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<p>This study investigates the performance of 35 recent ponds (which are under tendering, under construction, and finished in Erbil City), focusing on their role in flood mitigation across 11 distinct catchment areas. The total storage capacity of these ponds is approximately 9,926,394 m³, significantly enhancing the city's ability to manage stormwater runoff and reduce flood risks. The Watershed Modeling System (WMS), along with the Soil Conservation Service Curve Number (SCS-CN) method, was utilized for hydrological modeling to evaluate runoff behavior and water retention performance. Calculated Retention Capacity Ratio (RCR) values vary from as low as 21 % in the smallest system to 136 % in the Kasnazan catchment, with Chamarga similarly exceeding full capacity at 131 %. These over-capacity networks not only attenuate peak flows but also promote groundwater recharge, improve downstream water quality by trapping sediments and nutrients, and create valuable aquatic and riparian habitats. Our findings demonstrate the multifaceted benefits of high-capacity retention ponds and provide a replicable model for integrating green infrastructure into urban planning to build flood resilience and sustainable water management in rapidly urbanizing regions.</p>

ABSTRACT The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness.

Introduction Impacts of climate change on human health receive increasing attention. However, the connections of climate change with well-being and mental health are still poorly understood. Objective As part of the Horizon Europe project TRIGGER, we aim to deepen the understanding of the relationships between climate change and human mental health and well-being in Europe by focusing on environmental and socio-individual determinants. Methods This study is a systematic literature review based on the PRISMA guidelines using Embase, Medline and Web of Science. Results 143 records were retrieved. The results show that climate change and its specific hazards (air pollution, floods, wildfires, meteorological variables, and temperature extremes) impact human well-being and mental health. Discussion Mental health and well-being outcomes are complex, extremely individual, and can be long lasting. Determinants like the living surrounding, human’s life activities as well as socio-individual determinants alter the linkage between climate change and mental health. The same determinant can exert both a pathogenic and a salutogenic effect, depending on the outcome. Knowing the effects of the determinants is of high relevance to improve resilience. Several pathways were identified. For instance, higher level of education and female gender lead to perceiving climate change as a bigger threat but increase preparedness to climate hazards. Elderly, children and adolescents are at higher risks of mental health problems. On the other hand, social relation, cohesiveness and support from family and friends are generally protective. Green and blue spaces improve well-being and mental health. Overall, comparing the different hazard-outcome relationships is difficult due to varying definitions, measurement techniques, spatial and temporal range, scales, indicators and population samples. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/home , identifier CRD42023426758.

Abstract Background Relating the geographical distribution of intermediate freshwater snail hosts (viz. vectors of schistosomes) to local environmental attributes offers value for understanding the epidemiological landscape of schistosomiasis transmission in a changing aquatic environment. Schistosomiasis—both urogenital and intestinal—causes significant human suffering, affecting approximately 240 million people globally and grouped within the neglected tropical disease (NTD) umbrella. This study addresses the following questions: 1. Where are the most suitable habitats for intermediate host snails in the Lower Shire Valley (LSV) in Malawi? 2. Which environmental factors are strongly associated with the geographical distribution of such snails in the LSV? Methods This paper presents the first species distribution models (SDMs) for intermediate snail hosts for urogenital and intestinal schistosomiasis in Chikwawa and Nsanje Districts, which together form the LSV). The SDMs developed for this study are ensemble machine learning approaches based on Random Forest (RF), Support Vector Machines (SVM), and multilayer perceptron (MLP) and are specific to the Bulinus africanus group and Biomphalaria pfeifferi . The former transmits urogenital schistosomiasis ( Schistosoma haematobium ), while the latter transmits intestinal schistosomiasis ( Schistosoma mansoni ). Results The SDMs reveal the following: 1) currently, Bu. africanus group not only has a wide distribution across central Chikwawa and eastern Nsanje but is also concentrated in floodplains, and the LSV has few habitats that can support Bi. pfeifferi , and 2) vegetation cover is the most important predictor of Bu. africanus group distribution, whereas precipitation variables are most important for Bi. pfeifferi in the LSV. Thus, Bu. africanus group habitat is the most dominant and abundant, while Bi. pfeifferi suitable habitat is patchy and scarce. Conclusion The distribution of suitable habitats for potential urogenital and intestinal schistosomiasis transmission across LSV is not uniform and typically non-overlapping. Understanding the spatial and temporal distributions of these snails is important for controlling and eliminating schistosomiasis. Graphical Abstract

Abstract Market-based instruments, including competitive tenders, are central to funding global environmental restoration and management projects. Recently, tenders have been utilised to fund Nature-based Solutions schemes for Natural Flood Management, with the explicit purpose of achieving co-benefits; flood management and reducing inequities. While multiple studies consider the efficacy of Nature-based Solutions for tackling inequities, no prior research has quantified whether the resource allocation for these projects has been conducted equitably. We analyse two national natural flood management programmes funded through competitive tenders in England to explore who benefits by considering the characteristics of projects, including socio-economic, geographical (e.g. rurality) and flood risk dynamics. Our results suggest that inequity occurs at both the application and funding stages of Nature-based Solutions projects for flood risk management. This reflects wider international challenges of using market-based instruments for environmental resource allocation. Competitive tenders have the potential to undermine the equitable benefits of Nature-based Solutions.

