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Flood risk is changing over time. Climate change, land-use change, human interventions and socio-economic developments have an influence on the evolution of flood risk. Thus, the future dynamics of drivers influencing hazard, exposure and vulnerability and consequently flood risk evolution is uncertain. Therefore, flood risk management is confronted with deep uncertainties and need to continuously adapt to future circumstances. New management strategies are required to ensure the safety level of humans and their assets and reduce losses from floods. Adaptive flood risk management is a way to cope with such uncertainties. However, the implementation of adaptive flood risk management requires a flood risk monitoring system that screens critical developments of hazard, exposure, or vulnerability and warns the user when a critical point in flood risk evolution is approached. In order to develop a conceptual framework for a flood risk monitoring system, we conducted a systematic review of flood risk evolution assessments. We analysed how flood risk is conceptualised, which factors are assessed to analyse evolutions in one or more risk component, which methods are used to assess flood risk evolution and which risk outcomes are identified. We discuss the main concepts of monitoring the spatiotemporal changes of the components of risks and how the changes of these components contribute to the evolution of risk. We furthermore discuss the data sources, issues of spatial and temporal scales, and how the components of risk coevolve.

The scarcity of model input and calibration data has limited efforts in reconstructing scenarios of past floods in many regions globally. Recently, the number of studies that use distributed post-flood observation data collected throughout flood-affected communities (e.g. face-to-face interviews) are increasing. However, a systematic method that applies such data for hydrodynamic modelling of past floods in locations without hydrological data is lacking. In this study, we developed a method for reconstructing plausible scenarios of past flood events in data-scarce regions by applying flood observation data collected through field interviews to a hydrodynamic model (CAESAR-Lisflood). We tested the method using 300 spatially distributed flood depths and duration data collected using questionnaires on five river reaches after the 2017 flood event in Suleja and Tafa region, Nigeria. A stepwise process that aims to minimize the error between modelled and observed flood depth and duration at the locations of interviewed households was implemented. Results from the reconstructed flood depth scenario produced an error of ± 0.61 m for all observed and modelled locations and lie in the range of error produced by studies using comparable hydrodynamic models. The study demonstrates the potential of utilizing interview data for hydrodynamic modelling applications in data-scarce regions to support regional flood risk assessment. Furthermore, the method can provide flow depths and durations at houses without observations, which is useful input data for physical vulnerability assessment to complement disaster risk reduction efforts.

In recent times water-related hazards have captured public attention and led to an increased interest in developing hydrologic forecasting systems, and quantifying their reliability. Despite major investments in flood forecasting and flood protection measures undertaken after recurring widespread inundations (e.g. 2002 and 2013 in Eastern Europe), heavy rainfall events led to severe flooding in Western Europe during July of the past summer, resulting in severe impacts and massive losses, including over two hundred deaths. Extreme precipitation, breaking observed records, is expected to have an increased likelihood under global warming, all the more we should question and scrutinize our knowledge about the frequency and severity of floods. Reliable estimates of these, and a better quantification of their uncertainty are key information for improving our preparedness and developing adaption measures in the future.For this purpose we explore the potential of running pooled weather reforecasts through the flexible hydrological model framework DECIPHeR (Coxon et al.2019) in Switzerland, modified to include snow, glaciers and the effect of lakes and reservoirs on the river network. For three case studies of different climatic regions (one including regulated lakes), we compare floods generated by the 10 largest precipitation events and precipitation events of low frequency (return period >= 100 years), both extracted from the pooled data, for three accumulation periods (1, 3, 5 days) with the official flood frequency curves and flood frequency curves generated with long continuous simulation using the pooled data (> 1000 years). This analysis will show us possible deficiencies of record-based flood frequency curves, if running selected precipitation events according to a return period already gives us a representative “flood sample”, and what is the gain of running long continuous simulations (e.g. are there overlooked events, summoning hydrological extremes through particular spatio-temporal patterns? What is the effect of different initial conditions?). ReferencesCoxon G., Freer J., Lane R., Dunne T., Knoben W.J.M., Howden N.J.K., Quinn N., Wagener T. and Woods R. : DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology. Geosci Model Dev.,12,2285-2306, https://doi.org/10.5194/gmd-12-2285-2019, 2019.

