While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water‐stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO 2 fertilization effect compared to an unconstrained GHG emission scenario.

1 Introduction Climate change poses a real threat to global food security [Schmidhuber and Tubiello, 2007; Lang and Heasman, 2015] with some regions being more at risk than others [Lobell et al., 2008; Wheeler and von Braun, 2013]. One of the most beneficial adaptation measures to tackle the detrimental impacts of climate change is irrigation [Rosenzweig and Parry, 1994], which, thanks to crop yields on average 2.7 times larger than their rainfed counterparts, supports 40% of global food production on only 20% of total cultivated land [UNESCO, 2012]. Expanding irrigation can contribute to increasing global production but can be costly and have serious environmental impacts [Reilly and Schimmelpfennig, 1999], including contributing to increased greenhouse gas (GHG) emissions [Carlson et al., 2017]. Another essential constraint to irrigated cropland expansion is freshwater availability. Food production is the largest user of freshwater with 70% of global withdrawal [UNESCO, 2012] and many areas are already water stressed [Wada et al., 2011]. Future climate change could exert further pressure on irrigation capabilities by altering water resources and water uses. More specifically, climate change is expected to affect water availability by altering the geographic distribution of water resources [Arnell, 1999, 2004], its temporal distribution [Middelkoop et al., 2001], and irrigation water requirements [Fischer et al., 2007; Konzmann et al., 2013; Wada et al., 2013]. Under those conditions, are current irrigation patterns sustainable? Which regions will be most affected? What will be the consequences of water shortages on irrigated crop production? Are current modeling frameworks, which generally do not account for changes in irrigation water availability, appropriate? While the impact of climate change on crops has been extensively studied, both at the regional level [e.g., Lobell et al., 2011; Auffhammer et al., 2012; Blanc, 2012; Tao et al., 2012] and at the global level [e.g., Arnell et al., 2013; Teixeira et al., 2013; Deryng et al., 2014], understanding the effect of climate change on irrigated crop yields is more challenging due to the complexity of the system to consider. Biophysical crop models are specifically designed to estimate crop yields under different climatic conditions, but they usually consider only two irrigation scenarios [Rosenzweig et al., 2014]: no irrigation (rainfed yield) or perfect irrigation with no water stress experienced by the crops (optimal irrigated yield). Water resources system models account for competing water uses but are not capable of estimating the effect of the resulting potential water limitations on crop yields. In the most extensive assessment to date, Elliott et al. [2014] assess the impact of future irrigation water availability on crop productivity at the global level using an ensemble of water supply and demand projections from 5 global climate models, 10 global hydrological models, and 6 global gridded crop models, thus accounting for the uncertainty in projections of climate change, hydrology, and crop modeling. This study, however, only considers a single GHG concentration scenario and does not simulate the possible benefits of abatement policies. Also, it considers water use and resources without spatial or temporal optimization of water allocation. The lack of optimization is a crude assumption that is not representative of current water management practices. Focusing on the U.S., Hejazi et al. [2015] do include a river routing and reservoir operations models in an integrated assessment framework but do not account for any uncertainty in projections of climate change other than two GHG emissions scenarios. In this U.S.‐focused study, we evaluate the impacts of climate change and socioeconomic stressors on water resources and crop production using a large ensemble of scenarios. To this end, we use the Water Resource System for the United States (WRS‐US) model version 2.0 [Blanc et al., 2014; Blanc, 2015] within the MIT Integrated Global System Model‐Community Atmosphere Model (IGSM‐CAM) modeling framework [Monier et al., 2013]. We extend the WRS‐US model to include a crop yield reduction module that estimates the effect of irrigation water shortage on crop yields. This framework allows for a spatially detailed analysis by covering 99 river basins in the US. Our study is driven by a large ensemble of 45 integrated economic and climate scenarios developed for the U.S. Environmental Protection Agency's Climate Change Impacts and risk analysis (CIRA) project [Waldhoff et al., 2015], which includes three different GHG mitigation scenarios, different global climate responses and initial conditions to account for the large uncertainty in climate change projections [Monier et al., 2015]. While our modeling framework allows us to track the impact of climate change and socioeconomic stressors on irrigated crop yields, we choose to keep irrigated areas fixed. We project changes in crop production that will be caused by climate stress and increases in water demand by other sectors such as energy production and municipal use, but in the absence of adaptation in the agricultural sector. This allows us to identify regions where we can expect future transitions in irrigated agriculture, either to rainfed crops or where agricultural production will decrease or disappear.

