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Article

Impacts of Ongoing Land-Use Change on Watershed Hydrology and Crop Production Using an Improved SWAT Model

by
Baogui Li
1,
Gary W. Marek
2,
Thomas H. Marek
3,
Dana O. Porter
4,
Srinivasulu Ale
5,
Jerry E. Moorhead
2,
David K. Brauer
2,
Raghavan Srinivasan
6 and
Yong Chen
1,*
1
College of Land Science and Technology, China Agricultural University, 2 Yuanmingyuan W. Rd., Haidian District, Beijing 100193, China
2
USDA-ARS Conservation and Production Research Laboratory, 300 Simmons Rd., Unit 10, Bushland, TX 79012, USA
3
Texas A&M AgriLife Research and Extension Center at Amarillo, 6500 Amarillo Blvd. W., Amarillo, TX 79106, USA
4
Texas A&M AgriLife Research and Extension Center at Lubbock, 1102 E. Drew St., Lubbock, TX 79403, USA
5
Texas A&M AgriLife Research and Extension Center at Vernon, 11708 Highway 70 South, Vernon, TX 76384, USA
6
Ecology and Conservation Biology, Texas A&M University, 2138 TAMU, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Land 2023, 12(3), 591; https://0-doi-org.brum.beds.ac.uk/10.3390/land12030591
Submission received: 31 January 2023 / Revised: 25 February 2023 / Accepted: 27 February 2023 / Published: 1 March 2023
(This article belongs to the Section Water, Energy, Land and Food (WELF) Nexus)
Browse Figures
Versions Notes

Abstract

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

1. Introduction

The Ogallala Aquifer serves as an important groundwater source for agricultural production in the semi-arid Texas High Plains (THP). The THP region is one of the highly productive irrigated agricultural regions in the United States (U.S.). However, long-term intensive irrigation pumping, coupled with limited recharge has resulted in reduced water levels in the southern zones of the aquifer [1,2]. The saturated thickness of the aquifer declines with a southward gradient in general (Figure 1), and the related decrease in pumping capacity efficiently divides the THP into the Northern High Plains of Texas (NHPT) and the Southern High Plains of Texas (SHPT). The NHPT consists of 25 counties in the northern Texas panhandle, while the SHPT is composed of 16 counties expanding from northwest Lubbock to Midland (Figure 1).
The aquifer’s saturated thickness and associated well capacities have a directly influence on land use and crop composition in each region. As a relatively less water-demanding crop, cotton (Gossypium hirsutum L.) is the major cultivated crop under both irrigated and rainfed conditions in the SHPT, with approximately 100 years of production history [3]. The SHPT produces nearly 15% of U.S. cotton [3]. In the NHPT region, the major irrigated summer crop is grain corn (Zea mays L.), and several counties of this region with the largest average corn yields in the nation [3]. However, large supplemental irrigation amounts are often needed for the relatively water-intensive corn production in this semi-arid environment because of inadequate and unpredictable in-season precipitation. Furthermore, the essential dual-purpose crop of winter wheat (Triticum aestivum L.) in the NHPT is also grown for both cattle grazing and grain production, planted and managed in immense acreage under both irrigated and rainfed conditions (Figure 1). In addition, the general irrigation amount of the corn and winter wheat throughout the growing stage in THP is 417 mm and 218 mm, respectively.
Recently enacted regulations limiting groundwater withdrawals for irrigation to mitigate depletion of the Ogallala Aquifer [4], construction of several new large-capacity cotton gin facilities in the NHPT, and the potential for higher farm profitability with cotton production systems are driving forces for increased cotton production acreage in the NHPT. In fact, first-hand knowledge of the research team and NHPT land use maps from 2015 to 2017 affirm such land-use conversion is already in progress in the NHPT [3]. Subsequently, agricultural land use and agronomic practices in the NHPT could further migrate toward those of the SHPT, with the dominant agricultural land use of cotton (Figure 1). Changes in crop composition or land-use regime at a regional level should markedly influence the water budget by altering the irrigation demands, surface runoff, groundwater recharge, and crop evapotranspiration (ETc) [5,6,7,8]. For example, Neupane and Kumar [9] predicted the surface runoff increased by 4% using the Soil and Water Assessment Tool (SWAT) model in the Big Sioux River watershed due to the conversion of 10% of grassland to corn. Therefore, land-use change from corn or winter wheat to cotton in the NHPT may also dramatically affect the hydrologic cycle of the region. However, it requires time for producers to adapt and optimize their management strategies for this emerging cotton production in the NHPT.
It is crucial to select suitable models for hydrological assessment and crop predictions. Recently, hydrological models are commonly used to access and predict hydrologic processes such as the watershed-scale model of the SWAT, the small watershed/field-scale model of the Agricultural Policy/Environmental eXtender (APEX), the crop model of the Decision Support System for Agrotechnology Transfer (DSSAT), and the field-scale model of the Root Zone Water Quality Model (RZWQM) [10,11,12]. In this study, the SWAT model was chosen to evaluate the land-use change. The SWAT model is a semi-distributed, continuous-time, basin-scale, and process-based hydrological model, which is more suitable for a large watershed simulation with a high computation efficiency [13]. In addition, SWAT is an open-source model. In this study, we used an improved SWAT model with the MAD auto-irrigation method, and the unrestricted source code of SWAT 2012 revision 664 for the developers was conveniently obtained from the official website http://swat.tamu.edu/ (accessed on 28 February 2023) [10]. Management strategies related to land-use change need to be evaluated at the watershed scale before their adoption largely extension [14,15,16,17]. Therefore, assessments of the watershed scale land-use change impacts are deemed crucial for informing and carrying out applicable policies and strategies to lessen any negative effects. Studies assessing the impacts of replacing a food (corn) or a dual-purpose winter crop (winter wheat) with a drought-tolerant oilseed and fiber crop (cotton) on the water cycle are deficiency in the arid or semi-arid regions such as the NHPT. To the best of our knowledge, none of the previous studies have supplied a comprehensive assessment of the identified ongoing change of land-use under both irrigated and dryland conditions on a watershed scale.
In this study, the Palo Duro watershed (PDW) in the NHPT was chosen to fill the research gap. In addition, the Double Mountain Fork Brazos watershed (DMFBW) located in the SHPT, which has approximately 30% cotton land use (40% and 60% under irrigated and dryland conditions, respectively) [3], will be used as a reference for conventional cotton management practices (Figure 1). The irrigation scheduling of management allowed depletion (MAD) method has been widely utilized in actual irrigation scheduling worldwide [18,19,20,21]. However, the SWAT model with current auto-irrigation functions is incapable to reproduce this representative MAD-based irrigation scheduling. Therefore, a physically-based MAD auto-irrigation function was developed [13], which considered not only the soil properties in the profile but also the crop growth parameters to schedule irrigation. Further, we used this improved MAD auto-irrigation function in the SWAT model for land-use change assessment. The purpose of this study is to evaluate the influence of land-use change of corn or winter wheat to cotton on irrigation, ETa, crop production, and surface runoff in the PDW in the NHPT. Specifically, (1) the SWAT management allowed depletion (SWAT-MAD) model was evaluated for irrigated cotton land use in the DMFBW of the SHPT, where the MAD auto-irrigation trigger value for cotton was identified by comparing with actual irrigation, streamflow, total nitrogen (TN) load, and cotton lint yield data; and (2) the long-term effects of adopting identified cotton management practices from the DMFBW to the PDW on the crop production and hydrologic cycle were explored under both irrigated and rainfed conditions.

