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Modeling Climate Change Impacts on the Effectiveness of Stormwater Control Measures in Urban Watersheds

dc.contributor.authorAlamdari, Nasrinen
dc.contributor.committeechairSample, David J.en
dc.contributor.committeememberEaston, Zachary M.en
dc.contributor.committeememberIrish, Jennifer L.en
dc.contributor.committeememberSridhar, Venkataramanaen
dc.contributor.departmentBiological Systems Engineeringen
dc.date.accessioned2018-08-31T08:00:19Zen
dc.date.available2018-08-31T08:00:19Zen
dc.date.issued2018-08-30en
dc.description.abstractClimate change (CC) science has made significant progress in development of predictive models. Despite these recent advances, the assessment of CC impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Urban watersheds have been the focus of considerable efforts to restore hydrology and water quality, and the aquatic habitat of receiving waters, yet CC impacts threaten to reduce the effectiveness of these efforts. Thus, assessing the impacts of CC in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. Simulations of CC for the 2041-2068 period were developed using downscaled Global Climate Models (GCMs) from the North American Regional CC Assessment Program (NARCCAP) and Coupled Model Intercomparison Project Phase 5 (CMIP5) to forecast precipitation and temperature time series. This data were then used to force a Storm Water Management Model (SWMM) of the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. NARCCAP uses a scenario represents a medium-high greenhouse gas emissions assumption, A2; the latter, uses five GCMs, and two Representative Concentration Pathways (RCP 4.5 and 8.5) scenarios in an ensemble approach to better assess variability of model predictions in presenting precipitation, temperature, runoff quantity and quality. Then, the effects of CC on runoff peak, volume, and nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment performance of a very common stormwater control measure (SCM), retention ponds, was assessed. Rainwater Harvesting (RWH) systems are an unusual SCM in that they recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of RWH systems for projected CC for these dual purposes was assessed. NARCAAP data for selected locations across the U.S. were statistically downscaled using a modified version of the equiratio cumulative distribution function matching method to create a time series of projected precipitation and temperature. These data were used to force a simulation model, the Rainwater Analysis and Simulation Program (RASP) to assess the impacts of CC on RWH with respect to the reliability of water supply and runoff capture. To support CC modeling, an easy-to-use software tool, RSWMM-Cost, was developed. RSWMM-Cost automates the execution of SWMM, which is commonly used for simulating urban watersheds. Several features were incorporated into the RSWMM-Cost tool, including automated calibration, sensitivity analysis, and cost optimization modules; the latter can assist in identifying the most cost-effective combination of SCMs in an urban watershed. As an example, RSWMM-Cost was applied to a headwater subcatchment the Difficult Run watershed.en
dc.description.abstractgeneralUrban watersheds have been the focus of considerable efforts to restore water quantity and quality, and the aquatic habitat of receiving waters, yet climate change impacts threaten to reduce the effectiveness of these efforts. The assessment of climate change impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Thus, assessing the impacts of climate change in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. In this study, simulations of climate change for the 2041-2068 period were developed to forecast precipitation and temperature data. These data were then used to force a hydrologic model for the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. Then, the effects of climate change on runoff, nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment efficiency of a very common management practice called retention ponds, was assessed. Rainwater harvesting systems are an unusual management practice that recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of rainwater harvesting systems for projected climate change with respect to the reliability of water supply and runoff capture was assessed for the 2041-2068 period. To support climate change modeling, an easy-to-use tool, was also developed to select the most cost-optimized combination of best management practices in urban watersheds considering site constraints, limitations, and size. As an example, the tool was applied to a headwater subcatchment of the Difficult Run watershed. The ability to assess the impact of climate change on both hydrologic and water quality treatment could assist in the selection of the most appropriate management practices to address water management goals and conserve limited financial resources.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:16911en
dc.identifier.urihttp://hdl.handle.net/10919/84940en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectclimate changeen
dc.subjectcost-optimizationen
dc.subjectretention ponden
dc.subjectrainwater harvesting systemsen
dc.subjectcost effectivenessen
dc.subjectload reductionen
dc.titleModeling Climate Change Impacts on the Effectiveness of Stormwater Control Measures in Urban Watershedsen
dc.typeDissertationen
thesis.degree.disciplineBiological Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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