The southern Ogallala Aquifer continues to deplete due to decades of irrigation with minimal recharge. Recently enacted regulations limiting groundwater withdrawals and the potential for farm profitability with cotton production systems indicate driving forces for increased cotton production acreage in the Northern High Plains of Texas (NHPT). This study focused on evaluating the land-use change from corn or winter wheat to cotton under irrigation and dryland conditions in the Palo Duro watershed (PDW) in the NHPT using an improved Soil and Water Assessment Tool (SWAT) model. Land-use change from irrigated corn to irrigated cotton led to reductions in average (2000–2014) annual irrigation, actual evapotranspiration (ETa), and surface runoff by 21%, 7%, and 63%, respectively. Nevertheless, the replacement of irrigated wheat with irrigated cotton caused irrigation and ETa to increase by 46% and 18%, respectively. Land-use conversion from dryland wheat to dryland cotton showed 0.1% and 15% decreases in ETa and surface runoff, respectively. More than 40% reductions in simulated cotton yields were found when the cotton planting area was moving northward to the cooler NHPT. The ongoing change in land use provided an option to lengthen the water availability of the southern Ogallala Aquifer for irrigation.

Fast-response infrared gas analyzers (IRGAs) have been widely used over 3 decades in many ecosystems for long-term monitoring of water vapor fluxes in the surface layer of the atmosphere. While some of the early IRGA sensors are still used in these national and/or regional ecoflux networks, optically improved IRGA sensors are newly employed in the same networks. The purpose of this study was to evaluate the performance of water vapor density and flux data from three generations of IRGAs – LI-7500, LI7500A, and LI-7500RS (LI-COR Bioscience, Inc., Nebraska, USA) – over the course of a growing season in Bushland, Texas, USA, in an irrigated maize canopy for 90 d. Water vapor density measurements were in generally good agreement, but temporal drift occurred in different directions and magnitudes. Means exhibited mostly shift changes that did not impact the flux magnitudes, while their variances of water vapor density fluctuations were occasionally in poor agreement, especially following rainfall events. LI-7500 cospectra were largest compared to LI-7500RS and LI-7500A, especially under unstable and neutral static stability. Agreement among the sensors was best under the typical irrigationcooled boundary layer, with a 14 % interinstrument coefficient of variability under advective conditions. Generally, the smallest variances occurred with the LI-7500RS, and highfrequency spectral corrections were larger for these measurements, resulting in similar fluxes between the LI-7500A and LI-7500RS. Fluxes from the LI-7500 were best representative of growing season ET based on a world-class lysimeter reference measurement, but using the energy balance ratio as an estimate of systematic bias corrected most of the differences among measured fluxes.

Corn (Zea mays L.) was grown under full and deficit irrigation in two research field locations near Bushland, TX, in 2018 to compare seasonal water use of two irrigation management approaches. Full irrigation was achieved in both fields by allowing no more than 55% depletion of plant available soil water. However, irrigation depth and frequency were different in each field. The USDA-ARS Conservation and Production Research Laboratory (CPRL) weighing lysimeter fields were generally irrigated twice weekly using irrigation depths ranging from 19 to 32 mm. The Texas A&M AgriLife Research Emeny field was irrigated only once per week, having greater application depths ranging from 35 to 42 mm. Deficit irrigation treatments of 75% of full irrigation were also performed in both research fields. Yield and crop water productivity values for the 100 and 75% lysimeter field irrigation treatments were greater than corresponding values for the Emeny field. Emeny field yields may have been slightly reduced by heat stress incurred between irrigations during early grain fill whereas more frequent irrigations on the lysimeter fields may have reduced heat stress during that period. Results from this study suggest that evaporative losses associated with the more frequent, smaller application depth irrigations on the lysimeter fields did not contribute to appreciably lower CWP values, as losses were likely mitigated by the rapid development of the corn canopy. These findings suggest that corn yield is principally dependent upon seasonal water inputs and losses from frequent, smaller depth irrigations are minimal outside of incomplete canopy conditions.

