Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets

The São Francisco River Basin (SFRB) in Brazil In semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability increase the recurrence intensity extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecasts support management resources, reservoir operations agriculture. this context, an Ensemble Kalman Filter framework applied predict sub-basin-scale runoff employing global freely available datasets reanalysis precipitation (ERA5-Land) well bias-corrected spatially disaggregated (SEAS5-BCSD). Runoff estimated using least squares predictions, exploiting covariance structures between precipitation. performance assimilation was assessed different ensemble skill scores. Our results show that quality predictions are closely linked allows skillful up two months ahead most sub-basins. anthropogenic conditions Western Bahia state, however, must be taken under consideration, since non-stationary time-series have poorer unnatural variations can not captured by covariances. sub-basins which dominated little influence, presented provides a promising easily transferable approach for operational on sub-basin scale.