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Simulating the impact of climate change on the growth of Chinese fir plantations in Fujian province,… Kang, Haijun; Seely, Brad; Wang, Guangyu; Cai, Yangxin; Innes, John; Zheng, Dexiang; Chen, Pingliu; Wang, Tongli Oct 6, 2017

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RESEARCH ARTICLE Open AccessSimulating the impact of climate changeon the growth of Chinese fir plantationsin Fujian province, ChinaHaijun Kang1,2, Brad Seely2, Guangyu Wang1,2, Yangxin Cai1, John Innes1,2, Dexiang Zheng1*, Pingliu Chen1and Tongli Wang2AbstractBackground: Climate change represents a considerable source of uncertainty with respect to the long-term healthand productivity of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations in southeastern China.Methods: We employed the process-based, stand-level model FORECAST Climate to investigate the potentialimpact of four alternative climate-change scenarios on the long-term growth and development of Chinese firplantations in Fujian province, China. The capability of the model to project seasonal patterns of productivityrelated to variation in temperature and moisture availability was evaluated using 11 years of 8-day compositeMODIS remote sensing data.Results: Simulation results suggest climate change will lead to a modest increase in long-term stemwood biomassproduction (6.1 to 12.1% after 30 to 60 years). The positive impact of climate change was largely attributable toboth a lengthening of the growing season and an increase in nutrient-cycling rates. The increase in atmosphericCO2 concentrations associated with the different emission scenarios led to an increase in water-use efficiency and asmall increase in productivity. While the model predicted an overall increase in dry-season moisture stress, it didnot predict increased levels of drought-related mortality.Conclusions: Climate change is expected have positive impact on the growth of Chinese fir in the Fujian region ofChina. However, the projected increase in plantation productivity associated with climate change may not berealised if the latter also results in enhanced activity of biotic and abiotic disturbance agents.Keywords: FORECAST Climate, Process simulation, Climate change, Cunninghamia lanceolata, Forest productivity,Nutrient cycling, MODISBackgroundChinese fir (Cunninghamia lanceolata (Lamb.) Hook.) isan evergreen conifer species and is one of the mostimportant commercial species in China. Not only is it avaluable timber species useful for construction and fur-niture manufacturing (Huang 2013; Zhang et al. 2013),but it is also commonly used for pulp and biomass en-ergy production (Nie et al. 1998; Li et al. 2013). Chinesefir has been widely planted throughout subtropicalChina (Yu 1997; Wu 1984), and according to the resultsof the 8th National Forest Inventory, the planting area ofChinese fir is about 11.0 million ha, accounted for 15.8%of all plantations in the country (SFA 2014). In additionto its importance as a source of fibre, Chinese fir planta-tions also play an important role in water and soilconservation, and in climate regulation through theirfunction as carbon sinks (Tian et al. 2002; Wang et al.2009). Considering their large area of cultivation, andhigh potential for atmospheric CO2 fixation (Yao et al.2015), Chinese fir plantations represent a key compo-nent of China’s greenhouse gas mitigation strategy.There is considerable uncertainty surrounding thepossible effects of climate change on the long-term healthand productivity of Chinese fir plantations throughout* Correspondence: fjzdx123@163.com1Forestry College, Fujian Agriculture and Forestry University, Fuzhou, Fujian350002, ChinaFull list of author information is available at the end of the articleNew Zealand Journal of             Forestry Science© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made.Kang et al. New Zealand Journal of Forestry Science  (2017) 47:20 DOI 10.1186/s40490-017-0102-6China. Deviations from historical temperature and pre-cipitation regimes can influence a wide range of ecologicalprocesses associated with forest productivity including de-composition of dead organic matter and nutrient mineral-isation rates (Gholz et al. 2000), photosynthetic rates andlength of growing season (Boisvenue and Running 2006),and water stress and drought-related mortality (Allen etal. 2010). Moreover, relatively little is known about howclimate change may influence the long-term growth anddevelopment of Chinese fir plantations in different partsof its range. In general, Chinese fir is adapted to warmand relatively moist climate regimes although it is alsoconsidered to be tolerant of periods of moisture stress(Wei et al. 1991). Past studies on the relationship betweenthe growth of Chinese fir and climate factors have primar-ily focused on the analysis of suitable climate envelopesthroughout its distribution (Wei et al. 1991; Guan 1989;Shi 1994; Wu and Hong 1984; Zhang 1995). While suchstudies are useful, they do not provide adequate informa-tion to inform managers how current plantations mayrespond to shifting climate regimes. Such information isessential to support the development of sustainable andresilient plantation management systems.Another valuable approach to examining and monitoringthe impacts of climate change is through the application ofremote-sensing technologies. Satellite imagery and otherremotely sensed data have been shown to be effective toolsfor detecting subtle long-term impacts of climate changeon extensive forest areas (e.g. Keenan et al. 2014).Such data, when used in combination with ecosystemmodels, represent