Characteristic Width and Infiltration for Continuous SWMM

Brink, Philip; TenBroek, Mark J.

doi:10.14796/jwmm.r183-17

Cited by 4 publications

(5 citation statements)

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“…Similar arguments apply to the subcatchment geometry: for instance, the subcatchment "width" parameter is best understood to control the mean non-channelized flow path length 𝐴𝐴 L𝐿 , as 𝐴𝐴 𝐴𝐴= L𝐿𝐿𝐿 (Rossman & Huber, 2016). Travel time, runoff attenuation, and hydrograph properties are sensitive to this parameter (Brink & TenBroek, 1995;Li et al, 2014;James et al, 2010). SWMM allows users to calibrate W, but it remains difficult to interpret whether calibrated values are "reasonable."…”

Section: Effective Model Parameters In Swmmmentioning

confidence: 99%

“…Similar arguments apply to the subcatchment geometry: for instance, the subcatchment “width” parameter is best understood to control the mean non‐channelized flow path length

\bar{L}

, as

A=\bar{L}W

(Rossman & Huber, 2016). Travel time, runoff attenuation, and hydrograph properties are sensitive to this parameter (Brink & TenBroek, 1995; Li et al., 2014; James et al., 2010). SWMM allows users to calibrate W , but it remains difficult to interpret whether calibrated values are “reasonable.” Expressing the width parameter as W = k width A (Guo & Urbonas, 2009; James et al., 2010), where

{k}_{width}=\frac{1}{\bar{L}}

allows the finite maximum overland flow path length (i.e., the length prior to channel formation, usually in the range 0–150 m (James et al., 2010; Rossman & Huber, 2016)), to provide a physical bound on k width .…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM

Sytsma

Crompton

Panos

et al. 2022

Water Resources Research

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Predictions of urban runoff are heavily reliant on semi‐distributed models, which simulate runoff at subcatchment scales. These models often use “effective” model parameters that average across the small‐scale heterogeneity. Here we quantify the error in model prediction that arises when the optimal calibrated value of effective parameters changes with model forcing. The uncertainty this produces, which we refer to as “calibration parameter transfer uncertainty,” can undermine the usefulness of important applications of urban hydrologic models, for example, to predict the hydrologic response to novel climate or development scenarios. Using the urban hydrologic model SWMM (“Stormwater Management Model”) as a case study, we quantify the transferability of two calibrated effective parameters: subcatchment “width” and “connected impervious area.” Through numerical experiments, we simulate overland flow across a highly simplified synthetic urban landscape subject to a range of scenarios (combinations of storm events, soil types, and impervious areas). For each scenario, we calibrate SWMM “width” and “connected impervious area” parameters to the outcomes of a distributed model. We find that the calibrated values of these parameters vary with soil, storm, and land cover forcing. This variation across forcing parameters can result in prediction errors up to a magnitude of 60% when a calibrated SWMM is used to predict runoff following changes in climate and land cover. Such calibration transfer uncertainty is largely unaccounted for in urban hydrologic modeling. These results point to a need for additional research to determine how to use urban hydrologic models to make robust predictions across future conditions.

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Section: Effective Model Parameters In Swmmmentioning

confidence: 99%

“…Similar arguments apply to the subcatchment geometry: for instance, the subcatchment “width” parameter is best understood to control the mean non‐channelized flow path length

\bar{L}

, as

A=\bar{L}W

{k}_{width}=\frac{1}{\bar{L}}

Section: Introductionmentioning

confidence: 99%

Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM

Sytsma

Crompton

Panos

et al. 2022

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Previous studies have demonstrated that reducing widths by large amounts to simulate delayed inflow significantly reduces the amount of water entering the system. Water in these basins has a longer time of travel, and therefore experience increased amounts of infiltration and evaporation (Brink and TenBroek, 1995). For this reason, the basins representing indirect inflow were placed in a separate RUNOFF block in which evaporation was turned off and the infiltration rates were high.…”

Section: ----------------------------mentioning

confidence: 99%

A GIS and SWMM Modeling Application for Sewer Separation Design

Ho¹,

Brocard²,

Soucie³

et al. 2001

JWMM

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Partial or complete separation of combined sewers is one ofthe strategies that can be employed to control or eliminate combined sewer overflow (CSO) discharges. Separation of combined sewers can be expensive and dismptive to communities, since it involves digging up the street. In many urban areas, roof drains from houses are co1111ected to sewers via downspouts (outside piping), or through roof leaders (internal plumbing). Disco1111ection of roof drainage co1111ected to the sewer via internal plumbing can be very expensive and disruptive. In addition, experience has shown that it is very difficult to remove 100% of rainfall-induced inflow in a sewer. When storm drainage is removed from combined sewers, they become sanitary sewers carrying a small amount of residual inflow. The residual inflow may originate from a variety of sources such as leaky manholes, leaks in pipes, or building sump pumps. Quantification of this residual inflow is important since it affects the CSO activation frequency and thus the degree of roof drainage separation that must be undertaken to achieve CSO elimination goals. Many hydrologic models employ coefficients to represent the directly co1111ected impervious area (DCIA). These coefficients represent the fraction of the drainage basin that contributes flow to a drainage system. In general, this coefficient is propmiional to the total amount of impervious area in the region. If sufficient geographic information and field data is available, it is possible to determine the contribution of runoff from different land-uses, and thus estimate the contribution of runoff from co1111ected buildings to a combined system. This information in tum can be used for hydraulic modeling to estimate the

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“…To adjust this, the GDRSS model was initially formulated using calculated widths that assumed 15 meters (50 feet) of overland flow. A new element, a routing channel, was developed to simulate the flow routing that takes place in the collection system within each subbasin or in the concentrated flow channel areas, as documented by Brink and TenBroek (1994). This routing channel was initially formulated as a single flat channel with dimensions tied to the subbasin area.…”

Section: Large Subbasin Issuesmentioning

confidence: 99%

Detroit Water and Sewerage Department Model Extensions and Project Overview

TenBroek

Fujita²,

Brink

et al. 1999

JWMM

Self Cite

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The Detroit Water & Sewerage Department (DWSD) collection system and WWTP serves the majority in Southeastem Michigan. This collection system has been modeled in detail since 1987. The primary model used for these evaluations has been the US EPA Stormwater Management Model (SWMM). While this model provides significant capability, the combined sewer evaluations performed for this area have required modifications both to the methods used to prepare the model and changes to the calculations performed by the model. This chapter highlights the changes that have been made to the model to provide an appropriate evaluation of the collection system performance. Many of the new features added to the SWMM model have been driven by the need to evaluate different methods to control combined sewer overflows. Some changes were required by the sheer scope of the modeled area and by the complexity ofthe collection and treatment system. The changes made include better methods to simulate flow routing; improved algorithms to simulate dry weather flow (DWF), rainfall dependent inflow/infiltration (RDIII), and their seasonal variation; better methods to evaluate regulators, outfaHs, and storage; and finally, better fonnats used to present the output to fairly evaluate t~e different con.sidered. Miilly of these ,.

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Characteristic Width and Infiltration for Continuous SWMM

Cited by 4 publications

References 0 publications

Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM

Quantifying the Uncertainty Created by Non‐Transferable Model Calibrations Across Climate and Land Cover Scenarios: A Case Study With SWMM

A GIS and SWMM Modeling Application for Sewer Separation Design

Detroit Water and Sewerage Department Model Extensions and Project Overview

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