Urbanization alters land cover by increases in impervious areas, resulting in large increases in runoff, sediment, and nutrient loadings downstream. These changes cause flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) are used to address these concerns and are designed based on inflow loads. Thus, estimating nutrient and sediment loads from developed watersheds is vitally important for meeting the impacts of urbanization. Today, stormwater events are characterized mainly by watershed models using little, if any, actual field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common practice used by practitioners for estimating loads. Pollutants accumulate on surfaces during dry periods, making EMC a function of antecedent dry period (ADP). An EMC results from wash-off of accumulated pollutants from catchment surfaces during runoff events. However, it assumes concentration is constant across events from a particular land use and several studies found little to no correlation between constituent concentrations in stormwater and ADP. Build-up/wash-off equations were developed to account for variation of concentrations between events; however, the required parameters are difficult to estimate. This study applied machine learning approaches with a national dataset along with monitoring and modeling studies at watershed scales to improve predictions of stormwater quantity and quality. First, we obtained stormwater quality data from the National Stormwater Quality Database (NSQD), which is the largest data repository of stormwater quality data in the U.S., and used Bayesian Network Structure Learner (BNSL), a machine learning approach, to discover which climatological or catchment characteristics most significantly affect stormwater quality. Second, we developed and applied Random Forest (RF), a data-driven method, to predict nutrients and sediment EMCs in urban runoff. Third, we applied the Storm Water Management Model (SWMM), a widely used urban watershed model, to an urban watershed and assessed the best fit estimates of SWMM parameters and hydrological response of the watershed during dry and wet hydroclimatic conditions. Last, we conducted a monitoring and modeling study at a catchment scale and assessed the role of land use on stormwater quantity and quality to optimize and investigate the build-up/wash-off parameters for multiple urban land uses for nutrients and sediment. The results presented in this dissertation can help stakeholders, urban planners, and SCM designers improve estimates of nutrients and sediment loads and thus achieve more effective treatment of stormwater, better attain water quality goals, and protect downstream water bodies. / Doctor of Philosophy / Urban development results in increased hardscapes (impervious surfaces), which increases runoff and subsequent pollution from nutrients and sediment carried off land surfaces. This negatively impairs the health of receiving streams, lakes, rivers, and estuaries. A variety of management practices are available for reducing these impacts. Practice size is based on the water quantity and quality it will receive. Thus, estimating the quantity of nutrients and sediment from developed areas is crucial to meet water quality goals. However, designs of stormwater management practices typically use historical data based on land use; rather than conducting new monitoring studies to determine actual pollution loads.
Event mean concentration (EMC) is a common method used to estimate wash-off of pollutants from the land. Pollutants accumulate on surfaces during dry periods, making EMC a function of antecedent dry period (ADP) which is the time between storm events. An EMC results from wash-off of accumulated pollutants from urban areas during a storm event. However, EMC assumes pollutant concentration is constant across any storm event from a particular land use. Several studies found little to no correlation between nutrients and sediment concentrations in stormwater and ADP. Build-up/wash-off equations were developed to account for variability of concentrations between storm events; however, there are several parameters that are difficult to estimate. This study applied machine learning approaches to a national stormwater quality dataset and conducted monitoring and modeling studies at progressively smaller scales to improve the predictions of stormwater quantity and quality.
First, we obtained stormwater quality data from the National Stormwater Quality Database (NSQD), which is the largest data repository of its type in the U.S., and used Bayesian Network Structure Learner (BNSL), a machine learning method, to discover which climatological or catchment characteristics most significantly affect stormwater quality. Second, we developed and applied Random Forest (RF), also a machine learning method, to predict nutrients and sediment EMCs in stormwater. Third, we applied the Storm Water Management Model (SWMM), which is the most widely used rainfall/runoff model, to an urban area and assessed the best fit estimates of SWMM parameters during dry and wet years. Last, we conducted a monitoring and modeling study at smaller scales and assessed the role of land use on stormwater quantity and quality and estimated build-up/wash-off parameters for multiple urban land uses for nutrients and sediment using optimization. The results presented in this dissertation can help stakeholders, urban planners, and stormwater practice designers improve estimates of the quantity of nutrients and sediment in stormwater, achieve more effective treatment of stormwater, attain water quality improvement goals, and protect the health of receiving streams and downstream water bodies.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/111078 |
| Date | 30 June 2022 |
| Creators | Shahed Behrouz, Mina |
| Contributors | Biological Systems Engineering, Sample, David J., Shortridge, Julie Elizabeth, Daniels, W. Lee, Easton, Zachary M., Scott, Durelle T. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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