an efficient approach for projectinglong-term impacts of climate change and testingmodel performance.In this study we employ the process-based, forestmanagement model FORECAST Climate to evaluate thepotential impact of alternative climate change scenarioson the long-term growth and development of Chinesefir plantations in Fujian province, China. The FORE-CAST model (without the climate change component)has been previously applied in subtropical Chinese firplantations to evaluate the impact of intensive short-rotation management on soil productivity (Bi et al. 2007;Xin et al. 2011). The capability of FORECAST Climateto project seasonal patterns of productivity related tovariation in temperature and moisture availability wasassessed using 11 years of 8-day composite MODISremote sensing data. The broader evaluation includes anassessment of the impacts of climate change on keyecosystem processes regulating growth response.MethodsStudy area descriptionThe study area was located in Shunchang County in thenorth central part of Fujian province (117° 29′–118° 14′E and 26° 38′–27° 121′ N). The terrain in the north andsouthwest is generally higher than south central areas,which contain the major rivers. Shunchang has a sub-tropical maritime monsoon climate, but is also influ-enced by a continental climate. The annual averagetemperature in the study area is 16.9 °C, the frost-freeperiod lasts 305 days and the average annual rainfall is1628 mm. With a mild and humid climate and amplesunshine and rainfall, Shunchang is suitable for thegrowth of Chinese fir. The specific study area includesShuangxi Town and Yangkou Town with a total area ofabout 29,361 ha (Fig. 1). Spatial forest resource surveydata of Shunchang County in 2007 were used as theprincipal data source for the analysis. Productive forestswithin the study area are dominated by stands ofChinese fir of ranging in age from 1 to 52 years anddeveloped on soils with variable fertility.Soil descriptionSoils within the study area are largely classified as “redearth” under the Chinese classification system (equiva-lent to Ultisol under USDA soil taxonomy). Soils in thisregion tend to be acidic, with depths greater than 1 m.The texture is mostly loam and clay, and they tend to berich in organic matter.Model descriptionThe FORECAST Climate model (Seely et al. 2015) wasdeveloped as an extension of the hybrid forest growthmodel FORECAST (Kimmins et al. 1999) createdthrough the dynamic linkage of FORECAST with thestand-level hydrology model ForWaDy (Seely et al.1997). The linked model is capable of representing theimpact of climate and climate change on forest growthdynamics. Specifically, it includes detailed representa-tions of the relationships between temperature andwater availability on growth rates and as well as theeffect of soil temperature and moisture contents ondecomposition and nutrient cycling. The model also in-cludes a function to represent mortality associated withsevere drought events. The following sections includedescriptions of the two underlying models and theirlinkage to form FORECAST Climate.The FORECAST modelThe Forestry and Environmental Change AssessmentTool (FORECAST) is an ecosystem-based, stand-levelgrowth and forest ecosystem management simulator.The model was designed to accommodate a wide varietyof harvesting and silvicultural systems in order to com-pare and contrast their effect upon forest productivity,stand dynamics and a series of biophysical indicators ofnon-timber values. FORECAST employs a hybrid ap-proach whereby local growth and yield data are used toKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 2 of 14derive estimates of the rates of key ecosystem processesrelated to the productivity and resource requirements ofselected species. This information is combined with datadescribing rates of decomposition, nutrient cycling, lightcompetition, and other ecosystem properties to simulateforest growth under changing management conditions.Decomposition and dead organic matter dynamics aresimulated using a method in which specific biomasscomponents are transferred, at the time of litterfall, toone of a series of independent litter types. Decompos-ition rates used for the main litter types represented inthe model are based on the results of extensive field in-cubation experiments (Camiréet et al. 1991; Prescott etal. 2000; Trofymow et al. 2002). Residual litter mass andassociated nutrient content is transferred to active andpassive humus pools at the end of the litter decompos-ition period (when mass remaining is approximately 15to 20% of original litter mass). Mean residence times foractive and passive humus types are typically in the rangeof 50 and 600 years, respectively. In FORECAST Cli-mate, these decomposition rates are modified throughthe use of annual indices of temperature and moisture.FORECAST has been widely used in Canada, Scotland,Norway, China and other countries in the world. It hasbeen applied in variety of forest ecosystems includinglodgepole pine forest (Wei et al. 2000; Wei et al. 2003),mixed aspen and white spruce forest (Seely et al. 2002;Welham et al. 2002), Scots pine forest (Blanco et al.2006), coastal Douglas-fir forest (Blanco et al. 2007;Morris et al. 1997), Korean larch (Sun et al. 2012), andChinese fir plantations (Bi et al. 2007; Xin et al. 2011).The model has been used in a variety of applications andevaluated against field data for growth, yield, ecophysio-logical and soil variables. A detailed description ofFORECAST is provided in Kimmins et al. (1999).The forest water dynamics (ForWaDy) modelThe ForWaDy model simulates the hydrologic dynamicsof