Surface energy imbalance problems have been underexplored especially under advective environments. Here, we present a novel analysis of surface energy fluxes from three distinct approaches—weighing lysimeter, eddy covariance (EC), and surface energy balance modeling for the 2014 sorghum and 2016 corn crop seasons in Bushland, Texas. Our results suggest that energy imbalance problems in the EC system were evidently associated with two opposite evapotranspiration (ET) hysteresis patterns with respect to net radiation and water vapor pressure deficit (VPD). The lysimeter ET had the smallest ET hysteresis while the eddy covariance ET results indicated the strongest hysteresis with respect to net radiation. Conversely, the lysimeter ET hysteresis related to water VPD was stronger than that of the eddy covariance method. This study provides understanding of ET hysteresis patterns, energy imbalance, and advective conditions associated with turbulent flux dynamics for both diel and crop growing seasonal timescales in Bushland, Texas.

HighlightsSWAT soil water assessment was performed using soil water measurements.Dryland SWAT model soil water content was greater than the irrigated SWAT model.Using SWAT soil water estimates for real-time (daily) irrigation management purposes with the existing SWAT soil water subroutines and available soils data is considered risky.The surface layer showed the greatest soil water variability compared to deeper layers.Abstract. Soil water content (SWC) is a challenging measurement at the field, watershed, and regional scales. Soil and Water Assessment Tool (SWAT) soil water estimates were evaluated at three locations: the St. Joseph River watershed (SJRW) in northeast Indiana, the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and the USDA-ARS Limited Irrigation Research Farm (LIFR) at Greeley, Colorado. The soil water estimates were evaluated under two scenarios: (1) for the defined soil profile, and (2) by individual layer. Each site’s soil water assessment was performed based on the existing management conditions during each experiment, whether dryland or irrigated, and for various periods depending on SWC measurement availability at each site. The SWAT soil water was evaluated as follows: the Indiana site was evaluated under dryland conditions using daily soil water observations for one year; the Texas site was evaluated for a ten-year period under irrigated and dryland conditions using weekly soil water observations from four lysimeters; and the Colorado site was evaluated under irrigated conditions for a four-year period. The simulated soil water was evaluated by comparing the model simulations with observed daily and weekly soil water measurements at the three sites. Based on the results, even though all the SWAT models were considered to perform as good models following calibration (streamflow, ET, etc.), the soil water simulations were unacceptable for the defined soil profile and for individual layers at the three sites. Deeper soil layers had observations greater than field capacity values, indicating poor soil parameterization. The dryland model had greater water content than the irrigated model, contradicting the soil water measurements. This greater soil water simulation with the dryland model is a result of SWAT model uncertainties with ET reduction under dryland conditions due to water stress. This study indicated that soil water estimation using the default SWAT soil water equations has many sources of uncertainties. Two apparent sources resulted in the SWAT model’s poor performance: (1) SWAT soil water routines that do not fully represent soil water moving between layers to meet plant demand and (2) uncertainty in soil parameterization. Keywords: Hydrologic modeling, Soil moisture, Soil moisture sensor, Soil water, Soil and Water Assessment Tool.

The SWAT model is a well-documented hydrologic model. However, some studies report that the existing SWAT auto-irrigation methods are unable to represent actual irrigation management, particularly in intensively irrigated regions. In the U.S. Great Plains, the SWAT model does not reproduce the management allowed depletion (MAD) irrigation scheduling commonly used by researchers and producers. To this purpose, the SWAT source code has been modified to include the MAD auto-irrigation function. This study evaluated the performance of the soil water content (SWC), corrected soil water content (CSWC), plant water demand (PWD), and MAD auto-irrigation methods in streamflow and irrigation simulations by comparison with observed data. The CSWC and MAD methods performed the best in streamflow simulations with NSE > 0.75 and PBIAS within ±11%. Comparisons of simulated irrigation with the field irrigation also indicated the CSWC and MAD methods outperformed other methods with the NSE > 0.75 and PBIAS within ±5%.

