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

• Autor(a) principal: Borne, Maurus
• Data de Publicação: 2022
• Outros Autores: Lorenz, Christof, Portele, Tanja C., Martins, Eduardo Sávio Passos Rodrigues, Vasconcelos Júnior, Francisco das Chagas, Kunstmann, Harald
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/69372
• topic: Hydro-Meteorology; Seasonal forecast; River basin management; Data-Assimilation

Study region: The São Francisco River Basin (SFRB) in Brazil Study focus: 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 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. The performance of the assimilation framework was assessed using different ensemble skill scores. New hydrological insights for the region: Our results show that the quality of runoff predictions are closely linked to the performance of the rainfall seasonal predictions and allows skillful predictions up to two months ahead in most sub-basins. The anthropogenic conditions such as in the Western Bahia state, however, must be taken under consideration, since non-stationary runoff time-series have poorer skill as such unnatural variations can not be captured by long-term covariances. In sub-basins which are dominated by little anthropogenic influence, the presented framework provides a promising and easily transferable approach for skillful operational seasonal runoff predictions on sub-basin scale.