Improving Watershed Models to Achieve a Better Prediction of Water Quantity and Quality

dc.contributor.authorKaveh Garna, Rojaen
dc.contributor.committeechairEaston, Zachary M.en
dc.contributor.committeememberBosch, Darrell J.en
dc.contributor.committeememberWhite, Robin R.en
dc.contributor.committeememberBenham, Brian L.en
dc.contributor.departmentBiological Systems Engineeringen
dc.description.abstractWatershed models are powerful tools for simulating different scenarios to understand the impact of management practices and are used to support and guide decision-making. However, there are often challenges and limitations to using watershed models in some areas of watershed modeling; 1) model calibration in the areas with data limitations; 2) acquiring complete weather data that accurately reflect watershed model responses; 3) accurate representation of manure operation in watershed models. This dissertation addresses each of the aforementioned challenges using new approaches and tools in three studies with the main objective of achieving a better prediction of water quality and quantity and enhancing watershed models. Chapter 2 presents a method (multi-basin calibration (MBC)) to estimate watershed model parameters that lack long-term streamflow records. In the MBC method, first, the Soil and Water Assessment Tool (SWAT) models are initialized individually for several similar neighboring watersheds with a short period of measured streamflow. Then, we aggregate the simulated and observed flows from each initialization with short histories to generate a combined observed-simulated streamflow record that is longer than the initial length of each individual member in order to increase the information content. The Nash-Sutcliffe efficiency (NSE) from this merged time series was used as the basis for calibrating using a differential evolution algorithm. To evaluate the MBC, SWAT models for three newly instrumented USGS gages in Lake Champlain Basin of Vermont, USA, were compared to the commonly used similarity-based regionalization (SBR) approach. Results demonstrate that short periods of hydrological measurement from multiple locations in a basin can represent a system similar to long-term measurements. Chapter 3 develops a method to generate a complete weather data time series with the integration of multiple Global Historical Climatology Network (GHCN) stations and to assess the benefit of much higher density, lower reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data that was integrated into the GHCN. To evaluate the performance of the methodology, generated weather data is used to force the Soil and Water Assessment Tool (SWAT) models of 21 United States Department of Agriculture (USDA)-Agricultural Research Service (ARS)-Natural Resource Conservation Service (NRCS)-Conservation Effects Assessment Project (CEAP) watersheds to simulate daily streamflow. The results demonstrated that integration of multiple GHCN stations including higher-density, but perhaps lower-quality weather data can enhance model performance. A comparison with published SWAT model results further corroborated improved model performance using newly combined GHCN data. Chapter 4 develops a hybrid SWAT model, SWAT-Dairy, to accurately represent the impact of manure operation on nutrient transport. The SWAT-Dairy model incorporates process-based livestock routines, developed in the R platform, which quantify daily manure production, stored manure, daily total nitrogen (N) and phosphorus (P), organic and mineral N and P, and dynamic manure nutrient fractions based on animal characteristics, feed characteristics, and environmental conditions. Outputs are then used in SWAT to simulate the impact of livestock manure production. The new model, with simulated manure application management, is applied to a farm in the Little Otter Creek Basin in Vermont, US. Subbasin- and farm-level N and P losses from manure management using the new model were compared for different feed management scenarios.en
dc.description.abstractgeneralIn the past few decades, watershed management has become more challenging due to rapid population growth, climate change, and agricultural practices. In order to achieve better watershed management strategies, it is essential to understand the complex interaction between different biological, physical, and chemical processes occurring in the watershed. Watershed models are useful tools that help scientists and engineers to understand and predict how climate and land-use changes and agricultural management practices affect different components of a watershed system. While watershed models have many advantages, they are often limited by challenges and obstacles, such as model parameter estimations in the areas with limited measured streamflow data, acquiring complete and accurate weather data, and explicit representation of animal management impacts on water quality in manure applications. This dissertation addresses the challenges mentioned earlier by developing new approaches and methods that improve water quality and quantity using watershed models. A long record of measured streamflow data is necessary for watershed models to accurately represent watershed systems and estimate the parameters that cannot be directly measured. However, many watersheds worldwide are not monitored or are newly instrumented with a short period of recorded data. Chapter 2 introduces a new approach (multi basin calibration (MBC)) that integrates short periods of recorded data from several watersheds to provide a similar representation of the watershed system as long-term records. MBC was compared with a commonly used method that requires long recorded streamflow data from a neighboring watershed. The results showed that MBC improved model results and captured hydrological processes better for the watershed with a short period of recorded data than the traditionally used method. Obtaining accurate weather data for a watershed model can also be challenging since land-based weather stations often contain missing data. In recent years, hydrological modelers and researchers have access to the much higher density of weather measurements from private citizens that collect data with inexpensive equipment. However, no study has evaluated the benefits of using much higher-density data from private citizens for watershed modeling. Chapter 3 presents a new methodology to acquire complete weather data time series with the integration of all weather stations, including higher density private citizen-based measurements. Then the weather data were used to force watershed models of 21 watersheds across the United States. The results showed that the new methodology provides weather data that reflect the watershed model response with satisfactory performance ratings in 18 out of 21 watersheds. Lastly, chapter 4 develops a dairy model and integrates it into one of the most commonly used watershed models, the Soil and Water Assessment Tool (SWAT), to investigate how different farm management scenarios impact manure production and nutrient contents as well as their consequent effect on water quality during manure application on farm fields.en
dc.description.degreeDoctor of Philosophyen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.subjectCalibration; Multi-basin calibration; Regionalization; SWAT model; Global Historical Climatology Network; Weather forcing data; Manure application; Farm managementen
dc.titleImproving Watershed Models to Achieve a Better Prediction of Water Quantity and Qualityen
dc.typeDissertationen Systems Engineeringen Polytechnic Institute and State Universityen of Philosophyen
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