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Integrating High-resolution Wetland and Depression Water Storage Data in Major Basin Hydrologic Modeling
Citation:
Rajib, A., Q. Wu, C. Lane, H. Golden, J. Christensen, T. Dahl, J. Ryder, AND B. McFall. Integrating High-resolution Wetland and Depression Water Storage Data in Major Basin Hydrologic Modeling. 2021 American Geophysical Union Fall Meeting, New Orleans, LA, December 13 - 17, 2021.
Impact/Purpose:
The development of this database of wetland and depression storage for integration into watershed hydrology models at the large scale of 1 million km2, allows us to investigate the influence of small water bodies on large scale watersheds and their relative importance in watershed dynamics.
Description:
The increasing availability of surface water inundation data has encouraged modelers and managers to include small yet abundant surface water storage systems (e.g., wetlands and other landscape depressions) in process-based models. Yet, these model applications have been largely limited to small- to meso- watershed scales, with drainage areas ranging from a few hectares to several thousand square kilometers. The conventional practice of overlooking these surface water storage systems in basin-scale (e.g., >10,000 m2) hydrologic modeling may be missing the total picture of flood and drought hazards. To fill this gap, we developed a 30-m resolution topography-based wetland and depression storage (maximum surface area and storage volume) database for the Upper Mississippi, Ohio, and Missouri River Basins ⎼ encompassing the 2.35 million km2 upstream domain of the Mississippi River system. Further, we integrated this depression dataset into a process-based model to simulate sub-catchment and river reach-scale hydrologic fluxes (surface runoff, soil wetness, evapotranspiration) and flows (streamflow). Compared with a “no depression” conventional model constructed for the Missouri and Upper Mississippi River Basins, our exploratory analyses demonstrate that a depression-integrated model (i) significantly alters the spatial patterns and magnitudes of water yields, (ii) improves streamflow simulation accuracy, and (iii) provides realistic spatial distributions of landscape wetness conditions. These emerging findings provide us with new insights into the effects of small surface water storage and stimulates a reassessment of current practices for basin-scale hydrologic modeling and water management.
