climate change and irrigation demand uncertainty and adaptation

G Model ARTICLE IN PRESS EJRH-36; No of Pages 18 Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Contents lists available at ScienceDirect Journal of Hydrology: Regional Studies journal homepage: www.elsevier.com/locate/ejrh Climate change and irrigation demand: Uncertainty and adaptation Sean A Woznicki a, A Pouyan Nejadhashemi a,∗, Masoud Parsinejad b a Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, United States b Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran a r t i c l e i n f o Article history: Received January 2014 Received in revised form 12 December 2014 Accepted 17 December 2014 Available online xxx Keywords: Irrigation demand Climate change SWAT Crop yield Adaptation Uncertainty a b s t r a c t Study region: The Kalamazoo River Watershed, southwest Michigan, USA Study focus: Climate change is projected to have significant impacts on agricultural production Therefore, understanding the regional impacts of climate change on irrigation demand for crop production is important for watershed managers and agricultural producers to understand for effective water resources management In this study, the Soil and Water Assessment Tool was used to assess the impact of climate change on corn and soybean irrigation demand in the Kalamazoo River Watershed Bias-corrected statistically downscaled climate change data from ten global climate models and four emissions scenarios were used in SWAT to develop projections of irrigation demand and yields for 2020–2039 and 2060–2079 Six adaptation scenarios were developed to shift the planting dates (planting earlier and later in the growing season) to take advantage of periods with greater rainfall or lower temperature increases New hydrological insights for the region: Uncertainty in irrigation demand was found to increase moving from 2020–2039 to 2060–2079, with demand generally decreasing moving further into the future for corn and soybean A shift in timing of peak irrigation demand and increases in temperature lead to corn yield reductions However, soybean yield increased under these conditions Finally, the adaptation strategy of planting earlier increased irrigation demand and water available for transpiration, while delaying planting resulted in demand decreases for both crops © 2014 The Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) ∗ Corresponding author at: Department of Biosystems & Agricultural Engineering, Farrall Agriculture Engineering Hall, 524 S Shaw Lane, Room 225, Michigan State University, East Lansing, MI 48824, United States Tel.: +1 517 432 7653; fax: +1 517 432 2892 E-mail address: pouyan@msu.edu (A.P Nejadhashemi) http://dx.doi.org/10.1016/j.ejrh.2014.12.003 2214-5818/© 2014 The Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 xxx.e2 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Introduction Population growth and land use change due to agricultural expansion and deforestation have significantly increased pressure on global freshwater resources (Nejadhashemi et al., 2012) As climate change becomes more prevalent globally, the future availability of fresh water for human consumption, agricultural production, and manufacturing becomes more uncertain By the end of the 21st century, the projected range of global temperature increases relative to 1980–1999 is between 1.1 and 6.4 ◦ C, depending on greenhouse gas emissions (Solomon et al., 2007) Meanwhile, the magnitude of projected precipitation changes varies widely depending on geographic region and spatial scale (Bates et al., 2008), while there is still disagreement regarding the magnitude and sign of potential impacts On a day-to-day basis, more heavy precipitation events are predicted, even in some regions where mean rainfall is likely to decrease (Solomon et al., 2007) These changes are expected to exert additional pressure on agricultural production, while atmospheric CO2 has the potential to improve photosynthesis by up to 30% (Long and Ort, 2010) A number of studies have attempted to understand the effects of climate change on water use in agriculture in the form of changes in net irrigation requirements, demand, and crop water use This is important because the agricultural industry is the largest user of fresh water–water withdrawals for irrigation account for 70% of all water use globally (Fischer et al., 2007) However, a majority of these