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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.

Deep-seated gravitational slope deformations (DSGSDs) pose serious threats to buildings and infrastructure in mountain regions. The understanding of past movement behavior are essential requirements for enhancing process knowledge and potential mitigation measures. In this context historical aerial imagery provides a unique possibility to assess and reconstruct the deformation history of DSGSDs. This study investigates the feasibility of 3D point clouds derived from historical aerial imagery using free and open-source (FOSS) photogrammetric tools for analyzing the long-term behavior of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) and assessing related secondary processes as changes in creep velocity, rockfall or debris flows. For the photogrammetric analyses, scanned analogue and digital imagery of six acquisition flights, conducted in 1954, 1971/1973, 2007, 2010, and 2019, have been processed using the FOSS photogrammetric suite MicMac. Further point cloud processing was carried out in CloudCompare. An improved version of the image correlation approach (IMCORR) implemented in SAGA GIS was used for the area-wide assessment of slope deformation. For the georeferencing and scaling an airborne laser scanning (ALS) point cloud of 2008 provided by the Federal State of Tyrol (Austria) was used. In total five photogrammetric 3D point clouds covering the period from 1954 to 2019 were derived and analyzed in terms of displacement, velocity and acceleration. The accuracy assessment with computed Multiscale Model to Model Cloud Comparison (M3C2) distances between photogrammetric 3D point clouds and reference ALS 3D point cloud, showed an overall uncertainty of about ±1.2 m (95% quantile) for all 3D point clouds produced with scanned analogue aerial images (1954, 1971/1973 and 2007), whereas 3D point clouds produced with digital aerial imagery (2010, 2019) showed a distinctly lower uncertainty of about ±0.3 m (95% quantile). Also, digital elevation models (DEM) of difference (DoD) for each epoch were calculated. IMCORR and DoD results indicate significant displacements up to 40 meters in 65 years for the central part of the landslide. The historical datasets further indicate a change of spatio-temporal patterns of movement rates and a minor but overall acceleration of the landslide. The main challenges were the (i) gaps in the 3D point clouds on areas of steep, shadowed slopes and high vegetation, (ii) ground filtering on the photogrammetric point clouds for accurate calculation of digital terrain models (DTMs) and (iii) the quality of the scanned aerial imagery showing scratches, cuts, color irritations and linear artefacts. This research enabled the characterization of the spatio-temporal movement patterns of the Reissenschuh DSGSD over more than six decades. Further research will use the results as a reference for modelling the discussed multi-hazard processes.This research was partly conducted within the project EMOD-SLAP funded by the Tyrolean Science Fund (TWF).

Globally, between 1950 and 2011 nearly 80,000 debris flow fatalities occurred in densely populated regions in mountainous terrain. Mitigation of these hazards includes the construction of check dams, which limit coarse sediment transport and in the European Alps number in the 100,000s. Check dam functionality depends on periodic, costly maintenance, but maintenance is not always possible and check dams often fail. As such, there is a need to quantify the long-term (10–100 years) geomorphic response of rivers to check dam failures. Here, for the first time, a landscape evolution model (CAESAR-Lisflood) driven by a weather generator is used to replicate check dam failures due to the lack of maintenance, check dam age, and flood occurrence. The model is applied to the Guerbe River, Switzerland, a pre-Alpine catchment containing 73 check dams that undergo simulated failure. Also presented is a novel method to calibrate CAESAR-Lisflood’s hydrological component on this ungauged catchment. Using 100-year scenarios of check dam failure, the model indicates that check dam failures can produce 8 m of channel erosion and a 322% increase in sediment yield. The model suggests that after check dam failure, channel erosion is the remobilization of deposits accumulated behind check dams, and, after a single check dam failure channel equilibrium occurs in five years, but after many check dam failures channel equilibrium may not occur until 15 years. Overall, these findings support the continued maintenance of check dams.

