Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods
As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, includin...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/241 |
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| author | Yuanhong You Yuhao Zhang Yanyu Lu Ying Hao Zhiguang Tang Haiyan Hou |
| author_facet | Yuanhong You Yuhao Zhang Yanyu Lu Ying Hao Zhiguang Tang Haiyan Hou |
| author_sort | Yuanhong You |
| collection | DOAJ |
| description | As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. |
| format | Article |
| id | doaj-art-11af84bd9d3846318e19c620c06653c1 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-11af84bd9d3846318e19c620c06653c12025-01-24T13:47:50ZengMDPI AGRemote Sensing2072-42922025-01-0117224110.3390/rs17020241Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial MethodsYuanhong You0Yuhao Zhang1Yanyu Lu2Ying Hao3Zhiguang Tang4Haiyan Hou5School of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241002, ChinaAnhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, ChinaHuaihe River Basin Meteorological Center, Anhui Meteorological Bureau, Hefei 230031, ChinaNational-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, School of Earth Sciences and Geospatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaNational-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, School of Earth Sciences and Geospatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaAs climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts.https://www.mdpi.com/2072-4292/17/2/241standardized indexWRF-Hydro modelsoil moisturedrought characteristics |
| spellingShingle | Yuanhong You Yuhao Zhang Yanyu Lu Ying Hao Zhiguang Tang Haiyan Hou Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods Remote Sensing standardized index WRF-Hydro model soil moisture drought characteristics |
| title | Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods |
| title_full | Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods |
| title_fullStr | Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods |
| title_full_unstemmed | Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods |
| title_short | Spatiotemporal Dynamics of Drought in the Huai River Basin (2012–2018): Analyzing Patterns Through Hydrological Simulation and Geospatial Methods |
| title_sort | spatiotemporal dynamics of drought in the huai river basin 2012 2018 analyzing patterns through hydrological simulation and geospatial methods |
| topic | standardized index WRF-Hydro model soil moisture drought characteristics |
| url | https://www.mdpi.com/2072-4292/17/2/241 |
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