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Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.

Flooding in urban area is a major natural hazard causing loss of life and damage to property and infrastructure. The major causes of urban floods include increase in precipitation due to climate change effect, drastic change in land use–land cover (LULC) and related hydrological impacts. In this study, the change in LULC between the years 1966 and 2009 is estimated from the toposheets and satellite images for the catchment of Poisar River in Mumbai, India. The delineated catchment area of the Poisar River is 20.19 km 2 . For the study area, there is an increase in built-up area from 16.64 to 44.08% and reduction in open space from 43.09 to 7.38% with reference to total catchment area between the years 1966 and 2009. For the flood assessment, an integrated approach of Hydrological Engineering Centre-Hydrological Modeling System (HEC-HMS), HEC-GeoHMS and HEC-River analysis system (HEC-RAS) with HEC-GeoRAS has been used. These models are integrated with geographic information system (GIS) and remote sensing data to develop a regional model for the estimation of flood plain extent and flood hazard analysis. The impact of LULC change and effects of detention ponds on surface runoff as well as flood plain extent for different return periods have been analyzed, and flood plain maps are developed. From the analysis, it is observed that there is an increase in peak discharge from 2.6 to 20.9% for LULC change between the years 1966 and 2009 for the return periods of 200, 100, 50, 25, 10 and 2 years. For the LULC of year 2009, there is a decrease in peak discharge from 10.7% for 2-year return period to 34.5% for 200-year return period due to provision of detention ponds. There is also an increase in flood plain extent from 14.22 to 42.5% for return periods of 10, 25, 50 and 100 years for LULC change between the year 1966 and year 2009. There is decrease in flood extent from 4.5% for 25-year return period to 7.7% for 100-year return period and decrease in total flood hazard area by 14.9% due to provisions of detention pond for LULC of year 2009. The results indicate that for low return period rainfall events, the hydrological impacts are higher due to geographic characteristics of the region. The provision of detention ponds reduces the peak discharge as well as the extent of the flooded area, flood depth and flood hazard considerably. The flood plain maps and flood hazard maps generated in this study can be used by the Municipal Corporation for flood disaster and mitigation planning. The integration of available software models with GIS and remote sensing proves to be very effective for flood disaster and mitigation management planning and measures.

With the climate change and the acceleration of urbanization, urban flood disaster is becoming increasingly frequent, leading to more severe impact than in the past. The traditional disaster alleviation strategies have gradually expanded to non-engineering disaster reduction strategies. As urban public property, public facility is more available than private land, making it the preferred strategy of site selection for urban land flood diversion. However, due to the limited government finance, how to select public facilities as the multi-use detention basin to maximize the disaster reduction and external benefits is an issue that needs to be considered before planning and decision-making. This study builds an operable decision model of site selection of urban public facilities as multi-use detention basin from the perspective of environmental efficiency. The decision model analyzes the expected costs and benefits of the multi-use detention basin based on data envelopment analysis (DEA) and cross-efficiency analysis, so as to establish the optimal combination of alternative schemes of site selection. It further compares with the traditional detention basin considering only disaster reduction efficiency to summarize how to improve the strategy of selecting multi-use detention basin site within the watershed in the future. This paper uses the watershed of Dajiaxi as a case study, and finds that (I) there is a certain trade-off relationship between the optimized schemes established from the perspective of environmental efficiency and disaster reduction efficiency evaluation. The scheme with the highest disaster reduction efficiency does not necessarily increase the external benefit significantly; (II) for the inefficient schemes, the difference between the site selection schemes and the ideal efficiency value can be measured through slack variable analysis.

South Kalimantan Province has experienced floods during the rainy seasons due to high intensity of precipitation. In mid-January 2021, flood inundated the Banjarmasin Campus I area of Lambung Mangkurat University (ULM Campus I). In mid-December 2021, other flood case inundated Campus I ULM. The research aims to measure the effectiveness of using detention basin as flood control in Banjarmasin campus area of ULM. Data collection is based on hydrological analysis of rain data to obtain a designed flood discharge with a return period of 20 years using the Der Weduwen method. The hydraulic analysis of the transverse and longitudinal sections on the river section of Campus I ULM was analyzed using the HEC-RAS. Based on the value of the flood volume, an analysis of the volume of the detention basin is carried out, where the volume of the detention basin must be greater than the volume of the flood. Spatial data is used to obtain contours using ArcGIS, to determine the optimal location of the detention basin. Based on the simulation, cumulative planned flood volume was 36,010 m 3 with a discharge value of 89.30 m 3 /s. It was designed two detention basin. First detention basin dimension is 160 m x 130 m x 1.5 m located closed to General Building. The second one, located at Master of Law Building with dimension is 120 m x 35 m x 1.5 m First detention basin dimension is 160 m x 130 m x 1.5 m located closed to General Building. The second one, located at Master of Law Building with dimension is 120 m x 35 m x 1.5 m.

