Water Management Model Research Articles

Overflow Simulation and Optimization of a Drainage System in an Urban Area in the Northern Anhui Plain

Quantitative simulation of urban waterlogging using computer models is an effective technical means for urban storm water management, especially for predicting and preventing waterlogging. In this study, a city in the northern Anhui Plain, China, was selected as the study site. The Storm Water Management Model was applied to simulate the dynamic changes in the pipeline overload, node overflow, and discharge port runoff characteristics from three perspectives: surface runoff, pipe network transmission, and flow control of low-impact development. The operation of the rainwater pipe network under different return periods and the real-time operation of the rainwater pipe network were simulated to seek solutions to urban waterlogging problems caused by flat terrain and slow drainage. The results revealed that surface runoff is the primary source of rainfall in the study area, with a runoff coefficient of 0.599. The drainage pipe network was optimized by expanding the diameter of the pipe from ≤1.5 mm to ≥2 mm. The water reduction rate was more than 50%, and overload did not occur after optimization. Therefore, sinking green space technology and optimization methods for expanding a pipe diameter can reduce urban waterlogging.

Community water management model in Toluca: a tactical and integrated approach

El presente trabajo propone un modelo de gestión comunitaria de agua que ayude a resolver de forma integrada los problemas prioritarios (económicos, técnico-operativos y sociopolíticos) que padecen los comités de agua potable -de origen antiguo- del municipio de Toluca. Este artículo se realizó con investigación documental y análisis de gabinete fundado en los enfoques teóricos de gobernanza, socio-histórico, bienes comunes, gestión comunitaria, cogestión y gestión integrada de los recursos hídricos; también se consideraron los indicadores clave obtenidos por Anzurez et al. (en prensa) a través de la metodología de planeación estratégica participativa con enfoque integrado, y la evidencia empírica de trabajo de campo y participativo. Resultó un modelo híbrido de gestión comunitaria de agua acorde con las características específicas de Toluca y las necesidades prioritarias de sus comunidades originarias (históricas, culturales, sociales, políticas, administrativas y normativas), acompañado de estrategias de gobernanza colaborativa (asociatividad), opciones para obtener el reconocimiento legal, e instrumentos normativos (manual, reglamento, guía). El modelo sirve a los actores que prestan servicios de agua e instrumentan políticas públicas, para que entiendan el proceder comunitario, funcionamiento, organización social, contexto, visión del agua. Se concluyó que la Asamblea General del Pueblo es el elemento fundamental para garantizar el éxito del modelo, su importancia y valor no se limita a la toma de decisiones en las comunidades, sino a su funcionamiento a través de la participación activa, el compromiso continuo, la confianza, la voluntad de cambio y el consenso comunitario.

Integrated Assessment of the Runoff and Heat Mitigation Effects of Vegetation in an Urban Residential Area

Urban vegetation has an essential role in maintaining the hydrological and energy balance. These processes in urban areas have been long overlooked due to the fragmentation and uneven feature of land use and vegetation distribution. Recent advances in remote sensing and the ease of data acquisition have allowed a more precise mapping of vegetation and land cover, making it possible to simulate the above processes at micro scales. This research selects a small typical residential catchment in Japan as the study area and the purpose of this research is to investigate the impact of urban vegetation on mitigating urban runoff and the heat island effect. The remote-sensed Normalized Difference Vegetation Index (NDVI) data were used to represent vegetation spatial distribution and seasonal variation. A single layer canopy model and the Storm Water Management Model were coupled to simulate interception, evapotranspiration, and runoff generation processes. The effects of vegetation amount and landscape patterns on the above processes were also considered. The results showed that the coupled model had a satisfactory performance in the modeling of these processes. When the vegetation amount was set to 1.4 times its original value, the summer total runoff had a 10.7% reduction and the average surface temperature had a 2.5 °C reduction. While the vegetation amount was 0.8 times its original value, the total runoff increased by 6%, and the average surface temperature in summer increased by 1.5 °C. The combination of green roof and dense street trees showed the best mitigation performance among the different landscape patterns. The results of this study could be used as a reference for future green infrastructure development in areas with similar climate and vegetation characteristics.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Numerical simulation study on the effect of underground drainage pipe network in typical urban flood