2. Materials and Methods

2.1. Study Watersheds

The DMFBW (32°42′44″–33°43′30″ N; 100°8′36″–102°45′47″ W) in the SHPT and the PDW (35°47′12″–36°14′21″ N; 101°16′36″–102°26′19″ W) in the NHPT have delineated areas of approximately 6000 and 2600 km2, respectively (Table 1). The average annual precipitation in the DMFBW varies from 457 to 559 mm, and the average annual maximum and minimum temperatures are about 24 °C and 9 °C. As for the PDW, the long-term average annual precipitation varies from 320 to 425 mm, and the average annual maximum and minimum temperatures are 22 °C and 5 °C, respectively. The topography in both watersheds is relatively flat. The cotton cultivation in the DMFBW and the irrigated corn as well as the dual-purpose winter wheat production in the PDW has a long history (Table 1). The irrigation source is the Ogallala Aquifer. The main soil types in the DMFBW are Amarillo sandy loam and Acuff sandy clay loam. However, Sherm silty clay loam and Perryton silty clay loam are the major soils in the PDW [22].
In the DMFBW, seven weather stations from the National Oceanic and Atmospheric Administration-National Centers for Environmental Information (NOAA-NCEI) were accessed for daily precipitation, and minimum and maximum air temperature data from 1990 to 2009. Two U.S. Geological Survey (USGS) gages (08079600 and 08080500; Gage I and Gage II) with streamflow data from 1994 to 2009 are located in the DMFBW (Figure S1 in Supplementary Materials). Three research weather stations operated by the Texas High Plains Evapotranspiration (TXHPET) network [23] measured daily precipitation, minimum and maximum air temperatures, solar radiation, wind speed, and relative humidity from 1995 to 2014 were used in the PDW (Figure S1). Streamflow data from 2000 to 2014 at the USGS gage (07233500) were obtained in the PDW.

