. As the spatial dimension under consideration grows larger, so does the variety of sediment sources and mechanisms of erosion. The size of these basins varies between 1 and 100 km2 - a range in spatial scale that has been examined rarely in the Middle Mountains. Five hydrometric stations in the Jhikhu River basin have been monitored during most of the 1992-1994 study period and two additional stations during 1993-1994. Suspended-sediment sampling and stream-flow measurements were carried out at five of these sites during flood events at all times of the rainy season. The relation between suspended-sediment concentration and discharge forms the basis of the sediment-rating-curve technique. Discharge is determined from a relation with gauge height as explained in section 3.3.1. Observed patterns in suspended-sediment transport can be explained by linking these patterns to storm-period variables, inherent characteristics of the basins, and landuse activities. 5.4.1 Controlling factors A wide range of factors is responsible for shaping a basin's sediment-discharge relation. Rainfall characteristics of importance include peak intensity, spatial variability, and total storm rainfall. Soil-surface condition is important because it directly affects the entrainment process. Management activities can shape both the susceptibility of the surface to erosion and the rainfall-runoff process itself. Soil moisture, antecedent flood history, sediment storage, and sediment exhaustion can also be important factors as discussed in section 5.2.1. The next section characterises and contrasts the prevailing suspended-sediment regimes of all study basins. 5.4.2 Seasonal regimes The entire suspended-sediment data set consists of 2287 high-flow samples and 820 low-flow samples taken during 1992-1994 at seven hydrometric stations. For each sample taken, the measured sediment concentration is coupled with the corresponding discharge and presented in its station's C-Q 121 Figure 5.6 Sediment rating curves for monitored hydrometric stations based on entire data set (1992-1994): (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) Dhap, and (e) Jhikhu. (d) Dhap; St. 3 lli£b>o ET^ O • 0 ^ 1000 01 0.1 1 3io Discharge (m /sec) ~ • - 3 — Discharge (m /sec) 100 o 1992 • 1993 A 1994 .01 0.1 1 310 Discharge (m /sec) 100 122 graph. Figure 5.6 presents the C-Q distributions for the major hydrometric stations located within the Jhikhu basin stratified only by the year in which the samples were taken. In particular, Figures 5.6a and 5.6b present the results for the steep, headwater catchments (stations 10 and 9 respectively) within the Andheri basin. Figures 5.6c and 5.6d show the data for the Lower Andheri and Dhap catchments (stations 2 and 3 respectively), catchments of similar area but of strongly contrasting topography. Finally, the data for the entire Jhikhu basin (station 1) are found in Figure 5.6e. Stage-discharge relations are not available for the mid-reach Andheri basins (stations 11 and 12) so these distributions cannot be presented. These unstratified C-Q graphs reveal the general nature of suspended sediment transport in these basins. The small size and steepness of Kukhuri basin is reflected in steep C-Q behaviour. As basin scale increases, the distributions shift toward higher discharge values and weaker relations with Q. The graphs in Figure 5.6 show that the data derive from a wide cross-section of the period of study (1992-1994) and hence can be expected to reflect a wide variety of controls present during that period. A comparison of sediment transport in these basins is made possible by developing relations based on functional analysis (Kendall and Stewart 1979) for each station. Section 5.3 demonstrated a strong seasonal discontinuity in the rate of soil loss at the plot scale. This knowledge is used to improve the relations by applying an initial stratification by season before applying functional analysis. Seasons important to erosion and sediment transport need definition. Elevated levels of suspended sediment were readily observed at the beginning of the pre-monsoon season with lower values resulting during the later part of the rainy season. Where is it appropriate to define the extent of each season? To determine the end of the pre-monsoon season, sediment data were added incrementally to those of the beginning of the rainy period (generally early June). High sediment concentrations were maintained until the end of June then started to decline indicating the beginning 123 of a transition season. The same process was carried out from the end of the rainy period adding incrementally sediment data earlier in the monsoon. Levels remained low until those before July 20 were included and this pattern was present at all five hydrometric stations. As a result of these sensitivity analyses, the pre-monsoon season is defined to last until and including the last day of June, the monsoon season from July 20 to the end of the rainy season, and the transition season (showing intergrade behaviour) from July 1 to 19 inclusive. The concept of a floating rating curve has also been considered to model the change in suspended sediment concentration through the rainy season. This approach would assume specific (daily) rates of decline in suspended sediment concentration through the entire rainy season. In this situation, the rate(s) would reflect changing supplies due to increased vegetative cover during the pre-monsoon season and perhaps an additional supply-exhaustion effect during the monsoon season. Though such a model may provide a valid simulation for these basins, synoptic-scale variation in suspended sediment concentration within the data available obscures the observation of a floating rating-curve pattern. For instance, samples taken during May at the Upper Andheri station reveal a heightened response during the early stage of the pre-monsoon season. Unfortunately, storms and floods are less frequent at this time than in June and too few samples are available at any station (including Upper Andheri) for adequate development of a floating rating curve model for the pre-monsoon season. In addition, a systematic decline in suspended sediment concentration within either the transition or monsoon seasons was not evident; only a September drop at Lower Andheri appeared defensible. This remains a useful area for further research C-Q relations derived from these data are used both to compare the sensitivity of the basins with respect to the operating controls and to predict C values for given Q values. The comparison demands functional analysis whereas the prediction requires simple log-linear regression. Mark and Church (1977) have provided equations for the computation of the functional relation in terms of the marginal regression derived for prediction. Because both are required here (prediction - Chapter 8), 124 their equations are used and both relations presented. The results of simple log-linear regression are expressed in terms of C = aQb and are given in Table 5.7 for each of stations 10, 9, 2, 3, and 1. The correlation coefficient (R2) and the standard error (sr) of the regression are provided in terms of the transformed Oog10) values. These values are used in Chapter 8 for predictive purposes and a standard bias correction is applied in that application to better predict the influential high-flow estimates (Miller 1984). Table 5.7 Sediment-rating-curve relations based on seasonally-stratified data using log-linear regression (1992-1994, assuming Q known without error) excluding data from the transition season. Station No. Season a, b, n R2 Kukhuri 10 P 36.13 1.431 45 0.397 0.413 M 16.91 1.520 110 0.604 0.199 Upper Andheri 9 P 30.33 0.385 31 0.225 0.170 M 8.27 0.583 75 0.390 0.218 Lower Andheri 2 P 12.20 0.586 150 0.592 0.0913 M 3.15 0.740 243 0.678 0.0911 Dhap 3 P 11.53 0.284 62 0.402 0.0312 M 8.24 0.429 128 0.218 0.122 Jhikhu 1 P 1.44 0.872 201 0.546 0.0937 M 0.45 0.950 489 0.688 0.0819 Rating curves based on C = aQb power law relation; n=number of samples; R2=correlation coefficient; sr=standard error of the regression (log,0 g/1); P - pre-monsoon season; T - transition season; M - monsoon season To determine the functional relations for each station, it is necessary to estimate the ratio of the error variances of C and Q (X = E^/EQ2) as explained by Mark and Church (1977). The calculation of E Q 2 is straightforward using the standard error of the regression for the stage discharge relations presented in Appendix A5. A geometrical approach is followed to determine E C 2 . It is assumed that any additional departures in the C-Q relation between the measured and expected C