ALBERT

Beschreibung: A continuous SCS CN method that considers time-varied SCS CN values was developed based on the original SCS CN method with a revised soil moisture accounting approach to estimate runoff depth for long-term discontinuous storm events. The method was applied to spatially distributed long-term hydrologic simulation of rainfall-runoff flow with an underlying assumption for its spatial variability using a GIS-based spatially distributed Clark's unit hydrograph method (Distributed-Clark; hybrid hydrologic model), which is a simple few parameter runoff routing method for input of spatiotemporally varied runoff depth, incorporating conditional unit hydrograph adoption for different runoff precipitation depth-based direct runoff flow convolution. Case studies of spatially distributed long-term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimates (QPEs) demonstrate overall performances of Nash-Sutcliffe efficiency ( E NS ) 0.62, coefficient of determination ( R 2 ) 0.64, and percent bias ( PBIAS ) 0.33% in direct runoff and E NS 0.71, R 2 0.72, and PBIAS 0.15% in total streamflow for model result comparison against observed streamflow. These results show better fit (improvement in E NS of 42.0% and R 2 of 33.3% for total streamflow) than the same model using spatially averaged gauged rainfall. Incorporation of logic for conditional initial abstraction in a continuous SCS CN method, which can accommodate initial runoff loss amounts based on previous rainfall, slightly enhances model simulation performance; both E NS and R 2 increased by 1.4% for total streamflow in a 4-year calibration period. A continuous SCS CN method-based hybrid hydrologic model presented in this study is, therefore, potentially significant to improved implementation of long-term hydrologic applications for spatially distributed rainfall-russnoff generation and routing, as a relatively simple hydrologic modeling approach for the use of more reliable gridded types of QPEs.