Improving Predictions of Stormwater Quantity and Quality through the Application of Modeling and Data Analysis Techniques from National to Catchment Scales

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Biological Systems Engineering

Department dc:contributor.department

Biological Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Shahed Behrouz, Mina

Chair dc:contributor.committeechair

• Sample, David J.

Committee members dc:contributor.committeemember

• Shortridge, Julie Elizabeth
• Daniels, W. Lee
• Easton, Zachary M.
• Scott, Durelle T.

• urban runoff
• machine learning
• water quality modeling
• SWMM
• land use
• optimization

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35106

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111078