Translating seasonal climate forecasts into water balance forecasts for decision making

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way.