studies were performed on large spatial scales (e.g global, continental, or regional) at low resolution (e.g monthly temperature and precipitation data), with a few watershed-scale exceptions (Gondim et al., 2012; Save et al., 2012) Save et al (2012) modeled changes in irrigation requirements in a Spanish watershed using dynamically downscaled daily climate data and the Soil and Water Assessment Tool (SWAT) Depending on the crop (among corn, apples, and alfalfa), irrigation requirements were projected to increase by 40–250% by the end of the 21st century, attributed directly to decreases in growing season water availability, increases in evapotranspiration, and changes in crop phenology In a Brazilian watershed, Gondim et al (2012) determined that irrigation water demand will increase by 8–9% by the mid-21st century using monthly climate projections Demand increases were attributed to projected rainfall decreases (11–18%) and evapotranspiration increases (6.5–8%) Harmsen et al (2009) used statistically downscaled climate change data to understand changes in monthly cumulative precipitation, reference evapotranspiration (ET0 ), and the resulting precipitation deficit between the two in Puerto Rico Precipitation deficit in February increased up to 90 mm depending on emissions scenario and location, while in September precipitation excess increased by up to 277 mm Xiong et al (2010) projected that irrigation demand of three cereal crops in China will slightly decrease in lower emissions scenarios and increase by 44% and 36% in the 2020s and 2040s, respectively for higher emissions scenarios Thomas (2008) examined irrigation demand across China for the 2030s, projecting both increases and decreases in demand depending on the region For example, decreasing evapotranspiration in some locations result in demand decreases by 100 mm, while drier regions in western China can expect increases in irrigation demand Globally, Fischer et al (2007) projected that by 2080 two-thirds of irrigation requirement increases can be attributed to warming and changes in precipitation patterns, while the remaining third of increases are due to extended growing seasons in temperature and sub-tropical regions Overall, global net irrigation requirements increase by over 50% in developing regions and 16% in developed regions Schaldach et al (2012) examined changes in irrigation water requirements in 2050 in North African and Europe based on climate and land use change Depending on the region, climate model, and socioeconomic conditions, demand may decrease by up to 30% or increase by up to 264%, indicating large uncertainties in prediction For example, in Zimbabwe corn irrigation water requirements were projected to increase by 66% by the 2050s and 99% by the 2090s, although there was considerable uncertainty in these projections (Nkomozepi and Chung, 2012) while for paddy rice irrigation requirements in Korea, mean increases by 2050s and 2090s were projected to be 2.4% and 7.9%, respectively (Chung and Nkomozepi, 2012) Previous studies that determined the impacts of climate change on irrigation demand examined a small number of models and scenarios and therefore could not capture the wide range of climate model predictions The proposed study is unique in that ten global climate models (GCMs) combined with four emissions scenarios are used to consider the wide range of uncertainty in climate models and their impact on irrigation demand Using a limited number of GCMs results in a limited view of Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e3 plausible future climate, unreliable projections of the future, and consequently poor decision-making and adaptation (Gohari et al., 2013) In addition, most studies consider large spatial scales (e.g global or regional) with low resolution, limiting applicability for producers in need of adaptation strategies In this study, the climatological data and irrigation demand is examined at a fine temporal resolution (daily), while most studies consider monthly or annual time scales The goal of this study is to understand how irrigation demand is impacted by climate change for corn and soybean, two common crops grown in the Kalamazoo River Watershed, Michigan The specific objectives of this study are to: (1) determine the impacts of climate change on key elements in water balance; (2) understand the future change in irrigation demand; (3) map the