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Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, hydrological models that estimate such discharges often lack transparency regarding the uncertainty in their predictions. This was evidenced by the devastating flood that occurred in July 2021, which was not captured by the existing model for estimating design discharges. This article proposes an approach to obtain uncertainty estimates for extremes with structured expert judgment using the classical model (CM). A simple statistical model was developed for the river basin, consisting of correlated generalized extreme value (GEV) distributions for discharges from upstream tributaries. The model was fitted to seven experts' estimates and historical measurements using Bayesian inference. Results were fitted only to the measurements were solely informative for more frequent events, while fitting only to the expert estimates reduced uncertainty solely for extremes. Combining both historical observations and estimates of extremes provided the most plausible results. The classical model reduced the uncertainty by appointing the most weight to the two most accurate experts, based on their estimates of less extreme discharges. The study demonstrates that with the presented Bayesian approach that combines historical data and expert-informed priors, a group of hydrological experts can provide plausible estimates for discharges and potentially also other (hydrological) extremes with relatively manageable effort. [ABSTRACT FROM AUTHOR]

The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts. [ABSTRACT FROM AUTHOR]

Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model's parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a 'best' approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts' judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units' components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts' ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants' field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence. [ABSTRACT FROM AUTHOR]

Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, existing statistical and hydrological models that estimate these discharges often lack transparency regarding the uncertainty of their predictions, as evidenced by the devastating flood event that occurred in July 2021 which was not captured by the existing model for estimating design discharges. This article proposes an alternative approach with a central role for expert judgment, using Cooke's method. A simple statistical model was developed for the river basin, consisting of correlated GEV-distributions for discharges in upstream sub-catchments. The model was fitted to expert judgments, measurements, and the combination of both, using Markov chain Monte Carlo. Results from the model fitted only to measurements were accurate for more frequent events, but less certain for extreme events. Using expert judgment reduced uncertainty for these extremes but was less accurate for more frequent events. The combined approach provided the most plausible results, with Cooke's method reducing the uncertainty by appointing most weight to two of the seven experts. The study demonstrates that utilizing hydrological experts in this manner can provide plausible results with a relatively limited effort, even in situations where measurements are scarce or unavailable. [ABSTRACT FROM AUTHOR]

Immersed tunnels are positive buoyant structures during installation and negative buoyant after installation. A tunnel is composed of sequential immersed elements that are coupled to each other in joints. Tunnel elements consist of segments which are compressed to each other by longitudinal post-tensioning. After immersion the tunnel is supported by the seabed and the longitudinal post-tension is cut at the joints between segments. Therefore, the structure is a segmented lining which is sensitive for settlements due to non uniform circumstances over the length of the tunnel. An uneven response of the bedding underneath the tunnel introduce shear forces in joints of an immersed tunnel. Because immersed tunnels need to be buoyant during installation, they have limitations on weight and geometry, the size and therefore the capacity of these shear keys is limited because the height of the tunnel, as shear keys are applied in the walls of the tunnel. The foundation response is influenced by many factors related to subsoil but also to construction and dredging tolerances. The shear forces were derived as a function of different covariance lengths for subsoil stiffness and dredging tolerances for different tunnel layouts. In reliability analyses, using two different probabilistic methods, exceedance probabilities of maximum shear forces are derived for one lay out using Non Parametric Bayesian Networks and Vine Copulas. The analyses give more insight in to the magnitude of the shear forces in joints both in conditioned and unconditioned situations and this can be used for the design of immersed tunnels. • Incorporate subsoil uncertainty in structural behaviour of immersed tunnels. • Uncertainty of dredging tolerances described by Gaussian random fields. • Influence of bedding variation on shear key forces of immersed tunnels. • Probabilistic analysis based on non-parametric Bayesian networks and Vine Copulas. • Deriving exceedance probability of maximum shear key forces. [ABSTRACT FROM AUTHOR]