Rapid urbanization and climate change have heightened urban flood risks. An in-depth examination of the underground drainage pipe network’s effect on urban flood inundation can enhance urban stormwater management and mitigate disaster risks. This study presents a full hydrodynamic urban flood model (FHUM) coupling the pipe transport module of the Storm Water Management Model (SWMM) with a shallow water model. Based on unstructured grid cells, runoff yield and confluence calculations are carried out to effectively separate the underground drainage pipe network from other factors. The model is applied to Minzhi Area and Haidian Island to quantitatively assess the influence of the underground drainage system on urban flooding. Results indicate that FHUM performs well, accurately reflecting the process from rainfall to inundation. The underground drainage pipe network reduces surface inundation in the study area and improves the city’s stormwater drainage capacity. With increasing rainstorm intensity, the impact of the pipe network does not exhibit a consistent trend. During heavy rainstorms, the pipe network consistently plays a significant and stable role, particularly in reducing high inundation depths. However, attention should be paid to the potential flood risk transfer associated with using pipe networks in urban flood management. This work provides a scientific basis for urban stormwater management, holding significant importance in improving flood control and disaster reduction capabilities.

Rainwater runoff regulation effect in sponge reconstruction area based on rainstorm flood management model

The study adopts the rainwater regulation strategy based on the rainstorm water management model, and designs a set of rainwater runoff regulation system by monitoring the amount of water, collecting rainfall data, and evaluating the quality of rainwater and natural rainwater, aiming at conserving groundwater, restoring hydrological cycle, reducing urban waterlogging, and optimizing rainwater resources. In the implementation method, the effectiveness of this strategy is evaluated by simulating various scenarios such as without LID facilities, with LID facilities, and secondary rainwater pipe network regulation. The results showed that the Nash efficiency coefficient of the overall model was 0.89, and the changes in chemical oxygen demand, total nitrogen, and total phosphorus water quality indicators were very close to the actual monitoring results. The comparative data before and after the renovation showed that the three water quality indicators were much higher before the renovation. The peak flow rate and outflow time significantly decreased, for example, the peak flow rate decreased from 0.41 m³ /s to 0.39 m³ /s. The outflow time was reduced from 9 min to 6 min. It can be seen that the strategies proposed in the study have effectively improved the quality of urban environment and residents' lives, providing important reference basis for urban rainwater management and water resource protection.

Key drivers of vulnerability to rainfall flooding in New Orleans

IntroductionFuture urban stormwater flood risk is determined by the confluence of both climate-driven changes in precipitation patterns and the effectiveness of flood mitigation systems, such as urban drainage and pump systems. This is especially true in coastal cities protected by levee systems like New Orleans, where even present-day rainfall would be enough to cause serious flooding in the absence of extensive stormwater drainage and pumping. However, while the uncertainties associated with climate change have been well studied, uncertainties in infrastructure performance and operation have received less attention.MethodsWe investigated how these interrelated sets of uncertainties drive flood risk in New Orleans using a Robust Decision Making (RDM) approach. RDM is a framework for Decision Making Under Deep Uncertainty (DMDU) that leverages simulation models to facilitate exploration across many possible futures and the identification of decision-relevant scenarios. For our work, we leveraged a detailed Storm Water Management Model (SWMM) representation of the New Orleans urban stormwater management system to examine flood depths across the city when faced with different levels of future precipitation, sea-level rise, drainage pipe obstruction, and pumping system failure. We also estimated direct flood damage for each neighborhood in the city for this scenario ensemble. These damage estimates were then subjected to vulnerability analysis using scenario discovery—a technique designed to determine which combinations of uncertainties are most stressful to the system in terms of an outcome of interest (excess flood damage).ResultsOur results suggest that key drivers of vulnerability depend on geographic scale. Specifically, we find that possible climate-driven precipitation increases are the most important determinant of vulnerability at the citywide level. However, for some individual neighborhoods, infrastructure operation challenges under present day conditions are a more significant driver of vulnerability than possible climate-driven precipitation increases.

