Browsing by Author "Bailey, Ryan T., committee member"
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Item Open Access Finding land and water management practices to reduce selenium and nitrate concentrations in an agricultural river valley applying a regional-scale stream-aquifer model(Colorado State University. Libraries, 2017) Shultz, Christopher David, author; Gates, Timothy K., advisor; Bailey, Ryan T., committee member; Hoag, Dana K., committee memberThe long-term practice of irrigated agriculture within the Lower Arkansas River Valley (LARV) in southeast Colorado has contributed to a number of land and water management concerns, including elevated concentrations of dissolved selenium (Se) and nitrate (NO3) in the stream-aquifer system. The goal of this study was to develop and calibrate a stream-aquifer flow and reactive transport model to simulate conditions within a representative region of the LARV, then to apply the model to evaluate the potential effectiveness of alternative land and water best management practices (BMPs) to improve conditions. Using a MODFLOW-SFR model to simulate groundwater and stream flow, linked to an RT3D-OTIS model to simulate reactive transport of solutes, enabled comprehensive regional-scale modeling of the coupled stream-aquifer system. Through an extensive calibration and testing process, including manual and automated calibration using PEST, parameter values were estimated and runs were conducted to describe spatiotemporal distributions of groundwater levels and concentrations, mass and return flow rates to streams, and stream concentrations for baseline conditions. Similar runs were conducted for individual and combined BMPs to analyze their effectiveness in reducing groundwater and stream water pollution from Se and NO3, assuming their broad implementation over the study regions. The considered BMPs include two land BMPs, namely reducing applied fertilizer application (RF), and enhancing riparian buffer zones (ERB); and three water BMPs, reducing applied irrigation (RI), lease-fallowing irrigated land (LF), and canal sealing to reduce seepage (CS). Results reveal substantial spatial and temporal variability in Se and NO3 concentrations over the region. Moreover, they show that by implementing such BMPs, Se and NO3 groundwater concentrations could be lowered by as much as 23% and 40%, respectively, and stream concentrations of Se and NO3 could be lowered by as much as 57% and 33%, respectively. The most effective stand-alone land BMP was ERB, and the most effective stand-alone water BMP was CS. By coupling groundwater and stream flow modeling, this study has provided a number of insights not perceived in precursor modeling studies in the study region which examined only groundwater concentrations and mass loading. Some of these findings include: (1) BMPs which alter water management alone are likely to result in an increase in NO3 concentration in the streams (this is because the chemical reduction of groundwater return flows through the riparian zone is so effective under baseline conditions that practices which lower rates of return flow, without also substantially lowering concentrations, diminish the dilution effect on stream flow), (2) lower mass loading of Se and NO3 to streams due to a BMP does not necessarily imply a lowering of stream concentration since there are interactive effects of concurrent reductions in return flow rates, and (3) though there are prospects for substantial lowering of total Se concentrations in streams in the LARV, it is unlikely that the current Colorado chronic standard of 4.6 µg L-1 for total Se could ever be achieved practically. Furthermore, the linked models presented in this thesis could be applied to other irrigated stream-aquifer systems to simulate reactive transport of Se and NO3.Item Embargo Improvements in GRACE-based terrestrial water storage anomalies for groundwater depletion and ecohydrological analyses(Colorado State University. Libraries, 2022) Ukasha, Muhammad, author; Niemann, Jeffrey D., advisor; Grigg, Neil S., committee member; Bailey, Ryan T., committee member; Ronayne, Michael J., committee memberTo view the abstract, please see the full text of the document.Item Open Access Improving hydrologic modeling of runoff processes using data-driven models(Colorado State University. Libraries, 2021) Han, Heechan, author; Morrison, Ryan, advisor; Grigg, Neil S., committee member; Bailey, Ryan T., committee member; Kampf, Stephanie, committee memberAccurate rainfall–runoff simulation is essential for responding to natural disasters, such as floods and droughts, and for proper water resources management in a wide variety of fields, including hydrology, agriculture, and environmental studies. A hydrologic model aims to analyze the nonlinear and complex relationship between rainfall and runoff based on empirical equations and multiple parameters. To obtain reliable results of runoff simulations, it is necessary to consider three tasks, namely, reasonably