values (beyond that attributed to Q) can be attributed to real variance in C. Hence by geometry, E C 2 = E [dC]2 = E [(Cmea8 - C e x p - br(dQ)]2 125 where Cmau is the actual measured suspended sediment concentration (g/1), C e x p is the expected value based on the marginal regression, br is from the marginal regression, and dQ is the error (m3/s) associated with the measurement of discharge. Using this approach, all synoptic variability in suspended sediment concentration including hysteresis is attributed to the C variate. This is appropriate since adjustments for these synoptic effects are typically unavailable. Following Mark and Church (1977), this relation is used to calculate values of bf for each station/season combination: bf = {(br2/R2-X)-tV[(br2/R2-X)2+4Xbr2]}/2br where R2 is from the marginal regression (Table 5.7). Church and Mark (1980, p. 385 and erratum) provide confidence limits for bf in terms of the one-tailed Student's t for n-2 degrees of freedom: X t^anJtan-^ fX-^  + ^ sin-^  All calculations are carried out here using log10 transformed units. Figure 5.7 illustrates the results of these calculations (for the expected relations) and the equations of the resulting lines appear in Table 5.8. This table indicates that the error variance associated with the measurement of C is between 2 and 19 times greater than that of Q. A small set of replicate samples (21 pairs) taken at Upper and Lower Andheri, Dhap, and Jhikhu stations under conditions representative of high flow has a small associated error variance in comparison to the values calculated to yield Table 5.8 (0.0019 versus a range of 0.087-0.45). This contrast suggests that the unstructured variance in C is due largely to the effect of synoptic controls on suspended sediment operating within seasonal scales with a minimal contribution derived from random fluctuations during sampling. The estimated error associated with Q is at times larger than the one suggested by the standard error of the regression for the stage discharge relations. At Dhap station (3), the stage discharge relation is not developed over the highest flows putting a larger error expectation on the high-flow values calculated through extrapolation. In addition, bed control difficulties at Upper Andheri station (9) and some uncertainty in reading the gauge at the highest flows at Jhikhu (1) and 126 Figure 5.7 Seasonally-stratified sediment rating curves based on all entire data set: (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) Dhap, and (e) Jhikhu. Uiooo (a) Kukhuri; St. 10 "3> IOOO e o c o c 0 U + J c 1 • l - H <L> C/3 (b) Upper Andheri; St. 9 100 h 10 O i . r i 0.1 - • HD 0.01 0.1 10 100 0.01 0.1 Discharge (m /sec) 1 310 100 Discharge (m /sec) Uiooo §1000 fl I 100 J - l fl fl-o U 10 "8 o.i 00 e l § H o.i C/3 (d) Dhap; St. 3 K i n D - D M • 0.01 0.1 Discharge (m /sec) 0.01 0.1 1 310 100 Discharge (m /sec) • Pre-Monsoon • Monsoon 0.01 0.1 Discharge (m /sec) 127 Table 5.8 Sediment rating-curve relations derived using functional analysis. Station Season X b, % Name No. expected range expected range Kukhuri 10 P 18.47 1.68 1.28-2.11 49.5 29.9-84.5 M 10.44 1.72 1.55-1.90 21.9 17.6-27.5 Upper 9 P 12.18 0.402 0.221-0.585 31.6 20.7-48.2 . Andheri M 9.68 0.616 0.500-0.733 8.70 7.27-10.4 Lower 2 P 11.59 0.598 0.545-0.650 12.4 11.7-13.1 Andheri M 12.51 0.755 0.712-0.798 3.154 3.14-3.15 Dhap 3 P 2.29 0.299 0.239-0.361 11.5 11.5-11.5 M 8.99 0.462 0.362-0.563 8.13 7.80-8.47 Jhikhu 1 P 15.53 0.907 0.832-0.983 1.34 1.13-1.58 M 14.60 0.976 0.938-1.01 0.42 0.38-0.46 Each a, calculated by applying its respective bf to the means of C and Q from each data set (a f=Cm e a n-b fQm e m). Ranges are based on 90% confidence. X is the ratio of the error variances (E C 2/EQ 2). P = pre-monsoon season; M — monsoon season. Lower Andheri (2) increases the expected error of the Q values for these situations. However, due to their limited nature, these are not incorporated further in the present analysis. The pre-monsoon sediment response is greater by up to an order of magnitude in comparison with its associated monsoon response and shows a tendency to merge with the monsoon-season result at the highest flows (Figure 5.7). This pattern is evident within all basins including that of the largest Jhikhu basin. The similarities suggest that the same controls are operating over all scales (1-100 km2), with their relative influence changing with season and scale. The influence of basin character on these sediment regimes can be examined further by contrasting the parameters of these ten equations and bf). Figure 5.8 compares the expected values and ranges (here at 90% confidence) in bf and % between all five basins. This comparison reveals the relative vulnerability of these basins to floods, vis-a-vis suspended sediment. The exponent of the relation (bf) expresses the extent of coupling Figure 5.8 Seasonal variation in % and bf with basin area (C=aQb). (a) Relation Exponent (bf) 2.5 :ip Monsoon Season I 2 J. 3 1 J. 0.5 1 10 100 200 Basin Area (krn )^ 0.5 1 10 100 200 Basin Area (km )^ (b) Relation Coefficient (af) O SS <w o u 0.5 1 100 200 Basin Area (km )^ 100 Monsoon Season 80 60 40 -10 20 g. 9 3 0 0.5 1 10 100 200 Basin Area (km )^ Error limits based on 90% confidence. 10 - Kukhuri 2 - Lower Andheri 1 - Jhikhu 9 - Upper Andheri 3 - Dhap 129 between Q and C. Relief has a strong but not exclusive effect on this coupling. The higher bf is, the greater is the possible transport limitation. The coefficient (aj) provides an indication of the magnitude of the relation between Q and C. Higher values of a, (for equivalent bf) indicate a greater sediment availability. Considered together, especially in light of basin characteristics (including area), the comparison can shed light on the relative behaviour of these basins. It is important to realise that these two parameters are not independent because they are coupled through the bivariate mean in the linear regression (and functional analysis) and hence they are related to one another through R2. For example, a higher % and lower bf of one relation in comparison to another may suggest higher sediment concentration at low flow and lower concentration at high flow. Figure 5.8a shows the seasonal sensitivity of each basin to changes in discharge. The pattern -consistent within each season - suggests a decline in sensitivity with scale within the major tributary basins then an increase to the largest scale (100 km2). In particular, Kukhuri demonstrates a highly coupled C-Q behaviour with slopes (range of 1.18 to 2.11) higher than those generally quoted elsewhere (section 5.2.1). In this steep basin where cultivation is found on over 63% of the land area (see Table 2.5), discharge has a dominant influence on sediment regime. At low flows, sediment contribution is negligible (Figure 5.7) while at medium and high flows, sediment concentrations climb rapidly, yielding the highest value recorded in the study, 123.7 g/1. The low values of bf for the Upper Andheri basin are anomalous: this basin is proportionately as steep as the Kukhuri basin yet yields a range of bf similar to the two 5-km2 study basins. Its larger area (2.5 times the area of Kukhuri basin) may contribute to an attenuation of this strong headward coupling. The effect may also be due to the presence of pockets of degraded land in the vicinity of the hydrometric station (easily eroded at low flow), exaggerated by measurement difficulties that were faced only at this station. Additionally, sampling during May when it is suspected that concentrations of suspended sediment are considerably higher (in comparison to those of June) was successful at this station and certainly provided an unusual number of high-C/low-Q pre-monsoon samples to raise the low-Q end of this 130 relation. The relations for the entire Lower Andheri basin (2) show steeper slopes than that of the Upper Andheri basin (9), though having three times the area. Management factors which are not as influential on the sediment regime within the Upper Andheri and Kukhuri basins come into play in the lower reaches of the overall basin. Two of these factors, surface degradation and sediment storage within the irrigation system, are discussed respectively in detail in sections 5.5.2 and 5.5.3. Dhap, though of the same size as Lower Andheri has a significantly lower sensitivity to Q. Dhap is dominated by modest relief containing little steep land like that characterising the upland of Lower Andheri (Table 2.5). The sensitivity of Jhikhu basin increases over that of the major tributaries suggesting a change in dominant processes between the 10- and 100-km2 scales. The most likely explanation