Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

Solander, Kurt C.; Reager, J. T.; Thomas, Brian F.; David, Cédric H.; Famiglietti, J. S.

doi:10.1175/jhm-d-15-0056.1

Cited by 22 publications

(17 citation statements)

References 37 publications

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“…The water management modules of large-scale hydrology models have to date relied on relatively simple heuristics to simulate releases, such as monthly storage and release targets based on average climate (Hanasaki et al, 2006;Döll et al, 2009;Biemans et al, 2011;Solander et al, 2016;Voisin et al, 2013aVoisin et al, , 2017 or year-ahead, perfect foresight (Haddeland et al, 2006). Important nuances, such as the appropriate environmental release, are typically applied uniformly across all dams.…”

Section: Discussionmentioning

confidence: 99%

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Turner

Voisin

2020

Hydrol. Earth Syst. Sci.

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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 (1553 ± 50 out of 1927 dams with at least 10 Mm 3 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.

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Section: Discussionmentioning

confidence: 99%

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Turner

Voisin

2020

Hydrol. Earth Syst. Sci.

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show abstract

“…The water management modules of large-scale hydrology models have to-date relied on relatively simple heuristics to simulate releases, such as monthly storage and release targets based on average climate (Hanasaki et al, 2006;Döll et al 2009, Biemans et al, 2011Solander et al, 2016;Voisin et al, , 2017 or year-ahead, perfect foresight (Haddeland et al 2006). Important nuances, such as the appropriate environmental release, are typically applied uniformly across all dams.…”

Section: Discussionmentioning

confidence: 99%

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Turner¹,

Wang²,

Voisin³

2019

Preprint

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Abstract. 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 remains 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. 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 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 (1553 ± 50 out of 1927 dams with at least 10 Mm3 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 six 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 a selection of key reservoirs demonstrates that forecast-informed models of reservoir operations outperform models that neglect the horizon curve – including during flood and drought conditions.

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“…Similar to Hanasaki et al (2006), the model of Haddeland et al (2006) is favorable due to its generic formulation and capability to operate multipurpose reservoirs and to extract water for irrigation from the reservoir. These make the model applicable for large-scale hydrologic models, when data on operational policies are limited (Adam et al, 2007;van Beek et al, 2011). One limitation of Haddeland et al (2006) is that it requires knowledge of the future inflow for each reservoir so that the optimization can be conducted to determine the optimal release.…”

Section: Inflow-and-demand-based Methodsmentioning

confidence: 99%

“…The algorithm does not represent reservoirs with multi-year operational policies (Adam et al, 2007) and also requires running the model many times to optimize the reservoir release. Adam et al (2007) modified the Haddeland et al (2006) reservoir model parameterization to include (1) estimated minimum flow based on observed mean winter flow, (2) the reservoir-filling phase, (3) a storage-area-depth relationship following the regular shape approximation of Liebe et al (2005), and (4) a seasonally varying hydropowerproduction economic value that can be calibrated for hydropower production instead of a constant one; van Beek et al (2011) further modified the retrospective inflow assumption to the prospective model by approximating the upcoming operational year inflow based on previous years' inflow (requires historical inflow observation) and then adjusting the release and demand every month using the actual inflow as estimated from a hydrologic model. Solander et al (2016) tested and compared six generic equations to represent reservoir release and storage simulations.…”

Section: Inflow-and-demand-based Methodsmentioning

confidence: 99%

Representation and improved parameterization of reservoir operation in hydrological and land-surface models

Yassin

Razavi

Elshamy

et al. 2019

Hydrol. Earth Syst. Sci.

122

139

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Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land-surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise-linear relationships between reservoir storage, inflow, and release to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a "generalized" parameterization that requires a relatively limited amount of data and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH (Modélisation Environmentale-Surface et Hydrologie) modeling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both parameter-identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent ef-fects of the simulation accuracy of natural processes and that of reservoir operations on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.

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Simulating Human Water Regulation: The Development of an Optimal Complexity, Climate-Adaptive Reservoir Management Model for an LSM

Cited by 22 publications

References 37 publications

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Representation and improved parameterization of reservoir operation in hydrological and land-surface models

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