a forest stand on a daily time step for a given set ofclimatic and vegetation conditions. It has been shown toperform well for predicting the effect of forest manage-ment on evapotranspiration (Seely et al. 2006) andtemporal patterns in soil moisture content under fieldconditions (Dordel et al. 2011; Titus et al. 2006). Themodel represents potential evapotranspiration (PET)using an energy balance approach based on a modifiedversion of the Priestley-Taylor equation (Priestley andTaylor 1972). This equation has shown to be effective inpredicting evapotranspiration under a wide variety offorest types and conditions (Rao et al. 2011; Stagnitti etal. 1989; Sumner and Jacobs 2005). Net shortwave solarradiation interception is used to drive the PET calcula-tions. It is calculated for each tree and plant speciesfrom the light competition submodel built into FORE-CAST (Kimmins et al. 1999) and surface albedo.ForWaDy includes a representation of the vertical flowof water through canopy and soil layer compartments.Storage and movement of water in and through each soilFig. 1 A map showing the location of the specific study area and Fujian Climate StationKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 3 of 14layer is regulated by physical properties that dictatemoisture holding capacity, permanent wilting pointmoisture content, and infiltration rate.Water stress is calculated for each species on a dailytime step and expressed as a transpiration deficit index(TDI). The TDI is the relative difference between poten-tial energy-limited transpiration demand and actualtranspiration:TDIi;d ¼ CanTDemand;i;d−CanTActual;i;d =CanTDemand;i;dð1Þwhere:CanTDemad, i,d = energy-driven transpiration demandfor species i (mm) on day d, as a function of leaf areaindex (LAI), intercepted short-wave radiation, canopyalbedo, and canopy resistance,CanTActual, i,d = actual tree transpiration for species i(mm) on day d, as a function of CanTDemad, i,d, rootoccupancy, and available soil moisture.A detailed description of the ForWaDy model includ-ing its general data requirements are provided in Seelyet al. (2015, 1997).Linking tree growth with hydrologyIn the general version of the FORCAST model, forestproductivity is simulated based primarily upon light andnutrient availability. FORECAST Climate was designedto incorporate explicit representations of the impact ofmoisture availability and temperature on forest growthprocesses to expand the application of the model toaddress potential climate change. The foundation for theexpanded model was established through the creation ofa dynamic linkage between the detailed representation offorest biomass growth and structure in FORECAST withthe hydrological processes represented in ForWaDy(Seely et al. 2015).Accounting for climate impacts on ecosystem processesThe impact of climate on tree growth and decompositionprocesses in FORECAST Climate is focused primarily ontheir relationship to temperature and water availability.These relationships are represented using curvilinear re-sponse functions, simulated on a daily time step and sum-marised annually. The temperature growth responsefunctions are designed to encapsulate the physiologicalgrowth processes governing the response of trees andminor vegetation growth to mean daily temperature. Therelative effect of temperature as a limiting factor on treegrowth is captured annually through the sum of dailyvalues. The positive effect of a lengthening growing sea-son, for example, may be captured with this approach.The effect of moisture availability on plant growth rates iscalculated using daily TDI values, which represent thedegree that a given tree species is able to meet its energy-driven transpiration demands. As TDI increases, plantstend to close stomata to conserve water and there isan associated reduction in photosynthetic production(McDowell et al. 2008). The model also includes a repre-sentation of the effect of increasing atmospheric CO2concentration on water use efficiency (Seely et al. 2015).The temperature and moisture response functions areincorporated into FORECAST Climate through their in-clusion in a climate growth response index. Specifically,GRIy is calculated for each climate year (y) as the sum ofthe daily product of the temperature (TGrowth) and waterstress (SGrowth) indices (Eqs. 2 and 3). A similarapproach is used to represent the daily effect oftemperature (TDecomp) and moisture content (MDecomp)on dead organic matter decomposition rates through thecalculation of an annual climate decomposition responseindex (DRIy, Eq. 4).GRId ¼ TGrowth;d  SGrowth;d  ð2ÞGRIy ¼X365d¼1GRId ð3ÞDRIy ¼X365d¼1 TDecomp;d MDecomp;d  ð4Þwhere:TGrowth,d = The temperature growth index (range 0–1;dimensionless) on day d.SGrowth,d = The water stress growth index (range 0–1;dimensionless) on day d.TDecomp,d = The temperature decomposition index(range 0–1; dimensionless) on day d.MDecomp,d = The moisture decomposition index (range0–1; dimensionless) on day d.FORECAST climate model calibrationThe calibration of FORECAST Climate includes threesteps: (1) the calibration of the FORECAST model, (2)the parameterisation of the ForWaDy model and (3) thecalibration of the climate response functions in FORE-CAST Climate. The calibration of the base FORECASTmodel for Chinese fir in the Fujian region is described inBi et al. (2007) and Xin et al. (2011) and the main cali-bration parameters employed in the model are providedin Additional file 1. The calibration of the climateresponse functions was verified using remotely sensedmeasures of NPP from the study area.Parameterisation of the ForWaDy modelThe calibration of descriptive