Dam removal can generate geomorphic disturbances, including channel bed and bank erosion and associated abrupt/pulsed release and downstream transfer of reservoir sediment, but the type and rate of geomorphic response often are hard to predict. The situation gets even more complex in systems which have been impacted by multiple dams and a long and complex engineering history. In previous studies one-dimensional (1-D) models were used to predict aspects of post-removal channel change. However, these models do not consider two-dimensional (2-D) effects of dam removal such as bank erosion processes and lateral migration. In the current study the impacts of multiple dams and their removal on channel evolution and sediment delivery were modeled by using a 2-D landscape evolution model (CAESAR-Lisflood) focusing on the following aspects: patterns, rates, and processes of geomorphic change and associated sediment delivery on annual to decadal timescales. The current modeling study revealed that geomorphic response to dam removal (i.e., channel evolution and associated rates of sediment delivery) in multiple dam settings is variable and complex in space and time. Complexity in geomorphic system response is related to differences in dam size, the proximity of upstream dams, related buffering effects and associated rates of upstream sediment supply, and emerging feedback processes as well as to the presence of channel stabilization measures. Modeled types and rates of geomorphic adjustment, using the 2-D landscape evolution model CAESAR-Lisflood, are similar to those reported in previous studies. Moreover, the use of a 2-D method showed some advantages compared to 1-D models, generating spatially varying patterns of erosion and deposition before and after dam removal that provide morphologies that are more readily comparable to field data as well as features like the lateral re-working of past reservoir deposits which further enables the maintenance of sediment delivery downstream.

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.

Irrespective of the magnitude and spatial extend, the consequences of a natural hazard are strongly connected to the vulnerability of elements at risk (e.g. people, buildings and infrastructure). It is, therefore, obvious that an analysis and quantification of vulnerability is required for successful disaster risk reduction. The need for vulnerability assessment is crucial due to the effects of climate change as well as the socio-economic dynamics that may directly impact vulnerabilities and coping capacities of affected communities. Vulnerability is multidimensional (physical, social, economic, etc.), however, the dimension which is mainly related to the direct costs and threat to human lives is the physical one. We focus here on the physical vulnerability of buildings subject to torrential hazards occurring in small and steep mountain catchments. These processes include fluvial sediment transport, debris floods, and debris flows. Furthermore, we included flash flood hazards if these are related to torrential catchments. Physical vulnerability to torrential hazards in mountain areas is a topic that has been under scientific investigation over the last 20 years. Several methods to assess physical vulnerability of buildings towards flash floods, debris flows and hyper-concentrated flows can be found in the literature. The plethora of methods and approaches may be classified under the following three categories: vulnerability matrices, vulnerability curves and vulnerability indices. We provide a short review of these methods which became available over the last decade and which dominate the scientific debate in mountain hazard risk management, giving an emphasis to vulnerability curves. The approaches presented herein are highlighted through case studies from the European Alps and beyond. Furthermore, challenges in vulnerability assessment including data requirements, need for improved event documentation, uncertainties and challenges related to future climate and socio-economic changes are outlined. Finally, a discussion on progress-driving factors such as new technologies (e.g. mobile apps, drones), citizen science and new innovative assessment methods is provided.

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.

Mountain social‐ecological systems (MtSES) are vital to humanity, providing ecosystem services to over half the planet's human population. Despite their importance, there has been no global assessment of threats to MtSES, even as they face unprecedented challenges to their sustainability. With survey data from 57 MtSES sites worldwide, we test a conceptual model of the types and scales of stressors and ecosystem services in MtSES and explore their distinct configurations according to their primary economic orientation and land use. We find that MtSES worldwide are experiencing both gradual and abrupt climatic, economic, and governance changes, with policies made by outsiders as the most ubiquitous challenge. Mountains that support primarily subsistence‐oriented livelihoods, especially agropastoral systems, deliver abundant services but are also most at risk. Moreover, transitions from subsistence‐ to market‐oriented economies are often accompanied by increased physical connectedness, reduced diversity of cross‐scale ecosystem services, lowered importance of local knowledge, and shifting vulnerabilities to threats. Addressing the complex challenges facing MtSES and catalyzing transformations to MtSES sustainability will require cross‐scale partnerships among researchers, stakeholders, and decision makers to jointly identify desired futures and adaptation pathways, assess trade‐offs in prioritizing ecosystem services, and share best practices for sustainability. These transdisciplinary approaches will allow local stakeholders, researchers, and practitioners to jointly address MtSES knowledge gaps while simultaneously focusing on critical issues of poverty and food security. ISSN:2328-4277

