The challenge of unprecedented floods and droughts in risk management.

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3.

Developing a large-scale dataset of flood fatalities for territories in the Euro-Mediterranean region, FFEM-DB.

This data paper describes the multinational Database of Flood Fatalities from the Euro-Mediterranean region FFEM-DB that hosts data of 2,875 flood fatalities from 12 territories (nine of which represent entire countries) in Europe and the broader Mediterranean region from 1980 to 2020. The FFEM-DB database provides data on fatalities' profiles, location, and contributing circumstances, allowing researchers and flood risk managers to explore demographic, behavioral, and situational factors, as well as environmental features of flood-related mortality. The standardized data collection and classification methodology enable comparison between regions beyond administrative boundaries. The FFEM-DB is expandable, regularly updated, publicly available, and with anonymized data. The key advantages of the FFEM-DB compared to existing datasets containing flood fatalities are its high level of detail, data accuracy, record completeness, and the large sample size from an extended area.

Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction.

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data-driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a "sample selection bias." In this article, we enhance data-driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.

Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe.

Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2-19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.

Causative classification of river flood events.

A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large-scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph-based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space-time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods. This article is categorized under:Science of Water > Water ExtremesScience of Water > Hydrological ProcessesScience of Water > Methods.

Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification.

Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate.

Sociohydrology: Scientific Challenges in Addressing the Sustainable Development Goals.

The Sustainable Development Goals (SDGs) of the United Nations Agenda 2030 represent an ambitious blueprint to reduce inequalities globally and achieve a sustainable future for all mankind. Meeting the SDGs for water requires an integrated approach to managing and allocating water resources, by involving all actors and stakeholders, and considering how water resources link different sectors of society. To date, water management practice is dominated by technocratic, scenario-based approaches that may work well in the short term but can result in unintended consequences in the long term due to limited accounting of dynamic feedbacks between the natural, technical, and social dimensions of human-water systems. The discipline of sociohydrology has an important role to play in informing policy by developing a generalizable understanding of phenomena that arise from interactions between water and human systems. To explain these phenomena, sociohydrology must address several scientific challenges to strengthen the field and broaden its scope. These include engagement with social scientists to accommodate social heterogeneity, power relations, trust, cultural beliefs, and cognitive biases, which strongly influence the way in which people alter, and adapt to, changing hydrological regimes. It also requires development of new methods to formulate and test alternative hypotheses for the explanation of emergent phenomena generated by feedbacks between water and society. Advancing sociohydrology in these ways therefore represents a major contribution toward meeting the targets set by the SDGs, the societal grand challenge of our time.

Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.

Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.

A Review of Flood Loss Models as Basis for Harmonization and Benchmarking.

Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss-or flood vulnerability-relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.

The 2011 flood event in the Mekong Delta: preparedness, response, damage and recovery of private households and small businesses.

Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.

Adaptive flood risk management planning based on a comprehensive flood risk conceptualisation.

Densely populated deltas are so vulnerable to sea level rise and climate change that they cannot wait for global mitigation to become effective. The Netherlands therefore puts huge efforts in adaptation research and planning for the future, for example through the national research programme Knowledge for Climate and the Delta Programme for the Twenty-first century. Flood risk is one of the key issues addressed in both programmes. Adaptive management planning should rely on a sound ex-ante policy analysis which encompasses a future outlook, establishing whether a policy transition is required, an assessment of alternative flood risk management strategies, and their planning in anticipation without running the risk of regret of doing too little too late or too much too early. This endeavour, addressed as adaptive delta management, calls for new approaches, especially because of uncertainties about long-term future developments. For flood risk management, it also entails reconsideration of the underlying principles and of the application of portfolios of technical measures versus spatial planning and other policy instruments. To this end, we first developed a conceptualisation of flood risk which reconciles the different approaches of flood defence management practice and spatial planning practice in order to bridge the gap between these previously detached fields. Secondly, we looked abroad in order to be better able to reflect critically on a possible Dutch bias which could have resulted from many centuries of experience of successful adaptation to increasing flood risk, but which may no longer be sustainable into the future. In this paper, we explain the multiple conceptualisation of flood risk and argue that explicitly distinguishing exposure determinants as a new concept may help to bridge the gap between engineers and spatial planners, wherefore we show how their different conceptualisations influence the framing of the adaptation challenge. Also, we identify what the Netherlands may learn from neighbouring countries with a different framing of the future flood risk challenge.

