Search

Coastal flood risk and sea-level rise require decisions on investment in coastal protection and, in some cases, the relocation of urban areas. Models that formalize the relations between flooding costs, protective investments, and relocation can improve the analysis of the processes and issues involved and help to support decision-making better. In this paper, an agent-based model of a coastal city is represented in NetLogo. This model is based on the VIABLE modeling framework and describes adaptive dynamic agent behavior in a changing system. The hypothetical city faces damage caused by gradually rising sea levels and subsequent extreme sea-level events. To mitigate these risks, an "urban planner" agent has two adaptation measures at their disposal: developing coastal defenses or, as a more extreme measure, relocating vulnerable areas inland. As the simulation progresses and the decisions change with rising sea levels, the agent alters investments in these two measures to increase its value function, resulting in dynamic reactive behavior. Additionally, gradual sea-level rise is implemented in various modes, along with extreme sea-level events that cause severe short-term damage. The results of simulations under these modes and with multiple scenarios of agent action are presented. On average, agent behavior is quite reactive under limited foresight. Individual simulations yield a 'priming' effect when comparing different timings of extreme sea-level events, wherein an earlier extreme event primes the agent to adapt and thus be better prepared for subsequent events. Agent success with adaptation is also found to be sensitive to the costs involved, and these varying degrees of adaptation success are quantified using three parameters of adaptation success. [ABSTRACT FROM AUTHOR]

Drought and heat events are becoming more frequent in Europe due to human-induced climate change, affecting many aspects of human well-being and ecosystem functioning. However, the intensity of these drought and heat events is not spatially and temporally uniform. Understanding the spatial variability of drought impacts is important information for decision makers, supporting both planning and preparations to cope with the changing climatic conditions. Currently, data relating to the damage caused by extended drought episodes is scattered across languages and sources such as scientific publications, governmental reports and the media. In this review paper, we compiled data of damages caused by the drought and heat of 2018 until 2022 in forest ecosystems and relate it to large European data sets, providing support for decision making both on the regional and European levels. We partitioned data from 16 European countries to the following regions: Northern, Central, Alpine, and South. We focused on drought and heat damage to forests, and categorized them as (1) physiological (2) pest, and (3) fire damage. We were able to identify the following key trends: (1) Relative defoliation rates of broadleaves is higher than of conifers in every country with the exception of Czech Republic (2) the incidence of wood destroyed by insects is extremely high in Central Europe and Sweden (3) Although forest fires can be related to heat and drought, they are superimposed by other anthropogenic influences (4) In this period (2018–2022), forests in central Europe are particularly affected, while forests in the Northern and Alpine zones are less affected, and adaptations to heat and drought can still be observed in the Southern zone. (5) Although in several regions 2021 was an average year still high levels of damages were observed indicating strong legacy effects of 2018–2020. We note that the inventory should be continuously updated as new data appear. [ABSTRACT FROM AUTHOR]

Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, socio-demographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM 10 , NO, NO2 , SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2=0.62 –0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes. [ABSTRACT FROM AUTHOR]

Southwest Asia has different climate types including arid, semiarid, Mediterranean, and temperate regions. Due to the complex interactions among components of the Earth system, forecasting precipitation is a difficult task in such large regions. The aim of this paper is to propose a learning approach, based on artificial neural network (ANN) and random forest (RF) algorithms for post-processing the output of forecasting models, in order to provide a multi-model ensemble forecasting of monthly precipitation in southwest Asia. For this purpose, four forecasting models, including GEM-NEMO, NASA-GEOSS2S, CanCM4i, and COLA-RSMAS-CCSM4, included in the North American multi-model ensemble (NMME) project, are considered for the ensemble algorithms. Since each model has nine different lead times, a total of 108 different ANN and RF models are trained for each month of the year. To train the proposed ANN an RF models, the ERA5 reanalysis dataset is employed. To compare the performance of the proposed algorithms, four performance evaluation criteria are calculated for each model. The results indicate that the performance of the ANN and RF post-processing is better than that of the individual NMME models. Moreover, RF outperformed ANN for all lead times and months of the year. [ABSTRACT FROM AUTHOR]

Variability in iceberg discharge and deep-ocean circulation in the North Atlantic during the last glacial period is inferred from the grain-size distribution and trace elemental composition of terrigenous sediments in a deep-sea core taken on Reykjanes Ridge, south of Iceland. End-member modeling of the grain-size distributions is used to unmix the signals of varying bottom-current speed and iceberg discharge. The size distribution within the silt fraction appears to be influenced by both factors. Based on Th-Sc-La relationship, we established that during the ice-rafted detritus events, continental material of likely Greenlandic origin increased to 87%, and that bottom-current-derived material contains to 40% mid-oceanic ridge fines, probably of Icelandic origin. Our results have important implications for the use of silt grain size as an indicator for paleocurrent speed in the glacial North Atlantic. We show that reconstructions of variations in bottom-current speed based on the raw grain-size data are opposite to inferences from the unmixed record. The latter indicates that deep-water convection decreased during periods of enhanced iceberg discharge, which is in general agreement with paleoceanographic reconstructions of the North Atlantic. Keywords: ocean circulation, iceberg discharge, brain-size distribution, end-member modeling, trace element geochemistry.