This study aims to determine the contribution of glacial meltwater to streams in mountainous regions based on stable water isotope measurements (δ18O and δ2H). For this purpose, three partially glaciated catchments were selected as the study area in the central Swiss Alps being representative of catchments that are used for hydropower energy production in Alpine regions. The glacial meltwater contribution to the catchments’ stream discharges was evaluated based on high-resolution δ18O and δ2H measurements of the end-members that contribute to the stream discharge (ice, rain, snow) and of the discharging streams. The glacial meltwater contribution to the stream discharges could be unequivocally quantified after the snowmelt in August and September when most of the annual glacial meltwater discharge occurs. In August and September, the glacial meltwater contribution to the stream discharges corresponds to up to 95 ± 2 % and to 28.7 % ± 5 % of the total annual discharge in the evaluated catchments. The high glacial meltwater contribution demonstrates that the mountainous stream discharges in August and September will probably strongly decrease in the future due to global warming-induced deglaciation, which will be, however, likely compensated by higher discharge rates in winter and spring. Nevertheless, the changing mountainous streamflow regimes in the future will pose a challenge for hydropower energy production in the mountainous areas. Overall, this study provides a successful example of an Alpine catchment monitoring strategy to quantify the glacial meltwater contribution to stream discharges based on stable isotope water data, which leads to a better validation of existing modelling studies and which can be adapted to other mountainous regions.

In all regions of the Alps, natural hazards risk management practice actually is facing different challenges: the effects of climate change, the rapid increase of damage potential in endangered areas, the increase of vulnerability of endangered objects, the decrease in acceptability of risks caused by natural events, and last but not least, a mismatch of increasing responsibilities of public institutions and decreasing financial resources. As these challenges in risk management affect all stakeholders in the Alpine Space nearly in the same way, a simultaneous effort in finding solutions to deal with in all states and regions in the Alps is likely to be wasted time and wasted funds. Transnational collaboration offers many possibilities for benefiting from synergies: ï‚· Some stakeholders could find the time for specialization to an important topic while learning from other specialist in other topics of the risk management. ï‚· Instead of finding solutions from nil, stakeholder or responsible institutions can learn from others and contribute to the further enhancement of best practices. ï‚· Due to the limited public fonts, a transnational collaboration could make possible a satisfying budget for further developments and adaptation of risk management practice putting together many small financial budgets.

Because effects of climate change and an increase in elements at risk, mountain hazard loss increased throughout Europe. Yet, factors influencing loss, i.e. vulnerability, have gained less attention to date. Vulnerability is defined as the degree of loss resulting from the hazard impact on buildings. Recent studies have focused on evaluating vulnerability to dynamic flooding using proxies from case studies and based on empirical ex-post approaches. However, the transferability to other case studies and, therefore, the ability of such models to actually predict future losses is limited. To overcome this gap, we present a beta model based on loss data from the European Alps, which clearly shows that a single vulnerability function is sufficient to predict losses resulting from different types of torrential hazards and to provide probabilities of destruction under specific scenarios. As a result, the curves are transferable and may significantly increase the predictive power of risk analyses.

Surface water floods (SWFs) do not only increasingly threaten cities, but also affect rural areas. So far, little research has been dedicated to the prediction of SWFs in rural environments, although in practice the process is already being considered in deterministic flood hazard assessments. To test the validity of such assessments, we select four raster-based models with differing complexity and evaluate whether they reliably predict inundated areas by SWF in rural areas. The uncalibrated models are first applied to four artificial surfaces and second, to eight case studies covering manifold geographical and meteorological settings. For the case studies, the models' prediction skills are assessed based on inundated areas inferred from various sources. The models’ performance is rather low for all case studies, which highlights the necessity for calibration and/or validation of such models. Moreover, the case studies provide more general conclusions concerning the modeling of SWFs in rural areas.

