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The purpose of this study is to evaluate the state of knowledge regarding water for food within the water-energy-food (WEF) nexus framework. Three topics are considered: improved plant genetics; irrigation technology and practices; and urban agriculture. Research in plant genetics has sought to understand plant mechanisms that produce higher-yielding crops, identify and map genetic loci regions responsible for desirable plant traits, and introduce new and safe methods of precision plant breeding. In addition, new discoveries in irrigation practices are crucial in sustaining and further increasing yields as a result of improved irrigation water use efficiency through near-real time spatio-temporal monitoring of soil moisture, evapotranspiration, and rainfall. Urban agriculture has been proliferating in recent years, and recent research demonstrates the possibility of non-traditional, urban farming methods to produce food in a water- and land-efficient way. Opportunities remain to increase water use efficiency and/or decrease water usage throughout the food supply chain. Food producers need to achieve greater value from the water used through enhanced management supported by new technology and guided by scientific findings evolving from multidisciplinary research. To achieve this, constant exchange is needed between regulatory agencies, farmers, food manufacturers, and researchers to ensure all stakeholders are up to date on the issues, policies, technologies, and discoveries related to water and food production sustainability.

Interest in cover crops has been increasing in the Texas Rolling Plains (TRP) region, mainly to improve soil health. However, there are concerns that cover crops could potentially reduce soil water and thereby affect the yield of subsequent cash crops. Previous field studies from this region have demonstrated mixed results, with some showing a reduction in cash crop yield due to cover crops and others indicating no significant impact of cover crops on subsequent cotton fiber yield. The objectives of this study were to (1) evaluate the CROPGRO-Cotton and CERES-Wheat modules within the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for the TRP region, and (2) use the evaluated model to assess the long-term effects of growing winter wheat as a cover crop on water balances and seed cotton yield under irrigated and dryland conditions. The two DSSAT crop modules were calibrated using measured data on soil water and crop yield from four treatments: (1) irrigated cotton without a cover crop (CwoC-I), (2) irrigated cotton with winter wheat as a cover crop (CwC-I), (3) dryland cotton without a cover crop (CwoC-D), and (4) dryland cotton with a winter wheat cover crop (CwC-D) at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2015. The average percent error (PE) between the CSM-CROPGRO-Cotton simulated and measured seed cotton yield was -10.1% and -1.0% during the calibration and evaluation periods, respectively, and the percent root mean square error (%RMSE) was 11.9% during calibration and 27.6% during evaluation. For simulation of aboveground biomass by the CSM-CERES-Wheat model, the PE and %RMSE were 8.9% and 9.1%, respectively, during calibration and -0.9% and 21.8%, respectively, during evaluation. Results from the long-term (2001-2015) simulations indicated that there was no substantial reduction in average seed cotton yield and soil water due to growing winter wheat as a cover crop. Keywords: CERES-Wheat, Cover crop, Crop simulation model, CROPGRO-Cotton, DSSAT, Seed cotton yield, Soil water.

Grazing management practices affect watershed hydrology by altering vegetation cover and soil properties. Long-term success of grazing management depends on how well increased forage harvest efficiency is balanced with the need to maintain soil aggregate stability. The overall objective of this study was to assess the impacts of alternate grazing management practices including the light continuous (LC), heavy continuous (HC), adaptive multipaddock (MP) grazing, and no grazing (EX; exclosure) on hydrological processes at the ranch and watershed scales in a rangeland-dominated (71% rangeland) Clear Creek watershed (CCW) in north central Texas using the Soil and Water Assessment Tool (SWAT). Measured data on vegetation, soil physical and hydrological properties, and grazing management at four study ranches within the watershed (two under MP and one each under LC and HC grazing management) were used to parameterize the SWAT model. The SWAT model was calibrated and validated using the measured standing crop biomass and soil moisture data at the study ranches, and streamflow data at the watershed outlet over a 34-year period from 1980 to 2013. At the ranch scale, when the management was changed from the baseline MP grazing to HC grazing, the simulated average (1980 to 2013) annual surface runoff increased within a range of 106% to 117% and water yield increased within a range of 39% to 53%. While surface runoff was found to be a major contributor (52% to 67%) to streamflow under the HC grazing, baseflow was the dominant (55% to 66%) component of streamflow under the MP and EX practices. At the watershed scale, shifting grazing management from the baseline HC grazing to the improved MP grazing decreased surface runoff by about 47%, increased infiltration by 5%, and decreased streamflow by 29.5%. In addition, improvements to grazing decreased the simulated highest annual streamflow over the 1980 to 2013 period from 8.3 m3 s−1 ([293.1 ft3 sec−1] baseline scenario) to 6.2 m3 s−1 ([219 ft3 sec−1] MP grazing). This reduction in the maximum flow has a potential to reduce the risk of flooding downstream. However, these hydrologic responses vary according to the extent of grazing lands in a watershed. Overall, the MP grazing was found to be the best grazing management practice in terms of water conservation, vegetation regrowth, and the potential to reduce flood risk.

Increasing concerns over dependence on fossil fuels instigated the demand for biofuel production over the past two decades, which has led to an increase in competition for productive agricultural land, shifts in land use among different crops, and conversion of land from other uses into biofuel production. These biofuel-induced changes in land use and management can have significant impacts on the environment, which vary widely across landscapes, bioenergy feedstocks, and production practices. Extensive production of conventional first-generation bioenergy crops such as corn (Zea mays L.) and harvest of corn stover for biofuel production increase soil erosion, water and air pollution, and biodiversity losses. It also causes substantial carbon debt due to the release of a greater quantity of carbon dioxide (CO2) from land use changes than the greenhouse gas reductions from the displacement of fossil fuels. In contrast, replacing annual crops with perennial grasses, such as switchgrass (Panicum virgatum L.) and Miscanthus x giganteus, provides many environmental benefits, such as reduced sediment and nutrient losses, and increased soil carbon levels and biodiversity, and helps in minimizing climate change impacts. However, the use of productive cropland for bioenergy crops could lead to the competition between food and fiber versus fuel. Marginal and degraded lands where food crops may not be profitable and the conservation reserve areas offer the opportunity to develop economically and ecologically sustainable cellulosic bioenergy systems. However, as the market for cellulosic biofuel is under significant pressure due to highly volatile oil prices, biofuel production systems need to be developed as multifunctional landscapes along with conventional cropping systems to maximize ecosystem functions and minimize food versus fuel competition. This chapter provides an overview of biofuel-related changes in land use and management, discusses associated environmental impacts, and describes potential ways to ensure environmentally sustainable biofuel production.

Declining water levels in the southern Ogallala Aquifer and associated reduction in irrigation capacities and increasing pumping costs necessitate adoption of deficit/limited irrigation practices for sustaining cotton production in the Texas High Plains (THP) region. The overall goal of this study was to evaluate the response of cotton crop to water stress in different growth stages and suggest efficient growth-stage-based deficit (GSBD) irrigation schedules for maximizing yield, crop water productivity (CWP) and economic returns under contrasting weather conditions using the CROPGRO-Cotton model. Five growth stages including seedling emergence/germination, squaring, early bloom/flower initiation, peak bloom, and boll opening/late bloom were considered. A combination of five irrigation scenarios (S1 through S5 with seasonal irrigation amounts of 240, 300, 360, 420 and 480 mm) and six irrigation treatments (T1 through T5: no irrigation in one of the five growth stages, and T6: irrigation applied in all five growth stages) were then simulated with a center pivot irrigation system. Skipping irrigation during the peak bloom growth stage (T4 treatment) resulted in the lowest yield, CWP and net returns under all weather conditions. The T1 irrigation treatment in which irrigation was skipped during the seedling emergence/germination stage was identified as the most efficient irrigation strategy for maximizing yield, CWP and net returns among all irrigation scenarios. Application of more than 360, 420 and 480 mm of irrigation water in wet, normal and dry years, respectively, did not significantly improve yield or net returns, and resulted in a decrease in CWP. These results imply that cotton responses to water deficit vary by growth stages, and adoption of appropriate GSBD irrigation strategies could optimize the use of limited water resources and extend the life of the southern Ogallala Aquifer.