We believe that the transdisciplinary studies on water-related multi-hazards are innovative and critical research by the water community, thus answering the call of the recent Nature Sustainability Editorial ‘Too much and not enough’1 for water science ideas that are not derivative or stagnant. This domain of water studies focuses on the specific contexts where water-related hazardous events occur simultaneously, in cascade or cumulatively with other events. Characteristic of the field is the intensive collaboration of scientists and practitioners from different disciplines working together to better understand, assess and manage water-related multi-hazards. At the recent Asia Oceania Geosciences Society–European Geosciences Union Joint Conference on New Dimensions for Natural Hazards in Asia, we discussed the statement ‘Too much and not enough’1 and here suggest three reasons why transdisciplinary collaborations have led to many new ideas and notable advancements in the field of water-related multi-hazard research in recent years.

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.

The knowledge about potential flood damage is a key issue for disaster risk reduction. However, the scarcity of empirical data has limited flood damage modeling in several regions. As a result, studies in data-scarce regions have mostly been restricted to either building exposure assessment or identification of vulnerability indicators without a further linkage to probable damage. As expert-based approaches do not require empirical damage data, they have a high potential for flood damage modeling in data-scarce regions. In this study, we carried out a comparative assessment between an expert-based and a data-driven approach. The expert-based approach systematically combines the vulnerability indicator method and synthetic what-if analysis based on the knowledge of regional experts. The data-driven approach integrates empirical flood damage data in the analysis applying a multivariate random forest model. Flood damage data, collected through interviews after two flood events in 2017 and 2019 at separate locations in Nigeria, were used to evaluate the performance of both methods based on developed damage grades. Results from both methods showed i) a predictive accuracy of 30% and 38% for the expert-based and data-driven approaches respectively, ii) that distance to channel, wall material, building condition, and building quality are significant regional damage drivers, and iii) comparable model performance can be achieved even with a reduced number of variables. Furthermore, the study demonstrated how experts are likely to underestimate damage at low water depths and how a difference in conformity to building standards can add to challenges in flood damage prediction.

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.

Implementing effective flood risk strategies is an essential task for policy-makers which will gain in importance as flood losses are expected to increase due to socio-economic and climatic drivers in near future. Flood risk mitigation incorporates structural and non-structural measures such as the declaration of flood hazard zones, both of which are associated with high financial expenses. Essential information to ensure maximum effectiveness and cost efficiency of flood protection measures is provided by quantitative flood loss analyses based, for example, on data from insurance claims.This project aims to model the expected flood damage, thus the vulnerability to buildings by examining country-wide, empirical flood loss data of Switzerland of the past 35 years. The developed method includes several steps: First, the loss data are statistically analysed, second the spatial distribution of the loss data in the different hazard zones is assessed and third, vulnerability models for each hazard zone are developed including further parameters such as building values or building zones. A further objective is to provide an overview of possible methods which differ in complexity and data requirement and can be adapted for other applications outside of Switzerland. First results show that the extent of loss increases as the degree of hazard rises. In contrast, however, the number of damage events is highest in flood zones with a lower degree of hazard. Further possibilities how risk adaptation strategies can be supported or complemented by flood loss data are presented within this project.

Regardless of the frequency and magnitude, the consequences of flood hazards are strongly connected to the vulnerability of elements at risk (e.g. buildings, people, and infrastructure). It is, therefore, obvious that an analysis and quantification of vulnerability is required for successful risk reduction. Vulnerability is multidimensional (physical, social, economic, institutional, etc.), however, the primary driver of direct costs and threat to human lives is the physical one. We focus here on the physical vulnerability of buildings subject to dynamic flooding occurring in mountain environments. These processes include debris floods, fluvial sediment transport, and debris flows. Furthermore, we included flash flood hazards if these are related to torrential catchments.Physical vulnerability to dynamic flooding in mountain areas is a topic that has been under scientific investigation over the last 20 years. Several methods to assess physical vulnerability of buildings towards flash floods, debris flows and hyper-concentrated flows can be found in the literature. The plethora of methods and approaches may be classified under the following three categories: vulnerability matrices, vulnerability curves and vulnerability indices. We provide a short review of these methods which became available over the last decade and which dominate the scientific debate in mountain hazard risk management, giving an emphasis to vulnerability curves. The approaches presented herein are highlighted through case studies from the mountain areas of Europe and beyond, and challenges in vulnerability assessment including data requirements, need for improved event documentation, uncertainties and challenges related to future climate and socio-economic changes are outlined. Finally, a discussion on progress-driving factors such as new technologies (e.g. mobile apps, drones), citizen science and new innovative assessment methods is provided.

