Over recent decades, hydro-meteorological disasters appear to be becoming more intense and frequent. Nature-Based Solutions (NBS) have been introduced to address hydro-meteorological risks as they offer the possibility of working closely with nature. This provides solutions to adapt to future changes in climate and society, as well as to achieve multiple benefits to services and functions of ecosystems. However, the performance and efficiency of NBS for hydro-meteorological risk reduction are still highly uncertain. Scientists and decision-makers require holistic perspectives and frameworks to help understand, evaluate and design NBS in such a way that can minimize social and economic losses, reduce environmental impacts and increase resilience to hydro-meteorological events. Therefore, methods or frameworks that can be used to evaluate NBS performance are necessary. In this work, a framework for evaluating large-scale NBS for hydro-meteorological risks is presented. The evaluation framework is separated into three main stages; identification of Indicators, before implementation (ex-ante) evaluation and after implementation (ex-post) evaluation. Developing a framework will be useful in assisting and supporting communities that wish to implement NBS for hydro-meteorological risk reduction, as well as communities that have implemented NBS and wish to assess their effectiveness. The work presented here is part of the EC-funded HORIZON 2020 RECONECT project (Regenerating Ecosystems with Nature-based solutions for hydro-meteorological risk rEduCTion).

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Adverse effects of climate change are increasing around the world and the floods are posing significant challenges for water managers. With climate projections showing increased risks of storms and extreme precipitation, the use of traditional measures alone is no longer an option. Nature-Based Solutions (NBS) offer a suitable alternative to reduce the risk of flooding and provide multiple benefits. However, planning such interventions requires careful consideration of various factors and local contexts. The present paper provides contribution in this direction and it proposes a methodology for allocation of large-scale NBS using suitability mapping. The methodology was implemented within the toolboxes of ESRI ArcMap software in order to map suitability for four types of NBS interventions: floodplain restoration, detention basins, retention ponds, and river widening. The toolboxes developed were applied to the case study area in Serbia, i.e., the Tamnava River basin. Flood maps were used to determine the volume of floodwater that needs to be stored for reducing flood risk in the basin and subsequent downstream areas. The suitability maps produced indicate the potential of the new methodology and its application as a decision-support tool for selection and allocation of large-scale NBS.

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Climate change is presenting one of the main challenges to our planet. In parallel, all regions of the world are projected to urbanise further. Consequently, sustainable development challenges will be increasingly concentrated in cities. A resulting impact is the increment of expected urban flood risk in many areas around the globe. Adaptation to climate change is an opportunity to improve urban conditions through the implementation of green-blue infrastructures, which provide multiple benefits besides flood mitigation. However, this is not an easy task since urban drainage systems are complex structures. This work focuses on a method to analyse the trade-offs when different benefits are pursued in stormwater infrastructure planning. A hydrodynamic model was coupled with an evolutionary optimisation algorithm to evaluate different green-blue-grey measures combinations. This evaluation includes flood mitigation as well as the enhancement of co-benefits. We confirmed optimisation as a helpful decision-making tool to visualise trade-offs among flood management strategies. Our results show that considering co-benefits enhancement as an objective boosts the selection of green-blue infrastructure. However, flood mitigation effectiveness can be diminished when extra benefits are pursued. Finally, we proved that combining green-blue-grey measures is particularly important in urban spaces when several benefits are considered simultaneously.