The selection of vulnerability models has a significant influence on the overall uncertainty when quantifying flood loss. Several scholars reported a limited spatial transferability of available vulnerability functions to case studies other than those they have been empirically deduced from. As a result, there is a need for computation and validation of regionally specific vulnerability functions. As in many data-scarce regions this option is not feasible, the physical processes of flood impact model chains can be developed using synthetic vulnerability function and validating them by expert opinion. The function presented in our study is based on expert heuristics using a small sample of representative buildings. We applied the vulnerability function in a meso-scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region-specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that synthetic information can be a valuable alternative for developing flood vulnerability models in regions without any data or only few data on flood loss.

Understanding the different dimensions of vulnerability to floods is instrumental to gaining knowledge on flood impacts, to guide the development of appropriate risk analysis methods and to make critical decisions in risk management. Vulnerability assessment of complex systems, such as transportation infrastructure, demands an integrated framework to include various analytical methods to investigate the problem from the different characteristic perspectives related to their topological, functional, logic and dynamic properties. One approach to understand the impacts of transportation infrastructure disruptions on people is the accessibility-based vulnerability approach. Accessibility-based vulnerability analysis examines changes of access levels across a traffic network disrupted by floods, thereby providing insight on the impacts to a broader range of socio-economic aspects and to the society as a whole.The presented study evaluates two different approaches. The first approach computes direct impacts and investigates different measures for extreme flood impacts to the road network. The second approach computes indirect impacts andi) incorporates detailed information about the local road network in the accessibility-based vulnerability analysis by modifying the approach of calculating travel time between zones,ii) includes additional contributing factors to the accessibility-based vulnerability analysis by considering residents and socio-economic opportunities in flood-affected areas,iii) effectively identifies the most vulnerable traffic zones with respect to selected extreme flood scenarios, andiv) investigates the influence of different spatial patterns of floods on accessibility-based vulnerability assessment.We used three measures to assess direct flood impacts on the road network towards selecting the flood scenarios, which are representative for different flood patterns. Namely, Loss Index (LI), the total value of normalized edge betweenness centrality (Total-EBC), and the average normalized edge betweenness centrality (Mean-EBC). The Hansen integral accessibility approach was modified for two vulnerability indices considering traffic zones along with average shortest travel time as cost and applied for selected flood scenarios. The resulted vulnerability indices were additionally analyzed to identify the most vulnerable traffic zones for each approach and the spatial influence of the flood and network pattern as well as the distribution of population and opportunities. Finally, effects of the contributing factors to the vulnerability were investigated using correlation and comparison between the flood scenarios.The results of the direct impact assessment show that different flood scenario and varying spatial extent are selected as extreme events based on Total-EBC and Mean-EBC. The comparisons of these different measures in assessing direct impact of extreme floods to road network allows to plan different services on disaster mitigation to place mitigation policies to be efficient. Most of the highly vulnerable traffic zones are related to the flood extent in these zones and affected population and opportunities in the traffic zones. However, the most remote traffic zones were also highly vulnerable in flood scenario, if some parts of the important connecting roads for these remote traffic zones were disturbed by a flood in traffic zones faraway. The overall results implicate those different types of flood scenarios could be classified into several groups according to their patterns of vulnerability.

Aims Climate change is expected to markedly change site factors, tree species composition and finally ecosystem services provided by forests. Here, we describe the development of a framework for modelling how these changes may transform forest site types. Site types capture information on site conditions like climate, topography and soil, all factors with strong influence on tree species occurrence. Location Switzerland. Methods We elicited expert knowledge and followed a participatory modelling approach for quantifying upward shifts of altitudinal vegetation belts and the changes in the zonal distribution of main tree species, as a basis for assessing transformation pathways of forest site types for three climate projections. Results The model results show marked range shifts of altitudinal vegetation belts. The change in the vegetation belt and a rule base for forest site type transformations allow for assessing the location-specific and long-term transformation pathway from the current to a future forest site type. Conclusions The resulting maps enable forest managers to take climate change into account when selecting tree species. The presented method complements statistical distribution models as it considers more site information, integrates expert knowledge and is based on a forest site type classification which is already widely used by forest practitioners., Applied Vegetation Science, 24 (4), ISSN:1402-2001, ISSN:1654-109X