We develop a technique for reconstructing floods in small-scale data scarce regions using field interview data and hydro-dynamic modelling. The field interview data consist of flood depths and duration data collected from 300 buildings from a flood event in 2017 in Suleja/Tafa area, Nigeria. The flood event resulted from an overflow of water from five river reaches. The hydrodynamic model utilized, called CAESER LisFLOOD, is an integration of a landscape evolution model (CAESER) and a hydraulic model (LisFLOOD-FP). We employ three steps to reconstruct the 2017 Suleja/Tafa flood event. Firstly, we use a linearly increasing hydrograph to; (a) calibrate Manning’s coefficient and (b) determine optimal peak discharge on each reach. This was carried out by minimizing the Root Mean Square Error (RMSE) between the distributed observed flood depths and the simulated flood depths. Secondly, we use synthetic hydrographs with durations between 6, 12, 18, 20, 24 hours, having peak discharge (extracted from the previous step), to simulate flows on all upstream reaches. Using collected flood duration data, we minimized RMSE between distributed observed flood duration and simulated flood duration to determine optimal flow durations on each upstream reach. In the last step, utilizing peak discharge and flow duration for all upstream reaches, we carried out multiple spatial and temporal iterations to match downstream peak discharge. Thereafter, we use determined upstream hydrographs with their relative catchment response timing to simulate the entire river network. Minimum RMSE computed for the entire river network was between ±15 cm of many current studies that use distributed observed data to calibrate flood models. The method developed in this study is useful for simulating floods in regions where data such as high resolution DEMs, river bathymetry and river discharge are limited. In addition, the study extends current knowledge, on utilizing distributed flood data to determine peak discharge, from a single to multiple river networks.

Disasters induced by natural hazards or extreme events consist of interacting human and natural components. While progress has been made to mitigate and adapt to natural hazards, much of the existing research lacks interdisciplinary approaches that equally consider both natural and social processes. More importantly, this lack of integration between approaches remains a major challenge in developing disaster risk management plans for communities. In this study, we made a first attempt to develop a conceptual model of a coupled human-landscape system in Swiss Alpine communities. The conceptual model contains a system dynamics (e.g. interaction, feedbacks) component to reproduce community level, socio-economic developments and shocks that include economic crises leading to unemployment, depopulation and diminished community revenue. Additionally, the conceptual model contains climate, hydrology, and geomorphic components that are sources of natural hazards such as floods and debris flows. Feedbacks between the socio-economic and biophysical systems permit adaptation to flood and debris flow risks by implementing spatially explicit mitigation options including flood defences and land cover changes. Here we justify the components, scales, and feedbacks present in the conceptual model and provide guidance on how to operationalize the conceptual model to assess risk and community resilience of Swiss Alpine communities.

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.

Although the vulnerability indicator method has been applied to several data-scarce regions, a missing linkage with damage grades had hindered its application for loss evaluation to complement disaster risk reduction efforts. To address this gap, we present a review of physical vulnerability indicators and flood damage models to gain insights on best practice. Thereafter, we present a conceptual framework for linking the vulnerability indicators and damage grades using three phases (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking vulnerability index classes with damage grades. The vulnerability index comprehensively integrates elements of the hazard using a Building Impact Index (BII) on one hand, and exposure, susceptibility and local protection elements using a Building Resistance Index (BRI) on the other hand. For the damage grades, local expert assessments are used for identifying and categorizing frequently observed regional damage patterns. Finally, by means of synthetic what-if analysis, experts are asked to estimate damage grades for each interval of the BII and class of BRI to develop a vulnerability curve. The proposed conceptual framework can be used for damage prediction in data-scarce regions to support loss assessment and to provide guidance for disaster risk reduction.

