In this study, output of the Hadley Centre Regional Circulation Model (RCM) (HadRM3P, 0.44°× 0.44°resolution) was used as input to the Canadian Forest Fire Weather Index (FWI) for the present and 2 future IPCC climate scenarios (Special Report on Emissions Scenarios [SRES], A2 and B2 scenarios). The aim was to investigate the effects of climate change on fire risk (number of days with fire risk, length of fire risk season, etc.) for the EU Mediterranean countries. Results indicated a general increase in fire risk in both future scenarios over the whole study area. The increase in fire risk was mainly due to 3 components: (1) increase in the number of years with fire risk; (2) increase in the length of the season with fire risk; (3) increase of extreme events (e.g. total number of days with FWI > 45 and episodes with FWI > 45 for 7 consecutive days) during the fire season. As expected, A2 scenario showed a greater increase in risk than B2 scenario. These general increases in fire risk may have a very strong impact in areas where forest land cover is high (e.g. the Alps region in Italy, the Pyrenees in Spain and mountains of the Balkan region).

This paper provides an overview of the aims, objectives, research activities undertaken, and a selection of results generated in the European Commission-funded project entitled "Modelling the Impact of Climate Extremes" (MICE) -a pan-European end-to-end assessment, from climate model to impact model, of the potential impacts of climate change on a range of economic sectors important to the region. MICE focussed on changes in temperature, precipitation and wind extremes. The research programme had three main themes -the evaluation of climate model performance, an assessment of the potential future changes in the occurrence of extremes, and an examination of the impacts of changes in extremes on six activity sectors using a blend of quantitative modelling and expert judgement techniques. MICE culminated in a large stakeholder-orientated workshop, the aim of which was not only to disseminate project results but also to develop new stakeholder networks, whose expertise can be drawn on in future projects such as ENSEMBLES. MICE is part of a cluster of three projects, all related to European climate change and its impacts. The other projects in the cluster are PRUDENCE (Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) and STARDEX (Statistical and Regional Dynamical Downscaling of Extremes for European Regions).

Most of the recent studies and projections of precipitation patterns, based on records observed in the past and climate change scenarios for the Mediterranean basin, suggest a relatively slow decrease in rainfall amounts over the years but an increase in the frequency of extreme precipitation events. These are key factors in desertification processes and these will cause social and environmental impacts in the short term, mainly because changes in heavy rainfall events may have severe implications and impacts on soil erosion, resulting in increased risk of soil degradation.The main objective of the present work is to evaluate the spatial-temporal dynamics of extreme precipitation events in southern Portugal, using a direct sequential simulation algorithm (DSS models) in order to assess the relationships between spatial and temporal extreme rainfall patterns. Local probability density functions (pdfs) and spatial uncertainty are evaluated by a set of equiprobable simulated images of the chosen extreme precipitation indices.The used dataset in this work comprises a set of 105 station records of observed daily precipitation within the period 1961-2000. Two indices of extreme precipitation were selected: one representing the frequency of extremely heavy precipitation events (R30) and another characterizing the occurrence of dry events (RL10), both obtained from observed daily precipitation series.Results show that the spatial continuity of extreme precipitation events has increased in the last 40 years in southern Portugal. It also demonstrates a decrease in spatial variability, implying that extreme precipitation events tend to be more spatially homogeneous, which may have a severe impact on water resources, agriculture and soil erosion, particularly when associated with desertification risks.

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Abstract. The topographic characteristics and spatial climatic diversity are significant in the South of continental Portugal where the rainfall regime is typically Mediterranean. Direct sequential cosimulation is proposed for mapping an extreme precipitation index in southern Portugal using elevation as auxiliary information. The analysed index (R5D) can be considered a flood indicator because it provides a measure of medium-term precipitation total. The methodology accounts for local data variability and incorporates space-time models that allow capturing long-term trends of extreme precipitation, and local changes in the relationship between elevation and extreme precipitation through time. Annual gridded datasets of the flood indicator are produced from 1940 to 1999 on 800 m×800 m grids by using the space-time relationship between elevation and the index. Uncertainty evaluations of the proposed scenarios are also produced for each year. The results indicate that the relationship between elevation and extreme precipitation varies locally and has decreased through time over the study region. In wetter years the flood indicator exhibits the highest values in mountainous regions of the South, while in drier years the spatial pattern of extreme precipitation has much less variability over the study region. The uncertainty of extreme precipitation estimates also varies in time and space, and in earlier decades is strongly dependent on the density of the monitoring stations network. The produced maps will be useful in regional and local studies related to climate change, desertification, land and water resources management, hydrological modelling, and flood mitigation planning.

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Abstract. Most of the actual studies and previews of future rainfall patterns, based on past observed records for Mediterranean climate areas, focus on the decline of the rainfall amounts over the years, and also on the increase of the frequency of heavy/intense rainfall events particularly in the winter season. These changes in heavy rainfall events may have severe implications and impacts on soil erosion resulting in increased soil degradation risks.The objective of the present work is to evaluate the spatial distribution of extreme precipitation events in Southern Portugal, using a geostatistical approach to assess the relationships between spatial and temporal extreme rainfall patterns. The used dataset comprises a set of 105 stations' records of daily precipitation within the period 1960-1999. Two indices of extreme precipitation were selected to be computed based on the daily precipitation observation series: one representing the frequency of extremely heavy precipitation events (R30) and another one characterizing flood events (R5D).The space-time patterns of the precipitation indices were evaluated and simulated using a geostatistical approach. Despite no significant temporal trends were detected on the calculated indices series, the space-time decadal patterns are becoming more continuous in the last two decades than the previous ones.

Despite reductions in atmospheric sulfur (S) concentrations due to abatement policies in some countries, modeling the dispersion of this pollutant and disentangling anthropogenic sources from natural ones is still of great concern. Lichens have been used as biomonitors of the impacts of S for over 40 years, but their potential as source-tracers of specific sources, including natural ones, remains unexplored. In fact, few attempts have been made to try to distinguish and spatially model different sources of S using lichens. We have measured S concentrations and isotopic values in lichens within an industrial coastal region where different sources of S, natural and anthropogenic, interplay. We detected a prevailing influence of natural sea-originated S that mixed with anthropogenic sources of S. We were then able to disentangle the sources of S, by removing the ocean influence on S isotopic values, enabling us to model the impact of different anthropogenic sources on S deposition and highlighting the potential use of lichens to evaluate the weight of different types of anthropogenic sources.