Evapotranspiration (ET) is one of the biggest data gaps in water management due to limited ET measurements, and further, spatial variability in ET is difficult to capture. Satellite-based ET estimation has great potential for water resources planning as it allows estimation of agricultural water use at field, landscape, and watershed scales. However, uncertainties with satellite data derived ET are a major concern. This study evaluates hourly satellite-based ET from 2001–2010 for the growing season (May–October) under irrigated and dryland conditions for both tall and short crops. The evaluation was conducted using observed ET from four large weighing lysimeters at the United States Department of Agriculture Agricultural Research Service (USDA-ARS) Conservation and Production Research Laboratory in Bushland, Texas. Hourly ET from satellite data were derived using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model. Performance statistics showed that satellite-based hourly estimates compared to lysimeter measurements provided good performance with an root-mean-square error(RMSE) of 0.14 mm, Nash–Sutcliffe efficiency (NSE) of 0.57, and R2 of 0.62 for ET for dryland crops, and RMSE of 0.16, NSE of 0.63, and R2 of 0.65 for irrigated crops. METRIC provided accurate hourly ET estimates that may be useful for irrigation scheduling and other water resources management purposes based on the hourly assessment.

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

The use of soil water sensors is commonly advocated to aid and improve irrigation management in crop production systems. However, there are concerns about how sensor type, installation technique, sensor orientation, and soil texture may affect sensor accuracy. A field study was conducted to compare the performance of three commercially available soil water sensors (Acclima 315L, Decagon GS1, and Campbell Scientific 655) and a soil water potential sensor (Watermark 200SS) using different installation orientations of horizontal insertion, laid horizontal placement, and vertical insertion at depths of 15, 46, and 76 cm (6, 18, and 30 in.) in an irrigated clay loam soil field. Results indicated all sensors demonstrated similar trends of soil water content in response to wetting events (precipitation and irrigation) at the 15 cm depth following a 4-month settling period prior from the start of the growing season. Comparatively, the Acclima 315L performed well using horizontal insertion compared to calibrated neutron moisture meters (NMMs) at depths of 46 and 76 cm with R2 of 0.73 and 0.96 and slopes of 1.36 and 1.47, respectively. In addition, water storage in the 0.9 m soil profile integrated using the horizontally inserted Acclima 315L across the three depths matched closely with profile water storage determined by the NMMs with a mean difference (MD) and root mean square error (RMSE) of 25.7 and 36.4 mm. However, site-specific corrections or calibrations for each sensor type are required for accurate soil water content estimations with this clay loam soil for irrigation management applications.

Evapotranspiration (ET) is one of the largest data gaps in water management due to the limited availability of measured evapotranspiration data, and because ET spatial variability is difficult to characterize at various scales. Satellite-based ET estimation has been shown to have great potential for water resource planning and for estimating agricultural water use at field, watershed, and regional scales. Satellites with low spatial resolution, such as NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer), and those with higher spatial resolution, such as Landsat (Land Satellite), can potentially be used for irrigation water management purposes and other agricultural applications. The objective of this study is to assess satellite based-ET estimation accuracy using measured ET from large weighing lysimeters. Daily, seven-day running average, monthly, and seasonal satellite-based ET data were compared with corresponding lysimeter ET data. This study was performed at the USDA-ARS Conservation and Production Research Laboratory (CPRL) in Bushland, Texas, USA. The daily time series Landsat ET estimates were characterized as poor for irrigated fields, with a Nash Sutcliff efficiency (NSE) of 0.37, and good for monthly ET, with an NSE of 0.57. For the dryland managed fields, the daily and monthly ET estimates were unacceptable with an NSE of −1.38 and −0.19, respectively. There are various reasons for these results, including uncertainties with remotely sensed data due to errors in aerodynamic resistance surface roughness length estimation, surface temperature deviations between irrigated and dryland conditions, poor leaf area estimation in the METRIC model under dryland conditions, extended gap periods between satellite data, and using the linear interpolation method to extrapolate daily ET values between two consecutive scenes (images).

