Distributed urban storm water modeling within GIS integrating analytical probabilistic hydrologic models and remote sensing image analyses

Luciani, Peter; Li, Kairong; Banting, D.

doi:10.2166/wqrjc.2011.113

“…Analysis of event rainfall-runoff response is often used to inform hydrologic modelling for flood forecasting and for stormwater planning and design (Luciani et al, 2011). Rainfall-runoff analyses can also be used for inter-catchment comparisons of the relative influence of watershed characteristics, meteorological factors, and antecedent moisture conditions on hydrologic response (Ross et al, 2019).…”

mentioning

confidence: 99%

Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers

Ariano

¹

,

Oswald

²

2022

Hydrological Processes

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Urbanization is characterized by increased impervious cover, artificial drainage via sewers, soil compaction, and vegetation removal, which fundamentally alters how water moves across landscapes. Less rainwater and snowmelt infiltrate the subsurface and stream responses to precipitation are faster and larger compared to natural environments. While the impacts of catchment imperviousness on hydrologic processes have been widely investigated, the relationship between stream hydrologic response and infrastructure‐mediated hydrologic connectivity across a wide range of urban watersheds has not. This study examines the relative magnitude of control that catchment characteristics, total impervious area (TIA), and sewer‐corrected TIA have on broad‐scale spatial variability in hydrologic response. Sewer‐corrected TIA accounts for inter‐catchment water transfer via storm and/or combined sewer pipes and is feasible to estimate at the watershed‐scale using urban drainage asset data. Daily stream discharge data were used to calculate the Richards‐Baker Flashiness Index for 96 watersheds located across southern Ontario, Canada. For a subset of watersheds (n = 39) with suitable stream discharge and rainfall data, watershed average event runoff ratio (RR) was calculated, and watersheds with spatial sewer network data (n = 24), were used to assess if sewer‐corrected TIA is a better predictor of hydrologic response than TIA. As expected, stream flashiness and RR increased linearly as a function of TIA and sewer‐corrected TIA, however, sewer‐corrected TIA did not explain any additional variability in the response variables. Likewise, the relative control of catchment characteristics on the spatial variability in streamflow generation were negligible. Despite sewer‐corrected TIA not improving our understanding of broad scale variation in runoff response, it could be a useful metric to consider when assessing inter‐event variability in runoff response of, heavily urbanized sub‐catchments for improved understanding of flooding and water quality.

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“…Another main tool is called stream burning or DEM reconditioning and, as can be inferred from the name, it was initially developed for use in natural landscapes in order to enforce the known location of streams or rivers (Djokic, 2008). However, in urban drainage modelling, this same tool can be modified for use in the burning-in of various features, such as the curb lines, roads or underground sewer pipe locations (Baker et al 2006;Luciani et al 2011). The depths of the aforementioned features are then typically specified to be lowered between 0.1 to 5 metres because this "improves the representation of anthropogenic features in urban DEMs" (Gironás et al 2010, p. 2).…”

Section: Wet Weather Flow Subcatchment Delineation Methodsmentioning

confidence: 99%

“…The Arc Hydro data model extension was designed to support GIS-based hydrologic simulation models of natural landscapes and because it is in the public domain, it is freely available for use. Thus, it provides a framework that conveniently packages most of the ArcGIS tools required to generate subcatchments that go beyond what is provided in the standard Hydrology Toolbox though this too has been used to generate subcatchments (Luciani, 2005;Dongquan et al 2009). For example, a flow direction grid can be generated using the D8 method, which can then be used as the basis for creating subcatchment polygons by following a series of processing steps that are provided as built-in functions (Maidment, 2002;Johnson, 2009).…”

Section: Wet Weather Flow Subcatchment Delineation Methodsmentioning

confidence: 99%

Automating GIS input for distributed urban drainage modelling

Shamead¹

2021

Preprint

0

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The development of distributed urban drainage models is becoming more important as cities prepare for the challenges associated with climate change such as more intense precipitation events (McCarthy et al. 2010; Allan, 2011; Simõeset et al. 2011; Blumensaat et al. 2012; Leitãoet al. 2012). GIS-based tools were developed to generate input datasets for a 1-D distributed urban drainage model for part of Toronto's combined area, resulting in an efficient model development process compared to those utilizing manual approaches. These automatic GIS-based tools included the delineation of Wet Weather Flow (WWF) subcatchments (stormwater) and Dry Weather Flow (DWF) subcatchments (sanitary). It also included the determination of the intensity of rainfall on a more detailed scale than the coarse coverage provided by the City's rain gauges and the traditional Thiessen polygon interpolation method. Through testing the new tools designed in ModelBuilder, it was determined that 66% and 52% of DWF and WWF subcatchments respectively, were automatically delineated to a degree where they would be "Acceptable" for input into the urban drainage model, InfoWorks CS. Although the rainfall tools were able to continuously interpolate measured rainfall (on a seemingly unprecedented basis),and generate over 700 virtual rain gauges, the validity of the approach remains imperfect due to irresolvable inconsistencies between the City's gauges and those used for validation purposes.

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“…Such a refocus in research has occurred because traditional lumped models do not provide the level of detail that is required to make certain decisions (Rodriguez et al 2008;Hamel et al 2013). In a distributed model, modellers are able to determine whether a particular building is differentially prone to flooding, which would be nearly impossible to do using traditional lumped models because they are too aggregated (Luciani, 2005;Elliott & Trowsdale, 2007). However, distributed models also have a disadvantage when compared to lumped models.…”

Section: Problem Definitionmentioning

confidence: 99%

Automating GIS input for distributed urban drainage modelling

Shamead¹

2021

Preprint

1

0

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The development of distributed urban drainage models is becoming more important as cities prepare for the challenges associated with climate change such as more intense precipitation events (McCarthy et al. 2010; Allan, 2011; Simõeset et al. 2011; Blumensaat et al. 2012; Leitãoet al. 2012). GIS-based tools were developed to generate input datasets for a 1-D distributed urban drainage model for part of Toronto's combined area, resulting in an efficient model development process compared to those utilizing manual approaches. These automatic GIS-based tools included the delineation of Wet Weather Flow (WWF) subcatchments (stormwater) and Dry Weather Flow (DWF) subcatchments (sanitary). It also included the determination of the intensity of rainfall on a more detailed scale than the coarse coverage provided by the City's rain gauges and the traditional Thiessen polygon interpolation method. Through testing the new tools designed in ModelBuilder, it was determined that 66% and 52% of DWF and WWF subcatchments respectively, were automatically delineated to a degree where they would be "Acceptable" for input into the urban drainage model, InfoWorks CS. Although the rainfall tools were able to continuously interpolate measured rainfall (on a seemingly unprecedented basis),and generate over 700 virtual rain gauges, the validity of the approach remains imperfect due to irresolvable inconsistencies between the City's gauges and those used for validation purposes.

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Distributed urban storm water modeling within GIS integrating analytical probabilistic hydrologic models and remote sensing image analyses

Abstract: Analytical probabilistic hydrologic models (APMs) are computationally efficient producing validated storm water outputs comparable to continuous simulation for storm water planning level analyses.

Cited by 10 publications

References 20 publications

Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers

Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers

Automating GIS input for distributed urban drainage modelling

Automating GIS input for distributed urban drainage modelling

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