spatial variability in irrigation demand; and (4) examine possible adaptation strategies based on shifting planting date Materials and methods 2.1 Study area The Kalamazoo River Watershed, hydrologic unit code (HUC) 04050003, was the subject of this study (Fig 1) Located in southwest Michigan, the watershed area is approximately 4844 km2 , with the Kalamazoo River draining into Lake Michigan Elevation ranges from 176 m to 386 m above sea level, while 50% of the watershed area is less than 270 m The watershed is primarily agricultural and forested About 46% of the land is dedicated to agriculture, primarily corn, soybean, and pasture Deciduous forest and forested wetlands cover 35% of the watershed, while urban land occupies 14% of the land The remaining land use is comprised of various vegetable crops Soils within the watershed are predominantly sandy Sandy loam, loam, sand, and loamy sand make up 32%, 24%, 11%, and 9%, respectively Hydrologic soil groups A and B are dominant, covering 35% and 49% of the watershed, respectively Meanwhile, D soils only occur on 3% of the watershed area 2.2 SWAT model description The Soil and Water Assessment Tool (SWAT), developed by the United States Department of Agriculture-Agricultural Research Service (USDA-ARS,) simulates the impact of varying topography, soils, land use, and management practices on hydrology, water quality, and over long time periods (Arnold et al., 1998) SWAT is a physically based, semi-distributed, hydrological/water quality model that is capable of simulation on a daily time-step SWAT delineates a watershed into subwatersheds based on topography characteristics Subwatersheds are further discretized into hydrologic response units (HRUs) based on homogeneous land use, soil type, and slope characteristics Calculations are generally competed at the HRU level and aggregated to the subwatershed and watershed scales There are multiple components simulated by the SWAT model, including weather, hydrology, soil erosion and sediment transport, nutrient cycling and transport, plant growth, and land management practices (Gassman et al., 2007) Overland hydrology in SWAT is based on the water balance (Neitsch et al., 2011) Surface runoff is calculated using the SCS curve number equation (Mockus, 1972), which is based on rainfall, surface storage, interception, infiltration prior to runoff, and a retention parameter based on soils, land use management, slope, and soil water content Multiple pathways of water in the soil are simulated, including plant uptake, evaporation, percolation into shallow and deep aquifers, and lateral flow for streamflow contribution Main components of the land phase of the hydrologic cycle are change in soil water content, precipitation, surface runoff, evapotranspiration, percolation out of the root zone, and return flow from groundwater into the root zone Potential evapotranspiration is simulated in SWAT using the Penman–Monteith method (Monteith, 1965), which accounts for energy necessary to sustain evaporation, surface resistance, aerodynamic resistance, and strength of water vapor removal mechanisms Actual evapotranspiration is calculated by accounting for evaporation of rainfall intercepted by the plant canopy, potential transpiration based on plant growth under ideal conditions, and soil water evaporation Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 xxx.e4 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Fig Kalamazoo River Watershed; (a) land use, (b) weather station and streamflow gauging station locations Crop growth in SWAT is simulated by accumulation of heat units (difference between daily mean temperature and base temperature for crop growth) throughout the growing season (Neitsch et al., 2011) Maturity is reached when the number of accumulated heat units equals potential heat units Actual Potential growth is simulated using leaf area development, light interception, and biomass production assuming species-specific radiation use efficiency Canopy cover, height, and root development are also simulated based on heat units Water, nitrogen, and phosphorus uptake by the plant Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e5 are simulated based on soil water content, fraction of nitrogen in plant biomass, and fraction of phosphorus in plant biomass, respectively SWAT simulates crop growth constraints such as water stress, temperature stress and nutrient stress (Neitsch et al., 2011) Water stress is based on the relationship between actual and potential plant transpiration Temperature stress is modeled exponentially with daily average air temperature and optimal temperatures for plant growth, where divergence from optimal conditions results in growth-reducing stress Irrigation application in SWAT can be manually scheduled or automatically applied based on soil water deficit below field capacity (Neitsch et al., 2011) Water can be withdrawn from a reach, reservoir, shallow aquifer, deep aquifer, or source outside the watershed Limits can be placed on withdrawal based on minimum streamflow, fraction of total streamflow that can be withdrawn, or a maximum irrigation amount that cannot be exceeded in one day Other parameters for consideration are an irrigation efficiency factor (accounting for conveyance and evaporative losses) and a surface runoff ratio (fraction of irrigated water that is converted to runoff) 2.3 Data collection 2.3.1 Physiographic data SWAT model setup and parameterization requires the use of multiple spatial datasets Topography data was obtained from the United States Geological Survey National Elevation Dataset in the form of a 10 m resolution digital elevation model, which was used in watershed delineation The 2011 Cropland Data Layer (30 m resolution) developed by the United States Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) was selected for land use/land cover representation Spatial and tabular soil data was obtained from the USDA Natural Resources Conservation Service Soil Survey Geographic Database at a 1:24,000 resolution Common agricultural practices and rotations within the watershed were implemented for corn, soybean, and winter wheat land uses 2.3.2 Climate data Historical climate data from 1980 to 1999 was obtained from the National Climatic Data Center Daily precipitation, maximum temperature, and minimum temperature variables were available for nine precipitation and temperature gauges within the watershed Projected future climate scenarios were obtained from the United States Geological Survey (USGS) Geo Data Portal (http://cida.usgs.gov/climate/gdp/) The scenarios are a suite of bias-corrected statistically downscaled daily precipitation, maximum temperature, and minimum temperature data with a 1/8◦ spatial resolution, available from 1960 to 2100 (Hayhoe et al., 2013) Based on this resolution, 34 grid cells were used in SWAT Data from ten coupled atmosphere–ocean general circulation models (AOGCMs) driven by four Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) storylines (Nakicenovic et al., 2000) were used AOGCMs included were the following: Parallel Climate Model (PCM), Community Climate System Model version (CCSM3), Geophysical Fluid Dynamics Laboratory (GFDL) 2.0, GFDL 2.1, Hadley Centre Coupled Model version (HADCM3), Bergen Climate Model version (BCM2), Coupled Global Climate Model T47 (CGCM3T47), CGCM3-T63, Centre National de Recherches Meteorologiques (CNRM), ECHAM5 (developed by the Max Planck Institute), and ECHO (developed by Meteorological Institute of the University of Bonn (Germany), Institute of KMA (Korea), and Model and Data Group) Each model was forced with at least two SRES storylines developed using projections of population change, demographics, technology, and socio-economic factors to estimate greenhouse gas emissions (Dalton and Jones, 2010) The SRES storylines and their greenhouse gas emissions projections were: higher (A1FI), mid-high (A2), mid (A1B), and lower (B1) Using multiple climate projections and storylines allows for consideration of various parametric, structural, and forcing uncertainties (Knutti et al., 2010) The resulting number of scenarios (combination of GCMs and SRES storylines) was 35 Atmospheric CO2 concentrations were obtained from the BERN carbon cycle model, which vary based on year and SRES storyline (IPCC, 2001) CO2 concentrations for SRES storylines and time slices used are presented in Table Two future time slices were selected for analysis: 2020–2039 and 2060–2079 SWAT model projections of irrigation demand for these time slices were compared to the 1980–1999 model control Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model ARTICLE IN PRESS EJRH-36; No of Pages 18 xxx.e6 S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Table Atmospheric CO2 concentrations Time slice A1B (ppm) A1FI (ppm) A2 (ppm) B1 (ppm) 