soil variables including soiltexture, coarse fragment contents and depths of soillayers and the values for key parameters describing soilwater extraction and transpiration rates for Chinese firand minor vegetation are provided in Table 1. TheKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 4 of 14original version ForWaDy has been modified to facilitateits application for climate change analysis includingdifferent CO2 emissions scenarios (Seely et al. 2015). Adetailed description of the representation of the effect ofincreasing atmospheric CO2 concentrations on stomatalconductance and water use efficiency (WUE) is providedin Additional file 1. The model does not include a repre-sentation of increased atmospheric CO2 concentrationson photosynthetic rates as there is little evidence tosupport the long-term effects of CO2 fertilisation onforest productivity (See Seely et al. 2015 for a detaileddiscussion).Calibration of climate response functionsGrowth response functions A temperature growth re-sponse function for Chinese fir (Fig. 2a) was establishedbased upon reported optimal temperatures from a num-ber of studies (Zhang 1995; Wang 2006; Zhang and Xu2002). The curve shows a sigmoidal increase in growthrate with increasing temperature up to 20 to 22 °C,representing the optimal temperature range for growth.This is followed by a declining trend as the respirationrate increases with increasing temperature (Wang 2006;Zhang and Xu 2002). Similarly, a water stress responsecurve was established to represent the relationshipbetween plant growth and daily water stress (Fig. 2b).The shape of the curve is based on the model default.Climate impacts on decomposition The decompos-ition of litter and soil organic matter in FORECAST isrepresented by grouping litter, created through the deathof specific biomass components, into different littertypes with defined mass loss rates based on litter qualityand field studies (Kimmins et al. 1999). In FORECASTClimate, these base litter decomposition rates and theirassociated nutrient mineralisation rates are adjustedbased on mean air temperature and moisture content.The climate-influenced decomposition functions for thestudy area are shown in Fig. 2c, d. The shape of thetemperature-decomposition response curve was basedupon a Q10 relationship of 2 (Zhou et al. 2008).Drought-related mortality rateFORECAST Climate includes a drought mortality func-tion to capture the potential impacts of prolongeddrought events on tree and plant mortality rates. Thefunction simulates drought mortality using a responsecurve in which a 2-year running average of species-specific TDI is used as a predictor of the annual mortalityrate (Fig. 3). This relationship is based upon the widelyheld assumption that extended periods of drought willlead to carbon starvation (Hogg et al. 2008). The shape ofthe curve is based on the model default derived fromtesting with unpublished data from several tree species inwestern Canada and is assumed to be relatively consistentacross species.Reference climate dataThe FORECAST Climate model requires daily climatedata to drive the simulation of climate change. Specific-ally, 30 years of historical climate data are suggested toprovide a baseline against which any climate changescenarios can be evaluated. The Fujian climate station(see Fig. 1), near the study area, was selected for thispurpose. It is located at 117.17° E and 26.9° N, with anelevation of 208 m. Daily data from 1961 to 1990 were se-lected to represent the 30-year reference period, includingmaximum temperature, minimum temperature, meantemperature and total precipitation. The average monthlyTable 1 Parameter values in the ForWaDy submodel relevant for the simulation of plant available water, transpiration and waterstress on a mesic siteSoil variables Edaphic class Soil texture class Coarse fragment (%) Mineral soil depth (cm) Field capacity moisture content (θ)Mesic site Silt loam 25 85 0.25Soil water extractionand transpirationSpecies Maximum LAIa Canopy parameters Permanent wilting pointd(%)Maximum root depthe (cm)Albedob Resistancec Humus Mineral soilChinese fir 4.5 0.12 0.3 0.07 0.09 100Shrubs NA 0.12 0.25 0.08 0.1 100Grass NA 0.12 0.2 0.07 0.09 75aSets the upper limit for LAI by species. LAI is determined as a function of simulated foliage biomass. Not applicable (NA) for understorey vegetationbEstimated valuesc“Canopy resistance” represents a general measure of the resistance to water loss from foliage via stomata and cuticle. It is used to adjust the α value in thePriestley-Taylor equation to represent the amount of stomatal control on transpiration from a dry canopy based upon the relationship RCan = 1 − (α/1.26)(Seely et al. 2015). An α value of 1.26 represents a freely evaporating surface, a canopy resistance value of 0.3 would reduce the α value to approximately 0.88.The value 0.3 for Chinese fir was estimated based upon the physical characteristics of its foliage and its known climate niche in comparison to other species forwhich values of canopy resistance have been measured (dry pine = 0.45, Douglas fir = 0.33, non-sclerophyllic broad leaves 0.13, see Seely et al. (2015))dRefers to the volumetric moisture content at which the species can no longer extract moisture from the soil. It is related to the soil texture classeIndicates the maximum rooting depth within the total soil profile. Estimated valuesKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 5 of 14temperature and monthly total precipitation for the refer-ence period are shown in Fig. 4.Calculation of climate normalsFORECAST Climate simulates the impact of climatechange on growth