Publications

Liu, SN; Wang, J; Ang, H; Ge, SL (2023). Influence of Underlying Surface Datasets on Simulated Hydrological Variables in the Xijiang River Basin. JOURNAL OF HYDROMETEOROLOGY, 24(7), 1209-1223.

Abstract
Hydrological models play an important role in water resources management and extreme events forecasting, and they are sensitive to the underlying conditions. This study aims to evaluate the impact of different soil-type maps and land-use maps on hydrological simulations and watershed responses by applying the WRF-Hydro (Weather Research and Forecasting Model Hydrological modeling system) distributed hydrological model to the Xijiang River basin. WRF-Hydro runs for four different scenarios for the period 1992-2013. FAO (Food and Agriculture Organization) and GSDE (Global soil-type maps and land-use maps are freely combined to form four scenarios. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation is found to be the least sensitive to soil-type maps. Absorbed shortwave radiation and heat flux are sensitive to land-use maps. The model performance of simulating soil moisture has increased when the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC for most stations. When the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC, the biases of simulating streamflow decrease. This study shows that the performance of the offline WRF-Hydro is significantly influenced by soil-type and land-use maps, and better simulation results can be obtained with more realistic underlying surface maps.

DOI:
10.1175/JHM-D-22-0095.1

ISSN:
1525-7541