Persistent neural calibration for discharges modelling in drought-stressed catchments

Abstract

Cross-sector coordination between all water uses and the environmental flows is an essential target for achieving sustainable management in any hydrographic region. In this framework, a novel neural approach was developed and implemented, by the software ANNPI 1.0, to characterise and infer of the discharges regime in a specific basin using only a few attributes as independent variables. The calibration procedure is controlled by the Persistence Index (PI), which is function of a determined estimation lead-time, to facilitate the dynamic character of these simulations. A model validation was carried out in the Lower Guadiana Transboundary Basin, in the Southwest Iberian Peninsula, characterised by moderate and severe drought cyclical events. The best neural approaches included as input variable, between others, the Standardized Precipitation Index at a twelve-month scale SPI(12) that is indicator of hydrological drought, obtaining results statistically very good with determination coefficients higher to 0.77, Nash-Sutcliffe Efficiency coefficients higher to 0.75, Kling-Gupta Efficiency coefficients higher to 0.87 and Persistence Indexes higher to 0.60 in three of the four reservoirs analysed. These accuracy measures showed the ability of the software ANNPI 1.0 to reduce the naïve effect in the forecasting of streamflows time series and could therefore facilitate the development of decision-support systems to make reliable reservoir water balance simulations which will allow to assess future water availability to ensure the main ecosystem services.