Non-parametric Bayesian networks (NPBNs) are graphical tools for statistical inference widely used for reliability analysis and risk assessment and present several advantages, such as the embedded uncertainty quantification and limited computational time for the inference process. However, their implementation in hydrological studies is still scarce. Hence, to increase our understanding of their applicability and extend their use in hydrology, we explore the potential of NPBNs to reproduce catchment-scale hydrological dynamics. Long-term data from 240 river catchments with contrasting climates across the United States from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) data set will be used as actual means to test the utility of NPBNs as descriptive models and to evaluate them as predictive models for maximum daily river discharge in any given month. We analyse the performance of three networks, one unsaturated (hereafter UN-1), one saturated (hereafter SN-1), both defined only by hydro-meteorological variables and their bivariate correlations, and one saturated network (hereafter SN-C), consisting of the SN-1 network and including physical catchments' attributes. The results indicate that the UN-1 network is suitable for catchments with a positive dependence between precipitation and discharge, while the SN-1 network can also reproduce discharge in catchments with negative dependence. The latter can reproduce statistical characteristics of discharge (tested via the Kolmogorov–Smirnov statistic) and have a Nash–Sutcliffe efficiency (NSE) ≥0.5 in ∼40% of the catchments analysed, receiving precipitation mainly in winter and located in energy-limited regions at low to moderate elevation. Further, the SN-C network, based on similarity of the catchments, can reproduce discharge statistics in ∼10% of the catchments analysed. We show that once a NPBN is defined, it is straightforward to infer discharge and to extend the network itself with additional variables, i.e. going from the SN-1 network to the SN-C network. However, the results also suggest considerable challenges in defining a suitable NPBN, particularly for predictions in ungauged basins. These are mainly due to the discrepancies in the timescale of the different physical processes generating discharge, the presence of a "memory" in the system, and the Gaussian-copula assumption used for modelling multivariate dependence. [ABSTRACT FROM AUTHOR]

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions. [ABSTRACT FROM AUTHOR]

Continental or global studies of coastal flood hazard in the context of climate change encounter several obstacles. The primary concern is the limited coverage of sea level data, especially the high-frequency sort needed to analyse sea level extremes. In this paper we present the calculations of return periods of storm surge heights and water levels for the European coast. The analysis utilized simulations using Delft3D hydrodynamic model driven by meteorological data with temporal and spatial resolution, created under EURO-CORDEX activities. The simulations were calibrated using short- and long-term sea levels from over 150 gauges. Annual maxima of water levels were extracted from five simulations: 1971–2000 historical run as well as 2021–50 and 2071–2100 simulations based on two emissions scenarios each. Spatially varying sea level rise projections were also included. Annual maxima were then fitted to probability distributions in order to obtain the return periods. The results were combined with more than 70,000 coastal sections, so that they would be complimentary with a river flood hazard dataset developed in parallel. The study showed a good match between simulated and observed storm surge heights. It also shows large differences in future trends of water levels in Europe.

Non-Parametric Bayesian Networks (NPBNs) are graphical tools for statistical inference widely used for reliability analysis and risk assessment. However, few hydrological applications can be found in the literature. We therefore explore here the potential of NPBNs to reproduce catchment-scale hydrological dynamics by investigating 240 catchments with contrasting climate across the United States from the CAMELS dataset. First, two networks, one unsaturated (UN-1) and one saturated network (SN-1) based on hydro-meteorological variables are used to generate monthly maximum river discharge considering the catchment as a single element. Then, the saturated network SN-C, based on SN-1 but additionally including physical catchments attributes, is used to model a group of catchments and infer monthly maximum river discharge in ungauged basins based on the attributes similarity. The results indicate that the UN-1 model is suitable for catchments with a positive dependence between precipitation and river discharge, while the SN-1 model can reproduce discharge also in catchments with negative dependence. Furthermore, in ~40 % of the catchments analysed the SN-1 model can reproduce statistical characteristics of discharge, tested via the Kolmogorov-Smirnov (KS) statistic, and Nash-Sutcliffe Efficiencies (NSE) ≥ 0.5. Such catchments receive precipitation mainly in winter and are located in energy-limited regions at low to moderate elevation. Further, the SN-C model, in which the inference process benefits from catchment similarity, can reproduce river discharge statistics in ~10 % of the catchments analysed. However, in these catchments a common dominant physical attribute was not identified. In this study, we show that, once a NPBNs is defined, it is straightforward to infer discharge, when the remaining variables are known. We also show that it is possible to extend the network itself with additional variables, i.e. going from SN-1 to SN-C. Despite these advantages, the results also suggest that there are considerable challenges in defining a suitable NPBN, in particular for predictions in ungauged basins. These are mainly due to the discrepancies in the time scale of the different physical processes generating discharge, the presence of a “memory” in the system, and the Gaussian-copula assumption used by NPBNs for modelling multivariate dependence. [ABSTRACT FROM AUTHOR]