Bioenergy crop production has the potential to protect marginal crop lands that generate high surface runoff and produce poor crop yields. Long-term evaluation of the impacts of such land use change on hydrologic fluxes and biofuel production potential is necessary before adopting such strategies on a large scale. In this study, the hydrologic impacts of replacing cotton (Gossypium hirsutum L.) on marginal lands in an intensive agricultural watershed in the Texas High Plains with Alamo switchgrass (Panicum virgatum L.) as a bioenergy crop were evaluated using the Agricultural Policy/Environmental eXtender (APEX) model. The surface runoff to cotton yield ratio was used as a criterion to identify marginal cotton subareas (homogenous spatial units delineated by APEX) in the study watershed, and three replacement scenarios (low (9 %), medium (33 %), and high (57 %) extents of cotton acreage replaced by switchgrass) were implemented in the scenario analysis. The average (1994–2009) annual surface runoff decreased by about 84 and 66 %, and the percolation increased by 106 and 57 % in the irrigated and dryland subareas, respectively, when cotton was replaced by switchgrass under the high replacement scenario. Spatial analysis showed that switchgrass was a feasible bioenergy crop for replacing cotton, especially in the western part of the study watershed, due to its higher water use efficiency and better water conservation effects compared to cotton. It is estimated that 193 and 381 million liters of ethanol could be produced from the dryland and irrigated subareas of the study watershed, respectively, under the high replacement scenario.

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

The Southern High Plains (SHP) region of Texas in the United States, where cotton is grown in a vast acreage, has the potential to grow cellulosic bioenergy crops such as perennial grasses and biomass sorghum (Sorghum bicolor). Evaluation of hydrological responses and biofuel production potential of hypothetical land use change from cotton (Gossypium hirsutum L.) to cellulosic bioenergy crops enables better understanding of the associated key agroecosystem processes and provides for the feasibility assessment of the targeted land use change in the SHP. The Soil and Water Assessment Tool (SWAT) was used to assess the impacts of replacing cotton with perennial Alamo switchgrass (Panicum virgatum L.), Miscanthus giganteus (Miscanthus sinensis Anderss. [Poaceae]), big bluestem (Andropogon gerardii) and annual biomass sorghum on water balances, water use efficiency and biofuel production potential in the Double Mountain Fork Brazos watershed. Under perennial grass scenarios, the average (1994-2009) annual surface runoff from the entire watershed decreased by 6% to 8% relative to the baseline cotton scenario. In contrast, surface runoff increased by about 5% under the biomass sorghum scenario. Perennial grass land use change scenarios suggested an increase in average annual percolation within a range of 3% to 22% and maintenance of a higher soil water content during August to April compared to the baseline cotton scenario. About 19.1, 11.1, 3.2 and 8.8 Mg ha−1 of biomass could potentially be produced if cotton area in the watershed would hypothetically be replaced by Miscanthus, switchgrass, big bluestem and biomass sorghum, respectively. Finally, Miscanthus and switchgrass were found to be ideal bioenergy crops for the dryland and irrigated systems, respectively, in the study watershed due to their higher water use efficiency, better water conservation effects, greater biomass and biofuel production potential, and minimum crop management requirements. This article is protected by copyright. All rights reserved

The Texas High Plains (THP) region contributes to about 25% of the US cotton production. Dwindling groundwater resources in the underlying Ogallala aquifer, future climate variability and frequent occurrences of droughts are major concerns for cotton production in this region. Assessing the impacts of climate change on cotton production enables development and evaluation of irrigation strategies for efficient utilization of groundwater resources in this region. In this study, the CROPGRO-Cotton module within the Cropping System Model (CSM) that is distributed with the Decision Support System for Agrotechnology Transfer (DSSAT) was evaluated for the THP region using measured data from cotton water use efficiency experiments at Halfway over a period of four years (2010–2013). Simulated seed cotton yield matched closely with observed yield during model calibration (average percent error of 0.1) and validation (average percent error of 6.5). The evaluated model was able to accurately simulate seed cotton yield under various irrigation strategies over the four growing seasons. The evaluated CROPGRO-Cotton model was later used to simulate the seed cotton yield under historic (1971–2000) and future (2041–2070) climate scenarios projected by three climate models. On an average, when compared to historic seed cotton yield, simulated future seed cotton yield across the THP decreased within a range of 4–17% when carbon dioxide (CO2) concentration was assumed to be constant at the current level (380 ppm) under three climatic model scenarios. In contrast, when the CO2 concentration was assumed to increase from 493 ppm (in year 2041) to 635 ppm (in year 2070) according to the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario, the simulated future average seed cotton yield in the THP region increased within a range of 14–29% as compared to historic average yield. When the irrigation amount was reduced by 40% (from 100% to 60%), the average (2041–2070) seed cotton yield decreased by 37% and 39% under the constant and increasing CO2 concentration scenarios, respectively. These results imply that cotton is sensitive to atmospheric CO2 concentrations, and cotton production in the THP could potentially withstand the effects of future climate variability under moderate increases in CO2 levels if irrigation water availability remains at current levels.

Surface runoff is often poorly quantified in hydrologic studies of subsurface drained fields, as it is a relatively minor component of the water balance and difficult to measure in large fields. However, conservation practices that seek to mitigate pollutant transport through subsurface drainage may increase surface runoff, and therefore it needs to be better understood. The goal of this study was to determine the frequency and extent of occurrence of surface ponding and runoff, and to understand their generation processes in a seasonally frozen, subsurface drained agricultural field in eastern Indiana. Three different methods were used. Surface ponding was monitored with a time-lapse camera at the edge of the field for three years. A water table threshold for surface ponding was determined using photo evidence of ponding together with water table depth measurements and used to estimate ponding. The DRAINMOD hydrologic model was calibrated and validated by comparing model predictions of subsurface drainage and water table depth with 10 years of field observations and used to predict surface ponding and runoff. The simulation results indicated that surface runoff represented 1–10% of annual precipitation, while subsurface drainage represented between 26 and 45%. On average, 45% of simulated ponding occurred during the cold season (December-March) indicating the importance of soil freezing and snow accumulation. However, during parts of the cold season, DRAINMOD simulations of snow accumulation and melt were poor, resulting in drain flow under-prediction and runoff over-prediction during these periods. Water table depth measurements above the defined threshold provided a simple alternate for prediction of saturation excess ponding events in the absence of direct measurements. Results from both simulations and observations indicated that all of the ponding events in this location were generated by saturation excess rather than infiltration excess processes.