appears to be a loss of sediment to storage within the large valley meanders between the Dhap and Andheri Rivers' confluences with the Jhikhu River. It is only for the major tributary basins (Dhap and Lower Andheri) where the sensitivity to discharge changes significantly between seasons. Both Jhikhu and Upper Andheri show a trend of increasing sensitivity under the monsoon regime in comparison with the pre-monsoon regime, however, the differences are not statistically significant. Kukhuri basin shows no change in sensitivity with season, a fact consistent with its small area and steep topography. Not unexpectedly, aj shows a consistent decline with basin scale as a direct result of increased storage opportunities. The only exception to this is the Dhap basin during the monsoon season - its high coefficient (given the relatively consistent behaviour of bf in Figure 5.8a) suggests a high rate of sediment output during the monsoon season. This situation is related to this basin's degraded condition and is examined in detail in section 5.5.1. During the pre-monsoon season, the coefficient rises to very high values for small basin areas indicating - especially in light of the strong C-Q coupling observed in Figure 5.8a - a high degree of sediment mobilisation and an important area of concern for soil-erosion management. In Figure 5.9, the regression results for all five stations are overlaid for the pre-monsoon and 131 Figure 5.9 Seasonal sediment rating curves overlain for all study basins showing both the expected functional relations and envelopes representing confidence limits to these relations (at 90%): a) pre-monsoon regime b) monsoon regime. S o i l d l i n e s : w i t h i n A n d h e r i K u k h u r i (10) U p p e r A n d h e r i (9) L o w e r A n d h e r i (2) ( a ) P r e - M o n s o o n S e a s o n Dashed l ines : outside A n d h e r i D h a p (3) Jh ikhu (1) < 1000 a •a 100 h a O a o U •(^  CS B Expected Relations 1000 Envelope of Expected Relations 100 -10 1 -0.1 -0.01 1 100 0.01 0.1 10 100 Discharge (m /sec) Discharge (m /sec) ( b ) M o n s o o n S e a s o n Qiooo Expected Relations 1000 Envelope of Expected Relations 100 Discharge (m /sec) 100 Discharge (m /sec) 132 monsoon seasons. These overlays present an "integrated" comparison of af and bf between basins. It appears from this figure that within the 1 to 10 km2 range of scale, differences between the relations are due to rotation of the curve as a result of the rapid reduction in specific relief over these small scales. Between 10 and 100 km2, the change is dominantly one of translation - basin sensitivity to discharge remains similar but specific suspended sediment transport declines, perhaps due to alluvial storage. This pattern is stronger during the monsoon season because the plentiful sediment supply of the pre-monsoon season raises suspended sediment concentrations at all scales and relatively more at low Q, reducing the dominance of Q during this season. This may provide a conceptual framework within which to examine further the sediment regimes in this agricultural system. The sediment rating curves presented in this section corroborate the findings of section 5.3 in which pre-monsoon surface erosion was seen to be over an order of magnitude higher than the level in the monsoon season. However during the monsoon season and over all studied scales, the rate of sediment production from these upland cultivated fields is not sufficient to equal the amount of sediment which is transported in the streams. It must be concluded that there are other sediment sources present particularly during the monsoon season over scales larger than that of the individual field. These sources are streambed and streambank erosion and terrace damage and are of particular importance during "major" storm events (see section 4.6.1) and at high flow rates. The distribution of floods of the monsoon season tends to have higher peak flows than their pre-monsoon counterparts: this corresponds well to a seasonal increase in erosion due to mass wasting and is backed up by observation (discussed in Chapter 8). Another important source of suspended sediment is revealed by considering the regimes of the transition season. Figures 5.10a through 5.10e show the suspended-sediment data of the transition season for stations 10, 9, 2, 3, and 1 respectively and the seasonal regression lines for reference. In each case, the transition-season data show intergrade behaviour between the other two seasons. The vegetative cover - discussed in section 5.3 - does not develop instantaneously at all locations within 133 Figure 5.10 Suspended sediment data of based on the entire data set: Dhap, and (e) Jhikhu. Uiooo the transition season and the seasonal regression regimes (a) Kukhuri, (b) Upper Andheri, (c) Lower Andheri, (d) "5 IOOO 0 1 % o U *-> fl B •l-H T3 <D OO .01 0.1 1 310 Discharge (m /sec) 100 ^1000 .01 0.1 1 310 Discharge (m /sec) 100 "S iooo c o • i-H 3 o fl o U a •I-H C/3 .01 0.1 1 310 100 Discharge (m /sec) 0.01 0.1 1 ,10 100 Discharge (m /sec) ^1000 Discharge (m /sec) Pre-Monsoon Season Monsoon Season A Transition 134 the basin and during this period, heavy erosive rains may or may not occur at any particular location within a basin. This mechanism is operating throughout the transition season to a variable degree. At the onset of the monsoon season, the vegetative cover is ostensibly complete and this production opportunity gone. On-site observation during this period supported by the seasonal contrasts provide the evidence for these statements. In addition, the sand component of the suspended sediment load may show a declining availability through the transition season as this fraction (mobilised during the pre-monsoon season) is flushed through the basins (see Chapter 6). The data from the mid-reach Andheri stations (11 and 12) provide further evidence of the trends observed in this section. Seasonally-stratified suspended-sediment data from these stations are given in Figure 5.10 for stations 12 and 11 respectively. In both cases, higher regimes occur during the pre-monsoon season. Also, the transition season displays intergrade behaviour between that of the pre-monsoon and monsoon seasons. Due to the lack of stage-discharge relations at these stations, it is not possible to comment on the relative influence of discharge compared to the other basin scales. In conclusion, the suspended-sediment data from the streams reinforce and extend the results of section 5.3. The effect of surface cover in the pre-monsoon is evident clearly and it takes almost three weeks for a complete surface cover to develop in order to virtually eliminate this sediment production mechanism. The presence of significant suspended sediment during the monsoon season points to additional sediment sources beyond surface erosion from individual cultivated fields. Controls on suspended-sediment delivery change with scale; sensitivity to discharge declines with basin scale whereas management controls become increasingly complex with basin scale as explored in the following section. 5.5 Landuse signatures Human manipulation through the indigenous agricultural system is pervasive in the study basins, affecting nearly every aspect of erosion and sediment transport. Terracing, cropping practices, 135 Figure 5.11 Seasonally-stratified suspended-sediment data for the mid-reach Andheri River station based on 1993-1994 data: (a) mid-reach #1 (11), and (b) mid-reach #2 (12). O a a D O o U +-> a B •l-H CO 1000 100 -10 1 -0.1 10 100 Gauge Height (cm) o Pre-Monsoon A Transition + Monsoon 2 0 0 "3> 1000 o 2 100 -h o o U +^ a a T 3 <D 10 100 G a u g e H e i g h t ( c m ) 200 136 water diversion, and many other soil and water management techniques control the movement of soil and sediment from its source location to its eventual discharge from a basin. Three analyses are presented here. In the first, two basins are compared - Dhap (3) and Lower Andheri (2). These basins are similar in area but have contrasting character and management regimes. This comparison studies the effect of degradation on annual and seasonal sediment regimes. In the second analysis, the effect of degradation is studied within the Lower Andheri basin. This basin contains contrasting landuse between its upper- and lower-elevation areas. The effects of this spatial distribution in surface condition is examined particularly in light of the spatial variability in rainfall delivery which was quantified in Chapter 4. The final analysis looks at water diversion to estimate the extent to which this management activity recaptures previously eroded soil, redistributing soil from the upland rather than losing it from the larger basin. 