Watershed In Oklahoma Research Articles

Using regional characteristics to improve uncalibrated estimation of rootzone soil moisture from optical/thermal remote-sensing

Remote-sensing methods based on optical and thermal satellite imagery have been proposed to estimate fine-resolution (30 m) rootzone soil moisture θ¯. In these methods, θ¯ is most commonly estimated using a single empirical relationship with evaporative fraction ΛSEB or evaporative index ΛPET. Methods have been proposed recently to estimate these relationships based on regional climate, soil, and vegetation characteristics, but those methods have not yet been applied to estimate θ¯ from remote sensing data. The objective of this study is to evaluate the θ¯ estimates from remote sensing when the ΛSEBvs.θ¯ and ΛPETvs.θ¯ relationships are inferred from regional characteristics using the previously proposed methods. Four study regions are considered including the Walnut Gulch Experimental Watershed in Arizona, the Piñon Canyon Maneuver Site and Lower Arkansas River Valley in Colorado, the Little Washita and Fort Cobb Experimental Watersheds in Oklahoma, and the Mississippi Delta region in Mississippi. The θ¯ estimates from the regionally adapted relationships are compared to θ¯ estimates from the single empirical relationship and to in situ θ¯ measurements. The estimates from the regionally adapted relationships consistently outperform the estimates form the single empirical relationship. The performance typically improves as more regional information is used in the relationships and reduces the root mean squared error of θ¯ by an average of 45% among the four regions. The regional method typically performs better for the arid and semiarid regions with root mean squared errors of 0.05 cm3cm−3 and 0.04 cm3cm−3, respectively. The regionally adapted relationships better capture both spatial and temporal variations of soil moisture than the single empirical relationship.

Quantifying Water Fluxes of Irrigated Fields in an Agricultural Watershed in Oklahoma

AbstractEvaluating the adequacy and efficiency of irrigation practices and identifying potential irrigation management improvements in agricultural watersheds require accurate estimates of water fl...

An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals

AbstractIn the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9- or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9- and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2) Walnut Gulch Watershed in Arizona; 3) Little Washita Watershed in Oklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network in China, and 7) the OzNet in Australia. Soil moisture measurements of the in situ networks were upscaled to the corresponding SMAP reference pixels at 9 km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSE when comparing to the matched-up in situ datasets as well. In general, this method is promising for operational generation of fine-resolution soil moisture data product.

Field scale nitrogen load in surface runoff: Impacts of management practices and changing climate

The use of Nitrogen (N) fertilizer boosted crop production to accommodate 7 billion people on Earth in the 20th century but with the consequence of exacerbating N losses from agricultural landscapes. Land management practices that can prevent high N load are constantly being sought for mitigation and conservation purposes. This study was aimed at evaluating the impacts of different land management practices under projected climate scenarios on surface runoff linked N load at the field scale level. A framework to analyze changes in N load at a high spatiotemporal resolution under high greenhouse emission climate projections was developed using the Pesticide Root Zone Model (PRZM) for the Willow Creek Watershed in the Fort Cobb Experimental Watershed in Oklahoma. Specifically, 12 combinations of land management and climate scenarios were evaluated based on their N load via surface runoff from 2020 to 2070. Results showed that crop rotation practices lowered both the N load and the probability of high N load events. Spring application reduced the negative effects in summer and fall from other land management practices but at the risk of increased probability of generating high N load in April and May. The fertilizer application rate was found to be the most critical factor that affected the amount and the probability of high N load events. By adopting a target application management approach, the monthly maximum N can be decreased by 13% while the annual mean N load by 6%. The model framework and analysis method developed in this research can be used to analyze tradeoffs between environmental welfare and economic benefits of N fertilizer at the field scale level.

Projected Climate Could Increase Water Yield and Cotton Yield but Decrease Winter Wheat and Sorghum Yield in an Agricultural Watershed in Oklahoma

