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.

Spatial and temporal characterisation of ground deformation recorded by geodetic techniques

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).