Valuing year‐to‐go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

Rheinheimer, David E.; Bales, R. C.; Oroza, Carlos A.; Lund, Jay R.; Viers, Joshua H.

doi:10.1002/2015wr018295

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…In summary, cloudiness influences snowmelt and streamflow in different ways across the mountainous WUS and within the snowmelt season, that is, the cloud‐precipitation effect in early and late snowmelt season and the cloud shading effect in peak snowmelt season. The relatively inactive snow ablation in February–March, followed by peak activity in April–May and decline in June–July, underpins a link between snowpack dynamics and annual streamflow, which Rheinheimer et al () similarly noted from their work at Yuba River watershed in northern California. The low‐to‐moderate strengths in these linear associations suggest that nonlinear processes are important in cloudiness‐snowmelt‐streamflow dynamics and that other factors than cloudiness variability are involved in snowmelt and streamflow fluctuations.…”

Section: Discussionmentioning

confidence: 89%

The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States

Sumargo

Cayan

2018

Water Resources Research

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This study investigates snowmelt and streamflow responses to cloudiness variability across the mountainous parts of the western United States. Twenty years (1996–2015) of Geostationary Operational Environmental Satellite‐derived cloud cover indices (CC) with 4‐km spatial and daily temporal resolutions are used as a proxy for cloudiness. The primary driver of nonseasonal fluctuations in daily mean solar insolation is the fluctuating cloudiness. We find that CC fluctuations are related to snowmelt and snow‐fed streamflow fluctuations, to some extent (correlations of <0.5). Multivariate linear regression models of daily snowmelt (MELT) and streamflow (ΔQ) variations are constructed for each month from February to July, when snowmelt is most active. Predictors include CC from five antecedent days up to the current day. The CC‐MELT and CC‐ΔQ associations vary with time and location. The results show the dominance of negative correlations between CC and MELT, exemplifying the cloud‐shading (or clear‐sky) effect on snowmelt. The magnitude of the CC‐MELT association (R2) amounts to 5–61%, typically peaking in May. These associations fade earlier in summer during dry years than wet years, indicating the differing responses of thicker versus thinner snowpack. The CC‐ΔQ association displays a less consistent pattern, with R2 amounting to 2–47%. Nevertheless, MELT and ΔQ fluctuations exhibit spatially extensive patterns of correlations with daily cloudiness anomalies, indicating that the effects of cloudiness often operate over regional spatial scales.

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Section: Discussionmentioning

confidence: 89%

The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States

Sumargo

Cayan

2018

Water Resources Research

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“…Our results empirically confirm this water manger behavior. We presented empirical evidence of the behavioral responses to uncertainty across basins with diverse operations-hydropower, water supply for cities and agriculture, flood control, and environmental flowsextending prior estimates that were limited to forecast skill (Anghileri et al 2016) or hydropower operations (Rheinheimer et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Reservoir Operators React to Uncertainty in Snowmelt Runoff Forecasts

Stillinger

Costello

Bales

et al. 2021

J. Water Resour. Plann. Manage.

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Over 1 billion people rely on snow and ice for their water supply, and in many areas, reservoirs store the water after the snow melts. Deciding when to release water depends partly on forecasts of how much more rain and snowmelt will flow into the reservoirs. Often these forecasts express uncertainty, reporting a range of possible flows. We examined how those responsible for releasing water from reservoirs respond to forecasts using a case study of the historical record of past forecasts and water releases in California's Sierra Nevada. Results from a panel data model with fixed effects showed that these water managers hedge their bets against the possibility of less water than forecasted. Greater uncertainty in a forecast was associated significantly with a reduced amount of released water, and the effect of uncertainty on releases declined as the forecast volume increased (adjusted R 2 ¼ 0.70, F-test p ≪ 0.001). Our results suggest that increasing water managers' confidence in upcoming flows by reducing forecast uncertainty would allow more water to be released from reservoirs earlier in the year, without the need for direct coordination with these managers.

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“…These data can support better real-time monitoring of hydrologic fluxes across the landscape (see Section 3.2 ). As demonstrated by [ 40 ], better hydrologic information can potentially increase hydropower revenue. To that end, this deployment of WSNs demonstrates the capability of collecting more comprehensive hydrologic data, which can potentially translate into lower uncertainty in streamflow forecasts at various temporal and spatial scales and improved economic viability of hydropower.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Alpine Measurement System Using Wireless Sensor Networks

Malek

Avanzi

Brun‐Laguna

et al. 2017

Sensors

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Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

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Valuing year‐to‐go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

Abstract: We assessed the potential value of hydrologic forecasting improvements for a snow-dominated highelevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model.

Cited by 19 publications

References 38 publications

The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States

The Influence of Cloudiness on Hydrologic Fluctuations in the Mountains of the Western United States

Reservoir Operators React to Uncertainty in Snowmelt Runoff Forecasts

Real-Time Alpine Measurement System Using Wireless Sensor Networks

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