Climate change impacts on agricultural watersheds are highly variable and uncertain across regions. This study estimated the potential impacts of the projected precipitation and temperature based on the downscaled Coupled Model Intercomparison Project 5 (CMIP-5) on hydrology and crop yield of a rural watershed in Oklahoma, USA. The Soil and Water Assessment Tool was used to model the watershed with 43 sub-basins and 15,217 combinations of land use, land cover, soil, and slope. The model was driven by the observed climate in the watershed and was first calibrated and validated against the monthly observed streamflow. Three statistical matrices, coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and percentage bias (PB), were used to gauge the model performance with satisfactory values of R2 = 0.64, NS = 0.61, and PB = +5% in the calibration period, and R2 = 0.79, NSE = 0.62, and PB = −15% in the validation period for streamflow. The model parameterization for the yields of cotton (PB = −4.5%), grain sorghum (PB = −27.3%), and winter wheat (PB = −6.0%) resulted in an acceptable model performance. The CMIP-5 ensemble of three General Circulation Models under three Representative Concentration Pathways for the 2016–2040 period indicated an increase in both precipitation (+1.5%) and temperature (+1.8 °C) in the study area. This changed climate resulted in decreased evapotranspiration (−3.7%), increased water yield (23.9%), decreased wheat yield (−5.2%), decreased grain sorghum yield (−9.9%), and increased cotton yield (+54.2%) compared to the historical climate. The projected increase in water yield might provide opportunities for groundwater recharge and additional water to meet future water demand in the region. The projected decrease in winter wheat yield—the major crop in the state—due to climate change, may require attention for ways to mitigate these effects.

Quantifying Sediment Provenance Using Multiple Composite Fingerprints in a Small Watershed in Oklahoma.

Quantitative information on sediment provenance is needed for improved calibration and validation of process-based soil erosion models. However, sediment source data are often limited due to difficulties in directly measuring source contributions at a watershed scale. Our objectives in this study were to estimate sediment source contributions in a 15-km watershed using analytical solutions to a three end-member mixing model using multiple composite fingerprints and to compare the results with those estimated with a single radionuclide, Cs. Surface soil samples were collected from 23 croplands, 19 rangelands, and 26 gully banks in the watershed, and 31 geochemical elements were analyzed for each sample. The elements served as tracers and were screened using statistical tests and range checks. The mean concentrations of all the nonconflict tracer pairs were used in the mixing model to calculate source contributions for the three sources. Results showed that although source contributions were strongly influenced by topography and land use, gully or subsoil erosion was found to be the main source of fine sediment in most subwatersheds. This study demonstrates that estimated source contributions may vary substantially among different composite fingerprints and that the use of multiple composite fingerprints greatly improves accuracy while reducing uncertainty. The source contributions estimated using multiple composite fingerprints agreed well with those estimated with Cs, with a correlation coefficient of 0.69 for gully contributions. This good agreement increases our confidence in using the multiple composite fingerprint method to identify sediment provenance in relatively small watersheds.

Upper washita river experimental watersheds: nutrient water quality data.

Climate variability, changing land use and management, and dynamic policy environments are the main reasons why long-term research is needed to understand and predict possible water quality outcomes to alternative future scenarios. Long-term water quality data sets are needed to address these water issues. Such data sets were acquired by the USDA-ARS in three watersheds in Oklahoma: the Southern Great Plains Research Watershed (SGPRW), the Little Washita River Experimental Watershed (LWREW), and the Fort Cobb Reservoir Experimental Watershed (FCREW). We provide: (i) a description of these water quality data sets, (ii) the sample collection and processing procedures used and an assessment of the data quality, (iii) summary analyses of the variability in each data set, and (iv) details about how to access these data sets. Water quality data collection in the SGPRW began in the 1960s and continued through 1978, while that in the LWREW covered the 1960s to 1990 period. Data collection began in the FCREW in 2004 and continues through the present. The data were collected from streams, unit source watersheds, groundwater wells, and reservoirs. The water quality data described for a given site are generally complete for a given period of record; however, not all sites were monitored continuously and were not necessarily analyzed for the same water quality parameters. These data sets are expected to improve modeling and assessments of conservation practices in relation to climate variability, land use changes, and other environmental factors and may be useful in developing strategies to mitigate these environmental impacts.

Upper washita river experimental watersheds: multiyear stability of soil water content profiles.

