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

Area of Science:

• Hydrology
• Climate Science
• Data Science

Background:

• Accurate streamflow forecasting is crucial for water resource management.
• Existing multimodel ensemble techniques have limitations in optimizing forecast combination.

Purpose of the Study:

• To develop and evaluate a novel approach for creating multimodel streamflow forecasts.
• To improve forecast skill by adaptively weighting individual models based on predictor states.

Main Methods:

• A new weighting scheme using rank probability score (RPS) contingent on predictor states.
• Combining forecasts from statistical models using sea-surface temperature as predictors.
• Comparison of seven multimodel techniques against individual models and simple pooling.

Main Results:

• The proposed algorithm significantly reduces RPS compared to individual models and existing multimodel techniques.
• Incorporating climatological ensembles further enhances multimodel performance.
• The technique improves forecast reliability by reducing Brier scores and false alarms.

Conclusions:

• The proposed state-dependent weighting method offers a statistically significant improvement in streamflow forecasting.
• Adaptive model weighting is more effective than static or simple pooling methods.
• While improving reliability, ensemble additions may slightly reduce forecast resolution in specific scenarios.