Browsing by Author "Niemann, Jeffrey D., advisor"
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Item Open Access A method for assessing impacts of parameter uncertainty in sediment transport modeling applications(Colorado State University. Libraries, 2009) Ruark, Morgan D., author; Niemann, Jeffrey D., advisor; Kampf, Stephanie, committee member; Griemann, Blair, committee memberNumerical sediment transport models are widely used to evaluate impacts of water management activities on endangered species, to identify appropriate strategies for dam removal, and many other applications. The SRH-1D (Sedimentation and River Hydraulics - One Dimension) numerical model, formerly known as GST ARS, is used by the U.S. Bureau of Reclamation for many such evaluations. The predictions from models such as SRH-1D include uncertainty due to assumptions embedded in the model 's mathematical structure, uncertainty in the values of parameters, and various other sources. In this paper, we aim to develop a method that quantifies the degree to which parameter values are constrained by calibration data and determines the impacts of the remaining parameter uncertainty on model forecasts. Ultimately, this method could be used to assess how well calibration exercises have constrained model behavior and to identify data collection strategies that improve parameter certainty. The method uses a new multi-objective version of Generalized Likelihood Uncertainty Estimation (GLUE). In this approach, the likelihoods of parameter values are assessed using a function that weights different output variables using their first order global sensitivities, which are obtained from the Fourier Amplitude Sensitivity Test (FAST). The method is applied to SRH-1D models of two flume experiments: an erosional case described by Ashida and Michiue (1971) and a depositional case described by Seal et al. (1997). Overall, the results suggest that the sensitivities of the model outputs to the parameters can be rather different for erosional and depositional cases and that the outputs in the depositional case can be sensitive to more parameters. The results also suggest that the form of the likelihood function can have a significant impact on the assessment of parameter uncertainty and its implications for the uncertainty of model forecasts.Item Open Access A method to downscale soil moisture to fine-resolutions using topographic, vegetation, and soil data(Colorado State University. Libraries, 2014) Ranney, Kayla J., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Kampf, Stephanie K., committee memberVarious remote-sensing and ground-based sensor methods are available to estimate soil moisture over large regions with spatial resolutions greater than 500 m. However, applications such as water management and agricultural production require finer resolutions (10 - 100 m grid cells). To reach such resolutions, soil moisture must be downscaled using supplemental data. Several downscaling methods use only topographic data, but vegetation and soil characteristics also affect fine-scale soil moisture variations. In this thesis, a downscaling model that uses topographic, vegetation, and soil data is presented, which is called the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model. The EMT+VS model assumes a steady-state water balance involving: infiltration, deep drainage, lateral flow, and evapotranspiration. The magnitude of each process at each location is inferred from topographic, vegetation, and soil characteristics. To evaluate the model, it is applied to three catchments with extensive soil moisture and topographic data and compared to an Empirical Orthogonal Function (EOF) downscaling method. The primary test catchment is Cache la Poudre, which has variable vegetation cover. Extensive vegetation and soil data were available for this catchment. Additional testing is performed using the Tarrawarra and Nerrigundah catchments where vegetation is relatively homogeneous and limited soil data are available for interpolation. For Cache la Poudre, the estimated soil moisture patterns improve substantially when the vegetation and soil data are used in addition to topographic data, and the performance is similar for the EMT+VS and EOF models. Adding spatially-interpolated soil data to the topographic data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than the EOF method, in which the soil data are not highly weighted. These results suggest that the soil data must have greater spatial detail to be useful to the EMT+VS model.Item Open Access A nonlinear synthetic unit hydrograph method that accounts for channel network type(Colorado State University. Libraries, 2018) Czyzyk, Kelsey A., author; Niemann, Jeffrey D., advisor; Gironás, Jorge, committee member; Ronayne, Michael J., committee memberStormflow hydrographs are commonly estimated using synthetic unit hydrograph (UH) methods, particularly for ungauged basins. Current synthetic UHs either consider very limited aspects of basin geometry or require explicit representation of the basin flow paths. None explicitly considers the channel network type (i.e., dendritic, parallel, pinnate, rectangular, and trellis). The goal of this study is to develop and test a nonlinear synthetic UH that explicitly accounts for the network type. The synthetic UH is developed using kinematic wave travel time expressions for hillslope and channel points in the basin. The effects of the network structure are then isolated into two random variables whose distributions are estimated based on the network type. The proposed method is applied to ten basins from each classification and compared to other related methods. The results suggest that considering network type improves the estimated UHs with the largest improvements seen for dendritic, parallel, and pinnate networks.Item Open Access Advanced Bayesian framework for uncertainty estimation of sediment transport models(Colorado State University. Libraries, 2018) Jung, Jeffrey Youngjai, author; Niemann, Jeffrey D., advisor; Greimann, Blair P., committee member; Julien, Pierre Y., committee member; Wang, Haonan, committee memberNumerical sediment transport models are widely used to forecast the potential changes in rivers that might result from natural and/or human influences. Unfortunately, predictions from those models always possess uncertainty, so that engineers interpret the model results very conservatively, which can lead to expensive over-design of projects. The Bayesian inference paradigm provides a formal way to evaluate the uncertainty in model forecasts originating from uncertain model elements. However, existing Bayesian methods have rarely been used for sediment transport models because they often have large computational times. In addition, past research has not sufficiently addressed ways to treat the uncertainty associated with diverse sediment transport variables. To resolve those limitations, this study establishes a formal and efficient Bayesian framework to assess uncertainty in the predictions from sediment transport models. Throughout this dissertation, new methodologies are developed to represent each of three main uncertainty sources including poorly specified model parameter values, measurement errors contained in the model input data, and imperfect sediment transport equations used in the model structure. The new methods characterize how those uncertain elements affect the model predictions. First, a new algorithm is developed to estimate the parameter uncertainty and its contribution to prediction uncertainty using fewer model simulations. Second, the uncertainties of various input data are described using simple error equations and evaluated within the parameter estimation framework. Lastly, an existing method that can assess the uncertainty related to the selection and application of a transport equation is modified to enable consideration of multiple model output variables. The new methodologies are tested with a one-dimensional sediment transport model that simulates flume experiments and a natural river. Overall, the results show that the new approaches can reduce the computational time about 16% to 55% and produce more accurate estimates (e.g., prediction ranges can cover about 6% to 46% more of the available observations) compared to existing Bayesian methods. Thus, this research enhances the applicability of Bayesian inference for sediment transport modeling. In addition, this study provides several avenues to improve the reliability of the uncertainty estimates, which can help guide interpretation of model results and strategies to reduce prediction uncertainty.Item Open Access An assessment of streamflow production mechanisms for dam safety applications in the Colorado Front Range(Colorado State University. Libraries, 2019) Woolridge, Douglas, author; Niemann, Jeffrey D., advisor; Schumacher, Russ S., committee member; Morrison, Ryan R., committee memberHydrologic analyses are used for dam safety evaluations to determine the flow a dam must pass without failing. Many current guidelines model flood runoff solely by an infiltrationexcess mechanism. Saturation-excess runoff and subsurface stormflow mechanisms are known to be important for common events in forested regions, but few studies have analyzed their role for extreme events. The objectives of this study are to determine the active streamflow mechanisms for large historical storms and design storms in the Colorado Front Range and to propose methods to model these mechanisms that can be used by consultants. Hydrologic models were developed for five basins to simulate historical events in 1976, 1997, and 2013. The model results show saturation-excess was the dominant mechanism during the 2013 storm, which had a long duration and low rainfall intensities. Infiltration-excess runoff was dominant for the 1976 storm, which had a short duration and high intensities. Surface runoff was not observed during the 1997 storm. Similarly, infiltration-excess dominates for short duration design storms, and saturation-excess dominates for longer design storms.Item Open Access Assessing the influence of model inputs on performance of the EMT+VS soil moisture downscaling model for a large foothills region in northern Colorado(Colorado State University. Libraries, 2024) Fischer, Samantha C., author; Niemann, Jeffrey D., advisor; Scalia, Joseph, advisor; Stright, Lisa, committee memberSoil moisture is an important driving variable of the hydrologic cycle and a key consideration for decision-making in off-road vehicle mobility, crop modeling, drought forecasting, flood prediction, and a variety of other applications. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing or land surface models; however, coarse resolution estimates are unsuitable for many applications. Downscaling these products to finer resolutions (~10 m) creates soil moisture maps that are more useful. This study applies the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model to Maxwell Ranch, a 4,000-ha cattle ranch in Northern Colorado that represents a diverse range of topographic, vegetation, and soil characteristics and a wide range of soil moisture conditions. The EMT+VS model is a physically based geo-information method that downscales coarse resolution soil moisture estimates using ancillary fine resolution datasets of topography and vegetation. Input data to the EMT+VS model contain inherent sources of error that can impact the uncertainty of downscaled estimates. The objective of this study is to identify sources of uncertainty in inputs and assess their influence on the error of the EMT+VS model output. The study finds changes in vegetation input or digital elevation model (DEM) resolution introduce substantial errors in the EMT+VS model output; however, these errors can be mostly overcome when recalibration with local in-situ data is possible. The highest errors (RMSE = 0.20 cm3/cm3) tend to occur in locations with thick vegetation and high contributing area, which are difficult to accurately estimate with available remote sensing data sources.Item Open Access Downscaling soil moisture over regions that include multiple coarse-resolution grid cells(Colorado State University. Libraries, 2016) Hoehn, Dylan C., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Kampf, Stephanie K., committee memberMany applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be downscaled to reach fine resolutions. When large spatial extents are considered, the downscaling procedure must consider multiple coarse-resolution grid cells, yet little attention has been given to the treatment of multiple grid cells. The objective of this paper is to compare the performance of different methods for addressing multiple coarse grid cells. To accomplish this goal, the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) downscaling model is generalized to accept multiple coarse grid cells, and two methods for their treatment are implemented and compared. The first method (fixed window) is a direct extension of the original EMT+VS model and downscales each coarse grid cell independently. The second method (shifting window) replaces the coarse grid cell values with values that are calculated from windows that are centered on each fine grid cell. The window values are weighted averages of the coarse grid values within the window extent, and three weighting methods are considered (box, disk, and Gaussian). The methods are applied to three small catchments with detailed soil moisture observations and one large region. The fixed window typically provides more accurate estimates of soil moisture than the shifting window, but it produces abrupt changes in soil moisture at the coarse grid boundaries, which may be problematic for some applications. The three weighting methods produce similar results.Item Open Access Effects of gully topography on space-time patterns of soil moisture in a semiarid grassland(Colorado State University. Libraries, 2009) Melliger, Joshua J., author; Niemann, Jeffrey D., advisor; Butters, Greg, committee member; Bledsoe, Brian, committee memberGullies are pervasive topographic features in semiarid grasslands in North America. At the Army’s Piñon Canyon Maneuver Site (PCMS) in southeastern Colorado, gullies are important because they restrict the mobility of troops and vehicles in training exercises, and they represent areas that are potentially vulnerable to further erosion. Substantial research has examined the temporal evolution of gullies as well as the factors that initiate gullies and control their morphology. In particular, prolonged periods of low soil moisture (droughts), frequent flash floods, and human activity are thought to reduce vegetative cover and promote gully development. Much less is understood about the feedback of gully topography on space-time patterns of soil moisture. The presence of gullies may produce feedbacks to soil moisture that either enhance or diminish gully development. In this study, field observations from PCMS are used to study the effects of gullies on space-time patterns of soil moisture and to describe the interactions of soil moisture, soil texture, and vegetation around gullies. Three study sites at PCMS have been extensively instrumented. These sites are located in the same broad valley, but one site (~1500 m2) is ungullied while the other two sites (also ~1500 m2) each contain a gully. The gully sites are adjacent to each other and their two gullies are approximately parallel. Hourly soil moisture observations have been collected for 8 months at two sites and 4 months at one site using time domain reflectometry (TDR) probes installed along four transects within each site. Each transect contains 6-8 probes that are positioned at the mid-points between topographic breakpoints. Meteorological data are also collected at the ungullied site and between the two gullied sites. Overall, the occurrence of gullies was observed to not affect the spatial average soil moisture within