Thesis (M.S.)--University of Wyoming, 2009. / Title from PDF title page (viewed on May 7, 2010). Includes bibliographical references (p. 116-118).

Groundwater-derived phosphorus has often been dismissed as a significant contributor towards surface water eutrophication, however, this dismissal is unwarranted, making the quantification of phosphorus concentrations in groundwater systems immensely important. Machine learning models have been employed to quantify the concentrations of various contaminants in groundwater, but to our best knowledge have never been used for the quantification of groundwater phosphorus. The goal of this research was to use a boosted regression tree framework to produce the first believed machine learning model of phosphorus variability in groundwater, with the High Plains aquifer serving as the study area. Results display a boosted regression tree model that was not capable of explaining and predicting the statistical variance of phosphorus throughout the aquifer under standard conditions, however important variable correlation data that can potentially be incorporated into future studies that aim to further understand phosphorus dynamics in groundwater was obtained from this research.

Groundwater monitoring in regional scales using conventional methods is challenging since it requires a dense network monitoring well system and regular measurements. Satellite measurement of time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) mission since 2002 provided an exceptional opportunity to observe the variations in Terrestrial Water Storage (TWS) from space. This study has been divided into 3 parts: First different satellite and hydrological model data have been used to validate the TSW measurements derived from GRACE in High Plains Aquifer (HPA). Terrestrial Water Storage derived from GRACE was compared to TWS derived from a water budget whose inputs determined from independent datasets. The results were similar to each other both in magnitude and timing with a correlation coefficient of 0.55. The seasonal groundwater storage changes are also estimated using GRACE and auxiliary data for the period of 2004 to 2009, and results are compared to the local in situ measurements to test the capability of GRACE in detecting groundwater changes in this region. The results from comparing seasonal groundwater changes from GRACE and in situ measurements indicated a good agreement both in magnitude and seasonality with a correlation coefficient of 0.71. This finding reveals the worthiness of GRACE satellite data in detecting the groundwater level anomalies and the benefits of using its data in regional hydrological modelling. In the second part of the study the feasibility of the GRACE TWS for predicting groundwater level changes is investigated in different locations of the High Plains Aquifer. The Artificial Neural Networks (ANNs) are used to predict the monthly groundwater level changes. The input data employed in the ANN include monthly gridded GRACE TWS based on Release-05 of GRACE Level-3, precipitation, minimum and maximum temperature which are estimated from Parameter elevation Regression on Independent Slopes Model (PRISM), and the soil moisture estimations derived from Noah Land Surface Model for the period of January 2004 to December 2009. All the values for mentioned datasets are extracted at the location of 21 selected wells for the study period. The input data is divided into 3 parts which 60% is dedicated to training, 20% to validation, and 20% to testing. The output to the developed ANNs is the groundwater level change which is compared to the US Geological Survey's National Water Information well data. Results from statistical downscaling of GRACE data leaded to a significant improvement in predicting groundwater level changes, and the trained ensemble multi-layer perceptron shows a "good" to a "very good" performance based on the obtained Nash-Sutcliff Efficiency which demonstrates the capability of these data for downscaling. In the third part of this study the soil moisture from 4 different Land Surface models (NOAH, VIC, MOSAIC, and CLM land surface models) which are accessible through NASA Global Land Data Assimilation System (GLDAS) is included in developing the ANNs and the results are compared to each other to quantify the effect of soil moisture in the downscaling process of GRACE. The relative importance of each predictor was estimated using connection weight technique and it was found that the GRACE TWS is a significant parameter in the performance of Artificial Neural Network ensembles, and based on the Root Mean Squared (RMSE) and the correlation coefficients associated to the models in which the soil moisture from Noah and CLM Land Surface Models are used, it is found that using these datasets in process of downscaling GRACE delivers a higher correlated simulation values to the observed values.

