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Surface depression water storage improves continental-scale modeling for watershed management
Citation:
Rajib, A., H. Golden, C. Lane, AND Q. Wu. Surface depression water storage improves continental-scale modeling for watershed management. 2019 AGU Fall Meeting, San Francisco, CA, December 09 - 13, 2019.
Impact/Purpose:
Presented at American Geophysical Union Fall Meeting 2019
Description:
Surface water storage in small yet abundant landscape depressions (e.g., wetlands) promotes hydrological and biogeochemical processes that may influence downstream water quality and quantity. However, process-based model simulations of droughts, floods, and water quality in response to watershed management typically focus on integrating large, managed water bodies (such as lakes and reservoirs) into the modeling efforts. Surface water storage systems – including non-floodplain wetlands, ponds, and other small water bodies – are typically ignored in conventional model simulations. This may lead to potentially inaccurate flow response simulations to variations in climate and land cover changes across the world’s major river basins. To advance current understanding of these challenges, we developed the first-ever surface depression-integrated continental-scale modeling approach, focusing on the ~450,000 km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography-based algorithm to estimate areas and volumes of ~455,000 surface depressions across the UMRB and then aggregated their effects per subbasin. This approach afforded model parsimony and computational efficiency. Compared to the basic “no depression” model that represents conventional modeling practices, our new depression-integrated model showed (i) increased streamflow simulation accuracy, (ii) altered spatial patterns and decreased magnitude of water yields, and (iii) improved spatial distributions of root zone wetness. The relative improvements in subbasin streamflow were correlated with upstream abundance of surface depression storage, suggesting enhanced process representation in the depression-integrated model. Our depression-integrated model also indicates improved subbasin water balance simulations, which was corroborated with remotely sensed data. These findings provide us with new insights on the effects of surface depression storage at large river basin scales and stimulates a reassessment of current conventional practices for continental-scale hydrologic modeling and management.