Browsing by Author "Green, Timothy R., committee member"
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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 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 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 ̄) 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 ̄ and Λ_PET " vs." θ ̄ to estimate rootzone soil moisture (θ ̄) 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 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 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 Open Access Modeling and field evaluation of the strength of surface soils for vehicle mobility(Colorado State University. Libraries, 2019) Pauly, Matthew J., author; Scalia, Joseph, advisor; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Butters, Gregory, committee memberSurficial soil strength is a critical variable in vehicle mobility and terrain trafficability analysis and varies substantially in time and space with soil moisture and texture. Fine-resolution (5-30 m grid cell) patterns of soil strength and soil moisture are necessary for routing of off-road vehicle operations and must be estimated for applications when direct measurement is too expensive, labor-intensive, or dangerous. Rating cone index (RCI) is the in-situ method typically used in mobility applications to empirically evaluate the strength of surficial soils. The RCI method provides one simple parameter to evaluate soil trafficability, but in doing so fails to separately characterize the various mechanisms (compressibility, stress independent shear strength, stress dependent shear strength) that govern soil behavior in relation to vehicle traffic. Alternatively, the Bekker soil strength framework, which encompasses pressure-sinkage and shear strength soil properties, offers a mechanics-based representation of soil behavior and has received increased interest from the terramechanics community in recent years. However, because RCI has been the focus of the terramechanics community over several decades, predictive relationships to estimate Bekker parameters using basic spatially- and temporally-variable input data (soil moisture and soil composition) do not exist. The objective of this study is to develop and evaluate a framework for prediction of Bekker parameters (cohesion and friction angle) as a function of soil moisture and soil texture (percentage of sand and clay). A model, termed the Strength of Surface Soils (STRESS) model, is introduced to estimate shear strength of surface soils using soil moisture, pedotransfer functions based on soil texture, and unsaturated soil mechanics. The STRESS model is paired with an existing soil moisture downscaling model, the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model. The pre-existing EMT+VS model includes two untested simplifications that make the model inconsistent with the STRESS model framework, so two previously neglected soil-related hydrologic considerations are introduced to the EMT+VS model: runoff and residual water content. The impacts of runoff and residual on soil moisture downscaling performance and spatial patterns of soil moisture are assessed at a test region in northeastern Colorado called Drake Farm with measured soil moisture data for model calibration and evaluation. The additions are successfully included in the EMT+VS model but the assumptions made in the pre-existing EMT+VS model are shown to be adequate for soil moisture downscaling. After assessing EMT+VS model additions, the STRESS model is applied to Drake Farm to produce spatial patterns of estimated friction angle and cohesion. Model estimates are compared to measured shear strength using a human-powered shear strength bevameter to evaluate the predictive capability of the STRESS model. The model is found to underpredict friction angle and overpredict cohesion at Drake Farm due in part to the use of class-average effective shear strength parameters that do not appear to adequately reflect the properties of surficial soils. Finally, the design and construction of two bevameters are summarized for field and laboratory measurement of Bekker parameters. The results of laboratory tests on the human-powered shear strength bevameter used in STRESS model evaluation are compared to traditional geotechnical strength testing to validate field-testing results and ensure repeatability of measurements. Additionally, the design and construction of a fully automated, laboratory-focused bevameter device with pressure-sinkage and shear strength testing capabilities are described, but this bevameter is not used for testing in this study.Item Open Access Procedure for measurement of surficial soil strength via bevameter(Colorado State University. Libraries, 2020) Bindner, Joseph R., author; Scalia, Joseph, IV, advisor; Niemann, Jeffrey D., advisor; Butters, Gregory, committee member; Green, Timothy R., committee memberSpatial prediction of moisture-variable soil strength is critical for forecasting the trafficability of vehicles across terrain. The Strength of Surface Soils (STRESS) model calculates soil strength properties as a function of soil texture from SSURGO data (or locally available data) and soil moisture from the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model. The STRESS model yields soil strength properties (friction angle and moisture-variable cohesion) that vary with soil texture