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Assessing the impacts of biofuel-induced land use change on hydrology, water quality and crop yield under the current and future climate scenarios enables selection of appropriate land uses and associated best management practices under the changing climate. In this study, the impacts of land use change from cotton (Gossypium hirsutum L.) to perennial grasses in the Double Mountain Fork Brazos watershed in the Texas High Plains were assessed using the Soil and Water Assessment Tool (SWAT). While switchgrass (Panicum virgatum L.) was assumed to replace cotton in irrigated areas, dryland cotton was replaced by Miscanthus × giganteus under the hypothetical land use change scenarios. Climate change impacts were assessed based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections of 19 General Circulation Models (GCMs) under two Representative Concentration Pathway (RCP) emission scenarios of RCP4.5 and RCP8.5 during two 30-year periods of middle (2040–2069) and end (2070–2099) of the 21st century. Median irrigation water use of cotton was simulated to decrease by 41%–61% in the future when compared to historic (1994–2009) period based on projections by 19 GCMs. Under the future climate change scenarios, when compared to cotton, median annual irrigation water use by switchgrass reduced by 62%–89%. Simulated future median total nitrogen load decreased by 30%–40% under perennial grasses when compared to future cotton land use. The median irrigated switchgrass yield decreased by 16%–28%, but the median dryland Miscanthus yield increased by 32%–38% under the future climate change scenarios.

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.

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.

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.

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 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.

Arundo donax (hereafter referred to as Arundo), a robust herbaceous plant, has invaded the riparian zones of the Rio Grande River and the rivers of the Texas Hill Country over the last two decades. Arundo was first observed along the Nueces River in central Texas in 1995 by the Nueces River Authority (NRA). It then spread rapidly downstream due to its fast growth rate and availability of streamflow for its consumptive use, and it completely displaced the native vegetation, primarily Panicum virgatum (hereafter referred to as switchgrass) in the riparian zone. It was hypothesized that Arundo reduced streamflows due to higher water use by Arundo when compared to switchgrass. The overall goal of this study was to assess the impacts of Arundo invasion on hydrology of the headwaters of the Nueces River through observed long-term streamflow and precipitation data analysis and simulation modeling with the Soil and Water Assessment Tool (SWAT). The observed data analysis indicated that while there was no significant change in monthly precipitation between the pre-Arundo invasion (1979–1994) and post-Arundo invasion (1995–2010) periods, streamflows changed significantly showing a positive (slightly increasing) trend during the pre-invasion period and a negative (slightly decreasing) trend during the post-invasion periods. The simulated average (1995–2010) annual evapotranspiration of Arundo in the seven Hydrologic Response Units (HRUs) in which Arundo invaded, was higher by 137 mm when compared to switchgrass. The water uptake by Arundo was therefore higher by 7.2% over switchgrass. Higher water uptake by Arundo resulted in a 93 mm higher irrigation (water use from the reach/stream) annually when compared to switchgrass. In addition, the simulated average annual water yield (net amount of water that was generated from the seven Arundo HRUs and contributed to streamflow) under Arundo was less by about 17 mm as compared to switchgrass. In conclusion, model simulations indicated that Arundo invasion in the Nueces River has caused a statistically significant increase in water uptake and reduction in streamflow compared to the native switchgrass, which previously dominated the headwaters.

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.

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 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.

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.

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.