Accessibility is a key measure of the vulnerability of road networks to disruptions such as floods. However, studies comparing the contribution of parameters to the accuracy of accessibility-based vulnerability assessment are lacking. We propose modifying two accessibility measures to include flood-affected populations, opportunities, and average shortest travel time. We also applied three methods including the divergent ranking method to identify the direct impact of extreme floods on road networks. The shortest travel time pathway calculation was enhanced with the inclusion of spatially distributed settlements as an input. The results indicate a strong relationship between parameter weights and the accessibility measures, irrespective of the evaluated approaches. The results of the study highlight that measures of overall vulnerability, with respect to inter-comparisons of flood scenarios alone, do not fully capture the local vulnerability of some traffic zones. This is particularly evident with the flooding of highly connected roads that serve these zones.

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.

The abstract is available here: https://uscholar.univie.ac.at/o:894617

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

Disasters induced by natural hazards or extreme events consist of interacting human and natural components. While progress has been made to mitigate and adapt to natural hazards, much of the existing research lacks interdisciplinary approaches that equally consider both natural and social processes. More importantly, this lack of integration between approaches remains a major challenge in developing disaster risk management plans for communities. In this study, we made a first attempt to develop a conceptual model of a coupled human-landscape system in Swiss Alpine communities. The conceptual model contains a system dynamics (e.g. interaction, feedbacks) component to reproduce community level, socio-economic developments and shocks that include economic crises leading to unemployment, depopulation and diminished community revenue. Additionally, the conceptual model contains climate, hydrology, and geomorphic components that are sources of natural hazards such as floods and debris flows. Feedbacks between the socio-economic and biophysical systems permit adaptation to flood and debris flow risks by implementing spatially explicit mitigation options including flood defenses and land cover changes. Here we justify the components, scales, and feedbacks present in the conceptual model and provide guidance on how to operationalize the conceptual model to assess risk and community resilience as well as determine which shocks overcome the buffering capacity of Swiss Alpine communities.

The devastating 2015 earthquakes in Nepal highlighted the need for effective disaster risk reduction (DRR) in mountains, which are inherently subject to hazards and increasingly vulnerable to extreme events. As multiple UN policy frameworks stress, DRR is crucial to mitigate the mounting environmental and socioeconomic costs of disasters globally. However, specialized DRR guidelines are needed for biodiverse, multi-hazard regions like mountains. Ecosystem-based disaster risk reduction (Eco-DRR) emphasizes ecosystem conservation, restoration, and sustainable management as key elements for DRR. We propose that integrating the emerging field of Eco-DRR with community-based DRR (CB-DRR) will help address the increasing vulnerabilities of mountain people and ecosystems. Drawing on a global mountain synthesis, we present paradoxes that create challenges for DRR in mountains and examine these paradoxes through examples from the 2015 Nepal earthquakes. We propose four principles for integrated CB- and Eco-DRR that address these challenges: (1) governance and institutional arrangements that fit local needs; (2) empowerment and capacity-building to strengthen community resilience; (3) discovery and sharing of constructive practices that combine local and scientific knowledge; and (4) approaches focused on well-being and equity. We illustrate the reinforcing relationship between integrated CB- and Eco-DRR principles with examples from other mountain systems worldwide. Coordinated community and ecosystem-based actions offer a potential path to achieve DRR, climate adaptation, sustainable development, and biodiversity conservation for vulnerable ecosystems and communities worldwide.

Beside the flood hazard analysis, a comprehensive flood risk assessment requires the analysis of the exposure of values at risk and their vulnerability. Currently, the main focus of such analysis is on losses on building structure. However, loss on household contents accounts for up to 30% of the total losses on buildings due to floods. Based on insurance claim records, we developed and (cross-)validated two functions. The models based on linear regressions estimate the monetary loss and the degree of loss of household contents by the monetary and degree of loss for building structure, respectively. The main focus herein is to develop functions which provide robustness in prediction and transferability to other regions. Both models generate appropriate results with a comparative advantage of the relative over the absolute loss model. Our results indicate that the ratio of household content to building structure loss is decreasing relatively in regions with comparatively high losses or degrees of loss. A detailed examination of the model residuals, shows that the Box-Cox transformation works well to accurately fit a standard regression model to general right-skewed loss data as the transformed data meet the assumptions of a regression model.