1980–1999 2020–2039 2060–2079 352 447 601 352 449 702 352 444 620 352 432 518 Adapted from IPCC (2001) period By analyzing the changes between the control and future time slices across all models and SRES storylines (hereby referred to as a “scenario”), an ensemble of projected impacts of climate change on irrigation demand is developed Monthly precipitation and temperature for the control and future time slices are presented in Figs and 3, respectively 2.4 Calibration and validation SWAT model calibration and validation of streamflow and crop yield were performed to ensure that the model replicates observed physical behavior before extending the study to understand the impacts of change on water quantity and crop growth in the watershed Daily streamflow calibration and validation occurred at three USGS gauging stations in the watershed: 04105000 (Battle Creek at Battle Creek, MI), 04105500 (Kalamazoo River near Battle Creek, MI), and 04106000 (Kalamazoo River at Comstock, MI) The USGS station no 04106000 was calibrated from 1985 to 1992 and validated from 1993 to 1999 The remaining stations (04105000 and 04105500) were calibrated from 1980 to 1989 and validated from 1990 to 1999 Calibration and validation was completed using observed climate data from nine weather stations displayed in Fig 1) The periods of calibration and validation were selected based on the time period of available data Three statistical criteria were used to evaluate goodness-of-fit between the simulated and observed streamflow data: Nash–Sutcliffe efficiency (NSE), root mean square error-observations standard deviation ratio (RSR), and percent bias (PBIAS) Ranging from negative infinity to one (with a value of one indicating perfect fit), NSE determines the relative magnitude of the residual variance compared to the observed data variance (Moriasi et al., 2007) RSR standardizes root mean square error using the Fig Kalamazoo River Watershed monthly total precipitation for model control period (1980–1999) versus (a) 2020–2039 and (b) 2060–2079 across all climate scenarios Data from Hayhoe et al (2013) Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e7 Fig Kalamazoo River Watershed mean daily maximum and minimum temperature for model control period (1980–1999) versus (a) 2020–2039 and (b) 2060–2079 across all climate scenarios) Data from Hayhoe et al (2013) standard deviation of observations, with values ranging from zero to a large positive number (where a lower number indicates better model performance) PBIAS measures the tendency of the simulated data to over-predict or under-predict compared to the observed data, where the optimal value is zero and small positive and negative percentages are acceptable Based on streamflow goodness-of-fit recommendations by Moriasi et al (2007) for a monthly timestep, a satisfactory NSE is greater than 0.50, satisfactory RSR is less than or equal to 0.70, and satisfactory PBIAS lies between −25% and +25% Annual crop yield calibration was performed for the two major crops in the Kalamazoo River Watershed: corn and soybean Observed crop yields from the control period (1980–1999) were obtained at the county level from the National Agricultural Statistics Service (http://www.nass.usda.gov/Quick Stats/) Area weighted crop yield for the Kalamazoo River Watershed was calculated based on the portion of each county in which the watershed resides County-level crop yields are reported by NASS in bushels/ac, while SWAT estimates crop yield in metric tons/ha with 20% moisture content at harvest (Srinivasan et al., 2010) PBIAS was used as the evaluation criteria following a previous study on crop yield calibration by Srinivasan et al (2010) 2.5 Irrigation demand Determination of irrigation demand by agricultural crops is based on the soil–water balance (NEH, 1993), presented in Eq (1) IRR = ET − (P − Dp − RO) − GW − SW (1) Where IRR is irrigation demand (mm), P is precipitation (mm), Dp is deep percolation from the crop root zone (mm), RO is surface runoff (mm), GW is groundwater contribution to the crop root zone (mm), and SW is the change in soil water in the crop root zone (mm) On days in which IRR was negative (usually due to a precipitation event) the number was forced to zero 2.6 Adaptation strategies Planting dates were shifted in order to examine possible climate change adaptation For the control scenario, the total irrigation demand by corn and soybean