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Small Island Developing States (SIDS) are vulnerable to sea-level rise and hydro-meteorological hazards. In addition to the efforts to reduce the hazards, a holistic strategy that also addresses the vulnerability and exposure of residents and their assets is essential to mitigate the impacts of such hazards. Evaluating the socioeconomic vulnerability of SIDS can serve the purpose of identification of the root drivers of risk. In this paper, we present a methodology to assess and map socioeconomic vulnerability at a neighbourhood scale using an index-based approach and principal component analysis (PCA). The index-based vulnerability assessment approach has a modular and hierarchical structure with three components: susceptibility, lack of coping capacities and lack of adaptation, which are further composed of factors and variables. To compute the index, we use census data in combination with data coming from a survey we performed in the aftermath of Irma. PCA is used to screen the variables, to identify the most important variables that drive vulnerability and to cluster neighbourhoods based on the common factors. The methods are applied to the case study of Sint Maarten in the context of the disaster caused by Hurricane Irma in 2017. Applying the combined analysis of index-based approach with PCA allows us to identify the critical neighbourhoods on the island and to identify the main variables or drivers of vulnerability. Results show that the lack of coping capacities is the most influential component of vulnerability in Sint Maarten. From this component, the "immediate action" and the "economic coverage" are the most critical factors. Such analysis also enables decision-makers to focus their (often limited) resources more efficiently and have a more significant impact concerning disaster risk reduction.

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Flood adaptation measures implemented at the household level play an important role in reducing communities' vulnerability. The aim of this study is to enhance the current modelling practices of human-flood interaction to draw new insights for flood risk management (FRM) policy design. The paper presents a coupled agent-based and flood model for the case of Hamburg, Germany, to explore how individual adaptation behaviour is influenced by flood event scenarios, economic incentives and shared and individual strategies. Simulation results show that a unique trajectory of adaptation measures and flood damages emerges from different flood event series. Another finding is that providing subsidies increases the number of coping households in the long run. Households' social network also has a strong influence on their coping behaviour. The paper also highlights the role of simple measures such as adapted furnishings, which do not incur any monetary cost, in reducing households' vulnerability and preventing millions of euros of contents damages. Generally, we demonstrate that coupled agent-based and flood models can potentially be used as decision support tools to examine the role of household adaptation measures in flood risk management. Although the findings of the paper are case-specific, the improved modelling approach shows the potential to be applied in testing policy levers and strategies considering heterogeneous individual behaviours.

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Hydro-meteorological risks are a growing issue for societies, economies and environments around the world. An effective, sustainable response to such risks and their future uncertainty requires a paradigm shift in our research and practical efforts. In this respect, Nature-Based Solutions (NBSs) offer the potential to achieve a more effective and flexible response to hydro-meteorological risks while also enhancing human well-being and biodiversity. The present paper describes a new methodology that incorporates stakeholders’ preferences into a multi-criteria analysis framework, as part of a tool for selecting risk mitigation measures. The methodology has been applied to Tamnava river basin in Serbia and Nangang river basin in Taiwan within the EC-funded RECONECT project. The results highlight the importance of involving stakeholders in the early stages of projects in order to achieve successful implementation of NBSs. The methodology can assist decision-makers in formulating desirable benefits and co-benefits and can enable a systematic and transparent NBSs planning process.

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Hydro-meteorological risks due to natural hazards such as severe floods, storm surges, landslides and droughts are causing impacts on different sectors of society. Such risks are expected to become worse given projected changes in climate, degradation of ecosystems, population growth and urbanisation. In this respect, nature-based solutions (NBSs) have emerged as effective means to respond to such challenges. A NBS is a term used for innovative solutions that are based on natural processes and ecosystems to solve different types of societal and environmental challenges. The present paper provides a critical review of the literature concerning NBSs for hydro-meteorological risk reduction and identifies current knowledge gaps and future research prospects. There has been a considerable growth of scientific publications on this topic, with a more significant rise taking place from 2007 onwards. Hence, the review process presented in this paper starts by sourcing 1608 articles from Scopus and 1431 articles from the Web of Science. The full analysis was performed on 146 articles. The analysis confirmed that numerous advancements in the area of NBSs have been achieved to date. These solutions have already proven to be valuable in providing sustainable, cost-effective, multi-purpose and flexible means for hydro-meteorological risk reduction. However, there are still many areas where further research and demonstration are needed in order to promote their upscaling and replication and to make them become mainstream solutions.

