Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model
Abstract
Abstract Large‐scale hydrological and water resource models (LHMs) are used increasingly to study the vulnerability of human systems to water scarcity. These models rely on generic reservoir release schemes that often fail to capture the nuances of operations at individual dams. Here we assess whether empirically derived release‐availability functions tailored to individual dams could improve the simulation performance of an LHM. Seasonally varying, linear piecewise relations that specify water release as a function of prevailing storage levels and forecasted future inflow are compared to a common generic scheme for 36 key reservoirs of the Columbia River Basin. When forced with observed inflows, the empirical approach captures observed release decisions better than the generic scheme—including under conditions of drought. The inclusion of seasonally varying inflow forecasts used by reservoir operators adds further improvement. When exposed to biases and errors inherent in the LHM, data‐driven policies fail to offer a robust improvement; inclusion of forecasts deteriorates LHM reservoir simulation performance in some cases. We perform sensitivity analysis to explain this result, finding that the bias inherent in LHM streamflow is amplified by a reservoir model that relies on forecasts. To harness the potential of interpretable, data‐driven reservoir operating schemes, research must addressmore »
- Authors:
-
[1];
[2];
[3]
- Energy and Environment Directorate Pacific Northwest National Laboratory Seattle WA USA
- Department of Biological and Environmental Engineering Cornell University Ithaca NY USA
- Energy and Environment Directorate Pacific Northwest National Laboratory Seattle WA USA, Department of Civil and Environmental Engineering University of Washington Seattle WA USA
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1668426
- Alternate Identifier(s):
- OSTI ID: 1669036; OSTI ID: 1786918
- Report Number(s):
- PNNL-SA-153063
Journal ID: ISSN 0043-1397; e2020WR027902
- Grant/Contract Number:
- 59534; AC05‐76RL01830; AC05-76RL01830; DE‐AC05‐76RL01830
- Resource Type:
- Published Article
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Name: Water Resources Research Journal Volume: 56 Journal Issue: 10; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; dams, reservoirs, water resources model, hydrological simulation
Citation Formats
Turner, Sean W. D., Doering, Kenji, and Voisin, Nathalie. Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model. United States: N. p., 2020.
Web. doi:10.1029/2020WR027902.
Turner, Sean W. D., Doering, Kenji, & Voisin, Nathalie. Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model. United States. https://doi.org/10.1029/2020WR027902
Turner, Sean W. D., Doering, Kenji, and Voisin, Nathalie. Mon .
"Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model". United States. https://doi.org/10.1029/2020WR027902.
@article{osti_1668426,
title = {Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model},
author = {Turner, Sean W. D. and Doering, Kenji and Voisin, Nathalie},
abstractNote = {Abstract Large‐scale hydrological and water resource models (LHMs) are used increasingly to study the vulnerability of human systems to water scarcity. These models rely on generic reservoir release schemes that often fail to capture the nuances of operations at individual dams. Here we assess whether empirically derived release‐availability functions tailored to individual dams could improve the simulation performance of an LHM. Seasonally varying, linear piecewise relations that specify water release as a function of prevailing storage levels and forecasted future inflow are compared to a common generic scheme for 36 key reservoirs of the Columbia River Basin. When forced with observed inflows, the empirical approach captures observed release decisions better than the generic scheme—including under conditions of drought. The inclusion of seasonally varying inflow forecasts used by reservoir operators adds further improvement. When exposed to biases and errors inherent in the LHM, data‐driven policies fail to offer a robust improvement; inclusion of forecasts deteriorates LHM reservoir simulation performance in some cases. We perform sensitivity analysis to explain this result, finding that the bias inherent in LHM streamflow is amplified by a reservoir model that relies on forecasts. To harness the potential of interpretable, data‐driven reservoir operating schemes, research must address LHM flow biases arising from inaccuracies in climate input, runoff generation, flow routing, and water withdrawal and consumption data.},
doi = {10.1029/2020WR027902},
journal = {Water Resources Research},
number = 10,
volume = 56,
place = {United States},
year = {2020},
month = {9}
}
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https://doi.org/10.1029/2020WR027902
https://doi.org/10.1029/2020WR027902
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Figure 1: Study region. Columbia River basin, with 117 dams modeled in MOSART‐WM. The data‐driven operating rules with and without forecasts are implemented on the 36 dams highlighted. The eight labeled dams are those with storage capacity greater than 1,000 MCM.
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