In flood risk management, the choice of vulnerability functions has a remarkable impact on the overall uncertainty of modelling flood damage. The spatial transferability of empirical vulnerability functions is limited, leading to the need for computation and validation of region-specific vulnerability functions. In data-scarce regions however, this option is not feasible. In contrast, the physical processes of flood impact model chains can be developed in these regions because of the availability of global datasets. Here we evaluated the implementation of a synthetic vulnerability function into a flood impact model. The function bases on expert heuristics on a targeted sample of representative buildings (targeted heuristics). We applied the vulnerability function in a meso-scale river basin and evaluated the new function by comparing the resulting flood damage with the damage computed by other approaches, (1) an ensemble of vulnerability functions available from the literature, (2) an individual vulnerability function calibrated with region-specific data, and (3) the vulnerability function used in flood risk management by the Swiss government. The results show that targeted heuristics can be a valuable alternative for developing flood impact models in regions without any data or only few data on flood damage., Journal of Flood Risk Management, 14 (4)

Flood risk assessment and the design of risk reduction strategies often neglect the influence of socio-economic development on future exposure and vulnerability to floods and their development over time. Flood risks will increase or decrease depending on proactive adaptations of both households and government. A cooperation between household and government is therefore essential and may be reached by encouraging a constructive flood risk dialog. To overcome communication barriers, ease knowledge transfer between stakeholders and allow an integral rise in flood risk awareness the web-based tool “Flood damage simulator” is introduced.As a communication and awareness-raising instrument for flood risk management, the tool is directed at various user groups such as policy makers, local authorities, spatial planners as well as researchers. The tool offers scenarios which represent the magnitude of flood damages to be expected today and show possible trends in the near future. Furthermore, it is possible to customize a user defined scenario in which the key flood risk drivers exposure, vulnerability and flood extension can be individually adapted, thus breaking down a complex topic in more comprehensible subunits. Generated knowledge and awareness might promote proactive adaptation of both households and government leading to a reduction of flood risk.

Effective flood risk management requires a realistic estimation of flood losses. However, available flood damage estimates are still characterised by significant levels of uncertainty, questioning the capacity of flood damage models to depict real damages. With a joint effort of eight international research groups, the objective of this study was to compare the performances of different damage models for the estimation of the direct flood damage to the residential sector at the building level (i.e. micro scale) in a blind validation test. The test consisted in a common flood case study characterised by high availability of hazard and building data, but with undisclosed information on observed losses in the implementation stage of the models. The selected nine models were chosen in order to guarantee a good mastery of the models by the research teams, variety of the modelling approaches and heterogeneity of the original calibration context, in relation to both hazard and vulnerability features. By avoiding possible biases in model implementation, this blind comparison provided more objective insights on the transferability of the models and on the reliability of their estimations, especially regarding the potentials of local and multi-variable models. From another perspective, the exercise allowed to increase authors’ awareness on strengths and limits of flood damage modelling, which are summarised in the paper in the form of take-home messages from a modeller's perspective.

