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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., BT/Environmental Biotechnology

Water is a vital resource essential for both sustaining life and a healthy environment as well as being a critical hazard in the form of floods or droughts which can destroy people’s livelihoods and property. This gives rise to a multi-faceted set of concerns and issues that affect everybody. For example, when contaminated with pathogens, wastewater can carry and rapidly transmit disease. The global distribution of freshwater is uneven and the problems this creates are likely to get worse due to climate change and the uncertainties associated with changing rainfall patterns and the emergence of more extreme weather events. Water has been described as the “new oil,” with potential conflicts arising out of disputed access to scarce water resources in the rest of this century. Billions of people around the world still do not have access to adequate safe water supplies or basic sanitation facilities, so in bringing basic water services to all there is much still to be done.

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground-based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real-time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

Water distribution networks (WDNs) are a vital component of urban water infrastructure. They transport water from production sites (sources) to spatially distributed consumers (sinks). Multiobjective optimization procedures are often used to minimize construction costs and at the same time maximize the resilience of such systems, which is usually a very computationally expensive task. Recently, highly efficient approaches based on complex network analysis (CNA) have been developed to solve this task more computationally efficiently. With CNA, very large WDNs can be optimized, considering network topology and demand distribution (using, e.g., demand edge betweenness centrality). However, existing CNA approaches do not consider network topography (i.e., height differences between sources and sinks). Comparing design solutions based on CNA with those found by evolutionary algorithms shows that the least-cost CNA design cannot compete with the latter. In this work, a hybrid approach is developed, where low-cost design CNA solutions are evaluated with a hydraulic solver (Epanet2), and subsequently the demand edge betweenness centrality distribution is iteratively altered for nodes with pressure deficits. This enhanced CNA-based optimization is tested on two different large case studies from the literature and shows promising results (2% cost increase). These solutions were obtained using significantly less computational effort (at least factor 1,000 faster), enabling solving very large WDN optimization problems (>150,000 decision variables)., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Sanitary Engineering

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., Sanitary Engineering, Hydraulic Structures and Flood Risk