Landscape Evolution Models (LEMs) simulate the movement of water and sediment over the landscape. Although much progress has been made in the development of LEMs, few have been tested in rivers subject to anthropogenic impacts that produce high energy flows, transporting large amounts of sediment and causing significant geomorphic changes. As such, it remains uncertain if LEMs are useful and stable under relatively short term ‘extreme’ geomorphic conditions. To shed light on this topic we use a LEM (CAESAR-Lisflood) and historical documents to develop a detailed reach scale model of the Kander river (Switzerland). This model was used to simulate the unintended impacts of engineering works, occurring in 1714, that deviated the Kander river into a lake and resulted in a large decrease in base level of the river. In 10 years, the model simulates knickpoint propagation that rapidly erodes 2.5 million m3 of sediment and produces a remarkable 27 m of channel erosion. Simultaneously, the model develops the formation of a delta via frequent avulsions. Model testing is performed by comparing model predictions against historical observations of channel incision, knickpoint location, and delta spatial extent. Overall, model error is low and the model remained stable as results do not contain erratic erosion or deposition. Importantly the model suggests that downstream processes occurring at and near the delta have an effect on upstream channel erosion. We also recommend that studies replicating historic landscape changes with LEMs reduce uncertainty in hydrological inputs.

Assessing the flood exposure of infrastructure is crucial for sustainable flood risk management. We propose to define road exposure to floods as “any road segment/element located within a flood hazard zone”. On the example of Switzerland, we develop and compare three methods to assess the flood exposure of the road infrastructure: absolute road area potentially flooded (method A), relative road area potentially flooded (method B), and a network-based approach using the edge betweenness centrality index (method C). The results are aggregated and ranked in grid cells of 2 km by 2 km for comparison. The ranks are directly proportional to exposure levels. The results present distinct spatial patterns; grid cells with higher values in method A indicate where more road surfaces are in a flood hazard zone (urban or high road density areas in flood plains), while method B highlights where most of the road surface is in flood hazard zones of predominantly mountainous areas of Switzerland. Higher values in method C present areas where the most-connected road links within a network are exposed to floods. Most of the grid cells that have higher values in method B than in method C indicate isolated mountain valleys that can be accessed only by a single road exposed to floods. The study demonstrates the importance of an appropriate choice of methods in flood exposure analysis. As exposure analysis is a key step in flood risk assessment, the presented results provide information essential for decision making in disaster risk reduction.

European mountain regions are diverse, from gently rolling hills to high mountain areas, and from low populated rural areas to urban regions or from communities dependent on agricultural productions to hubs of tourist industry. Communities in European mountain regions are threatened by different hazard types: for example floods, landslides, or glacial hazards, mostly in a multi-hazard environment. Due to climate change and socioeconomic developments they are challenged by emerging and spatially as well as temporally highly dynamic risks. Consequently, over decades societies in European mountain ranges developed different hazard and risk management strategies on a national to local level, which are presented below focusing on the European Alps. Until the late 19th century, the paradigm of hazard protection was related to engineering measures, mostly implemented in the catchments, and new authorities responsible for mitigation were founded. From the 19th century, more integrative strategies became prominent, becoming manifest in the 1960s with land-use management strategies targeted at a separation of hazardous areas and areas used for settlement and economic purpose. In research and in the application, the concept of hazard mitigation was step by step replaced by the concept of risk. The concept of risk includes three components (or drivers), apart from hazard analysis also the assessment and evaluation of exposure and vulnerability; thus, it addresses in the management of risk reduction all three components. These three drivers are all dynamic, while the concept of risk itself is thus far a static approach. The dynamic of risk drivers is a result of both climate change and socioeconomic change, leading through different combinations either to an increase or a decrease in risk. Consequently, natural hazard and risk management, defined since the 21st century using the complexity paradigm, should acknowledge such dynamics. Moreover, researchers from different disciplines as well as practitioners have to meet the challenges of sustainable development in the European mountains. Thus, they should consider the effects of dynamics in risk drivers (e.g., increasing exposure, increasing vulnerability, changes in magnitude, and frequency of hazard events), and possible effects on development areas. These challenges, furthermore, can be better met in the future by concepts of risk governance, including but not limited to improved land management strategies and adaptive risk management.

