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ATEAM final report Section 5 and 6 (2001-2004) 32 change. Table 8 summarises the total numbers of people (within the basins shown in Figure 23) living in watersheds with less than 1700 m 3 capita -1 year -1 . The table shows that a large proportion of Europe’s population live in such watersheds, and that beyond the 2020s climate change increases the numbers affected. Table 8. Numbers of people (millions) living in watersheds with less than 1700 m 3 capita -1 year -1 , by socio-economic storyline (A1f, A2, B1, B2), assuming no climate change and climate change calculated by four GCMs (HadCM3, CGCM2, CSIRO2, PCM) in the years 2025, 2055 and 2085. The change in number of people (millions) due to climate change is shown as well. millions Change (millions) total population no climate change HadCM3 CGCM2 CSIRO2 PCM HadCM3 CGCM2 CSIRO2 PCM 2025 A1f 633.0 357.5 356.2 -1.3 A2 646.4 365.6 364.2 364.2 364.2 364.2 -1.4 -1.4 -1.4 -1.4 B1 633.0 357.5 360.8 3.2 B2 609.4 342.0 346.3 4.3 2055 A1f 610.5 286.0 350.8 64.8 A2 656.1 331.0 376.7 379.6 374.5 375.7 45.7 48.7 43.5 44.8 B1 610.5 286.0 352.9 67.0 B2 565.9 250.7 287.7 36.9 2085 A1f 570.1 249.0 324.6 75.6 A2 716.3 437.4 465.5 461.4 449.0 444.9 28.1 24.0 11.6 7.5 B1 570.1 249.0 298.8 49.8 B2 557.2 246.4 297.0 50.6 Two papers have been published in the journal Global Environmental Change, other articles are in preparation (see Annex 2). 6.2.2.5 Biodiversity and nature conservation Principal investigators: Sandra Lavorel, Wilfried Thuiller, Miguel B. Araújo We used the BIOMOD framework (Thuiller 2003) to predict the current distribution of more than 2000 species across Europe (1350 plants, 157 mammals, 108 herptiles (reptiles and amphibians) and 383 breeding birds) using five bioclimatic variables. Species distributions were projected across Europe under current and future climate change scenarios. For 2080, we derived future distributions of the 2000 species under seven climate change scenarios (A1-A2-B1-B2 HadCM3, A2 CSIRO2, A1-A2 CGCM2). In contrast to results from the other sectors the biodiversity estimates have a coarser spatial resolution of 50 x 50 km. In order to reduce the tremendous uncertainty associated with selection of methods in niche-based modeling (Thuiller 2004, Thuiller et al. 2004), we used eight different models for each species and selected for each scenario the most consensual one (the one closest to the average across models). We then derived projections under two extreme cases of dispersal, namely zero and full instantaneous dispersal (Thuiller 2004). These approximations bracket the most pessimistic and optimistic estimates of future species range as a way to capture unknown actual dispersal rates. Major results for 2080 show great sensitivity of biodiversity under all climate change scenarios (Figure 24). A1-HadCM3 (A1f emissions calculated with climate model HadCM3) represents the most threatening scenario for species diversity with some regions expected to lose more than 80% of current
ATEAM final report Section 5 and 6 (2001-2004) 33 species richness. This scenario also produces the greatest variability across Europe while B1-HadCM3 appears to produce the lowest species loss and species turnover by pixel. These projections were also averaged and mapped by biogeographical regions (using the environmental classification proposed by Metzger et al. 2004). Assuming no dispersal, the regions most at risk under A1-HadCM3 are Mediterranean mountains, the Lusitanian and Pannonian regions (mean percentage of species loss = 55%), while the regions in which the lowest extinction by pixel is expected are the Alpine north, Mediterranean south, boreal and Atlantic regions (Figure 25). The ranking of regional sensitivities was consistent across scenarios. The detailed examination of the projections for plants in 2050 under the full dispersal hypothesis revealed that 93% of species were projected to have overlapping populations with current distributions, while 2% had totally non-overlapping future distributions and 5% lost all available habitat (Araùjo et al. 2004). These changes meant that, over a range of reserve selection methods, 6% to 11% of species modelled would be potentially lost from selected reserves in a 50-year period. The modeling framework used here was developed to allow reserve area selection to optimize the protection goal of nature conservation areas under restricted resources and changing climate. A flexible management of nature reserve areas is necessary to maintain the conservation effect under changing conditions. Numerous papers have been produced which are listed in Annex 2, including a contribution to Nature. 6.2.2.6 Mountains Principal investigators: Harald Bugmann and Bäbel Zierl Mountains and their ecosystems provide many goods and services to European societies, including freshwater supply, carbon storage, protection from natural hazards, and tourism related services such as maintaining a reliable snow cover for skiing. Some of these services have been dealt with in previous sections (carbon storage, water), this section provides an additional Alpine focus, based on a special approach that is suited for mountainous systems. The continued capacity of mountain regions to provide ecosystem services is threatened by the impact of environmental change. Therefore, the objective of our contribution to ATEAM was to perform high-resolution case studies in mountain catchments in order to assess the vulnerability of sectors that rely on mountain ecosystem services to global change. We used five catchments representing different major climatic zones in the Alpine area: the Alptal, the Hirschbichl, the Dischma, the Saltina and the Verzasca catchment (Table 9). Plans to extend the approach to other mountain ranges of Europe had to be abandoned because of time constraints and limited data availability. Table 9. Description of case study catchments from various climate zones in the alpine region. The category ‘rest’ in the column ‘land cover’ includes predominantly rocks, but also sealed areas such as cities, streets, or parking places. case study region area precipi- altitude land cover (%) catchment tation (km 2 ) (mm) (m) Forest Grass Rest Alptal (CH) western prealpine 46.75 2110 1150 (850 – 1860) 52 42 6 Hirschbichl (D) eastern prealpine 44.61 1640 1460 (680 – 2560) 49 36 15 Dischma (CH) high alpine 43.16 1310 2370 (1680 – 3110) 10 54 36 Saltina (CH) inner dry alpine 77.02 1340 2010 (680 – 3400) 35 34 31 Verzasca- Lavertezzo (CH) southern prealpine 44.13 2320 1680 (540 – 2500) 64 19 17 The simulation system RHESSys (http://typhoon.sdsu.edu/Research/Projects/RHESSYS, Tague and Band, 2004, Band et al., 1993) was used for simulating the fluxes of water, carbon and nitrogen through mountain catchments. First, the model was adapted to alpine conditions, particularly regarding maintenance respiration, phenology, snow accumulation and melting; second, it was validated against
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