2.2. Descriptions of SWAT and SWAT-MAD Models

As a semi-distributed, continuous-time, basin-scale, and process-based hydrological model [10], the primary components of the SWAT model are comprised of crop growth, water quality, and hydrology, and the elevation, land use, soil, climate, and management practices are the major data needed for setting up the model [11]. The SWAT model has been successfully utilized for simulating water quality and quantity in many watersheds worldwide [24,25,26,27].
The default options for auto-irrigation in the model are soil water content (SWC; Equation (1)) and plant water demand (PWD; Equation (2)) (widely used method) methods [28]. A manual irrigation method is also available in the model, and it allows users to input the irrigation amount for a particular date [29]. Recently, a more representative MAD auto-irrigation method (Equation (3)) was developed by Chen et al. [13] based on ten years of measured data from a large weighing lysimeter experiment at the USDA-ARS Conservation and Production Research Laboratory (CPRL), Bushland, TX and integrated into the SWAT model (hereafter referred to as SWAT-MAD model). The MAD method triggers irrigation according to a user-defined allowable depletion percentage of plant available water, determined by the crop-specific maximum rooting depth and site-specific soil properties [13]. The equations for the MAD algorithm can be found in the Supplementary Materials.
SWC method: sol_sumfc − sol_sw > auto_wstr
where sol_sumfc is the amount of water in the soil profile at field capacity (mm), sol_sw is the amount of water in soil profile on a given day (mm), and auto_wstr is the water stress value that triggers irrigation (mm).
PWD method: strsw < auto_wstr
where strsw is the fraction of potential plant growth achieved on the day where the reduction is caused by water stress, and auto_wstr is the water stress value that triggers an irrigation event (0–1). The recommended range is 0.90 to 0.95.
MAD method: (sol_sumfc - sol_sw)/PAW > MAD
where PAW is plant available water, determined by both soil properties and plant maximum rooting depth, and MAD expressed as a decimal value ranging from 0 to 1. MAD values approaching 0 indicate irrigation management that allows relatively less depletion of soil water before triggering irrigation, resulting in low plant water stress. By contrast, values approaching 1 denote irrigation management that allows relatively more depletion of soil water before applying irrigation, leading to high plant water stress.
In this study, the ArcSWAT (version 2012.10_2.19; revision 664) for the ArcGIS 10.2.2 platform was used for two watersheds. The SWAT model calibration and validation for streamflow and TN load with the purpose of maximizing Nash-Sutcliffe efficiency (NSE) through using the model Calibration and Uncertainty Procedures (SWAT-CUP 2012) with the Sequential Uncertainty Fitting version-2 (SUFI-2) [30].

2.3. Scenario Design and Analysis

In a previous study, we evaluated the SWAT model for the DMFBW using the PWD auto-irrigation method [31]. In a more recent study [32], we evaluated the SWAT-MAD model for the PDW. The newly developed MAD auto-irrigation method was evaluated for irrigated cotton land use in the DMFBW in this study. Also, the MAD threshold for this watershed was identified by comparing simulated values with actual irrigation, streamflow, TN load, and cotton yield data. The NSE [33], coefficient of determination ( R 2 ) [34], and percent bias (PBIAS) [35] were used to evaluate the performance of the SWAT-MAD model in the DMFBW. The calibrated hydrology, water quality, and cotton growth parameters using the SWAT-MAD model in the DMFBW are listed in Table S1 in the Supplementary Materials.
As for the scenario analysis of land-use change, the growth parameters and management practices identified for cotton using SWAT-MAD in the DMFBW were used in the PDW. The land-use change impacts of the replacement of corn or winter wheat with cotton under both irrigated and dryland conditions were assessed. In the THP, corn is usually planted in mid-May and harvested in the middle of October with irrigation management. Winter wheat is generally cultivated in mid-October and harvested at the end of June under both irrigated and dryland conditions. Cotton is commonly planted in mid-May and harvested around the end of October with irrigation and rainfed management. According to the crop types and management practices, we designed four land-use change scenarios (Table 2).

3. Results and Discussion

3.1. Evaluating the Performance of the SWAT-MAD Model in the Double Mountain Fork Brazos and Palo Duro Watersheds

According to the NASS Irrigation and Water Management Survey conducted during 1988–1994, 1994–1998, 1998–2003, and 2003–2008, the average annual irrigation amount applied for cotton was 345.4 mm in the counties within the DMFBW. The simulated results for cotton irrigation by the MAD auto-irrigation method (346.9 mm) were very close to the survey data as compared to that simulated by the PWD auto-irrigation function (324.5 mm). The MAD irrigation method in the THP is commonly used for actual irrigation management, which may partially account for the good irrigation simulation of the MAD auto-irrigation method compared to the PWD auto-irrigation function. As an essential and significant component of water balance, evapotranspiration is critical for the hydrologic cycle from a local scale to a global scale [36]. Crop ET is a primary component of terrestrial water cycling, particularly in irrigated land use [37]. According to the detailed water balance analysis for the DMFBW, results also showed that approximately 90% of the water inputs (irrigation + precipitation) were lost because of ETa in this semi-arid watershed (Table 3). Therefore, ETa is essential as it controls the variation of water and heat energy between the atmosphere and land surface [38]. Less than 1% of the water inputs yielded as surface runoff under cotton land use for irrigated conditions (Table 3). A negligible percolation amount was simulated in the cotton land use, which was consistent with local reports [39,40]. Finally, the increase in simulated irrigation amount using the SWAT-MAD model compared to the SWAT-PWD model (346.9 vs. 324.5 mm) was mainly attributed to ETa under irrigated cotton land use.
Improvement of model performance statistics in simulating irrigated cotton yield was observed using the MAD auto-irrigation method compared to the PWD auto-irrigation method in the DMFBW (Figure S2). According to Tables S2 and S3, the improvement in the prediction of cotton irrigation water use and yield did not lead to evidently improved prediction of streamflow and TN load at the stream gages in the DMFBW. In contrast, improved irrigation simulation for intensively irrigated corn (overall PBIAS: −9.9% vs. −22.1% for SWAT-MAD vs. SWAT-PWD) and the winter crop of wheat (overall PBIAS: 0.98% vs. 17.3% for SWAT-MAD vs. SWAT-PWD) at Moore County in the PDW resulted in improved streamflow prediction (Table S4). The NSE, R 2 , and PBIAS in streamflow simulations were 0.74, 0.89, & −13.7% and 0.77, 0.91, & −9.4% using the SWAT-PWD and SWAT-MAD models during the calibration (2000–2006) in the PDW, respectively. Those model simulation statistics were 0.91, 0.92, & 12.4% and 0.92, 0.92, & 10.8% during the validation (2007–2014). The findings indicated that improving the simulation of the terrestrial system processes to a certain extent might not be converted into the improvement of simulation results of the riverine system at the watershed outlet. The complex processes of the watershed were most likely to cause offsets of the positive and negative water imbalance during the hydrological processes [41]. In the end, the results at the watershed outlet might not change.