the study sites, but the gullies do promote spatial variability in soil moisture. Gully bottoms tend to be wetter. Although the evidence here is not conclusive, this tendency may be due to gradual lateral inflows, thicker vegetation (which protects the soil against surface crusting and promotes infiltration), and the lower local elevations (which protect against higher wind speeds and evapotranspiration). The gully sidewalls tend to be drier because of rapid drainage during and after precipitation events and in some cases increased solar insolation.Item Open Access Effects of woody vegetation on shallow soil moisture at a semiarid montane catchment(Colorado State University. Libraries, 2013) Traff, Devin, author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Butters, Greg, committee memberSoil moisture plays an integral role in many ecohydrologic processes and applications, particularly in semiarid environments. While interactions between vegetation and soil moisture at greater depths are relatively well understood, less is known about soil moisture at depths of 5 cm or less. In this study we investigate the impact of woody vegetation on shallow soil moisture dynamics for forested and shrubland ecosystems in a semiarid montane catchment. Instrumentation was installed on a forested north-facing hillslope (NFS) and a south-facing hillslope (SFS) vegetated primarily by shrubs at three types of locations: open or intercanopy, under mountain mahogany (Cercocarpus montanus) shrubs, and under ponderosa pine (Pinus ponderosa) trees. Rain gauges and pyranometers were installed to assess the impact of interception and shading, while time-domain reflectometry (TDR) probes were inserted into the top 5 cm of the soil to monitor hourly soil moisture. The observations suggest that interception reduces throughfall to about 25-50% of rainfall under the mountain mahogany and ponderosa pines. Shading is important for all locations on the NFS (PET ~ 20% of the SFS open location), but less shading occurs under the SFS mountain mahogany (PET ~ 40% of the SFS open location). Shallow soil under all vegetation types is typically wetter than at the SFS open location for dry conditions and drier than the SFS open location for wet conditions. Average shallow soil moisture is higher under all vegetation types than in the open, suggesting that the shading effect is stronger than the interception effect for the conditions studied.Item Embargo Enhancing rootzone soil moisture estimation using remote sensing, regional characteristics, and machine learning(Colorado State University. Libraries, 2023) Sahaar, Ahmad Shukran, author; Niemann, Jeffrey D., advisor; Chavez, Jose Luis, committee member; Green, Timothy R., committee member; Butters, Gregory, committee memberAccurate estimation of root-zone soil moisture (θ Ì„) is essential for various agricultural applications, including crop yield estimation, precision irrigation, and groundwater management. This dissertation encompasses three interconnected studies that collectively investigate different approaches for improving soil moisture estimation. The first study delves into the utilization of remote sensing methods, particularly optical and thermal satellite imagery, to estimate fine-resolution (30 m) root-zone soil moisture across diverse regions. Traditionally, these methods relied on empirical relationships with evaporative fraction Λ_SEB or evaporative index Λ_PET. However, it has been shown that a single relationship does not universally apply to all regions. This study evaluates the influence of regional soil, vegetation, and climatic conditions on the shape and strength of these relationships using global sensitivity analysis. The results highlight that soil characteristics, such as clay and silt content, and vegetation properties, like leaf area index and rooting depth, play pivotal roles in determining these relationships. Moreover, the impact of annual precipitation in defining climatic regions is crucial. Consequently, region-specific relationships are proposed, adapting to local conditions and potentially enhancing soil moisture estimates. The second study extends this investigation by applying the regionally adapted relationships for the Λ_SEB " vs." θ Ì„ and Λ_PET " vs." θ Ì„ to estimate rootzone soil moisture (θ Ì„) from remote sensing data across four study regions. The results consistently demonstrate the superior performance of the regionally adapted relationships over a single empirical relationship, with a substantial reduction in root mean squared error. These adapted relationships are particularly effective in arid and semiarid regions. The third study explores the application of machine learning models, including XGBoost, CatBoost, RF, LightGBM, and artificial neural networks, to predict soil moisture levels across various climates and depths in the contiguous United States. The findings emphasize the high accuracy and effectiveness of machine learning models, especially XGBoost, in predicting soil moisture across diverse climate regions. XGBoost outperforms other models, making it a potentially valuable tool for soil moisture prediction in environmental monitoring and management. The study also highlights the influence of climate and soil depth on prediction accuracy, with deeper layers having improved forecasts. Additionally, feature importance analysis identifies key predictors for predicting soil moisture, such as elevation, aridity index, soil composition, and depth. These findings contribute to the advancement of soil moisture monitoring and management, with practical applications in agriculture and environmental sciences.Item Open Access Estimating changes in streamflow attributable to wildfire in multiple watersheds using a semi-distributed watershed model(Colorado State University. Libraries, 2023) Wells, Ryan, author; Niemann, Jeffrey D., advisor; Kampf, Stephanie, committee member; Nelson, Peter, committee memberOver half of western U.S. water supply is sourced from forested lands that are increasingly under wildfire risk. Studies have begun to isolate the effects of wildfire on streamflow, but they have used coarse temporal resolutions that cannot account for the numerous, interconnected watershed processes that control the responses to rainfall events. To address these concerns, we developed a method to isolate fine-scale (daily) effects of fire from climate. Wildfire effects were represented by the difference between measured post-fire daily streamflow and simulated unburned post-fire daily streamflow from a hydrologic model calibrated to pre-fire conditions. The method was applied to track hydrologic recovery after wildfires in six burned watersheds across the western U.S.: North Eagle Creek, NM (2012 Little Bear Fire), Lopez Creek, CA (1985 Las Pilitas Fire), and City Creek, Devil Canyon Creek, East Twin Creek, and Plunge Creek, CA (2003 Old Fire). All six watersheds experienced prolonged increases of post-fire streamflow, with the most consistent changes occurring during periods of low streamflow. Following 6 years of increased streamflow, Lopez Creek experienced 6 years of reduced streamflow, before returning to pre-fire streamflow behavior 12 years after the fire. North Eagle Creek and the four watersheds affected by the Old Fire continued to demonstrate elevated streamflow 9 and 18 years post-fire, respectively. This study demonstrates the utility of examining post-fire streamflow at daily resolution over multiple years. In particular, these results captured the variability of change across flow frequencies during recovery periods that would not be quantifiable otherwise.Item Open Access Estimation of catchment-scale soil moisture patterns from topography and reconstruction of a preserved ash-flow paleotopography(Colorado State University. Libraries, 2012) Coleman, Michael Lee, author; Niemann, Jeffrey D., advisor; Salas, Jose D., committee member; Green, Timothy R., committee member; Kampf, Stephanie, committee memberThis dissertation consists of three parts, two of which examine methods for estimating spatial soil moisture patterns while the third investigates the reconstruction of a fluvially-eroded paleotopography. Part I of the dissertation evaluates unsupervised machine-learning techniques' effectiveness for estimating soil moisture patterns and compares them with linear regression. Physical processes that impact soil moisture are typically expressed as nonlinear functions, but most previous research on the estimation of soil moisture has relied on linear techniques. In the present work, two machine learning techniques, a spatial artificial neural network (SANN) and a mixture model (MM), that can infer nonlinear relationships are compared with multiple linear regression (MLR) for estimating soil moisture patterns using topographic attributes as predictor variables. The methods are applied to time-domain reflectometry (TDR) soil moisture data collected at three catchments with varying characteristics (Tarrawarra, Satellite Station, and Cache la Poudre) under different wetness conditions. The methods' performances with respect to the number of predictor attributes, the quantity of training data, and the attributes employed are compared using the Nash-Sutcliffe Coefficient of Efficiency (NSCE) as the performance measure. The performances of the methods are dependent on the site studied, the average soil moisture and the quantity of training data provided. Although the methods often perform similarly, the best performing method overall is the SANN, which incorporates additional predictor variables more effectively than the other methods. Next, Part II of the dissertation presents the development and testing of a new conceptually-based model for estimating soil moisture patterns and describes the investigation of the climatic, vegetation and soil characteristics that affect pattern organization and temporal stability with the model. Soil moisture is a key hydrologic state variable for the Earth's surface affecting both energy and precipitation partitioning. Additionally, the nonlinear dependence of hydrologic processes on soil moisture means that not only is the average moisture condition important for many applications, but the spatial patterns of soil moisture are also important. At the catchment scale, soil moisture patterns have been observed to exhibit different types of dependence on topography. Some catchments have their wettest locations in the valley bottoms, while others have their wettest locations on hillslopes that are oriented away from the sun. Additionally, some catchments have moisture patterns that maintain a similar organization at all times (time