Natural hazards affect many types of tangible assets, the most valuable of which are often residential assets, comprising buildings and household contents. Yet, information necessary to derive exposure in terms of monetary value at the level of individual houses is often not available. This includes building type, size, quality, or age. In this study, we provide a universal method for estimating exposure of residential assets using only publicly available or open data. Using building footprints (polygons) from OpenStreetMap as a starting point, we utilized high-resolution elevation models of 30 European capitals and pan-European raster datasets to construct a Bayesian-network-based model that is able to predict building height. The model was then validated with a dataset of (1) buildings in Poland endangered by sea level rise, for which the number of floors is known, and (2) a sample of Dutch and German houses affected in the past by fluvial and pluvial floods, for which usable floor space area is known. Floor space of buildings is an important basis for approximating their economic value, including household contents. Here, we provide average national-level gross replacement costs of the stock of residential assets in 30 European countries, in nominal and real prices, covering the years 2000–2017. We either relied on existing estimates of the total stock of assets or made new calculations using the perpetual inventory method, which were then translated into exposure per square metre of floor space using data on countries' dwelling stocks. The study shows that the resulting standardized residential exposure values provide much better coverage and consistency compared to previous studies. [ABSTRACT FROM AUTHOR]

Natural hazards affect many types of tangible assets, the most valuable of which are often residential assets, comprising buildings and household contents. Yet, information necessary to derive exposure in terms of monetary value at the level of individual houses is often not available. This includes building type, size, quality or age. In this study, we provide a universal method for estimating exposure of residential assets using only publicly-available or open data. Using building footprints (polygons) from OpenStreetMap as a starting point, we utilized high-resolution elevation models of 30 European capitals and a set of pan-European raster dataset to construct a Bayesian Network-based model that is able to predict building height. The model was then validated with a dataset of: (1) buildings in Poland endangered by sea level rise, for which the number of floors is known, and (2) a sample of Dutch and German houses affected in the past by fluvial and pluvial floods, for which usable floor space area is known. Floor space of buildings is an important basis for approximating their economic value, including household contents. Here, we provide average national-level gross replacement costs of the stock of residential assets in 30 European countries, in nominal and real prices, covering years 2000–2017. We relied either on existing estimates of the total stock of assets or made new calculations using the Perpetual Inventory Method, which were then translated into exposure per m² of floor space using data on countries' dwelling stocks. The study shows that the resulting standardized residential exposure values provide much better coverage and consistency compared to previous studies. [ABSTRACT FROM AUTHOR]

Governmental climate change mitigation targets are typically developed with the aid of forecasts of greenhouse-gas (GHG) emissions. The robustness and credibility of such forecasts depends, among other issues, on the extent to which forecasting approaches can reflect prevailing uncertainties. We apply a transparent and replicable method to quantify the uncertainty associated with projections of gross domestic product growth rates for Mexico, a key driver of GHG emissions in the country. We use those projections to produce probabilistic forecasts of GHG emissions for Mexico. We contrast our probabilistic forecasts with Mexico’s governmental deterministic forecasts. We show that, because they fail to reflect such key uncertainty, deterministic forecasts are ill-suited for use in target-setting processes. We argue that (i) guidelines should be agreed upon, to ensure that governmental forecasts meet certain minimum transparency and quality standards, and (ii) governments should be held accountable for the appropriateness of the forecasting approach applied to prepare governmental forecasts, especially when those forecasts are used to derive climate change mitigation targets. POLICY INSIGHTS No minimum transparency and quality standards exist to guide the development of GHG emission scenario forecasts, not even when these forecasts are used to set national climate change mitigation targets. No accountability mechanisms appear to be in place at the national level to ensure that national governments rely on scientifically sound processes to develop GHG emission scenarios. Using probabilistic forecasts to underpin emission reduction targets represents a scientifically sound option for reflecting in the target the uncertainty to which those forecasts are subject, thus increasing the validity of the target. Setting up minimum transparency and quality standards, and holding governments accountable for their choice of forecasting methods could lead to more robust emission reduction targets nationally and, by extension, internationally. [ABSTRACT FROM AUTHOR]

