Mi-Hyun Park

The modified SWAT model for predicting fecal coliforms in the Wachusett Reservoir Watershed, USA

This study assessed fecal coliform contamination in the Wachusett Reservoir Watershed in Massachusetts, USA using Soil and Water Assessment Tool (SWAT) because bacteria are one of the major water quality parameters of concern. The bacteria subroutine in SWAT, considering in-stream bacteria die-off only, was modified in this study to include solar radiation-associated die-off and the contribution of wildlife. The result of sensitivity analysis demonstrates that solar radiation is one of the most significant fate factors of fecal coliform. A water temperature-associated function to represent the contribution of beaver activity in the watershed to fecal contamination improved prediction accuracy. The modified SWAT model provides an improved estimate of bacteria from the watershed. Our approach will be useful for simulating bacterial concentrations to provide predictive and reliable information of fecal contamination thus facilitating the implementation of effective watershed management.

Accuracy and precision of the volume–concentration method for urban stormwater modeling

Land use based models have been widely used to estimate stormwater pollutant loads for establishing standards and selecting best management practices. Land use information is required to assign imperviousness or runoff coefficients and event mean concentrations for selected pollutants to the areas of the watersheds. This approach is useful to estimate the total mass discharges, but is dependent on various assumptions for parameters, which have rarely been validated with full-scale field data. This paper compares the assumptions, methodologies and predictions of six independent modeling efforts using literature and local data, and shows the variability of the different approaches and methodologies for the Upper Ballona Creek Watershed in Los Angeles CA. Differences in land use definitions among the six studies produced up to 14 % differences in average load predictions for TSS. Differences in runoff coefficient and EMC assumptions produced -70 to 124% differences in average TSS load. The combined effects of all assumptions produced differences in the estimated TSS mass loads by -68 to 118%. The most inaccurate estimates were based on the Nationwide Urban Runoff Program data, which were far too high. These results underscore the need for additional data collection and model validation, and provide error bounds for the use of this modeling approach for regulation and best management practice selection.

Assessment of nutrient distributions in Lake Champlain using satellite remote sensing

The introduction of nutrients to lakes causing eutrophic conditions is a major problem around the world. Proper monitoring and modeling are important to effectively manage eutrophication in lake waters. The goal is to develop remote sensing models for nutrients, total phosphorus and total nitrogen, in Lake Champlain. The remote sensing models were created using multivariate linear regression with the unique band combinations of Landsat Enhanced Thematic Mapper Plus (ETM+) imagery based on the empirical relationship with the field observations. The resulting models successfully showed nutrient distributions in the most eutrophic part of Lake Champlain, Missisquoi Bay, with reasonable adjusted coefficient of determination values (R(2)=0.81 and 0.75 for total phosphorus and total nitrogen, respectively). The results show the feasibility and the utility of satellite imagery to detect spatial distributions of lake water quality constituents, which can be used to better understand nutrient distributions in Lake Champlain. This approach can be applicable to other lakes experiencing eutrophication assisting decision making when implementing Best Management Practices and other mitigation techniques to lakes.

Water Quality Improvement Policies: Lessons Learned from the Implementation of Proposition O in Los Angeles, California

This article evaluates the implementation of Proposition O, a stormwater cleanup measure, in Los Angeles, California. The measure was intended to create new funding to help the city comply with the Total Maximum Daily Load requirements under the federal Clean Water Act. Funding water quality objectives through a bond measure was necessary because the city had insufficient revenues to deploy new projects in its budget. The bond initiative required a supermajority vote (two-thirds of the voters), hence the public had to be convinced that such funding both was necessary and would be effective. The bond act language included project solicitation from the public, as well as multiple benefit objectives. Accordingly, nonprofit organizations mobilized to present projects that included creating new parks, using schoolyards for flood control and groundwater recharge, and replacing parking lots with permeable surfaces, among others. Yet few, if any, of these projects were retained for funding, as the city itself also had a list of priorities and higher technical expertise in justifying them as delivering water quality improvements. Our case study of the implementation of Proposition O points to the potentially different priorities for the renovation of urban infrastructure that are held by nonprofit organizations and city agencies and the importance of structuring public processes clearly so that there are no misimpressions about funding and implementation responsibilities that can lead to disillusionment with government, especially under conditions of fiscal constraints.

Remote sensing models using Landsat satellite data to monitor algal blooms in Lake Champlain

Lake Champlain is significantly impaired by excess phosphorus loading, requiring frequent lake-wide monitoring for eutrophic conditions and algal blooms. Satellite remote sensing provides regular, synoptic coverage of algal production over large areas with better spatial and temporal resolution compared with in situ monitoring. This study developed two algal production models using Landsat Enhanced Thematic Mapper Plus (ETM(+)) satellite imagery: a single band model and a band ratio model. The models predicted chlorophyll a concentrations to estimate algal cell densities throughout Lake Champlain. Each model was calibrated with in situ data compiled from summer 2006 (July 24 to September 10), and then validated with data for individual days in August 2007 and 2008. Validation results for the final single band and band ratio models produced Nash-Sutcliffe efficiency (NSE) coefficients of 0.65 and 0.66, respectively, confirming satisfactory model performance for both models. Because these models have been validated over multiple days and years, they can be applied for continuous monitoring of the lake.

