Season‐based rainfall–runoff modelling using the probability‐distributed model (PDM) for large basins in southeastern Brazil.

Abstract

Southeastern Brazil is characterized by seasonal rainfall variability. This can have a great social, economic, and environmental impact due to both excessive and deficient water availability. During 2014 and 2015, the region experienced one of the most severe droughts since 1960. The resulting water crisis has seriously affected water supply to the metropolitan region of São Paulo and hydroelectric power generation throughout the entire country. This research considered the upstream basins of the southeastern Brazilian reservoirs Cantareira (2,279 km2; water supply) and Emborcação (29,076 km2), Três Marias (51,576 km2), Furnas (52,197 km2), and Mascarenhas (71,649 km2; hydropower) for hydrological modelling. It made the first attempt at configuring a season-based probability-distributed model (PDM-CEMADEN) for simulating different hydrological processes during wet and dry seasons. The model successfully reproduced the intra-annual and interannual variability of the upstream inflows during 1985–2015. The performance of the model was very satisfactory not only during the wet, dry, and transitional seasons separately but also during the whole period. The best performance was obtained for the upstream basin of Furnas, as it had the highest quality daily precipitation and potential evapotranspiration data. The Nash–Sutcliffe efficiency and logarithmic Nash–Sutcliffe efficiency were 0.92 and 0.93 for the calibration period 1984–2001, 0.87 and 0.88 for the validation period 2001–2010, and 0.93 and 0.90 for the validation period 2010–2015, respectively. Results indicated that during the wet season, the upstream basins have a larger capacity and variation of soil water storage, a larger soil water conductivity, and quicker surface water flow than during the dry season. The added complexity of configuring a season-based PDM-CEMADEN relative to the traditional model is well justified by its capacity to better reproduce initial conditions for hydrological forecasting and prediction. The PDM-CEMADEN is a simple, efficient, and easy-to-use model, and it will facilitate early decision making and implement adaptation measures relating to disaster prevention for reservoirs with large-sized upstream basins.