and decomposition rates usingclimate normals calculated from reference climate data(Kimmins et al. 1999). Mean values for climate growthand decomposition response indices (GRINormal, andDRINormal) were calculated by running the model inclimate calibration mode using the 30 years of dailyreference climate data to drive the model without cli-mate feedback on growth. The normal climate indicesare subsequently stored for use in climate simulationruns where they are compared against simulated futureclimate indices to determine relative changes in baselinegrowth and decomposition rates.Application of the FORECAST climate modelModel validation using MODIS dataMeasurements of net photosynthesis from the ModerateResolution Imaging Spectroradiometer (MODIS) on theNASA satellites, Terra and Aqua (Zhao et al. 2006), wereused to validate the parameterised model. MODIS datawere selected for evaluation as previous studies havedemonstrated that MODIS estimates of GPP provide agood approximation for ground-based measures ofproductivity derived from eddy flux measurements inChinese fir forests located in neighbouring JiangxiProvince (Wang et al. 2014). The data were collectedduring an 11-year period (2000–2010) with 8-day com-posite time steps. The MODIS product (MOD 17A2)provides an estimate of GPP and net photosynthesis(PSNnet) based upon direct measures of the absorptionof photosynthetically active radiation (PAR) (Zhao et al.2005).Fig. 2 Climate response functions used in FORECAST Climate related to Chinese fir. These functions illustrate the relationship between dailygrowth rate for Chinese fir and a mean daily temperature, b daily water stress; and the response of decomposition rates to c mean daily airtemperature (based upon a Q10 of 2), and d relative daily moisture content, for litter, humus and soilFig. 3 Annual drought-related mortality rate in relationship to 2-yearaverage water stressKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 6 of 14The MODIS data, available at (http://modis.gsfc.nasa.-gov/data/), are formatted as a HDF EOS (HierarchicalData Format – Earth Observing System) tile with a1 km × 1 km grid in a sinusoidal projection. A total of 18tiles with predominant Chinese fir cover were identifiedwithin the Shunchang study region (Fig. 1). A relativePSNnet value was calculated for each 8-day period foreach tile and combined to produce a study-area aver-age. Relative 8-day composite values were calculatedusing annual maxima for each tile. The relative valueshelp to isolate the effects of climate on PSNnet ascompared to absolute values, which are subject to dif-ferences in forest cover and other factors among tiles.Quality control data, included as part of the MOD17A2 product (Zhao et al. 2005), were used to ex-clude periods for which there was excessive cloudcover or other error factors.Daily climate data from the Fujian climate station(2000–2010) were used to drive the validation simula-tion of the study area with FORECAST Climate. Aninitial condition of a medium site quality Chinese firplantation age 20 was established as a starting conditionfor the model validation exercise. For the purposes ofcomparison, daily output of the modelled climate growthresponse index (GRId) was averaged for the equivalent8-day periods used with the MODIS data.Development of climate change scenariosTo illustrate the potential impact of climate change onlong-term stand growth and development of forests inthe study area, it was necessary to develop a set of alter-native climate change scenarios. Two general circulationmodels (HadGEM2 and CNRM-CM5) included as partof the International Panel on Climate Change FifthAssessment Report (IPCC 2013) were selected incombination with two different emission scenarios togenerate four alternative climate change projections forthe study site location. The Climate models were se-lected to represent a general range in potential changepatterns where HadGEM2 = “warm and wet” andCNRM = “cool and dry”. The two emission scenarios se-lected were RCP4.5 and RCP8.5, derived from the IPCCAR5 analysis (Meinshausen et al. 2011; Peters et al.2012). Monthly outputs for the 2025, 2055 and 2085from these models were downscaled to daily data andextrapolated for a 100-year period (2013–2112) using adirect approach linked to the daily reference climatedata. In other words, the variability present in the dailyreference data was projected forward in the futurescenarios with the daily temperatures and precipitationamounts adjusted according to the monthly trends fromthe GCM models. Time periods after 2080 were as-sumed to have no further change other than the inher-ent interannual variation. Projected patterns of changein mean temperature and precipitation for the four cli-mate change scenarios are shown in Fig. 5. Each climatedata set also includes projections for annual changes inatmospheric CO2 concentrations that are consistent withthe associated emission scenarios.Establishment of initial conditionsPrior to conducting a simulation run, it is necessary toestablish initial soil conditions that are representative ofpast management activities. This is achieved by runningthe model in setup mode to generate an ECOSTATE filethat contains values for state variables describingamounts of soil humus, decomposing litter and soilnutrient capital. We created an initial ECOSTATE file torepresent a medium site (where top height = 13 m atage 20) within the study area following steps describedin Bi et al. (2007).Fig. 4 Average monthly temperature and precipitation calculated with data from the Fujian climate station, years 1961–1990Kang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 