Suchergebnisse

Abstract. Heat waves (HWs) and cold waves (CWs) can have considerable impact on
people. Mapping risks of extreme temperature at local scale, accounting for
the interactions between hazard, exposure, and vulnerability, remains a
challenging task. In this study, we quantify risks from HWs and CWs for the
Trentino-Alto Adige region of Italy from 1980 to 2018 at high spatial
resolution. We use the Heat Wave Magnitude Index daily (HWMId) and the Cold
Wave Magnitude Index daily (CWMId) as the hazard indicators. To obtain HWs
and CW risk maps we combined the following: (i) occurrence probability maps of the hazard
obtained using the zero-inflated Tweedie distribution (accounting directly
for the absence of events for certain years), (ii) normalized population
density maps, and (iii) normalized vulnerability maps based on eight
socioeconomic indicators. The methodology allowed us to disentangle the
contributions of each component of the risk relative to total change in
risk. We find a statistically significant increase in HW hazard and
exposure, while CW hazard remained stagnant in the analyzed area over the
study period. A decrease in vulnerability to extreme temperature spells is
observed through the region except in the larger cities where vulnerability
increased. HW risk increased in 40 % of the region, with the increase
being greatest in highly populated areas. Stagnant CW hazard and declining
vulnerability result in reduced CW risk levels overall, except for the four
main cities where increased vulnerability and exposure increased risk
levels. These findings can help to steer investments in local risk
mitigation, and this method can potentially be applied to other regions
where there are sufficient detailed data.

Climate change will impact many economic sectors and aspects of natural and human wellbeing. Quantifying these impacts as they vary across regions, sectors, time, and social and climatological scenarios supports detailed planning, policy, and risk management. This article summarises and compares recent climate impact assessments in Europe (the JRC PESETA III project) and the USA (the American Climate Prospectus project). Both implement a multi-sector perspective combining high resolution climate data with sectoral impact and economic models. The assessments differ in their coverage of sectors and scenarios, mix of empirical and process-based methods, handling of uncertainty, and representation of damages. Despite the dissimilarities, projected relative economic impacts are comparable, with human mortality as the dominant impact category. Both studies further show a large spatial heterogeneity of impacts that may amplify pre-existing economic inequality in the EU and US, and that mitigation can considerably reduce economic impacts. The comparison highlights the various decision-points involved in interdisciplinary climate impact modelling and lessons learnt in both projects, on the basis of which we provide recommendations for further research.

Abstract. The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia–Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

Abstract. River floods pose a significant threat to road transport infrastructure in
Europe. This study presents a high-resolution object-based continental-scale
assessment of direct flood risk of the European road network for the present
climate, using high-resolution exposure data from OpenStreetMap. A new set
of road-specific damage functions is developed. The expected annual direct
damage from large river floods to road infrastructure in Europe is EUR 230 million per year. Compared to grid-based approaches, the object-based
approach is more precise and provides more action perspective for road
owners because it calculates damage directly for individual road segments
while accounting for segment-specific attributes. This enables the
identification of European hotspots, such as roads in the Alps and along the
Sava River. A first comparison to a reference case shows that the new
object-based method computes realistic damage estimates, paving the way for
targeted risk reduction strategies.

River floods generate a large share of the socio-economic impact of weather-driven hazards worldwide. Accurate assessment of their impact is a key priority for governments, international organization, reinsurance companies and emergency responders. Yet, available databases of flood losses over large domains are often affected by gaps and inconsistencies in reported figures. In this work, a framework to reconstruct the economic damage and population affected by river floods at continental scale is applied. Pan-European river flow simulations are coupled with a high-resolution impact assessment framework based on 2-D inundation modelling. Two complementary methods are compared in their ability to estimate the climatological average flood impact and the impact of each flood event in Europe between 1990 and 2013. The event-based method reveals key features, such as the ability to include changes in time of all three components of risk, namely hazard, exposure and vulnerability. Furthermore, it skilfully reproduces the socio-economic impact of major flood events in the past two decades, including the severe flooding hitting central Europe in June 2013. On the other hand, the integral method is capable of reproducing the average flood losses which occurred in Europe between 1998 and 2009. Strengths and limitations of the proposed model are discussed to stress the large potential for filling in the gaps of current datasets of flood impact.

