Use of seasonal streamflow forecasts in water resources management

Chiew, Fhs; Zhou, Senlin; McMahon, Thomas A.

doi:10.1016/s0022-1694(02)00292-5

Cited by 113 publications

(80 citation statements)

References 4 publications

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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]. The skill of seasonal forecasts is derived from knowledge of the state of the climate system and of catchment conditions before the forecast period.…”

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

137

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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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

137

107

View full text Add to dashboard Cite

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“…The teleconnection between southeast Australian rainfall and ENSO has been extensively described (e.g. Schepen et al, 2012a;Chiew et al, 1998;Wang et al, 2009) including, as already noted, the link between flooding and ENSO (Kiem et al, 2003). We use five indices to describe ENSO: NINO3, NINO3.4, NINO4, the ENSO Modoki index (EMI) (Ashok et al, 2007) and the southern oscillation index (SOI) (Troup, 1965).…”

Section: Predictorsmentioning

confidence: 99%

The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

Bennett

Wang

Pokhrel

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km 2 ) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean-atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25 %) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

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“…In the Iberian Peninsula, SST fields of Atlantic Ocean were demonstrated to be useful in seasonal streamflow forecast (Gamiz-Fortis et al, 2008). In Australia, information based on ENSO streamflow teleconnection and serial correlation in streamflow was demonstrated to help irrigators to take more-informed risk-based management decisions (Chiew et al, 2003;Wang et al, 2010). The North Atlantic Oscillation (NAO) or the Southern Oscillation Index (SOI) was found to provide the magnitude of seasonal streamflow in Iran (Araghinejad et al, 2006).…”

Section: Pal Et Al: Predictability Of Western Himalayan River Flowmentioning

confidence: 99%

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

Pal

Lall

Robertson

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an understudied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

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Use of seasonal streamflow forecasts in water resources management

Cited by 113 publications

References 4 publications

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

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

The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

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