期刊
WATER RESOURCES RESEARCH
卷 55, 期 10, 页码 8238-8251出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2019WR025164
关键词
Ecohydrology; Watershed Storage; Machine Learning; Budyko Analysis; Hydroclimate; Land Cover
资金
- National Science Foundation [DGE-1252376, EAR-1558675]
- Department of Forestry and Environmental Resources at North Carolina State University
Watershed studies often rely on the assumption that interannual storage changes are negligible in the hydrologic balance of a watershed. The assumption can be useful and is sometimes necessary, but it is widely acknowledged as unrealistic. Identifying and understanding systematic deviations from hydrologic steady state has important implications for both hydrologic research and water management. To that end, we evaluated the magnitude of interannual changes in storage for nearly 1000 watersheds in the conterminous United States for the 10-year period 2002 to 2011 using ground-based and remotely sensed data. We evaluated relationships between changes in storage (i.e., deviations from hydrologic steady state), vegetation cover, and hydroclimatic variables. Analysis of results using a Budyko framework revealed that, in general, greater evaporative partitioning led to smaller deviations from hydrologic steady state. Additional analysis using gradient boosted regression tree modeling identified an inverse relationship between forest cover and the magnitude of deviations from hydrologic steady state. In fact, modeling showed forest cover to be a stronger driver of variability in deviations from steady state than any hydroclimatic variable. We discuss ecohydrological feedbacks capable of contributing to steady-state conditions in forested watersheds, and we discuss implications of these results for the coevolution of watersheds, vegetation, and climate.
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