Abstract. River floods generate a large share of the socio-economic impact of weather-driven hazards worldwide. Accurate assessment of their impact is a key priority for governments, international organization, reinsurance companies and emergency responders. Yet, available databases of flood losses over large domains are often affected by gaps and inconsistencies in reported figures. In this work, a framework to reconstruct the economic damage and population affected by river floods at continental scale is applied. Pan-European river flow simulations are coupled with a high-resolution impact assessment framework based on 2-D inundation modelling. Two complementary methods are compared in their ability to estimate the climatological average flood impact and the impact of each flood event in Europe between 1990 and 2013. The event-based method reveals key features, such as the ability to include changes in time of all three components of risk, namely hazard, exposure and vulnerability. Furthermore, it skilfully reproduces the socio-economic impact of major flood events in the past two decades, including the severe flooding hitting central Europe in June 2013. On the other hand, the integral method is capable of reproducing the average flood losses which occurred in Europe between 1998 and 2009. Strengths and limitations of the proposed model are discussed to stress the large potential for filling in the gaps of current datasets of flood impact.

River floods generate a large share of the socio-economic impact of weather-driven hazards worldwide. Accurate assessment of their impact is a key priority for governments, international organization, reinsurance companies and emergency responders. Yet, available databases of flood losses over large domains are often affected by gaps and inconsistencies in reported figures. In this work, a framework to reconstruct the economic damage and population affected by river floods at continental scale is applied. Pan-European river flow simulations are coupled with a high-resolution impact assessment framework based on 2-D inundation modelling. Two complementary methods are compared in their ability to estimate the climatological average flood impact and the impact of each flood event in Europe between 1990 and 2013. The event-based method reveals key features, such as the ability to include changes in time of all three components of risk, namely hazard, exposure and vulnerability. Furthermore, it skilfully reproduces the socio-economic impact of major flood events in the past two decades, including the severe flooding hitting central Europe in June 2013. On the other hand, the integral method is capable of reproducing the average flood losses which occurred in Europe between 1998 and 2009. Strengths and limitations of the proposed model are discussed to stress the large potential for filling in the gaps of current datasets of flood impact.

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national
scales has taken place during recent years, with a number of large-scale
models emerging as tools for hotspot identification, support for
international policymaking, and harmonization of climate change adaptation
strategies. There is, however, limited insight into the scaling effects and
structural limitations of flood risk models and, therefore, the underlying
uncertainty. In light of this, we examine key sources of epistemic
uncertainty in the coastal flood risk (CFR) modelling chain: (i) the
inclusion and interaction of different hydraulic components leading to
extreme sea level (ESL), (ii) the underlying uncertainty in the digital
elevation model (DEM), (iii) flood defence information, (iv) the assumptions
behind the use of depth–damage functions that express vulnerability, and
(v) different climate change projections. The impact of these uncertainties
on estimated expected annual damage (EAD) for present and future climates is
evaluated in a dual case study in Faro, Portugal, and on the Iberian
Peninsula. The ranking of the uncertainty factors varies among the different
case studies, baseline CFR estimates, and their absolute and relative
changes. We find that uncertainty from ESL contributions, and in particular
the way waves are treated, can be higher than the uncertainty of the two
greenhouse gas emission projections and six climate models that are used. Of
comparable importance is the quality of information on coastal protection
levels and DEM information. In the absence of large datasets with
sufficient resolution and accuracy, the latter two factors are the main
bottlenecks in terms of large-scale CFR assessment quality.

