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In the present work, the role played by vegetation parameters, necessary to the hydrological distributed modeling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the vegetation status is characterised through a data-set of multi-temporal NDVI images. The model adopted uses one layer of vegetation whose status is defined by the Leaf Area Index (LAI), which is often obtained from NDVI images. The inherent problem is that the vegetation heterogeneity - including soil disturbances - has a large influence on the spectral bands and so the relation between LAI and NDVI is not unambiguous. We present a rationale for the basin scale calibration of a non-linear NDVI-LAI regression, based on the comparison between NDVI values and literature LAI estimations of the vegetation cover in recognized landscape elements of the study catchment. Adopting a process-based model (DREAM) with a distributed parameterisation, the influence of different NDVI-LAI regression models on main features of water balance predictions is investigated. The results show a significant sensitivity of the hydrological losses and soil water regime to the alternative LAI estimations. These crucially affects the model performances especially in lowflows simulation and in the identification of the intermittent regime

Influences of Leaf Area Index estimations on water balance modeling in a Mediterranean semi-arid basin