1. Introduction
Impacts of climate change on agricultural production and water resources have been reported globally [
1,
2,
3,
4,
5]. Ray et al. [
6] found that more than 60% of the variability in crop yield in top global production regions is associated with climate, with both positive and negative responses noted depending upon geographic locations and irrigation applications [
7,
8,
9]. For example, future climate change could increase corn and wheat yields in high latitudes and reduce them in middle to low latitudes [
9,
10]. Kang et al. [
8] found that yields of wheat, rice, and maize were more sensitive to precipitation than temperature and generally increased with increased precipitation. On the other hand, Kang et al. [
8] and others indicated increased crop production with a modest rise in average temperature of 1–3 °C, but decreasing yields above this range. From the hydrological modeling perspective, the Soil and Water Assessment Tool (SWAT) [
11] has been used to assess quality and quantity issues [
12,
13] to identify critical source areas [
14] and impacts on crop-yield [
15,
16] due to changes in climate and land uses in order to suggest improved management practices [
17].
The Southern Great Plains in the U.S. is a water-limited and sometimes highly irrigated agricultural and oil-producing region which has experienced recurring droughts, which in turn have caused surface water losses and variability in crop yield [
18]. Climate models project increased variability in precipitation and temperature for this region and thus suggest significant future variability in crop yield [
19,
20,
21], as well as policy challenges related to the management of water required for food and energy-related economic interests [
18,
22].
Oklahoma is located in the Southern Great Plains, and similar to many states in that region, agriculture plays a key role in the state’s economy. Therefore, understanding the effects of a changing climate on water resources and agricultural yields is crucial to developing sustainable and resilient mitigation measures. The water needs for crop irrigation, livestock, and aquaculture in Oklahoma amount to nearly 50% of the state’s total water use and that percentage is projected to rise by 11–16% by 2060 [
23,
24]. It is projected that future precipitation and temperature will vary significantly in the state [
25,
26], but it is unknown if these changes will result in an increase or decrease in water and crop yields. For example, an increase in temperature in the region may increase crop water requirements leading to higher irrigation costs, stress on groundwater sources, and reduced profitability for farmers [
27,
28]. Alternately, increased precipitation or changes to seasonality may offset the impact of increased temperature, increasing crop yields. Understanding such changes is important in the water-stressed basins of central and western Oklahoma.
4. Discussion
The potential future changes in precipitation and temperature and how they interact to affect the water balance in agricultural watersheds is crucial. Overall, the nine climate scenarios based on three downscaled CMIP5 climate projections indicated increases in both precipitation and temperature for the study watershed over the 2016–2040 period modeled. The overall modeled increase in precipitation of nearly 1.5%, could mean increased water availability and opportunity for groundwater recharge (
Table 4). Our estimates of increased precipitation in the study area are similar to the values estimated by Qiao et al. [
26] who used a 39-member ensemble of the downscaled CMIP5 climate projections for the Arkansas Red River Basin, but differs from the estimates by Garbrecht et al. [
18] of decreased precipitation in the region. However, the projected increase in average annual temperature of 1.9 °C in the watershed is similar to the increase Garbrecht et al. [
18] estimated. The diverging precipitation estimates between the Garbrecht et al. [
18] and our study could be due to the use of two different climate datasets; we used CMIP-5 projections while CMIP-3 was used by Garbrecht et al. [
18]. Compared to CMIP-3, the CMIP-5 models include an improved physical representation and integration of the processes in the atmosphere, ocean, and land with higher resolution and a new representation of anthropogenic forcing of climate [
46,
47]. It was found that compared to CMIP-3 simulations, CMIP-5 ensembles have improved regional-scale temperature distributions with no systematic change for precipitation [
46].