This study aims to conduct a grid-scale extreme precipitation risk assessment in Xuanwu District, Nanjing, so as to fill the gaps in existing indicator systems and improve the precision of risk characterization. By integrating physical, social, and environmental indicators, a risk assessment framework was constructed to comprehensively represent the characteristics of extreme precipitation risk. This study applied the entropy weight method to calculate indicator weights, combined with ArcGIS technology and the K-means clustering algorithm, to analyze the spatial distribution characteristics of risk under a 100-year extreme precipitation scenario and to identify key influencing indicators across different risk levels. The results showed that extreme precipitation risk levels in Xuanwu District exhibited significant spatial heterogeneity, with an overall distribution pattern of low risk in the central area and high risk in the surrounding areas. The influence mechanisms of key indicators showed tiered response characteristics: the low-risk areas were mainly controlled by the submerged areas of urban and rural, industrial and mining, and residential lands, water body area, soil erosion level, and normalized difference vegetation index (NDVI). The medium-risk areas were influenced by the submerged areas of urban and rural, industrial and mining, residential lands, the submerged areas of forest land, emergency service response time to disaster-affected areas, soil erosion level, and NDVI. The high-risk areas were jointly dominated by the submerged areas of urban and rural, industrial and mining, residential lands, the submerged areas of forest land, and NDVI. The extremely high-risk areas were driven by three factors—the submerged areas of forest land, emergency service response time to disaster-affected areas, and the proportion of the largest patch to the landscape area. This study improves the indicator system for extreme precipitation risk assessment and clarifies the tiered response patterns of risk-driving indicators, providing a scientific basis for developing differentiated flood control strategies in Xuanwu District while offering important theoretical support for improving regional flood disaster resilience. © 2025 Editorial Office of Journal of Disaster Prevention and Mitigation Engineering. All rights reserved.

Rapid urban expansion has significantly altered land use patterns, resulting in a decrease in pervious surface areas and a disruption of hydrologic connectivity between surface water and groundwater systems. Combined with inadequate drainage systems and poorly managed runoff, these changes have intensified urban flooding, leading to fatalities and significant infrastructure damage in many rapidly growing and climate-vulnerable urban areas around the world. This study presents an integrated economic-hydrologic model to assess the effectiveness of Low Impact Development (LID) measures—specifically permeable pavement, infiltration trenches, bio-retention cells, and rain barrels—in mitigating flood damage in the Bronx river watershed, NYC. The Storm Water Management Model (SWMM) was employed to simulate flood events and assess the effectiveness of various LIDs, applied individually and in combination, in reducing peak discharge. Flood inundation maps generated using HEC-GeoRAS were integrated with the HAZUS damage estimation model to quantify potential flood damages. A benefit-to-cost (BC) ratio was then calculated by comparing the monetary savings from reduced flood damage against the implementation costs of LID measures. Results indicate that the combined LID scenario offers the highest peak flow reduction, with permeable pavement alone reducing flow by 57%, outperforming other techniques under equal area coverage. Among all individual options, permeable pavement yields the highest cumulative BC ratio under all scenarios (4.6), whereas rain barrels are the least effective (2.6). The proposed evaluation framework highlights the importance of economic efficiency in flood mitigation planning and provides a structured foundation for informed decision-making to enhance urban resilience through LID implementation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

Study region: This study aims at the Kunhar River Basin, Pakistan, that has been facing repeated flood occurrences on a recurring basis. As the flood susceptibility of this area is high, its topographic complexity demands correct predictive modeling for strategic flood planning. Study focus: We developed a system of flood susceptibility mapping based on Geographic Information Systems (GIS), Principal Component Analysis (PCA), and Support Vector Machine (SVM) classification. Four kernel functions were applied, and the highest-performing was the Radial Basis Function (SVM-RBF). The model was validated and trained using historical flood inventories, morphometric parameters, and hydrologic variables, and feature dimensionality was reduced via PCA for increased efficiency. New hydrological insights: The SVM-RBF model recorded an AUC of 0.8341, 88.02% success, 84.97% predictability, 0.89 Kappa value, and F1-score of 0.86, all of which indicated high predictability. Error analysis yielded a PBIAS of +2.14%, indicating negligible overestimation bias but within limits acceptable in hydrological modeling. The results support the superiority of the SVM-RBF approach compared to conventional bivariate methods in modeling flood susceptibility over the complex terrain of mountains. The results can be applied in guiding evidence-based flood mitigation, land-use planning, and adaptive management in the Kunhar River Basin. © 2025 The Author(s)