7 of 14Simulation of climate change impactsFollowing the establishment of an initial ECOSTATE file,the model was used to simulate the long-term impact ofclimate change on the growth of Chinese fir. The fourclimate change scenarios described above (HG4.5,HG8.5, CNRM4.5 and CNRM8.5) and a referenceclimate scenario were used to drive climate simulationruns for three consecutive 30-year rotations starting in2013. A total of five simulations were conducted.Sensitivity analysisA two-part sensitivity analysis was conducted to evaluatethe sensitivity of the model response to changes in at-mospheric CO2 concentrations and to potential changesin growing-season rainfall patterns. The impact of chan-ging CO2 concentrations was assessed by running theclimate change scenarios described in the “Developmentof climate change scenarios” section without the corre-sponding changes in atmospheric CO2 concentrations(atmospheric CO2 was held at 396 ppm). To evaluatethe impact of changes in growing season rainfall pat-terns, a new set of climate change scenarios weregenerated in which the frequency of growing season(Mar–Oct) precipitation events were reduced by 35%while maintaining the total monthly rainfall amounts.The scenarios were created using the StatisticalDownscaling Model (SDSM v5.2) developed by Wilbyand Dawson (2013). These were intended to representpotential increases in rainfall intensity associated withclimate warming.ResultsModel evaluation against MODIS dataMODIS 8-day composite Net PSN data, averaged across18 Chinese fir-dominated 1 km × 1 km tiles within thestudy area, were used to validate the capability of themodel to project temporal patterns in productivity as in-dicated by simulated GRId. As described above, relativevalues of Net PSN were calculated for each tile to betterisolate the impact of climate variations on changes inproductivity. There was a good correlation (r = 0.84,p < 0.0001) between the model and the MODIS datasuggesting the model is able to represent the temporalpatterns in seasonal productivity associated withtemperature and moisture availability with reasonableaccuracy (Fig. 6).Effects of climate change on tree growthThe influence of the alternative projected climate changescenarios on the simulated annual growth responseFig. 5 Historical reference climate data and projected pattern of change. a, b Mean annual air temperature and total annual precipitation for thenext 100 years based on four downscaled climate change projections, respectively. Lines represent the 10-year moving average for each seriesKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 8 of 14index for Chinese fir is shown in Fig. 7a. In general, theclimate change scenarios showed modest increases inGRIy relative to the reference scenario. The selection ofclimate model had a greater impact than the emissionscenario with the HadGEM model showing the largestincrease. Simulation results for stemwood biomassproduction followed a similar pattern (Fig. 7b) withincreases in productivity of 5.4 to 8.1% by the end of thefirst rotation (years 1–30), 6.1 to 12.1% for the secondrotation (years 31–60), and 4.5 to 17.1% for the thirdrotation (years 61–90).The projected impact of the different climate changescenarios on the annual water stress index is illustratedin Fig. 7c. Water stress varies substantially year-to-yearin all climate scenarios as a result of interannual vari-ability inherent in the reference data. Scenarios basedupon the CNRM-CM5 model show the highest waterstress while those based upon the HadGEM2 model tendto be slightly lower than the reference climate. These re-sults are consistent with projected rainfall trends shownin Fig. 5. Consecutive years of elevated water stress wererare in all scenarios (Fig. 7c) and, accordingly, the modelprojected low levels of drought-related mortality.Effects of climate change on daily growth response indexTo evaluate long-term trends in seasonal climate changeimpacts on forest growth rates, the 90-year simulations(2013–2102) were divided into three 30-year future pe-riods (coinciding with each rotation) for each climatescenario. Model results showing the average dailygrowth response index of Chinese fir for the differentperiods and climate scenarios are provided in Fig. 8. Thedaily growth response index (GRId) is the product oftemperature response index and water stress responseindex. Increases in average GRId were greatest duringthe spring and fall for all climate change scenarios. Incontrast, average GRId tended to show modest declinesin the future climate periods relative to the referenceperiod during mid- to late summer due to higher-than-optimal temperatures and greater than normal moisturedeficits. However, this negative impact was offset by asimulated lengthening of the growing season. Forexample, compared to reference climate, the averagelength of the growing season (determined as the numberof calendar days in which TGrowth ≥ 0.25) for the periodfrom 2073 to 2102 increased by 26 days and 34 daysunder the HG4.5 and HG8.5 scenarios, respectively.Fig. 6 Relationship between FORECAST Climate and MOIDS data formodel evaluation. Based on the comparison between simulateddaily growth response index (GRId) (summarised with 8-day means)and average 8-day composite MODIS Net PSN values for the studyarea (converted to relative values using annual maxima) for theperiod from 2000 through 2010. The regression line was forcedthrough the originFig. 