Master of Science / Department of Biological & Agricultural Engineering / Kyle R. Douglas-Mankin / Sediment is considered as one of the important pollutant of concern in the U.S. In order to develop watershed management plans that address sediment pollution, it is essential to identify all sources of sediment in a watershed. The overall goal of this research is to quantify the total sediment from a watershed by integrating the outputs of three types of sediment sources: sheet and rill erosion, ephemeral gully erosion, and streambank erosion, that each operates at different spatial and temporal scales. This approach will be demonstrated in Black Vermillion River Watershed using AnnAGNPS (overland flow/erosion model), REGEM (ephemeral gully erosion model) and field measured values of streambank erosion. The study area includes three subwatersheds (Irish Creek, the Black Vermillion River Main Stem, and North Fork of the Black Vermillion), each monitored for continuous stream flow, base flow and event-based suspended sediment subwatershed export, annual streambank erosion, for 2 years. NASS land use, SSURGO soils data, 30-m DEMs, and local weather data were used to generate input data needed by the models. Stream monitoring data were used to calibrate the models. This paper will present results from independently calibrated and validated combinations of AnnAGNPS, REGEM, and filed measured streambank erosion. Our hypothesis is that use of separate models to simulate sediment load contributions for each sediment source will improve model agreement with measured watershed sediment yield data.

The previously developed watershed hydrological and water quality model for St. Louis Bay watershed by Kieffer (2002) was refined and calibrated. The aspects of model development refinement included development of fertilization-related nutrient input parameters, evaluation of nutrient input methods, development of plant uptake-related nutrient input parameters, non-cropland simulation using PQUAL module, and recalibration of hydrology in Jourdan River. The related information of typical cropland management practice based on consultation from Mississippi State University Extention Service personnel was integrated into the watershed model. In addition, the Mississippi Department of Environmental Quality (MDEQ) observed water quality data were analyzed to evaluate the appropriateness of current watershed delineation and assess the health of the stream based on the MDEQ proposed numerical water quality target. The refined watershed model was calibrated in Wolf Rover and Jourdan River using both USGS and MDEQ observed water quality data. The concentrations of water quality constituents calculated from the developed watershed model will be provided as boundary conditions for the developed Bay hydrodynamic and water quality model for Total Maximum Daily Load studies.

For an agricultural watershed, best management practice (BMP) is a conservational way to prevent non-point source pollution, soil and water loss and mitigate groundwater declination. In this dissertation, several BMPs of tail water recovery system, conservation tillage system and crop rotation were selected and evaluated in order to demonstrate the impacts of those activities on stream water quality and quantity. Besides, a land use change scenario was also evaluated. In order to evaluate the scenarios comprehensively, Soil and Water Assessment Tool (SWAT) and Annualized Agricultural Non-point Source Pollution (AnnAGNPS) were applied to simulate surface hydrology scenarios, and Modular flow (MODFLOW) models was used to simulate groundwater level change. This dissertation contains several novel methods regarding to model simulation including (i) using satellite imagery data to detect possible tail water recovery ponds, (ii) simulating surface and groundwater connected, (iii) selecting land use change area based on local trend and spatial relationship, (iv) comparing scenarios between two models. The outcomes from this dissertation included scenarios comparison on surface water quantity and quality, groundwater level change for long term simulation, and comparison between surface water models.

No description available.

The emergence of antibiotic resistance in riverine systems has become a growing issue worldwide. The use of watershed-scale models is popular with many other water quality issues but not in the case of antibiotic resistance. In this study, we introduce a watershed-scale bacteria fate and transport model to simulate antibiotic resistance in E. coli. This model was developed through amendments to an existing watershed-scale physically based hydrological model (SWAT), and the newly modified model was called SWAT-ARB. The SWAT-ARB model was employed in the receiving environment of a WWTP in the Adyar River basin, India. The SWAT-ARB model simulations of resistant fractions (resistant E. coli concentration/E. coli concentration) in stream water were analyzed by the flow levels with the application of a range of parameter values. It is concluded that the model can be used to test prevailing hypotheses and evaluate the current state of knowledge. For instance, model simulations suggest that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. We used the SWAT-ARB model to quantify the impact of climate change on antibiotic resistance. Six climate models were used to obtain the future climates in two distinct scenarios. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher simulated resistant fractions. In case of Adyar basin, resistant fractions are alleviated in high flow conditions, while aggravated in low flow conditions. / Doctor of Philosophy / The antibiotic resistance occurs when bacteria no longer responds to antibiotics. Hence, the diseases that caused by resistant bacteria are harder to treat. These antibiotic resistant bacteria end up in our rivers because of our heavy use of antibiotics in human and animal treatments. Thus, the spread of antibiotic resistance has become a water quality issue in the rivers worldwide. Scientists generally use computer models to understand water quality issues in rivers. These computer models are important because of high cost of monitoring and their use in finding how environment works. Up to the date of this publication, there is no sophisticated enough model to simulate antibiotic resistance in rivers. Hence, we created a river basin scale model to simulate antibiotic resistance. We found that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher antibiotic resistant bacteria compared to susceptible bacteria.

This thesis explores the potential impacts of the implementation of best management practices (BMPs) in Upper Roberts Creek (URC) watershed in northeast Iowa as part of the Iowa Nutrient Research Center (INRC). The INRC was formed in response to the United States Environmental Protection Agency (EPA) requirement that the states along the Mississippi River develop and implement strategies for reducing the nutrient load leaving their states and entering the Gulf of Mexico. The impacts of BMP implementation in URC were evaluated through the use of HydroGeoSphere which was used to develop a three dimensional, coupled surface/subsurface model of the watershed. The URC model was used to evaluate the hypothetical impacts of the widespread implementation of cover crops on agricultural land within the watershed, the construction of eight Iowa Conservation Enhancement Reserve Program (CREP) style wetlands, and the combination of these two BMPs. Through the comparison of these simplified, hypothetical scenarios to a baseline condition, potential nitrate load reduction estimates were made for each practice or combination of practices. These estimates indicate that neither of the individual practices would be likely to achieve the nitrogen reductions targeted by the EPA and in order to achieve these goals a combination of practices would likely be required.

