Browsing by Author "Shahed Behrouz, Mina"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Do Maryland's Stormwater Management Regulations Protect Channel Stability?
    Thompson, Theresa M.; Sample, David J.; Al-Samdi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew; Butcher, Jon (2024-06-20)
    Webinar for the Maryland Stream Restoration Association. 84 participants
  • Thumbnail Image
    Effectiveness of environmental site design in protecting stream channel stability
    Thompson, Theresa M.; Sample, David J.; Al-Smadi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew (2023-05-08)
  • Thumbnail Image
    Effectiveness of stormwater management practices in protecting stream channel stability
    Thompson, Theresa M.; Sample, David J.; Al-Smadi, Mohammad; Towsif Khan, Sami; Shahed Behrouz, Mina; Miller, Andrew (2024-06-11)
    Presentation made as part of the Stream Restoration Webinar Series: Finding Common Ground. Webinar had 284 participants.
  • Thumbnail Image
    Improving Predictions of Stormwater Quantity and Quality through the Application of Modeling and Data Analysis Techniques from National to Catchment Scales  
    Shahed Behrouz, Mina (Virginia Tech, 2022-06-30)
    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.