was calculated for the growing season Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model ARTICLE IN PRESS EJRH-36; No of Pages 18 xxx.e8 S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Table Daily streamflow calibration and validation results Location Time period NSEd PBIASe RSRf 04105000a Calibration (1980–1989) Validation (1990–1999) 0.56 0.51 4.28% 0.73% 0.66 0.70 04105500b Calibration (1980–1989) Validation (1990–1999) 0.52 0.59 7.91% 2.13% 0.70 0.64 04106000c Calibration (1985–1992) Validation (1993–1999) 0.65 0.56 1.15% 8.66% 0.59 0.69 a b c d e f http://waterdata.usgs.gov/usa/nwis/uv?04105000 http://waterdata.usgs.gov/usa/nwis/uv?04105500 http://waterdata.usgs.gov/usa/nwis/uv?04106000 Nash–Sutcliffe efficiency Percent bias Root mean square error-observations standard deviation ratio (May–September) Meanwhile, planting date was shifted by ±10, ±20, and ±30 days as an adaptation to projected future climates This allows for beneficial changes in crop water use or harvest yield by avoiding temperature stress during key crop development times or utilizing greater precipitation volumes Finally, the total irrigation demand and yields were compared against the control scenarios for corn and soybean Results and discussion 3.1 Calibration and validation 3.1.1 Streamflow calibration Daily streamflow calibration and validation were satisfactory for all locations according to guidelines developed by Moriasi et al (2007) Goodness-of-fit results for all locations are presented in Table However, only the time-series comparison of simulated and observed streamflow for one station (04105000) is presented in Fig Fig Observed vs SWAT simulated streamflow for USGS station 04105000 Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e9 Fig Observed vs SWAT simulated (a) corn yield and (b) soybean yield 3.1.2 Crop yield calibration A time-series comparison of observed and simulated annual corn and soybean yields for the entire Kalamazoo River Watershed are presented in Fig 5a and b, respectively The area-weighted average PBIAS values for corn and soybean at the watershed level were 1.9% and 9.4%, respectively Low positive PBIAS values indicate that the SWAT model slightly under-predicted yields, particularly for soybean, though the values are within satisfactory limits Poor calibration in some years for soybean (e.g 1992) is due to the wide range of yields observed at the county level, while the Kalamazoo River Watershed lies in ten counties This makes it difficult to achieve an accurate simulated value when multiple locations in the watershed have very different yields 3.2 Key elements in water balance The dynamics of the water balance are projected to be altered due to climate change, although some uncertainty is apparent regarding the magnitude of these changes, but the trends among water quantity variables are relatively consistent Fisher’s least significant difference (LSD) was performed to determine significant differences between water balance variables across time slices Average annual water quantity variables under all scenarios for the Kalamazoo River Watershed are presented in Fig Average annual precipitation is generally projected to increase moving from the control period to 2060–2079, as indicated by increases in the scenario means However, some climate scenarios project annual precipitation decreases up to 60 mm Almost 80% of the scenarios predict annual precipitation increases by 2020–2039, while by 2060–2079 90% of scenarios predict increases Almost all variables resulted in statistically significant changes moving from the control period to 2020–2039 or 2060–2079 Percolation trends are correlated with precipitation, as most scenarios projections result in percolation increases following greater precipitation volumes Finally, decreases in spring snowmelt occur in almost all scenarios because more winter precipitation falls in the form of rain due to warmer temperatures Changes in surface runoff were statistically insignificant, with an equal number of scenarios projecting slight increases or decreases These changes depend on the magnitude of the precipitation change and resulting ET, soil water holding capacity, and percolation Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 xxx.e10 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Fig Watershed-wide water balance, where bars represent means and error bars represent maximum and minimum