Ensemble kalman-filter-based seasonal runoff predictions for the rio são francisco river basin

Abstract: In semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability and increase the recurrence and intensity of extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecasts to support the seasonal management of water resources, reservoir operations and agriculture. In this context, an Ensemble Kalman Filter (EnKF) framework is applied to predict sub-basin-scale runoff employing global freely available datasets of reanalysis precipitation (ERA5-Land) as well as Bias-Corrected and Spatially Disaggregated seasonal forecasts (SEAS5-BCSD). Runoff is estimated using least squares predictions, exploiting the covariance structures between runoff and precipitation. This repository contains the runoff observations, the final EnKF-based runoff predictions, reference precipitation from ERA5-Land, bias-corrected and spatially disaggregated seasonal precipitation forecats from SEAS5-BCSD as well as shapefiles delineating the sub-basin-boundaries within the Rio São Francisco River Basin.