ALBERT

Description: Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties for both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic wind–damage relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two sources of wind gust data, direct observations by the German Weather Service and ERA-Interim reanalysis data. While the choice of gust data has little impact on the evaluation of German storm loss, spatially resolved coefficients of variation reveal dependence between model and data choice. The comparison shows that the probabilistic models by Heneka et al. (2006) and Prahl et al. (2012) both provide accurate loss predictions for moderate to extreme losses, with generally small coefficients of variation. We favour the latter model in terms of model applicability. Application of the versatile deterministic model by Klawa and Ulbrich (2003) should be restricted to extreme loss, for which it shows the least bias and errors comparable to the probabilistic model by Prahl et al. (2012).

Description: In contrast to recent advances in projecting sea levels, estimations about the economic impact of sea level rise are vague. Nonetheless, they are of great importance for policy making with regard to adaptation and greenhouse-gas mitigation. Since the damage is mainly caused by extreme events, we propose a stochastic framework to estimate the monetary losses from coastal floods in a confined region. For this purpose, we follow a Peak-over-Threshold approach employing a Poisson point process and the Generalised Pareto Distribution. By considering the effect of sea level rise as well as potential adaptation scenarios on the involved parameters, we are able to study the development of the annual damage. An application to the city of Copenhagen shows that a doubling of losses can be expected from a mean sea level increase of only 11 cm. In general, we find that for varying parameters the expected losses can be well approximated by one of three analytical expressions depending on the extreme value parameters. These findings reveal the complex interplay of the involved parameters and allow conclusions of fundamental relevance. For instance, we show that the damage typically increases faster than the sea level rise itself. This in turn can be of great importance for the assessment of sea level rise impacts on the global scale. Our results are accompanied by an assessment of uncertainty, which reflects the stochastic nature of extreme events. While the absolute value of uncertainty about the flood damage increases with rising mean sea levels, we find that it decreases in relation to the expected damage.

Description: The assessment of coastal flood risks in a particular region requires the estimation of typical damages caused by storm surges of certain characteristics and annualities. Although the damage depends on a multitude of factors, including flow velocity, duration of flood, precaution, etc., the relationship between flood events and the corresponding average damages is usually described by a stage-damage function, which considers the maximum water level as the only damage influencing factor. Starting with different (microscale) building damage functions we elaborate a macroscopic damage function for the entire case study area Kalundborg (Denmark) on the basis of multiple coarse-graining methods and assumptions of the hydrological connectivity. We find that for small events, the macroscopic damage function mostly depends on the properties of the elevation model, while for large events it strongly depends on the assumed building damage function. In general, the damage in the case study increases exponentially up to a certain level and then less steep.

Description: In contrast to recent advances in projecting sea levels, estimations about the economic impact of sea level rise are vague. Nonetheless, they are of great importance for policy making with regard to adaptation and greenhouse-gas mitigation. Since the damage is mainly caused by extreme events, we propose a stochastic framework to estimate the monetary losses from coastal floods in a confined region. For this purpose, we follow a Peak-over-Threshold approach employing a Poisson point process and the Generalised Pareto Distribution. By considering the effect of sea level rise as well as potential adaptation scenarios on the involved parameters, we are able to study the development of the annual damage. An application to the city of Copenhagen shows that a doubling of losses can be expected from a mean sea level increase of only 11 cm. In general, we find that for varying parameters the expected losses can be well approximated by one of three analytical expressions depending on the extreme value parameters. These findings reveal the complex interplay of the involved parameters and allow conclusions of fundamental relevance. For instance, we show that the damage always increases faster than the sea level rise itself. This in turn can be of great importance for the assessment of sea level rise impacts on the global scale. Our results are accompanied by an assessment of uncertainty, which reflects the stochastic nature of extreme events. While the uncertainty of flood damage increases with rising sea levels, we find that the error of our estimations in relation to the expected damage decreases.

Description: Most climate change impacts manifest in the form of natural hazards. For example, sea-level rise and changes in storm climatology are expected to increase the frequency and magnitude of flooding events. In practice there is a need for comprehensive damage assessment at an intermediate level of complexity. Answering this need, we reveal the common grounds of macroscale damage functions employed in storm damage, coastal-flood damage, and heat mortality assessment. The universal approach offers both bottom-up and top-down damage evaluation, employing either an explicit or an implicit portfolio description. Putting emphasis on the treatment of data uncertainties, we perform a sensitivity analysis across different scales. We find that the behaviour of intrinsic uncertainties on the microscale level (i.e. single item) does still persist on the macroscale level (i.e. portfolio). Furthermore, the analysis of uncertainties can reveal their specific relevance, allowing for simplification of the modelling chain. Our results shed light on the role of uncertainties and provide useful insight for the application of a unified damage function.

Description: Winter storms are the most costly natural hazard for European residential property. We compare four distinct storm damage functions with respect to their forecast accuracy and variability, with particular regard to the most severe winter storms. The analysis focuses on daily loss estimates under differing spatial aggregation, ranging from district to country level. We discuss the broad and heavily skewed distribution of insured losses posing difficulties on both the calibration and the evaluation of damage functions. From theoretical considerations, we provide a synthesis between the frequently discussed cubic damage-wind relationship and recent studies that report much steeper damage functions for European winter storms. The performance of the storm loss models is evaluated for two wind data sources, direct observation by the German Weather Service and ERA Interim reanalysis data. While the choice of wind data indicates little impact for the evaluation of German storm loss, local variability exhibits dependence between model and data choices. Based on our analysis, we favour the application of two probabilistic approaches which fare best in terms of the accuracy of their expected value and overall exhibit the lowest amount of variability.

Description: Water is an essential input to the majority of human activities. Often, access to sufficient water resources is limited by quality and infrastructure aspects, rather than by resource availability alone, and each activity has different requirements regarding the nature of these aspects. This paper develops an integrated approach to assess the adequacy of water resources for the three major water users: the domestic, agricultural and industrial sectors. Additionally, we include environmental water requirements. We first outline the main determinants of water adequacy for each sector. Subsequently, we present an integrated approach using fuzzy logic, which allows assessing sector-specific as well as overall water adequacy. We implement the approach in two case study settings to exemplify the main features of the approach. Using results from two climate models and two forcing RCPs (representative concentration pathways), as well as population projections, we further assess the impacts of climate change in combination with population growth on the adequacy of water resources. The results provide an important step forward in determining the most relevant factors, impeding adequate access to water, which remains an important challenge in many regions of the world. The methodology allows one to directly identify the factors that are most decisive in determining the adequacy of water in each region, pointing towards the most efficient intervention points to improve conditions. Our findings underline the fact that, in addition to water volumes, water quality is a limitation for all sectors and, especially for the environmental sector, high levels of pollution are a threat to water adequacy.

Description: Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions – and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions.