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To avoid the nuisance of frequent flooding during rainy season, designing an efficient stormwater drainage system has become the need of the hour for present world engineers and urban planners. The present case study deals with providing a solution to stormwater management problem in an urbanized area. Mann–Kendall and Sen’s slope tests are used to perform the trend analysis of rainfall events using daily rainfall data (1956–2012), while the L-moments-based frequency analysis method is employed to estimate the design storm for a small urbanized area in West Bengal, India, using daily annual maximum rainfall (1975–2013). SWMM (Storm Water Management Model) and MIKE URBAN models are used to design an efficient drainage system for the study area. Two-dimensional (2D) MIKE URBAN model is primarily used to overcome the limitation of one-dimensional (1D) SWMM in simulating flood extent and flood inundation. Model simulation results from MIKE URBAN are shown for an extreme rainfall event of July 29, 2013. A multi-purpose detention pond is also designed for groundwater recharge and attenuating the peak of outflow hydrograph at the downstream end during high-intensity rainfall. This study provides an insight into the importance of 2D model to deal with location-specific flooding problems.

Design storms are key components for planning drainage networks and flood risk management. Due to atmospheric processes, precipitation accumulations across multiple temporal intervals are often correlated and can combine to shape flood intensities. However, current design storm guidance overlook the observed correlations between return periods of different duration intervals within storms and may thereby lead to under‐ or over‐estimation of the flood risk. We present a new approach for generating plausible design storms that accounts for joint return periods. Focusing on short‐duration extreme precipitation events, potentially leading to urban pluvial flooding, we analyze the dependencies between critical precipitation intensities over the 10‐min, 30‐min, 1‐hr, 3‐hr, and 6‐hr intervals, for data from Zurich (Switzerland). We then propose a method based on a canonical vine copula model for sampling precipitation intensities that reflect the observations' dependencies. Using this model, we then generate realistic design storms with a constrained micro‐canonical cascade model. Our results shows that the common block methods (e.g., the Chicago and Euler design storms) tend to overestimate total precipitation volumes on average, by up to 56%. Furthermore, we highlight the variability in possible duration‐frequency profiles, leading to both higher and lower total precipitation volumes compared to standard approaches. This underscores the need to switch from traditional block methods to a more realistic sampling of design storms, incorporating multiple design storm scenarios for robust risk assessment. The model is applicable to any time series of precipitation, regardless of its location or climate. The code is freely available.

Climate change, driven by human activities and increasing greenhouse gas emissions, is pushing Earth’s climate toward a warmer state, as evidenced by long-term observations. The frequency and intensity of unprecedented rainfall events have increased in recent years, underscoring the urgent need to revise design storms and depth-duration frequency (DDF) curves to better adapt to and mitigate the impacts of climate change. This study used a serial type of stochastic rainfall generator (SRG) that is capable of simulating daily rainfall series by embedding unprecedented events to study extreme precipitation scenarios under the changing climate. By perturbing values of power law tuning parameters in the SRG model, we developed thirty-six precipitation scenarios, some of which directly correlate with the current climate change scenario, while others represent very extreme conditions. High-performance computing is employed to run the computationally intensive SRG for simulating thirty-six scenarios across the entire Indian region. These simulated scenarios were analyzed to prepare rainfall return level maps and DDF curves. The findings reveal substantial increases in rainfall return levels across all frequencies when unprecedented events are considered, with pronounced impacts in coastal, northeastern, and Himalayan regions. The spatial pattern of simulated extreme precipitation was consistent across all generated scenarios from SRG irrespective of the return periods. Minimal spatial uncertainty in return level estimates across climate zones is observed which confirms the robustness of the SRG model and spatial clusters of extreme rainfall are identified irrespective of SRG being a point model. The analysis in this study based on SRG simulated climate change scenarios offers crucial insights for revising design storms and for devising climate resilience and flood management strategies.

Managing stormwater under climate uncertainty is a concern in both built-out communities and those continuing to undergo land use change. In this study, a suite of climate change scenarios were developed to represent a probable range of change in the 10-year recurrence interval design storm. The Environmental Protection Agency’s Stormwater Management Model was used to predict flooding due to undersized drainage components within watersheds representing a traditional, built-out urban area and a developing suburban area with intact green infrastructure corridors. Despite undersized infrastructure and flooding in both study watersheds, the risk of property damage in the suburban watershed was negligible across the range of scenarios even at projected build-out, due in part to flood storage capacity of the green infrastructure network. Adaptation approaches – including pipe upsizing, underground storage, and bioinfiltration – and costs were also modeled in both watersheds. In the built-out site, bioinfiltration practices were predicted to moderate both flooding and total adaptation costs even when implemented over a relatively modest (10 %) portion of the watershed; however, a substantial upgrade to gray stormwater infrastructure (pipes and storage chambers) was also needed to mitigate impacts. In the urbanizing community, maintaining an intact green infrastructure network was surmised to be the most cost-effective approach for enhancing the resilience of urban stormwater systems to climate uncertainties and urbanization.