7 Simulation results showing the impacts of climate change onthe growth of Chinese fir. a The relative change in the annualgrowth response index (GRIy) from the climate normal (GRINormal),lines represent 5-year moving averages; b stemwood biomassproduction; and c the annual water stress index (TDI) for four climatechange projections and the reference climate for Chinese fir on amedium siteKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 9 of 14Effects of climate change on decomposition rates andnutrient cyclingIn addition to its impact on tree growth, climate changealso had a positive impact on site productivity throughits impact on litter decomposition rates. The litterdecomposition rate index is a measure of the effect oftemperature and moisture content on mass loss rates.Five-year running averages show that the litter decom-position rate increased substantially in all climate changescenarios relative to the reference climate data (Fig. 9a).The increased rates of litter decomposition led to signifi-cant increases in the rate of litter N release relative tothe reference scenario (Fig. 9b), which had a positive im-pact on site productivity.Results of the sensitivity analysisResults from the sensitivity analysis described aboveshow that the calculation of water stress in the model issensitive to both changes in atmospheric CO2 concen-tration and the frequency and intensity of growingseason precipitation events. Removing the effect of CO2on water use efficiency (by holding CO2 constant) led toaverage increase of water stress of 22% above the levelsobserved for the original scenarios. Similarly, decreasesin the frequency of growing season precipitation eventsled to an average increase of 24% in the mean annualwater stress index (TDI) relative to the original scenarios.However, despite the increase in water stress, biomassproduction at rotation only declined slightly (1 to 2%) inthe sensitivity analysis runs. The impact of the increasedwater stress was small because although it increased,water stress was still low enough that it had only aminimal impact on growth and mortality. Detailedresults from the sensitivity analysis are provided inAdditional file 1.DiscussionProjections of future climate regimesThe projection of future climate regimes is invariably asource of uncertainty in modelling studies of climatechange impacts on forest health and productivity. Theapproach taken here was to provide a range of potentialpatterns of change derived from the combination of tworeputable climate models and two possible emissionscenarios derived as part of the IPCC AR5 analysis. Itshould be noted that the prediction of future trends ofinterannual variability is extremely challenging consider-ing most of the output from global climate models onlyreport trends as changes in annual and monthly meansand often only for particular time slices. The directdownscaling approach applied here is based upon the as-sumption that future patterns of interannual variabilitywill reflect past observations. However, there is mountingevidence that this may not be the case (Wilby and Dawson2013), particularly with respect to precipitation patterns.The sensitivity analysis conducted herein provided someFig. 8 Simulation results showing the average daily growth response index. Based on three 30-year future time periods (coinciding with eachrotation) relative to the reference period for the a HG4.5 scenario, b HG8.5 scenario, c CNRM4.5 scenario and d CNRM8.5 scenarioKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 10 of 14insight as to potential impact changes in the frequency ofand intensity of growing season precipitation events butmore work should be done in this area.Effects of climate change on Chinese fir productivityModel results suggest that Chinese fir plantationsdeveloped in the subtropical climate of Fujian provincewill show modest increases in productivity (6.1 to 12.1%)over the next 30 to 60 years due primarily to a lengthen-ing of the growing season. These results are consistentwith a recent study by Wang et al. (2014), who observedthat productivity of Chinese fir in the Fujian regionincreased during a 10-year warming trend from 2000 to2010. They suggested this pattern was linked to aprolonging of the photosynthetically active period. Thisconclusion is supported by a spatial-temporal climateanalysis conducted by Liu et al. (2009), who reported anincrease in the growing season in southeastern China of6.9–8.7 days during the period from 1955 to 2000.While net annual growth rates are projected toincrease, FORECAST Climate predicts variably reducedgrowth during the dry season as a result of increasedwater stress and greater-than-optimal daytime tempera-tures depending on the climate model selected (seeFig. 5). Predicted elevations in peak summer tempera-tures will exceed optimal levels causing reductions ingrowth during these periods, but the net positive im-pacts of increased growing season length were greater.Wang (2006) found that the optimal temperature andhumidity required during the main period of the Chinesefir growing season were 18–20 °C and 80%, respectively.In addition, he observed that growth of Chinese fir waslimited when daily temperature exceeded 28 °C and/ormonthly precipitation was less than 50 mm.Our modelling results suggest that warming associatedwith climate change will also benefit productivity byincreasing rates of litter decomposition and associatednutrient cycling. These results are consistent with thosereported by Moore et al. (1999), in a study of litterdecomposition rates in Canadian forests in which litterdecomposition rates were projected to increase by 4 to7% above contemporary levels because of warmingFig. 