Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing
Abstract High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an...
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| Format: | Article |
| Language: | English |
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Wiley
2024-08-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036450 |
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| author | Yongzhe Chen Yiming Wang Luoqi Li Yanhong Cui Xingwu Duan Di Long |
| author_facet | Yongzhe Chen Yiming Wang Luoqi Li Yanhong Cui Xingwu Duan Di Long |
| author_sort | Yongzhe Chen |
| collection | DOAJ |
| description | Abstract High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China. |
| format | Article |
| id | doaj-art-d564590a3a8c413eb047988c92cd2e64 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-d564590a3a8c413eb047988c92cd2e642025-08-20T04:00:32ZengWileyWater Resources Research0043-13971944-79732024-08-01608n/an/a10.1029/2023WR036450Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote SensingYongzhe Chen0Yiming Wang1Luoqi Li2Yanhong Cui3Xingwu Duan4Di Long5Department of Hydraulic Engineering Tsinghua University Beijing ChinaDepartment of Hydraulic Engineering Tsinghua University Beijing ChinaDepartment of Hydraulic Engineering Tsinghua University Beijing ChinaDepartment of Hydraulic Engineering Tsinghua University Beijing ChinaInstitute of International Rivers and Eco‐security Yunnan University Kunming ChinaDepartment of Hydraulic Engineering Tsinghua University Beijing ChinaAbstract High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China.https://doi.org/10.1029/2023WR036450monthly inundated areareservoir water storagemultisource remote sensingChina |
| spellingShingle | Yongzhe Chen Yiming Wang Luoqi Li Yanhong Cui Xingwu Duan Di Long Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing Water Resources Research monthly inundated area reservoir water storage multisource remote sensing China |
| title | Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing |
| title_full | Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing |
| title_fullStr | Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing |
| title_full_unstemmed | Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing |
| title_short | Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing |
| title_sort | monthly monitoring of inundated areas and water storage dynamics in china s large reservoirs using multisource remote sensing |
| topic | monthly inundated area reservoir water storage multisource remote sensing China |
| url | https://doi.org/10.1029/2023WR036450 |
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