Scaling in situ soil water content time series data to a large spatial domain is a key element of watershed environmental monitoring and modeling. The primary method of estimating and monitoring large-scale soil water content distributions is via in situ networks. It is critical to establish the stability of in situ networks when deploying them to study hydrologic systems. Two watersheds in Oklahoma, the Little Washita River Experimental Watershed (LWREW) and the Fort Cobb Reservoir Experimental Watershed (FCREW), are two prime examples of well-equipped research watersheds that provide long-term measurements of atmospheric and soil water content from in situ networks. The soil water content measurement network on the LWREW has been in operation since 2002, with 20 stations available for investigating soil water dynamics at the watershed scale. Temporal stability analysis of the network is complicated by the changing configuration of the network, but it is possible to determine a singular long-term average for the network. The FCREW consists of 15 soil water content stations and began operation in 2007, providing detailed information across a mixed agricultural domain and was determined to be stable and representative of the region. This study reinforces the applicability of temporal stability analysis to very long time scales, which are now becoming available for soil moisture monitoring. Each of these networks is temporally stable with respect to soil water content at each depth on a spatial basis. The LWREW has a persistent pattern through the root zone profile, but the FCREW does not, which requires further investigation.

Development and testing of an in-stream phosphorus cycling model for the soil and water assessment tool.

The Soil and Water Assessment Tool is widely used to predict the fate and transport of phosphorus (P) from the landscape through streams and rivers. The current in-stream P submodel may not be suitable for many stream systems, particularly those dominated by attached algae and those affected by point sources. In this research, we developed an alternative submodel based on the equilibrium P concentration concept coupled with a particulate scour and deposition model. This submodel was integrated with the SWAT model and applied to the Illinois River Watershed in Oklahoma, a basin influenced by waste water treatment plant discharges and extensive poultry litter application. The model was calibrated and validated using measured data. Highly variable in-stream P concentrations and equilibrium P concentration values were predicted spatially and temporally. The model also predicted the gradual storage of P in streambed sediments and the resuspension of this P during periodic high-flow flushing events. Waste water treatment plants were predicted to have a profound effect on P dynamics in the Illinois River due to their constant discharge even under base flow conditions. A better understanding of P dynamics in stream systems using the revised submodel may lead to the development of more effective mitigation strategies to control the impact of P from point and nonpoint sources.

Soil moisture at watershed scale: Remote sensing techniques

Summary Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low – around 25 km. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) as well as SMOS (Soil Moisture and Ocean Salinity satellite) to produce the 1 km downscaled soil moisture over the Little Washita Watershed in Oklahoma for the growing season in 2010 and 2011.The disaggregated soil moisture has been compared to in situ observations. The results of this approach are very encouraging.

Environmental effects of agricultural conservation: A framework for research in two watersheds in Oklahoma's Upper Washita River Basin

Agriculture in the Upper Washita River Basin represents mixed crop-livestock systems of the Southern Plains. Research in the Little Washita River Experimental Watershed and the Fort Cobb Reservoir Experimental Watershed addresses interactive effects of variable climate, land use, and management on environmental quality. The Little Washita River watershed provides opportunities to explore impacts of flood retarding impoundments within a watershed. The Fort Cobb Reservoir watershed provides opportunities to study effects of agricultural conservation on a large eutrophic reservoir. Analysis of 1940 to 2005 data from the Fort Cobb Reservoir watershed showed that precipitation increased 33%, corresponding runoff increased 101%, and sediment yield increased 183% when comparing multi-year wet periods to multi-year dry periods. Depth to groundwater exhibited seasonal and interannual variation. A rapid geomorphic assessment indicated that unstable stream channels dominate the stream networks. Phosphorus concentration in streams was correlated to multiple attributes of the contributing areas, including contributing area, slope, stream density, and channel stability. Anticipated outcomes are improved understanding of environmental effects of conservation, new approaches to mitigation of water quality problems, and tools to support strategic placement of conservation practices on the landscape to achieve environmental goals.

Suitability of SWAT for the Conservation Effects Assessment Project: Comparison on USDA Agricultural Research Service Watersheds