This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10 -year simulations and 25×10 -year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and 2.0 ∘C global warming are quantified. These indices include number of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-year return period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C. [ABSTRACT FROM AUTHOR]

For the development of sustainable, efficient risk management strategies for the hydrological extremes of droughts and floods, it is essential to understand the temporal changes of impacts, and their respective causes and interactions. In particular, little is known about changes in vulnerability and their influence on drought and flood impacts. We present a fictitious dialogue between two experts, one in droughts and the other in floods, showing that the main obstacles to scientific advancement in this area are both a lack of data and a lack of commonly accepted approaches. The drought and flood experts "discuss" available data and methods and we suggest a complementary approach. This approach consists of collecting a large number of single or multiple paired-event case studies from catchments around the world, undertaking detailed analyses of changes in impacts and drivers, and carrying out a comparative analysis. The advantages of this approach are that it allows detailed context- and location-specific assessments based on the paired-event analyses, and reveals general, transferable conclusions based on the comparative analysis of various case studies. Additionally, it is quite flexible in terms of data and can accommodate differences between floods and droughts. [ABSTRACT FROM AUTHOR]

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

Abstract: Reliable flood damage assessment is important for decision‐making in flood risk management. Flood damage assessment is often done with damage curves based only on water depth. These depth‐damage curves are usually developed based on data from a specific location and specific flood conditions. Such depth‐damage curves tend to be applied outside the scope of their validity. Validation studies show that in such cases depth‐damage curve are not very reliable, probably due to excluded influencing variables. The expectation is that the inclusion of more variables in a damage function will improve its transferability. We compare multi‐variable models based on Bayesian Networks and Random Forests developed on the basis of flood damage data sets from Germany and The Netherlands. The performance of the models is tested on a validation sub‐set of both countries' data. The models are also updated with data from the other country and then tested again. The results show that the German models (BN/RF‐FLEMOps) perform better in the Netherlands than the Dutch models (BN/RF‐Meuse) perform in Germany. This is probably because the FLEMOps models are based on more heterogeneous data than the Meuse models. The FLEMOps models, therefore, are better able to capture damages processes from other events and in other locations. Model performance improves via updating the models with data from the location to which the model is transferred to. The results show that there is high potential to develop improved damage models, by training multi‐variable models with heterogeneous data, for example from multiple flood events and locations. [ABSTRACT FROM AUTHOR]

As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro-climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur. [ABSTRACT FROM AUTHOR]

Flood damage assessment is usually done with damage curves only dependent on the water depth. Recent studies have shown that data-mining techniques applied to a multi-dimensional dataset can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive dataset, which is rarely available and this can limit the application of these new techniques. In this paper we enrich a dataset of residential building and content damages from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2D flood simulations are used to add information on flow velocity, flood duration and the return period to the dataset, and cadastre data is used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched dataset in combination with the data-mining techniques delivers a significant improvement over a simple model only based on the water depth. We find that with our dataset, the trees based methods perform better than the Bayesian Network. [ABSTRACT FROM AUTHOR]

With projected changes in climate, population and socioeconomic activity located in flood-prone areas, the global assessment of flood risk is essential to inform climate change policy and disaster risk management. Whilst global flood risk models exist for this purpose, the accuracy of their results is greatly limited by the lack of information on the current standard of protection tofloods, with studies either neglecting this aspect or resorting to crude assumptions. Here we present a first global database of FLOod PROtection Standards, FLOPROS, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales. FLOPROS comprises three layers of information, and combines them into one consistent database. The design layer contains empirical information about the actual standard of existing protection already in place; the policy layer contains information on protection standards from policy regulations; and the model layer uses a validated modelling approach to calculate protection standards. The policy layer and the model layer can be considered adequate proxies for actual protection standards included in the design layer, and serve to increase the spatial coverage of the database. Based on this first version of FLOPROS, we suggest a number of strategies tofurther extend and increase the resolution of the database. Moreover, as the database is intended to be continually updated, while flood protection standards are changing with new interventions, FLOPROS requires input from the flood risk community. We therefore invite researchers and practitioners to contribute information to this evolving database by corresponding to the authors. [ABSTRACT FROM AUTHOR]

The article proposes a framework of cost assessment cycle for the integrated cost assessment of natural hazards. It describes a four-phase continuous costing, emphasizing the importance of contextualization of the aim and scope of the assessment and the relevant hazards. It stresses that informed decisions for natural hazard risk management are important under global environmental change.

Recent major flood disasters have shown that single extreme events can affect multiple countries simultaneously, which puts high pressure on trans-national risk reduction and risk transfer mechanisms. So far, little is known about such flood hazard interdependencies across regions and the corresponding joint risks at regional to continental scales. Reliable information on correlated loss probabilities is crucial for developing robust insurance schemes and public adaptation funds, and for enhancing our understanding of climate change impacts. Here we show that extreme discharges are strongly correlated across European river basins. We present probabilistic trends in continental flood risk, and demonstrate that observed extreme flood losses could more than double in frequency by 2050 under future climate change and socio-economic development. We suggest that risk management for these increasing losses is largely feasible, and we demonstrate that risk can be shared by expanding risk transfer financing, reduced by investing in flood protection, or absorbed by enhanced solidarity between countries. We conclude that these measures have vastly different efficiency, equity and acceptability implications, which need to be taken into account in broader consultation, for which our analysis provides a basis. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)