Sensitivity analysis (SA) describes how varying inputs to a model subsequently varies its outputs. Its inclusion can support the systematic calibration of numerical models to back-calculate intensity properties of past torrent events that would otherwise be difficult or impossible to collect during their occurrence. Sensitivity analysis for model calibration is assessed with the back-calculation of a known torrent event. In particular, FLO-2D, a cell-based numerical model, is used to simulate the 2005 debris flow event that occurred in Brienz, Switzerland. Under 4000 simulations were completed with ranges of physically reasonable parameter values. Model results were compared in 3-dimensions with both sediment deposition extents (x, y) and estimated deposition heights (z) from available post-event images. The comparisons highlighted that more accurate input and validation data, namely the flow behavior of hazardous processes and post-event deposition heights, are required to produce stronger agreements between simulated and observed results. Furthermore, the application of SA for model calibration supports systematic exploration of large parameter spaces characteristic of complex phenomena like natural hazard events. These findings demonstrated how important model input factors can be identified, which provide guidance for future data collection efforts to capture both the rheology and the spatial distribution of hazards more accurately.

Quantitative flood risk analyses support decisions in flood management policies that aim at cost efficiency. Risk is commonly calculated by a combination of the three quantified factors: hazard, exposure, and vulnerability. Our paper focuses on the quantification of exposure, in particular on the relevance of building value estimation schemes within flood exposure analyses at regional to national scales. We compare five different models that estimate the values of flood exposed building. Four of them refer to individual buildings, whereas one is based on values per surface area, differentiated by land use category. That one follows an approach commonly used in flood risk analyses at regional or larger scales. Apart from the underlying concepts, the five models differ in complexity, in data and in computational expenses required for parameter estimations, and in the data they require for model application. The model parameters are estimated by using a database of more than half a million building insurance contracts in Switzerland, which are provided by 11 (out of 19) Cantonal insurance companies for buildings that operate under a monopoly within the respective Swiss Cantons. Comparing the five models' results with the spatially referenced insurance data applied directly suggests that models based on individual buildings produce better results than the model based on surface area, but only when they include an individual building's volume. Applying the five models to all of Switzerland produces results that are very similar with regard to the spatial distribution of exposed building values. Therefore, for spatial prioritizations, simpler models are preferable. In absolute values, however, the five models' results differ remarkably. The two simplest models underestimate the overall exposure, and even more so the extreme high values, upon which risk management strategies generally focus. In decision-making processes based on cost-efficiency, this underestimation would result in suboptimal resource allocation for protection measures. Consequently, we propose that estimating exposed building values should be done based on individual buildings rather than on areas of land use types. In addition, a building's individual volume has to be taken into account in order to provide a reliable basis for cost-benefit analyses. The consideration of other building features further improves the value estimation. However, within the context of flood risk management, the optimal value estimation model depends on the specific questions to be answered. The concepts of the presented building value models are generic. Thus, these models are transferrable with minimal adjustments according to the application's purpose and the data available.

A sound understanding of flood risk drivers (hazard, exposure and vulnerability) is essential for the effective and efficient implementation of risk-reduction strategies. In this paper, we focus on ‘exposure’ and study the influence of different methods and parameters of flood exposure analyses in Switzerland. We consider two types of exposure indicators and two different spatial aggregation schemes: the density of exposed assets (exposed numbers per km2) and the ratios of exposed assets (share of exposed assets compared to total amount of assets in a specific region) per municipality and per grid cells of similar size as the municipalities. While identifying high densities of exposed assets highlights priority areas for cost-efficient strategies, high exposure ratios can suggest areas of interest for strategies focused on the most vulnerable regions, i.e. regions with a low capacity to cope with a disaster. In Switzerland, the spatial distribution of high exposure densities and exposure ratios tend to be complementary. With regards to the methods, we find that the spatial cluster analysis provides more information for the prioritization of flood protection measures than ‘simple’ maps of spatially aggregated data represented in quantiles. In addition, our study shows that the data aggregation scheme influences the results. It suggests that the aggregation based on grid cells supports the comparability of different regions better than aggregation based on municipalities and is, thus, more appropriate for nationwide analyses.