Optimizing rural residential areas (RRAs) in environmentally fragile areas such as flood detention basins is of great significance for improving the human–land relationship and achieving sustainable rural development. This study took Xun County in central China as a case study, established a dual minimum cumulative resistance model (DMCR) that considered the dual factors of natural attributes and policy regulations to evaluate the optimization resistance of RRAs and determined the optimization directions and strategies accordingly. The main results are: (1) RRAs are relatively small and scattered, and there are spatial conflicts with policy regulations such as flood detention basin and urban development boundaries. (2) The spatial difference in optimization resistance of RRAS is higher in the central and northern towns and lower in the western and eastern towns. The factors with significant effects include policy resistance, location resistance, and production resistance. (3) The optimization of RRAs is divided into three directions: annexation or evacuation, consolidation and improvement, and clustering and upgrading, with their respective area proportions of 31.17%, 48.12%, and 20.72%. (4) RRAs in the direction of clustering and upgrading allow for moderate expansion; RRAs in the direction of consolidation and improvement achieve smart reduction through the renovation of homesteads; RRAs in the direction of annexation or evacuation are gradually integrated into the urban area in the suburbs and completely demolished in the outer suburbs.

The Edwards Aquifer is the primary water resource for over 2 million people in Texas and faces challenges including fecal contamination of water recharging the aquifer, while effectiveness of best management practices (BMPs) such as detention basins in mitigating fecal pollution remains poorly understood. For this study, the inlet and outlet of a detention basin overlying the aquifer’s recharge zone were sampled following storm events using automated samplers. Microbial source tracking and culture-based methods were used to determine the occurrence and removal of fecal genetic markers and fecal coliform bacteria in collected water samples. Markers included E. coli (EC23S857), Enterococcus (Entero1), human (HF183), canine (BacCan), and bird (GFD). Fecal coliforms, EC23S857, and Entero1 were detected following each storm event. GFD was the most frequent host-associated marker detected (91% of samples), followed by BacCan (46%), and HF183 (17%). Wilcoxon signed rank tests indicated significantly lower outlet concentrations for fecal coliforms, EC23S857, and Entero1, but not for HF183, GFD, and BacCan. Higher GFD and BacCan outlet concentrations may be due to factors independent of basin design, such as the non-point source nature of bird fecal contamination and domestic dog care practices in neighborhoods contributing to the basin. Mann–Whitney tests showed marker concentrations were not significantly higher during instances of fecal coliform water quality criterion exceedance, except for E. coli , and that fecal coliform concentrations were not significantly different based on marker detection. Overall, results suggest that the detention basin is effective in attenuating fecal contamination associated with fecal coliforms and the general markers, but not for host-associated markers. Consequently, management efforts should focus on mitigating dog and bird-associated fecal pollution in the study region.

Under the dual pressures of global climate change and rapid urbanization, urban drainage systems (UDS) face severe challenges caused by extreme precipitation events and altered surface hydrological processes. The drainage paradigm is shifting toward resilient systems integrating grey and green infrastructure, necessitating a comprehensive review of the design and operation of grey infrastructure. This study systematically summarizes advances in urban stormwater process-wide regulation, focusing on drainage network design optimization, siting and control strategies for flow control devices (FCDs), and coordinated management of Quasi-Detention Basins (QDBs). Through graph theory-driven topological design, real-time control (RTC) technologies, and multi-objective optimization algorithms (e.g., genetic algorithms, particle swarm optimization), the research demonstrates that decentralized network layouts, dynamic gate regulation, and stormwater resource utilization significantly enhance system resilience and storage redundancy. Additionally, deep learning applications in flow prediction, flood assessment, and intelligent control exhibit potential to overcome limitations of traditional models. Future research should prioritize improving computational efficiency, optimizing hybrid infrastructure synergies, and integrating deep learning with RTC to establish more resilient and adaptive urban stormwater management frameworks.

