Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

Abstract

[1] A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two lowdimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast probabilities.Citation: Devineni, N., A. Sankarasubramanian, and S. Ghosh (2008), Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W09404,

Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall–runoff regime

“…This study quantified the additional skill that could be gained using precipitation forecasts from ECHAM4.5 forecasts over the climatological forcings. This study uses precipitation forecasts from one GCM; however, combining climate information from multiple models has been shown to result in improved streamflow forecasts (Devineni et al, 2008). The climatological forcings were run as ensemble and the mean of the streamflow ensemble was used to quantify the skill.…”

An intercomparison of approaches for improving operational seasonal streamflow forecasts

“…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.…”

Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments

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