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Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model

Turner, Sean W. D. ; Doering, Kenji ; Voisin, Nathalie

Water resources research, 2020-10, Vol.56 (10), p.n/a [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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  • Título:
    Data‐Driven Reservoir Simulation in a Large‐Scale Hydrological and Water Resource Model
  • Autor: Turner, Sean W. D. ; Doering, Kenji ; Voisin, Nathalie
  • Assuntos: dam ; Dam failure ; Dams ; dams, reservoirs, water resources model, hydrological simulation ; Data ; Drought ; Empirical analysis ; ENVIRONMENTAL SCIENCES ; Hydrologic data ; Hydrologic models ; Hydrology ; Inflow ; Large‐scale hydrological and water resource model ; Operators (mathematics) ; reservoir ; Reservoirs ; River basins ; Routing ; Runoff ; Sensitivity analysis ; Simulation ; Storage ; Stream discharge ; Stream flow ; Vulnerability ; Water inflow ; Water resources ; Water scarcity ; water supply
  • É parte de: Water resources research, 2020-10, Vol.56 (10), p.n/a
  • Notas: 59534; AC05‐76RL01830; AC05-76RL01830; DE‐AC05‐76RL01830
    PNNL-SA-153063
    USDOE Office of Science (SC)
  • Descrição: 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. Key Points Data‐driven release‐availability functions are more accurate than generic reservoir schemes when forced with observed inflows Data‐driven schemes fail to provide robust simulation improvements under conditions of inflow bias common to large‐scale hydrological models Water supply vulnerability varies widely across reservoir schemes, but large‐scale hydrological simulations fail to expose this sensitivity
  • Editor: Washington: John Wiley & Sons, Inc
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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