PARAMETERIZATION OF A DISTRIBUTED RAINFALL-RUNOFF MODEL BASED ON SOIL TEXTURE INFORMATION

Abstract

 This study investigated the potential to use soil texture information for promoting the grid-based regionalization of a distributed rainfall-runoff model (1K-DHM) for flash-floods predictions, which aims at assigning representative model parameter sets to 30-second grid cells in ungauged basins. A previous study explored the parameterization based on land-use and soil types data, generating model parameter sets on soil-type classification (PS-STC). This study reclassified soil types with soil texture information, creating three new soil types based on soil grain composition: coarse-grain soil, fine-grain soil and gravelgrain soil. Representative model parameter sets of soil types for the grain size composition (PS-GSC) were identified through calibration in 27 donor catchments and a cross-verification within each donor group. Besides the final optimized parameter set, five additional parameter sets generated during the calibration processes were also selected and included in the cross-verification. The newly identified model parameter sets (PS-GSC) demonstrate different water flow characteristics in soils in terms of the depthdischarge relationship embedded in 1K-DHM. In the validation, the simulation results using the PS-GSC were compared with the ones using the PS-STC at 52 heterogenous catchments with 541 rainfall events, evaluated by the Nash-Sutcliffe efficiency (NSE) and peak discharge signatures. Results show that PS-GSC demonstrate a comparable performance to PS-STC in terms of NSE, while PS-GSC shows stronger performance in terms of peak discharge estimation and peak discharge time delay. These analyses reconfirm that the grid-based regionalization considering geospatial types can be used to achieve discharge forecast in ungauged basins and indicate that soil texture information has potential to enhance these forecasts by yielding more reliable peak discharges.