diagnosing the modeling performance, managing the uncertainties in the modeling outcome, and simulating runoff considering various conditions. Recently, with the advancement of computing systems, technology, resources, and information, data-driven models are widely used in various fields such as language translation, image classification, and time-series analysis. In addition, as spatial and temporal resolutions of observations are improved, the applicability of data-driven models, which require massive amounts of datasets, is rapidly increasing. In hydrology, rainfall–runoff simulation requires various datasets including meteorological, topographical, and soil properties with multiple time steps from sub-hourly to monthly. This research investigates whether data-driven approaches can be effectively applied for runoff analysis. In particular, this research aims to explore if data-driven models can 1) reasonably evaluate hydrologic models, 2) improve the modeling performance, and 3) predict hourly runoff using distributed forcing datasets. The details of these three research aspects are as follows: First, this research developed a hydrologic assessment tool using a hybrid framework, which combines two data-driven models, to evaluate the performance of a hydrologic model for runoff simulation. The National Water Model, which is a fully distributed hydrologic model, was used as the physical-based model. The developed assessment tool aims to provide easy-to-understand performance ratings for the simulated hydrograph components, namely, the rising and recession limbs, as well as for the entire hydrograph, against observed runoff data. In this research, four performance ratings were used. This is the first research that tries to apply data-driven models for evaluating the performance of the National Water Model and the results are expected to reasonably diagnose the model's ability for runoff simulations based on a short-term time step. Second, correction of errors inherent in the predicted runoff is essential for efficient water management. Hydrologic models include various parameters that cannot be measured directly, but they can be adjusted to improve the predictive performance. However, even a calibrated model still has obvious errors in predicting runoff. In this research, a data-driven model was applied to correct errors in the predicted runoff from the National Water Model and improve its predictive performance. The proposed method uses historic errors in runoff to predict new errors as a post-processor. This research shows that data-driven models, which can build algorithms based on the relationships between datasets, have strong potential for correcting errors and improving the predictive performance of hydrologic models. Finally, to simulate rainfall-runoff accurately, it is essential to consider various factors such as precipitation, soil property, and runoff coming from upstream regions. With improvements in observation systems and resources, various types of forcing datasets, including remote-sensing based data and data-assimilation system products, are available for hydrologic analysis. In this research, various data-driven models with distributed forcing datasets were applied to perform hourly runoff predictions. The forcing datasets included different hydrologic factors such as soil moisture, precipitation, land surface temperature, and base flow, which were obtained from a data assimilation system. The predicted results were evaluated in terms of seasonal and event-based performances and compared with those of the National Water Model. The results demonstrated that data-driven models for hourly runoff forecasting are effective and useful for short-term runoff prediction and developing flood warning system during wet season.Item Open Access Long-term-robust adaptation strategies for reservoir operation considering magnitude and timing of climate change: application to Diyala River Basin in Iraq(Colorado State University. Libraries, 2020) Waheed, Saddam Qahtan, author; Grigg, Neil S., advisor; Ramirez, Jorge A., committee member; Bailey, Ryan T., committee member; Fassnacht, Steven R., committee memberVulnerability assessment due to climate change impacts is of paramount importance for reservoir operation to achieve the goals of water resources management. This requires accurate forcing and basin data to build a valid hydrology model and assessment of the sensitivity of model results to the forcing data and uncertainty of model parameters. The first objective of this study is to construct the model and identify its sensitivity to the model parameters and uncertainty of the forcing data. The second objective is to develop a Parametric Regional Weather Generator (RP-WG) for use in areas with limited data availability that mimics observed characteristics. The third objective is to propose and assess a decision-making framework to evaluate pre-specified reservoir operation plans, determine the theoretical optimal plan, and identify