Enhancing the predictability of seasonal streamflow with a statistical-dynamical approach
journal contributionposted on 25.06.2018, 08:36 by Louise Slater, Gabriele Villarini
Seasonal streamflow forecasts facilitate water allocation, reservoir operation, flood risk management, and crop forecasting. They are generally computed by forcing hydrological models with outputs from general circulation models (GCMs) or using large-scale climate indices as predictors in statistical models. In contrast, hybrid statistical-dynamical forecasts (combining statistical methods with dynamical climate predictions) are still uncommon and their skill is largely unknown. Here, we conduct systematic forecasting of seasonal streamflow using eight GCMs from the North-American Multi-Model Ensemble, 0.5-9.5 months ahead, at 290 streamgauges in the U.S. Midwest. Probabilistic forecasts are developed for low to high streamflow using predictors that reflect climatic and anthropogenic influences. Results indicate that GCM forecasts of climate and antecedent climatic conditions enhance seasonal streamflow predictability; while land cover and population density predictors decrease biases or enhance skill in certain catchments. This paper paves the way for novel forecasting approaches using dynamical GCM predictions within statistical frameworks.
This study was supported in part by the Broad Agency Announcement (BAA) Program and the Engineer Research and Development Center (ERDC)–Cold Regions Research and Engineering Laboratory (CRREL) under Contract No. W913E5-16-C-0002, and by the National Science Foundation under CAREER Grant AGS-1349827.
- Geography and Environment