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2 Multimodel ensembles of streamflow forecasts: Role of predictor 3 state in developing optimal combinations
 

Summary: 2 Multimodel ensembles of streamflow forecasts: Role of predictor
3 state in developing optimal combinations
4 Naresh Devineni,1
A. Sankarasubramanian,1
and Sujit Ghosh2
5 Received 22 December 2006; revised 19 May 2008; accepted 6 June 2008; published XX Month 2008.
6 [1] A new approach for developing multimodel streamflow forecasts is presented. The
7 methodology combines streamflow forecasts from individual models by evaluating
8 their skill, represented by rank probability score (RPS), contingent on the predictor state.
9 Using average RPS estimated over the chosen neighbors in the predictor state space,
10 the methodology assigns higher weights for a model that has better predictability under
11 similar predictor conditions. We assess the performance of the proposed algorithm by
12 developing multimodel streamflow forecasts for Falls Lake Reservoir in Neuse River
13 Basin, North Carolina (NC), through combining streamflow forecasts developed from two
14 low-dimensional statistical models that use sea-surface temperature conditions as
15 underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of
16 seven multimodels that include existing multimodel combination techniques such as
17 combining based on long-term predictability of individual models and by simple pooling
18 of ensembles. Detailed nonparametric hypothesis tests comparing the performance of
19 seven multimodels with two individual models show that the reduced RPS from

  

Source: Arumugam, Sankar - Department of Civil, Construction, and Environmental Engineering, North Carolina State University

 

Collections: Environmental Sciences and Ecology; Engineering