Spatially distributed long‐term hydrologic simulation using a continuous SCS CN method‐based hybrid hydrologic model. Issue 7 (1st March 2018)

• Record Type: Journal Article
• Authors: Cho, Younghyun; Engel, Bernard A.
• Is Part Of: Hydrological processes. Volume 32:Issue 7(2018)
• Page Start: 904
• Page End: 922
• Publication Date: 2018-03-01
• Subjects: continuous SCS CN method -- GIS -- hybrid hydrologic model -- NEXRAD QPEs -- spatially distributed long‐term rainfall‐run‐off flow estimation; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.11463
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: A continuous Soil Conservation Service (SCS) curve number (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 run‐off depth for long‐term discontinuous storm events. The method was applied to spatially distributed long‐term hydrologic simulation of rainfall‐run‐off flow with an underlying assumption for its spatial variability using a geographic information systems‐based spatially distributed Clark's unit hydrograph method (Distributed‐Clark; hybrid hydrologic model), which is a simple few parameter run‐off routing method for input of spatiotemporally varied run‐off depth, incorporating conditional unit hydrograph adoption for different run‐off precipitation depth‐based direct run‐off flow convolution. Case studies of spatially distributed long‐term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimations demonstrate overall performances of Nash–Sutcliffe efficiency ( E NS ) 0.62, coefficient of determination ( R 2 ) 0.64, and percent bias 0.33% in direct run‐off and E NS 0.71, R 2 0.72, and percent bias 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 run‐off 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‐run‐off generation and routing, as a relatively simple hydrologic modelling approach for the use of more reliable gridded types of quantitative precipitation estimations.