Adaptation measures are required to enhance climate change resilience of agricultural systems and reduce risks associated with climate change at both regional and global scales. The Texas High Plains is a semi-arid region that faces major challenges from climate change risks and dwindling groundwater supply from the exhaustible Ogallala Aquifer for sustaining irrigated agriculture. The overall goal of this study was to assess the impacts of climate change on yield and water use of grain sorghum and identify optimum climate change adaptation strategies for three study sites in the Texas High Plains. Future climate data projected by nine Global Circulation Models (GCMs) under two Representative Concentration Pathways (RCPs) of greenhouse gas emissions (RCPs 4.5 and 8.5) were used as input for the DSSAT CSM-CERES-Sorghum model. The climate change adaptation strategies were designed by modifying crop genotype parameters to incorporate drought tolerance, heat tolerance, high yield potential, and long maturity traits. Irrigated and dryland grain sorghum yield and irrigation water use were projected to decrease at varying percentages at the study sites in the future. On an average (of 9 GCMs), irrigated grain sorghum yield is expected to decrease by 5–13 % and 16–27 % by mid-century (2036–2065) and late-century (2066–2095), respectively under RCP 8.5 compared to the baseline (1976–2005). The irrigation water use is expected to decrease by 7–9% and 14–16 % by the mid-century and late-century, respectively. Among the adaptation strategies, an ideotype with high yield potential trait (10 % higher partitioning to the panicle, radiation use efficiency, and relative leaf size than the reference cultivar) resulted in maximum grain sorghum yield gains in the future under both irrigated (6.9 %–17.1 %) and dryland (7.5 %–17.1 %) conditions, when compared to the reference cultivar. Enhancing drought tolerance by increasing root density at different soil depths also resulted in a significantly higher irrigated grain sorghum yield than the reference cultivar. A longer maturity cultivar will likely increase irrigation water use and, therefore, is not recommended for water limited conditions.

Water is the limiting factor for crop production within the southern US Great Plains and it has become a critical resource for multiple stakeholders. Hence, efficient irrigation and cropping systems are of paramount importance to conserve water resources. The objective of this study was to determine the effect of irrigation timing and quantity, cover crop use and tillage on cotton production in an established conservation tillage system. Evaluated tillage systems included: 1) conventional tillage (CT); 2) strip-tillage (ST); 3) no-till (NT); and 4) NT with a terminated wheat cover crop (NT-W). Irrigation treatments included 1) 5.08-6.35 mm d−1 initiated mid-season (LOW); 2) 6.35-8.38 mm d−1 initiated mid-season (MED); and 3) 5.08-6.35 mm d−1 initiated early-season (HIGH). No significant differences in lint yield or irrigation water use efficiency (IWUE) were observed between MED and HIGH. Although HIGH resulted in 12 % greater lint yields than LOW, HIGH resulted in 67 % greater irrigation water applications. LOW resulted in significantly greater IWUE than MED and HIGH. No-till systems, with and without a cover crop, had significantly greater lint yields and IWUE than CT. Furthermore, inclusion of wheat in NT increased yields and IWUE compared with ST. Applying irrigation water at a critical growth stage proved to be more water efficient than early season irrigation that was used to bank moisture in the soil profile. Delaying irrigation application until critical growth stages and using cover crops should be considered as best management approaches to conserve water resources while sustaining cotton production in the Southern Great Plains.

The increasing use of hydrologic and water quality (H/WQ) models for technical, policy, and legal decision making calls for greater transparency in communicating the methods used and decisions made when using H/WQ models. The objectives of this article are to: (1) provide guidelines to properly document H/WQ model calibration, validation, and use, and (2) raise issues about how to improve model documentation and reproducibility, and encourage open discussion of these topics in the H/WQ modeling community. First, eight recommended elements of model documentation and reporting are identified and described, and good examples are provided for each. Next, steps to move the H/WQ modeling community toward a culture of full model reproducibility are discussed. The use by model practitioners of the consistent and comprehensive elements described herein for documentation and reporting of H/WQ model calibration, validation, and use will allow better interpretation of published modeling studies, improve the utility of modeling studies, and allow more systematic advancement of H/WQ models.

Cotton is one of the major crops cultivated in the Texas Rolling Plains region, and it is a major contributor to the regional economy. Cotton cultivation in this region is facing severe challenges due to an increase in the frequency of droughts and a projected decrease in rainfall in the future. Development and evaluation of deficit irrigation strategies for this region could potentially conserve water while maintaining cotton yields. In this study, the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) CROPGRO-Cotton was extensively tested and then used for evaluating various deficit irrigation strategies for this region. The model inputs were obtained from field experiments conducted at Chillicothe, Texas, during four growing seasons: 2008-2010 and 2012. The model was first calibrated using the data from a 100% evapotranspiration (ET) replacement irrigation scheduling experiment conducted in 2012 and then validated on three other irrigation scheduling treatments (75% ET replacement, soil moisture based, and tensiometer based) conducted in the same year. The model was further evaluated using the data from cotton tillage and irrigation experiments conducted in an adjacent field during 2008-2010. The model calibration, validation, and evaluation results were satisfactory except under dry conditions (0% ET replacement and 33% ET replacement). Simulated maximum seed cotton yields under normal and dry weather conditions were achieved at 100% and 110% ET replacement, respectively. Percentage decrease in seed cotton yield was marginal (3.5% to 8.8%) when the amount of irrigation water applied was decreased from 100% to 66% ET replacement under normal rainfall conditions. However, under less than normal rainfall (drier) conditions, the percentage decrease in seed cotton yield was substantial (about 17.5%) when the irrigation strategy was switched from 100% to 70% ET replacement. The simulations demonstrate that adopting deficit irrigation practices under normal weather conditions can conserve water without adversely affecting seed cotton yields. However, under dry conditions, there is a risk of increased yield loss, and therefore producers should consider that risk when adopting deficit irrigation strategies.

Agricultural (tile) drainage enables agricultural production on millions of hectares of arable lands worldwide. Lands where drainage or irrigation (and sometimes both) are implemented, generate a disproportionately large share of global agricultural production compared to dry land or rain-fed agricultural lands and thus, these water management tools are vital for meeting the food demands of today and the future. Future food demands will likely require irrigation and drainage to be practiced on an even greater share of the world’s agricultural lands. The practice of agricultural drainage finds its roots in ancient societies and has evolved greatly to incorporate modern technologies and materials, including the modern drainage plow, plastic drainage pipe and tubing, laser and GPS-guided installation equipment, and computer-aided design tools. Although drainage brings important agricultural production and environmental benefits to poorly drained and salt-affected arable lands, it can also give rise to the transport of nutrients and other constituents to downstream waters. Other unwanted ecological and hydrologic environmental effects may also be associated with the practice. The goal of this article is to familiarize the reader with the practice of subsurface agricultural drainage, the history and extent of its application, and the benefits commonly associated with it. In addition, environmental effects associated with subsurface drainage including hydrologic and water quality effects are presented, and conservation practices for mitigating these unwanted effects are described. These conservation practices are categorized by whether they are implemented in-field (such as controlled drainage) versus edge-of-field (such as bioreactors). The literature cited and reviewed herein is not meant to be exhaustive, but seminal and key literary works are identified where possible.

Rapid groundwater depletion has raised grave concerns about sustainable development in many parts of Texas, as well as in other parts of the world. Previous hydrologic investigations on groundwater levels in Texas were conducted mostly on aquifer-specific basis, and hence lacked state-wide panoramic view. The aim of this study was to present a qualitative overview of long-term (1930-2010) trends in groundwater levels in Texas and identify spatial patterns by applying different statistical (boxplots, correlation-regression, hierarchical cluster analysis) and geospatial techniques (Moran's I, Local Indicators of Spatial Association) on 136,930 groundwater level observations from Texas Water Development Board's database. State-wide decadal median water-levels declined from about 14 m from land surface in the 1930s to about 36 m in the 2000s. Number of counties with deeper median water-levels (water-level depth100 m) increased from 2 to 13 between 1930s and 2000s, accompanied by a decrease in number of counties having shallower median water-levels (water-level depth25 m) from 134 to 113. Water-level declines across Texas, however, mostly followed logarithmic trends marked by leveling-off phenomena in recent times. Assessment of water-levels by Groundwater Management Areas (GMA), management units created to address groundwater depletion issues, indicated hotspots of deep water-levels in Texas Panhandle and GMA 8 since the 1960s. Contrasting patterns in water use, landcover, geology and soil properties distinguished Texas Panhandle from GMA 8. Irrigated agriculture is the major cause of depletion in the Texas Panhandle as compared to increasing urbanization in GMA 8. Overall our study indicated that use of robust spatial and statistical methods can reveal important details about the trends in water-level changes and shed lights on the associated factors. Due to very generic nature, techniques used in this study can also be applied to other areas with similar eco-hydrologic issues to identify regions that warrant future management actions.