Recent interest in tracking environmental benefits of conservation practices on agricultural watersheds throughout the United States has led to the development of the U.S. Department of Agriculture’s (USDA) Conservation Effects Assessment Project (CEAP). The purpose of CEAP is to assess environmental benefits derived from implementing various USDA conservation programs for cultivated, range, and irrigated lands. Watershed scale, hydrologic simulation models such as the Soil and Water Assessment Tool (SWAT) will be used to relate principal source areas of contaminants to transport paths and processes under a range in climatic, soils, topographic, and land use conditions on agricultural watersheds. To better understand SWAT’s strengths and weaknesses in simulating streamflow for anticipated applications related to CEAP, we conducted a study to evaluate the model’s performance under a range of climatic, topographic, soils, and land use conditions. Hydrologic responses were simulated on five USDA Agricultural Research Service watersheds that included Mahantango Creek Experimental Watershed in Pennsylvania and Reynolds Creek Experimental Watershed in Idaho in the northern part of the United States, and Little River Experimental Watershed in Georgia, Little Washita River Experimental Watershed in Oklahoma, and Walnut Gulch Experimental Watershed in Arizona in the south. Model simulations were performed on a total of 30 calibration and validation data sets that were obtained from a long record of multigauge climatic and streamflow data on each of the watersheds. A newly developed autocalibration tool for the SWAT model was employed to calibrate eleven parameters that govern surface and subsurface response for the three southern watersheds, and an additional five parameters that govern the accumulation of snow and snowmelt runoff processes for the two northern watersheds. Based on a comparison of measured versus simulated average annual streamflow, SWAT exhibits an element of robustness in estimating hydrologic responses across a range in topographic, soils, and land use conditions. Differences in model performance, however, are noticeable on a climatic basis in that SWAT will generally perform better on watersheds in more humid climates than in desert or semidesert climates. The model may therefore be better suited for CEAP investigations in wetter regions of the eastern part of the United States that are predominantly cultivated than the dryer regions of the West that are more characteristically rangeland.

Use of limited soil property data and modeling to estimate root zone soil water content

Estimation of soil water content in the root zone with time in different parts of a watershed is important for both strategic and tactical management of water resources, as well as of agricultural production, water quality, and soil resources. This estimation requires detailed knowledge of rainfall intensities and meteorological variables over space and time, as well as the physical and hydraulic properties of the soil horizons and plant growth information. However, all this detailed spatial information is extremely expensive and time consuming to obtain. New technologies are helping to increase the spatial sampling of rainfall and other meteorological variables, but spatially detailed measurement of soil properties is still not practical. The best we can obtain from the existing soil survey database is the spatial distribution of soil textural class. We investigated the use of a hierarchy of limited soil input data, ranging from soil textural class of soil horizons alone, to measured soil texture and bulk densities of horizons, additional lab or field measurement of −33 kPa soil water content, to additional field measurement of average saturated hydraulic conductivity. These five modeling scenarios, along with meteorological and plant information, were input to the Root Zone Water Quality Model (RZWQM) to estimate 0–60 cm soil water content over a 30-day period in 1997 at the Little Washita River Experimental Watershed in Oklahoma. The estimated water contents were compared with time-domain reflectometry (TDR) profile measurements and gravimetric samplings of soil surface moisture. In addition to the five scenarios using limited input data, a more detailed set of data based on laboratory measured soil water retention curves and field measured saturated conductivity was supplied to the model for all Brooks–Corey function parameters (full description mode). Estimates of root zone soil water content using detailed input were compared to estimates obtained using minimum input data. Adjustments in specific hydraulic parameters were also made in an effort to calibrate the model to the soils in this region. Overall, reasonable agreement was found between TDR-measured and RZWQM-predicted average water contents for 0–60 cm depths. Surprisingly, the smallest errors in the predicted water contents were achieved using either the textural class only or the hydraulic properties determined in situ, with root mean square errors ranging from 0.012 to 0.018 m 3 m −3. Hence, the model provided adequate estimates of average profile soil water content based on textural class-name only which was considered the most limited input data condition.

Soluble potassium transport in agricultural runoff water

Potassium transport in runoff from three agricultural soils was investigated in laboratory and field experiments using a kinetic equation describing soil K desorption. In the laboratory, this equation was used to study the effect of rainfall intensity and soil slope, cover, and residue incorporation on the effective depth of interaction between surface soil and runoff ( edi), an important parameter in the kinetic equation. Using simulated rainfall, edi increased linearly (from 1.6 to 22.0 mm) with an increase in rainfall intensity (50 to 160 mm h −1) and soil slope (2 to 20%). edi was reduced an average 82% following incorporation of 5.0 t ha −1 of wheat straw ( Triticum aestivum L. sp.) and 44% by a 0.5-mm 2 mesh screen, simulating crop cover, compared to the control (4.5 mm). This reduction was attributed to a decreased turbulent mixing of water at the soil surface brought about by an increased physical protection of soil. For all soils and treatments, edi was logarithmically related to soil loss ( r 2 = 0.80), allowing estimation of edi under variable rainfall intensity, soil slope, type, and cover conditions from measured or estimated soil loss. The values of edi, thus estimated were used in the kinetic equation to predict solution K transport in runoff from eight agricultural watersheds in Oklahoma, U.S.A. Measured and predicted mean annual flow-weighted concentrations of solution K were not significantly different and were strongly correlated ( r 2 = 0.92). These results improve our capability to predict K transport in runoff under field conditions. These methods could also be applied to other agricultural chemicals transported in runoff and results used to improve management of soil and water.