3.2. Hydrological Responses of Simulated Land-Use Changes in the Palo Duro Watershed Using the SWAT-MAD Model

Changes in hydrological variables of irrigation, ETa, water yield, surface runoff, and percolation in the PDW under different land-use change scenarios were compared in Table 2 and Table 3. In this semi-arid watershed, more than 90% of water input (precipitation + irrigation) was lost through ETa, and hence the irrigation requirement and management practices of winter wheat, corn, and cotton in the THP were found to have a substantial influence on the water balances under different land-use change scenarios. Spera et al. [42] also revealed that land-use change affects the water cycle to the atmosphere via evapotranspiration. Cotton (May to November) has a longer growing period compared to corn (May to October) in the THP, and the growing period of cotton is opposite to winter wheat (October to June). Therefore, the hydrologic impacts can be very different when changing land use from corn to cotton or from winter wheat to summer cotton.
Results in this study indicated that the average (2000–2014) annual irrigation amount was reduced by ~21% (Table 4) when land use in the baseline irrigated corn Hydrologic Response Units (HRUs) was changed to irrigated cotton. Cotton has a lower irrigation demand compared to corn even with a longer heat unit accumulation to maturity in the THP. Compared with the irrigated corn land use, the reduction in irrigation amount further caused the decrease in water yield, ETa, surface runoff, and percolation under irrigated cotton scenario by approximately 62%, 7%, 63%, and 96%, respectively. However, when irrigated winter wheat was replaced with irrigated summer cotton, irrigation water use was increased by 46%. The associated increase in ETa was 18%. Nonetheless, surface runoff, percolation, and water yield were reduced by 20%, 85%, and 20%, respectively. The dual-purpose winter wheat is commonly not adequately irrigated as negligible increases in yield do not make up for management input costs in the NHPT [28,43]. In light of this, replacing irrigated winter wheat with irrigated cotton might be more economically feasible in the semi-arid NHPT. Under the dryland conditions, land-use conversion from winter wheat to cotton only showed a 0.1% decrease in ETa but with a 15% reduction in water yield and surface runoff (Table 4). Finally, the combined effect of replacing all the corn and winter wheat land uses with the corresponding cotton management resulted in the reductions of irrigation, surface runoff, percolation, and water yield by 2%, 43%, 96%, and 43%, respectively, while ETa increased by 1.6%.
On the entire watershed scale of all agricultural land use types, results showed the changes in irrigation ranged from −10.3% (replacement of irrigated corn scenario) to 8.9% (replacement of irrigated winter wheat scenario) among the four land-use changes compared with the baseline scenario (Table 5). However, the change in ETa was within 3% among land-use change scenarios. The land-use change from irrigated corn to irrigated cotton exhibited a high percentage of reduction (~19%) in water yield and surface runoff compared to other land-use change scenarios. In general, all land-use scenarios generated negligible percolation (<1.5 mm) in this semi-arid region with heavy clay soil.

3.3. Spatial Variations of Simulated Irrigation, ETa, Surface Runoff, and Stream Discharge under Cotton Land-Use Change Scenarios in the Palo Duro Watershed