Urban Roadside Flooding Analysis Using SWMM: A Case Study of a Road Section in Pokhara Metropolitan City, Nepal

Pokhara City, in Nepal, experiences heavy rainfall during four months of monsoon, causing stress on drainage infrastructures. This situation is exacerbated by poor drainage systems, ineffective urban planning, and rapid urbanization. This study aims to develop mitigation strategies for urban roadside flooding in the Barahi Chowk region in Pokhara City. The focus is to identify the causes and potential solutions for the recurring issue of urban roadside flooding. For this purpose, the study utilizes Storm Water Management Model (SWMM) computer modeling to analyze the hydraulic capacity of existing drainage systems during peak flows. The SWMM modeling reveals that the current drainage usage is between 80% and 100% during peak times, leading to flooding. Consequently, resizing or expanding various drainage sections using SWMM modeling is suggested. The study introduces Low Impact Development (LID) controls, such as rain gardens and permeable pavements, to manage surface runoff. Implementing rain gardens on just 3% of the impervious area in each sub-catchment showed an average 21.5% reduction in peak runoff and an average 6.68% reduction in the total runoff while implementing 5% in each sub-catchment, permeable pavements reduced peak runoff by 22-26% and total runoff by 8-11%. The research explores the impact of land use and land cover change and unplanned urban growth on roadside flooding, resulting in impermeable urban surfaces that disturb the natural drainage system and infiltration pattern. Thus, the outcomes of this study should be carefully considered by policymakers and stakeholders to address issues through sustainable urban planning and infrastructure development. Also, implementing measures like resizing drains and LID controls appears to be effective in controlling flooding during the study period and should be considered for future studies as well.

Effectiveness of stormwater control measures in protecting stream channel stability

AbstractWhile research on the hydrologic impact of different types of stormwater control measures (SCMs) is extensive, little research exists linking urbanization, widespread implementation of SCMs and channel stability in headwater streams. This study evaluated whether the unified stormwater sizing criteria (USSC) regulations in the state of Maryland, USA, which require the use of both end‐of‐pipe and distributed, small‐scale SCMs, protect channel stability. To achieve this goal, a coupled hierarchical modelling approach utilizing the Storm Water Management Model (SWMM) and the Hydrologic Engineering Center River Analysis System 6.3 (HEC‐RAS) was developed to predict changes in streamflow and sediment transport dynamics in a first‐order gravel‐bed, riffle‐pool channel. Storm event discretization revealed that 88% of observed storm events during the 16 years (2004–2020) had durations less than 18 h and that the greatest peak flows resulted from storm events with durations less than 24 h. HEC‐RAS simulation results also showed that both channel degradation and aggradation, as high as 1.2 m, will likely occur due to regulations which require the use of 24 h duration design storms with a target stormwater detention time rather than bed material sediment transport limits. Overall, this study provides valuable insights into the complex interactions between SCM practises, flow regimes and sediment transport dynamics in heavily urbanized watersheds. It is recommended that SCMs be designed using a continuous simulation model with at least 10 years of continuous rainfall data. Furthermore, to protect channel stability, the SCM design goal should focus on maintaining pre‐development sediment transport regimes across a range of flows.

Impacts of future climate and land use/land cover change on urban runoff using fine-scale hydrologic modeling

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model—Personal Computer Storm Water Management Model (PCSWMM)—was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC.

Assessment of the sub-urban drainage system resilience on the increased flooding scenario using Storm Water Management Model (SWMM)

Assessment of the sub-urban drainage system resilience on the increased flooding scenario using Storm Water Management Model (SWMM)

Integrated nonurban-urban flood management using multi-objective optimization of LIDs and detention dams based on game theory approach

Flood events in urban and non-urban areas have long-term devastating impacts on communities resulting in casualties and economic damages. Different interconnected concepts of flood management lead decision-makers to broaden their focus from traditional points of view to transcend the disciplinary boundaries of hydrological modeling. So, evaluating interconnected aspects of flood control requires configuring an Integrated Flood Management (IFM) system. In this research, a novel comprehensive framework is proposed to control floods in urban and non-urban areas using Low-Impact Development (LID) practices and detention dams, simultaneously. In the first step, urban and non-urban areas of the Darakeh catchment in Tehran, Iran, as the study area, are modeled using Storm Water Management Model (SWMM). Next, by coupling the SWMM with Pareto Envelope-based Selection Algorithm II (PESA-II), optimal management scenarios are generated considering four hydrological-economic objectives. Each management scenario specifies the optimal sites and dimensions of detention dams and the optimal location and area of LIDs in non-urban and urban areas, respectively. Indeed, the innovative IFM framework provides a multi-dimensional trade-off between upstream and downstream flood protection. Then, interactions among the main stakeholders with conflicting interests are incorporated into utility functions. Ultimately, using COmplex PRoportional ASsessment (COPRAS) and Nash bargaining methods the most capable management scenarios are selected based on stakeholders' preferences. Results showed that the IFM framework can be significantly effective in flood severity attenuation. Based on the findings, proposed method warrants peak flow reduction up to 60.04 and 53.50 percent at the inlet and outlet of the urban area. Besides, the Nash bargaining approach tends to select a scenario with a higher peak flow reduction compared to the COPRAS method. Also, results signify that each stakeholder's relative importance and preferences can remarkably impact the decision-making process. In addition to resolving conflicts arising from implementing flood control measures, this framework can incorporate divergent aspects of flood management into a reliable system.

