Influence of Underlying Surface Datasets on Simulated Hydrological Variables in the Xijiang River Basin

Songnan Liu aCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
cKey Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Jun Wang bNansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
aCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Huijun Wang aCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
cKey Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Shilong Ge dSchool of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China

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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 Dataset for Earth System Science) soil-type maps, and MODIS (Moderate-Resolution Imaging Spectroradiometer) and CNLUCC (China Land Use Land Cover Remote Sensing Monitoring Dataset) land-use maps are used in this study. These 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.

Significance Statement

The purpose of this study is to evaluate the impacts of land-use and soil-type maps on hydrological processes at the watershed scale by applying a distributed hydrological model WRF-Hydro model for the Xijiang River basin and reveal the importance of choosing land-use and soil-type maps. In this study, two soil-type maps and two land-use maps are used. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation and heat flux are sensitive to land-use maps. When using GSDE soil-type and CNLUCC land-use maps, the performance of the model is improved. The underlying conditions should be considered when applying the models in practice.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Wang, wangjun@mail.iap.ac.cn

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 Dataset for Earth System Science) soil-type maps, and MODIS (Moderate-Resolution Imaging Spectroradiometer) and CNLUCC (China Land Use Land Cover Remote Sensing Monitoring Dataset) land-use maps are used in this study. These 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.

Significance Statement

The purpose of this study is to evaluate the impacts of land-use and soil-type maps on hydrological processes at the watershed scale by applying a distributed hydrological model WRF-Hydro model for the Xijiang River basin and reveal the importance of choosing land-use and soil-type maps. In this study, two soil-type maps and two land-use maps are used. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation and heat flux are sensitive to land-use maps. When using GSDE soil-type and CNLUCC land-use maps, the performance of the model is improved. The underlying conditions should be considered when applying the models in practice.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jun Wang, wangjun@mail.iap.ac.cn

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