Inferred inflow forecast horizons guiding reservoir release decisions across the United States
<p>Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data des...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/24/1275/2020/hess-24-1275-2020.pdf |
| _version_ | 1818908440482807808 |
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| author | S. W. D. Turner W. Xu N. Voisin N. Voisin |
| author_facet | S. W. D. Turner W. Xu N. Voisin N. Voisin |
| author_sort | S. W. D. Turner |
| collection | DOAJ |
| description | <p>Medium- to long-range forecasts often guide reservoir
release decisions to support water management objectives, including
mitigating flood and drought risks. While there is a burgeoning field of
science targeted at improving forecast products and associated decision
support models, data describing how and when forecasts are applied in
practice remain undeveloped. This lack of knowledge may prevent
hydrological modelers from developing accurate reservoir release schemes for
large-scale, distributed hydrology models that are increasingly used to
assess the vulnerabilities of large regions to hydrological stress. We
address this issue by estimating seasonally varying, regulated inflow
forecast horizons used in the operations of more than 300 dams throughout
the conterminous United States (CONUS). For each dam, we take actual forward
observed inflows (perfect foresight) as a proxy for forecasted flows
available to the operator and then identify for each week of the year the
forward horizon that best explains the release decisions taken. Resulting
“horizon curves” specify for each dam the inferred inflow forecast horizon
as a function of the week of the water year. These curves are analyzed for
strength of evidence for contribution of medium- to long-range forecasts in
decision making. We use random forest classification to estimate that
approximately 80 % of large dams and reservoirs in the US (<span class="inline-formula">1553±50</span>
out of 1927 dams with at least 10 Mm<span class="inline-formula"><sup>3</sup></span> storage capacity) adopt medium- to
long-range inflow forecasts to inform release decisions during at least part
of the water year. Long-range forecast horizons (more than 6 weeks ahead)
are detected in the operations of reservoirs located in high-elevation
regions of the western US, where snowpack information likely guides the
release. A simulation exercise conducted on four key western US reservoirs
indicates that forecast-informed models of reservoir operations may
outperform models that neglect the horizon curve – including during flood
and drought conditions.</p> |
| first_indexed | 2024-12-19T22:11:03Z |
| format | Article |
| id | doaj.art-b77711a341ef494080c7eb8585e2107c |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-19T22:11:03Z |
| publishDate | 2020-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-b77711a341ef494080c7eb8585e2107c2022-12-21T20:03:55ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382020-03-01241275129110.5194/hess-24-1275-2020Inferred inflow forecast horizons guiding reservoir release decisions across the United StatesS. W. D. Turner0W. Xu1N. Voisin2N. Voisin3Energy and Environment Directorate, Pacific Northwest National Laboratory, Seattle, WA, USAEnergy and Environment Directorate, Pacific Northwest National Laboratory, Seattle, WA, USAEnergy and Environment Directorate, Pacific Northwest National Laboratory, Seattle, WA, USADepartment of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA<p>Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (<span class="inline-formula">1553±50</span> out of 1927 dams with at least 10 Mm<span class="inline-formula"><sup>3</sup></span> storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.</p>https://www.hydrol-earth-syst-sci.net/24/1275/2020/hess-24-1275-2020.pdf |
| spellingShingle | S. W. D. Turner W. Xu N. Voisin N. Voisin Inferred inflow forecast horizons guiding reservoir release decisions across the United States Hydrology and Earth System Sciences |
| title | Inferred inflow forecast horizons guiding reservoir release decisions across the United States |
| title_full | Inferred inflow forecast horizons guiding reservoir release decisions across the United States |
| title_fullStr | Inferred inflow forecast horizons guiding reservoir release decisions across the United States |
| title_full_unstemmed | Inferred inflow forecast horizons guiding reservoir release decisions across the United States |
| title_short | Inferred inflow forecast horizons guiding reservoir release decisions across the United States |
| title_sort | inferred inflow forecast horizons guiding reservoir release decisions across the united states |
| url | https://www.hydrol-earth-syst-sci.net/24/1275/2020/hess-24-1275-2020.pdf |
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