2 Methods 2.1 Integrated Assessment Framework In this study, the interaction between water resources and anthropogenic water requirements is analyzed using the IGSM‐WRS‐US integrated assessment framework. This section provides an overview of the framework schematized in Figure 1 (further details can be found in Blanc et al. [2014] and Blanc [2015]). Figure 1 Open in figure viewer PowerPoint Schematic of the IGSM‐WRS‐US framework illustrating the connections between the different components of the IGSM framework and the WRS‐US components. Within the integrated assessment framework, the global economy is represented by the Economic Projection and Policy Analysis (EPPA) model [Paltsev et al., 2005]. U.S. national‐level economic projections from EPPA are used to provide boundary conditions to the U.S. Regional Energy Policy (USREP) model [Rausch et al., 2010], a general equilibrium model of the U.S. economy with subnational detail. USREP's projections of economic activity in different regions of the U.S. are then used to determine water requirements, as detailed below. The USREP model is also coupled with the National Renewable Energy Lab Regional Energy Deployment System (ReEDS) model [Short et al., 2009; Rausch and Mowers, 2012] to provide highly resolved projections of electricity production and the corresponding withdrawal and consumption of water for thermal power generation cooling. The Earth system component of the integrated assessment framework includes land surface, atmospheric, and ocean processes, and provides the required variables to estimate crop water needs and geophysical water availability input into the WRS‐US model presented on the right‐hand side of Figure 1. The water resources considered in WRS‐US are composed of runoff (estimated using the IGSM‐CAM) and groundwater resources (see Blanc [2015] for more details). Anthropogenic water requirements are estimated for five sectors: irrigation, thermoelectric cooling (estimated directly by the ReEDS model), public supply (drinking water and other domestic uses by public utilities), self‐supply (mostly industrial) and the mining sector. Changes in requirements from the last three sectors are estimated as a function of population and gross domestic product per capita projections from USREP. Water withdrawals for irrigation are estimated with the CliCrop model [Fant et al., 2012], which simulates daily crop water requirements driven by daily accumulated precipitation, mean temperature, and temperature range from the IGSM‐CAM. These crop water requirements account for the effect of CO 2 concentrations on crop water use (via stomatal closure and biomass development), management practices as well as conveyance and field efficiency. Environmental water requirements are representative of policies protecting water ecosystems through the regulation of water levels and flows. See Fant et al. [2012] and [Blanc et al., 2014] for further details regarding the calculations of irrigation requirements. The estimated water resources and requirements are inputs to a Water System Management (WSM) module. For each of the 99 river basins (see Figure S1 and Table S1, Supporting Information, for a spatial representation of the river basin structure), the model allocates available water among users, each month, while minimizing annual water deficits (i.e., water requirements that are not met) and smoothing deficit across months. The allocation of water is solved simultaneously for all months of each year, and for all basins while respecting upstream/downstream relationships. This solving structure captures cooperation across basins by optimizing water allocation depending on water requirements and resources across all basins within the same water‐shed [Blanc, 2015]. Irrigation is a residual user [Molle and Berkoff, 2007] and water is allocated to this sector once the requirements of all the other sectors have been met. Water deficit is represented by the water supply requirement ratio (SRR), which is calculated monthly as the ratio of total water supplied over total water required for all sectors (including irrigation). This water stress indicator represents the physical constraints on anthropogenic water use. Stress to the irrigation sector in particular is represented by the SRR for irrigation, IR_SRR, calculated monthly as the ratio of water supplied for irrigation over water required by the agriculture sector. This stress indicator is used to calculate irrigated yield reductions due to insufficient of irrigation caused by water shortages. 