Summary Although numerous studies have expounded on depletion of the Ogallala aquifer, very few researchers have studied groundwater quality therein which relates to the ‘usability’ of available groundwater resources. The objective of this study was to elucidate regional trends in groundwater quality and salinization in the Ogallala aquifer, underlying 49 counties and two Groundwater Management Areas (GMA 1 and 2) in Texas, on a decadal scale between 1960 and 2010. Contrasting groundwater quality distinguished GMA 1 (northern Ogallala) from GMA 2 (southern Ogallala), and shallow wells (depth 50 m) wells. The GMA 2 was characterized by pronounced groundwater nitrate (NO 3 − ) contamination accompanied by elevated levels of sulfate (SO 4 2− ), chloride (Cl − ) and salinity (TDS), marked by an abundance of mixed cation SO 4 –Cl and Na–Cl facies. In contrast, Ca–Mg–HCO 3 and Ca–HCO 3 facies prevailed in GMA 1 with substantially lower salinization, SO 4 2− , Cl − and NO 3 contamination. In shallow wells, more abundant in GMA 2, about 80% and 32% of observations exceeded the United State Environmental Protection Agency’s Secondary Maximum Contaminant Level (SMCL, 500 mg L −1 ) for total dissolved solids (TDS) and MCL (44 mg L −1 ) for NO 3 , respectively in the 2000s (2000–2010), with progressive increases in both parameters since the 1960s (1960–1969). Majority (>60%) of the shallow observations since the 1980s (1980–1989) have exceeded the natural background of 11 mg L −1 of NO 3 − indicating anthropogenic sources, The NO 3 − contamination was more apparent in domestic wells indicating substantial human health risk. Groundwater salinization in this aquifer resulted from a combination of natural (e.g. upwelling of highly mineralized groundwater from the underlying formations, seepage from playas and saline plumes, and evaporative enrichment) and anthropogenic processes (irrigated agriculture and hydrocarbon exploration activities). Natural processes were largely aggravated by anthropogenic practices such as lowering of hydraulic heads in the Ogallala aquifer due to prolonged irrigational pumping, inducing cross-formational flow from underlying highly mineralized older formations (Edwards Trinity (High Plains)) which led to groundwater mixing between the formations and rise in salinity levels in the Ogallala aquifer over time.

Identification of efficient crop-growth-stage-based deficit irrigation strategies for cotton (Gossypium hirsutum L.) can play a pivotal role in optimizing the use of available irrigation water in the Southern High Plains (SHP) region, which is facing severe challenges from rapidly declining groundwater levels in the underlying Ogallala Aquifer. The objective of this study was to suggest efficient crop-growth-stage-based deficit irrigation strategies for cotton under nine different climate variability classes using the CROPGRO-Cotton module available in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM). Cotton growth stages considered in this study include: i) germination and seedling emergence (GS1), ii) squaring (GS2), iii) flower initiation/ early bloom (GS3), iv) peak bloom (GS4), and v) cutout, late bloom and boll opening stage (GS5). The amount of seasonal irrigation water applied was varied from 120 to 540 mm under eight different irrigation scheduling scenarios with four irrigation application rates of 3, 6, 8 and 9 mm d−1 using the subsurface drip irrigation method. Under each scenario, six growth-stage-based irrigation treatments were adopted, resulting in a total of 48 irrigation strategies. Results indicated that imposing water deficit in the initial (GS1 to GS2) or final (GS5) growth stages had little effect on seed cotton yield. The peak bloom growth stage (GS4) was found to be the most sensitive stage to water stress, and imposing water deficit during this stage resulted in the lowest irrigation water use efficiency (IWUE) and seed cotton yield under most climate variability classes. Application of higher than 420 mm irrigation did not significantly contribute to an increase in seed cotton yield and resulted in a decline in IWUE. The results from this study are useful for the SHP producers to make appropriate crop-growth-stage-based deficit irrigation management decisions for achieving higher seed cotton yield while conserving precious irrigation water resources from the Ogallala Aquifer.

Winter wheat is one of the major crops in the Texas High Plains (THP) region, which is facing challenges from climate change (CC) and exhausting irrigation water supplies from the Ogallala Aquifer. The goal of this study was to assess the impacts of CC on winter wheat production in the THP and evaluate potential adaptation strategies using the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Wheat model. A thorough calibration of the model against field data resulted in a satisfactory simulation of phenology, leaf area index, grain yield (percent error, |PE

The Texas High Plains (THP) region, a vital part of U.S. grain and fiber production, is experiencing the effects of conflicting interests in the diminishing Ogallala Aquifer, making necessary the adoption of more efficient irrigation strategies. Decision Support System for Agrotechnology Transfer (DSSAT) is a process-based model that uses meteorological, soil, and crop management data to predict crop growth, development, and yield. A well-evaluated DSSAT model is useful for simulation of efficient crop and irrigation management strategies. This study details the evaluation of CROPGRO-Cotton module in the DSSAT model based on measured in-season biomass and canopy height, and crop yield data from a field study as well as the use of the evaluated model for determining the best irrigation strategy for cotton (Gossypium hirsutum L. var. hirsutum) in terms of crop yield and irrigation water use efficiency. Irrigation simulation experiments were conducted over a testing range for four separate irrigation scheduling strategies —Time Temperature Threshold (TTT)-5.5 h, TTT-7.5 h, Daily Irrigation (DI), and percent ET replacement —to determine the most efficient irrigation strategy that results in maximum yield with minimum irrigation water input. The DSSAT CROPGRO-Cotton model demonstrated potential to simulate the effects of various irrigation strategies on cotton yield and water use efficiency. The 12 mm, 7.5 h TTT strategy was found to be the best strategy to achieve a maximized yield with the greatest irrigation water use efficiency, with a modelled yield of 5887 kg ha−1 using 195 mm of irrigation throughout the season.

Recently, the Soil and Water Assessment Tool (SWAT) was revised to improve the partitioning of runoff and tile drainage in poorly drained soils by modifying the algorithm that computes the soil moisture retention parameter. In this study, the Revised SWAT model was used to evaluate the sensitivity and long-term effects of different nitrogen (N) application rates and tile drain spacing (SDRAIN) and depths (DDRAIN) on nitrate-nitrogen (NO3-N) losses through tile drains. Monitoring data for the 1983–1996 period measured on three experimental plots on a poorly drained Webster clay loam soil (fine-loamy, mixed, superactive, mesic Typic Endoaquoll) in southern Minnesota was used. Sensitivity analysis covered the 1983–1996 period and long-term simulations were made for the 1915–1996 period. Sensitivity analysis showed a decrease in tile flow as DDRAIN decreased and/or SDRAIN increased. The predicted NO3-N losses in tile drain decreased by 16% (from 33.8 to 28.4 kg ha−1) and 14% (from 34.0 to 29.4 kg ha−1) when SDRAIN was increased by 122% (from 27 to 60 m) and 40% (from 1.5 to 0.9 m), respectively. However, NO3-N losses were decreased by 67% (from 33.8 to 11.1 kg ha−1) when N application rate was decreased by 50% (from 200 to 100 kg ha−1). Long-term simulations results indicated that much greater reductions in NO3-N losses can be achieved with reduction in the N application rates than with changing the tile drain spacing and depth. Reductions in NO3-N losses were consistent with the results reported using the Agricultural Drainage and Pesticide Transport (ADAPT) model, which was developed specifically for understanding effects of tile drainage on water quality in the Upper Midwest U.S. Overall, results from sensitivity analysis and long term simulation indicated that Revised SWAT can be used to adequately evaluate the effects of tile drain configurations on drainage and associated NO3-N losses.

