Economic assessment of nature-based solutions to reduce flood risk and enhance co-benefits

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.

Real time control of nature-based solutions: Towards Smart Solutions and Digital Twins in Rangsit Area, Thailand

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.

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Effectiveness of small- and large-scale Nature-Based Solutions for flood mitigation: The case of Ayutthaya, Thailand

There is growing evidence that traditional response to floods and flood-related disaster is no longer achieving desirable results. Nature-Based Solutions (NBS) represent a relatively new response towards disaster risk reduction, water security, and resilience to climate change, which has a potential to be more effective and sustainable than traditional measures. However, in practice, these measures are still being applied at a slow rate while traditional grey infrastructure remains as a preferred choice. This can be attributed to several barriers which range from political and governance to social and technological/technical. More generally, there is a lack of sufficient knowledge base to accelerate their wider acceptance and uptake. The present work provides contribution in this direction and addresses the question of effectiveness of different types of NBS (i.e., small- and large-scale NBS) and their hybrid combinations with grey infrastructure. The work has been applied on the case of Ayutthaya, Thailand. The results suggest that the effectiveness of small-scale NBS is limited to smaller rainfall events whereas the larger (or extreme) events necessitate combinations of different kinds of measures with different scales of implementation (i.e., hybrid measures).

Incorporating stakeholders' preferences into a multi-criteria framework for planning large-scale Nature-Based 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.

Exploring trade-offs among the multiple benefits of green-blue-grey infrastructure for urban flood mitigation

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.

Nature-based solutions for hydro-meteorological hazards: Revised concepts, classification schemes and databases

Hydro-meteorological hazards (HMHs) have had a strong impact on human societies and ecosystems. Their impact is projected to be exacerbated by future climate scenarios. HMHs cataloguing is an effective tool to evaluate their associated risks and plan appropriate remediation strategies. However, factors linked to HMHs origin and triggers remain uncertain, which pose a challenge for their cataloguing. Focusing on key HMHs (floods, storm surges, landslides, droughts, and heatwaves), the goal of this review paper is to analyse and present a classification scheme, key features, and elements for designing nature-based solutions (NBS) and mitigating the adverse impacts of HMHs in Europe. For this purpose, we systematically examined the literature on NBS classification and assessed the gaps that hinder the widespread uptake of NBS. Furthermore, we critically evaluated the existing literature to give a better understanding of the HMHs drivers and their interrelationship (causing multi-hazards). Further conceptualisation of classification scheme and categories of NBS shows that relatively few studies have been carried out on utilising the broader concepts of NBS in tackling HMHs and that the classification and effectiveness of each NBS are dependent on the location, architecture, typology, green species and environmental conditions, as well as interrelated non-linear systems. NBS are often more cost-effective than hard engineering approaches used within the existing systems, especially when taking into consideration their potential co-benefits. We also evaluated the sources of available data for HMHs and NBS, highlighted gaps in data, and presented strategies to overcome the current shortcomings for the development of the NBS for HMHs. We highlighted specific gaps and barriers that need to be filled since the uptake and upscaling studies of NBS in HMHs reduction is rare. The fundamental concepts and the key technical features of past studies reviewed here could help practitioners to design and implement NBS in a real-world situation.

Flood risk management in Sint Maarten - A coupled agent-based and flood modelling method

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.

Assessing the Co-Benefits of green-blue-grey infrastructure for sustainable urban flood risk management

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.

Assessment of long-term radon concentration measurement precision in field conditions (Serbian Schools) for a survey carried out by an international collaboration

In an international collaboration, a long-term radon concentration survey was carried out in schools of Southern Serbia with radon detectors prepared, etched and read-out in Italy. In such surveys it is necessary to evaluate measurement precision in field conditions, and to check whether quality assurance protocols were effective in keeping uncertainties under control, despite the complex organisation of measurements. In the first stage of the survey, which involves only some of the total number of municipalities, paired detectors were exposed in each monitored room in order to experimentally assess measurement precision. Paired passive devices (containing CR-39 detectors) were exposed for two consecutive 6-month periods. Two different measurement systems were used to read out CR-39s of the first and second period, respectively. The median of the coefficient of variation (CV) of the measured exposures was 8 % for 232 paired devices of the first 6-month period and 4 % for 242 paired devices of the second 6-month period, respectively. This difference was mainly due to a different track count repeatability of the two read-out systems, which was 4 and 1 %, respectively, as the median value of CV of repeated countings. The in-field measured precision results are very similar to the precision assessed in calibration conditions and are much lower than the room-to-room variation of radon concentration in the monitored schools. Moreover, a quality assurance protocol was followed to reduce extra-exposures during detector transport from Rome to schools measured and back.

Effects of model schematisation, geometry and parameter values on urban flood modelling

One-dimensional (1D) hydrodynamic models have been used as a standard industry practice for urban flood modelling work for many years. More recently, however, model formulations have included a 1D representation of the main channels and a 2D representation of the floodplains. Since the physical process of describing exchanges of flows with the floodplains can be represented in different ways, the predictive capability of different modelling approaches can also vary. The present paper explores effects of some of the issues that concern urban flood modelling work. Impacts from applying different model schematisation, geometry and parameter values were investigated. The study has mainly focussed on exploring how different Digital Terrain Model (DTM) resolution, presence of different features on DTM such as roads and building structures and different friction coefficients affect the simulation results. Practical implications of these issues are analysed and illustrated in a case study from St Maarten, N.A. The results from this study aim to provide users of numerical models with information that can be used in the analyses of flooding processes in urban areas.

A complementary modelling approach to manage uncertainty of computationally expensive models

The fact that the models applied in the 'water domain' are far from reality can be attributed to many reasons. In this context, a systematic analysis of uncertainties reflected by the model error can provide insight into the level of confidence in the model results and how to approach estimation of optimal model parameters. This paper discusses the four commonly used approaches for estimation of model parameters and suggests that an alternative complementary modelling approach should be considered in cases where the traditional model calibration gives limited results and particularly in cases where the computationally expensive models are concerned. It treats uncertainty as modelling the total discrepancy between the model and physical process. The proposed approach combines the results from a physically-based model and Support Vector Machine model into the final solution.

Dynamic least-cost optimisation of wastewater system remedial works requirements

In recent years, there has been increasing concern for wastewater system failure and identification of optimal set of remedial works requirements. So far, several methodologies have been developed and applied in asset management activities by various water companies worldwide, but often with limited success. In order to fill the gap, there are several research projects that have been undertaken in exploring various algorithms to optimise remedial works requirements, but mostly for drinking water supply systems, and very limited work has been carried out for the wastewater assets. Some of the major deficiencies of commonly used methods can be found in either one or more of the following aspects: inadequate representation of systems complexity, incorporation of a dynamic model into the decision-making loop, the choice of an appropriate optimisation technique and experience in applying that technique. This paper is oriented towards resolving these issues and discusses a new approach for the optimisation of wastewater systems remedial works requirements. It is proposed that the optimal problem search is performed by a global optimisation tool (with various random search algorithms) and the system performance is simulated by the hydrodynamic pipe network model. The work on assembling all required elements and the development of an appropriate interface protocols between the two tools, aimed to decode the potential remedial solutions into the pipe network model and to calculate the corresponding scenario costs, is currently underway.