2.2 Crop Yield Factor Module Allen et al., 1998 relative yield reduction is related to the corresponding relative reduction in evapotranspiration’. The yield factor, YF, which corresponds to the ratio of actual yield to optimal irrigated yield, is then calculated for each crop and growing season, gsc, as: (1) As shown in the right‐hand side of Figure 1 , the WRS‐US modeling framework was extended with a new crop Yield Factor Module (YFM) in order to estimate the effect of irrigation water shortages on crop yields. Following the CropWat model [], the ‘’. The yield factor, YF, which corresponds to the ratio of actual yield to optimal irrigated yield, is then calculated for each crop and growing season,, as: where the ratio of actual yield, Ya, and maximum yields, Yx, representing the crop yield factor are a function of actual and maximum crop evapotranspiration (ETa and ETx, respectively). Ky is a crop yield response factor that represents the sensitivity of crop yields to a reduction in evapotranspiration due to water shortage. Values for this parameter are also sourced from the CropWat model and reported in Table S2. For crops that are very sensitive to water shortage have Ky > 1 and the yield reduction is proportionally larger than the reduction in water use. Ky < 1 applies to crops that are more tolerant to water deficits and for which yields decrease less than proportionally to water use reduction. Crop water requirements depend on the crop‐growing stage [Brouwer et al., 1989]. Out of the four stages (initial, development, mid‐season, and late season) usually considered, the third ‘mid‐season’ stage, corresponding to the flowering and yield formation, is the period of greatest water need. Therefore, a water shortage within this season will have the largest detrimental effect on crop yields. We therefore use values of Ky which are specific to each of the four growing stages, gsc. The values are consistent with those employed by the CliCrop model which provides growing stages and water requirements to the crop YFM . asr), the crop yield factor, YF, is calculated annually as: (2) When considering water stress due to the lack of water availability for irrigation at the river basin level (), the crop yield factor, YF, is calculated annually as: USDA, 2003 USGS, 2011 Blanc et al. [ 2014 Blanc [ 2015 aS, is calculated as: (3) where IRarea at the county level, cnt, is the crop‐specific irrigated area []; see] and] for further details. Crop evapotranspiration under water stress, ET, is calculated as: where IR_SRR is calculated for each growing stage using the monthly IR_SRR estimated with the WSM module prorated by the share of each month within each growing stage. The term (ETx crop , cnt , gsc − ETa crop , cnt , gsc ) represents the crop irrigation requirements at the root to obtain maximum yield. An IR_SRR = 1 would imply that all the water required for irrigation is available. On the other hand, an IR_SRR = 0 means that none of the water necessary for irrigation is available and therefore irrigated crop yields are similar to rainfed crop yields. 2.3 Major Assumptions A set of major assumptions are made in the modeling framework regarding: (1) Conveyance and field efficiencies: they are assumed to remain constant over time to be consistent with our objective of estimating the effect of climate change without adaptation in the irrigation sector; (2) Groundwater resources: they are estimated to remain constant at 2005 levels unless groundwater extraction is greater than groundwater recharge; (3) the allocation of irrigation water to the various crop considered: we assume that all crops are affected equally by a shortage of water for irrigation, i.e., no specific crop has priority access to water over another crop; and (4) irrigated areas: we assume that they remain fixed at current levels with the explicit aim to estimate the effect of climate change on irrigated crops under actual cropping conditions (i.e., without adaptation) and identify the areas most vulnerable to irrigation shortages in the future. 2.4 Scenarios Water uses and resources are projected out to 2050 using a large ensemble of integrated economic and climate simulations from the IGSM‐CAM modeling framework [Monier et al., 2013] prepared for the CIRA project [Waldhoff et al., 2015]. This ensemble comprises three consistent socioeconomic and GHG emissions scenarios: a reference scenario (REF) with unconstrained emissions, similar to the Representative Concentration Pathway RCP8.5 [Van Vuuren et al., 2011] and two GHGs mitigation scenarios: POL4.5, a moderate mitigation scenario reaching 4.5 W m−2 by 2100, similar to RCP4.5; and POL3.7, a more stringent mitigation scenario reaching 3.7 W m−2, corresponding to an intermediate stabilization scenario between RCP4.5 and RCP2.6. More details on the emissions scenarios and their economic implications are given in Paltsev et al. [2015]. For each emission scenario, the IGSM‐CAM is run with three different values of climate sensitivity (CS = 2.0, 3.0 and 4.5°C), which are obtained by changing the strength of the cloud feedback in the climate model using a radiative cloud adjustment method (see Sokolov and Monier [2012]. For each set of emissions