The floodplains of rivers are relevant living spaces for population globally and provide favorable locations for economic development. However, these areas are commonly exposed to floods, and the increasing population together with the changes in storminess as a result of global warming mean that the risks from flooding are expected to rise. Most studies investigating the impact that climatic change has on flood risk are based on a cascade of global climate model simulations coupled with regional climate models, hydrologic models, inundation models, and flood impact models. However, this approach is subject to uncertainties. Model results are found to be sensitive to climate forcing, the structure of the underlying models, the choice of methods used for downscaling and bias correction, and the use of extreme value analysis for both current and future climate conditions. Moreover, uncertainties are expected to propagate through the model cascade. To overcome these problems, we propose a method for analyzing and mapping the sensitivity of population exposure in floodplains to changes in flood magnitude. The method is based on downward counterfactuals, namely perturbations of a selected flood scenario by increasing its magnitude, interpreted in this case as the worsening of a today’s design flood event as a result of climatic changes. The increase in the impact of a current design flood compared to its counterfactual illustrates the sensitivity to changes in hazard. We calculate the normalized gradients of the flood exposure curves, that is, the increase in the exposure and magnitude of the perturbed event relative to the exposure and magnitude of the current scenario. We test the applicability of the method on local, national, and global scale by using existing data sets, including flood hazard maps, flood protection standards, floodplain delineation, river network definition, and spatial population distribution. The gradients were found to vary remarkably across the globe and are overall smaller in the upper range of flood magnitudes that in the lower range. Based on these results, we compare the drivers of the sensitivity in different parts of the world and identify river reaches with the highest relative gradients. These river reaches might be the most affected by climate change and thus deserve an indepth investigation of the underlying characteristics of the floodplains and the need for climate change adaptation., Mobiliar Lab for Natural Risks, Oeschger Centre for Climate Change Research, University of Bern, European Union (EU) 754446, UGR Research and Knowledge Transfer Fund-Athenea, University of Granada

The estimation of flood losses implies the use of vulnerability/exposure models for flood damage and risk assessment (Meyer et al. 2013; Zischg et al. 2018; Wagenaar et al. 2018; Molinari et al. 2019). According to Gerl et al. (2016), in central Europe there are 28 models with 652 functions to assess flood losses, whereas almost half of the functions refer to residential buildings. The main differences among damage models are: (i) spatial scale, (ii) metric (i.e. absolute or relative loss), (iii) exposure assessment (i.e. whole building or affected floors, market values or reconstruction-replacement costs), (iv) number of input variables, (v) calibration/validation with observed losses. These differences pose the issue of model transferability to other urban or environmental contexts. With a joint effort of eight research groups, the objective of this study is to test and compare damage models for residential buildings used or developed by each group, by applying them in a “blind exercise”, i.e. a common flood case study characterised by high availability of hazard and building data, with unknown information on observed losses in the implementation stage of the models. As the research groups use approaches representing many different types and characteristics of models (e.g. univariable – multivariable; absolute – relative; graduated – regression – machine learning), being calibrated based on empirical data stemming from different countries (Austria, France, Germany, Italy, Japan, Netherlands), with different landscapes, the blind exercise provides an extensive comparison of models and their transferability. The analysis of the differences in the ensemble of model outcomes aims at pointing out common patterns or divergent behaviours between model outcomes and with respect to observed losses.

In comparison to a local-scale flood risk analysis, modeling flood losses and risks at the river basin scale is challenging. Particularly in mountainous watersheds, extreme precipitation can be distributed spatially and temporally with remarkable variability. Depending on the topography of the river basin and the topological characteristics of the river network, certain rainfall patterns can lead to a synchronization of the flood peaks between tributaries and the main river. Thus, these complex interactions can lead to high variability in flood losses. In addition, flood inundation modeling at the river basin scale is computationally resource-intensive and the simulation of multiple scenarios is not always feasible. In this study, we present an approach for reducing complexity in flood-risk modeling at the river basin scale. We developed a surrogate model for flood loss analysis in the river basin by decomposing the river system into a number of subsystems. A relationship between flood magnitude and flood losses is computed for each floodplain in the river basin by means of a flood inundation and flood loss model at sub-meter resolution. This surrogate model for flood-loss estimation can be coupled with a hydrological-hydraulic model cascade, allowing to compute a high number of flood scenarios for the whole river system. The application of this model to a complex mountain river basin showed that the surrogate model approach leads to a reliable and computationally fast analysis of flood losses in a set of probable maximum precipitation scenarios. Hence, this approach offers new possibilities for stress test analyses and Monte-Carlo simulations in the analysis of system behavior under different system loads.