Spatial variability in simulating annual average irrigation, ETa, and surface runoff under cotton land-use change scenarios are shown in Figure 2 and Figure 3. The land-use conversion from irrigated corn to irrigated cotton indicated decreases in irrigation from 13% to 27%, with a slight variation across the entire watershed (Figure 2a). However, the variation from irrigated wheat to irrigated cotton exhibited an increase in irrigation. The high alterations (ranging from 7% to 189%) were mainly focused on the upstream of the PDW (Figure 2a). This is mainly because the changes of ETa may also be driven by water supply in addition to land use changes in both upstream and downstream [44]. The changes of ETa under land-use changes from irrigated corn or irrigated wheat to irrigated cotton scenarios were consistent with those of irrigation, with slight decreases (ranging from 1% to 8%) and increases (varied from 3% to 46%) in ETa, respectively (Figure 2b). A negligible variation (±1%) in ETa for land-use change from dryland wheat to dryland cotton was presented in the whole watershed. Land-use change from corn or wheat to cotton under irrigation and rainfed conditions led to reductions in surface runoff at various degrees. Specifically, the replacement of irrigated corn with irrigated cotton caused an obvious decline in the surface runoff with a range of 58%–71% on the entire watershed (Figure 3). The land-use change from irrigated wheat to irrigated cotton resulted in a more than 16% decline in surface runoff upstream. Nevertheless, reductions in surface runoff for the dryland cotton scenario differed from 3% to 33% compared to the baseline scenario of dryland wheat, which was concentrated downstream of the PDW (Figure 3). All land-use change scenarios indicated reductions in stream discharge compared to the dryland wheat, particularly for the scenarios of land-use change from irrigated corn to irrigated cotton and replacing all corn and wheat land uses with cotton (Figure 4). In a previous study, Hurkmans et al. [45] estimated that the projected land use change scenarios in the Rhine basin lead to an increase in streamflow, the opposite results possibly due to the studied watershed and model utilization. Intuitively, the upstream and tributary of the PDW had relatively small reductions in stream discharge compared to the downstream and main channels (Figure 4). This was partially explained by the most of the rivers in the upstream and tributary of the PDW are seasonal or long-term non-flow rivers.

3.4. Seasonal Variability of Modeled Hydrologic Parameters in the Palo Duro Watershed

Compared to the baseline corn land use, the land-use change from irrigated corn to cotton indicated the reductions in irrigation amounts during the period of the major growing season of summer crops in June, July, and August by 66%, 42%, and 10%, respectively (Figure 5a). In addition, the ETa was generally decreased in the land use of irrigated cotton, except for the planting month (May) and late growing season (September and October) (Figure 5b), which was most probably because of the longer growing season of cotton than corn. This is an important water conservation finding given the depleting groundwater levels in the southern Ogallala Aquifer region. Monthly surface runoff had a good response to the precipitation pattern, particularly in June and August (Figure 5c). The land-use change to cotton resulted in a continuous decline in surface runoff (ranging from 52% to 95%) and soil water content (ranging from 44% to 79%) in the study watershed (Figure 5c,d). Cotton, a perennial woody shrub, has a relatively more extended root depth compared to corn [46,47], which also means more soil profile water is available for cotton growth relative to corn. This may partially explain the lower soil moisture and associated lower surface runoff in the cotton land use than corn. Jia et al. [48] also found that a well-developed root system can loosen and enhance the porosity of the soil, and thus decrease the surface runoff.
A substantial shift was determined for monthly irrigation amounts of winter wheat and cotton owing to the very different growing periods (Figure 6a). The irrigation requirements were larger in July and August for cotton land use compared to other months regardless of the land uses. The ETa increased apparently in July, August, and September by 284%, 283%, and 224%, respectively, when replacing irrigated winter wheat with irrigated cotton (Figure 6b). However, the reductions in ETa were found from November to May with a change range of 55% to 71%. Once again, a continuous decrease in surface runoff (varied from 3% to 94%) and soil water content (varied from 0.1% to 63%) was also identified under the land use of irrigated cotton compared to the irrigated winter wheat land use (Figure 6c,d). Based on 14 years of measurements among five land-use types, Wei et al. [49] reported that cropland had the highest surface runoff. The second was pastureland, followed by woodland and grassland. However, the lowest surface runoff was measured under the shrubland. Cotton has perennial shrub characteristics, which are managed as an annual cash crop in the THP [47].
Under dryland conditions, year-round precipitation was the same for winter wheat and cotton land uses. Results have shown that the ETa was increased evidently in June, July, August, and September by 21%, 82%, 51%, and 25%, respectively, for dryland cotton relative to the dryland winter wheat (Figure 7a). Nevertheless, the ETa reductions were discovered from October to May with a range of 16% to 61%. A good response of surface runoff to rainfall distribution was detected under rainfed land uses (Figure 7b). The decreases in surface runoff changed from 1% to 83% under the dryland cotton land use compared to the dryland winter wheat, except for a 5% increase in June. Under cotton land use, the distinctly different growing period was the dominant reason for the decreased surface runoff. The majority of rainfall was allocated in the cotton-growing season, and the established ground cover was in favor of a decrease in surface runoff generation. With the exception of May (13% increase), the soil moisture reductions varied from 5% to 78% when the replacement of dryland winter wheat was with dryland cotton (Figure 7c).