Master of Science / Department of Geology / Matthew Kirk / Groundwater from the High Plains aquifer is vital for food production and a growing human population in the Great Plains region of the United States. Understanding how groundwater quality is changing in response to anthropogenic and natural processes is critical to effectively managing this resource. Our study considers variation in groundwater geochemistry in the Great Bend Prairie aquifer, a portion of the High Plains aquifer in southcentral Kansas. We collected samples during summer 2016 from 24 monitoring wells and compared our results to data collected previously from the same wells from 1979 to 1987. We sampled 13 wells screened in the upper portion of the aquifer (avg. depth 72 ft), 10 wells screened near the aquifer base (avg. depth 141 ft), and one well screened in underlying bedrock. Compared to initial samples, samples we collected tended to have higher total dissolved solids (TDS) and nitrate content, particularly those we collected from the upper aquifer. Compared to initial samples, TDS was 78 mg/L higher in samples we collected from the upper aquifer and 373 mg/L lower in samples we collected from the aquifer base on average. Nitrate exceeded the U.S. standard for public supplies of drinking water (10 mg/L as N) in seven of the samples we collected, compared to only two samples collected previously. Compared to previous samples, nitrate concentrations were 9.5 and 3.9 mg/L as N higher on average in samples collected from the upper aquifer and aquifer base, respectively. Based on a mixing analysis, variation in the salinity of our samples primarily reflects the dilution of natural Permian brines by freshwater recharge throughout the area. However, salinity decreases observed in four samples reflects flushing of initial oil brine contamination over time, salinity increases in two samples may be due to evapotranspiration, and salinity increases in two samples may reflect migration of oil-brine contamination towards the site. Stable nitrogen (15N/14N) and oxygen (18O/16O) isotope ratios in our samples primarily fall within the range typical of nitrification of ammonium-based fertilizers with potential contributions from manure or sewage. In our analysis of the microbial community, we observed groups capable of denitrification, including genera within Nitrospirae, Firmicutes, and Proteobacteria. Despite their presence, our results demonstrate that water quality in the aquifer has degraded over the past 30 to 40 years due to nitrate accumulation.

Doctor of Philosophy / Department of Agricultural Economics / Jeffrey M. Peterson / The High Plains aquifer is a primary source of irrigation in western Kansas. Since World War II, producers increased irrigation and the irrigated acreage with the widespread adoption of newer irrigation technologies, causing a reduction in the saturated thickness of the High Plains aquifer. In an effort to conserve water and reduce further decline of the aquifer, the state of Kansas administered cost-share programs to producers who upgraded to an efficient irrigation system. But evidence suggests that the efforts to reduce water consumption have been undermined by producers, who under certain conditions have increased irrigation and irrigated acreage of high-valued and water-intensive crops. The state of Kansas is in a quandary to reduce water consumption and stabilize the saturated thickness of the aquifer while maintaining the economic viability of irrigated agriculture. A producer is faced with the choice of crop, irrigation timing and irrigation technology at the start of the season. This research identifies the conditions for risk-efficient crop choices and estimates the effect of an irrigation technology upgrade on the aquifer. Simulation models based on data from Tribune, Kansas were executed under various scenarios, varying by crop (corn or sorghum), irrigation system (conventional center-pivot or center-pivot with drop nozzles) and well capacity (190, 285 or 570 gallons per minute). Each well capacity was associated with a pre-season soil moisture level (0.40, 0.60 or 0.80 of field capacity). Each scenario was simulated over weather data observed during the 36-year period (1971-2006). Results indicate that producers with slower wells could maximize their net returns while conserving water by choosing less water-intensive crops like sorghum, while irrigating with a conventional center-pivot irrigation system. Producers with faster wells could maximize net returns by choosing water-intensive crops like corn and irrigate with the more efficient center-pivot with drop nozzle irrigation system. In order to reduce groundwater consumption and maintain the saturated thickness of the aquifer, water policies should internalize the interests of all stakeholders and be a combination of irrigation technology, economic factors, hydrological conditions, agronomic practices, conservation practices and local dynamics of the region.

Master of Science / Department of Civil Engineering / David R. Steward / Accurate modeling of the High Plains Aquifer depends on the availability of good data that represents and quantities properties and processes occurring within the aquifer. Thanks to many previous studies there is a wealth of good data available for the High Plains Aquifer but one key component, groundwater-surface water interaction locations and rates, is generally missing. Without these values accurate modeling of the High Plains Aquifer is very difficult to achieve. This thesis presents methods for simplifying the modeling of the High Plains Aquifer using a sloping base method and then applying mathematical optimization techniques to locate and quantify points of groundwater-surface water interaction. The High Plains Aquifer has a base that slopes gently from west to east and is approximated using a one-dimensional stepping base model. The model was run under steady-state predevelopment conditions using readily available GIS data representing aquifer properties such as hydraulic conductivity, bedrock elevation, recharge, and the predevelopment water level. The Levenberg-Marquardt and particle swarm algorithms were implemented to minimize error in the model. The algorithms reduced model error by finding locations in the aquifer of potential groundwater-surface water interaction and then determining the rate of groundwater to surface water exchange at those points that allowed for the best match between the measured predevelopment water level and the simulated water level. Results from the model indicate that groundwater-surface water interaction plays an important role in the overall water balance in the High Plains Aquifer. Findings from the model show strong groundwater-surface water interaction occurring in the northern basin of the aquifer where the water table is relatively shallow and there are many surface water features. In the central and southern basins the interaction is primarily limited to river valleys. Most rivers have baseflow that is a net sink from groundwater.

