Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Biological and Environmental Sciences


Biological Sciences

First Advisor

Soni Mulmi Pradhanang


A comprehensive assessment of the stormwater BMPs’ in terms of flow volume reduction, peak flow attenuation and runoff pollutant concentration reduction was made to evaluate the effectiveness of existing stormwater best management practices (BMPs) on the campus of the University of Rhode Island, RI, USA.

Urban stormwater runoff became a pressing issue in the later part of the 1980s with the recognition that point source controls were not sufficient to protect and restore water quality. In the last 15 years, the observed annual total rainfall in the study area was usually greater than long-term average yearly rainfall, suggesting the climate is towards wetter conditions. In addition to changing climate conditions, the areal extent of urban and suburban land uses also increased in Rhode Island, i.e., there is a clear link between increased stormwater runoff and the increasing extent of impervious surfaces in build-over areas. Compared to predevelopment conditions, impervious surfaces can lead to a rapid rise in peak flows in systems receiving stormwater runoff. About 10% to 15% of the total impervious areas in the USA are parking lots. This percentage is expected to increase in the future. Changing drainage patterns can cause floods, channel erosion but also carries the potential for the decreased baseflow and streambed alterations.

Roadside best management practices (BMPs) are techniques or methods that aim to prevent or reduce the overall negative impacts of stormwater runoff flow and improve the quality of stormwater runoff cost-effectively. These BMPs can be characterized into three types based on their performance: (i) source control, (ii) flow control and (iii) runoff treatment. Structural stormwater treatment practices are the most common type of water quality control BMP in Rhode Island. They include 1) wet vegetated treatment systems, 2) infiltration practices, 3) filtering systems, 4) green roofs, and 5) open channel practices. Therefore, it is essential to integrate monitoring and modeling of the treatment of water stormwater pollutants by these BMPs and to evaluate water quality risks of surface runoff along roadsides. Therefore, the proposed study was conducted to evaluate the effectiveness of the roadside BMPs under peak flow conditions as a function of runoff depth and in terms of runoff pollutant reduction. In addition, a stormwater management model was applied, and the various types of Low Impact Development (LID) control were analyzed.

This study conducted a four-pronged analysis based on 1) RS-GIS based SCS-CN model developed; 2) EPA SWMM 5.1 for hydrologic and hydraulic simulation; 3) parameter estimation using the Bayesian approach, and 4) predictive modeling through Artificial Neural Network.

Specifically, this study focuses on:

(i) EPA SWMM5.1 model set up for runoff and stormwater pollutants,

(ii) Calibration and validation of the developed model

(iii) Estimation of parameter uncertainty of SWMM using Bayesian statistics

(iv) Application of Artificial Neural Network (ANN) is predicting on the of important storm water pollutants

The developed model confirmed the significant role of LID in reducing runoff depth and peak flow. The established LID structures (permeable pavement, bioretention, and vegetative swale) are effective in runoff depth, peak flow, and pollutants reduction (Total suspended sediments, Nitrate-N, and Orthophosphate-P) for the smaller rainfall intensities (1-yr, 2-yr, 5-yr, and 10-yr) or rainfall design. The structures are not much effective in terms of 50-yr and 100-yr rainfall design.

In conclusion, this study provides development and evaluation of the stormwater management model in assessing the effectiveness of stormwater best management practices and provides a decision-making tool for the stormwater managers and planners.



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