Flood risks are dynamically changing over time. Over decades and centuries, the main drivers for flood risk change are influenced either by perturbations or slow alterations in the natural environment or, more importantly, by socio-economic development and human interventions. However, changes in the natural and human environment are intertwined. Thus, the analysis of the main drivers for flood risk changes requires a disentangling of the individual risk components. Here, we present a method for isolating the individual effects of selected drivers of change and selected flood risk management options based on a model experiment. In contrast to purely synthetic model experiments, we built our analyses upon a retro-model consisting of several spatio-temporal stages of river morphology and settlement structure. The main advantage of this approach is that the overall long-term dynamics are known and do not have to be assumed. We used this model setup to analyse the temporal evolution of the flood risk, for an ex-post evaluation of the key drivers of change, and for analysing possible alternative pathways for flood risk evolution under different governance settings. We showed that in the study region the construction of lateral levees and the consecutive river incision are the main drivers for decreasing flood risks over the last century. A rebound effect in flood risk can be observed following an increase in settlements since the 1960s. This effect is not as relevant as the river engineering measures, but it will become increasingly relevant in the future with continued socio-economic growth. The presented approach could provide a methodological framework for studying pathways for future flood risk evolvement and for the formulation of narratives for adapting governmental flood risk strategies to the spatio-temporal dynamics in the built environment.

Comprehensive flood risk modeling is crucial for understanding, assessing, and mitigating flood risk. Modeling extreme events is a well-established practice in the atmospheric and hydrological sciences and in the insurance industry. Several specialized models are used to research extreme events including atmospheric circulation models, hydrological models, hydrodynamic models, and damage and loss models. Although these model types are well established, and coupling two to three of these models has been successful, no assessment of a full and comprehensive model chain from the atmospheric to local scale flood loss models has been conducted. The present study introduces a model chain setup incorporating a GCM/RCM to model atmospheric processes, a hydrological model to estimate the catchment's runoff reaction to precipitation inputs, a hydrodynamic model to identify flood-affected areas, and a damage and loss model to estimate flood losses. Such coupling requires building interfaces between the individual models that are coherent in terms of spatial and temporal resolution and therefore calls for several pre- and post-processing steps for the individual models as well as for a computationally efficient strategy to identify and model extreme events. The results show that a coupled model chain allows for good representation of runoff for both long-term runoff characteristics and extreme events, provided a bias correction on precipitation input is applied. While the presented approach for deriving loss estimations for particular extreme events leads to reasonable results, two issues have been identified that need to be considered in further applications: (i) the identification of extreme events in long-term GCM simulations for downscaling and (ii) the representativeness of the vulnerability functions for local conditions.

Flood impact modelling requires reliable models for the simulation of flood processes. In recent years, flood inundation models have been remarkably improved and widely used for flood hazard simulation, flood exposure and loss analyses. In this study, we validate a 2D inundation model for the purpose of flood exposure analysis at the river reach scale. We validate the BASEMENT simulation model with insurance claims using conventional validation metrics. The flood model is established on the basis of available topographic data in a high spatial resolution for four test cases. The validation metrics were calculated with two different datasets; a dataset of event documentations reporting flooded areas and a dataset of insurance claims. The model fit relating to insurance claims is in three out of four test cases slightly lower than the model fit computed on the basis of the observed inundation areas. This comparison between two independent validation data sets suggests that validation metrics using insurance claims can be compared to conventional validation data, such as the flooded area. However, a validation on the basis of insurance claims might be more conservative in cases where model errors are more pronounced in areas with a high density of values at risk.

As financing protection against mountain hazards becomes increasingly challenging and therefore investments have to be prioritized, dilemmas of justice emerge: some local governments and individuals benefit from natural hazard protection schemes, whereas others loose. Decisions on whom to protect often caused contradicting concepts of political understanding, which differ in interpretations of fair resource allocation and distribution. This paper analyses the impact of different philosophical schools of social justice on mountain hazard management in Austria. We used data from a spatially explicit, object-based assessment of elements at risk and compared potential distributional effects of three political jurisdictions. We found that—depending on the respective political direction—various local governments gain and others loose within the actual distributional system of mitigation strategies. The implementation of a utilitarian policy approach would cause that high income communities in hazard-prone areas would mainly benefit. Consequently, this policy direction would encourage the public administration to ignore their own failure in the past natural hazards management and prevention. On the other hand, following a Rawlsians approach mainly peripheral communities would gain from new policy direction who often show besides natural hazards problem mainly large socio-economic challenges. Finally, the most radical change would include the implementation of a liberalism policy, whereabouts the state only provides hazard information, but no further mitigation measures. These findings highlight the distributional consequences of future mountain hazard management strategies and point to the crucial selection of policy direction in navigating the selection of various adaptation schemes.