5.5.1 Surface degradation: comparison of two basins There are frequent pockets of degraded land in the Middle Mountains, especially below 1400 m where red soils occur. Though these soils can be highly productive, they can also be difficult to manage and require high inputs of fertilizer and compost. Once these lands degrade, it can be difficult to bring them back into production. To what extent do these degraded areas contribute to the basin sediment budget? How important are they in modifying the patterns which have been discovered in the previous two sections? To help answer these questions, two basins of equal size and of differing character (especially with respect to their level of degradation) are compared in this section. Figure 5.12 contrasts the seasonal sediment rating curves for the Dhap basin with those of the Lower Andheri basin. These two basins are of equal size but the Dhap basin has a more gently-sloping topography and is in a lower elevation range. Despite these characteristics which should reduce this basin's tendency to erode, the sediment rating curve for the Dhap basin is higher than that of Lower Andheri during each season except at high-flow during the pre-monsoon season. 137 Figure 5.12 Seasonal sediment rating curves for Lower Andheri and Dhap basins overlaid for comparison. 1000 a o a G <D C o U a B C/3 15 03 ^ 10 c O o U 100 10 1 -0.1 0.01 0.1 1 3 10 Discharge (m /sec) Lower Andheri Dhap - P M : I p M -0-_ T Lower Andheri 100 Dhap 138 The monsoon-season regression curves of these two basins are further separated than are their pre-monsoon counterparts. This enlarged separation develops because the sediment-yield response in Lower Andheri basin drops in the monsoon whereas that of the Dhap basin does not. Recall the anomalous pattern of af shown in Figure 5.8 with bf consistendy lower in Dhap basin relative to Lower Andheri (due to Dhap's modest relief). These contrasting seasonal changes can be explained by surface cover and soil degradation. The Lower Andheri basin contains large amounts of well-managed cultivated steeplands. These soils experience a seasonal change in surface cover as examined in sections 5.3 and 5.4. The Dhap basin contains a greater proportion of seriously degraded land -gullied soil with little or no vegetation. In the Dhap basin, 24% of the land is seriously degraded whereas in the Lower Andheri basin, there is only 15% in this state. This apparently small difference must have a large effect on sediment production within the basin especially given the limited relief of Dhap in comparison to Lower Andheri. Because these degraded lands do not experience a change in surface cover with the onset of the monsoon season, these small areas - if present - play a big role in the basin sediment budget. This finding underlines the importance of preventing land from becoming degraded. In the Andheri basin, 25% of the area is in a moderately degraded condition (minor gullies or limited crown cover) whereas only 18% of the Dhap basin is moderately degraded. If the moderately-degraded land in the Andheri basin is allowed to slip further out of production, the suspended sediment regime of the Andheri basin could become similar to that of the Dhap basin or even higher due to its steeper topography. Re-examination of Figure 5.7d indicates that the seasonal difference in the regression results for the Dhap basin is a result of low sediment data derived from monsoon-season samples. The high-sediment data show no seasonal dependence at any discharge suggesting that the soil surface responds similarly to heavy rainfall in both seasons. The seasonal difference in low-sediment data may be the result of local (<5 km2) convective showers over the less degraded parts of Dhap basin. 139 It is important to also consider flood frequency to understand the overall significance of the seasonal suspended-sediment behaviour. The sediment loads during the pre-monsoon season, though generally quite high throughout the Middle Mountains, are short-lived because this season is brief and rainfall infrequent. The monsoon season, in contrast, lasts longer and experiences a greater number of flood events. The increased flood frequency in the monsoon season underlines the importance of the lower surface erosion during the monsoon season. In a basin with degraded surface conditions like the Dhap basin, elevated pre-monsoon sediment levels are maintained during almost twice as many floods through the monsoon season. Local management which encourages the rapid return of a complete vegetative cover is therefore the key to reducing net sediment output. To determine the overall and relative importance of these processes, annual budgets need to be calculated and this is done in Chapter 8. 5.5.2 Surface degradation: within Lower Andheri basin The effect of surface degradation on stream suspended sediments can also be examined within the Andheri basin because the basin contains large areas of contrasting landuse. The lowlands are dominated by bare red soils, often gullied, while the majority of the headwaters is under intensively-managed, steepland agriculture on non-red soils. In Chapter 4, it was learned that flood-producing rainfall over this basin is local in nature (<5 km2) for about half the storms. By linking the rainfall database to the flood database, suspended sediments in floods resulting from storms over each of the two landuse classes noted above can be isolated. Section 5.3 demonstrated that high levels of surface erosion occur on steep cultivated fields (like those in the headwaters of the Andheri basin) during the pre-monsoon season. In the previous section, basin sediment rating curves were used to conclude that the gullied badlands yield high sediment levels though no plot measurements were provided to substantiate those statements. In several gullies of the Andheri lowlands, gully pins were maintained in place to monitor the change in 140 gully dimensions during 1992-1994. Gully growth is highly variable: one gully showed little or no growth in size while the other two changed greatly in dimensions, reflecting the instability of their vertical headwalls. These results confirm that this gullied area is an important sediment source and also suggest that gully management may be focused on the individual gullies which show particularly active behaviour. To carry out the comparison of suspended sediments arising from storm events over these two areas, it must first be clarified what constitutes upland-dominated versus lowland-dominated floods as it concerns suspended sediments. The approach taken here extends the classification described in Chapter 4 in which basin, lowland, and upland events were defined based on the average rainfall for 12 gauges within each of the lowland and upland clusters. If the average of the two clusters was not significantly different, then the event was considered a basin event. If the difference was significant, it was a lowland or an upland event depending on which area received more rainfall. Of the events which are erosive and cause floods, it is necessary to stratify them removing those events which yield suspended sediment from a mixture of upland and lowland source areas. Then the amount of suspended sediment can be related to the source area directly. To accomplish this, the phosphorus content of the sediment is used as a fingerprint to calibrate a threshold which identifies relative source area contributions. The red soils of the degraded lowland are low in phosphorus whereas the non-red soils of the upland are high in phosphorus. This contrast is exploited to identify suspended sediment originating from these two source areas (see section 6.5 for more details). For each flood within Andheri basin, the ratio of the average rainfall in the upland versus that of the lowland was calculated. Beginning with all the suspended sediments for upland and lowland rain events on one C-Q plot, the requirement for a minimum ratio of rainfall in one area to the rainfall in the other area was made incrementally more stringent and the resulting phosphorus content of the sediments at the hydrometric station examined. At a threshold of 1.7 (one area delivering 1.7 141 Figure 5.13 Sediment-phosphorus rating curves stratified by rainfall location and colour for Lower Andheri basin (2) based on (a) rainfall differences only, and (b) both rainfall differences and their relative upland-lowland sediment contributions. Discharge (m /sec) 100 (a) Upland/lowland classes based on rainfall differences only • Lowland A Upland 100 0.1 A A A A ArV . A . ^ f M ^ 4 ! A A A A • • • AA A . • • 1 • 0.01 0.1 1 3 10 100 