Nature-Based solutions (NBS) are the measures supported by natural processes that can adapt to changing climates and generate diverse social, economic, and environmental benefits. Recognising the potential for additional NBS benefits, and quantifying these benefits is essential as it encourages decision-makers to implement and scale-up NBS initiatives. This paper presents findings from a systematic literature review. The review focused on tools and methodologies used for assessing the environmental benefits of implementing NBS. This review provides a detailed compilation of environmental indicators supported by assessment tools. It also includes a catalogue of tools for evaluating environmental benefits, thereby identifying research gaps. Moreover, this research proposes a methodology that uses an ArcGIS (Architecture of Geographic Information Systems) toolbox to identify habitat changes resulting from the implementation of NBS. The methodology translates CORINE (Coordination of Information on the Environment) land cover classes to EUNIS (European Nature Information System) habitat classes. The developed toolbox was applied to two case studies: Denmark (12 NBS) and the Netherlands (3 NBS). The assessment aimed to compare the habitat changes between 2000 and 2018 as two extreme time points for NBS implementation for both case studies. Results indicate that NBS implementation can change habitats leading to an increase in the Red-necked Grebe population in Denmark and a decline in the Black-tailed Godwit population in the Netherlands (two threatened species). The population change highlights the potential positive and potential negative impacts of NBS in their respective cases. These findings suggest Denmark could benefit from lake construction and restoration projects. At the same time, the Netherlands could invest in wetlands and meadows construction and restoration projects to protect the respective species. They could establish designated breeding zones to ensure their population does not decline rapidly.

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Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models.

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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.@en

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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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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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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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

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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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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