Modeling spatiotemporal bacterial variability with meteorological and watershed land-use characteristics.

Bacteria are a primary contaminant in natural surface water. The instream concentration of fecal coliform, a potential indicator of pathogens, is influenced by meteorological conditions and land-use characteristics. However, the relationships between these conditions and fecal coliforms are not fully understood. Furthermore, the sources of large variability in fecal coliform counts, e.g., temporal or spatial sources, remain unexplained, especially at large scales. This study proposes the use of Bayesian overdispersed Poisson models, whereby the combined effects of temperature, rainfall, and land-use characteristics on fecal coliform concentration are quantified with predictive uncertainty, and the sources of variability in fecal coliform concentration are assessed. The models were developed using 8-year weekly observations of fecal coliforms obtained from the Wachusett Reservoir watershed in Massachusetts, USA. The results highlight the importance of interactions among meteorological and land-use characteristics in controlling the instream fecal coliform concentration; the increase in fecal coliform concentration with temperature increase was more drastic when rainfall occurred. Also, the responses of fecal coliforms to temperature increases were more pronounced in forest-dominated than in urban-dominated areas. In contrast, the fecal coliform concentration increased more rapidly with rainfall increases in urban-dominated than in forest-dominated areas. The models also demonstrate that among the sources of variability, the monthly component made the most significant contribution to the variability in fecal coliform concentrations. Our results suggest that seasonally dependent processes, including surface runoff, are critical factors that regulate fecal coliform concentration in streams.

Evaluation of stormwater BMPs for implementing industrial stormwater permitting strategy.

This study assesses the performance of stormwater best management practices (BMPs) in industrial sectors and their effluent quality to facilitate the development of technology-based numerical effluent criteria. Generally, retention ponds outperform other BMP types for reducing total suspended solids, and media filter and wetland basins outperform other BMPs for metal removal. Detention basins were not effective in reducing stormwater pollution although they can retain the stormwater before entering surface waters. However, many BMPs show high variability of influent and effluent concentrations and no significant difference between them, which makes it difficult to determine the effectiveness of the BMP. In some cases, low influent concentrations govern the distribution of effluent concentrations and effluent concentrations are often greater than inflow concentrations. The analysis results can be used to assist in the developing a watershed based multisector industrial stormwater general permit to ensure compliance with total maximum daily loads. The results also suggest the need for additional monitoring data.

Water quality improvement by implementation of Proposition O in the Los Angeles river watershed, California

Proposition O was created to help the City of Los Angeles comply with the Total Maximum Daily Load (TMDL) requirements under the Clean Water Act. In this study, the effectiveness of the Proposition O projects in Los Angeles River watershed was examined to show whether it achieves the goal of meeting water quality standards. Our analysis shows the most effective single project will remove at most 2% of pollutant loads from Los Angeles River Watershed and will not achieve TMDL compliance, although several projects can make important contributions to achieve compliance. The ranking results show that the projects that treat the runoff from the largest drainage area have the greatest impact on the water quality of Los Angeles river.

Identification of Roads for Urban Runoff Pollution Management

Urban roads are identified as major sources of many pollutants and the runoff from these areas discharges high pollution mass loads to receiving waters. Identifying roads is important for effective urban runoff and non point source pollution management. However, public land use information usually does not provide detailed road information. In this research, we identified roads and streets in an urban area to estimate their runoff pollution. We used high resolution multi-spectral imagery i.e. IKONOS. The classified image was used to estimate urban runoff pollutant mass loads and compared them to the estimates using public land use information. The results show that the pollutant load estimation was improved by using high resolution imagery because of the extracted road information. The overall results of this work suggest that using high resolution multi-spectral data is a promising way to estimate overall stormwater pollutant loads. This method could be used as an alternative method of estimating limits for the US EPAs total maximum daily loads control strategies.

Identifying main areas generating stormwater pollution from satellite imagery and ancillary data

Urban stormwater runoff has been recognized as a major source of pollutants in Santa Monica Bay watershed because of the watersheds highly developed, impervious landuses. Estimating stormwater pollutant mass emissions is inherently difficult and often requires land use information. Many approaches have been developed to estimate land use from satellite imagery. This research uses an alternative approach, which estimates stormwater pollutant loadings from satellite imagery. We classified a Landsat ETM/sup +/ image of the areas in the Santa Monica Bay watershed using Bayesian networks. Identified water quality parameters were heavy metals such as copper, lead and zinc, and oil and grease. The parameters were classified into low, medium and high loads and the most polluting areas are the target area to be identified. We examined the effect of incorporating ancillary data: locational data, such as X and Y coordinate values of each pixel, and elevation data such as SRTM. The results show that incorporating coordinate values or elevation data improves overall accuracy and lowers the omission error compared with using Landsat ETM/sup +/ spectral data only. In fact, incorporating coordinate values was more useful than incorporating elevational data to provide better accuracies and less omission errors. These results will be useful in developing best management strategies for stormwater pollution.