A Statistical Modelling Approach to Site Suitability of Small Agricultural Reservoirs

Agricultural production increasingly relies on irrigation to withstand droughts, precipitation variability or support agricultural intensification. Small Agricultural Reservoirs (SmAR) can contribute to sustainable agricultural water management by providing additional water without increasing pressure on surface and groundwater resources. The construction of new SmAR is usually subject to a phase of suitability analysis, which helps discern suitable places within a large area, before exploring the potential locations with major details. This task is traditionally performed using deductive approaches relying on multi-criteria analysis (MCDA), which are based on relevant macro criteria for the location of SmAR, often supported by hydrological modelling. In this work, we present an inductive approach based on data-driven statistical modelling based on a large database of existing SmAR locations. We compare this empirical approach with the conventional MCDA to show the potential advantages of data-driven suitability analysis within a case application in the Italian region of Tuscany. Our results can directly support high level suitability in Tuscany, while the proposed approach can be further extended and applied in different contexts, scales and applications.