stability), while other catchments have soil moisture patterns that change through time (time instability). Although these tendencies are well known, the reasons for their occurrence at a particular catchment are not well understood. In this paper, we investigate the conditions under which the different types of topographic dependence and different degrees of time instability occur through the use of a new conceptual model. The type of topographic dependence and the degree of instability are quantified by two metrics that are also introduced in the paper, and the effects of soil, vegetation, and climatic parameters on these metrics are then evaluated. The evaluations indicate that saturated horizontal hydraulic conductivity, pore disconnectedness, vegetation evapotranspiration efficiency, and an exponent relating the horizontal hydraulic gradient to the topographic slope have the strongest effects on the organization and instability of the soil moisture patterns. In contrast, annual potential evapotranspiration alone does not strongly impact the organization or its stability. Finally, Part III of the dissertation describes the modification of a previously-developed interpolation scheme for fluvial topography and the reconstruction of a paleotopography that may be potentially important to groundwater movement by the modified method. Many applications in geology require estimation of the depth and thickness of lithologic layers based on limited observations. The boundaries of such layers are typically estimated using Kriging or other estimation methods that produce smooth surfaces. In some cases, however, smooth surfaces may be inappropriate. A boundary that is formed by a preserved hillslope and valley paleotopography, in particular, is expected to exhibit drainage characteristics and inherent roughness that are not consistent with standard estimation methods. This paper discusses the generalization of a technique originally designed to interpolate fluvially-eroded topography. The method incorporates a simple river basin evolution model to generate realistic topography and adjusts an erodability parameter in space to match observed elevations. The method is generalized to allow flow to enter from outside the interpolation region, which is a likely scenario when reconstructing paleotopography. The method is then applied to the lower boundary of the Tshirege Member of the Bandelier Tuff, which underlies Los Alamos National Laboratory and Bandelier National Monument in north-central New Mexico. The method produces surfaces with major valleys that are consistent with previous observations. The method is also applied in a framework that estimates the likelihood that any particular point within the interpolation region drains through a specified boundary. Although the surfaces vary between simulations, most portions of the interpolation domain drain through consistent boundaries.Item Open Access Evaluating the parameter identifiability and structural validity of a probability-distributed model for soil moisture(Colorado State University. Libraries, 2007) Tripp, Danielle R., author; Niemann, Jeffrey D., advisor; Butters, Greg, committee member; Oad, Ramchand, committee memberModels that use probability distributions to describe spatial variability within a watershed have been proposed as a parsimonious alternative to fully distributed hydrologic models. This study evaluates the performance of a probability-distributed model that simulates local and spatial average soil moisture in a watershed. The model uses well-known expressions for infiltration, evapotranspiration, and groundwater recharge to describe soil moisture dynamics at the local scale. Then, the spatial mean soil moisture is simulated by integrating the local behavior over a probability distribution that characterizes the spatial variability of soil saturation. Ultimately, the model requires time series for precipitation and potential evapotranspiration and calibration of six parameters to simulate the dynamics of the spatial average soil moisture. The model is applied to the Fort Cobb watershed in Oklahoma using one year of data from September 2005 through August 2006. Model performance is evaluated in three main ways. First, the model's ability to reproduce observed local and spatial average soil moisture through calibration is examined. Second, the identifiability and stability of the parameter values are evaluated to assess parameter uncertainty and errors in the mathematical structure of the model. Third, the identifiability and stability of the sensitivities to changes in annual precipitation and potential evapotranspiration are evaluated to assess the impacts of parameter uncertainty and structural errors on forecasts for unobserved conditions. At the local scale, the calibrated model reproduces the soil moisture with a similar degree of accuracy as a more physically-based model (HYDRUS ID), and both models exhibit some structural errors. For the spatial average soil moisture, the calibration is acceptable simulating soil moisture with a similar degree of accuracy as the model applied at the local scale. Among all the parameters, the standard deviation of soil saturation is the most stable and identifiable. The probability-distributed model produces a relatively wide range of plausible sensitivities for