Enhancing the Predictability of Seasonal Streamflow With a Statistical-Dynamical Approach

Louise J. Slater, Gabriele Villarini

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

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 stream gauges 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.

Original languageEnglish (US)
Pages (from-to)6504-6513
Number of pages10
JournalGeophysical Research Letters
Volume45
Issue number13
DOIs
StatePublished - Jul 16 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geophysics
  • General Earth and Planetary Sciences

Keywords

  • NMME
  • forecast
  • land cover
  • precipitation
  • streamflow
  • temperature

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