A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin

Regonda, Satish Kumar; Rajagopalan, Balaji; Clark, Martyn P.; Zagona, Edith

doi:10.1029/2005wr004653

Cited by 120 publications

(131 citation statements)

References 75 publications

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“…There are several measures and ways to evaluate a forecast, but given the short dataset single point cross-validated R 2 is acceptable (e.g., Grantz et al, 2005;Regonda et al, 2006). In this analysis, the observations for a year are dropped and the model is fitted using the remaining observations.…”

Section: Forecast Modelsmentioning

confidence: 99%

Seasonal forecasting of East Asian summer monsoon based on oceanic heat sources

Lee

Chase

Rajagopalan

2007

Intl Journal of Climatology

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We use the upper-level divergence zone at 150 hPa to define the areas of study for the East Asian summer monsoon (EASM) and to show the advances and retreats of the EASM. We find that the EASM can be subdivided into a northern and southern component with distinctly different driving mechanisms. The northern EASM (NEASM) is affected by heat sources in the tropical oceans related to El Niño events while the southern EASM (SEASM) is affected by the subtropical oceans related to a North Pacific sea surface temperature (SST) dipole mode. A stronger NEASM is related to above-normal western North Pacific (WNP) anticyclonic anomalies, while a stronger SEASM is related to below-normal WNP anticyclonic anomalies. These WNP anticyclonic anomalies are connected to SST anomalies in the tropical and subtropical Pacific during the pre-monsoon season (December∼May). We also find that NEASM precipitation can be predicted from regional oceanic heat sources, i.e. SST and ocean heat content, in the tropical Pacific and Indian Oceans during the pre-monsoon season using a linear regression model. SEASM precipitation can be predicted from pre-monsoon SST in the eastern North Pacific. The NEASM forecast model is more skillful than that for the SEASM.

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Section: Forecast Modelsmentioning

confidence: 99%

Seasonal forecasting of East Asian summer monsoon based on oceanic heat sources

Lee

Chase

Rajagopalan

2007

Intl Journal of Climatology

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“…Several statistical approaches can be found in the literature, encompassing different degrees of complexity (e.g., Garen, 1992;Piechota et al, 1998;Grantz et al, 2005;Tootle et al, 2007;Pagano et al, 2009;Moradkhani and Meier, 2010). Other studies have tested multi-model combination techniques for purely statistical seasonal forecasts, using objective performance criteria (e.g., Regonda et al, 2006), both performance and predictor state information (Devineni et al, 2008), and Bayesian model averaging (e.g., Mendoza et al, 2014), among others.…”

Section: Introductionmentioning

confidence: 99%

An intercomparison of approaches for improving operational seasonal streamflow forecasts

Mendoza

Wood

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches -statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) -and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction -HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

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“…Therefore, the utility of the bestglm technique in the multiple linear regression scenario based on selective information criteria was skillfully used to forecast spring seasonal streamflow at the upstream of Bhakra Dam over the Satluj River at different lead times. We compared the forecasts to PCR and PLSR, which were more widely used for the central and southern Asian rivers (Schar et al, 2004;Tippet et al, 2004;Barlow and Tippet, 2008) and also in the US (Regonda et al, 2006). We find that overall, the proposed method is equally skillful to (and actually more skillful than) existing operational models while tending to better predict seasonal streamflow 1-4 months in advance.…”

Section: Summary and Discussionmentioning

confidence: 91%

“…The K-NN local polynomials and the local weighted polynomial (LOC-FIT) approaches are very similar. Some of these methods were also used in conjunction with the multimodel ensemble forecasting framework that helps in determining the probability of exceedances of various thresholds useful for the water resource management (Regonda et al, 2006;Bracken et al, 2010). Existing studies also show that multimodel ensemble forecasts tend to perform much better than a singlemodel forecast, particularly in short-term and seasonal climate forecast (Krishnamurti et al, 1999(Krishnamurti et al, , 2000Rajagopalan et al, 2002;Hagedorn et al, 2005;Wood and Lettenmaier, 2006;Singla et al, 2012).…”

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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A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin

Cited by 120 publications

References 75 publications

Seasonal forecasting of East Asian summer monsoon based on oceanic heat sources

Seasonal forecasting of East Asian summer monsoon based on oceanic heat sources

An intercomparison of approaches for improving operational seasonal streamflow forecasts

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

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