3.5. Production Potential of Cotton Yield When Moving Northward

Although the same cotton management practices and growth parameters from the DMFBW in the SHPT were adopted in the PDW in the NHPT, it is worth noting that the irrigated cotton yield in the baseline irrigated corn HRUs in the PDW was ~52% lower than that in the DMFBW (Table S5). A 53% reduction in irrigated cotton yield was also found in the baseline irrigated winter wheat HRUs in the PDW compared to that in the DMFBW. The dryland cotton yield in the baseline dryland winter wheat HRUs in the PDW was ~40% less than that of the DMFBW (Table S5). The decrease in cotton yield was mainly attributed to the shorter heat unit accumulation in the PDW in the north compared to the DMFBW in the south, which was caused by the decrease in solar radiation and air temperatures in the PDW. Reduced heat unit accumulation might have decreased the time duration for cotton to effectively assimilate CO2 and use solar radiation, which might have adversely altered the accumulation of cotton biomass and final yield [50,51,52,53]. Cotton has a high heat unit accumulation to maturity (close to 2400 °C-day), and the optimal air temperature for cotton growth is approximately 27 °C to 28 °C [54]. The decrease in air temperature by ~3 °C in the PDW would result in adverse conditions for cotton growth compared to the temperature conditions in the DMFBW. Different management practices and cotton cultivars may be better suited for the distinct climate in the NHP and may improve yield while reducing irrigation amounts.

4. Conclusions

In this study, the SWAT-MAD model was evaluated in the DMFBW using observed streamflow, TN load, and NASS reported irrigation and cotton yield data. The MAD auto-irrigation method improved the simulations of irrigation and cotton yield compared to the PWD auto-irrigation function. However, no clear improvement in streamflow and TN load predictions was found at the outlet of the DMFBW in which the dominant agricultural land use is less water-demanding cotton. In the PDW, an evident improvement in irrigation simulation for intensively irrigated corn using the MAD method resulted in improved model performance statistics in streamflow simulation.
From the entire watershed perspective, the emerging land-use change from irrigated corn to irrigated cotton in the PDW showed reductions in irrigation, ETa, surface runoff, and water yield of 10.3%, 1.5%, 19.5%, and 19.2%, respectively. However, the land-use change from winter wheat to cotton under the irrigated conditions indicated increased irrigation and ETa of 8.9% and 2.4%. A continuous reduction in soil moisture was identified under cotton land use compared to corn and winter wheat no matter the irrigation or dryland management. Less than 3% of reductions in the studied hydrologic parameters were found when replacing dryland winter wheat with dryland cotton. At the watershed level, the replacement of all corn and winter wheat land uses with the corresponding cotton management had the reductions of irrigation, surface runoff, percolation, and water yield by 1.4%, 23.4%, 6.1%, and 23%, respectively. Overall, these emerging cotton land-use changes demonstrated the potential for water conservation, particularly by replacing irrigated corn land use. However, irrigated and dryland cotton yields in the PDW were 50% and 40% lower compared to those in the DMFBW, respectively. In this case, short-season cotton cultivars and best management practices, such as adjustment of planting window and improved irrigation management, need to be further investigated in the PDW.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/land12030591/s1. Table S1: Default and calibrated values of hydrology, water quality, and cotton growth parameters using SWAT-MAD in the Double Mountain Fork Brazos watershed. Table S2: Monthly statistical parameters for the model streamflow calibration and validation on two USGS gages in the Double Mountain Fork Brazos watershed. Table S3: Model performance statistics during calibration and validation for monthly total nitrogen loads at the watershed outlet in the Double Mountain Fork Brazos watershed. Table S4: Performance statistics for observed and simulated monthly stream-flow in the Palo Duro watershed using SWAT-PWD and SWAT-MAD. Table S5: Comparison of the average (2000–2009) annual cotton yield production potential (Mg ha−1) between the Double Mountain Fork Brazos watersheds (DMFBW) and the Palo Duro watersheds (PDW) over the overlay time period under both irrigated and dryland conditions. Figure S1: Locations of weather stations and USGS gaging stations in the Double Mountain Fork Brazos and Palo Duro watersheds. Figure S2: Comparison of simulated and observed cotton lint yield in Lynn County under irrigated conditions using the plant water demand (PWD) and management allowed depletion (MAD) auto-irrigation methods in the Double Mountain Fork Brazos watershed. References [13,28,55,56] are cited in the supplementary materials.

Author Contributions

Conceptualization, B.L. and Y.C.; Methodology, B.L. and Y.C.; Software, G.W.M., T.H.M., R.S. and Y.C.; Formal analysis, B.L.; Investigation, G.W.M.; Data curation, Y.C.; Writing—original draft, B.L. and S.A.; Writing—review & editing, G.W.M., T.H.M., D.O.P., J.E.M., D.K.B., R.S. and Y.C.; Visualization, B.L.; Supervision, G.W.M. and Y.C.; Project administration, Y.C.; Funding acquisition, G.W.M., D.K.B. and Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Chinese Universities Scientific Fund [grant numbers 1191-15051002, 1191-15052008, 1191-10092004, and 1191-31051204]; the National Institute of Food and Agriculture, U.S. Department of Agriculture [grant number NIFA-2021-67019-33684]. USDA-ARS [grant number 3090-13000-016D].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing is not applicable.

