Search

The methodological framework of a large-scale risk assessment for Austrian forests under scenarios of climatic change is presented. A recently developed 3D-patch model is initialized with ground-true soil and vegetation data from sample plots of the Austrian Forest Inventory (AFI). Temperature and precipitation data of the current climate are interpolated from a network of more than 600 weather stations to the sample plots of the AFI. Vegetation development is simulated under current climate (âcontrol runâ) and under climate change scenarios starting from today's forest composition and structure. Similarity of species composition and accumulated biomass between these two runs at various points in time were used as assessment criteria. An additive preference function which is based on Saatyâs AHP is employed to synthesize these criteria to an overall index of the adaptation potential of current forests to a changing climate. The presented methodology is demonstrated for a small sample from the Austrian Forest Inventory. The forest model successfully simulated equilibrium species composition under current climatic conditions spatially explicit in a heterogenous landscape based on ground-true data. At none of the simulated sites an abrupt forest dieback did occur due to climate change impacts. However, substantial changes occured with regard to species composition of the potential natural vegetation (PNV).

Our current knowledge on multi‐decadal to centennial changes of snow in different parts of the world is based largely on observations of snow depth and depth of snowfall from national weather and hydrographic services. Studies analysing these snow observations in the European Alps are predominantly based on national data and are therefore limited by their respective borders in the detection of robust, spatiotemporal snow trends. In order to overcome this limitation, data from Austria and Switzerland, which cover a substantial fraction of the Alps when taken together, are merged for this study (196 station‐records). Additionally, it is the first time that such an analysis is based on homogenized data. Our homogenization study shows that, although the detection of breaks in snow depth series works quite well with the existing methods, further research is needed to adequately correct snow depth series at a daily resolution. Roughly, 70% (139 station‐records) of the snow depth series could be homogenized and are used for further trend analysis. The findings concern seven climatologically different areas that are identified by a regionalization (using empirical orthogonal functions) using station records from 1961 to 2012. These regions share a high degree of inner similarity and outer separation, and the temporal trends detected are rather different across the Swiss‐Austrian domain. Regions in the south show a clear decrease in the snow depth of up to −12 cm/decade on average, while those in the northeast are characterized by almost no change. The declining trend in the southern regions intensifies as altitude increases. Comparisons of these variations in depth changes with concurrent changes in air temperature and precipitation totals reveal a clear dichotomy with respect to elevation. Snow depths in low elevated areas are highly sensitive to air temperature changes, whereas those at high elevations strongly depend on alterations in precipitation totals. Snow depth observations of the Swiss‐Austrian region for the past half century show strikingly different trends. Whereas in particular the South of Austria is characterized by strongly decreasing snow depths, in the northeastern part of Austria they are almost unchanged. Our analyses show, that snow depth changes to a greater extent are forced by air temperature for low elevation regions, whereas for high elevation regions changes are sensitive to precipitation. The figure shows the Mann Kendall trend over the period 1961‐2012 for mean snow depth (November to April) at 139 stations within the Swiss‐Austrian area. White: no significant trend; Orange: significantly negative at a 95% confidence level; Red: significantly negative at a 99% confidence level. [ABSTRACT FROM AUTHOR]

Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, that are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet, to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In two Austrian impact studies the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations and non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. In both case studies we employed global sensitivity analysis (GSA) to identify the impact of the scenario inputs, the model setup and the parametrization on the simulation of discharge and NO3--N loads. We accompanied the GSA with a visual analysis of the simulation outputs and their associated uncertainties that resulted from the simulations of the 7000 SWAT model combinations. We present visualizations of the results of the GSA and the simulation uncertainty bands that proved to be powerful diagnostic tools in this study. Based on the GSA we identified climate change and the model parametrization to be the most sensitive model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low and the changes in land use and point source emissions were found to have the least impact on the simulated discharge and NO3--N loads. Additionally, the visual analysis of the uncertainty bands illustrated that the precipitation of the climate scenarios dominated the changes in simulation outputs, rather than changes in air temperature in both case studies. [ABSTRACT FROM AUTHOR]

In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. Robust and reliable information on the extent of climate change and its projected future impacts on roads and railways are of prime importance for proactive planning and the implementation of targeted adaptation strategies. Among various menacing natural hazards, landslides stand out as most destructive hazards to the functional effectivity and structural integrity of land-bound transport systems, since they cause long-lasting downtimes and exceedingly expensive repair works. Periods of heavy precipitation persisting over several days are known to be a major trigger for increased landslide activity. Along with climate change such events can be expected to increase in frequency, duration and intensity over the decades to come. In this study, a Climate Index (CI) picturing rainfall patterns which trigger landslides in Central Europe is analyzed until the end of this century and compared to present day conditions. The analysis of potential future developments is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional scale climate change projections across Central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrences. While flat terrain at low altitudes exhibits increases of about two more landslide events per year until the end of this century, higher elevated regions are more affected and show increases of up to eight additional events. This general spatial distribution emerges already in the near future (2021-2050) but gets more pronounced in the remote future (2071-2100). Largest increases are to be found in the Alsace. Consequently, potential impacts of increasing landslide events are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with and additional geodata. Derived findings are suitable to support political decision-makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's land-bound transport network are at particularly high risk concerning landslide activity. This study supports proactive development of adaption strategies and the realization of cost-efficient and effective protection programmes as well as the generation of guidelines for climate proofing. This refers to the design of transport networks, intermodal logistics as well as the setting up of maintenance and reinforcement strategies in order to safeguard one of the most essential backbones of Europe's economic prosperity. [ABSTRACT FROM AUTHOR]