Spatial Patterns of Urban Development from Optimization of Flood Peaks and Imperviousness-Based Measures

Mejía, Alfonso; Moglen, Glenn E.

doi:10.1061/(asce)1084-0699(2009)14:4(416)

Cited by 60 publications

(56 citation statements)

References 36 publications

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“…This study agrees with Mejía & Moglen (2009), Wagner et al (2013) and Su et al (2014) who argue that urban imperviousness patterns within catchments can play an important role in determining changes in streamflow characteristics, particularly because of changes in the fraction of flow becoming surface runoff. When the settlement area is clustered, peak flows will increase without much affecting the flow volume compared to scattered settlement (Corbett et al, 1997).…”

Section: Discussionsupporting

confidence: 92%

Effects of changes in land use and climate on water availability of a tropical catchment

Marhaento

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Section: Discussionsupporting

confidence: 92%

Effects of changes in land use and climate on water availability of a tropical catchment

Marhaento

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“…There is a growing interest in using urban ecology and the urban landscape for managing and controlling storm water runoff [74]. Our study of the influence of the spatial pattern of the impervious surface on urban waterlogging risk spots at multiple scales could provide important insights and perspectives into urban waterlogging risk mitigation and urban planning.…”

Section: Implications For Urban Planning and Urban Waterlogging Mitigmentioning

confidence: 99%

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

Zhang

Cheng

et al. 2018

Sustainability

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An impervious surface is considered one of main factors affecting urban waterlogging. Previous studies found that spatial pattern (composition and configuration) of impervious surfaces affected urban waterlogging. However, their relative importance remains unknown, and the scale-effect of the spatial pattern on urban waterlogging has been ignored. To move forward, our research studied the relationship between spatial patterns on the impervious surface and its subcategories (building and pavement) on urban waterlogging risk spots using Pearson correlation, partial redundancy analysis and performed at three grid scales (1 km × 1 km, 3 km × 3 km, 5 km × 5 km) and the catchment scale based on different spatial resolution land cover maps (2 m, 10 m and 30 m). We identified positively-correlated metrics with urban waterlogging risk spots, such as the composition of impervious surface (i.e., total impervious surface, building, pavement) and the aggregation metric of the total impervious surface at most scales, as well as two negatively correlated metrics (i.e., proximity metric of building, fragmentation metric of total impervious surface). Furthermore, the total variance of urban waterlogging risk spots explained by the spatial pattern of the total impervious surface and its subcategories increased with studied grid and catchment scales while decreasing from a fine to a coarse resolution. The relative contribution of the impervious surface composition and configuration to the variation of urban waterlogging risk spots varied across scales and among impervious surface types. The composition contributed more than the configuration did for the total impervious surface at both grid and catchment scales. Similar to total impervious surface, the composition of buildings was more important than its configuration was at all the grid scales, while the configuration of buildings was more important at the catchment scale. Contrary to the total impervious surface, the configuration of pavement at both the grid and catchment scales mattered more than their compositions did. Furthermore, the composition of the building was more important than that of pavement, but its configuration mattered less. Our study could provide a multi-scale landscape perspective with detailed suggestions for controlling the area of impervious surface and optimizing its spatial configuration in urban waterlogging risk mitigation and urban planning.

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“…Although TIA and DCIA have both proved to be effective for predicting urban runoff, most previous research focuses on the fluvial runoff feedback in urbanized watersheds combined with mixed land cover (e.g., developed, rural, and natural surfaces) at various spatial scales of several hectares (Arnell, 1982;Boyd et al, 1993Boyd et al, , 1994Sillanpää and Koivusalo, 2014) to hundreds of square kilometers (Mejía and Moglen, 2009;Sheeder et al, 2002;Yang et al, 2011). In the urban built-up regions, previous studies tend to focus on relatively small and integrated drainage basins (e.g., residential sites) that usually cover less than 1 km 2 and incur severe pluvial risks (Dietz and Clausen, 2008;Lee and Heaney, 2003).…”

Section: Introductionmentioning

confidence: 99%

How does imperviousness impact the urban rainfall-runoff process under various storm cases?

Yao

Wei

Chen

2016

Ecological Indicators

121

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a b s t r a c tDramatic changes in imperviousness exert significant influence on the rainfall-runoff process in urban catchments. In urban rainwater management, imperviousness is generally adopted as an effective indicator for assessing potential runoff risk. However, the effects of imperviousness on rainfall-runoff at the scale of small urbanized drainage areas have not been fully determined, particularly when various storm characteristics are considered. In this paper, a model-based analysis is conducted in a typical urban residential catchment in Beijing, China, in which 69 subareas are delineated within the catchment as the basic drainage units. Two metrics, total impervious area (TIA) and directly connected impervious area (DCIA), are employed to quantify the spatial characteristics of imperviousness of the subareas. Three runoff variables within the delineated subareas including total runoff depth (Q t ), peak runoff depth (Q p ), and lag time (T lag ) are simulated by using the Storm Water Management Model (SWMM) to represent the specific rainfall-runoff characteristics. Moreover, model input storms are designated to several typical flood-induced rainfall events with varying amounts, locations of rainfall peak, and durations for holistic assessment of imperviousness. Regression analyses are conducted to explore the contributions and relative significances of impervious metrics in predicting runoff variables under various storm cases. The results indicate that the performances of imperviousness with fine spatial scale (<1 ha) and heavy rainfall conditions (>34 mm) may vary markedly according to storm conditions. Specifically, TIA rather than DCIA acts as a dominate factor affecting total runoff, and its significance maintains relatively stable with various storm conditions. In addition, the combined use of both TIA and DCIA are more effective for predicting peak runoff than that using a single impervious metric; however, rainfall amount, peak location, and duration alter the contribution gaps between TIA and DCIA and the overall performance of the regression model. Moreover, DCIA is more likely to affect runoff lag time without the contribution of TIA; however, an increase in rainfall peak ratio or duration will significantly limit its performance. These results can provide insight into the hydrologic performance of imperviousness, which is essential for landscape design and runoff regulation in small urban catchments.

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Spatial Patterns of Urban Development from Optimization of Flood Peaks and Imperviousness-Based Measures

Cited by 60 publications

References 36 publications

Effects of changes in land use and climate on water availability of a tropical catchment

Effects of changes in land use and climate on water availability of a tropical catchment

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

How does imperviousness impact the urban rainfall-runoff process under various storm cases?

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