Toward improved parameterizations of reservoir operation in ungauged basins: a synergistic framework coupling satellite remote sensing, hydrologic modeling, and conceptual operation schemes

  • 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 relativelyAssessments 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.show moreshow less

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Metadaten
Author:Ningpeng Dong, Mingxiang Yang, Jianhui Wei, Joël Arnault, Patrick LauxORCiDGND, Shiqin Xu, Hao Wang, Zhongbo Yu, Harald KunstmannORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1040479
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/104047
ISSN:0043-1397OPAC
ISSN:1944-7973OPAC
Parent Title (English):Water Resources Research
Publisher:American Geophysical Union (AGU)
Place of publication:Washington, DC
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/05/02
Tag:Water Science and Technology
Volume:59
Issue:3
First Page:e2022WR033026
DOI:https://doi.org/10.1029/2022wr033026
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Regionales Klima und Hydrologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)