Abstract
Assessments of water and energy security over historical and future periods require hydrologic models that can accurately simulate reservoir operations. However, scare reservoir operation data limits the accuracy of current reservoir representations in simulating reservoir behaviors. Furthermore, the reliability of these representations under changing inflow regimes remains unclear, which makes their application for long future planning horizons questionable. To this end, we propose a synergistic framework to predict the release, storage, and hydropower production of ungauged reservoirs (i.e., reservoirs without in-situ inflow, release, storage, and operating rules) by combining remotely sensed reservoir operating patterns and model-simulated reservoir inflow with conceptual reservoir operation schemes within a land surface-hydrologic model. A previously developed reservoir operation scheme is extended with a storage anomaly based calibration approach to accommodate the relatively short time series and large time intervals of remotely sensed data. By setting up controlled experiments in the Yalong River Basin in China, we show that remote sensing can improve the parameter estimation and simulations of ungauged reservoirs for all selected reservoir operation schemes, thereby improving the downstream flood and streamflow simulations. However, most of these schemes show degraded accuracies of reservoir operation simulations under a changing inflow regime, which could lead to unreliable assessments of future water resources and hydropower production. In comparison, our newly extended reservoir operation scheme can be more adaptable to flow regime variations. Our study provides a practical framework for reservoir impact assessments and predictions with the ongoing satellite altimetry projects such as Surface Water and Ocean Topography.
Original language | English (US) |
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Article number | e2022WR033026 |
Journal | Water Resources Research |
Volume | 59 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2023 |
Bibliographical note
Funding Information:The authors thank the editor, two anonymous reviewers, and the third reviewer, Dr. Xudong Zhou, for their in-depth reviews and comments. This work was financially supported by the National Key Research and Development Project of China (Grant 2021YFC3000202), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2021490311, 2020490711), the German Research Foundation through funding of the AccHydro project (DFG Grant KU, 2090/11-1), and the German Federal Ministry of Science of Education through funding of the MitRiskFlood project (BMBF Grant 01LP2005A). Open Access funding enabled and organized by Projekt DEAL.
Funding Information:
The authors thank the editor, two anonymous reviewers, and the third reviewer, Dr. Xudong Zhou, for their in‐depth reviews and comments. This work was financially supported by the National Key Research and Development Project of China (Grant 2021YFC3000202), the Belt and Road Special Foundation of the State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering (2021490311, 2020490711), the German Research Foundation through funding of the AccHydro project (DFG Grant KU, 2090/11‐1), and the German Federal Ministry of Science of Education through funding of the MitRiskFlood project (BMBF Grant 01LP2005A). Open Access funding enabled and organized by Projekt DEAL.
Publisher Copyright:
© 2023. The Authors.
Keywords
- hydrologic prediction
- hydrologic simulation
- remote sensing
- reservoir operation schemes
- satellite altimetry
- SWOT
ASJC Scopus subject areas
- Water Science and Technology