Discharge (m /sec) (b) Upland/lowland classes based on rainfall differences & relative sediment contributions 142 times the rainfall of the other area), the phosphorus content of the sediments separated as seen in Figure 5.13. Below this threshold, sediments sampled at Lower Andheri from upland rainfall events contain a significant number of red, low-phosphorus samples and those from lowland rainfall events contain brown, high-phosphorus samples. The threshold value represents the point at which the contribution from one area dominates overwhelmingly that of the other area. The sediments derived from "mixed" events are removed from the C-Q graph altogether. For example, if a lowland event brings 40 mm average rainfall to the lowland, it is retained as a lowland event in this analysis only if it also brings no more than 23.5 mm of average rainfall to the upland cluster. Those area events which do not satisfy this added constraint are termed basin events. In Figure 5.14, the locations of the rain gauges within and near the Andheri basin are overlaid on the red soil map. The areas of red soil in the Andheri basin lowland correspond to the bare and degraded land. This figure shows that if a storm event is a lowland one, it is dominantly over the degraded areas (on red soils) whereas if it is an upland event, it is dominantly over the intensively-managed cultivated (non-red) soils. C-Q data from the Lower Andheri (2) station introduced earlier in this chapter (Figure 5.7c) are stratified according to lowland and upland events and the result is presented in Figure 5.15 (data from basin-wide events are not shown). The upland displays a greater sensitivity to Q (as reflected in bf) than the lowland and the difference is significant (90%) during the monsoon season. In addition, a, for the upland is double that of the lowland during the pre-monsoon season and this difference is significant. The higher response during the pre-monsoon reflects the vulnerability of the steep upland during this season especially given the potential sediment production capability of the degraded lowland given the findings of the previous section. These functional relations are based on only 22 independent flood events. In addition, due to the more frequent occurrence of lowland events, 17 (P - 9; M - 8) of these are lowland events and only 5 (P - 3; M - 2) are from the upland. As a result, the upland data used to develop the relations 143 Figure 5.14 Locations of the rain gauges within and near the Andheri basin overlain on the red soil map. @ Red Soil • Non-Red Soil + Rain Gauge Locations — Subwatershed Boundaries Rivers 144 Figure 5.15 Sediment rating curves for Lower Andheri station (2) including sediments from only lowland and upland events (1992-1994). 1000 fl o • 1—I •*-> c <D O C o U w> C B • i -H TD <D 00 40 fl •g 20 93 o U 100 -10 0.1 • Lowland (P) A Lowland (M) • Upland (P) O Lowland (M) + Flood #1 M Functional Relations Lowland — — Upland - all —- — Upland - w/o #1 0.01 0.1 1 3 10 Discharge (m /sec) Coefficient (af) 100 Lowland Upland p M P M -A l l Data I - Without T - I F l o o d 1 J_ - 3: 1.0 g 0.5 -o Slope (bf) Lowland Upland oo P M p A l l Data M Without F l o o d 1 0.0 Error limits based on 90% confidence. 145 presented in Figure 5.15a are dominated by a well-sampled major event (see section 4.6.1 for definition of storm classes) of June 9, 1992 (Flood #1) identified on the figure. The effect of this event is investigated by recalculating the functional relation excluding these data. This relation is shown in Figure 5.15a and in the confidence ranges shown in Figure 5.15b and 5.15c. These recalculations show that without this one event, there is no difference during the pre-monsoon season between the upland and lowland flood events. Clearly, an analysis cannot rely on one independent event to reach a conclusion of statistical significance. This finding suggests caution in situations where the number of independent events is small. Table 5.9 Annual and seasonal sediment-rating-curve relations for Lower Andheri station based on lowland/upland data using log-linear regression. Area Marginal Regression X b f aj Seas on a b N R 2 sr expected range expected range lowland P 10.3 0 0.494 44 0.547 0.0589 8.03 0.506 0.414-0.600 10.45 9.35-11.71 upland P 21.2 3 0.704 19 0.637 0.124 19.59 0.715 0.541-0.891 21.30 20.32-22.34 lowland M 3.69 4 0.624 33 0.551 0.0806 10.15 0.644 0.509-0.783 3.77 3.29-4.33 upland M 3.39 8 0.875 32 0.892 0.0400 6.87 0.886 0.813-0.960 3.36 3.61-3.13 upland (w/o #1) P* 11.8 64 0.561 8 0.598 0.0472 4.80 0.585 0.308-0.881 12.10 9.58-15.52 Upland/pre-monsoon recalculated without the single large pre-monsoon event of June 9, 1992. A comparison of seasonal sediment yield between the upland and the lowland must also consider the relative frequency of upland versus lowland events. The functional relations suggest that upland events deliver a higher sediment concentration but in section 4.5.2, it was found that almost 43% more lowland events (>10 mm; 1992-1994) were monitored in comparison to upland events. Together, these results suggest that the degraded lowland may leak sediment throughout more of the rainy season whereas the upland's losses are episodic, particularly during the pre-monsoon season. 146 What does this analysis tell us about the effect of landuse on suspended sediment concentration within this 5-km2 basin? It suggests that event sediment yield is higher for events in the pre-monsoon season originating in the steep, well-managed upland. This increase is due to high-Q samples. This difference is not present during the monsoon season because high-C samples are limited to high-Q conditions. Although the degraded lowland did not generate C values as high as those from the upland, this area yielded higher C values for conditions of low and intermediate flows, especially during the monsoon season. This is consistent with the previous section where it was found that the Dhap basin retained an elevated rate of sediment output during the monsoon season. This analysis is useful because it confirms that we can also see this behaviour within a 5-km2 basin. According to Burt (1989), the mixing of runoff from widely differing source areas (e.g., in terms of precipitation inputs and surface response) obscures the process-response relations that can generally be identified in smaller basins. Burt pointed out the importance of basin scale in mediating the degree of obscurity: it is in the intermediate-sized basins where variability in surface response (surface cover and condition) and rainfall input is greatest. In the study area, this area appears to correspond to about 5 km2. Above and below this scale, process-response relations are identifiable. The analysis presented in this section reveals that surface response and variability in rainfall input contribute formatively to sediment regimes at these limited spatial scales (roughly 5 km2). This conclusion has major implications for monitoring in similar mountainous environments. 5.5.3 Sediment storage by water diversion for irrigation The irrigation system serves as a sediment trap in diverting flow and halting the movement of eroded material out of watersheds. But how effective is this process? To what extent are the farmers able to recapture soil and nutrients lost from their upland agricultural fields and entrained in the fluvial system? To answer this question, two analyses are presented. The first focuses on the diversion of water while the second looks specifically at the extent of sediment accumulation in the irrigated 147 fields. Water diversion As it progresses downstream, runoff is concentrated and thus streams generally grow in size. If rainfall is uniform over the basin and if there are not significant losses to the subsurface nor dramatic changes in the surface characteristics downstream, one would expect the stream to grow in volume in proportion to the ratio of the basin areas which contribute to their outflows. This principle is used here to evaluate the extent of diversion of floodwaters by diversion dams. Figure 5.16 shows the Andheri and Kukhuri Rivers and the locations of the 62 diversion dams which are built along this drainage. This figure also shows the locations of two automated hydrometric stations within this basin. Table 5.10 summarises the pertinent information about each of these hydrometric stations. Though there is irrigated land above the Kukhuri River station (10), it is in the basin headwaters where water is scarce and unreliable. Hence, the flow passing through the Kukhuri station is probably not much different than it would be in the absence of the structures of the irrigation