Is green infrastructure a viable strategy for managing urban surface water flooding?

The need to improve the urban drainage network to meet recent urban growth and the redevelopment of old industrial and commercial areas provides an opportunity for managing urban surface water infrastructure in a more sustainable way. The use of sustainable urban drainage systems (SUDS) can reduce urban surface water flooding as well as the pollution impact of urban discharges on receiving waters. However, these techniques are not yet well known by many stakeholders involved in the decision-making process, or at least the evidence of their performance effectiveness may be doubted compared with more traditional engineering solutions often promoted by existing 1D/2D drainage models. The use of geographic information systems (GIS) in facilitating the inter-related risk analysis of sewer surface water overflows and urban flooding as well as in better communication with stakeholders is demonstrated in this paper. An innovative coupled 1D/2D urban sewer/overland flow model has been developed and tested in conjunction with a SUDS selection and location tool (SUDSLOC) to enable a robust management approach to surface water flood risks and to improve the resilience of the urban drainage infrastructure. The paper demonstrates the numerical and modelling basis of the integrated 1D/2D and SUDSLOC approach and the working assumptions and flexibility of the application together with some limitations and uncertainties. The role of the SUDSLOC modelling component in quantifying flow, and surcharge reduction benefits arising from the strategic selection and location of differing SUDS controls are also demonstrated for an extreme storm event scenario.

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Urban surface water flood modelling – a comprehensive review of current models and future challenges

10.5194/hess-2020-655 ◽

2020 ◽

Author(s):

Kaihua Guo ◽

Mingfu Guan ◽

Dapeng Yu

Keyword(s):

Surface Water ◽

Urban Areas ◽

Scale Effects ◽

Water Flooding ◽

Urban Surface ◽

Economic Losses ◽

Intense Rainfall ◽

Urban Flood ◽

Comprehensive Review ◽

Flood Modelling

Abstract. Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

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Urban surface water flood modelling – a comprehensive review of current models and future challenges

Hydrology and Earth System Sciences ◽

10.5194/hess-25-2843-2021 ◽

2021 ◽

Vol 25 (5) ◽

pp. 2843-2860

Author(s):

Kaihua Guo ◽

Mingfu Guan ◽

Dapeng Yu

Keyword(s):

Surface Water ◽

Urban Areas ◽

Scale Effects ◽

Water Flooding ◽

Urban Surface ◽

Economic Losses ◽

Intense Rainfall ◽

Urban Flood ◽

Comprehensive Review ◽

Flood Modelling

Abstract. Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses, are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances, and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

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Development of a Vulnerability Diagnosis Model for Urban Surface Water Floods using Building Big Data

Journal of Climate Change Research ◽

10.15531/ksccr.2020.11.1.65 ◽

2020 ◽

Vol 11 (1) ◽

pp. 65-75

Author(s):

Eunseok Lee

Keyword(s):

Big Data ◽

Surface Water ◽

Urban Surface ◽

Diagnosis Model

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Research of Geostatistics on Spatial Distribution of Urban Surface Water Quality

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/657/1/012050 ◽

2021 ◽

Vol 657 (1) ◽

pp. 012050

Author(s):

Hongquan Sun ◽

Jing Wang ◽

Yachong Tian ◽

Yiran Shi

Keyword(s):

Water Quality ◽

Spatial Distribution ◽

Surface Water ◽

Surface Water Quality ◽

Urban Surface

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Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Remote Sensing ◽

10.3390/rs10111704 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1704 ◽

Cited By ~ 2

Author(s):

Wei Wu ◽

Qiangzi Li ◽

Yuan Zhang ◽

Xin Du ◽

Hongyan Wang

Keyword(s):

Surface Water ◽

High Resolution ◽

Urban Surface ◽

Mapping Technique ◽

Support Vector ◽

Svm Classifier ◽

P Value ◽

Urban Water ◽

Water Index ◽

Water Mapping

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

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