and moisture conditions. However, the STRESS model is hindered by a lack of surficial soil strength data linked directly to soil texture. The objective of this study is to develop and validate a bevameter procedure to improve measurement of near-surface moisture-variable soil strength. The bevameter is a test apparatus that measures in-situ surficial soil strength properties by rotational shearing of a shear annulus under a constant normal force at a constant rate. The bevameter allows for lab or field determination of Mohr-Coulomb surficial soil strength properties at a given moisture content in a manner that approximates how vehicles interact with surficial soils. Experimental variables evaluated include the shearing surface (grousers, sandpaper, or bonded angular sand) and the use of interior and exterior annular surcharge weights to minimize slip sinkage of the shear annulus. Based on the results of this study, a bevameter procedure is recommended that uses a coarse sandpaper as the shear interface with an internal and external surcharge of 2 kPa during shear testing. Using the revised bevameter procedure for field testing, the performance of predicted moisture-variable soil strength by the STRESS model is evaluated. Field validation illustrates the need to develop surficial-soil specific pedotransfer functions for use in the STRESS model.Item Open Access Spatial analysis of soil moisture at the catchment scale with applications for estimation and interpolation(Colorado State University. Libraries, 2006) Perry, Mark A., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Smith, Freeman M., committee memberThe spatial distribution of soil moisture is important to numerous applications in hydrology, agriculture, ecology and climatology. Soil moisture is a state variable for many physical processes in these fields such as infiltration and transpiration. Because these processes often have non-linear relationships with soil moisture, they depend on the spatial variation of soil moisture. The spatial variation of soil moisture can be complex because it can change through time. The goal of this thesis is to characterize the time varying properties of soil moisture patterns, from which to develop improved soil moisture estimation and interpolation methods. Here, soil moisture patterns are studied using Empirical Orthogonal Function (EOF) analysis. EOF analysis decomposes space-time variability into a series of time-invariant spatial patterns (EOFs) and spatially-invariant time series called expansion coefficients (ECs). This method is applied to soi l moisture data from the 10.5 ha Tarrawarra catchment in Australia. High-resolution soil moisture patterns are available for 13 days, spanning 14 months. The analysis shows that three EOFs explain 70% of the dataset variation. Connections are drawn between these EOFs and hydrologic processes that affect soil moisture. In particular, the most important EOF (EOF1) is most highly correlated with the topographic wetness index, which is conceptually related to steady lateral flow. The second most important EOF (EOF2) is most highly correlated with the potential solar radiation index, which is related to evapotranspiration. The third most important (EOF3) is most highly correlated to elevation and is related to the seasonal wetting-up and drying-down of the catchment. The EOFs and ECs are used for the purposes of estimation and interpolation. In the estimation problem, an estimate of the soil moisture pattern is desired for a time when only the spatial average soil moisture is known. It is assumed that the site's EOFs can be derived from fine resolution soil moisture data collected in a previous, short field campaign. The EC values are estimated from empirical relationships with the average soil moisture. Here, only ECs 1 and 2 are considered to be predictable through time, but this may be due to the limited temporal size of the dataset. An estimated soil moisture pattern is constructed form the average soil moisture, the observed EOFs 1 and 2, and the estimated ECs 1 and 2. This EOF-based estimation method is shown to outperform other available methods. Likewise in the interpolation problem, soi l moisture patterns are observed only at a coarse scale and a high resolution pattern is desired. In this case, the ECs from the coarse data are used directly, and the EOFs from the coarse data are interpolated to a higher resolution using either a distance-based method or multiple linear regression with topographic attributes. For spatial interpolation the number of useful EOFs is shown to vary with the coarse data spacing, but up to 4 EOFs are useful here. The EOF-based soil moisture interpolation provides better estimates of the fine-scale soil moisture patterns than direct soil moisture interpolation because the EOFs exhibit more consistent spatial behavior than measured soil moisture. This study shows that EOFs exhibit stronger topographic dependence than soil moisture, because important variation at Tarrawarra is related to topography and is partitioned into low order EOFs. Less important sources of variation and random noise are partitioned into high order EOFs. Low order EOFs are shown to exhibit distinct and higher linear correlations with common topographic attributes than soil moisture itself. Likewise in a geostatistical analysis, low order EOFs are shown to exhibit distinct and more consistent va1iogram