Effects of bottleneck blockage on the resilience of an urban stormwater drainage system

Mohammadiun, Saeed; Yazdi, Jafar; Hager, James; Neyshabouri, Seyed Ali Akbar Salehi; Sadiq, Rehan; Hewage, Kasun; Gharahbagh, Abdorreza Alavi

doi:10.1080/02626667.2019.1690657

Cited by 30 publications

(10 citation statements)

References 33 publications

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“…The calibrated curve number and Manning's roughness coefficient of each catchment can be found in Data Set S1 and S2. Detailed information about the calibration and validation of the SWMM model of EDC can be found in Yazdi and Khazaei (2019) and Mohammadiun et al (2020).…”

Section: Urban Runoff Simulationmentioning

confidence: 99%

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Amini

Dolatshahi

Kerachian

2023

Water Resources Research

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Climate change has increased the importance of rainfall forecasting, because it has changed the duration and frequency of floods and increased their magnitude Ashrafi, Khoie, et al., 2022). As a result of inundation, landslides, and debris flows in urban areas, heavy rainfall often leads to fatalities and significant financial damages (Seo et al., 2014). Furthermore, overflow resulting from heavy rainfall is a primary contributor to the transport of water pollutants (Lintern et al., 2018). Various preventive measures have been developed, including flood warning systems, to alleviate damages caused by rainfall and enable urban flood management infrastructures to operate more efficiently. Therefore, accurate and reliable rainfall forecasting is essential in urban areas (Poornima & Pushpalatha, 2019). Reliable rainfall nowcasting is a challenging and complex undertaking since rainfall features are influenced by various variables, such as pressure, temperature, relative humidity, and wind speed (Govindaraju, 2000). Due to the difficulty in obtaining meteorological data and the complexity of estimating relationships among variables, physical models are not usually suitable for rainfall nowcasting. An effective alternative is to use empirical models to determine the relationship between inputs like temperature and relative humidity and outputs like rainfall (Nasseri et al., 2008). To ensure optimal performance for urban drainage infrastructures, rainfall nowcasting frameworks should be able to make accurate forecasts with different lead times. Researches on rainfall forecasting and its influencing factors have been conducted for decades, and many approaches have been proposed. There are various traditional forecasting models in the literature, which have been developed using classic time series algorithms such as linear regression, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural networks (ANNs), numerical weather predictions (NWPs), and Fuzzy logic algorithm (

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Section: Urban Runoff Simulationmentioning

confidence: 99%

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Amini

Dolatshahi

Kerachian

2023

Water Resources Research

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“…However, most of the existing UDSs are designed on the basis of historical climate statistics and maintained by the assumption of the constant urban land surfaces (Arnbjerg-Nielsen et al, 2013). These conventional designs will be likely to overestimate the reliability of UDSs that consequently become vulnerable to future external or internal failures or disturbances such as extreme rainfall, urban impervious cover changes, sewer sedimentation, equipment malfunction, manhole collapse, and pipe blockage (Kim et al, 2017;Mohammadiun et al, 2020). The concerns for the performance deterioration in traditional UDSs are rising in metropolitan areas since the severity of flooding is predicted to continue or be aggravated due to future climate and land cover change (Huong and Pathirana, 2013;Semadeni-Davies et al, 2008;Skougaard Kaspersen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing Impacts of Future Land Cover and Rainfall Change on Urban Flooding Using An Event-based Resilience Index

Burian²,

Strong³

2023

Preprint

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Assessing the resilience of urban drainage systems requires the consideration of future threats that will disrupt the system performance and trigger urban flooding failures. However, most existing resilience assessments of urban drainage systems rarely consider the uncertainties from future urban redevelopment and climate change, which leads to the underestimation of future disturbances toward system performance. This paper fills in the gap of assessing the combined and relative impacts of future impervious land cover and rainfall changes on flooding resilience in the context of a densely-infilled urban catchment severed by an urban drainage system in Salt Lake City, Utah, the USA. An event-based flooding resilience index is proposed to measure climatic and urbanized impacts on flooding resilience from system-level to junction-level, enabling engineers to harvests high-resolution infrastructure adaptation strategies at the vulnerable spots. Impact comparison shows that imperious urban surface induces more effects on the system performance curves by magnifying the maximum failure level, lengthening the recovery duration, and aggravating the flooding severity than future rainfall changes. A nonlinear logarithm resilience correlation is found, and this finding shows that flooding resilience is more sensitive to the land imperviousness change due to urban redevelopment than rainfall intensity change in the case study. This research work predicts the system response to the uncertainties induced by climate change and urban redevelopment, improving the understanding of impacts analysis and contributing to the advancement of resilient urban drainage systems in changing environments.

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“…Urban flooding can be mitigated by upgrading and re-sizing stormwater infrastructure, such as pipes, pump stations, and storage tanks, which convey stormwater runoff into downstream receiving water bodies. Previous studies focused on enhancing resilience through rehabilitating and redesigning UDSs (Mohammadiun et al, 2020), adding system storage components (Mugume et al, 2015), and optimizing the drainage system's structure (Bakhshipour et al, 2021). The locations of structural facilities and the layout of pipelines affect UDS resilience (Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections

Burian²

2023

Preprint

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Traditional stormwater control measures are designed to handle system loadings induced by fixed-size storm events. However, climate change is predicted to alter the frequency and intensity of flooding events, stimulating the need to explore another more adaptive flooding solution like real-time control (RTC). This study assesses the performance of RTC to mitigate impacts of climate change on urban flooding resilience. A simulated, yet realistic, urban drainage system in Salt Lake City, Utah, USA, shows that RTC improves the flooding resilience by up to 17% under climatic rainfall changes. Compared with green stormwatrer infrastructure (GSI), RTC exhibits a lower resistibility, lower flooding failure level, and higher recovery rate in system performance curves. Results articulate that keeping RTC’s performance consistent under ‘back-to-back’ storms requires a tradeoff between upstream dynamical operation and downstream flooding functionality loss. This research suggests that RTC provides a new path towards smart and resilient stormwater management strategy.

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Effects of bottleneck blockage on the resilience of an urban stormwater drainage system

Cited by 30 publications

References 33 publications

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

Assessing Impacts of Future Land Cover and Rainfall Change on Urban Flooding Using An Event-based Resilience Index

Evaluating real-time control of stormwater drainage network and green stormwater infrastructure for enhancing flooding resilience under future rainfall projections

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