Neue Waldstandort-Hinweiskarte Graubündens zur Ermittlung sensitiver Standorte und Bestände Kenntnisse der Waldstandorttypen sind grundlegend, um den heutigen Zustand und die künftige Entwick-lung des Waldes zu verstehen. Insbesondere in Schutzwäldern ist entscheidend, wie widerstands- und anpassungsfähig die heutigen Bestände sind. Die komplett überarbeitete, flächendeckende Waldstandort- Hinweiskarte für den Kanton Graubünden ist ein wichtiges Planungsinstrument: Sie dient als Grundlage für die Bestimmung von Standorten und Beständen, die empfindlich auf den Klimawandel reagieren könnten. Darüber hinaus ermöglicht sie es, flächendeckende Baumartenempfehlungen für die Zukunft zu erstellen.

Deterministic rainfall-runoff modelling usually assumes stationary hydrological system, as model parameters are calibrated with and therefore dependant on observed data. However, runoff processes are probably not stationary in the case of a probable maximum flood (PMF) where discharge greatly exceeds observed flood peaks. Developing hydrodynamic models and using them to build coupled hydrologic-hydrodynamic models can potentially improve the plausibility of PMF estimations. This study aims to assess the potential benefits and constraints of coupled modelling compared to standard deterministic hydrologic modelling when it comes to PMF estimation. The two modelling approaches are applied using a set of 100 spatio-temporal probable maximum precipitation (PMP) distribution scenarios. The resulting hydrographs, the resulting peak discharges as well as the reliability and the plausibility of the estimates are evaluated. The discussion of the results shows that coupling hydrologic and hydrodynamic models substantially improves the physical plausibility of PMF modelling, although both modelling approaches lead to PMF estimations for the catchment outlet that fall within a similar range. Using a coupled model is particularly suggested in cases where considerable flood-prone areas are situated within a catchment.

This study presents the results of an experimental application of a multi-method measurement concept for the monitoring of alpine mass movements. Satellite-borne differential interferometric synthetic aperture radar (DInSAR) was applied as the key technology. To improve the information contents of the DInSAR displacement data for an individual mass movement, a complementary measurement was carried out with a three dimensional measurement system. The information on the 3D movement characteristics obtained by this complementary measurement was used to extrapolate subsequent DInSAR measurements to 3D. Terrestrial laser scanning (TLS) and Global Navigation Satellite System (GNSS) data were tested as complementary 3D measurement systems. The deviations between the single measurement systems were mainly controlled by the error budgets of the different methods. An exception were short term GNSS single point time series which included small scale surface movements that were not captured by the other methods. TLS proved to be the most suitable complementary method. A single TIS repeat measurement was sufficient to create a mask, which enables the projection of DInSAR displacement data to 3D. The application of satellite-borne DInSAR in alpine terrain is challenging; signal decorrelation is a problem due to fast terrain movements and snow coverage and can cause failure of the measurement system. (C) 2016 Elsevier B.V. All rights reserved.

The original paper was published not bearing Open Access copyright and north arrows are missing from the figures

Estimations of low-probability flood events are frequently used to plan infrastructure and to determine the dimensions of flood protection measures. Several well-established methods exist for estimating low-probability floods. However, a global assessment of the consistency of these methods is difficult to achieve because the “true value” of an extreme flood is not observable. A detailed comparison performed on a given case study brings useful information about the statistical and hydrological processes involved in different methods. In the present study, the following three methods of estimating low-probability floods are compared: a purely statistical method (ordinary extreme value statistics), a statistical method based on stochastic rainfall-runoff simulation (SCHADEX method), and a deterministic method (physically based estimation of the probable maximum flood, PMF). These methods are tested for two different Swiss catchments; the results show that the 10,000-year return level flood estimations exceed the PMF estimations by 3% and 18%. The analysis shows that the plausibility of an extreme flood estimation does not only depend on the applied method but also on its ability to represent flood-triggering processes, including precipitation input, spatio-temporal precipitation distribution, and runoff.