both the local soil moisture and the spatial mean soil moisture, suggesting that parameter uncertainty and model structural errors produce significant uncertainty for unobserved conditions.Item Open Access Evaluation of a surface energy balance method based on optical and thermal satellite imagery to estimate root-zone soil moisture(Colorado State University. Libraries, 2014) Alburn, Nathan E., author; Niemann, Jeffrey D., advisor; Chávez, José L., committee member; Butters, Greg L., committee memberVarious remote-sensing methods are available to estimate soil moisture, but few address the fine spatial resolutions (e.g., 30 m grid cells) and root-zone depth requirements of agricultural and other similar applications. One approach that has been previously proposed to estimate fine-resolution soil moisture is to first estimate the evaporative fraction from an energy balance that is inferred from optical and thermal remote-sensing images (e.g., using the ReSET algorithm) and then estimate soil moisture through an empirical relationship to evaporative fraction. A similar approach has also been proposed to estimate the degree of saturation. The primary objective of this study is to evaluate these methods for estimating soil moisture and degree of saturation, particularly for a semiarid grassland with relatively dry conditions. Soil moisture was monitored at twenty-eight field locations in southeastern Colorado with herbaceous vegetation during the summer months of three years. In-situ soil moisture and degree of saturation observations are compared with estimates calculated from Landsat imagery using the ReSET algorithm. The in-situ observations suggest that the empirical relationships with evaporative fraction that have been proposed in previous studies typically provide overestimates of soil moisture and degree of saturation in this region. However, calibrated functions produce estimates with an accuracy that may be adequate for various applications. The estimates produced by this approach are more reliable for degree of saturation than for soil moisture, and the method is more successful at identifying temporal variability than spatial variability in degree of saturation for this region.Item Open Access Evaluation of parameter and model uncertainty in simple applications of a 1D sediment transport model(Colorado State University. Libraries, 2011) Sabatine, Shaina M., author; Niemann, Jeffrey D., advisor; Greimann, Blair, committee member; Hoeting, Jennifer, committee memberThis paper aims to quantify parameter and model uncertainty in simulations from a 1D sediment transport model using two methods from Bayesian statistics. The first method, Multi-Variable Shuffled Complex Evolution Metropolis - Uncertainty Analysis (MSU), is an algorithm that identifies the most likely parameter values and estimates parameter uncertainty for models with multiple outputs. The other method, Bayesian Model Averaging (BMA), determines a combined prediction based on three sediment transport equations and evaluates the uncertainty associated with the selection of a transport equation. These tools are applied to simulations of three flume experiments. Results show that MSU's ability to consider correlation between parameters improves its estimate of the uncertainty in the model forecasts. Also, BMA results suggest that a combination of transport equations usually provides a better forecast than an individual equation, and the selection of a single transport equation substantially increases the overall uncertainty in the model forecasts.Item Open Access Evaluation of the portability of an EOF-based method to downscale soil moisture patterns based on topographical attributes(Colorado State University. Libraries, 2011) Busch, Frederick A., author; Niemann, Jeffrey D., advisor; Green, Tim, committee member; Kampf, Stephanie K., 1975-, committee memberSoil moisture influences many hydrologic applications including agriculture, land management, and flood prediction. Most remote-sensing methods that estimate soil moisture produce coarse-resolution patterns, so methods are required to downscale such patterns to the resolutions required by these applications (e.g., 10-30 m grid cells). At such resolutions, topography is known to impact soil moisture patterns. Although methods have been proposed to downscale soil moisture based on topography, they usually require the availability of past high-resolution soil moisture patterns from the application region. The objective of this paper is to determine whether a single topographic-based downscaling method can be used at multiple locations without relying on detailed local observations. The evaluated downscaling method is developed based on empirical orthogonal function (EOF) analysis of space-time soil moisture data at a reference catchment. The most important EOFs are then estimated from topographic attributes and the associated expansion coefficients (ECs) are estimated based on the spatial-average soil moisture. To test the portability of this EOF-based method, it is developed separately using four datasets (Tarrawarra, Tarrawarra2, Cache la Poudre, and Satellite Station), and the relationships that are derived from these datasets to estimate the EOFs and ECs are compared. In addition, each of these downscaling methods is applied not only for the catchment