among all climate scenarios Average annual ET decreases slightly for 80% of scenarios in 2020–2039 and 2060–2079, likely because atmospheric CO2 concentrations increase Increasing CO2 levels cause partial closure of plant stomata because they not need to open as widely to obtain necessary CO2 for photosynthesis (Sanderson et al., 2007) As stomatal conductance decreases, the plant transpires less 3.3 Irrigation demand Corn and soybean irrigation demand were projected to change in response to altered future climate, with changes varying depending on the month As observed with the water balance, monthly demand becomes more uncertain moving further into the future with a wider range of possibilities depending on the climate model and SRES storyline Monthly area-weighted irrigation demand for corn and soybean across all time slices are presented in Fig 7a and b, respectively Corn irrigation demand is greatest in June and July, with lowest demands occurring in May and September (Fig 7a) Average demand across all scenarios and scenarios increases in May, June, and September, while decreases in the averages occur for July and August These changes are more pronounced in 2060–2079 than in 2020–2039, which reflects the greater temperature increases and more pronounced precipitation changes projected in the late 21st century (Figs and 4) Peak irrigation demand shifts from occurring generally in July for the 1980–1999 control time slice to more commonly occurring in June This shift is more apparent moving further into the future: demand is slightly greater in June for 2020–2039, but July average demand for 2060–2079 is about 20 mm less than in June Greater uncertainty further into the future is apparent for June, July, and August, with a greater range between the first and third quartiles (boxes) and whiskers/outliers The increase in uncertainty is reflected in the coefficient of variation (CV) across all scenarios for each month/time slice For example, CV in July increases from 4% (1980–1999) to 12% (2060–2079), while August CVs move from 7% to 21% in the same time period These changes indicate the climate scenarios’ uncertainty in projecting future precipitation, particularly in July and August Meanwhile, the future uncertainty increases are smaller in May (CV from 5% to 11%) and September (CV from 6% to 11%) moving from 1980–1999 to 2060–2079 Average seasonal totals for corn irrigation demand and crop yield across all scenarios and time slices are presented in Table Changes in irrigation demand, whether they are increases or decreases, vary by model and SRES storyline By 2020–2039, the watershed-wide average change in corn irrigation demand over the entire growing season is between +20 mm and −30 mm Moving to 2060–2079, this change is between +19 mm and −41 mm from 1980 to 1999 Most scenarios generally project decreases in total seasonal irrigation demand (57% and 71% predict decreases by 2020–2039 and 2060–2079, respectively) Consequently, 97% of scenarios predict yield decreases by 2020–2039, while all of them Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e11 Fig Monthly average irrigation demand for (a) corn and (b) soybean predict decreases between 0.27 and 0.82 t/ha by 2060–2079 Decreases in irrigation demand are likely due to changes in July and August demand In these months daily maximum temperatures reach above optimal corn growing temperatures, leading to temperature stress and reduced ET Meanwhile, half of the climate scenarios project precipitation increases, further decreasing irrigation demand In cases where precipitation decreases are coupled with temperature increases, water stress could be a factor in reducing ET and subsequently irrigation demand Soybean irrigation demand peaks in July, while demand is lowest in May and September (Fig 7b) Seasonal patterns of irrigation demand generally following of those of irrigation demand based on growth stage However, there is no shift in peak demand as observed for corn Irrigation demand increases for May through June moving from the control period to 2020–2039 and 2060–2079, while demand decreases further into the future for August and September Uncertainty in irrigation demand is generally greater for months with greater magnitudes of demand, which coincides with months with more varying projections of precipitation and temperature change In addition, uncertainty is greater for 2060–2079 than for 