Modelling of the vegetation belts and the areas of beech and silver fir for Switzerland The vegetation belts and the distribution area of beech and silver fir in Switzerland are an essential basis for the classification of forest site types with ecograms and therefore for site-specific recommendations of tree species and for forestry. Their limits, which are to a large extend defined by climatic factors, were until now only defined on a qualitative basis. For the future usage of the ecograms for tree species recommendations the limits must be described quantitatively, so that they can be modelled for the future with climate scenarios. With maps in a high spatial resolution of the forest-relevant climate variables and with additional important site parameters, those parameters were selected, which were relevant for the actual limits of the vegetation belts as well as for the beech and silver fir areal. The altitudinal belts were modelled by means of a linear regression between the known position of a representative altitudinal limit and the respective climatic factors at that position. The areal limits were modelled with a fuzzy logic approach. The climatic variables for the time span 2070–2099 were calculated on the basis of the climate (emission) scenario A1B (regional models RegCM3 and CLM), so that the limits of the altitudinal belts and the areas could be modelled for the future. The results of the model approaches are ecologically plausible. This could be shown in a comparison with the forest site types on the sample points of the National Forest Inventory and in an expert evaluation. The developed vegetation and climate maps as well as the modelled projections of the altitudinal belts in the future are the basis for the inclusion of climate change in the recommendation of tree species. They serve as decision support for forest managers and authorities in the forestry planning which is confronted with many uncertainties.

Assessing the impacts of climatic changes on forests requires the analysis of actual climatology within the forested area. In mountainous areas, climatological indices vary markedly with the micro-relief, i.e., with altitude, slope, and aspect. Consequently, when modelling potential shifts of altitudinal belts in mountainous areas due to climatic changes, maps with a high spatial resolution of the underlying climatological indices are fundamental. Here we present a set of maps of climatological indices with a spatial resolution of 25 by 25 m. The presented dataset consists of maps of the following parameters: average daily temperature high and low in January, April, July, and October as well as of the year; seasonal and annual thermal continentality; first and last freezing day; frost-free vegetation period; relative air humidity; solar radiation; and foehn conditions. The parameters represented in the maps have been selected in a knowledge engineering approach. The maps show the climatology of the periods 1961–1990 and 1981–2010. The data can be used for statistical analyses of forest climatology, for developing tree distribution models, and for assessing the impacts of climatic changes on Swiss forests., Forests, 10 (8), ISSN:1999-4907

Information on possible changes in future flood risk is essential for successful adaptation planning and risk management. However, various sources of uncertainty arise along the model chains used for the assessment of flood risk under climate change. Knowledge on the importance of these different sources of uncertainty can help to design future assessments of flood risk, and to identify areas of focus for further research that aims to reduce existing uncertainties. Here we investigate the role of four sources of epistemic uncertainty affecting the estimation of flood loss for changed climate conditions for a meso-scale, pre-alpine catchment. These are: the choice of a scenario-neutral method, climate projection uncertainty, hydrological model parameter sets, and the choice of the vulnerability function. To efficiently simulate a large number of loss estimates, a surrogate inundation model was used. 46,500 loss estimates were selected according to the change in annual mean precipitation and temperature of an ensemble of regional climate models, and considered for the attribution of uncertainty. Large uncertainty was found in the estimated loss for a 100-year flood event with losses ranging from a decrease of loss compared to estimations for present day climate, to more than a 7-fold increase. The choice of the vulnerability function was identified as the most important source of uncertainty explaining almost half of the variance in the estimates. However, uncertainty related to estimating floods for changed climate conditions contributed nearly as much. Hydrological model parametrisation was found to be negligible in the present setup. For our study area, these results highlight the importance of improving vulnerability function formulation even in a climate change context where additional major sources of uncertainty arise.