system. The ratio of the contributing areas of the two stations is 7.4, as shown in Table 5.10. Table 5.10 Contributing areas and number of irrigation dams for Kukhuri and Lower Andheri hydrometric stations (stations 10 and 2 respectively). Kukhuri Basin (station 10) Lower Andheri Basin (station 2) Ratio Total Contributing Area (ha) 72 532 7.4 Irrigated area (ha) 7 37 5.3 Number of dams 14 62 4.4 Between these two stations, there are 21 large diversion dams. Without these in place, and assuming losses to the subsurface and groundwater are negligible, then we would expect to see about 7.4 times more flow at the Andheri station from rainfall events which provide even coverage over the entire basin. During the period of monitoring, 16 basin rainfall events (see section 4.5.2) were 149 monitored simultaneously at both stations by the automated equipment and are available for comparison. For each of these flood events, the total flow through the two stations has been calculated. The results are presented seasonally and in terms of the ratio of the lowland to upland stations. These results are summarised in Table 5.11. Table 5.11 Flow-ratio comparison of 16 individual floods for Kukhuri (station 10) and Lower Andheri (station 2) hydrometric stations. Outflow Ratio Class Pre-Monsoon and Monsoon Total Percentage (Andheri/Kukhuri) Transition Seasons Season Number of Total 0- 1.0 2 3 5 31 1.0-3.7 3 5 8 50 3.7 - 7.4 1 0 1 6 > 7.4 1 1 2 13 Total 7 9 16 100 In all but two cases, the ratios are far less than would be expected under unmanipulated hydrological conditions. In fact, 31% experience less flow at the lowland than the upland station. Further, the seasonal separation suggests that the tendency for a reduction in basin output may be greater during the pre-monsoon and transition seasons than it is during the monsoon season. However, because of the small number of events available, this seasonal consideration will have to be revisited when there are more events monitored in this comprehensive way. The extent of irrigation in the upland compared with the lowland and the surface soil characteristics both serve to make stronger the conclusion of this analysis. The upland has proportionately greater irrigated area than the lowland. If the dams are effective at diverting floodwaters in the steep upland, then this greater area should cause the flow ratio (lowland-flow/upland-flow) to be higher, not lower. Further, the water-holding capacity of the surface soils in the lowland is less than in the upland; this should further cause the ratio to be higher than if this factor was equal. Despite these differences, the low flow ratios persist. 150 The flow-ratio analysis suggests that the diversion dams are very effective at directing floodwaters out of the stream and into the irrigation system. In fact, it appears that a majority of the floodwaters is redirected into the irrigation system. Quantitative water budgets including measurement of runoff diverted into khet on a flood basis is a useful area of further research. In addition, we know from the hydrometric data that large amounts of suspended sediments are carried with the floodwaters. If the hypothesis is correct, then there should also be evidence of soil accumulation within the irrigation system. We know that the farmers annually maintain the canals removing considerable deposition (see Chapter 7) but what of the fields themselves? Sediment accumulation pins To examine this, pins were placed in a wide selection of khet fields before the onset of each flood season during the study period. The pins were collected after harvest and the soil level noted and compared to the level before the pre-monsoon season began. These results, summarised in Table 5.12, suggest that considerable deposition occurs within the khet fields themselves. For instance, of the 25 fields sampled in 1992, 76% showed accumulation and 40% showed more than 0.5 cm. Further, these enriched deposits enhance soil-nutrient condition: lab analyses show that all the base cations are higher in the deposited sediment than in the underlying field soil (see Figure 7.1). These two analyses suggest that management can recapture large amounts of previously-eroded fertile soil from upland cultivated fields. Due to its fertility, this redistribution of soil represents a redistribution of wealth from the upland farmers to those in the lowland. To what extent is soil redistributed and not lost from these headwater basins? This question and others will be addressed in Chapter 8 after a more detailed analysis of sediment dynamics is explored in Chapter 6. Each of the above three sections, considered individually, gives an indication of landuse effects on sediment regimes. Collectively, they provide a stronger indication of the effect on sediment regimes of landuse practices. 151 Table 5.12 Sediment accumulation in irrigated fields measured using pegs. Year Accumulation (mm) Accumulation Distribution (mm) n X s <0 >o, <£5 >5, <£15 >15, <25 >25 1992 25 10.5 21.2 6 9 5 3 2 24% 36% 20% 12% 8% 1993 33 7.8 6.1 2 11 16 4 0 6% 33% 49% 12% 0% 1994 47 1.4 1.3 12 34 1 0 0 26% 72% 2% 0% 0% Note: overall average accumulation = 6.6 mm/yr 5.6 Conclusions Soil losses are observed to be strongly seasonal with rates of surface erosion from upland bari being one to two orders of magnitude higher in the pre-monsoon season than in the monsoon season. High annual rates of erosion (up to 40 t/ha) are due primarily to a lack of surface cover during the pre-monsoon when high-intensity rains may fall on newly-cultivated sloping fields due to unfortunate timing. The high nutrient content of soils during this season means that the seasonal change in nutrient loss is proportionally higher than the change in soil loss. Soil characteristics influence erosion at the plot scale through two mechanisms. For a given storm, they determine the rate of runoff which directly affects the soil's rate of erosion. If the surface is exposed, they determine the soil's erodibility in relation to rainfall intensity. Thresholds of I10 =30 mm/h and CR = 5 to 10% were observed below which surface erosion rarely occurs. Above this I10 threshold, soil loss increases with I10. There is little evidence of seasonal changes in rainfall characteristics sufficient to cause observed seasonal changes in erosion and sediment regimes. In particular, high-intensity rainfall occurs during all seasons within the study period. Higher volume rainfall occurred more often during 152 the monsoon season. The likelihood of heavy rainfall falling on dry ground is measurably higher in the pre-monsoon season and this may contribute to the higher erosion regime of the pre-monsoon season. When coupled with spatial variability in surface condition, rainfall spatial variability can result in significantly different sediment regimes over spatial scales smaller than 500 ha. Degraded land with a year-round lack of surface cover (and often gullied) experiences an elevated rate of erosion due to rain in every season. Because these soil losses have little nutrient value, these losses are of greatest concern for downstream sedimentation rather than the farming system. Indigenous management techniques are their most effective at low and intermediate flows. The indigenous irrigation system captures a significant portion of storm runoff during the pre-monsoon season, thereby capturing a large amount of soil eroded from upland fields. However, they are also vulnerable as the pre-monsoon losses indicate. The limited surface erosion from bari during the monsoon season when rainfall is equally intense illustrates the potential effectiveness of indigenous management in these environments. Spatial and temporal variabilities place constraints on monitoring for erosion and sediment transport studies. Temporally and spatially, surface erodibility and rainfall erosivity operate simultaneously and show interactions. Within basins of 10 000 ha in size, monitoring resolution can be important over spatial scales from 100-1000 ha, to 0.01 ha. Within an annual period, monitoring resolution can be important over time periods of the season and the single event. These conclusions relate to the normal-regime behaviour of these basins. During the three-year period of study, heavy rainfall with significant erosivity occurred - including one event which may have been a 10-year storm event over 5 km2 - but absent were events sufficient in magnitude to destabilise these sediment regimes (e.g., deep-seated landsliding). Regardless, the conclusions suggest specific management recommendations germane to the normal-regime behaviour. Recommendations for management are discussed in Chapter 9 following the presentation of sediment budgets. 