Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower

Hamlet, Alan F.; Huppert, Daniel D.; Lettenmaier, Dennis P.

doi:10.1061/(asce)0733-9496(2002)128:2(91)

Cited by 265 publications

(177 citation statements)

References 16 publications

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“…Some studies have investigated sharpness and reliability simultaneously. For instance, Hamlet and Lettenmaier (1999) selected past precipitation based on categories of ENSO and PDO to feed a hydrological model for streamflow forecasting, and, later on, for reservoir operation (Hamlet et al, 2002). They noted that the conditioning improved forecast sharpness.…”

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in longrange streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

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show abstract

“…Reliable forecasts on seasonal streamflow have tremendous economic and environmental worth, ranging from more effective water supply planning [1][2][3], improved flood and drought management [4][5][6][7], and increased hydropower generation [8][9][10], among others. The worth is particularly evident in semi-arid areas including the State of California, which largely relies on snow melt from the Sierra Nevada to supply water in meeting the State's increasing demand induced from its growing economy and population.…”

Section: Introductionmentioning

confidence: 99%

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

He¹,

Russo²,

Anderson³

2016

Climate

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“…Their main use at present is to assist in planning the operation of water reservoirs for hydropower, agricultural and urban water supply, flood mitigation, and environmental flows [Cherry et al, 2005;Hamlet et al, 2002;Pagano et al, 2004]. Forecasts of the likelihood of above-or belowaverage streamflow levels can also help river water users, environmental water managers, and floodplain communities in decision making, as well as national or international organizations involved in water trading, policy making, regulation, and aid and emergency response [Chiew et al, 2003;Pappenberger et al, 2011;Ritchie et al, 2004;Sankarasubramanian and Lall, 2003].…”

Section: Introductionmentioning

confidence: 99%

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Dijk

Peña‐Arancibia

Wood

et al. 2013

Water Resources Research

138

107

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[1] Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1 resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km 2 ) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions. Citation: van Dijk, A. I. J. M., J. L. Peña-Arancibia, E. F. Wood, J. Sheffield, and H. E. Beck (2013), Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour.

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Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower

Cited by 265 publications

References 16 publications

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Predictability of Seasonal Streamflow in a Changing Climate in the Sierra Nevada

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

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