Evapotranspiration (ET) is a major component of the water cycle, which makes it an integral part of water resources management, especially in arid and semiarid environments. ET data are used for water management, irrigation scheduling, various modeling activities, and much more. Some areas of scarce water resources place limitations on water use, which are typically determined from various modeling approaches. As many models use ET as an input, or for validation, accurate ET data is essential to ensure accurate model outputs. In addition, most water management practices are done at the field scale; ET data of a similar scale is needed. Many ET measurement or estimation methods exist and vary widely in approach, instrumentation, complexity, and purpose. A lysimeter is considered the standard for ET measurement and is the most accurate. Other, more portable options are available, such as eddy covariance, scintillometer, Bowen ratio, and remote sensing, all capable of estimating actual field ET within approximately 30% of actual values. Although other methods may not be as accurate as a lysimeter, each has benefits in certain situations. Depending on the purpose, the level of accuracy may be suitable. ET estimation methods are constantly evolving, and accuracy should continually improve further.

Evapotranspiration (ET) or crop water use is the major consumptive use of irrigation and precipitation, commonly accounting for the largest component of the water balance. As water resources become scarce, agricultural research aims to increase water use efficiency by maintaining profitable crop yields while reducing ET. Advanced understanding of ET in the soil-plant-atmosphere continuum requires quantification of evaporation (E) and transpiration (T) components. However, the partitioning of E and T can be challenging even when using high quality ET data. Microlysimeters can be used to measure E from the soil surface and estimate T by subtraction using quality ET data. However, an understanding of the limitations of small, hydraulically isolated microlysimeters is required for meaningful interpretation of results. Proper design, fabrication, and operation of microlysimeters can yield reliable estimates of E from bare soil. Multiple microlysimeters and strategic placement may be required for accurate estimates of E in row crops, particularly during incomplete plant canopy conditions. Periodic weighing of lysimeter cores allows for time-averaged measurements of E but precludes E rate data at finer time scales. Design and testing of continuous weighing platforms to improve temporal density of microlysimeter measurements is needed. We present design and fabrication details of a compact, inexpensive, in-situ microlysimeter for field estimates of E in agricultural soils. Installation and operation techniques are also provided with discussion of example field data.

Modeling alternative irrigation strategies can be a cost-effective and time-saving approach to field-based experiments. However, the efficacy of irrigation scheduling algorithms should be verified using field data from multiple locations. In this study, an auto-irrigation algorithm recently developed for Soil and Water Assessment Tool (SWAT) was further evaluated using irrigation data for corn (Zea mays L.) grown at six research sites across the Southern Great Plains. Simulated monthly irrigation, based on the management allowed depletion (MAD) of plant available soil water, was compared to measured data for irrigation applied in accordance with crop water requirement guidelines outlined by the Food and Agriculture Organization Irrigation and Drainage Paper 56. Overall, results indicated the MAD algorithm simulated monthly field-based irrigation amounts well (Nash-Sutcliffe efficiency; NSE > 0.56). Comparisons revealed the MAD algorithm outperformed the plant water demand and soil water content approaches in SWAT, which tended to underestimate and overestimate irrigations, respectively.

Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET; however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%–30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%–15%, indicating that EC has potential for use in agricultural water management.

