Assessment of Folsom Lake response to historical and potential future climate scenarios

Yao, Haiyuan; Georgakakos, Aris P.

doi:10.3133/ofr00336

Cited by 116 publications

(33 citation statements)

References 9 publications

(10 reference statements)

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“…Nevertheless, this was seen in a few cases and, more often, a trade-off between reliability and sharpness was highlighted. This is in accordance with other studies (Hamlet and Lettenmaier, 1999;Yao and Georgakakos, 2001). …”

Section: Resultssupporting

confidence: 82%

“…] that low skill in reproducing regional precipitation is an index of the utility of GCM information for systems acting as low-pass filters, such as the hydrological and reservoir systems are". They found that this conditioning was particularly efficient to forecast the 30-day low-inflow volumes to the Folsom lake, and that the GCM-conditioned ensemble outperformed ESP (Yao and Georgakakos, 2001).…”

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

“…However, in 6 months of the year, climatology was more reliable than the conditioned ensembles in terms of observed streamflow falling within the forecast range. Yao and Georgakakos (2001) compared the method proposed by Carpenter and Georgakakos (2001) with the ESP approach and with a conditioned forecast ensemble based on historical streamflows and the latest observed reservoir inflows. An in-depth evaluation of the latter showed not only a gain in sharpness but also a loss in reliability as compared to historical streamflow data.…”

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

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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: Resultssupporting

confidence: 82%

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

Section: Selecting Ensembles For Long-range Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Such model evaluation is simpler for regions such as California that have consistently long dry seasons that reduce interannual dependency. Finally, the evaluation of the reliability of the longer-term ensemble flow forecasts is paramount to their effective use for water resources management ͑e.g., Georgakakos et al 1998;Yao and Georgakakos 2001͒.…”

Section: Operational Forecast Procedures and Modelsmentioning

confidence: 99%

Evaluation of the National Weather Service Operational Hydrologic Model and Forecasts for the American River Basin

et al. 2006

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This paper evaluates the National Weather Service operational hydrologic model and operational flow forecasts for several subbasins of the American River. The evaluation includes: ͑1͒ the quality of the 6-h operational flow forecasts with up to 5 days lead time; ͑2͒ the hydrologic model ability to reproduce observed mean daily flows; and ͑3͒ the reliability of the ensemble streamflow predictions of the hydrologic model to reproduce extremes of the monthly volume of full natural flow to Folsom Lake. The results indicate that the model represents the observed flow record well for sites and/or flow ranges unaffected by upstream regulation. Real time operational forecast produced by a forecaster that considers model predictions have good skill out to 18 h with precipitation forecast contributing significantly to forecast uncertainty. Certain high-flow events with a spatially distributed mix of snow/rain over the basin may not be reproduced well by the basic spatially lumped structure of the operational snow-soil-channel model. It is suggested to incorporate upstream regulation rules into the operational models for better reproduction of observed medium and low flows. Routine evaluation based on a national archive of operational flow forecasts and observations is also recommended.

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Assessment of Folsom Lake response to historical and potential future climate scenarios

Cited by 116 publications

References 9 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

Evaluation of the National Weather Service Operational Hydrologic Model and Forecasts for the American River Basin

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