Adequate knowledge on the movement of nitrate-nitrogen (NO 3 -N) under different subsurface (tile) drain configurations and management practices in the U.S. Midwest is essential for developing remedial measures for reducing hypoxic conditions in the Gulf of Mexico. In this study, DRAINMOD-NII, a daily time-step soil carbon (C) and N model, was calibrated and validated for subsurface drainage and associated NO 3 -N losses, and crop yield. Long term (1983–1996) monitoring data measured on three experimental plots under continuous corn ( Zea mays L.) with conventional tillage practice at the University of Minnesota's Southern Research and Outreach Center near Waseca, southern Minnesota was used for this purpose. Nash-Sutcliffe efficiency (NSE), Percent Error (PE) and Index of agreement ( d ) were used for assessing the model performance. DRAINMOD-NII predicted monthly subsurface drainage matched well with measured data during calibration (NSE = 0.81, PE = −7.8% and d = 0.94) and validation (NSE = 0.67, PE = −0.7% and d = 0.88) periods. Performance of DRAINMOD-NII for predicting monthly NO 3 -N losses in subsurface drainage was also good for both calibration (NSE = 0.64, PE = 0.8%, and d = 0.85) and validation (NSE = 0.62, PE = −5.3%, and d = 0.83) periods. DRAINMOD-NII predicted average (1983–1992) annual corn relative yield (93%), a ratio of crop yield in a year to the long-term average crop yield, was close to the observed relative yield (92.5%). DRAINMOD-NII simulation results were also compared and contrasted with those obtained by the Agricultural Drainage and Pesticide Transport (ADAPT) model with the same dataset. Both models performed equally well in predicting monthly subsurface drainage. However, DRAINMOD-NII performed slightly better in predicting monthly NO 3 -N losses and annual N budget, in addition to showing potential to simulate the effects of excess and deficit water stresses on crop yield. Studies comparing performances of different drainage models in the U.S. Midwest are useful to select an appropriate model for devising various strategies for reducing NO 3 -N losses from subsurface drainage systems, and thereby minimizing hypoxic conditions in the Gulf of Mexico.

Aquifer-based groundwater quality assessment offers critical insight into the major hydrochemical processes, and aids in making groundwater resources management decisions. The Texas Rolling Plains (TRP), spanning over 22 counties, is a major agro-ecological region in Texas from where highest groundwater nitrate (NO3 −) levels in the state have been reported. In this study, we present a comparative assessment of major hydrochemical facies pertaining to NO3 − contamination and a host of species such as sulfate (SO4 2−), chloride (Cl−), and total dissolved solids (TDS) in different water use classes in the Seymour and Blaine aquifers, underlying the TRP. Aquifer-stratified groundwater quality information from 1990 to 2010 was obtained from the Texas Water Development Board and aggregated over decadal scale. High groundwater salinization was found in the municipal water use class in the Blaine aquifer with about 100, 87 and 50 % of observations exceeding the secondary maximum contaminant level for TDS, SO4 2−, and Cl−, respectively in the 2000s (2000–2010). The NO3-contamination was more alarming in the Seymour aquifer with 82 and 61 % of observations, respectively, exceeding the maximum contaminant level (MCL) in the irrigation and municipal water use classes in the 2000s. Salinization was more influenced by SO4 2− and Cl− in the Blaine aquifer and by NO3 − in the Seymour aquifer. High NO3 − (>MCL) observations in the Seymour aquifer occurred in the Ca–HCO3 and Ca–Mg–HCO3 facies, the domains of fresh water recharge and anthropogenic influences (e.g., agricultural activities, waste disposal). High SO4 2−, Cl− and TDS observations in the Blaine aquifer dominated the Ca–Cl, Na–Cl, and mixed Ca(Mg)–SO4(Cl) facies indicating evaporite dissolution, mixing and solute exchange, and lack of fresh recharge.

A vast region in north-central Texas, centering on Dallas-Fort Worth metroplex, suffers from intense groundwater drawdown and water quality degradation, which led to inclusion of 18 counties of this region into Priority Groundwater Management Areas. We combined aquifer-based and county-based hydrologic analyses to (1) assess spatio-temporal changes in groundwater level and quality between 1960 and 2010 in the Trinity and Woodbine aquifers underlying the study region, (2) delve into major hydrochemical facies with reference to aquifer hydrostratigraphy, and (3) identify county-based spatial zones to aid in future groundwater management initiatives. Water-level and quality data was obtained from the Texas Water Development Board (TWDB) and analyzed on a decadal scale. Progressive water-level decline was the major concern in the Trinity aquifer with >50% of observations occurring at depths >100 m since the 1980s, an observation becoming apparent only in the 2000s in the Woodbine aquifer. Water quality degradation was the major issue in the Woodbine aquifer with substantially higher percentage of observations exceeding the secondary maximum contaminant levels (SMCL; a non-enforceable threshold set by the United State Environmental Protection Agency (USEPA)) and/or maximum contaminant level (MCL, a legally enforceable drinking water standard set by the USEPA) for sulfate (SO4(2-)), chloride (Cl(-)), and fluoride (F(-)) in each decade. In both aquifers, however, >70% of observations exceeded the SMCL for total dissolved solids indicating high groundwater salinization. Water-level changes in Trinity aquifer also had significant negative impact on water quality. Hydrochemical facies in this region sequentially evolved from Ca-Mg-HCO3 and Ca-HCO3 in the fluvial sediments of the west to Na-SO4-Cl in the deltaic sediments to the east. Sequentially evolving hydrogeochemical facies and increasing salinization closely resembled regional groundwater flow pattern. Distinct spatial zones based on homogenous hydrologic characteristics have become increasingly apparent over time indicating necessity of zone-specific groundwater management strategies.

Soil information is one of the crucial inputs needed to assess the impacts of existing and alternative agricultural management practices on water quality. Therefore, it is important to understand the effects of spatial scale at which soil databases are developed on water quality evaluations. In the United States, STATSGO (State Soils Geographic) and SSURGO (Soil Survey Geographic) are the most commonly available soil databases. The purpose of this paper was to quantify the effect of scale by employing STATSGO (1:250,000) and SSURGO (1:24,000) soil databases in predicting and comparing flow, sediment, nitrate and phosphorus losses for High Island Creek. This watershed is predominately agricultural and located in south-central Minnesota. The ADAPT (Agricultural Drainage and Pesticide Transport), model was calibrated for flow, sediment, nitrate and phosphorus losses over two years (2001-2002) using STATSGO and SSURGO soil databases. Then the calibrated model was used to evaluate alternative tillage and fertilizer management practices such as adoption of conservation tillage, and rate, timing and method of N- and P-fertilizer applications. Statistical comparison of calibration results with observed data indicated excellent agreement for both soil databases (STATSGO with r2 of 0.95, 0.97, 0.77 and 0.92 and SSURGO with r2 of 0.90, 0.97, 0.82 and 0.99 for flow, sediment, nitrate and phosphorus losses, respectively). However, STATSGO based predictions of annual nitrate-N losses were consistently greater than those with SSURGO database and vice-versa for predicted annual phosphorus losses for the alternative management practice that were evaluated.