153 6. Signatures of Erosional Sources and Sediment-Transport Behaviour in the Physical and Chemical Character and the Patterns of Movement of Suspended Sediments 6.1 Introduction In this chapter, suspended-sediment behaviour is examined in the context of both its behaviour during floods and of the physical and chemical character of the sediment itself. The examination of suspended sediment as a simple, single quantity masks important differences in how its concentration and composition are shaped by the range of controls. Sediment properties serve as sensitive indicators able to reveal a wider range of controls. Controls are generalised in relation to supply and transport limitations. Strengths and weaknesses of the sediment rating curve technique for prediction are identified. Three analyses are presented. An analysis of hysteresis provides further evidence of source and transport controls. Rating curves for individual particle-size classes are developed, illustrating the contrasting behaviours of separate size fractions. Sediment colour and phosphorus content are used as fingerprints to trace the origin of suspended sediment and relate this information to the conclusions reached regarding hysteresis and particle-size behaviour. 6.2 Research Background In all basins, sediment supply and the competence of transport mechanisms interact through time and space to yield a sediment regime. Chapter 5 showed how bulk behaviour changes as a function of specific controls. The exponent (b) and coefficient (a) of the power-law relation (C=aQb; C - suspended sediment concentration; Q - discharge) were used to discuss the effect of transport and supply limitations respectively. Patterns of hysteresis also reflect supply and transport limitations but their sensitivity is far greater than that of composite rating curves. And these patterns can be examined over various spatial and temporal scales to investigate embedded regime controls. Particle-size controls on sediment yield 154 are important yet poorly studied. Clay, silt, and sand behave in contrasting ways and reveal patterns which can also be linked to supply and transport concerns and related to hysteresis. And, finally, sediment properties can be used as tracers to further identify dominant controls, especially where interactions are occurring. This section reviews existing research findings relevant to these three areas - hysteresis, particle size, and tracers. 6.2.1 Hysteresis Hysteresis in suspended-sediment transport is used to indicate departures from a direct relation between discharge and sediment concentration. Hysteresis can impair predictive ability because the assumption of simple C-Q relations may lead to variously biased predictions. Fortunately, when multiple controls are operating in complex ways on suspended sediment, the presence of hysteresis can actually help to enhance understanding of sediment sources and dynamics. The term hysteresis is normally applied to the event or synoptic scale. Seasonal and annual changes are generally termed rating "shifts". The remainder of this discussion refers to the event and synoptic timescales. Many authors have observed hysteresis (Johnson 1942; Heidel 1956; Walling and Teed 1971; Walling 1974; Wood 1977; Paustian and Beschta 1979; Bogen 1980; van Sickle and Beschta 1983; Sidle and Campbell 1985) and generally attribute the phenomenon to the following primary factors: • supply exhaustion • supply thresholds • kinematic effects (e.g.: lag time, source variability) These controls dominate differentially through time and space and, in some instances, their effects are scale embedded. Since most basins are supply limited (Einstein 1943), supply-related controls are most often cited. Conceptual models of hysteretic behaviour have been presented by several authors in terms of 155 C-Q graphs (Olive and Rieger 1985; Williams 1989; Nistor 1996) and all include both primary and secondary behaviours. Table 6.1 presents a summary of primary hysteretic behaviour and its dominant controls. A wide variety of more complex behaviour is found in basins due to the superposition and sequencing of these primary behaviours (Williams 1989; Nistor 1996). Table 6.1 Theoretical models of primary hysteretic behaviour. Regime Behaviour Graphical Representation Explanation of Controls Single Line Straight Curved 0 -<2 Unlimited supply Transport is the only significant control. Curved line suggests the presence of thresholds for new supplies or for the transport of specific particle-size classes. Clockwise Loop Supply exhaustion important Width of loop indicates extent of supply exhaustion. Synchronous peaks (variable loop width) Nonsynchronous peaks (wide loop) -a Transport is the major control. Wide loop indicates high short-term sediment availability on rising limb. Relative importance of transport as a control declines due to severe sediment exhaustion. Counterclockwise Loop Supply threshold and/or spatial variability Synchronous peaks (variable loop width) Nonsynchronous peaks (wide loop) Transport is a strong control; some supply exhaustion present. Width of loop represents extent of source creation (e.g., discharge-induced supply) Relative importance of transport declines due to delayed sediment pulse: travel time of sediment wave, or rainfall-runoff variability causing late sediment production. 156 Single C-Q lines generally reflect unlimited sediment supply, suggesting that transport capability is the dominant control on suspended sediment concentration. If the line is straight, then the effect of discharge is equal throughout the event and supplies are likely indeed unlimited. If the line is curved (Williams 1989), then supplies may be sensitive to discharge, perhaps reflecting the presence of a supply threshold. For example, above a threshold discharge, coarser sediment may be accessed in the channel margins resulting in an increase in the slope of the C-Q relation. Supply exhaustion causes sediment production to drop in advance of the decline in transport capacity resulting in "clockwise-loop hysteresis". Exhaustion can occur within event and sub-event timescales (Wood 1977; Olive and Rieger 1985) through sub-seasonal (Walling and Teed 1971; Walling and Webb 1982; Beschta 1978), seasonal (Paustian and Beschta 1979; Bogen 1980; Tropeano 1991) and annual (Brown and Krygier 1971; Anderson and Potts 1987) timescales. Supply exhaustion relative to transport capacity can occur for several reasons. In general, as Williams (1989) points out, a limited supply flushed during the onset of a flood event, or a much larger supply subject to a prolonged event (Wood 1977) can both suffer relative exhaustion. Material deposited on a streambed at the end of one event, can form a sediment supply flushed during the rising stage of the subsequent event, contributing to this effect. In gravel-bed rivers, bed armour can be disturbed during the rising stage creating a short-term (exhaustible) supply of fine sediment with re-armouring occurring at or near the discharge peak (Paustian and Beschta 1979). The sediment peak does not need to occur in advance of the peak in discharge for clockwise hysteresis to occur. The sole criterion for clockwise loops to occur is that for every Q, C/Q must be higher on the rising limb (Williams 1989). However, if the peaks are temporally separate, this is a good indication that transport is greatly diminished in relative importance compared with the supply control. As Table 6.1 shows, the same principal applies whether or not the sediment peak leads or lags the discharge peak. Counterclockwise loops may result from kinematic effects involving differential travel time or 157 rainfall/runoff heterogeneity but can also result from a discharge-induced sediment supply. If sediment and discharge peaks are simultaneous and the relation forms a counterclockwise loop, transport remains a strong control with new sediment sources likely induced by the high flow or prolonged rainfall. For instance, Kung and Chiang (1977) studied 5- to 40-km long streams in the rolling loess area of China's Yellow River and found sediment concentration to peak after discharge due to prolonged soil erosion and to taper off slowly following peak discharge (in Williams 1989). Prolonged rainfall on unstable slopes can heighten pore-water pressures and cause widespread slope failures at or beyond the time of maximum discharge in a basin flood event. In some systems, sediment supplies at channel margins may be accessible only during high flow (Nistor 1996). If the peaks are not simultaneous and counterclockwise behaviour occurs, then transport is diminished in