Irrigazione e cambiamenti climatici

The aim of this PhD thesis, developed in the framework of the Italian Agroscenari research project, is to compare current irrigation volumes in two study area in Emilia-Romagna with the likely irrigation under climate change conditions. This comparison was carried out between the reference period 1961-1990, as defined by WMO, and the 2021-2050 period. For this period, multi-model climatic projections on the two study areas were available. So, the climatic projections were analyzed in term of their impact on irrigation demand and adaptation strategies for fruit and horticultural crops in the study area of Faenza, with a detailed analysis for kiwifruit vine, and for horticultural crops in Piacenza plan, focusing on the irrigation water needs of tomato. We produced downscaled climatic projections (based on A1B Ipcc emission scenario) for the two study areas. The climate change impacts for the period 2021-2050 on crop irrigation water needs and other agrometeorological index were assessed by means of the Criteria water balance model, in the two versions available, Criteria BdP (local) and Geo (spatial) with different levels of detail. We found in general for both the areas an irrigation demand increase of about +10% comparing the 2021-2050 period with the reference years 1961-1990, but no substantial differences with more recent years (1991-2008), mainly due to a projected increase in spring precipitation compensating the projected higher summer temperature and evapotranspiration. As a consequence, it is not forecasted a dramatic increase in the irrigation volumes with respect to the current volumes.

Development of SuperEnsemble Techniques for the Mediterranean Ocean Forecasting System

This research activity studied how the uncertainties are concerned and interrelated through the multi-model approach, since it seems to be the bigger challenge of ocean and weather forecasting. Moreover, we tried to reduce model error throughout the superensemble approach. In order to provide this aim, we created different dataset and by means of proper algorithms we obtained the superensamble estimate. We studied the sensitivity of this algorithm in function of its characteristics parameters. Clearly, it is not possible to evaluate a reasonable estimation of the error neglecting the importance of the grid size of ocean model, for the large amount of all the sub grid-phenomena embedded in space discretizations that can be only roughly parametrized instead of an explicit evaluation. For this reason we also developed a high resolution model, in order to calculate for the first time the impact of grid resolution on model error.