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In this paper, we describe a modelling framework that allows the integration of human and physical components of flood risk. Within this framework, flood risk management is conceptualized as a coupled human-flood system. The human subsystem includes individuals and their behaviour and institutions that shape human-flood interaction. The framework presents a dynamic integration between agent-based models of individuals and institutions and numerical flood models. We demonstrate the framework's modelling application by examining the effects of three institutions in the Caribbean island of Sint Maarten. The case study shows the capabilities of the framework by exploring impacts of existing policies on flood risk reduction. Coupled agent-based-flood models built using the framework are useful to analyse policy options that address flood hazard and communities' vulnerability and exposure to support policy decision making. These models also show how flood risk changes over time in relation to the human dynamics on the urban environment.

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This paper proposes a novel methodology for modelling the impacts of floods on traffic. Often, flooding is a complex combination of various causes (coastal, fluvial and pluvial). Further, transportation systems are very sensitive to external disturbances. The interactions between these two complex and dynamic systems have not been studied in detail so far. To address this issue, this paper proposes a methodology for a dynamic integration of a flood model (MIKE FLOOD) and a microscopic traffic simulation model (SUMO). The flood modelling results indicate which roads are inundated for a period of time. The traffic on these links will be halted or delayed according to the flood characteristics—extent, propagation and depth. As a consequence, some of the trips need to be cancelled; some need to be rerouted to unfavourable routes; and some are indirectly affected. A comparison between the baseline and a flood scenario yields the impacts of that flood on traffic, estimated in terms of lost business hours, additional fuel consumption and additional CO2 emissions. The proposed methodology will be further developed as a workable tool to evaluate the flooding impact on transportation network at city scale automatically.@en

Green-blue infrastructures in urban spaces offer several co-benefits besides flood risk reduction, such as water savings, energy savings due to less cooling usage, air quality improvement and carbon sequestration. Traditionally, these co-benefits were not included in decision making processes for flood risk management. In this work we present a method to include the monetary analysis of these co-benefits into a cost-benefits analysis of flood risk mitigation measures. This approach was applied to a case study, comparing costs and benefits with and without co-benefits. Different intervention strategies were considered, using green, blue and grey measures and combinations of them. The results obtained illustrate the importance of assessing co-benefits when identifying best adaptation strategies to improve urban flood risk management. Otherwise green infrastructure is likely to appear less efficient than more conventional grey infrastructure. Moreover, a mix of green, blue and grey infrastructures is likely to result in the best adaptation strategy as these three alternatives tend to complement each other. Grey infrastructure has good performance at reducing the risk of flooding, whilst green infrastructure brings in multiple additional benefits that grey infrastructure cannot offer.

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Disaster risk reduction is a major concern of small island developing states. Measures to reduce risk should not only be based on the magnitude of physical hazard, but also on the exposure and vulnerability of communities. In this article, we examine flood risk management policies in the Caribbean island of Sint Maarten using coupled agent-based and flood models. The agent-based model is used to model actors' behaviour in relation to urban building development and policies that are designed to reduce flood hazard and communities' vulnerability and exposure. The policies considered in the model are a Beach Policy, a Building and Housing Ordinance, a Flood Zoning policy and hazard mitigation structural measures. The flood model is used to simulate coastal and pluvial floods on the island. Agent behaviour such as building new houses and implementing hazard reduction measures affect the flood model as these actions affect the rainfall-runoff process. The flood maps generated from the updated flood model simulations are then used to assess the impact and update agents’ attributes and behaviour. The simulations results show that low-lying areas are populated, which increases the exposure, and the number of vulnerable houses is also high. Hence, out of the four policies, implementing hazard reduction measures is the most important. Reducing the flood hazard by widening existing drainage channels, constructing new ones and building dykes as coastal flood defence would reduce the hazard, hence reducing the number of flooded houses. As it affects all households on the island, the Building and Housing Ordinance is an important policy to reduce vulnerability. In general, the coupled model outputs can be used to inform policy decision making and provide insights to policymakers on the island.@en