Harnessing the runoff reduction potential of urban bioswales as an adaptation response to climate change

Nature-based solutions (NbS), including China's Sponge City Program (SCP), can address the challenges urban communities face due to surface runoff and flooding. The current capacity of SCP facilities in urban environments falls short of meeting the demands placed on communities by climate change. Bioswales are a form of SCP facility that plays an important role in reducing surface runoff by promoting infiltration. This study assesses the potential of SCP facilities to reduce runoff in urban communities under climate change using the storm water management model. The study site in Ningbo, China, was used to evaluate the potential role of bioswales in reducing runoff risks from climate change. We found that bioswales were most effective in scenarios when rainfall peaks occurred early and were less effective in right-skewed rainfall events. The overall performance of SCP facilities was similar across all climate scenarios. To maintain the current protection level of SCP facilities, bioswales would need to cover at least 4% of the catchment area. These findings from Ningbo provide a useful method for assessing NbS in other regions and indicative values for the increase in the bioswale coverage needed to adapt to climate change.

Assessing the Effects of Environmental Flows on Water Quality for Urban Supply

This paper analyses the effects of environmental flows on water quality within a highly regulated basin, focusing on the Turia River basin in the eastern Iberian Peninsula. Through water management and water quality models, a series of simulations were conducted, introducing variations in the outflows of the Loriguilla reservoir to evaluate the effects of different environmental flow scenarios on water quality, particularly at the location of the intake for the water supply to Valencia. Three environmental flow scenarios were analyzed, alongside an alternative management scenario, considering their implications on water quality and reliability of water demand. The findings of this paper, particularly the nitrate (NO3−) concentration evolution, highlight the influence of minimum e-flow and e-flow regimes on water quality within the basin. These results suggest that while modifying the current flow regime can lead to some improvements in nitrate concentrations at the Valencia supply intake point, the primary cause of high nitrate concentrations is attributed to irrigation return flow and the pre-existing contamination of the aquifer. This analysis offers valuable insights into the complexities of water quality management in regulated basins, emphasizing the need for a multi-faceted approach to address the diverse factors influencing water quality and demand supply reliability.

Environmental Option and Remedy for Resuscitating Dying Lake Chad

Lake Chad, a vital freshwater reservoir in the Sahel region, faces a severe decline attributed to climate change, population growth, and unsustainable water practices. This study proposes a comprehensive transboundary water transfer initiative, sourcing freshwater from the water-rich regions of south-south, south-east, and south-west Nigeria to rejuvenate Lake Chad and its surrounding basins. This initiative employs a robust methodology comprising hydrological assessments of potential water sources, pipeline design, and construction informed by successful international case studies. Environmental impact assessments, stakeholder engagement, water quality management, and phased pilot project implementation ensure a holistic and sustainable approach. Lessons from projects like China's South-North Water Transfer and Australia's Snowy 2.0 provide insights into best practices for environmental stewardship and community involvement. Anticipated outcomes encompass the restoration of Lake Chad's water levels, revitalization of ecosystems, and socio-economic improvements in local communities. The multi-regional approach involving south-south, south-east, and south-west Nigeria seeks to enhance resilience against climate variability while fostering equitable water distribution and regional collaboration. Continuous monitoring, adaptive management, and knowledge sharing with global initiatives are emphasized to ensure the long-term success and sustainability of the project. This study proposes a novel and inclusive approach to address the challenges facing Lake Chad by leveraging freshwater resources from multiple regions in Nigeria. The outlined methodology prioritizes environmental sustainability, community engagement, and draws upon international successes, positioning this initiative as a model for transboundary water management in regions grappling with water scarcity.

