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The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

This research examines the impact of climate change and urban expansion on urban drainage systems in Hyderabad (Zone-XII, Zone-IV&V), India. It employs a Markov chain-based framework to simulate future climate and land changes. Integrated 1D-2D PCSWMM model is used to assess the hazards posed by these changes. Present and future extreme rainfall event(s) (1–10 days) are simulated to determine maximum flooding hours, valuable for resilience studies. Future rainfall events are simulated under four SSP scenarios using CMIP6 Global Climate Models (GCMs): EC-Earth3-Veg, MPI-ESM-1-2-HR, and MPI-ESM-1-2-LR. The Markov Chain Precipitation Generator (MCPG) model downscales grid-scale precipitation data to station-scale. Future urban land expansion is simulated using the Markov Chain-Cellular Automata (MC-CA) model with Terrset. MCPG model is validated using performance measures, and it showed most increased rainfall events under EC-Earth3-Veg. The MC-CA model obtained a Kappa coefficient of 0.89, indicating an increase in imperviousness in future LULC; 6.1% of vegetation and 29.06% of barren land in 2022 will be urbanized by 2075. A significant increase in extreme flood hazard areas for the 1-day and above 7-day events in the both zones is observed from the PCSWMM results. The study highlighted the importance of Markov chains and event duration in flood hazard assessments.

The Bongani River is a primary source of polluted stormwater runof discharging into the shallow Ashmead Channel, a portion of the Knysna Estuary situated on the southern coast of South Africa. One of the ways to improve the quality of stormwater in the Bongani River is to introduce sustainable drainage systems (SuDS) into the catchment area to improve stormwater management. The feasibility of reducing nutrient loads using SuDS was investigated using a continuous hydrological model of the Bongani River and its catchment. Besides the current situation (Current Scenario), various scenarios were developed in PCSWMM (Personal Computer Stormwater Management Model). The total phosphorus reduction objective for SuDS set by the City of Cape Town (used in the absence of Knysna-specific stormwater quality objectives) is 45%. All the scenarios modelled showed pollutant load reductions of between 47% and 78%, exceeding the 45% target, but none approached the pre-development baseline which indicated some 89% and 90% lower concentrations of total nitrogen and total phosphorus, respectively, compared to current conditions. This performance gap highlights the extent of nutrient enrichment in the Bongani River catchment and suggests that, while SuDS can provide improvements, additional watershed-scale interventions are necessary to restore water quality conditions.

Existing sea-level rise models for coastal cities often neglect precipitation impacts on infrastructure. In tidally influenced areas, high water levels can overwhelm stormwater systems, causing drainage failure, corrosion, and backflow of contaminated water. Waikīkī, Honolulu’s tourism hub, faces increasing flood risks and infrastructure damage due to rising sea levels. Using PCSWMM modeling software, selected for its capacity to represent complex urban drainage systems, this study simulates drainage failure under present and projected sea levels with precipitation. Findings reveal a 5-year precipitation event at present sea level floods more inlets than three feet of sea-level rise, while a 10-year event floods three times more inlets than four feet of sea-level rise. By 2050, a 5-year event could disrupt transportation and contaminate 70% of stormwater inlets in Waikīkī. Accounting for precipitation, 100% of outfalls will fail and 85% of the drainage system will be full by 2040. Results indicate 22–50% more flooded inlets during precipitation events than passive models at present sea level. Salinity and water level data indicate severe corrosion risks, potentially worsening drainage failure. This study highlights the urgent need to integrate precipitation into sea-level rise modeling to strategically mitigate urban flood risks in Waikīkī and other coastal cities.

Land use and land cover (LULC) change is one of the primary contributors to hydrological change in urban watersheds and can potentially influence stream flow and flood volume. Understanding the impacts of LULC change on urban hydrological processes is critical to effective urban water management and minimizing flood risks. In this context, this study aims to determine the impacts of LULC change on hydrological response in a fast transitioning watershed for the predicted years of 2050 and 2080. This research employs the hybrid land use classification technique, Cellular Automata–Markov (CA–Markov) model to predict land use changes, utilizing land use data from 2001, 2013, and 2021. Additionally, it incorporates a calibrated, event-specific hydrologic model known as the Personal Computer Storm Water Management Model (PCSWMM) to assess alterations in hydrological responses for storm events of various magnitudes. The findings indicate a transition of the watershed into an urbanized landscape, replacing the previous dominance of agriculture and forested areas. The initial urban area, constituting 11.6% of the total area in 2021, expands to cover 34.1% and 44.2% of the total area by 2050 and 2080, respectively. Due to the LULC changes, there are increases in peak discharge of 5% and 6.8% and in runoff volume of 8% and 13.3% for the years 2050 and 2080 for a 100-year return period storm event. Yet, the extent of these changes intensifies notably during storm events with lower return periods. This heightened impact is directly attributed to the swift urbanization of the watershed. These results underscore the pressing necessity to regulate LULC change to preserve the hydrological equilibrium.