A delphi method expert survey to derive standards for flood damage data collection.

For the purpose of flood damage analyses reliable, comparable, comprehensive, consistent, and up-to-date data are an indispensable need. Like in many other countries a database with this kind of datasets does not exist in Germany. To establish it, standards have to be set for flood damage data collection. We approached this problem by questioning experts about their information needs for flood damage analysis. This survey is done by applying a Delphi survey approach. The aptitude of the Delphi approach to assess, structure, and standardize expert knowledge is evaluated in this article. In the survey a panel of 55 experts working in the field of flood damage analysis for insurances, engineering companies/consultancy, public water management, and universities and other scientific institutions helped to identify common information needs. The multi-step Delphi method proved to reduce the deviation of answers thereby enabling consensual results and also enhanced the quality by modifying group answers in the direction of experience based answers. There was also a high level of congruence in information needs between experts from different fields of employment that allowed the derivation of common standards.

The role of disaggregation of asset values in flood loss estimation: a comparison of different modeling approaches at the Mulde River, Germany.

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.

Improvements on flood alleviation in Germany: lessons learned from the Elbe flood in August 2002.

The increase in damage due to natural disasters is directly related to the number of people who live and work in hazardous areas and continuously accumulate assets. Therefore, land use planning authorities have to manage effectively the establishment and development of settlements in flood-prone areas in order to avoid the further increase of vulnerable assets. Germany faced major destruction during the flood in August 2002 in the Elbe and Danube catchments, and many changes have been suggested in the existing German water and planning regulations. This article presents some findings of a "Lessons Learned" study that was carried out in the aftermath of the flood and discusses the following topics: 1) the establishment of comprehensive hazard maps and flood protection concepts, 2) the harmonization of regulations of flood protection at the federal level, 3) the communication of the flood hazard and awareness strategies, and 4) how damage potential can be minimized through measures of area precaution such as resettlement and risk-adapted land use. Although attempts towards a coordinated and harmonized creation of flood hazard maps and concepts have been made, there is still no uniform strategy at all planning levels and for all states (Laender) of the Federal Republic of Germany. The development and communication of possible mitigation strategies for "unthinkable extreme events" beyond the common safety level of a 100-year flood are needed. In order to establish a sustainable and integrated flood risk management, interdisciplinary and catchment-based approaches are needed.

Insurability and mitigation of flood losses in private households in Germany.

In Germany, flood insurance is provided by private insurers as a supplement to building or contents insurance. This article presents the results of a survey of insurance companies with regard to eligibility conditions for flood insurance changes after August 2002, when a severe flood caused 1.8 billion euro of insured losses in the Elbe and the Danube catchment areas, and the general role of insurance in flood risk management in Germany. Besides insurance coverage, governmental funding and public donations played an important role in loss compensation after the August 2002 flood. Therefore, this article also analyzes flood loss compensation, risk awareness, and mitigation in insured and uninsured private households. Insured households received loss compensation earlier. They also showed slightly better risk awareness and mitigation strategies. Appropriate incentives should be combined with flood insurance in order to strengthen future private flood loss mitigation. However, there is some evidence that the surveyed insurance companies do little to encourage precautionary measures. To overcome this problem, flood hazards and mitigation strategies should be better communicated to both insurance companies and property owners.

Estimation of symbiotic N2 fixation in an Amazon floodplain forest.

Sustainable management for existing Amazonian forests requires an extensive knowledge about the limits of ecosystem nutrient cycles. Therefore, symbiotic nitrogen (N2) fixation of legumes was investigated in a periodically flooded forest of the central Amazon floodplain (Várzea) over two hydrological cycles (20 months) using the 15N natural abundance method. No seasonal variation in 15N abundance (delta 15N values) in trees which would suggest differences in N2 fixation rates between the terrestrial and the aquatic phase was found. Estimations of the percentage of N derived from atmosphere (%Ndfa) for the nodulated legumes with Neptunia oleracea on the one side and Teramnus volubilis on the other resulted in mean %Ndfa values between 9 and 66%, respectively. More than half of the nodulated legume species had %Ndfa values above 45%. These relatively high N gains are important for the nodulated legumes during the whole hydrological cycle. With a %Ndfa of 4-5% for the entire Várzea forest, N2 fixation is important for the ecosystem and therefore, has to be taken into consideration for new sustainable land-use strategies in this area.