Open Collections

Data for Forest Disturbance Thresholds and Cumulative Hydrological Impacts

Hou, Yiping

Forest disturbance threshold is defined as a critical disturbance level in forested landscapes above which significant hydrological impacts are detected. Determining disturbance thresholds is critically important for supporting forest management to ensure sustaining of ecological and hydrological functions and services. However, there are very few quantitative evaluations of forest disturbance thresholds regionally or globally. In this study, we applied a well-tested methodology (the modified double mass curve, MDMC) to derive the long-term, continuous hydrological response curves and then to quantify forest disturbance thresholds on annual streamflow in 42 forested watersheds in British Columbia, Canada. The results show that forest disturbance thresholds for significant and cumulative hydrological impacts vary from 7 to 52% of cumulative equivalent clear-cut area (CECA) with an average of 17% or from 8 to 52% of disturbed area with an average of 19%. Climate (inter-annual and intra-annual) and watershed properties exert critical controls on forest disturbance thresholds. Watersheds with greater snowfall proportions, more desynchronizations of energy demand and water supply, less diverse ecosystems, larger watershed sizes, lower water retention capacities, and steeper slopes have lower forest disturbance thresholds. Given the present forest disturbance levels in the central interior of British Columbia, about half (53%) of the forested watersheds have already crossed the average disturbance threshold. These results highlight that the one-threshold-fits-all application and management are inappropriate and watershed characteristics (climate and watershed properties) should be carefully considered to manage water supply and other hydrological functions under increasing forest disturbance and climate change impacts in the region. The methodology can be effectively and robustly extended elsewhere around the globe.

British Columbia

2023-07-26

CC-BY 4.0

https://doi.org/10.5683/SP3/GDCQZN

http://creativecommons.org/licenses/by/4.0

Dataverse