• Energy Research

Insurance programmes have been indicated as a tool to reduce the economic risk associated with climate change, and crop growth simulation models can be used effectively to assess future trends in crop insurance payouts. This paper assesses the economic role of increasing weather extremes under future climate change on the expected insurance payouts for durum wheat (Triticum turgidum L. spp. durum) over the Mediterranean basin, focusing attention on the effects of heat stresses (HSs). A crop growth simulation, Sirius Quality version 2 (SQ2), calibrated for three varieties (long, medium and short growth cycle) was applied on seven sites under present (1975-1990) and future climate conditions (2030-2050) obtained from five regional circulation models under SRES scenario A1b. The intensity of HSs at anthesis was included as reducing factor of yield originally simulated by SQ2 calculated according to a specific empirical model. Simulated yields were then fitted to the most appropriate distribution, which was used to calculate the expected payouts according to the probability of yields being below a guaranteed level. We found that the simulated crop yields were, in general, negatively skewed and that Weibull probability density function (PDF), admitting negative skewing, provided the best performances in their fitting. The simulation of HSs modified the original shape of the Weibull PDF by increasing the skewness of the distribution. The results of the insurance model indicated that the modification of crop PDFs induced by HSs led to a general increase in payouts with respect to unstressed conditions, with a marked difference between present (+11 %, on average for the selected sites) and future periods (+25 %). When compared to the present, a general decrease in payouts (-1.1 %) was observed when HSs were not included in the simulations. Conversely, HSs impact resulted in a general increase in payouts (+10.3 %) where the highest increase was detected for the long growth cycle variety (+16.6 %) and the lowest for that with short growth cycle (-1.6 %). These results emphasize the importance of the appropriate characterization of crop yield distribution, the economic implications of HSs in a risk management context and a possible strategy to cope with climate change and variability.

The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed.

This research simulates the impact of climate change on the distribution of the most important European wine regions using a comprehensive suite of spatially informative layers, including bioclimatic indices and water deficit, as predictor variables. More specifically, a machine learning approach (Random Forest, RF) was first calibrated for the present period and applied to future climate conditions as simulated by HadCM3 General Circulation Model (GCM) to predict the possible spatial expansion and/or shift in potential grapevine cultivated area in 2020 and 2050 under A2 and B2 SRES scenarios. Projected changes in climate depicted by the GCM and SRES scenarios results in a progressive warming in all bioclimatic indices as well as increasing water deficit over the European domain, altering the climatic profile of each of the grapevine cultivated areas. The two main responses to these warmer and drier conditions are 1) progressive shifts of existing grapevine cultivated area to the north-northwest of their original ranges, and 2) expansion or contraction of the wine regions due to changes in within region suitability for grapevine cultivation. Wine regions with climatic conditions from the Mediterranean basin today (e.g., the Languedoc, Provence, Ctes Rhne M,ridionales, etc.) were shown to potentially shift the most over time. Overall the results show the potential for a dramatic change in the landscape for winegrape production in Europe due to changes in climate.

This paper focuses on the effectiveness of passive adaptation measures against climate change, in the medium (2036–2065) and long term (2066–2095), for three case studies located in Florence (central Italy). In order to identify and highlight the passive measures which can provide comfort conditions with the lowest net heating and cooling energy demand, the input assumptions consider a constant thermal comfort level and don’t take into account either the effect of HVAC system’s performance and the degradation of the materials by ageing. The study results show that, in case of poorly insulated buildings, on the medium term, the reduction of energy needed for heating could be bigger than the increase for cooling, resulting in a total annual net energy need decrease, while in the long term the opposite happens. Conversely, considering a high level of thermal insulation, due to the large increase in cooling demand, the total annual energy need rises in both periods. Furthermore, attention should be paid to internal loads and solar gains that, due to the projected climate change, could become main contributors to the energy balance. In general, since the magnitude of energy need increase for cooling and decrease for heating is very significant on the long term, and varies in function of the type of building, the passive measure adopted and the level of thermal insulation, the research results lead to pay close attention to different types of energy refurbishment interventions, that should be selected in function of their effectiveness over time.

The association between air temperature and human health is described in detail in a large amount of literature. However, scientific publications estimating how climate change will affect the population's health are much less extensive. In this study current evaluations and future predictions of the impact of temperature on human health in different geographical areas have been carried out. Non-accidental mortality and hospitalizations, and daily average air temperatures have been obtained for the 1999-2008 period for the ten main cities in Tuscany (Central Italy). High-resolution city-specific climatologic A1B scenarios centered on 2020 and 2040 have been assessed. Generalized additive and distributed lag models have been used to identify the relationships between temperature and health outcomes stratified by age: general adults (<65), elderly (aged 65-74) and very elderly (≥75). The cumulative impact (over a lag-period of 30 days) of the effects of cold and especially heat, was mainly significant for mortality in the very elderly, with a higher impact on coastal plain than inland cities: 1 °C decrease/increase in temperature below/above the threshold was associated with a 2.27% (95% CI: 0.17-4.93) and 15.97% (95% CI: 7.43-24.51) change in mortality respectively in the coastal plain cities. A slight unexpected increase in short-term cold-related mortality in the very elderly, with respect to the baseline period, is predicted for the following years in half of the cities considered. Most cities also showed an extensive predicted increase in short-term heat-related mortality and a general increase in the annual temperature-related elderly mortality rate. These findings should encourage efforts to implement adaptation actions conducive to policy-making decisions, especially for planning short- and long-term health intervention strategies and mitigation aimed at preventing and minimizing the consequences of climate change on human health.

AbstractUnderstanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.