the anticipated best timeframe for implementation by considering all possible climate scenarios. To construct the model, the Variable Infiltration Capacity (VIC) platform was selected to simulate the characteristics of the Diyala River Basin (DRB) in Iraq. Several methods were used to obtain the forcing data and they were validated using the Kling–Gupta efficiency (KGE) metric. Variables considered include precipitation, temperature, and wind speed. Model sensitivity and uncertainty were examined by the Generalized Likelihood Uncertainty Estimation (GLUE) and the Differential Evolution Adaptive Metropolis (DREAM) techniques. The proposed RP-WG was based on (1) a First-order, Two-state Markov Chain to simulate precipitation occurrences; (2) use of Wilks' technique to produce correlated weather variables at multiple sites with conservation of spatial, temporal, and cross correlations; and (3) the capability to produce a wide range of synthetic climate scenarios. A probabilistic decision-making framework under nonstationary hydroclimatic conditions was proposed with four stages: (1) climate exposure generation (2) supply scenario calculations, (3) demand scenario calculations, and (4) multi-objective performance assessment. The framework incorporated a new metric called Maximum Allowable Time to examine the timeframe for robust adaptations. Three synthetic pre-suggested plans were examined to avoid undesirable long-term climate change impacts, while the theoretical-optimal plan was identified by the Non-dominated Sorting Genetic Algorithm II. The multiplicative random cascade and Schaake Shuffle techniques were used to determine daily precipitation data, while a set of correction equations was developed to adjust the daily temperature and wind speed. The depth of the second soil layer caused most sensitivity in the VIC model, and the uncertainty intervals demonstrated the validity of the VIC model to generate reasonable forecasts. The daily VIC outputs were calibrated with a KGE average of 0.743, and they were free from non-normality, heteroscedasticity, and auto-correlation. Results of the PR-WG evaluation show that it exhibited high values of the KGE, preserved the statistical properties of the observed variables, and conserved the spatial, temporal, and cross correlations among the weather variables at all sites. Finally, risk assessment results show that current operational rules are robust for flood protection but vulnerable in drought periods. This implies that the project managers should pay special attention to the drought and spur new technologies to counteract. Precipitation changes were dominant in flood and drought management, and temperature and wind speed changes effects were significant during drought. The results demonstrated the framework's effectiveness to quantify detrimental climate change effects in magnitude and timing with the ability to provide a long-term guide (and timeframe) to avert the negative impacts.Item Open Access Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law(Colorado State University. Libraries, 2019) Rohmat, Faizal Immaddudin Wira, author; Labadie, John W., advisor; Gates, Timothy K., advisor; Bailey, Ryan T., committee member; Anderson, Charles W., committee memberThe sustainability issues facing irrigated river basins are intensified by legal and institutional regulations imposed on the hydrologic system. Although solutions that would boost water savings and quality might prove to be feasible, such imposed institutional constraints could veto their implementation, rendering them legally ineffectual. The problems of basin-scale irrigation water management in a legally-constrained system are exemplified in the central alluvial valley of the Lower Arkansas River Basin (LARB) in Colorado, USA, and in the Tripa River Basin in Indonesia. In the LARB, water and land best management practices (BMPs) have been proposed to enhance the environment, conserve water, and boost productivity; however, the legal feasibility of their implementation in the basin hinder BMP adoption. In the Tripa river basin, the rapid growth of water demand pushes the proposal of new reservoir construction. However, inadequate water availability and the lack of water law enforcement requires the basin to seek water from adjacent basins, thereby raising legal and economic feasibility issues. To address these issues, an updated version of a decision support system (DSS) named River GeoDSS has been employed to model basin-scale behavior of the LARB for both historical (baseline) and BMP implementation scenarios. River GeoDSS uses GeoMODSIM as its water allocation component, which also handles water rights and uses a deep neural network (DNN) functionality to emulate calibrated regional MODFLOW-SFR2 models in modeling complex stream-aquifer interactions. The use of DNNs for emulation if critical for extrapolating the results of MODFLOW-SFR2 simulations to un-modeled portions of the basin and for compute-efficient analysis. The BMP