It is undeniable that coastal regions worldwide are facing unprecedented damages from catastrophic floods attributable to storm-tide (tidal) and extreme rainfall (pluvial). For flood-risk assessment, although recognizing compound impact of these drivers is a conventional practice, the marginal/individual impacts cannot be overlooked. In this letter, we propose a new measure, Tide-Rainfall Flood Quotient (TRFQ), to quantify the driver-specific flood potential of a coastal region arising from storm-tide or rainfall. A set of inundation and hazard maps are derived through a series of numerical and hydrodynamic flood model simulations comprising of design rainfall and design storm-tide. These experiments are demonstrated on three different geographically diverse flood-affected coastal regions in India. The new measure throws light on existing knowledge gaps on the propensity of coastal flooding induced by the marginal/individual contribution of storm-tide and rainfall. It shall prove useful in rationalizing long-term flood management strategies customizable for storm-tide and pluvial dominated global coastal regions.

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.

Estimation of urban runoff peak and volume is a fundamental step in determining the transferring capacity of urban drainage systems. The main aim of this study was to present an application of the Storm Water Management Model (SWMM) in order to estimate urban flooding of a semi-arid area (Zanjan city in the northwest of Iran). The performance of an urban drainage system in the study area was also investigated. According to the results, SWMM is an effective tool for urban flood estimation in a semi-arid area. In this study, urban peak flow was simulated via a calibrated model with acceptable accuracy. Based on the results of the model simulation, the capacity of the main canals in the study area is sufficient for peak runoff transferring for a design storm with 50 year return periods, without retrofitting. Whereas, based on local observation and model results, localized and surface flooding can be observed in some urban areas.

Flood frequency analysis and hydrological modelling are crucial for water resource management and flood mitigation, especially in regions vulnerable to extreme weather. This study utilises the HEC-HMS hydrological model to simulate rainfall-runoff processes and generate design storms for various return periods across 24 sub-watersheds of the Jhelum Basin, Kashmir. The model setup includes rainfall transformation using the ModClark method, baseflow estimation through the Linear Reservoir Method, and flood routing via the Muskingum approach. Satellite-based gridded rainfall data and sub-basin-specific hyetographs were used as meteorological inputs to ensure spatially distributed precipitation representation. Calibration and validation were performed using discharge data from Sangam, Ram Munshibagh, and Asham gauging stations (2020–2023), covering five high-flow events. This research marks the first application of event-based design storms at the sub-watershed scale in the Kashmir Valley using HEC-HMS, providing high-resolution insights into flood risk patterns. The model showed strong agreement with observed hydrographs (R² > 0.78, NSE > 0.56, RSR < 0.6, PBIAS within ± 25%). Sensitivity analysis identified curve number, time of concentration, and infiltration rates as key parameters influencing performance. Results indicated varied hydrological responses, with watersheds like Lower Jhelum, Sindh, Lidder, and Pohru showing higher peak discharges due to steep slopes, while low-lying areas such as Wular-II and Anchar exhibited prolonged flood retention. Urbanised watersheds like Dal and Wular-I showed moderate to high peaks, highlighting infrastructure vulnerability. Design storms for 2–500-year return periods identified critical flood-prone zones, offering insights for infrastructure planning and risk management. This research highlights the effectiveness of HEC-HMS model as an important non-structural flood mitigation measure in a mountainous region of Kashmir.

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

Design storm hyetographs are essential inputs for hydrological modeling and flood risk assessment, yet their applicability in hyper-arid climates remains poorly constrained. In Saudi Arabia, engineers have frequently relied on imported synthetic profiles—such as such as the Natural Resources Conservation Service (NRCS; formerly the Soil Conservation Service, SCS) Type II curve—which were originally derived from temperate regions and may misrepresent the temporal structure of local storms. This study develops dimensionless design storm hyetographs for the southwestern provinces of Saudi Arabia (Asir, Al-Baha, Makkah, and Jazan) using a dataset of 8923 storms recorded at 152 rain gauges between 2017 and 2024. Storms were classified into four duration groups (<3 h, 3–6 h, 6–12 h, and 12–24 h), normalized by depth and duration, and analyzed through Huff quartiles, Euler Type II, Alternating Block Method (ABM), and NRCS Type II. Model–data evaluation using root-mean-square error (RMSE) identified Huff Q1 as the most representative profile for short and intermediate storms, while Huff Q2 best captured longer events. The optimized profiles consistently reproduced the strong front-loaded character of Saudi convective rainfall and outperformed existing synthetic hyetographs. These findings provide robust, locally calibrated design storms for flood modeling and infrastructure design in arid regions. The methodology is transferable to other data-scarce environments where standard profiles may misrepresent storm dynamics.