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Prediction of Catchment-Scale Effici...

Almadani, Mohammad Ahmad.

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Prediction of Catchment-Scale Efficiency of Green Infrastructure in an Urban Watershed Using a Process-Based Modeling Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction of Catchment-Scale Efficiency of Green Infrastructure in an Urban Watershed Using a Process-Based Modeling Approach./
Author:	Almadani, Mohammad Ahmad.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	232 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
Subject:	Environmental engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27663826
ISBN:	9781392422410

Prediction of Catchment-Scale Efficiency of Green Infrastructure in an Urban Watershed Using a Process-Based Modeling Approach.
Almadani, Mohammad Ahmad.

Prediction of Catchment-Scale Efficiency of Green Infrastructure in an Urban Watershed Using a Process-Based Modeling Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 232 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Ph.D.)--The Catholic University of America, 2020.

This item must not be sold to any third party vendors.

Stormwater green infrastructures (GI)s are being widely used to reduce volume and peak of surface runoff and pollutant level through increased infiltration, evaporation, filtration or delayed release to the traditional sewer systems or receiving surface waters. To evaluate the effectiveness of the GI practices, it is important to consider the effects of GIs on surface runoff as a result of directly capturing overland flow but also through their impacts on infiltration, inter-flow, groundwater recharge and base-flow into the streams. In this study, the application of a process-based model to predict the long-term impacts of GIs on the hydrologic response of the highly urbanized Sligo Creek watershed in the suburbs of Washington DC, is demonstrated. The watershed system is represented using a number of connected blocks representing sub-catchments, unsaturated soil, groundwater and segments of the stream network. The soil columns underneath each catchment is discretized into several layers to more accurately capture infiltration and percolation processes. The overland and stream flow are modeled using diffusive wave model and the unsaturated flow in soil is modeled using Richards equation. The pre-retrofit version of the model is calibrated using observed hydrographs and the uncertainty in the parameter values have been quantified using Bayesian inference. The parameter values estimated is used to evaluate the post retrofit conditions of the catchment as a result of multiple scenarios of GI implementation. The results of this dissertation demonstrate that the Green Infrastructure practice is very effective for an urbanized watershed. The GI model results in this dissertation shows significant impact in term of runoff reduction, decrease flowrate peaks, and maintain baseflow level in the stream. However, the model results demonstrate no significant increasing of groundwater recharge and small increasing in infiltration rate. In addition, the dissertation approved that the GI practices work efficiently during trace and small rainfall events, while GI do not work during storm events.

ISBN: 9781392422410Subjects--Topical Terms:

548583
Environmental engineering.
Subjects--Index Terms:

Best management practice

Prediction of Catchment-Scale Efficiency of Green Infrastructure in an Urban Watershed Using a Process-Based Modeling Approach.
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300 $a 232 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
500 $a Advisor: Massoudieh, Arash.
502 $a Thesis (Ph.D.)--The Catholic University of America, 2020.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Stormwater green infrastructures (GI)s are being widely used to reduce volume and peak of surface runoff and pollutant level through increased infiltration, evaporation, filtration or delayed release to the traditional sewer systems or receiving surface waters. To evaluate the effectiveness of the GI practices, it is important to consider the effects of GIs on surface runoff as a result of directly capturing overland flow but also through their impacts on infiltration, inter-flow, groundwater recharge and base-flow into the streams. In this study, the application of a process-based model to predict the long-term impacts of GIs on the hydrologic response of the highly urbanized Sligo Creek watershed in the suburbs of Washington DC, is demonstrated. The watershed system is represented using a number of connected blocks representing sub-catchments, unsaturated soil, groundwater and segments of the stream network. The soil columns underneath each catchment is discretized into several layers to more accurately capture infiltration and percolation processes. The overland and stream flow are modeled using diffusive wave model and the unsaturated flow in soil is modeled using Richards equation. The pre-retrofit version of the model is calibrated using observed hydrographs and the uncertainty in the parameter values have been quantified using Bayesian inference. The parameter values estimated is used to evaluate the post retrofit conditions of the catchment as a result of multiple scenarios of GI implementation. The results of this dissertation demonstrate that the Green Infrastructure practice is very effective for an urbanized watershed. The GI model results in this dissertation shows significant impact in term of runoff reduction, decrease flowrate peaks, and maintain baseflow level in the stream. However, the model results demonstrate no significant increasing of groundwater recharge and small increasing in infiltration rate. In addition, the dissertation approved that the GI practices work efficiently during trace and small rainfall events, while GI do not work during storm events.
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