implementations are found to introduce significant alterations to streamflows in the LARB, including shortages in flow deliveries to water right demands and in flow deficits at the Colorado-Kansas Stateline. To address this, an advanced Fuzzy-Mutation Linear Particle Swarm Optimization (Fuzzy-MLPSO) metaheuristic algorithm is applied to determine optimal operational policies for a new storage account in John Martin Reservoir for use in mitigating the side-effects of BMP implementation on water rights and the interstate compact. Prior to the implementation of Fuzzy-MLPSO, a dedicated study is conducted to develop the integration between MLPSO and GeoMODSIM, where it is applied in addressing the water allocation issue in the Tripa River Basin. The coupling of simulation (GeoMODSIM) and optimization (MLPSO) models provides optimal sizing of reservoirs and transbasin diversions along with optimal operation policies. Aside from that, this study shows that MLPSO converges faster compared to the original PSO with sufficiently smaller swarm size. The implementations of Fuzzy-MLPSO in the LARB provided optimal operational rules for a new storage account in John Martin Reservoir dedicated to abating the undesirable impacts of BMP implementation on water rights and Stateline flows. The Fuzzy-MLPSO processes inflow, storage, seasonal, and hydrologic states into divert-to-storage/release-from-storage decisions for the new storage account. Results show that concerns over shortages in meeting water rights demands and deficits to required Stateline flow due to otherwise beneficial BMP implementations can be addressed with optimized reservoir operations.Item Open Access Modeling nonpoint-source uranium pollution in an irrigated stream-aquifer system: calibration and simulation(Colorado State University. Libraries, 2024) Qurban, Ibraheem A., author; Gates, Timothy K., advisor; Bailey, Ryan T., committee member; Grigg, Neil S., committee member; Ippolito, James A., committee memberThe Lower Arkansas River Valley (LARV) in southeastern Colorado has been a source of significant agricultural productivity for well over a century, primarily due to extensive irrigation practices. Mirroring trends seen in other semi-arid irrigated areas globally, however, irrigated agriculture in the LARV has resulted in several challenges for the region. In addition to the emergence of waterlogging and soil salinization, leading to decreased crop yields, elevated levels of nutrients and trace elements have appeared in the soil and water. Among these constituents, uranium (U), along with co-contaminants selenium (Se) and nitrate (NO3), has shown particularly high concentrations in groundwater, surface water, and soils. These heightened concentrations pose environmental concerns, impacting human health and the well-being of aquatic life such as fish and waterfowl. Careful monitoring and management practices are crucial to prevent potential harm to water resources. The main goal of this research is to develop a comprehensive numerical model for assessing U pollution in a stream-aquifer system within a large irrigated area. To achieve this, a computational model is built and tested that can predict with reasonable accuracy how U, along with Se and NO3, are mobilized and move within a coupled system of streams and groundwater. The approach combines two key modeling components: a MODFLOW package, which handles the simulation of groundwater and stream flow dynamics, and an RT3D package, which addresses the reactive transport of U, Se, and nitrogen (N) species in both groundwater and interconnected streams. RT3D relies on the simulated flows generated by MODFLOW to track the movement of U, Se, and N species between streams and the aquifer in the irrigated landscape, updating daily to adequately capture changes over time. This integrated model provides an understanding of how these contaminants behave and interact within the stream-aquifer system, aiding in effective pollution assessment and providing insights valuable to the planning of management strategies. The coupled MODFLOW-RT3D flow and reactive transport model is applied to a 550 km² area within the LARV, stretching from Lamar, Colorado, to the Colorado-Kansas border and spanning a period of 14 years. The flow package is compared with observations of groundwater hydraulic head and stream flow, along with estimates of return flow along the Arkansas River. The reactive transport package is assessed by comparing predicted U, Se, and NO3 concentrations against data collected from groundwater monitoring wells and stream sampling sites along with estimates of solute mass loads to the river. To calibrate and refine the model, the PESTPP-iES iterative ensemble smoother (iES) software is employed. This calibration process is dedicated to enhancing the model's accuracy in predicting both flow and transport dynamics. PESTPP-iES addresses calibration uncertainty by establishing prior frequency distributions for key model parameters based on data