functions than soil moisture. Previous studies have noted the difficulty of quantifying the time-varying relationship between dynamic soil moisture patterns and static topography. This study shows that time-invariant EOF patterns exhibit time-stable relationships to topography. The time-varying nature of the soil moisture-topography relationship can be quantified by the associated ECs. Finally, this thesis presents opportunities for future research. The Tarrawarra catchment has a strong seasonal climate, as well as spatially uniform soils and vegetation. Future studies should apply similar EOF analysis to sites without seasonal variation, with non-uniform vegetation and with non-uniform soils. In addition, analysis of the temporal behavior of ECs was limited here due to the dataset' s small temporal dimension. Unfortunately, there is a scarcity of soil moisture datasets with large space and time dimensions. One possible solution is computer simulation of soil moisture data. Simulation of large amounts of soil moisture data could allow better characterization of ECs. Based on results here, it is anticipated that ECs will exhibit more certain temporal behavior than soil moisture. This should allow better soil moisture forecasting when time-series modeling is done on ECs instead of on soil moisture itself.Item Open Access Stochastic analysis and probabilistic downscaling of soil moisture(Colorado State University. Libraries, 2018) Deshon, Jordan P., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Cooley, Daniel S., committee memberMany applications require fine-resolution soil-moisture maps that exhibit realistic statistical properties (e.g., spatial variance and correlation). Existing downscaling models can estimate soil-moisture based on its dependence on topography, vegetation, and soil characteristics. However, observed soil-moisture patterns also contain stochastic variations around such estimates. The objectives of this research are to perform a geostatistical analysis of the stochastic variations in soil moisture and to develop downscaling models that reproduce the observed statistical features while including the dependence on topography, vegetation, and soil properties. Extensive soil-moisture observations from two catchments are used for the geostatistical analysis and model development, and two other catchments are used for model evaluation. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model is used to downscale soil moisture, and the difference between the point measurements and the EMT+VS estimates are considered to be the stochastic variations. The stochastic variations contain a temporally stable pattern along with temporally unstable patterns. All of these patterns include spatially correlated and uncorrelated variations. Moreover, the spatial variance of the stochastic patterns increases with the mean moisture content. The EMT+VS model can reproduce the observed statistical features if it is generalized to include stochastic deviations from equilibrium soil moisture, variations in porosity, and measurement errors. It can also reproduce most observed properties if stochastic variations are inserted directly in its soil moisture outputs. These analyses and downscaling models provide insight into the nature of stochastic variations in soil moisture and can be further tested by application to other catchments and larger regions.Item Open Access Use of global datasets for downscaling soil moisture with the EMT+VS model(Colorado State University. Libraries, 2017) Grieco, Nicholas R., author; Niemann, Jeffrey D., advisor; Green, Timothy R., committee member; Butters, Gregory L., committee memberSatellite remote sensing and land-surface models provide coarse-resolution (9-40 km) soil moisture estimates, but various applications require fine-resolution (10-30 m) soil moisture patterns. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moisture using fine-resolution topography, vegetation, and soil data. It has been shown to reproduce temporally unstable soil moisture patterns (i.e. patterns where the spatial structure varies in time). It can also reproduce hillslope dependent patterns (wetter locations occur on hillslopes oriented away from the sun) and valley dependent patterns (wetter locations occur in valley bottoms). However, the EMT+VS model requires several parameters to characterize the local climate, soil, and vegetation characteristics. In previous applications, the parameters were calibrated using point soil moisture data, but many regions of interest may not have such data. The purpose of this study is to evaluate EMT+VS model performance when the parameters are estimated from global datasets without site-specific calibration. Reliable and accessible global datasets were identified and methods were developed to estimate the parameters from the datasets. The global model (without site-specific calibration) was applied to six study sites, and its results were compared to local soil moisture observations and the results from the locally calibrated model. The use of global datasets decreased downscaling performance and the spatial variability of soil moisture was underestimated. Overall, only 5 of the 16 parameters can be estimated from global datasets. However, the global model still provides more reliable soil moisture estimates than the coarse-resolution input for most sampling dates at all six study sites.