Both temporal and spatial magnitude, structure, and distribution of rangeland aboveground biomass (AGB) are important inputs for many necessities, in particular for estimating terrestrial carbon amount, ecosystem productivity, climate change studies, and potential bioenergy uses. Much of the remote sensing research previously completed has focused on determining carbon stocks in forested ecosystems with little attention directed to estimate AGB amount in rangelands. Our objectives were to: 1) identify and delineate individual redberry juniper (Juniperus pinchotii) plants from surrounding live vegetation using the support vector machine method for classifying two-dimensional (2D) geospatial imagery with a 1-m spatial resolution at two sites; and 2) develop regression models relating imagery-derived and fieldmeasured single tree canopy area and diameter for dry AGB estimation. The regression results show that there were very close and significant relationships between field measured juniper plant AGB and canopy area derived from the image classification with r2 > 0.90. These results suggest that spectral reflectance recorded on 2D high resolution imagery is capable to assess and quantify AGB as a quick, repeatable, and unbiased method over large land areas.

Nitrate (NO) is a major contaminant and threat to groundwater quality in Texas. High-NO groundwater used for irrigation and domestic purposes has serious environmental and health implications. The objective of this study was to evaluate spatio-temporal trends in groundwater NO concentrations in Texas on a county basis from 1960 to 2010 with special emphasis on the Texas Rolling Plains (TRP) using the Texas Water Development Board's groundwater quality database. Results indicated that groundwater NO concentrations have significantly increased in several counties since the 1960s. In 25 counties, >30% of the observations exceeded the maximum contamination level (MCL) for NO (44 mg L NO) in the 2000s as compared with eight counties in the 1960s. In Haskell and Knox Counties of the TRP, all observations exceeded the NO MCL in the 2000s. A distinct spatial clustering of high-NO counties has become increasingly apparent with time in the TRP, as indicated by different spatial indices. County median NO concentrations in the TRP region were positively correlated with county-based area estimates of crop lands, fertilized croplands, and irrigated croplands, suggesting a negative impact of agricultural practices on groundwater NO concentrations. The highly transmissive geologic and soil media in the TRP have likely facilitated NO movement and groundwater contamination in this region. A major hindrance in evaluating groundwater NO concentrations was the lack of adequate recent observations. Overall, the results indicated a substantial deterioration of groundwater quality by NO across the state due to agricultural activities, emphasizing the need for a more frequent and spatially intensive groundwater sampling.

Summary Drainage tiles buried beneath many naturally poorly drained agricultural fields in the Midwestern U.S. are believed to “short circuit” pools of NO 3 - -laden soil water and shallow groundwater directly into streams that eventually discharge to the Mississippi River. Although much is known about the mechanisms controlling this regionally pervasive practice of artificial drainage at the field-plot scale, an integrative assessment of the effect of drainage density (i.e., the number of tile drains per unit area) on the transport of nutrients and solutes in streams at the catchment scale is lacking. In this study, we quantified the flux and hydrological pathways of agricultural NO 3 - and road-salt Cl− from catchments lying within the Wabash River Basin, a major source of NO 3 - to the Mississippi River. The paired catchments differ primarily in drainage density (70% vs. 31%, by catchment area), with essentially all other agricultural management, land use, and soil drainage characteristics remaining equal. Our study revealed two significant hydrological responses to increased drainage density: (1) more near-surface storm event water (dilute in both NO 3 - and Cl - ) was transported early in the storm and (2) higher transport of Cl−-laden pre-event soil water relative to shallow groundwater elevated in NO 3 - occurred later in the storm. These patterns are consistent with a proposed conceptual model in which increased drainage density results in (1) greater transport of soil water to streams and (2) a delayed rise in the water table. With respect to nutrient management implications, these results indicate that increased drainage density impacts subsurface pools of Cl− and NO 3 - differently, a finding that we propose is linked to soil/ground water dynamics in artificially drained agricultural catchments.

Drainage water management, also known as controlled drainage, is the practice of using a water table control structure at the end of the subsurface drain pipe to reduce subsurface drainage, and thereby nitrate losses. Methods to quantify the potential effects of drainage water management for entire watersheds are needed to evaluate the impacts of large-scale adoption. A distributed modeling approach was developed to apply the field-scale DRAINMOD model at the watershed scale, and used to assess the impact of drainage water management on nitrate load from an intensively subsurface drained agricultural watershed in west central Indiana. The watershed was divided into 6460 grid cells for which drain spacing, soil parent material, and cropping pattern were estimated, resulting in 600 unique field conditions. The annual edge-of-field nitrate load from each grid cell was estimated as the product of DRAINMOD-predicted drain flow and the average annual nitrate concentration in drain flow, estimated from observations from related drainage sites in northern Indiana. Predicted monthly streamflow was in good agreement with the observed streamflow (Nash–Sutcliffe efficiency of 0.87 and 0.84 during the calibration and validation periods, respectively) and the predicted drain flow matched well with the measured drain flow (77.1 cm vs. 77.8 cm and 121.3 cm vs. 128.4 cm). Drainage water management decreased the average annual (1985–2009) predicted drain flow from 11.0 to 5.9 cm, and the total nitrate load through subsurface drainage from 236 to 126 ton (both about 47% reduction). The percent reduction in nitrate load varied between 40% and 53% for all combinations of drain spacing, soil parent material and cropping patterns, with drain spacing and soil parent material having a greater effect than cropping pattern. The methodology developed in this study showed potential for predicting the watershed-scale effects of subsurface drainage and drainage water management in drained agricultural watersheds.

The effects of climate variability, drain spacing, and growing season operational strategy on annual drain flow and crop yield were studied for a hypothetical drainage water management (DWM) system at Purdue University9s Water Quality Field Station using the DRAINMOD model. Drainage water management showed potential for reducing annual average (1915–2006) drain flow from all drain spacings (10–35 m) regardless of the growing season operational strategy, with reductions varying between 52 and 55% for the drain spacings considered. Approximately 81 to 99% of the annual drain flow reduction occurred during the non-growing season, depending on the operational strategy. Fixed DWM operational strategies led to an increase in mean predicted yield for narrower spacings compared with conventional drainage systems. Maximum yield was achieved with no control for drain spacings wider than 20 m in 50% of the years. Overall, the height of control had more influence on relative yield than the date of initiation of control. The greatest positive impacts of DWM on relative yield (1.2%) occurred in cool, dry years, while the greatest average negative impacts (−0.2%) occurred in cool, wet years. On average, with the best-case operation selected for annual weather conditions, DWM increased relative yield by approximately 0.8, 0.4, and 0.2% for the 10-, 20-, and 30-m drain spacing, respectively. Accumulated growing degree days and antecedent precipitation index show promise for identifying appropriate operational strategies for DWM.