Acknowledgments

This research was supported in part by the Ogallala Aquifer Program, a consortium between USDA-Agricultural Research Service, Kansas State University, Texas A&M AgriLife Research, Texas A&M AgriLife Extension Service, Texas Tech University, and West Texas A&M University. We gratefully thank the anonymous reviewers for their valuable comments and suggestions for improving this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Saturated thickness of the Ogallala Aquifer and major land uses of two representative watersheds in the Northern and Southern High Plains of Texas.
Figure 1. Saturated thickness of the Ogallala Aquifer and major land uses of two representative watersheds in the Northern and Southern High Plains of Texas.
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Figure 2. The spatial distribution of simulated annual irrigation and ETa under cotton land-use change scenarios in the Palo Duro watershed.
Figure 2. The spatial distribution of simulated annual irrigation and ETa under cotton land-use change scenarios in the Palo Duro watershed.
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Figure 3. The spatial distribution of simulated surface runoff under cotton land-use change scenarios in the Palo Duro watershed.
Figure 3. The spatial distribution of simulated surface runoff under cotton land-use change scenarios in the Palo Duro watershed.
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Figure 4. Comparison of stream discharge (m3/s) between the baseline and all land-use change scenarios in the Palo Duro watershed.
Figure 4. Comparison of stream discharge (m3/s) between the baseline and all land-use change scenarios in the Palo Duro watershed.
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Figure 5. Monthly comparison of hydrologic parameters (mm) under the baseline irrigated corn and the land-use change scenario of irrigated cotton in the Palo Duro watershed. Irrigation (a), actual evapotranspiration (b), surface runoff (c), and soil water content (d).
Figure 5. Monthly comparison of hydrologic parameters (mm) under the baseline irrigated corn and the land-use change scenario of irrigated cotton in the Palo Duro watershed. Irrigation (a), actual evapotranspiration (b), surface runoff (c), and soil water content (d).
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Figure 6. Monthly comparison of hydrologic parameters (mm) under the baseline irrigated winter wheat and the land-use change scenario of irrigated cotton in the Palo Duro watershed. Irrigation (a), actual evapotranspiration (b), surface runoff (c), and soil water content (d).
Figure 6. Monthly comparison of hydrologic parameters (mm) under the baseline irrigated winter wheat and the land-use change scenario of irrigated cotton in the Palo Duro watershed. Irrigation (a), actual evapotranspiration (b), surface runoff (c), and soil water content (d).
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Figure 7. Monthly comparison of hydrologic parameters (mm) under the baseline dryland winter wheat and the land-use change scenario of dryland cotton in the Palo Duro watershed. Actual evapotranspiration (a), surface runoff (b), and soil water content (c).
Figure 7. Monthly comparison of hydrologic parameters (mm) under the baseline dryland winter wheat and the land-use change scenario of dryland cotton in the Palo Duro watershed. Actual evapotranspiration (a), surface runoff (b), and soil water content (c).
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Table
Table 1. Description of study watersheds in the Texas High Plains.
Table 1. Description of study watersheds in the Texas High Plains.
AreaWeather StationSimulation PeriodLand UseSoil Type
Double Mountain Fork Brazos watershed (32°42′44″−33°43′30″ N; 100°8′36″−102°45′47″ W)
6000 km271990–2009
1990–1993 warmup		CottonAmarillo sandy loam
Acuff sandy clay loam
Palo Duro watershed (35°47′12″−36°14′21″ N; 101°16′36″−102°26′19″ W)
2600 km231995–2014
1995–1999 warmup		Corn
Winter wheat		Sherm silty clay loam
Perryton silty clay loam
Table
Table 2. Scenario design in the Palo Duro watershed.
Table 2. Scenario design in the Palo Duro watershed.
Scenario IDScenario Description
(a)Replacement of corn (mid-May to mid-October) in baseline irrigated corn HRUs with identified irrigated cotton (mid-May to end of October) management
(b)Replacement of winter wheat (mid-October to end of June) in baseline irrigated wheat HRUs with identified irrigated cotton management
(c)Replacement of winter wheat (mid-October to end of June) in baseline dryland wheat HRUs with identified dryland cotton (mid-May to end of October) management
(d)Replacement of corn and winter wheat in baseline irrigated corn and all wheat HRUs with respectively identified cotton management
HRUs denote Hydrologic Response Units.
Table