scenarios and climate sensitivity, a five‐member ensemble is created with a different representation of natural variability through initial condition perturbation. More details on the design of the climate ensemble and the analysis of the projections of temperature and precipitation changes over the U.S. can be found in Monier et al. [2015]. Contrary to Elliott et al. [2014], this ensemble is derived using a single climate model. However, Monier et al. [2016] shows that the range of agro‐climate projections from the IGSM‐CAM ensemble is similar to that of the Coupled Model Intercomparison Project Phase 5 (CMIP5) multimodel ensemble. That is because the IGSM‐CAM ensemble samples key sources of uncertainty, namely emissions levels, the global climate response (using different values of climate sensitivity) and the natural variability. In this study, we mainly focus on simulations with a climate sensitivity of 3.0°C (CS3.0) to identify the benefits of GHG mitigation. We present results from the five‐member ensemble mean to filter out noise associated with natural variability and thus extract the anthropogenic signal. While five initial conditions might not be enough to fully filter out natural variability, it is an improvement over current modeling studies and practices, which generally do not run with multiple initial conditions and thus do not filter out the role of natural variability. We further identify the range of projections associated with the uncertainty in natural variability to determine its contribution in our analysis. We also provide a brief analysis of the impact of the uncertainty in climate sensitivity for the unconstrained emissions scenario.

4 Discussion In this study, we project that by 2050, under a wide range of emissions scenarios and climate change projections, a number of U.S. basins will start experiencing water shortages while several basins will see their existing shortages severely accentuated. As a result, irrigated yields in these basins will be reduced, in extreme cases to levels that are only 10% of optimal irrigated yields. Our findings thus suggest that crop modeling studies that do not account for changes in the availability of irrigation water under varying socioeconomic drivers and climate change, in essence assuming optimal irrigated crop yields, can be misleading. However, the basins affected by water shortages generally do not contain most of the irrigated cropland areas. Therefore, while our analysis suggests that cropland expansion and land‐use change decisions can be constrained by water availability for irrigation, it also indicates a large potential for relocation of irrigated agriculture from water‐stressed regions to regions where irrigated agriculture is more sustainable. Taken together, these results demonstrate the importance of considering the integrated effect of climate change and socioeconomic stressors on water resources and crop yields at a detailed river basin level: water stress is highly localized and disaggregation at the 99 river basin level is necessary to estimate the impact of water shortage on irrigation water availability and resulting crop yields. At the U.S.‐wide level, our results show that under a no‐policy scenario, future irrigated yields factors, for all crops except forage and cotton, are projected to be higher than in the present. This increase in irrigated crop yield factors is driven by increased water availability in important growing basins but also by a reduction in irrigation demand thanks, in part, to increased crop water use efficiency caused by higher CO 2 concentrations. When considering GHG mitigation policies, results show that, in the absence of adaptation, mitigation policies enhance future yield factors for all crops, and even offset the projected decrease in irrigated cotton yield factor. In particular, we show that reductions in water stress associated with GHG mitigation under both policies far offsets the negative impact from reduced CO 2 concentrations compared to the reference scenario. Furthermore, the most ambitious GHG mitigation policy has the potential to reduce the number of basins affected by water stress, a finding that resonates with Strzepek et al. [2015] and Waldhoff et al. [2015]. Our analysis provides a unique and comprehensive effort to quantify the impact of water stress on irrigation while accounting for changes in water resources and competing uses from all sectors. This emphasizes the need to rely on integrated modeling frameworks that are capable of establishing better linkages between agriculture and water resources management in the face of climate change and socioeconomic stressors. It should be noted that this study only considers a single‐integrated assessment model and thus does not explore the structural uncertainty associated with different economic, climate, and water resources models. Existing