A review on climate-model-based seasonal hydrologic forecasting: physical understanding and system development

Climate-model-based seasonal hydrologic forecasting (CM-SHF) is an emerging area in recent decade because of the development of coupled atmosphere-ocean-land general circulation models (CGCMs) and land surface hydrologic models, and increasing needs for transferring the advances in climate research into hydrologic applications within the framework of climate services. In order to forecast terrestrial hydrology from monthly to seasonal time scales, a CM-SHF system should take advantage of important information from initial land surface conditions (ICs) as well as skillful seasonal predictions of atmospheric boundary conditions that mostly rely on the predictability of large-scale climate precursors such as the El Nino Southern Oscillation (ENSO). The progresses in the understanding of seasonal hydrologic predictability in terms of ICs and climate precursors are reviewed, and future emphases are discussed. Both the achievements and challenges of the CM-SHF system development, including multimodel ensemble prediction, seamless hydrologic forecasting, dynamical downscaling, hydrologic post-processing, and seasonal forecasting of hydrologic extremes with the hyper-resolution modeling framework that is able to address both the climate change and water resources management impacts on terrestrial hydrology, are presented. Regardless of great strides in CM-SHF, a grand challenge is the effective dissemination of the information provided by the seasonal hydrologic forecasting system to the decision-makers, which cannot be resolved without cross-disciplinary dialog and collaboration. (C) 2015 The Authors. WIREs Water published by Wiley Periodicals, Inc.