and expertise, then iteratively adjusts these parameters during calibration to align model predictions with observed data. Post-calibration, posterior distributions reflect updated parameter values and reduced uncertainties. Demonstrating a strong alignment with concentrations of CNO3, CSe, and CU values found in groundwater, streams, and the mass loading entering the Arkansas River, outcomes of the model-based simulations reveal a substantial violation of the Colorado chronic standard (85th percentile = 30 μg/L) for CU throughout the study region. On average, simulated CNO3, CSe, and CU values for groundwater in non-riparian areas in the region are 3.6 mg/L, 41 µg/L, and 126 µg/L, compared to respective averages of 4 mg/L, 53 µg/L, and 112 µg/L observed in monitoring wells. When considering the 85th percentile of simulated CNO3, CSe, and CU values, the figures for non-riparian groundwater are 6 mg/L, 50 µg/L, and 218 µg/L, respectively. Groundwater in riparian areas shows lower average simulated CNO3, CSe, and CU values of 3 mg/L, 26 µg/L, and 72 µg/L, respectively, and 85th percentile values of 5 mg/L, 41 µg/L, and 152 µg/L. Additionally, simulated average mass loading rates for NO3, Se, and U along the river are 8.8 kg/day per km, 0.05 kg/day per km, and 0.27 kg/day/km respectively, compared to stochastic mass balance estimates of 9.2 kg/day per km , 0.06 kg/day per km , and 0.23 kg/day per km. The simulated 85th percentile CNO3, CSe, and CU values in the Arkansas River are 1 mg/L, 11 μg/L, and 87 μg/L, respectively. Notably, the simulated U levels in groundwater exceed the chronic standard across 44% of the region. Along the Arkansas River, concentrations consistently surpass the chronic standard, averaging 2.9 times higher. Predicted Se concentrations also show significant exceedances of the chronic standard, while NO3 violations are slight to moderate. The varying pollutant levels across the region highlight specific areas of concern that require targeted attention, indicating potential contributing factors to these hotspots. Findings outline how serious and widespread the problem is in the LARV, providing a starting point for comparing potential pollution reduction from alternative water and land best management strategies (BMPs) to be explored in future applications of the calibrated model.Item Open Access Multiscale connections between a groundwater dependent ecosystem and socio-hydrology: insight gained from numerical modeling, geospatial informatics, and Bayesian statistics(Colorado State University. Libraries, 2023) Lurtz, Matthew R., author; Morrison, Ryan R., advisor; Bhaskar, Aditi S., committee member; Bailey, Ryan T., committee member; Ross, Matthew, committee memberThe connectivity between floodplain practices and groundwater dependent ecosystems (GDE) is undeniable, yet difficult to measure. Quantifying the connection between ecosystems would be ideal for the conjunctive management of groundwater and surface water resources in an irrigated river valley. In the research presented, a variety of methodologies are used to understand the socio-hydrologic connections between a semi-arid GDE and agro-pastoral practices in southeastern Colorado (USA). I investigated the socio-hydrologic relationships between a GDE and the surrounding floodplain using three approaches. First, I used the output from a calibrated groundwater model and a remote sensing evapotranspiration (ET) algorithm with exploratory statistics. Second, I used remotely sensed vegetation information and socio-hydrologic data in a Bayesian hierarchical time series and spatial statistics models to compliment the first approach by examining new explanatory covariates. Third, a simple regression framework examines the point-scale relationship between groundwater and ET to further dissect results from the first approach at a finer resolution. These three approaches yielded key results. From the first objective, the dual-model comparison agreed with previous ecological research showing a non-linear relationship between ET and groundwater depth (0-5 m), and a threshold was identified at three meters where the rate between ET and groundwater depth change. The time series and spatial statistics objective helped identify a spatial scale threshold to detect temporal trend, lagged intra-seasonal predictors of vegetation water use, and which floodplain characteristics impact vegetation density. This statistical analysis discovered that temporal trend is not detectable at spatial scales larger than catchment size (> 10 km). Monthly temperature and lagged monthly values of precipitation and stream gain-loss (i.e., an return flow indicator variable) are all predictive of temporal changes in riparian vegetation density. Based on the floodplain characteristics tested in the spatial statistics approach, perennial tributaries to the Arkansas River increase vegetation density while the conversion of agriculture to fallow land decrease riparian vegetation density. The third objective