Dry detention basins (DB) are commonly used to reduce the rate of runoff in urban areas and may provide open space for recreation between storms. However, most are not effective at nitrogen removal in comparison to other measures, such as constructed wetlands. The study goal was to assess the nitrogen treatment efficiency of a DB that exhibited some wetland characteristics, including saturated soil near the inlet and wetland vegetation that covered 40% of the surface area. Influent and effluent samples were collected during multiple stages of eight storm events for nitrogen concentration analyses. High-frequency water stage, pH, dissolved oxygen (DO), and temperature loggers were deployed at the inlet and outlet prior to anticipated rain. As stormwater passed through the DB, the event mean concentrations (EMCs) and masses of TN declined by 20.7% and 52.3%, respectively, while the DO and pH dropped by 62% and 20.5%, respectively. Load reductions of TN exceeding 93% were observed during two small storms with rain depths of less than 0.16 cm and when the outflow volumes were reduced by greater than 82%. Temperature was significantly correlated (p < 0.001; r = 0.964) with volume reductions (via infiltration and evapotranspiration), and, thus, the treatment was better during warmer periods. The DB was effective at removing inorganic nitrogen, likely via nitrification, denitrification, and immobilization, but frequently exported higher EMCs of organic nitrogen. Overall, the DB exceeded the 10% TN removal expectation for dry basins. The findings from this study suggest that the TN treatment efficiency of DBs may be improved by incorporating wetland characteristics.

It is widely recognized that urban development alters infiltration capacity and enhances its spatial variability, but also constrains watercourses into narrow channels making them unable to contain the runoff that is generated by relatively small, but intense, rainfall events. Network of detention basins are designed to reduce the flood peak by temporarily storing the excess storm water and then releasing the water volume at allowable rates over an extended period. This paper shows the use of a distributed hydrological model for the assessment of effectiveness of a network of detention facilities in a heavily urbanized river basin. The distributed hydrological model FEST was used to assess design hydrograph and, in parallel to design the seven detention basins optimized for the specific purpose of maintaining the flow rate within the range of the maximum allowable discharge. This permitted to estimate the design hydrograph considering both the spatial variability of soil infiltration capacity and routing characteristics induced by each detention basins along the main river. Results indicate that on-stream detention ponds can increase duration of the critical event and runoff volume of design flood with possible negative implications on downstream facilities.

Sustainable Urban Drainage Systems (SUDS) are commonly used to control flooding in urban areas. These structures store and treat stormwater runoff. Several studies in high-income countries have reported the presence of pathogens in runoff water, but it is expected that runoff water in developing countries contains higher pathogen concentrations given their lack of resources to properly manage sewage; this could result in higher risks of infection for people interacting with SUDS. In this study, we investigated pathogen concentrations (i.e., Salmonella spp. and E. Coli O157 ) at the micropool of a SUDS train composed of a grassed swale followed by a dry extended detention basin in Bogotá (Colombia) during a 25-week period. We also estimated the risk of infection with the analyzed pathogens, given the high level of exposure to the detention structure. Additionally, we investigated if any of the physicochemical or meteorological variables were associated with pathogen concentrations at the site. We found that pathogen concentrations greatly exceeded concentrations reported for stormwater runoff in developed countries, namely 1562 CFU/mL, on average, for Salmonella spp. and 9160 CFU/mL, on average, for E. Coli O157. The risk of infection from Salmonella spp. and E. Coli O157 greatly exceeded risks previously reported for recreational waters and SUDS. Pathogen concentrations were associated with precipitation and the concentration of suspended solids in the runoff. Given our findings, it is recommended that SUDS in developing countries should consider potential higher pathogen concentrations in stormwater runoff to reduce exposure. Article Highlights Pathogen concentrations in stormwater runoff may be higher in developing countries. Pathogen concentrations in the SUDS micropool are linked to TSS concentrations. High precipitation events result in high pathogen concentrations in the SUDS. High pathogen concentrations in the micropool yield a high infection risk in children. Understanding pathogen concentrations in stormwater runoff is key to operating SUDS.