studies of the effect of climate policies on water stress generally place little emphasis on uncertainty—for example, Hejazi et al. [2015] only consider two climate simulations from a single climate model. However, we know that the choice of pattern of precipitation change (associated with the climate model employed in this analysis) can greatly influences the outcome of the water model, with larger water stresses projected under a dry climate pattern than under a wet pattern [Blanc et al., 2014; Strzepek, et al., 2015]. In this study, we attempt to investigate the overall uncertainty in our results by considering multiple socioeconomic and GHG mitigation scenarios, different representations of natural variability, as well as different global climate system responses (via different climate sensitivities). Our results show a large range of impacts on irrigated crop yields when considering such a large ensemble of integrated economic and climate scenarios, and highlight the considerable uncertainty associated with natural variability in particular. Our modeling framework does not track feedbacks from sectoral water stress to economic activity. There is also no measure of adaptation taken to prevent water stress and no land‐use change from areas where water is scarce to locations with greater water availability. International trade is also not taken into account as a response to water‐stressed activities in the United States. These aspects are intentionally not considered in order to estimate the effect of climate change on irrigated cropping under actual conditions and therefore identify the areas the most vulnerable to irrigation shortages in the future. Also, our analysis focuses on crop yield factor relative to a potential fully irrigated crop. However, we do not simulate change in irrigated yield caused by changes in temperature. As shown in Sue Wing et al. [2015] using the same integrated economic and climate scenarios, climate change and the associated increase in CO 2 concentrations lead to heterogeneous changes to crop yields in the United States, which can be either negative and positive depending on the region.

5 Conclusion This study describes the application of the IGSM‐WRS‐US, a model of U.S. water resource systems, to estimate the effect of climate change and socioeconomic drivers on water stress and the resulting impact on crop productivity. To this end, a yield reduction module was integrated into the modeling framework. It is unique in its consistent treatment of the complex interactions between the climatic, biological, physical, and economic elements of the system. It identifies areas of potential stress in the absence of specific adaptive responses at the 99 river basin level for the continental United States through 2050 under a large ensemble of integrated economic and climate scenarios, including different GHG mitigation policies for the most commonly irrigated crops in the United States. On average, we find that irrigation in the Western part of the country will be affected by an increase in water shortages, with particular basins seeing severe increases in water stress. As a result we identify various basins where current irrigation is not sustainable. At the national level, however, climate and socioeconomic changes will entail an overall reduction in water stress and its effect on irrigated yields for all crops, except for forage and cotton. GHG mitigation policies are effective at limiting the detrimental effect of climate change on irrigated cotton and forage yields, but results show that a stringent policy (CS3.0 POL3.7) is necessary to considerably reduce the number of strongly affected basins. Overall, our study shows potential for adaptation strategies, such as improvements in irrigation efficiency to reduce irrigation demand, but also relocation of irrigated cropland to regions less prone to water stress, to further develop irrigated agriculture in the coming decades. At the same time, these adaptation measures will be costly, as they will require relocation of agricultural production and transport capacity. Additionally, regions which are projected to be irrigation‐constrained will lose irrigation's implicit value as an insurance mechanism against droughts and other adverse effects of climate change. Our study points to the areas and crops which will bear the burden of these costs.

Acknowledgments This work was partially funded by the U.S. Environmental Protection Agency's Climate Change Division, under Cooperative Agreement No. XA‐83600001 and by the U.S. Department of Energy, Office of Biological and Environmental Research, under grant DE‐FG02‐94ER61937. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. (For the complete list see http://globalchange.mit.edu/sponsors/current.html). The data used are listed in the references.

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