Climate Change and Tourism in Tuscany, Italy. What if heat becomes unbearable?

This paper investigates the empirical magnitude of climate conditions on tourist flows in Tuscany, exploring the use of a fine spatial scale analysis. In fact, we explore the use of an 8-year panel dataset of Tuscany’s 254 municipalities, examining how tourist inflows respond to variation in local weather conditions. In particular, as the area enjoys a fairly mild Mediterranean climate, our analysis focused on temperature extremes at key times of the tourist season, i.e., on maximum summer temperature and minimum winter temperature. Separate analyses are conducted for domestic and international tourists, so as to test the differences in the preferences among these distinct groups (or types of demand). Estimation results show the impact of climate change on tourist flows appears to vary significantly among destinations depending on the kind of attractions they offer, and those areas that host the main artistic and historical sights, affecting predominantly the domestic rather than the international tourists.Domestic Tourists, International Tourists, Municipalities, Maximum And Minimum Daily Temperature, Dynamic Model, Temperature Demand Elasticity, GMM

Data Integration and Modelling for the Assessment of Future Climate Change Impacts on Natural Pasturelands of the Alps

Evidence shows that in the last century in the Alps area warming was roughly three times the global average and, according to future projections, this trend is expected to worsen in the next decades. Moreover, the species-rich permanent grasslands characterizing the marginal areas of the Alpine landscape are acknowledged as very sensitive and vulnerable ecosystems to climate change (IPCC 2007). So far several studies have investigated the climate effects only on specific Alpine grassland species at a very small scale, while a comprehensive assessment of the impact of climate change on Alpine mountain grasslands distribution and composition at a territorial scale is still lacking. Building on these premises, ground-breaking tools (classification models coupled with data integration by GIS techniques) were used to identify and environmentally characterize the main pastoral communities over the Alpine chain and to assess future climate change impacts on these fragile resources

Pastoral suitability driven by future climate change along the Apennines

This work aims at evaluating the impacts of climate change on pastoral resources located along the Apennines chain. To this end, random forest machine learning model was first calibrated for the present period and then applied to future conditions, as projected by HadCM3 general circulation model, in order to simulate possible spatial variation/shift of pastoral areas in two time slices (centred on 2050 and 2080) under A2 and B2 SRES scenarios. Pre-existent spatial database, namely Corine land cover map and WorldClim, were integrated and harmonised in a GIS environment in order to extract climate variables (mean seasonal precipitation, mean maximum temperature of the warmest month and minimum temperature of the coldest month) and response variables (presence/absence of pastures) to be used as model predictors. Random forest model resulted robust and coherent to simulate pastureland suitability under current climatology (classification accuracy error=19%). Accordingly, results indicated that increases in temperatures coupled with decreases in precipitation, as simulated by HadCM3 in the future, would have impacts of great concern on potential pasture distribution. In the specific, an overall decline of pasturelands suitability is predicted by the middle of the century in both A2 (–46%) and B2 (–41%) along the entire chain. However, despite alarming reductions in pastures suitability along the northern (–69% and –71% under A2 and B2 scenarios, respectively) and central Apennines (–90% under both scenarios) by the end of the century, expansions are predicted along the southern areas of the chain (+96% and +105% under A2 and B2 scenarios, respectively). This may be probably due to expansions in pastures dominated by xeric and thermophiles species, which will likely benefit from warmer and drier future conditions predicted in the southern zone of the chain by the HadCM3. Hence, the expected climate, coupled with an increasing abandonment of the traditional grazing practices, will likely threat grassland biodiversity as well as pastoral potential distribution currently dominating the Apennines chain

An Assessment of Weather-Related Risks in Europe: Maps of Flood and Drought Risks

This technical report describes the adopted methodology and the outputs produced during the first 18 months of life of the 'ADAM' project. ADAM (Adaptation and Mitigation Strategies: Supporting European Climate Policy) is an Integrated Project financed under thematic priority 'Global Change and Ecosystems' of the 6th framework programme (for further information, see www.adam.info) The task 'A2.1 - An assessment of weather-related risks in Europe' has the following main objective: 'Quantify and map weather-related extreme-event risks to public and private capital assets, human lives, and agriculture/forestry/tourism, and identify high-risk areas (hot spots) on which to focus more detailed analysis.' The key innovative aspects of the work herein presented are manifold: - the quantification of the probabilistic monetary impact of extreme events; - the combined use of modelling techniques and of observed data to supply the lack of information at the various scales of relevance of the study; - the estimation of uncertainty arising from limitations in data availability and modelling assumptions; - the geographical scale (continental) of the exercise. The key outputs of task A2.1 are digital maps of risks from natural extremes at European scale identifying monetary/economic losses. The maps are furnished as input to other tasks of package A2 for successive modelling exercises and analysis. As defined in the project work-plan, task A.21 has duration of 24 months. The 18-month deliverables are maps of flood and drought risks. The report focuses on inland river flood damage to properties and infrastructures and on climatic stresses (drought and heat waves) in agriculture. Population exposure has only been addressed in a partial study and it's therefore not included in the final monetary losses assessment. The work on floods has been carried out by the Institute for Environment and Sustainability of the Joint Research Centre; the work on droughts and heat waves by the Department of Agronomy and Land Management - University of Florence. The methodology is centred on the risk paradigm of the research community. The risk is defined as a product of hazard, exposure and vulnerability where: - Hazard is the threatening natural event including its probability/magnitude of occurrence; - Exposure is the values/humans that are present at the location related to a given event; - Vulnerability is the lack of resistance to damaging/destructive forces (damage function). This definition has been applied to extreme events such as floods and heat/water stresses, with the due adjustments required by data availability and specific modelling techniques.JRC.H.7-Land management and natural hazard

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data

Relations between micrometeorological conditions and plant physiology

The changing climate and environmental conditions play a key role on plant physiology. In this context, crop simulation models represent a useful tool for investigating the main plant processes and provide a reliable estimation of crop productivity and quality. However, the most common crop models showed many limitations, with particular concern on the effect of some meteorological variables on plant processes during sensitive stages of development. Improving models by implementing the effect of such variables on crop processes may help to improve the accuracy of models, thus their usefulness. Here we focus on the analysis of the effect of high and low temperatures during flowering in grapevine. To this, the fruit-set index, developed for taking into account for the effect of temperature on setting the number of berries per cluster and the fruit-set percentage, was applied in a preliminary explorative study to assess the impact of different conditions during flowering at European scales. The sensitivity of the index allowed to identify the differential impact of temperature around flowering in different environment and for different varieties. Once meteorological variables are available at field or sub-field scale, the index can be used to provide information about the spatial variability of crop growth, thus allowing to identify the most appropriate interventions to improve productivity

Air temperature-related human health outcomes: Current impact and estimations of future risks in Central Italy

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Evaluating the effects of environmental changes on the gross primary production of italian forests

A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes

Open field screening of the productive parameters, protein content, phenolic compounds, and antioxidant capacity of maize (Zea mays L.) in the marginal uplands of southern Madagascar

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