A Robust Multimodel Framework for Ensemble Seasonal Hydroclimatic Forecasts

Monday, 15 December 2014
Pablo A Mendoza, University of Colorado at Boulder, Boulder, CO, United States, Balaji Rajagopalan, Univ Colorado, Civil, Environmental, and Architectural Engineering and Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Martyn P Clark, NCAR, Boulder, CO, United States, Gonzalo Cortés, University of California Los Angeles, Los Angeles, CA, United States and James P McPhee, University of Chile, Santiago, Chile
We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.