This paper focuses on utilizing Sentinel 2 MSI datasets to generate satellite-derived bathymetry (SDB) maps at a resolution of 10 m for two temporally varying datasets of the study region of Ameland Inlet, located in The Netherlands, by using support vector regression (SVR) technique. The relative performance of Landsat 8 OLI (30 m) datasets with SVR technique is also assessed to demonstrate the complementary nature of these freely available medium-resolution imageries. Further, the root mean square error and mean absolute error between the retrieved and measured bathymetries are estimated and reported to evaluate the capability of SVR in estimating depths. It is evident that the SDBs thus generated using this machine learning approach provide dependable estimations of depths that can further be utilized for various coastal engineering studies.@en

Continuous changes in climate conditions combined with urban population growth pose cities as one of the most vulnerable areas to increasing flood risk. In such an atmosphere of growing uncertainty, a more effective flood risk management is becoming crucial. Nevertheless, decision-making and selection of adequate systems is a difficult task due to complex interactions between natural, social and built environments. The combination of green (or sustainable) and grey (or traditional) options has been proposed as a way forward to ensure resilience in advance of extreme events, and at the same time to obtain co-benefits for society and the environment. The present paper describes a novel method for selection of urban flood measures, based on a multi-criteria analysis that includes flood risk reduction, cost minimization and enhancement of co-benefits. The aim of this method is to assist decision makers in selecting and planning measures, which afterwards can be part of either high level scoping analysis or more complex studies, such as model based assessment. The proposed method is implemented within a tool which operates as a standalone application. Through this tool, the method has been applied in three study cases. The findings obtained indicate promising potential of the method here introduced.

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Flood watermarks stipulate peak water depths from a flood event, indicating a magnitude of inundation that took place. Such information is invaluable for instantiation and validation of urban flood models. However, collecting and processing such data from land surveys can be costly and time-consuming. New remote sensing and data processing technologies offer improved opportunities to address these issues. The present paper deals with the new structure from motion (SfM) technology and its application in extracting flood watermarks. For this purpose, the first of its kind, side-view SfM surveys with two mobile units were utilised. Survey works were carried out in the vicinity of Ayutthaya heritage area (Thailand) and data obtained were used for setting up numerical models and simulations of the 2011 flood event. The work undertaken demonstrates the significant capability of SfM technology for extraction of flood watermarks. With such technology, it was possible to indicate façades, low-level structures, and susceptible openings, which in turn have improved schematizations of two-dimensional (2D) flood models. The resulting model simulations were found to be more accurate (i.e., more close to the measurements of flood watermarks) than those obtained from models with conventional top-view light detection and ranging (LiDAR) data.@en

Modelling floods and flood-related disasters has become priority for many researchers and practitioners. Currently, there are several options that can be used for modelling floods in urban areas and the present work attempts to investigate effectiveness of different model formulations in modelling supercritical and transcritical flow conditions. In our work, we use the following three methods for modelling one-dimensional (1D) flows: the MIKE 11 flow model, Kutija's method, and the Roe scheme. We use two methods for modelling two-dimensional (2D) flows: the MIKE21 flow model and a non-inertia 2D model. Apart from the MIKE11 and MIKE21 models, the code for all other models was developed and used for the purposes of the present work. The performance of the models was evaluated using hypothetical case studies with the intention of representing some configurations that can be found in urban floodplains. The present work does not go into the assessment of these models in modelling various topographical features that may be found on urban floodplains, but rather focuses on how they perform in simulating supercritical and transcritical flows. The overall findings are that the simplified models which ignore convective acceleration terms (CATs) in the momentum equations may be effectively used to model urban flood plains without a significant loss of accuracy.

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Recent research suggests future alterations in rainfall patterns due to climate variability, affecting public safety and health in urban areas. Urban growth, one of the main drivers of change in the current century, will also affect these conditions. Traditional drainage approaches using grey infrastructure offer low adaptation to an uncertain future. New methodologies of stormwater management focus on decentralized approaches in a long-term planning framework, including the use of Green Infrastructure (GI). This work presents a novel methodology to select, evaluate, and place different green-grey practices (or measures) for retrofitting urban drainage systems. The methodology uses a hydrodynamic model and multi-objective optimization to design solutions at a watershed level. The method proposed in this study was applied in a highly urbanized watershed to evaluate the effect of these measures on Combined Sewer Overflows (CSO) quantity. This approach produced promising results and may become a useful tool for planning and decision making of drainage systems.

