Probabilistic assessments of climate change impacts on durum wheat in the Mediterranean region

Recently, the availability of multi-model ensemble prediction methods has permitted a shift from a scenario-based approach to a risk-based approach in assessing the effects of climate change. This provides more useful information to decision-makers who need probability estimates to assess the seriousness of the projected impacts. <br><br> In this study, a probabilistic framework for evaluating the risk of durum wheat yield shortfall over the Mediterranean Basin has been exploited. An artificial neural network, trained to emulate the outputs of a process-based crop growth model, has been adopted to create yield response surfaces which are then overlaid with probabilistic projections of future temperature and precipitation changes in order to estimate probabilistic projections of future yields. The risk is calculated as the relative frequency of projected yields below a selected threshold. <br><br> In contrast to previous studies, which suggest that the beneficial effects of elevated atmospheric CO<sub>2</sub> concentration over the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying, the results of this study are of greater concern

Modelling climate change impacts on crop production for food security

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A model-based assessment of adaptation options for Chianti wine production in Tuscany (Italy) underclimate change

This paper covers a comprehensive economic analysis of climate change adaptation options for a specific wine producing region, namely Tuscany. As temperature increases under climate change, rainfall patterns will be different, and Chianti wine production in Tuscany therefore needs to adapt in the near future. We address the adaptation challenges and identify grape yield and quality loss as the main impact of climate change on wine production. Relocation of vineyards uphill and introducing drought-resistant varieties are considered as adaptation measures. We appraise these adaptation measures using an optimization framework, where regional wine producers maximize income subject to economic constraints including the climate change impacts on wine productivity and quality. Our simulation shows quantitatively to what extent a higher degree of climate change impact demands a higher degree of adaptation. We find that a combination of the two measures provides a better strategy because it leads to higher economic efficiency. However, uncertainty regarding the efficiency of the new variety discourages the use of this new drought-resistant variety, whereas a higher efficiency would make this choice more favourable. Sensitivity analysis for time horizon and discount rate confirms the theory of investment under uncertainty, showing a shorter time horizon (or more frequent investment) provides the possibility to postpone the decision to implement adaptation measures due to the value of flexibility, while a higher discount rate leads to a later adaptation decision, because uncertainty creates a value of waiting for new information

On-site earthquake early warning: a partially non-ergodic perspective from the site effects point of view

We introduce in the on-site earthquake early warning (EEW) a partially non-ergodic perspective from the site effects point of view. We consider the on-site EEW approach where the peak ground velocity (PGV) for S waves is predicted from an early estimate, over the P waves, of either the peak-displacement (PD) or cumulative squared velocity (IV2). The empirical PD-PGV and IV2-PGV relationships are developed by applying a mixed-effect regression where the site-specific modifications of ground shaking are treated as random effects. We considered a large data set composed of almost 31 000 selected recordings in central Italy, a region struck by four earthquakes with magnitude between 6 and 6.5 since the 2009 L’Aquila earthquake. We split the data set into three subsets used for calibrating and validating the on-site EEW models, and for exemplifying their application to stations installed after the calibration phase. We show that the partially non-ergodic models improve the accuracy of the PGV predictions with respect to ergodic models derived for other regions of the world. Moreover, considering PD and accounting for site effects, we reduce the (apparent) aleatory variability of the logarithm of PGV from 0.31 to 0.36, typical values for ergodic on-site EEW models, to about 0.25. Interestingly, a lower variability of 0.15 is obtained by considering IV2 as proxy, which suggests further consideration of this parameter for the design of on-site EEW systems. Since being site-specific is an inherent characteristic of on-site EEW applications, the improved accuracy and precision of the PGV predicted for a target protection translate in a better customization of the alert protocols for automatic actions