The hypothetical effects of drainage water management operational strategy on hydrology and crop yield at the Purdue University Water Quality Field Station (WQFS) were simulated using DRAINMOD, a field-scale hydrologic model. The WQFS has forty-eight cropping system treatment plots with 10 m drain spacing. Drain flow observations from a subset of the treatment plots with continuous corn (Zea mays L.) were used to calibrate the model, which was then used to develop an operational strategy for drainage water management. The chosen dates of raising and lowering the outlet during the crop period were 10 and 85 days after planting, respectively, with a control height of 50 cm above the drain (40 cm from the surface). The potential effects of this operational strategy on hydrology and corn yield were simulated over a period of 15 years from 1991 to 2005. On average, the predicted annual drain flows were reduced by 60% (statistically significant at 95% level). This is the most significant benefit of drainage water management since it may reduce the nitrate load to the receiving streams. About 68% of the reduced drain flow contributed to an increase in seepage. Drainage water management increased the average surface runoff by about 85% and slightly decreased the relative yield of corn crop by 0.5% (both are not statistically significant at 95% level). On average, the relative yield due to wet stress (RYw) decreased by 1.3% while relative yield due to dry stress (RYd) increased by 1%. Overall, the relative crop yield increased in 5 years (within a range of 0.8�6.9%), decreased in 8 years (within a range of 0.2�5.5%), and was not affected in the remaining 2 years. With simulated drainage water management, the water table rose above the conventional drainage level during both the winter and the crop periods in all years (except 2002 crop season). The annual maximum winter period rise ranged between 47 cm (1995) and 87 cm (1992), and the annual maximum crop period rise ranged between no effect (2002) and 47 cm (1993).

Detailed location maps of tile drains in the Midwestern United States are generally not available, as the tile lines in these areas were laid more than 75 years ago. The objective of this study is to map individual tile drains and estimate drain spacing using a combination of GIS-based analysis of land cover, soil and topography data, and analysis of high resolution aerial photographs to within the Hoagland watershed in west-central Indiana. A decision tree classifier model was used to classify the watershed into potentially drained and undrained areas using land cover, soil drainage class, and surface slope data sets. After masking out the potential undrained areas from the aerial image, image processing techniques such as the first-difference horizontal and vertical edge enhance filters, and density slice classification were used to create a detailed tile location map of the watershed. Drain spacings in different parts of the watershed were estimated from the watershed tile line map. The decision tree identified 79% of the watershed as potential tile drained area while the image processing techniques predicted artificial subsurface drainage in approximately 50% of the Hoagland watershed. Drain spacing inferred from classified aerial image vary between 17 and 80 m. Comparison of estimated tile drained areas from aerial image analysis shows a close agreement with estimated tile drained areas from previous studies (50% versus 46% drained area) which were based on GIS analysis and National Resource Inventory survey. Due to lack of sufficient field data, the results from this analysis could not be validated with observed tile line locations. In general, the techniques used for mapping tile lines gave reasonable results and are useful to detect drainage extent from aerial image in large areas. These techniques, however, do not yield precise maps of the systems for individual fields and may not accurately estimate the extent of tile drainage in the presence of crop residue in agricultural fields and/or existence of other spatial features with similar spectral response as tile drains.

Grazing management practices affect watershed hydrology and water quality by altering vegetation cover, soil properties and manure production. The objective of this study was to assess the impacts of alternate grazing management practices including light continuous (LC), heavy continuous (HC) and multi-paddock (MP) grazing, and no grazing (EX; exclosure) on watershed hydrology and water quality of a rangeland-dominated (71% rangeland) Clear Creek Watershed (CCW) in north central Texas using the Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using the measured streamflow and sediment concentration data at the watershed outlet over a 34-year (1980–2013) and 16-year (1994–2009) period, respectively. Results indicated that shifting grazing management in the watershed from the baseline HC grazing to the improved MP grazing decreased surface runoff by about 47%, increased infiltration by 29%, and decreased streamflow by 27%. However, there were no significant differences in evapotranspiration (ET) among the grazing scenarios. Sediment load decreased from 90.1A—10 3 to 54.4A—10 3 ton (39.6%) under the MP grazing when compared to the baseline HC scenario. Improvements to grazing management have also resulted in a gradual reduction in high (0–10% exceedance interval) streamflows (and sediment loads), and thereby reduced the chances of flooding. Overall, the MP grazing practice was found to be the best grazing management practice in terms of water conservation, vegetation regrowth, water quality improvement and minimization of flood risk.

Groundwater quality degradation is a major threat to sustainable development in Texas. The aim of this study was to elucidate spatiotemporal patterns of groundwater fluoride (F) contamination in different water use classes in 16 groundwater management areas in Texas between 1960 and 2010. Groundwater F concentration data were obtained from the Texas Water Development Board and aggregated over a decadal scale. Our results indicate that observations exceeding the drinking water quality threshold of World Health Organization (1.5 mg F L) and secondary maximum contaminant level (SMCL) (2 mg F L) of the USEPA increased from 26 and 19% in the 1960s to 37 and 23%, respectively, in the 2000s. In the 2000s, F observationsSMCL among different water use classes followed the order: irrigation (39%)domestic (20%)public supply (17%). Extent and mode of interaction between F and other water quality parameters varied regionally. In western Texas, high F concentrations were prevalent at shallower depths (50 m) and were positively correlated with bicarbonate (HCO) and sulfate anions. In contrast, in southern and southeastern Texas, higher F concentrations occurred at greater depths (50 m) and were correlated with HCO and chloride anions. A spatial pattern has become apparent marked by "excess" F in western Texas groundwaters as compared with "inadequate" F contents in rest of the state. Groundwater F contamination in western Texas was largely influenced by groundwater mixing and evaporative enrichment as compared with water-rock interaction and mineral dissolution in the rest of the state.

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO-N) loadings to hypoxic conditions in the Gulf of Mexico. Hydrologic and water quality models, such as the Soil and Water Assessment Tool, are widely used to simulate tile drainage systems. The Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile flow and the corresponding NO-N losses. In this study, long-term (1983-1996) monitoring plot data from southern Minnesota were used to evaluate the SWAT version 2009 revision 531 (hereafter referred to as SWAT) model for accurately estimating subsurface tile drain flows and associated NO-N losses. A retention parameter adjustment factor was incorporated to account for the effects of tile drainage and slope changes on the computation of surface runoff using the curve number method (hereafter referred to as Revised SWAT). The SWAT and Revised SWAT models were calibrated and validated for tile flow and associated NO-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile flow and associated NO-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile flow and NO-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile flow and NO-N losses within 8 and 2%, respectively, of the observed values. Therefore, the Revised SWAT model is expected to provide more accurate simulation of the effectiveness of tile drainage and NO-N management practices.

We assessed spatial distribution of total dissolved solids (TDS) in shallow (50 m), intermediate (50-150 m), and deep (150 m) municipal (domestic and public supply) wells in nine major aquifers in Texas for the 1960s-1970s and 1990s-2000s periods using geochemical data obtained from the Texas Water Development Board. For both time periods, the highest median groundwater TDS concentrations in shallow wells were found in the Ogallala and Pecos Valley aquifers and that in the deep wells were found in the Trinity aquifer. In the Ogallala, Pecos Valley, Seymour and Gulf Coast aquifers,60% of observations from shallow wells exceeded the secondary maximum contaminant level (SMCL) for TDS (500 mg L(-1)) in both time periods. In the Trinity aquifer, 72% of deep water quality observations exceeded the SMCL in the 1990s-2000s as compared to 64% observations in the 1960s-1970s. In the Ogallala, Edwards-Trinity (plateau), and Edwards (Balcones Fault Zone) aquifers, extent of salinization decreased significantly (p0.05) with well depth, indicating surficial salinity sources. Geochemical ratios revealed strong adverse effects of chloride (Cl(-)) and sulfate (SO4(2-)) on groundwater salinization throughout the state. Persistent salinity hotspots were identified in west (southern Ogallala, north-west Edwards-Trinity (plateau) and Pecos Valley aquifers), north central (Trinity-downdip aquifer) and south (southern Gulf Coast aquifer) Texas. In west Texas, mixed cation SO4-Cl facies led to groundwater salinization, as compared to Na-Cl facies in the southern Gulf Coast, and Ca-Na-HCO3 and Na-HCO3 facies transitioning to Na-Cl facies in the Trinity-downdip regions. Groundwater mixing ensuing from cross-formational flow, seepage from saline plumes and playas, evaporative enrichment, and irrigation return flow had led to progressive groundwater salinization in west Texas, as compared to ion-exchange processes in the north-central Texas, and seawater intrusion coupled with salt dissolution and irrigation return flow in the southern Gulf Coast regions.