In 1988, the counties of Appanoose, Davis, Monroe, and Wapello created the Soap Creek Watershed Board. This group put in place a plan to fund and construct 154 farm ponds in an effort to provide water for agriculture practices as well as provide flood protection for the residents inside the Soap Creek watershed. Through collaborative efforts and funding from federal, state, and local sources, to date 132 ponds have been constructed. Currently there is no stream monitoring in place in the watershed to observe stream conditions. This leads to no stored data on the benefits of the projects in the basin and the reduced flood impacts. With funding from the Iowa Watershed Projects (IWP) through the IIHR - Hydroscience & Engineering lab, a lumped parameter surface water model was created to show the benefits of the constructed projects. Using detailed LiDAR data, a Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was created. This model used arcHydro and ARC-GeoHMS, tools in ARCgis. Detailed LiDAR, SURGGO soil data, and land cover data was used to create the model parameters. Several design and historical storms were modeled to quantify the benefits in peak flow reductions and in amounts of water stored behind the projects.

Urbanization has considerably altered natural hydrology of urban watersheds by increasing runoff volume, producing higher and faster peak flows, and reducing water quality. Efforts to minimize or avoid these impacts, for example by implementing low impact development (LID) practices, are gaining momentum. Designing effective and economical stormwater management practices at a watershed scale is challenging; LIDs are commonly designed at site scales, considering local hydrologic conditions (i.e., one LID at a time). A number of empirical studies have documented hydrologic and water quality improvements achieved by LIDs. However, watershed scale effectiveness of LIDs has not been well studied. Considering cost, effort, and practicality, computer modeling is the only viable approach to assess LID performance at a watershed scale. As such, the United States Environmental Protection Agency’s Stormwater Management Model (SWMM) was selected for this study. It is well recognized that model predictions are plagued by uncertainties that arise from the lack of quality data and inadequacy of the model to accurately simulate the watershed. To scrutinize sensitivity of prediction accuracies to spatial resolution, four SWMM models of different spatial detail were developed for the Ballona Creek watershed, a highly urbanized watershed in the Los Angeles Basin, as a case study. Detailed uncertainty analyses were carried out for each model to quantify their prediction uncertainties and to examine if a detailed model improves prediction accuracy. Results show that there is a limit to the prediction accuracy achieved by using detailed models. Three of the four models (i.e., all but the least detailed model) produced comparable prediction accuracy. This implies that devoting substantial resources on collecting very detailed data and building fine resolution watershed models may not be necessary, as models of moderate detail could suffice. If confirmed using other urban watersheds, this result could benefit stormwater managers and modelers. All four SWMM models were then used to evaluate hydrologic effectiveness of implementing bioretention cells at a watershed scale. Event based analyses, 1-year, 2-year, 5-year and 10-year storms of 24-hours were considered, as well as data from October 2005 to March 2010 for a continuous simulation. The runoff volume reductions achieved by implementing bioretention cells were not substantial for the event storms. For the continuous simulation analysis, however, about twenty percent reductions in runoff volume were predicted. These results are in-line with previous studies that have reported ineffectiveness of LIDs to reduce runoff volume and peak for less frequent but high intensity storm events.

No description available.

This study used the Soil and Water Assessment Tool (SWAT) to model 2 watersheds in Mississippi, which are the Lower Pearl River Watershed (LPRW) and the Big Sunflower River Watershed (BSRW), to simulate streamflow, groundwater storage and recharge, sediments, nutrients, and bacteria transport. The LPRW model was calibrated and validated for daily streamflow at 4 locations with R2 ranging from 0.49 to .90 and Nash-Sutcliffe Efficiency (NSE) ranging from 0.49 to 0.84. In the BSRW, the model showed good to very good performance for daily streamflow simulation (R2 = 0.53-0.75 and NSE = 0.49-0.72) and seasonal groundwater table depth fluctuations (R2 = 0.76 to 0.86 and NSE = 0.71-0.79). The BSRW model was also calibrated and validated for total sediment (TS) load (R2 = 0.50-0.72, NSE = 0.47-0.66), total phosphorus (TP) load (R2 = 0.79-0.82, NSE = 0.73-0.77), and fecal coliform bacteria concentrations (R2 = 0.56-0.60 and NSE = 0.23-0.40). In the LPRW, the effectiveness of grassed waterways, detention ponds, and parallel terraces Best Management Practices (BMPs) to attenuate peak streamflow decreases significantly under increased rainfall and under increased CO2 concentration climate change scenarios; however, under increased temperature or decreased rainfall, the effectiveness of BMPs to reduce peak streamflows did not significantly change. In the BSRW, implementing crop rotations practices with rice resulted in the lowest groundwater storage (-10.7%), but it also led to the highest increases in monthly groundwater recharge (up to +60.1%). The crop rotations with corn and cotton usually resulted in the largest increases in groundwater storage (up to +27.2%). The BSRW was modeled to assess the sensitivity of bacteria concentrations to climate change, and this study determined that bacteria concentrations were most sensitive to rainfall, followed by temperature, solar radiation, and CO2 concentrations. The BSRW model also showed significant parameter uncertainty in the streamflow, TS load, TP load, and total nitrogen (TN) load simulations, and that equifinal parameter sets exist in the model. Moreover, the SWAT parameters that were sensitive to streamflow were also found to be sensitive to sediment and nutrient transport.