highlighted that the process between evapotranspiration and groundwater head is non-linear but depends on temporal scale and plant functional group. The results from these approaches is important for GDE preservation in the face of increasing demand on groundwater supply. The process between groundwater and ET is of particular importance in large scale water balance studies that include a groundwater and surface water interface with need to model the groundwater-ET relationship in natural and agricultural ecosystems simultaneously.Item Open Access Numerical modeling and hydrochemical analysis of the current and future state of seawater intrusion in the Todos Santos aquifer, Mexico(Colorado State University. Libraries, 2019) Fichera, Marissa M., author; Sanford, William E., advisor; Ronayne, Michael J., committee member; Bailey, Ryan T., committee memberThe Todos Santos aquifer, Baja California Sur, Mexico, provides the sole source of freshwater to the town of Todos Santos, and is utilized for domestic and agricultural needs crucial to the town's economy. The region is characterized by an arid climate. Major recharge to the aquifer is supplied from intermittent cyclones. Irregular and unpredictable recharge rates combined with population growth resulting from resort development put the Todos Santos aquifer at risk of overexploitation, causing potentially permanent water quality degradation by salinization as a result of seawater intrusion. Understanding the complex response of seawater intrusion to variable pumping rates and sea-level rise is critical to water resource management in Todos Santos. This study utilized numerical simulation of variable-density groundwater flow, using SEAWAT, in conjunction with temporal and spatial hydrochemical analysis, to evaluate the current and future extent of seawater intrusion in the area. Forecasting simulations were run for five, ten, and twenty years following 2017, for five different hydrologic scenarios, which implemented various pumping rates, sea-level rise, and overexploitation of significant surface water resources. Hydrochemical analysis shows an increase in groundwater specific conductance and chloride concentration within two kilometers of the coastline from 2007 to 2017. This combined with the distribution of groundwater samples exhibiting chloride concentration above the permissible limit for potable water (250 mg/L) suggest that the Todos Santos aquifer is experiencing effects of seawater intrusion up to 1.6 kilometers inland as of 2017. Analysis of groundwater cation exchange reactions indicates widening of the freshwater-seawater mixing zone from 2007 to 2017, further suggesting the exacerbation of seawater intrusion over this time span. Forecasting simulation results indicate that the extent of seawater intrusion is exacerbated by increased water withdrawal, overexploitation of surface water resources, the current rate of sea-level rise (~ 4 mm/yr), and an increased rate of sea-level rise of 25 mm/yr.Item Open Access Quantifying lawn irrigation contributions to semi-arid, urban stream baseflow with water-stable isotopes(Colorado State University. Libraries, 2020) Fillo, Noelle K., author; Bhaskar, Aditi S., advisor; Bailey, Ryan T., committee member; Kampf, Stephanie K., committee memberIn semi-arid cities, urbanization can lead to elevated baseflow during summer months. One potential source for the additional water is lawn irrigation. We sought to quantify the presence of lawn irrigation in Denver's summertime baseflow using water-stable isotope (δ18O and δ2H) analysis of surface water, tap water, and precipitation. If lawn irrigation contributed significantly to baseflow, we predicted the isotopic composition of Denver's urban streams would more closely resemble the local tap water than precipitation or streamflow from nearby grassland watersheds. We expected the tap water to be distinctive due to local water providers importing much of their source water from high elevations. Thirteen urban streams and two grassland streams were selected for sampling. The thirteen urban watersheds ranged from 3.9 km2 - 63.3 km2 in drainage area and 22% - 44% in imperviousness. The two grassland watersheds had drainage areas of 3.7 km2 and 7.5 km2 as well as 1% and 5% imperviousness. None of the streams had high-elevation headwaters or wastewater effluent, and the grassland streams did not receive irrigation. Tap water was sampled from five local water provider service areas. Wide spatial and temporal variation in isotopic composition was observed within the stream, tap and precipitation samples. Comparison of samples between nearby watersheds revealed that proximity did not imply similar isotopic values. Streamflow analysis focusing on summer 2019 revealed that the grassland watersheds flowed for 60% of the summer while urban watersheds flowed for 90% - 100% of the summer. A two end-member isotope mixing model using tap and precipitation end-members estimated that tap water contributed 61% - 97% of urban