PREVISIONI A SCALA REGIONALE DEL LIVELLO PIEZOMETRICO DELLA FALDA IPODERMICA. Ottimizzazione della rete di monitoraggio nel tempo e nello spazio

In this study, data from a monitoring network in a relevant agricultural area of Northern Italy (12,000 Km2) were used to develop a data driven model for predicting water table depth in space and time from meteorological data and longterm water table characteristics and to optimize sampling density in space and time. Evolutionary Polynomial Regressions (EPR) were used to calibrate a predictive tool. The model was applied to the whole study area, using the geostatistical estimates of the average water table depth as input. The impact of the degradation of data input in the temporal and spatial domain was then assessed. Reducing the average sampling frequency from 1.5 per month to 1 every 40 days did not impact significantly on the prediction capability of the proposed model. Reducing the sampling frequency to 1 every 4 months resulted in a loss of accuracy <3%, while removing more than half locations from the network, resulted in a global loss of information <15%.