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

Spatial allocation of bioretention cells considering interaction with shallow groundwater: A simulation-optimization approach

Green infrastructure (GI), as one type of ecological stormwater management practices, can potentially alleviate water problems and deliver a wide range of environmental benefits in urban areas. GIs are often planned and designed to reduce runoff and mitigate pollution. However, the influence of GI on groundwater hydrology and that of shallow groundwater on GI performance was seldom considered. This study utilized a calibrated surface-subsurface hydrological model, i.e., Storm Water Management Model coupled with USGS's modular hydrologic model (SWMM-MODFLOW) to consider the interaction between GI and groundwater into the process of GI planning. The optimal implementation ratio, aggregation level and upstream-downstream location of bioretention cells (BC, one type of GI) under different planning objectives and hydrogeologic conditions was explored. The consideration of groundwater management exerted a significant impact on the optimal spatial allocation of BCs. The results showed that when groundwater management was more concerned than runoff control, BCs were recommended to be allocated more apart from each other and more upstream in the catchment because more-distributed and upstream BCs can result in lower groundwater table rise which is beneficial. BCs were overall recommended to be allocated in areas of deeper groundwater tables, coarser soils, and flatter topographies. However, the spatial features of BCs are related to each other, the choice of them are affected by various hydrogeologic factors simultaneously. The exact location of BCs should be determined by considering the trade-off between runoff control efficiency and groundwater impact. The findings obtained in this study can provide guidance on GI planning in shallow groundwater areas.

A Review of Recent Developments on Modeling Low Impact Development (LID) Technique

Urban areas are more susceptible to flooding and water body contamination due to the detrimental effects of urbanization. As a result, a sustainable urban drainage system, also known as low impact development (LID) technique, is required. Although this technique can be extensively applied, the planning and design processes are multi-dimensional, multi-variable, and site-specific, which must consider various local conditions and factors. Consequently, these processes can be very complicated and time-consuming for professionals, necessitating support from computer modeling. This study intends to thoroughly explore the idea of LID modeling, various available computer models, and other tools for its optimization and decision-making processes. The most recent trustworthy journal publications that addressed the subjects under discussion were reviewed. This paper used the descriptive and comparative approaches as the analytical methods. According to the findings of the review, Storm Water Management Model (SWMM) is the computer model in LID modeling that is most frequently employed. This model is a fundamental package for dynamic urban rainfall-runoff modeling, and it has the benefits of being lightweight, simple to use, and an intuitive user interface. Besides, this model is public domain (free to use), open source, and interoperable with many hydro modeling applications. A specific LID editor module is also included in this model for modeling different LID units. To acquire the best LID planning and design from multiple criteria and alternatives, it is also necessary to use metaheuristic algorithms as an optimization model and a multi-criteria decision-making (MCDM) model in addition to the rainfall-runoff model. The authors believe combining the hydrologic and hydraulics models integrated with geographical information systems (GIS), metaheuristic algorithms, and MCDM is the most comprehensive and appropriate method for LID modeling in urban watersheds.

Climate‐driven runoff variability in semi‐mountainous reservoirs of the Vietnamese Mekong Delta: Insights for sustainable water management

AbstractThe Mekong Delta, South East Asia's ‘rice bowl’, sustains more than 18 million people through its agricultural output. This yield is secured by efficient water management systems but is susceptible to climatic changes. As Vietnam's policies aim to optimize the delta's semi‐mountainous regions reliant on rain‐fed agriculture, this study investigates drought risks and climate change impacts on runoff in the O Ta Soc and O Tuk Sa reservoirs, An Giang Province, Vietnam. Using simulation models, we determined runoff volumes for specific rainfall return periods and climate scenarios for the 2030s and 2050s. Using the storm water management model (SWMM), we simulated the reservoir water balance considering rainfall, evaporation and infiltration. Our findings suggest potentially increased runoff and reservoir storage due to intensified monsoons and reduced off‐season rainfall. The 4.77 km2 drainage of the O Ta Soc reservoir could benefit from this, while the 2.55 km2 drainage of the O Tuk Sa watershed may require alternative water‐sourcing strategies. This research offers insights for drought predictions, flood management and water strategies in An Giang. To refine these predictions, future research should consider upcoming rainfall patterns.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.