Modeling the effects of climate change on hydrology and crop yield provides opportunities for choosing appropriate crops for adapting to climate change. In this study, climate change impacts on irrigated corn and sorghum, dryland (rainfed) sorghum, and continuous fallow in the Northern High Plains of Texas were evaluated using an improved Soil and Water Assessment Tool (SWAT) model equipped with management allowed depletion (MAD) irrigation scheduling. Projected climate data (2020-2099) from the Coupled Model Intercomparison Project Phase 5 (CMIP 5) of 19 General Circulation Models (GCMs) were used. Climate data were divided into four 20-year periods of near future (2020-2039), middle (2040-2059), late (2060-2079), and end (2080-2099) of the 21st century under two Representative Concentration Pathway (RCP) emission scenarios (RCP 4.5 and RCP 8.5). For irrigated corn, median annual crop evapotranspiration (ET) and irrigation decreased by 8%-25% and 15%-42%, respectively, under the climate change scenarios compared to the historical period (2001-2010). The median yield was reduced by 3%-22% with exponentially decreases in the latter half of the 21st century. For sorghum, the reduction of median annual crop ET ranged from 6%-27%. However, the decline in the median annual irrigation was within 15%, except for the 2060-2079 and 2080-2099 periods under RCP 8.5 scenarios with 30% and 49% reductions in median annual irrigation. The median irrigated sorghum yield declined by 6%-42%. The median annual crop ET of dryland sorghum decreased by 10%-16%. The reduction in median yield was within 10% of the historical dryland sorghum yield. The decrease in median annual evaporation varied from 15%-23% under future continuous fallow conditions. The elevated CO2 level of future climate scenarios was the primary factor for the decrease in the ET and irrigation. The reduction in future crop yield was mainly attributed to the shortening of the maturity period caused by increased future temperature.

Evapotranspiration (ET) is a major component of the water cycle, which makes it an integral part of water resources management, especially in arid and semiarid environments. ET data are used for water management, irrigation scheduling, various modeling activities, and much more. Some areas of scarce water resources place limitations on water use, which are typically determined from various modeling approaches. As many models use ET as an input, or for validation, accurate ET data is essential to ensure accurate model outputs. In addition, most water management practices are done at the field scale; ET data of a similar scale is needed. Many ET measurement or estimation methods exist and vary widely in approach, instrumentation, complexity, and purpose. A lysimeter is considered the standard for ET measurement and is the most accurate. Other, more portable options are available, such as eddy covariance, scintillometer, Bowen ratio, and remote sensing, all capable of estimating actual field ET within approximately 30% of actual values. Although other methods may not be as accurate as a lysimeter, each has benefits in certain situations. Depending on the purpose, the level of accuracy may be suitable. ET estimation methods are constantly evolving, and accuracy should continually improve further.

A new agricultural drought index was developed for monitoring drought impacts on agriculture in Oklahoma. This new index, called the Soil Moisture Evapotranspiration Index (SMEI), estimates the departure of aggregated root zone moisture from reference evapotranspiration. The SMEI was estimated at five locations across Oklahoma representing different climates. The results showed good agreement with existing soil moisture-based (SM) and meteorological drought indices. In addition, the SMEI had improved performance compared to other indices in capturing the effects of temporal and spatial variations in drought. The relationship with crop production is a key characteristic of any agricultural drought index. The correlations between winter wheat production and studied drought indices estimated during the growing period were investigated. The correlation coefficients were largest for SMEI (r > 0.9) during the critical crop growth stages when compared to other drought indices, and r decreased by moving from semi-arid to more humid regions across Oklahoma. Overall, the results suggest that the SMEI can be used effectively for monitoring the effects of drought on agriculture in Oklahoma.

Performance evaluations and corrections of soil water sensors have not been studied using different installation orientations under various irrigation treatments in the Texas High Plains. This study evaluated the performance of four sensors using factory calibration and derived field corrections as compared to calibrated neutron moisture meters (NMMs). Sensor performance was assessed using horizontal insertion, laid horizontal placement, and vertical insertion at 15.2, 45.7, and 76.2 cm depths in a clay loam soil with three irrigation treatments. Results indicated the factory-calibrated Acclima 315 L performed satisfactorily using horizontal insertion as compared to NMM measurements at 45.7 and 76.2 cm depths with a ±2% mean difference (MD) and <3.5% root mean square error (RMSE). The factory-calibrated Acclima 315 L using horizontal insertion also performed satisfactorily across all irrigation treatments according to soil profile water storage (MD = 0.36% and RMSE = 3.25%). Generally, the factory-calibrated Decagon GS1 and Campbell Scientific 655 using vertical insertion agreed more closely with NMM measurements compared with other installation orientations. There was a significant underestimation of water storage (>60 mm) in the 0.9 m soil profile using the Watermark 200SS. In summary, field corrections are required for Decagon GS1, Campbell Scientific 655, and Watermark 200SS sensors.