Master of Landscape Architecture / Department of Landscape Architecture and Regional and Community Planning / Eric A. Bernard / The Kansas High Plains region is a key global agricultural production center (U.S. G.S, 2009). The High Plains physiography is ideal agricultural production landscape except for the semi-arid climate. Consequently, farmers mine vast groundwater resources from the High Plains Ogallala Aquifer formations to augment precipitation for crop production. Growing global population, current policy and subsidy programs, declining aquifer levels coupled with regional climatic changes call into question both short-term and long-term resilience of this agrarian landscape and food and water security. This project proposes a means to simulate future irrigated agriculture land use and crop cover patterns in the Kansas High Plains Aquifer region based on coupled modeling results from ongoing research at Kansas State University. A Cellular Automata (CA) modeling framework is used to simulate potential land use distribution, based on coupled modeling results from groundwater, economic, and crop models. The CA approach considers existing infrastructure resources, industrial and commercial systems, existing land use patterns, and suitability modeling results for agricultural production. The results of the distribution of irrigated land produced from the CA model provide necessary variable inputs for the next temporal coupled modeling iteration. For example, the groundwater model estimates water availability in saturated thickness and depth to water. The economic model projects which crops will be grown based on water availability and commodity prices at a county scale. The crop model estimates potential yield of a crop under specific soil, climate and growing conditions which further informs the economic model providing an estimate of profit, which informs regional economic and population models. Integrating the CA model into the coupled modeling system provides a key linkage to simulate spatial patterns of irrigated land use and crop type land cover based on coupled model results. Implementing the CA model in GIS offers visualization of coupled model components and results as well as the CA model land use and land cover. The project outcome hopes to afford decision-makers, including farmers, the ability to use the actual landscape data and the developed coupled modeling framework to strategically inform decisions with long-term resiliency.

Base flow recession curve during a dry period is a distinct hydrologic signature of a watershed. The base flow recession analysis for both streamflow and spring flow has been extensively studied in the literature. Studies have shown that the recession behaviors during the early stage and the late stage are different in many watersheds. However, research on the transition from early stage to late stage is limited and the hydrologic control on the transition is not completely understood. In this dissertation, a novel cumulative regression analysis method is developed to identify the transition flow objectively for individual recession events in the well-studied Panola Mountain Research Watershed in Georgia, USA. The streamflow at the watershed outlet is identified when the streamflow at the perennial stream head approaches zero, i.e., flowing streams contract to perennial streams. The identified transition flows are then compared with observed flows when the flowing stream contracts to the perennial stream head. As evidenced by a correlation coefficient of 0.90, these two characteristics of streamflow are found to be highly correlated, suggesting a fundamental linkage between the transition of base flow recession from early to late stages and the drying up of ephemeral streams. At the early stage, the contraction of ephemeral streams mostly controls the recession behavior. At the late stage, perennial streams dominate the flowing streams and groundwater hydraulics governs the recession behavior. The ephemeral stream densities vary from arid regions to humid regions. Therefore, the characteristics of transition flow across the climate gradients are also tested in 40 watersheds. It is found that climate, which is represented by climate aridity index, is the dominant controlling factor on transition flows from early to late recession stages. Transition flows and long-term average base flows are highly correlated with a correlation coefficient of 0.82. Long-term average base flow and the transition flow of recession are base flow characteristics at two temporal scales, i.e., the long-term scale and the event scale during a recession period. This is a signature of the co-evolution of climate, vegetation, soil, and topography at the watershed scale. The characteristics of early and late recession are applied for quantifying human impacts on streamflow in agricultural watersheds with extensive groundwater pumping for irrigation. A recession model is developed to incorporate the impacts of human activities (such as groundwater pumping) and climate variability (such as evapotranspiration) on base flow recession. Groundwater pumping is estimated based on the change of observed base flow recession in watersheds in the High Plains Aquifer. The estimated groundwater pumping rate is found consistent compared with the observed data of groundwater uses for irrigation. Besides streamflow recession analysis, this dissertation also presents a novel spring recession model for Silver Springs in Florida by incorporating groundwater head, spring pool altitude, and net recharge into the existing Torricelli model. The results show that the effective springshed area has continuously declined since 1988. The net recharge has declined since the 1970s with a significant drop in 2002. Subsequent to 2002, the net recharge increased modestly but not to the levels prior to the 1990s. The decreases in effective springshed area and net recharge caused by changes in hydroclimatic conditions including rainfall and temperature, along with groundwater withdrawals, contribute to the declined spring flow.