The nitrates (NO(3)-N) lost through subsurface drainage in the Midwest often exceed concentrations that cause deleterious effects on the receiving streams and lead to hypoxic conditions in the northern Gulf of Mexico. The use of drainage and water quality models along with observed data analysis may provide new insight into the water and nutrient balance in drained agricultural lands and enable evaluation of appropriate measures for reducing NO(3)-N losses. DRAINMOD-NII, a carbon (C) and nitrogen (N) simulation model, was field tested for the high organic matter Drummer soil in Indiana and used to predict the effects of fertilizer application rate and drainage water management (DWM) on NO-N losses through subsurface drainage. The model was calibrated and validated for continuous corn (Zea mays L.) (CC) and corn-soybean [Glycine max (L.) Merr.] (CS) rotation treatments separately using 7 yr of drain flow and NO(3)-N concentration data. Among the treatments, the Nash-Sutcliffe efficiency of the monthly NO(3)-N loss predictions ranged from 0.30 to 0.86, and the percent error varied from -19 to 9%. The medians of the observed and predicted monthly NO(3)-N losses were not significantly different. When the fertilizer application rate was reduced ~20%, the predicted NO(3)-N losses in drain flow from the CC treatments was reduced 17% (95% confidence interval [CI], 11-25), while losses from the CS treatment were reduced by 10% (95% CI, 1-15). With DWM, the predicted average annual drain flow was reduced by about 56% (95% CI, 49-67), while the average annual NO(3)-N losses through drain flow were reduced by about 46% (95% CI, 32-57) for both tested crop rotations. However, the simulated NO(3)-N losses in surface runoff increased by about 3 to 4 kg ha(-1) with DWM. For the simulated conditions at the study site, implementing DWM along with reduced fertilizer application rates would be the best strategy to achieve the highest NO(3)-N loss reductions to surface water. The suggested best strategies would reduce the NO(3)-N losses to surface water by 38% (95% CI, 29-46) for the CC treatments and by 32% (95% CI, 23-40) for the CS treatments.

The rapid development of canal irrigated areas and consequent supply of irrigation water at a highly subsidized price has disturbed hydrological equilibrium and caused adverse effects on soil and ground water quality in many canal command areas in India. There has been a rise in the water table and consequent degradation of soils through waterlogging and secondary salt build up in these command areas. This has caused millions of hectares of land gradually going out of production or experiencing reduced yields. It is estimated that an area of 8.4 million ha is affected by soil salinity and alkalinity in India. Large areas of prime agricultural land in some of the irrigation commands in Andhra Pradesh state of India are constantly waterlogged, while other commands are affected only during the post-monsoon season. The waterlogged and salt affected areas in Andhra Pradesh are estimated at around 0.46 million ha. Eight subsurface drainage pilot areas have been established in Andhra Pradesh as a part of various research projects over the past 25 years and the performance of these drainage systems has been evaluated. This paper reviews the experiences from these drainage pilot areas in Andhra Pradesh.

The potential impact of drainage water management (DWM) on the nitrate load from subsurface drainage in the Hoagland watershed in west central Indiana was assessed using the DRAINMOD 6.0 model. The watershed was divided into 6460 grid cells of 180 x 180 m size and the drain spacing, soil parent material and cropping pattern in each of the grid cells were identified from the analysis of high resolution aerial photographs and from the GIS-based analysis of soil, crop land and land cover datasets. The DRAINMOD model was applied to each cell and the nitrate loss through subsurface drains was estimated for the conventional and DWM cases. The delivery ratio (DR), which is the fraction of the nitrate load delivered from the field edge to the watershed outlet, was estimated for each of the grid cells using measured nitrate attenuation rates and calculated travel times. A 44% reduction from the current average (1987-2006) annual nitrate load of 155 metric tons was predicted with the implementation of DWM in all subsurface drained fields in the watershed. The effects of multiple management scenarios of DWM implementation based on drain spacing, soil parent material, DR and distance from the ditches/streams were evaluated with respect to the percent reduction in annual nitrate load at the watershed outlet. The highest percent reduction in average annual nitrate load, on a per unit area basis, was predicted from implementation of DWM in the grid cells formed from the Eolian sand parent material (58%) followed by the cells with

DRAINMOD, a computer simulation hydrologic model, was used to study the performance of a tile drainage system at the Purdue University Water Quality Field Station and simulate the hypothetical effects of drainage water management. The historic observations from a subset of the treatment plots with continuous corn were used to calibrate the model. The model was used to simulate the potential effects of drainage water management on hydrologic parameters and corn yield over a period of 7 years from 1997 to 2003. Preliminary results suggested that with drainage water management, the preferable dates of raising and lowering the outlet during the crop period were 40 and 118 days after planting (June 15 and August 31 when the date of planting was May 5), respectively. Seventy-eight days of control during the crop season and a control height of 50 cm (40 cm from the surface) were found to be optimum during this period. With drainage water management from June 15 to August 31 during crop season and from November 1 to March 31 in winter, on an average, the annual drain flows were reduced by 25% while runoff and relative yield of corn crop increased by about 37% and 2%, respectively. By practicing drainage water management, the water table was raised by a maximum of 72 cm during winter and by 21 cm during the crop period in the year 1999.

Both the evergreen redberry juniper (Juniperus pinchotii Sudw.) and deciduous honey mesquite (Prosopis glandulosa Torr.) are destructive and aggressive invaders that affect rangelands and grasslands of the southern Great Plains of the United States. However, their current spatial extent and future expansion trends are unknown. This study was aimed at: (1) exploring the utility of aerial imagery for detecting and mapping intermixed redberry juniper and honey mesquite while both are in full foliage using the support vector machine classifier at two sites in north central Texas and, (2) assessing and comparing the mapping accuracies between sites. Accuracy assessments revealed that the overall accuracies were 90% with the associated kappa coefficient of 0.86% and 89% with the associated kappa coefficient of 0.85 for sites 1 and 2, respectively. Z -statistics (0.102

Detailed location maps of tile drains in the Midwestern states of the United States are generally not available as the tile lines in these areas were laid more than 75 years ago. The objective of this study is to map tile drains in the Hoagland watershed in west--central Indiana using image processing tools such as decision tree classification and edge enhancement techniques applied to multiple spatial datasets. A decision tree classifier model that utilizes land cover, soil drainage, and surface slope data sets was used to classify the watershed into 'potential tile drained' and 'potential non--tiled' areas. After masking out the potential non--tiled areas from the aerial photograph, image processing techniques were used to create a detailed tile location map of the watershed. First difference horizontal and vertical filters were applied to enhance the edges that represent tiles and the edge enhanced images were classified in to 'tile' and 'no--tile' areas using density slice classification. Post--classification methods were applied to reclassify erroneous pixels. From the tile line map of the watershed, drain spacings in different parts of the watershed were estimated. The decision tree identified 79% of the watershed as potential tile drained area while the image processing techniques predicted tiles in 73% of the watershed. The average drain spacing in five subwatersheds varied from 41 to 43 m, which is within the recommended drain spacing of 23--45 m for the soils in this area. In general, the techniques used for mapping tile lines gave reasonably good results and hence are useful to detect tile lines from aerial photographs in large areas.