Adverse consequences of floods change in time and are influenced by both natural and socio-economic trends and interactions. In Europe, previous studies of historical flood losses corrected for demographic and economic growth (‘normalized’) have been limited in temporal and spatial extent, leading to an incomplete representation of trends in losses over time. Here we utilize a gridded reconstruction of flood exposure in 37 European countries and a new database of damaging floods since 1870. Our results indicate that, after correcting for changes in flood exposure, there has been an increase in annually inundated area and number of persons affected since 1870, contrasted by a substantial decrease in flood fatalities. For more recent decades we also found a considerable decline in financial losses per year. We estimate, however, that there is large underreporting of smaller floods beyond most recent years, and show that underreporting has a substantial impact on observed trends. [ABSTRACT FROM AUTHOR]

The interaction between storm surges and inland runoff has been gaining increasing attention recently, as they have the potential to result in compound floods. In Europe, several flood events of this type have been recorded in the past century in Belgium, France, Ireland, Italy and United Kingdom. Here, we investigate the probability of joint occurrence of storm surges, precipitation, river discharges and waves. A coincidence of those factors have a potential to cause compound floods. We use several datasets covering most of Europe, including observations and data from the European Flood Awareness System (EFAS), ERA-Interim climate reanalysis and a regional climate model within the CORDEX framework, and carry out a statistical analysis based on copulas to assess the likelihood of joint occurrence. Further, we synthesize the joint probability of occurrence of extreme compound events, and their intensity, in the form of a composite index, thus identifying areas where compound floods could be of most concern. The results show considerable regional differences in dependency structure and the resulting joint probability of extreme surge, precipitation and river discharge events. In southern Europe the probability of joint occurrence of storm surge and precipitation is relatively high due to significant flash flood hazard. In northern Europe, along the main corridor of winter storms, dependency between surges and river discharges is higher than elsewhere, with large differences between west-facing and east-facing coasts. The occurrence of compound floods in most of the Nordic countries and along the Black Sea is very unlikely. The results allow the identification of areas at risk from compound flooding. Future studies that utilize statistical and physical methods are recommended to assess interactions between surges and inland runoff at a local scale. [ABSTRACT FROM AUTHOR]

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information. [ABSTRACT FROM AUTHOR]

The influence of social and economic change on the consequences of natural hazards has been a matter of much interest recently. However, there is a lack of comprehensive, high-resolution data on historical changes in land use, population or Historical Analysis of Natural Hazards in Europe, which contains two parts: (1) HANZE-Exposure with maps for 37 countries and territories from 1870 to 2020 in 100 m resolution and (2) HANZE-Events, a compilation of past disasters with information on dates, locations and losses, currently limited to floods only. The database was constructed using high-resolution maps of present land use and population, a large compilation of historical statistics, and relatively disaggregation techniques and rule-based land-use reallocation schemes. Data encompassed in HANZE allow to normalize information on losses due to natural hazards by taking into account inflation as well as changes in population, production and wealth. Database of past events currently contains 1564 records (1870-2016) of flash, river, coastal and compound floods. HANZE database is freely available at https://doi.org/10.4121/collection:HANZE. [ABSTRACT FROM AUTHOR]

Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km²/ were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling. [ABSTRACT FROM AUTHOR]