where it was developed but also to the other three catchments. The results suggest that the EOF downscaling method performs well for the location where it is developed, but its performance degrades when applied to other catchments.Item Open Access Hydrologic alteration under hydropower dam operations and climate change: a case study in the Sesan River Basin, Lower Mekong Region(Colorado State University. Libraries, 2023) Ghalley, Wangmo, author; Niemann, Jeffrey D., advisor; Shrestha, Sangam, advisor; Ettema, Robert, committee member; Poff, N. LeRoy, committee memberHydropower dam developments exacerbated by climate change can significantly disrupt the natural flow regimes, leading to adverse effects on river ecosystems. The Sesan River, a major tributary of the Lower Mekong Basin, is renowned for its diverse biomes and is an important resource for nearby inhabitants. Rapid expansion of hydropower dams has occurred in recent years, but the hydrologic impacts remain poorly understood, particularly when combined with the effects of climate change. This study assessed the hydrologic alterations in Sesan River streamflow due to hydropower dams and potential climate change. Daily streamflow in the Sesan River was simulated using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS), which was calibrated and evaluated based on streamflow observations. Climate change projections were based on daily precipitation and temperature, which were estimated using an ensemble of three Earth system models from the Coupled Model Intercomparison Project Phase-6 under two Socioeconomic Pathways: SSP2-4.5 (Middle of the road) and SSP5-8.5 (Fossil-fueled development). Future projections spanned 2025 to 2100, which was divided into three 25-year periods called the Near Future (NF), Mid-Future (MF), and Far Future (FF). The projections were compared to a 30-year baseline (BL) period from 1984 to 2014. Results show a consistent rise in both precipitation and temperature for the Sesan basin across all future periods and SSP scenarios. Precipitation is projected to increase by 4% to 13% for SSP2-4.5 and 7% to 29% for SSP5-8.5. Minimum temperature is projected to increase by 8% to 16% for SSP2-4.5 and 10% to 26% for SSP5-8.5, and maximum temperature is projected to increase by 3% to 7% for SSP2-4.5 and 3% to 12% for SSP5-8.5. Hydrologic alterations were assessed using the Range of Variability Approach (RVA) within the Indicators of Hydrologic Alteration (IHA). The impact of dams was assessed by comparing streamflow with dams and without dams during the BL period. The dams significantly altered the hydrograph characteristics by decreasing the high flows and increasing the low flows. The overall alteration due to dams fell within the "moderate" category. The impact of climate change was assessed by comparing streamflow without dams between the BL and the future periods. Climate change increased the high flow rates, with the impact limited to September in the NF but impacting much of the year in the MF and FF periods. Another notable change was the shift in the timing of peak flow from August in the BL to September in the future periods. The hydrologic alteration due to climate change fell within the "low" category. Finally, the combined impact of dams and climate change was assessed by comparing the BL streamflow without dams to future streamflow with dams. Dams were found to mitigate some impacts of climate change by smoothing extreme high flows, especially in the FF period. Overall, the combined impact showed greater alteration than the individual scenarios but fell within the "moderate" category.Item Open Access Identification and characterization of dendritic, parallel, pinnate, rectangular and trellis networks based on deviations from planform self-similarity(Colorado State University. Libraries, 2006) MejÃa, Alfonso I., author; Niemann, Jeffrey D., advisor; Wohl, Ellen, committee member; RamÃrez, Jorge A., committee memberGeomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification methods are scale dependent and have no connection to an underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks based on their deviations from self-similarity. The planform geometry of dendritic networks is known to be self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to ten networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to be self-affine with Hurst exponents around 0.8 and 0.7, respectively. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test classification trees, which are found to perform well in classifying networks.Item Open Access Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief(Colorado State University. Libraries, 2016) Cowley, Garret S., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Butters, Gregory, committee memberMapping of soil moisture is important for many applications such as flood forecasting, soil protection, and crop management. Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple precipitation and PET downscaling methods are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek Watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables, and the fine-resolution soil moisture estimates improve when these downscaling methods are used. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.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.