With global warming and urbanization accelerating, urban flood disasters have become increasingly frequent, highlighting the need for reliable urban flood forecasting models. Traditional numerical simulation models and individual machine learning model often suffer from poor robustness and low efficiency, leading to inaccurate predictions. Meanwhile, current machine learning models have high requirements for sample size and quality of training data, which can be challenging to meet even with data interpolation. To address these limitations, this study proposes an urban flood forecasting method that combines the strengths of PCSWMM numerical simulation and stacking ensemble learning. The key objectives of this research are to: (1) Leverage the reliable data and features generated by the PCSWMM numerical simulation to train a robust machine learning model; (2) Employ the stacking ensemble learning algorithm to integrate multiple base learner models, thereby reducing the errors caused by individual model deficiencies and improving the overall prediction accuracy and stability. The results demonstrate that the data obtained through numerical simulation has stable predictive ability and can provide reliable datasets and features for training machine learning models. Compared with KNN, XGBoost, and LightGBM models, the stacking ensemble model has the highest accuracy, with RMSE improvements of 69.92%, 51.82% and 73.79% respectively. This indicates that the stacking ensemble learning method is superior to the individual machine learning model, reducing prediction errors and improving overall prediction performance. The findings of this study offer a new perspective for urban flood forecasting and provide a reliable basis for flood disaster simulation, contributing to the field of urban hydrology and disaster risk management.

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

Urbanization has led to a decrease in infiltration and an increase in surface runoff which intensifies the risk, frequency, and extent of urban flood disasters. Although studies have been conducted to reduce urban flood damage by restoring the natural water cycle and thereby increasing the capacity of low impact development (LID) practices, there are few of them on land-use optimization to reduce surface runoff in urban areas. Thus, this study proposes an optimization approach that reallocates land-use parcels to reduce surface runoff using the genetic algorithm (GA) and the PCSWMM model. Incheon Gyeyang Techno-valley, one of the target districts of the 3rd New Town Project in the Seoul Metropolitan Area, South Korea, was selected as the target site. GA was embedded in the delineated catchment using the PCSWMM scripting tool to relocate land-use planning. Four LID practices, such as green roofs, permeable pavements, bio-retention, and infiltration trenches, were applied to each cell after considering the type of land-use planning. As a result, the rate of peak runoff decreased by 2.16%, 7.09%, and 7.01% under 2-, 10-, and 50-year return period rainfall, respectively. Although the updated land-use plan was not able to dramatically decrease the amount of runoff and peak flow rate, it was found that the relocation of LID practices with limited changes in the land-use plan can mitigate the peak flow rate during storm events in urban areas. Optimized land-use allocation must be considered during the planning stage because the overall capacity of low impact development practices depends on the land-use plan.

Due to the dual pressure of rapid urbanization and climate change, urban flooding has become more common. Thus, for effective planning and mitigation strategies, it is of paramount interest to quantify the generated runoff and prioritize the urban critical sub-catchments. The present study investigates flood inundation in Hyderabad urban setting (zone-XII, zone-IV&V) using the Personal Computer Storm Water Management Model (PCSWMM) and prioritizes the critical sub-catchments using the compromise programming method (CPM) and PCSWMM. In addition, the system resilience is examined by integrating PCSWMM with GIS. The model simulation is performed for a 264 h (11 days) rainfall event that occurred in October 2020. The outcomes from the simulation are found to be satisfactory and in agreement with the field water logging points (WLPs). The inundation map results are validated with social media markers (SMMs). The critical sub-catchments are prioritized based on PCSWMM by runoff results and CPM by considering WLPs, slope and impervious percentage of sub-catchments as input criteria. The Integrated 1D-2D PCSWMM is used to examine the inundation velocity and depth. An urban flood hazard (UFH) map is generated to identify optimal low impact developments (LIDs). Subsequently, the present study showed how storage can improve the catchment capability and resilience of urban settings to tackle the excess stormwater.

Building information modeling (BIM) has been used by the architectural and engineering disciplines to streamline the building design, construction, and management process, but there has been much more limited experience in extending the application to landscape design and implementation. This study integrated BIM software (Autodesk InfraWorks 2024.1) with a dynamic, process-oriented, conceptual hydrologic/hydraulic model (PCSWMM 2023, version 7.6.3665) to enhance the analytical tools for sustainable landscape design. We illustrate the model integration through a case study that links an existing nature-based solution (NbS) development, the PTT Metro Forest Park, Bangkok, Thailand, with theoretical new-build NbS for an adjacent property. A BIM school building was virtually situated on an empty lot beside the Metro Forest Park and seven NbS scenarios were run with design storms having 2-year, 5-year, and 100-year return intervals. The combination of a rain garden, permeable pavement, a retention pond, and a green roof was effective in sustainably managing runoff from the theoretical new-build site discharging to the Metro Forest. NbS design characteristics such as rain garden substrate depth and green roof area were optimized using the hydrologic/hydraulic model. Model results showed that even with the 100-year rainfall event, the existing Metro Forest pond storage capacity was sufficient so that flooding on the property would not occur. The consideration of connectivity between NbS features is facilitated by the modeling approach, which is important for NbS planning and assessment at a regional scale.