Flooding is expected to increase due to climate change, urbanisation, and land use change. To address this issue, Nature-Based Solutions (NBSs) are often adopted as innovative and sustainable flood risk management methods. Besides the flood risk reduction benefits, NBSs offer co-benefits for the environment and society. However, these co-benefits are rarely considered in flood risk management due to the inherent complexities of incorporating them into economic assessments. This research addresses this gap by developing a comprehensive methodology that integrates the monetary analysis of co-benefits with flood risk reduction in economic assessments. In doing so, it aspires to provide a more holistic view of the impact of NBS in flood risk management. The assessment employs a framework based on life-cycle cost-benefit analysis, offering a systematic and transparent assessment of both costs and benefits over time supported by key indicators like net present value and benefit cost ratio. The methodology has been applied to the Tamnava basin in Serbia, where significant flooding occurred in 2014 and 2020. The methodology offers valuable insights for practitioners, researchers, and planners seeking to assess the co-benefits of NBS and integrate them into economic assessments. The results show that when considering flood risk reduction alone, all considered measures have higher costs than the benefits derived from avoiding flood damage. However, when incorporating co-benefits, several NBS have a net positive economic impact, including afforestation/reforestation and retention ponds with cost-benefit ratios of 3.5 and 5.6 respectively. This suggests that incorporating co-benefits into economic assessments can significantly increase the overall economic efficiency and viability of NBS.

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.

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.

The escalating impacts of climate change trigger the necessity to deal with hydro-meteorological hazards. Nature-based solutions (NBSs) seem to be a suitable response, integrating the hydrology, geomorphology, hydraulic, and ecological dynamics. While there are some methods and tools for suitability mapping of small-scale NBSs, literature concerning the spatial allocation of large-scale NBSs is still lacking. The present work aims to develop new toolboxes and enhance an existing methodology by developing spatial analysis tools within a geographic information system (GIS) environment to allocate large-scale NBSs based on a multi-criteria algorithm. The methodologies combine machine learning spatial data processing techniques and hydrodynamic modelling for allocation of large-scale NBSs. The case studies concern selected areas in the Netherlands, Serbia, and Bolivia, focusing on three large-scale NBS: rainwater harvesting, wetland restoration, and natural riverbank stabilisation. Information available from the EC H2020 RECONECT project as well as other available data for the specific study areas was used. The research highlights the significance of incorporating machine learning, GIS, and remote sensing techniques for the suitable allocation of large-scale NBSs. The findings may offer new insights for decision-makers and other stakeholders involved in future sustainable environmental planning and climate change adaptation.

Urbanization and climate change are producing an escalation in the prevalence of urban problems, particularly those connected to flooding, prompting authorities and stakeholders to recognize the need for sustainable solutions. Nature-Based Solutions are progressively replacing traditional engineering solutions as an alternative since they are more eco-friendly. By re-activating the urban hydrological cycle processes, NBS intends to increase the natural water storage capacity to help decrease urban flooding. The work described here outlines a framework for optimising the efficacy of NBS for flood risk reduction and its co-benefits, as well as defining the trade-offs among these co-benefits. The framework integrates 1D hydrodynamic models with multi-objective optimisation techniques. To demonstrate the applicability of the framework and its methods it has been used in Sint Maarten, which is an island located in the Caribbean Sea. Four NBS measure were identified as having good potential to be applied in the case study, namely: green roof, permeable pavement, bio-retention pond, and open detention basin. The results showed that the developed framework has the ability to represent the link between benefits and costs when evaluating various NBS, hence aiding the decision-making process to select and implement NBS.

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).

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.

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.

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.

Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications will become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure and on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other components, machine learning may not always be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.