The GCMs in RCP 8.5 had the largest range in water yield, with values from +92.7% to −32.7% of the modeled historical yields. This extreme range in water yields (
Table 5) appears to be related to rainfall, which had its highest value in MPI-ESM-LR and lowest in CCSM4 relative to the historical climate (
Table 4). The overall increase in water yield occurred despite a significant reduction in average annual surface runoff (−17.9%), and thus is contributed entirely by increase in groundwater recharge (+58.4%) which has likely been influenced by a reduction in modeled actual ET (
Table 4). The relatively low water yield increase in summer seen in
Figure 6d could be due to a combination of higher temperature (+2.1 °C) and reduced precipitation (−4.6%) in the months of June and August compared to the historical climate (
Figure 5a,b). Our finding of increased water yield in the watershed is similar to findings reported from other watersheds in the U.S. For example, in an agriculturally intensive watershed in the northern Great Plains, Neupane et al. [
48] found climate change increased average water yield by 8–67%. Similar to modeled results from this study, Neupane et al. [
48] reported that the increased water yield was due primarily to increases in groundwater contribution. Gautam et al. [
15] reported a 29% increase in median water yield in a heavily agricultural experimental watershed of Missouri, USA under the CMIP-5 climate.
Crop modeling using the climate scenarios decreased winter wheat and grain sorghum yields and increased the yield of cotton in the watershed (
Table 5). An increase in temperature of 2.9 °C and relatively unchanged precipitation in the critical growing season could have led to lower soil moisture and suppressed winter wheat production. Our result of decreased winter wheat yield is consistent with the estimates of Rosenzweig et al. [
9] and Delphine et al. [
10], who found that wheat yield would decrease in low to mid-latitude areas of the globe due to climate change. Winter wheat is Oklahoma’s most valuable crop and decreased wheat yield is of important concern because of its economic significance locally in the watershed and regionally. Nearly one third of the study watershed is traditionally planted with winter wheat, and Oklahoma is the fourth largest producer of winter wheat in the U.S.
Cotton production is sensitive to temperature, and according to Adhikari et al. [
16] cotton yields in the Texas High Plains increased with temperature and sufficient water under future climate projections, including increased atmospheric CO
2. Their results indicated that the increased cotton yield could be partly attributed to increased temperature in the future, and that with additional atmospheric CO
2, cotton could potentially withstand the impacts of future climate variability if irrigation water remains available at current levels. In this modeling study, we allowed cotton irrigation at historical levels throughout the future simulation and thus, similar to Adhikari et al. [
16], well-irrigated cotton was able to benefit from increased temperature. The modeled dryland cotton yields (807.9 kg/ha) were much smaller than the irrigated crop (
Table 6) and showed no essential change from the modeled historical yield (808.3 kg/ha). Therefore, the potential climate benefits for future cotton production in the study area depend on sustainable management of water resources for irrigation.
This study represents an important first step towards understanding and adapting to the uncertainty that projected change in climate poses to an agricultural watershed. Projecting downscaled future climate scenarios onto the current mix of agricultural practices produces an understanding of future changes based on a familiar frame of reference, which is among the types of information needed by stakeholders such as agricultural advisors to alert local farmers of the need to adapt. Travis and Huisenga [
49] found that the occurrence of extreme climate events increased the rate of adaptation to changing climate among farmers, which implies action after poor yields. Schattman et al. [
50] noted that farmers perceive climate change risks in terms of known experience, and therefore are more likely to respond to adaptation planning information that incorporates typical activities.
Our study has important limitations that need to be addressed in future research. The first limitation is related to modeling of sediment and nutrient loadings which we excluded in this study but are important in the selection and implementation of best management practices in agricultural watersheds. Any changes in climate, hydrological response, and/or on-farm practices will likely change the yields of nutrients or sediments, and therefore should be studied. The next limitation is related to hydrological and climate model related uncertainty in the estimated water and crop yield; in this study we examined climate model related uncertainty by including an ensemble of three GCMs and three RCPs as suggested by Brown et al. [
51], which yielded a general understanding of potential hydrologic and standard crop yield changes. The next step would be to utilize a sophisticated crop yield model, in the manner of Adhikari et al. [
16] and Bao et al. [
52], and at a regional scale with several sources and a mix of important model input and management scenarios as suggested by Daggupati et al. [
12] and Akkari and Bryant [
53], to better understand and prepare potential farm adaptation strategies at local and regional scale.