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Top-view data obtainedfrom LiDAR systemshas long been used as topographic-input data for urban flood modelling applications. This high-resolution input data has considerable potential to improve urban flood modelling predictions with more detail. However, the difficulty of employing top-view data is that it may create some missing urban features because this type ofdata cannot represent anyurban features,which are hiddenunderneath other objects. These hidden featuresmay play a substantial part in diverting floodwater flowing through,especially in complex urban areas. The recent advances in Photogrammetry and Computer Vision techniques offer an opportunity to create high-resolution topographic data. By using a consumer digital camera, 2D digital photoscan betaken from different viewpoints. The so-called Structure from Motion (SfM) techniquecan usethese overlappingphotos and reconstruct theminto3D pointcloud data with a high level of accuracy and resolution,usinga cost effective approach. In this work, we create street-view SfM point-cloud data obtained from street viewpoints. We also introduce a new multi-view approach by merging top-view LiDAR data withstreet-view SfM data. This new multi-view data can be used as topographic input data for a coupled 1D-2D model. When applyingsuch newdata, the flood simulation results can highlight some flood propagations much better than using the traditional top-view LiDAR data. Therefore, it has the potential toenhance the multi-view approach into practicable flood-modelling applications for the present and future urbanizing areas. @en

Coastal floods are one of the most dangerous and harmful natural hazards affecting urban areas adjacent to shorelines. The present paper discusses the FP7-ENV-2013 EU funded PEARL (Preparing for Extreme And Rare events in coastaL regions) project which brings together world leading expertise in both the domain of hydro-engineering and risk reduction and management services to pool knowledge and practical experience in order to develop more sustainable risk management solutions for coastal communities focusing on present and projected extreme hydro-meteorological events. The PEARL approach draws upon the complexity theory and the use of complex adaptive system (CAS) models as tools to identify root causes of vulnerabilities and their multi-stressors and to analyze risk and the behavior of key actors.@en

The present paper reviews several approaches that can be used in capturing urban features in coarse resolution two-dimensional (2D) models and it demonstrates the effectiveness of a new approach against the straightforward 2D modelling approach on a hypothetical and a real-life case study work. The case study work addresses the use of coarse grid resolutions in 2D non-inertia models. The 2D noninertia model used solves continuity andmomentumequations over the cells of the coarse model while taking the minimum elevation as a surface level. The volume stored in every cell is calculated as a volume-depth relationship. In order to replicate restriction in conveyances in x-y directions of fine resolution models due to building blocks, the friction values of the coarse-resolution model are adjusted to match the results of the high-resolution model. The work presented in this paper shows the possibility of applying a 2D non-inertia model more effectively in urban flood modelling applications whilst still making use of the high resolution of topographic data that can nowadays be easily acquired.

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There has been a rapid growth in the field of remote sensing and its various applications in the area of water management. Nowadays, there are several remote sensing techniques that can be used as a source to derive bathymetry data along coastal areas. The key techniques are: sonar (sound navigating and ranging), LiDAR (light detection and ranging) and high-resolution satellite images. The present paper describes a method which was developed and used to create a shallow water bathymetry data along the Dutch side of Sint Maarten Island by combining sonar measurements and satellite images in a nonlinear machine learning technique. The purpose of this work is to develop a bathymetry dataset that can be used to set up physically-based models for coastal flood modelling work. The nonlinear machine learning technique used in the work is a support vector machine (SVM) model. The sonar data were used as an output whereas image data were used as an input into the SVM model. The results were analysed for three depth ranges and the findings are promising. It remains to further verify the capacity of the new method on a dataset with higher resolution satellite imagery.@en