9 Simulation results showing the effects of climate change on decomposition rate (a) and litter nitrogen release (b). The lines in a represent5-year moving averagesKang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 11 of 14trends expected with climate change. Evidence of thepositive influence of climate enhanced decompositionand nutrient mineralisation rates on productivity hasbeen observed by several authors. For example, Kirwanand Blum (2011) found that organic matter decompos-ition rates increased by about 20% per degree of warmingin salt marsh experiments, which led to the enhancedproductivity of marsh vegetation. Further, Melillo et al.(2011), in a 7-year soil warming study, pointed out thatthe plant carbon storage increased with the support of theadditional inorganic nitrogen released by the warming-enhanced decay of soil organic matter.Projected impacts on mortality ratesWhile the model predicts an increase in the negativeimpact of dry season moisture stress on growth rates insome climate change scenarios, simulated stress levelswere not high enough in consecutive years to cause in-creases in drought-related mortality. Several studies haveobserved increases in drought-related tree mortality at-tributed to prolonged periods of moisture stress associ-ated with climate change (Breshears et al. 2005; Gitlin etal. 2006; van Mantgem et al. 2009; Allen et al. 2010;O'Grady et al. 2013). The reader should be remindedthat the direct downscaling approach employed in theanalysis presented here limits the interannual variabilityof future precipitation as it forces the data to follow his-toric patterns of variation. If interannual variation wereto increase as a result of climate change, it could lead toa significant increase in drought-related mortality, whichwould, in turn, have a negative impact on estimates offuture productivity in Chinese fir plantations. Further re-search is required to assess the potential of such events.It also is important to note that FORECAST Climatedoes not account for the potential impacts of climatechange on the activity of other disturbance agents. Thereis evidence that both biotic (insects and pathogens) andabiotic (fire and wind) disturbance are influenced byclimate change (van Mantgem and Stephenson 2007;Mutch and Parsons 1998). O’Grady et al. (2013) pointedout that trees suffering from soil-water deficits are moresusceptible to biotic disturbance agents due to signifi-cant changes in the energy and carbon balance of theplants. Thus, the predicted increases in Chinese fir plan-tations associated with climate change may not be rea-lised if changes in climate regime lead to increasedmortality from biotic and abiotic disturbance agents.ConclusionsThe process-based, stand-level model FORECASTClimate was applied to examine the potential impacts ofalternative climate change scenarios on the long-termgrowth and development of Chinese fir plantations inFujian province. Climate change is projected to have amodest positive impact on plantation productivity dueto a gradual lengthening of the growing season and anassociated increase in nutrient cycling rates. While themodel predicted an increase in dry-season water stressunder climate change, it did not project an increase indrought-related mortality. Moreover, the model pre-dicted that the increases in atmospheric CO2 concentra-tions associated with climate change would lead toincreases in WUE thereby reducing the development ofwater stress. However, additional research should beconducted to examine the potential sensitivity ofChinese fir forests in this region to significant changesin the interannual variability in precipitation patternsassociated with climate change. It is also important tonote that the predicted increases in Chinese fir planta-tions associated with climate change may not be realisedif changes in climate regime also lead to increasedmortality from biotic and abiotic disturbance agents.The work presented here demonstrates the value ofemploying a process-based model with relatively minorcalibration requirements to evaluate the potential im-pacts of climate change on key ecosystem processes andlong-term productivity.Additional fileAdditional file 1: Additional information describing the FORECASTClimate model. (DOCX 57 kb)AcknowledgementsThis research was conducted as part of the APF Net-funded project “Adaptationof Asia-Pacific Forests to Climate Change” (project # APFNet/2010/PPF/001)founded by the Asia-Pacific Network for Sustainable Forest Management andRehabilitation.Authors’ contributionsBS, GW and DZ conceived and designed the study; HK and BS analysed thedata; BS, JI, TW and PC contributed reagents/materials/analysis tools; HK andBS wrote the paper. All authors contributed to preparing the manuscript.All authors read and approved the final manuscript.Competing interestsThe authors declare no conflict of interest. The founding sponsors hadno role in the design of the study; in the collection, analyses, orinterpretation of data; in the writing of the manuscript, and in thedecision to publish the results.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Forestry College, Fujian Agriculture and Forestry University, Fuzhou, Fujian350002, China. 2Faculty of Forestry, University of British Columbia, Vancouver,BC V6T 1Z4, Canada.Kang et al. New Zealand Journal of Forestry Science  (2017) 47:20 Page 12 of 14Received: 23 September 2016 Accepted: 13 September 2017ReferencesAllen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier,M., Michel, K., Thomas, R., Andreas, B., David, D., Hogg, E. H., Gonzalez, P.,Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J. H., Allard, G., Running,S. W., Semerci, A., & Cobb, N. (2010). 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