Abstract. Coastal flooding related to marine extreme events has severe socioeconomic impacts, and even though the latter are projected to increase under the changing climate, there is a clear deficit of information and predictive capacity related to coastal flood mapping. The present contribution reports on efforts towards a new methodology for mapping coastal flood hazard at European scale, combining (i) the contribution of waves to the total water level; (ii) improved inundation modeling; and (iii) an open, physics-based framework which can be constantly upgraded, whenever new and more accurate data become available. Four inundation approaches of gradually increasing complexity and computational costs were evaluated in terms of their applicability to large-scale coastal flooding mapping: static inundation (SM); a semi-dynamic method, considering the water volume discharge over the dykes (VD); the flood intensity index approach (Iw); and the model LISFLOOD-FP (LFP). A validation test performed against observed flood extents during the Xynthia storm event showed that SM and VD can lead to an overestimation of flood extents by 232 and 209 %, while Iw and LFP showed satisfactory predictive skill. Application at pan-European scale for the present-day 100-year event confirmed that static approaches can overestimate flood extents by 56 % compared to LFP; however, Iw can deliver results of reasonable accuracy in cases when reduced computational costs are a priority. Moreover, omitting the wave contribution in the extreme total water level (TWL) can result in a  ∼  60 % underestimation of the flooded area. The present findings have implications for impact assessment studies, since combination of the estimated inundation maps with population exposure maps revealed differences in the estimated number of people affected within the 20–70 % range.

Quantitative estimates of the economic damages of climate change usually are based on aggregate relationships linking average temperature change to loss in gross domestic product (GDP). However, there is a clear need for further detail in the regional and sectoral dimensions of impact assessments to design and prioritize adaptation strategies. New developments in regional climate modeling and physical-impact modeling in Europe allow a better exploration of those dimensions. This article quantifies the potential consequences of climate change in Europe in four market impact categories (agriculture, river floods, coastal areas, and tourism) and one nonmarket impact (human health). The methodology integrates a set of coherent, high-resolution climate change projections and physical models into an economic modeling framework. We find that if the climate of the 2080s were to occur today, the annual loss in household welfare in the European Union (EU) resulting from the four market impacts would range between 0.2–1%. If the welfare loss is assumed to be constant over time, climate change may halve the EU's annual welfare growth. Scenarios with warmer temperatures and a higher rise in sea level result in more severe economic damage. However, the results show that there are large variations across European regions. Southern Europe, the British Isles, and Central Europe North appear most sensitive to climate change. Northern Europe, on the other hand, is the only region with net economic benefits, driven mainly by the positive effects on agriculture. Coastal systems, agriculture, and river flooding are the most important of the four market impacts assessed.

Quantitative estimates of the economic damages of climate change usually are based on aggregate relationships linking average temperature change to loss in gross domestic product (GDP). However, there is a clear need for further detail in the regional and sectoral dimensions of impact assessments to design and prioritize adaptation strategies. New developments in regional climate modeling and physical-impact modeling in Europe allow a better exploration of those dimensions. This article quantifies the potential consequences of climate change in Europe in four market impact categories (agriculture, river floods, coastal areas, and tourism) and one nonmarket impact (human health). The methodology integrates a set of coherent, high-resolution climate change projections and physical models into an economic modeling framework. We find that if the climate of the 2080s were to occur today, the annual loss in household welfare in the European Union (EU) resulting from the four market impacts would range between 0.2–1%. If the welfare loss is assumed to be constant over time, climate change may halve the EU's annual welfare growth. Scenarios with warmer temperatures and a higher rise in sea level result in more severe economic damage. However, the results show that there are large variations across European regions. Southern Europe, the British Isles, and Central Europe North appear most sensitive to climate change. Northern Europe, on the other hand, is the only region with net economic benefits, driven mainly by the positive effects on agriculture. Coastal systems, agriculture, and river flooding are the most important of the four market impacts assessed.

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk. ; The study was primarily supported by Grants from the European Commission's Joint Research Centre Seville (Research Contract ID: JRC/SVQ/2020/MVP/1654), Medical Research Council-UK (Grant ID: MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). The following individual Grants also supported this work: J.K and A.U were supported by the Czech Science Foundation, project 20-28560S. A.T was supported by MCIN/AEI/10.13039/501100011033, Grant CEX2018-000794-S. V.H was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 101032087. This work was generated using Copernicus Climate Change Service (C3S) information [1985–2019]. ; Peer reviewed