streamflow on specific days in late summer. After taking estimated contributions from infrastructure leakage into account, we conservatively determined the lawn irrigation return flows made up 4% - 75% of the modeled baseflow. Quantifying the contribution of lawn irrigation to urban baseflow will provide a basis for understanding how changes to lawn irrigation efficiency would affect water yield in the Denver metropolitan area.Item Open Access Three essays on energy inputs, technology, and conservation policy in irrigated agricultural production(Colorado State University. Libraries, 2019) Hrozencik, R., author; Suter, Jordan F., advisor; Manning, Dale T., advisor; Goemans, Christopher G., committee member; Bailey, Ryan T., committee memberThis dissertation explores the role of energy inputs, irrigation technology, and conservation policy in irrigated agricultural production. In the first chapter, I utilize empirical and simulation modeling to understand the impact of non-linear energy pricing on groundwater use decisions in the Republican River Basin of Colorado. The second chapter empirically investigates how peer effects and resource availability influence a producer's choice to adopt a resource-conserving irrigation technology using data from the Trifa Plain of Morocco. The third chapter develops a hydroeconomic model which pairs groundwater demand with a physical model of resource dynamics to quantify how a groundwater conservation policy implemented within a subsection of the Republican River Basin of Colorado creates resource and input market spillovers.Item Open Access Three essays on the use of spatial data to inform environmental and resource management(Colorado State University. Libraries, 2022) Sheng, Di, author; Suter, Jordan F., advisor; Manning, Dale T., committee member; Goemans, Christopher G., committee member; Bailey, Ryan T., committee memberThis dissertation consists of three essays that use of spatial data to inform trade-offs related to environmental and resource management. The first essay explores how a spatially targeted differentiated payment design can reduce the social cost of achieving a given level of ecosystem service (ES) provisions. Performance comparisons between uniform payments and differentiated payments for ecosystem services help to identify the context under which differentiated payments offer the largest advantage relative to a uniform payment. A mathematical programming model is developed to explore the performance of different payment schemes and to derive generalized lessons from simulations. Then generalized lessons are evaluated with two case studies related to water quality management. It is found that the simulations and case studies align with each other in terms of the total cost reductions, but they diverge in the payment rate choice due to the underlying distributional differences. The findings suggest that a higher payment rate for parcels that systematically provide higher levels of ES can reduce the social cost of providing the ES of interest, particularly for cases where the mean ES provision benefits across land types are different and ES provision targets are relatively low. In the second essay, I examine whether China's pilot carbon emission trading system (ETS) has the co-benefit of reducing local PM2.5 levels. Two ETS pilot provinces are selected to be the treated group, while the control group is constructed with institutional knowledge. Static and dynamic difference-in-differences designs are adopted and compared to reveal the ETS treatment effect. The spatial and temporal variation in the ETS pilot areas allows me to adopt a dynamic two-way fixed effects model to estimate heterogeneous treatment effects on the treated areas. I find that the ETS improves the local air quality in Hubei but not in Guangdong. A further analysis suggests that a sector-standards based allowance allocation mechanism can cause local air quality to deteriorate. The third essay revisits the groundwater resource value question in the Ogallala aquifer through estimation of an econometric model of agricultural land prices that includes fixed effects, with the repeated transactions from the ZTRAX data product. Saturated thickness is used to present the groundwater availability and the study includes irrigated parcels only. Heterogeneous responses in land values to groundwater stock changes are found across Colorado and Nebraska. The marginal value of groundwater stock is highest at low levels of groundwater availability, which implies that additional groundwater depletion in Colorado is more costly than depletion in Nebraska.Item Open Access Uncertainty in measuring seepage from earthen irrigation canals using the inflow-outflow method and in evaluating the effectiveness of polyacrylamide applications for seepage reduction(Colorado State University. Libraries, 2015) Martin, Chad Allen, author; Gates, Timothy K., advisor; Cooley, Daniel S., committee member; Bailey, Ryan T., committee memberSeepage losses from