Nutrient Runoff Losses as Predicted by Annual and Monthly Soil Sampling

AbstractThe transport of soluble and particulate P and N in runoff from several cropped and grassed watersheds in Oklahoma and Texas was related to the nutrient content of surface soil (0–50 mm) sampled monthly and annually (March) over a 2‐yr period in an effort to improve the prediction of nutrient transport. For both monthly and annual soil samplings, the prediction of soluble P was good for events > 0.75 mm runoff (r values ranged from 0.57 to 0.92), but smaller events were poorly predicted. The nitrate content of runoff was not closely related to surface soil content (r vlaues ranged from 0.17 to 0.58), precluding its accurate prediction in runoff. Prediction of both particulate P and total N concentrations of individual runoff events was improved using separate equations for grassed and cropped watersheds compared to a general equation. Even so, predicted mean annual flow‐weighted concentrations and amounts of soluble P, particulate P, and total N using either monthly or annual soil nutrient data were significantly related to measured values at the 0.01 level and above (r values ranged from 0.57 to 0.97). Results indicate that seasonal variation in soil nutrient content had less effect on the predictive equations than rainfall and management characteristics. Consequently, these characteristics must be accounted for before the transport equations can be reliably used on an individual runoff event basis.

Sediment Yield and Storm Flow Response to Clear‐Cut Harvest and Site Preparation in the Ouachita Mountains

Clear‐cut harvest and site preparation, including crushing the residual vegetation, burning, and contour ripping, were applied to three small watersheds in Oklahoma. Three undisturbed forested watersheds served as controls. First‐year sediment losses averaged 282 and 36 kg/ha and storm flow water yields averaged 22.9 and 31.7 cm on clear‐cut and uncut watersheds, respectively. The lower water yield on cut watersheds may have been due to contour ripping. Treatment differences in sediment yield were significant the second and third, but not the fourth, year following treatment. Storm flow water yield from clear‐cut watersheds was significantly higher than from uncut watersheds the second year but not the third or fourth years. Absolute levels of sediment yield from clear‐cut and control watersheds were relatively low in all years of the study. The overall impact of harvest and site preparation on total suspended solids levels in runoff was small and short‐lived.

PREDICTING BASE FLOW USING HYDROGEOLOGIC PARAMETERS1

ABSTRACT: A method is presented for predicting base flow using easily measured, or estimated, hydrogeologic parameters. A mathematical model based upon the theory of subsurface flow to parallel drains is applied to a small watershed in Oklahoma. An example of model application is presented for a five‐year period of record from this small watershed. Three years of data are used to calibrate the model, and two years of data are used for model validation. Hydrographs of observed and predicted base flow are presented for the five‐year period of record.We concluded from this limited application of the model, on a small watershed, that the modeling techniques discussed herein were valid and should be tested for longer time periods on a larger watershed to determine their general applicability.

Nutrient and Sediment Discharge from Agricultural Watersheds in Oklahoma

AbstractSeven cropland watersheds and four rangeland watersheds in central Oklahoma were monitored for surface hydrology and discharge of nitrogen, phosphorus, and sediment over a 1 year period. Precipitation and runoff were much above normal during the study. Sediment losses from the continuously grazed rangeland watersheds ranged from 18 to 23 metric tons/ha during the study. None of the sediment losses from the other watersheds exceeded 10 metric tons/ha.Total nutrients discharged in runoff ranged from 2 to 15 kg/ha of N and 1 to 11.5 kg/ha of P. Flow‐weighted mean concentrations ranged from 1 to 6 ppm of total N, 0.2 to 1.9 ppm of nitrate‐N, 0.5 to 4.8 ppm of total P, and 0.04 to 0.9 ppm of soluble P. Runoff losses of soluble inorganic nitrogen were generally less than those quantities received in rainfall. Concentrations of soluble phosphorus in runoff from the cropland watersheds were much greater than from the rangeland watersheds. Losses of fertilizer nitrogen and phosphorus did not exceed 5% of the most recent applications, although surface runoff was 4‐ to 10‐fold greater than that observed in previous years.