Table 3. Comparison of the average (1994–2009) annual water balance parameters using plant water demand and management allowed depletion auto-irrigation methods in the entire Double Mountain Fork Brazos watershed and cotton HRUs.
Table 3. Comparison of the average (1994–2009) annual water balance parameters using plant water demand and management allowed depletion auto-irrigation methods in the entire Double Mountain Fork Brazos watershed and cotton HRUs.
Unit (mm)Plant Water DemandManagement Allowed Depletion
(a) Entire watershed
Precipitation517.3517.3
Irrigation30.232.3 (6.9 *)
Actual evapotranspiration505.5502.2 (−0.7)
Surface runoff14.5514.30 (−1.7)
Percolation20.0625.47 (27.0)
Water yield22.3222.61 (1.3)
(b) Cotton HRUs (combined irrigated and dryland)
Precipitation496.3496.3
Irrigation97.5104.2 (6.9)
Actual evapotranspiration590.0596.4 (1.1)
Surface runoff2.262.33 (3.0)
Percolation0.0030.005
Water yield2.963.01 (1.6)
(c) Irrigated cotton HRUs
Precipitation483.1483.7
Irrigation324.5346.9 (6.9)
Actual evapotranspiration803.6824.5 (2.6)
Surface runoff2.413.32 (37.7)
Percolation00.003
Water yield3.704.58 (23.7)
* The number in the parentheses is the percent change using management allowed depletion method relative to plant water demand method.
Table
Table 4. Comparison of the average (2000–2014) annual water balance parameters (mm) under baseline and land-use change scenarios in the corn and winter wheat HRUs in the Palo Duro watershed.
Table 4. Comparison of the average (2000–2014) annual water balance parameters (mm) under baseline and land-use change scenarios in the corn and winter wheat HRUs in the Palo Duro watershed.
ScenarioBaseline ScenarioLand-Use Change Scenario
(a) Replacement of corn in the baseline irrigated corn HRUs with irrigated cotton
Precipitation375.5375.5
Irrigation416.8327.9 (−21.3 *)
Actual evapotranspiration731.1681.1 (−6.8)
Surface runoff60.622.7 (−62.6)
Percolation0.430.02 (−95.9)
Water yield60.822.8 (−62.4)
(b) Replacement of wheat in the baseline irrigated winter wheat HRUs with irrigated cotton
Precipitation368.9368.9
Irrigation218.2319.1 (46.2)
Actual evapotranspiration569.6672.9 (18.1)
Surface runoff19.315.4 (−20.0)
Percolation0.020.003 (−85.0)
Water yield19.415.6 (−19.8)
(c) Replacement of wheat in the baseline dryland winter wheat HRUs with dryland cotton
Precipitation411.2411.2
Actual evapotranspiration395.4394.8 (−0.1)
Surface runoff19.716.7 (−15.3)
Percolation0.010.00 (−100.0)
Water yield19.816.8 (−15.2)
(d) Replacement of corn and wheat in the baseline irrigated corn and all wheat HRUs with respectively managed cotton
Precipitation390.4390.4
Irrigation179.0175.2 (−2.1)
Actual evapotranspiration539.2547.7 (1.6)
Surface runoff32.218.2 (−43.3)
Percolation0.140.01 (−95.7)
Water yield32.318.4 (−43.1)
* The number in the parentheses is the percent change with each land-use change scenario relative to the baseline scenario.
Table
Table 5. Comparison of the average (2000–2014) annual water balance parameters (mm) under baseline and land-use change scenarios in the entire Palo Duro watershed.
Table 5. Comparison of the average (2000–2014) annual water balance parameters (mm) under baseline and land-use change scenarios in the entire Palo Duro watershed.
ScenarioBaselineIrrigated Corn to
Irrigated Cotton		Irrigated Wheat To
Irrigated Cotton		Dryland Wheat to Dryland CottonAll Corn and Wheat to Cotton
Rainfall394.8394.8394.8394.8394.8
Irrigation129.4116.1 (−10.3 *)140.8 (8.9)No change127.5 (−1.4)
ETa495.9488.4 (−1.5)507.6 (2.4)495.6 (−0.1)500.0 (0.8)
Surface runoff28.9523.29 (−19.5)28.51 (−1.5)28.28 (−2.3)22.16 (−23.4)
Percolation1.11.04 (−5.4)1.1 (0.01)1.1 (−0.2)1.03 (−6.1)
Water yield29.5723.9 (−19.2)29.13 (−1.5)28.89 (−2.3)22.78 (−23.0)
* The number in the parentheses is the percent change with each land-use change scenario relative to the baseline scenario.
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Li, B.; Marek, G.W.; Marek, T.H.; Porter, D.O.; Ale, S.; Moorhead, J.E.; Brauer, D.K.; Srinivasan, R.; Chen, Y. Impacts of Ongoing Land-Use Change on Watershed Hydrology and Crop Production Using an Improved SWAT Model. Land 2023, 12, 591. https://0-doi-org.brum.beds.ac.uk/10.3390/land12030591

AMA Style

Li B, Marek GW, Marek TH, Porter DO, Ale S, Moorhead JE, Brauer DK, Srinivasan R, Chen Y. Impacts of Ongoing Land-Use Change on Watershed Hydrology and Crop Production Using an Improved SWAT Model. Land. 2023; 12(3):591. https://0-doi-org.brum.beds.ac.uk/10.3390/land12030591

Chicago/Turabian Style

Li, Baogui, Gary W. Marek, Thomas H. Marek, Dana O. Porter, Srinivasulu Ale, Jerry E. Moorhead, David K. Brauer, Raghavan Srinivasan, and Yong Chen. 2023. "Impacts of Ongoing Land-Use Change on Watershed Hydrology and Crop Production Using an Improved SWAT Model" Land 12, no. 3: 591. https://0-doi-org.brum.beds.ac.uk/10.3390/land12030591

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