Flood hazard is being analysed with ever-more complex models on national, continental and global scales. In this paper we investigate an alternative, simplified approach, which combines statistical and physical models in order to carry out flood mapping for Europe. Estimates of extreme river discharges made using a Bayesian Network-based model from a previous study are employed instead of rainfall-runoff models. Those data provide flood scenarios for simulation of water flow in European rivers with a catchment area above 100 km2. The simulations are performed using a one-dimensional steady-state hydraulic model and the results are post-processed using geographical information system (GIS) software in order to derive flood zones. This approach is validated by comparison with Joint Research Centre's (JRC) pan-European map and five local flood studies from different countries. Overall, both our and JRC's maps have similar performance in recreating flood zones of local maps. The simplified approach achieved similar level of accuracy, while substantially reducing the computational time. The paper also presents the summarized results from the flood hazard maps, including future projections. We find that relatively small changes in flood hazard are observed (increase of flood zones area by 2–4 %). However, when current flood protection standards are taken into account, there is a sharp increase in flood-prone area in the future (28–38 % for a 1000 year return period). This is because in many parts of Europe river discharge with the same return period is projected to increase in the future, thus making the protection standards insufficient. [ABSTRACT FROM AUTHOR]

Traffic load plays an important role not only in the design of new bridges but also in the reliability assessment of existing structures. Weigh-in-motion systems are used to collect data to determine traffic loads. In this paper, the potential of hybrid nonparametric Bayesian networks (BNs) is demonstrated for modeling the complex data measured by the weigh-in-motion systems. The quantification process provides insight into the statistical buildup of the traffic load. The BN is shown to be a reliable traffic load model for use in bridge design. The model's value is shown with applications for prediction of missing data and calculation of extreme loads. A simulation that includes both a dynamic BN and a static component is performed. The model is able to generate the distribution function of section forces, such as bending moments, generated by multiple vehicles in several lanes. The model presented in this paper should serve as a benchmark for further applications. [ABSTRACT FROM AUTHOR]

Dams' safety is highly important for authorities around the world. The impacts of a dam failure can be enormous. Models for investigating dam safety are required for helping decision-makers to mitigate the possible adverse consequences of flooding. A model for earth dam safety must specify clearly possible contributing factors, failure modes and potential consequences of dam failure. Probabilistic relations between variables should also be specified. Bayesian networks (BNs) have been identified as tools that would assist dam engineers on assessing risks. BNs are graphical models that facilitate the construction of a joint probability distribution. Most of the time, the variables included in a model for earth dam risk assessment involve continuous quantities. The presence of continuous random variables makes the implementation of discrete BNs difficult. An alternative to discrete BNs is the use of non-parametric continuous BNs, which will be briefly described in this article. As an example, a model for earth dams' safety in the State of Mexico will be discussed. Results regarding the quantification of conditional rank correlations through ratios of unconditional rank correlations have not been presented before and are introduced herein. While the complete application of the model for the State of Mexico is presented in an accompanying paper, here some results regarding model use are shown for demonstration purposes. The methods presented in this article can be applied for investigating risks of failure of civil infrastructures other than earth dams. [ABSTRACT FROM AUTHOR]

Dams are civil engineering structures to hinder water flows. Criteria such as purpose, size and construction material are useful to categorise them. The latter is used to classify ‘earth dams’, which tend to have higher risk levels than other types. The failure of a dam leads to significant economic loss and usually to catastrophic impacts. In an effort to comprehensively examine the variables that influence earth dam breaks and describe their interactions, a model has been developed in such a way that it allows to assess risks and to prioritise the allocation of resources for maintenance activities. The research was carried out by systematically reviewing the literature, which led to the choice of Bayesian Networks (BNs) as a tool for assessing risks. Using data from seven case studies in Mexico, a model was built, which helped to rank the dams under study, leading to results comparable with those reported in the literature. While the particular type of BN used and its quantification is presented more extensively in an accompanying paper, the model may be of interest for dam owners, managers, practitioners and academics on their efforts to manage earth dams’ risks. [ABSTRACT FROM AUTHOR]

The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions. [ABSTRACT FROM AUTHOR]