unlined irrigation canals account for a large fraction of the total volume of water diverted for agricultural use, and reduction of these losses can provide significant water quantity and water quality benefits. Quantifying seepage losses in canals and identifying areas where seepage is most prominent are crucial for determining the potential benefits of using seepage reduction technologies and materials. In recent years, polymers have been studied for their potential to reduce canal seepage, and the use of linear-anionic polyacrylamide (PAM) was studied as part of this analysis. To quantify seepage reduction, seepage rates must be estimated before and after application of linear-anionic polyacrylamide (LA-PAM). In this study, seepage rates from four earthen irrigation canals in the Lower Arkansas River Valley (LARV) of southeastern Colorado were estimated with repeated measurements using the inflow-outflow volume balance procedure. It is acknowledged that a significant degree of measurement error and variability is associated with using the inflow-outflow method; however, as is often the case, it was selected so that canal operations were not impacted and so that seepage studies could be conducted under normal flow conditions. To account for uncertainty related to using the inflow-outflow procedure, detailed uncertainty analysis was conducted by assigning estimated probability distribution functions to volume balance components then performing Monte Carlo simulation to calculate possible seepage values with associated probabilities. Based upon previous studies, it was assumed that flow rates could be measured with +/- 5% accuracy, evaporation at +/- 20% accuracy, and water stage within 0.04 to 0.06 feet (all over the 90% interpercentile range). Spatial and temporal variability in canal hydraulic geometry was assessed using field survey data and was incorporated into the uncertainty model, as were temporal variability in flow measurements. Monte Carlo simulation provided a range of seepage rates that could be expected for each inflow-outflow test based upon the pre-defined probable error ranges and probability distribution functions. Using the inflow-outflow method and field measurements directly for assessing variables, deterministic seepage rates were estimated for 77 seepage tests on four canals in the LARV. Canal flow rates varied between 25.8 and 374.2 ft³/s and averaged 127.9 ft³/s, while deterministic estimates of seepage varied between -0.72 and 1.53 (ft³/s) per acre of wetted perimeter with an average of 0.36 (ft³/s)/acre for all 77 tests. Deterministic seepage results from LA-PAM application studies on the earthen Lamar, Catlin, and Rocky Ford Highline canals in southeastern Colorado indicated that seepage could be reduced by 34-35%, 84-100%, and 66-74% for each canal, respectively. Uncertainty analysis was completed for 60 seepage tests on the Catlin and Rocky Ford Highline canals. To describe hydraulic geometry within the seepage test reaches of these canals, canal cross-sections were surveyed at 25 and 16 locations, respectively. Probability distribution functions were assigned to parameters used to estimate wetted perimeter and top width for each cross-section to account for measurement error and spatial uncertainty in hydraulic geometry. Probability distributions of errors in measuring canal flow rates and stage, and in calculating water surface evaporation also were accounted for. From stochastic analysis of these 60 seepage tests, mean values of estimated seepage were between -0.73 (ft³/s)/acre (gain) and 1.53 (ft³/s)/acre, averaging 0.32 (ft³/s)/acre. The average of the coefficient of variation values computed for each of the tests was 240% and the average 90th interpercentile range was 2.04 (ft³/s)/acre. For the Rocky Ford Highline Canal reaches untreated with LA-PAM sealant, mean values of canal seepage rates ranged from -0.26 to 1.09 (ft³/s)/acre, respectively, and averaged 0.44 (ft³/s)/acre. For reaches on the Catlin Canal untreated with LA-PAM, mean values of seepage ranged from 0.02 to 1.53 (ft³/s)/acre, respectively, and averaged 0.63 (ft³/s)/acre. For reaches on the Rocky Ford Highline Canal and Catlin Canal treated with LA-PAM, mean canal seepage rates values ranged from 0.25 to 0.57 (ft³/s)/acre, averaging 0.33 (ft³/s)/acre, and from -0.73 to 0.55 (ft³/s)/acre, averaging -0.01 (ft³/s)/acre, respectively. Comparisons of probability distributions for several pre- and post-PAM inflow-outflow tests suggest likely success in achieving seepage reduction with LA-PAM. Sensitivity analysis indicates that while the major effect on seepage uncertainty is error in measured flow rate at the upstream and downstream ends of the canal test reach, but that the magnitude and uncertainty of storage change due to unsteady flow also is a significant influence. Based upon the findings, recommendations for future seepage studies were provided, which have the ability to account for and reduce uncertainty of inflow-outflow measurements.