relative importance in controlling the sediment regime. Source control related to spatial effects may become the dominant cause of hysteretic behaviour. A difference in travel time of the water and sediment waves for events in which rainfall is isolated over a distant part of the basin causes there to be a lag time in the arrival of peak sediment concentration. Heidel (1956) termed this "time-of-lead". In basins with spatially-diverse sediment sources subject to localised rainfall, desynchronised tributary inflow can result in high sediment concentrations arriving in the main stem during falling stage. For instance, Tropeano (1991) calculated that 30 to 50% of total input into the main stem of a 9.51-km2 basin in Northwestern Italy derived from a 0.75-km2 basin dominated by bare surfaces, representing only 8% of the larger basin's area. The effect is greatest in mid-sized basins: the effect is less likely as basin size becomes small and as basin size becomes large, the effect is lost. The basic behaviours and causes outlined in Table 6.1 and discussed above can be combined to yield other, more complex models. "Figure-eight" behaviour (Arnborg et al. 1967; Williams 1989; Nistor 1996) results from sediment exhaustion during the rising stage of an event followed by sediment replenishment. Olive and Rieger (1985) found that in events with multiple peaks, the 158 sediment peaks decreased successively to the point that the final peaks sometimes showed no sediment response. These combined behaviours can become so intractable that sediment response appears random: Olive and Rieger (1985) examined 39 storm hydrographs in Australia and found "an apparently random" response with "no identifiable pattern observed" to be by far the most common sediment response, occurring in almost half (16) of the events studied. The stratification of sediment rating curves on the basis of stage (rising-limb versus falling-limb) is a useful approach for identifying hysteretic behaviour if it operates consistently throughout a specific period (Walling 1978; Tropeano 1991). Supply limitations generally result in a difference between the two relations. Unfortunately, the lack of a difference between rising-stage and falling-stage behaviour does not confirm a lack of hysteresis. As implied in the above discussion, the direction of hysteretic behaviour can quickly reverse (even within a single event) due to changing supply and transport limitations with discharge causing a "null result" in the overall stage-stratified rating curve. Such contrasting hysteretic behaviour increases the variance of the sediment-discharge relation. 6.2.2 Particle-size behaviour The patterns of sediment yield studied in Chapter 5 were the net result of several systems operating simultaneously. These systems are governed by different particle-size classes which show distinctly different source and transport behaviour. More can be learned about the system under study if the size fractions are considered individually. Geomorphologists and hydrologists have traditionally emphasised measurements of total suspended sediment, ignoring both its physical and chemical characteristics. Sediment properties have a strong influence on entrainment and transport and are of environmental importance. In particular, chemical characteristics of suspended sediment are strongly influenced by particle-size distribution which in turn is determined by source and transport controls. 159 In contrast to geomorphological approaches, agricultural approaches to studying particle-size effects in basin sediment yield have generally been concerned with clarifying nutrient loss. Geomorphologists have shown a greater interest in sediment quantity while agriculturalists have emphasised sediment quality. Further, the geomorphological research has emphasised stream suspended sediment and the agriculturalists' measurements have been based on individual fields or plots. Sutherland and Bryan (1989) pointed out that little work has been done linking the findings from the hillslope to those of the stream. Results from the two approaches offer useful, complementary information about the importance of particle-size and are combined in the following discussion. Particle-size characteristics exercise a strong control on initial entrainment and continued suspension of river sediment. In gravel-bed rivers, flux concentration of the largest fractions is strongly affected by transport constraints (flow velocity) whereas transport of the finest sediment (clay) is completely unaffected. Every fraction is influenced by the size characteristics of the sediment supply. It is within the fine-fraction size distribution (less than 2 mm) where the greatest change occurs in how sediment responds to the supply and transport limitations discussed in the previous section (Hjulstrom 1935). Sundborg (1967) presents a modification of Hjulstrom's curve (Hjulstrom 1935) showing the relation between flow velocity (one metre from the stream bed), grain size and its state of movement for uniform material (specific gravity = 2.65). From this diagram, four classes of fine-sediment transport can be defined according to particle size: Clay (<0.002 mm) • minor velocities entrain particles unless highly cohesive • particles remain in suspension regardless of flow velocity Silt (0.002 to 0.063mm) • minor velocities entrain particles; begin to see evidence of threshold velocity for entrainment 160 • once entrained, particles generally remain suspended though coarser silts (0.010 to 0.063mm) show a very weak tendency to deposit at low velocities Fine Sand (0.063 to 0.180mm) • transition between washload-dominated and traction-dominated transport • threshold velocity required for initial entrainment • the tendency to deposit increases rapidly with particle size Coarse Sand (0.180 to 2.000mm) • particles are entrained and remain in suspension with only significant flow velocities (0.3 to 0.5 m/s); may move in traction With heterogeneous particles, lithology, and bed configurations, thresholds vary but the basic distinction between washload-dominated movement of the clay and silt and the energy-dominated motion of the fine and coarse sands remains. The 0.180-mm breakpoint between fine and coarse sand is consistent with the lower limit to traction-phase movement in rivers (Church, personal communication). Under energy-dominated motion, transport constraints dominate in shaping suspended-sediment concentrations. In non-episodic events, Hamlett et al. (1987) observed a coarsening of suspended sediment with flow in five nested agricultural basins (5 to 5055 ha) in east-central Iowa. This coarsening was partly attributed to the increased transport competence of the stream; as flow increases, thresholds are crossed for the movement of sand-sized particles thereby altering the overall sediment regime markedly. Nordin (1963) studied sand-bed rivers with extreme concentrations of suspended sediment (greater than 300 g/1), 1/3 to 2/3 of which was sand. Once hydraulic conditions were conducive to the suspension of sand particles, sand quickly came to dominate in the overall sediment distribution to the point that fluid properties changed so predictive relations were unavailable. Most work on the mobility of different size fractions has been in gravels due to the weaker discrimination in the fine fraction (Church, personal communication). 161 In contrast, the suspended concentration of clay and silt is largely unaffected by flow competence. Once entrained these particle sizes remain suspended even at low flow velocities. Peart and Walling (1982) found the particle size of suspended sediment to be insensitive to discharge in the River Dart - this may be due to the dominance of silt and clay in the material available for transport within the range of events considered. Griffiths (1981) found the lower range of sediment rating curves (of rivers in New Zealand) to be insensitive to discharge; in this flow range, transport capacity did not meet threshold requirements for sand transport hence washload was controlled by source characteristics alone. It remains useful to discriminate between clay and silt because clay shows no influence of discharge whereas behaviour within the silt range may vary greatly between the fine and coarse silts. The above discussion illustrates that source constraints affect the suspended concentration of all particle-size classes. If it is not available for transport, it won't be transported regardless of flow competence (Stone and Saunderson 1992). Climate, geology, soils, and landuse which Walling and Moorehead (1987) list as controlling factors on the particle size of suspended sediment a