2020–2039, as observed with corn For example, irrigation demand CVs in May increase from 6% to 8% moving from 1980–1999 to 2060–2079, while in August the change in CV is much greater (5% in 1980–1999 to 14% in 2060–2079) September is an exception with the highest uncertainty increase although it generally has lower demand than the peak growing season The irrigation demand CV in September moves from 6% in 1980–1999 to 9% in 2020–2039, and finally to 23% in 2060–2079 The uncertainty increase demonstrates the difficulty of projecting future precipitation and temperature, hydrologic conditions, and subsequently irrigation demand, further into the future Average seasonal totals for soybean irrigation demand and crop yield across all scenarios and time slices are presented in Table Similar to corn, the change in soybean irrigation demand between time slices varies based on model and SRES storyline Less than half of the scenarios project decreases in irrigation demand by 2020–2039, while 89% of scenarios project decreases moving to 2060–2079 Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 xxx.e12 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Table Seasonal corn irrigation demand and crop yield across all scenarios and time slices Scenario BCM2 A1B BCM2 A2 BCM2 B1 CCSM3 A1B CCSM3 A1FI CCSM3 A2 CCSM3 B1 CGCM3 t47 A1B CGCM3 t47 A2 CGCM3 t47 B1 CGCM3 t63 A1B CGCM3 t63 A2 CGCM3 t63 B1 CNRM A1B CNRM A2 CNRM B1 ECHAM5 A1B ECHAM5 A2 ECHAM5 B1 ECHO A1B ECHO A2 ECHO B1 GFDL 2.0 A2 GFDL 2.0 B1 GFDL 2.1 A1B GFDL 2.1 A1FI GFDL 2.1 B1 HADCM3 A1B HADCM3 A1FI HADGEM A1B HADGEM A2 PCM A1B PCM A1FI PCM A2 PCM B1 Irrigation demand (mm) Crop yield (t/ha) 1980–1999 2020–2039 2060–2079 1980–1999 2020–2039 2060–2079 263 264 263 283 281 282 281 267 268 267 274 274 274 260 257 257 254 254 254 269 269 269 258 258 262 262 263 259 259 264 264 265 256 276 269 257 256 262 272 253 269 262 261 259 267 260 250 244 266 268 267 260 264 250 290 275 280 246 278 249 266 253 262 257 264 266 262 261 269 273 256 269 266 254 252 259 254 256 249 256 234 240 269 258 267 264 243 241 264 270 266 274 277 270 258 240 257 269 238 259 256 248 232 251 258 4.83 4.84 4.84 4.88 4.86 4.87 4.87 5.14 5.15 5.15 4.90 4.91 4.91 4.96 4.93 4.92 4.96 4.96 4.99 4.97 4.97 4.97 5.04 5.04 4.99 4.99 4.99 4.84 4.86 4.85 4.87 5.02 4.90 5.13 5.07 4.79 4.92 4.76 4.64 4.40 4.57 4.79 4.69 4.76 4.76 4.56 4.59 4.50 4.80 4.87 4.89 4.93 4.85 4.96 4.68 4.72 4.91 4.52 4.84 4.53 4.71 4.69 4.82 4.69 4.53 4.75 4.83 4.88 4.91 5.06 4.40 4.51 4.46 4.53 4.50 4.45 4.60 4.55 4.33 4.75 4.37 4.25 4.49 4.44 4.59 4.62 4.54 4.67 4.60 4.51 4.55 4.58 4.38 4.43 4.32 4.21 4.50 4.50 4.39 4.21 4.07 4.63 4.49 4.71 4.69 from 1980–1999 This translates to changes in irrigation demand of between +35 mm and −33 mm for 2020–2039 and between +31 mm and −61 mm by 2060–2079 Unlike corn, soybean yields are generally projected to increase, where only 6% of scenarios project decreases in 2020–2039 and none so in 2060–2079 Median increases in soybean yield are 0.71 t/ha by 2020–2039 and 1.13 t/ha by 2060–2079, likely due to more uniform precipitation during the critical growing season, increases in atmospheric CO2 , decreases in ET, and crop phenology 3.4 Spatial variation in irrigation demand Examining the effect of climate change on irrigation demand spatially reveals trends demonstrating that not all locations within the Kalamazoo River Watershed will be impacted uniformly The GFLD 2.0 B1 scenario was selected to demonstrate spatial changes in irrigation demand and crop yield because its changes in precipitation and temperature are representative as an average of all studied scenarios Spatial changes in irrigation demand and crop yield from the control period to future time slices are presented in Figs and for corn and soybean, respectively Corn irrigation demand increases in the east and decreases in the west in 2020–2039 and generally decreases everywhere in 2060–2079 (Fig 8) Subbasins that increase in irrigation demand in 2020–2039 only experience increases of about 3%, while locations with irrigation demand decreases Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e13 Table Seasonal soybean irrigation demand and crop yield across all scenarios and time slices Scenario BCM2 A1B BCM2 A2 BCM2 B1 CCSM3 A1B CCSM3 A1FI CCSM3 A2 CCSM3 B1 CGCM3 t47 A1B CGCM3 t47 A2 CGCM3 t47 B1 CGCM3 t63 A1B CGCM3 t63 A2 CGCM3 t63 B1 CNRM A1B CNRM A2 CNRM B1 ECHAM5 A1B ECHAM5 A2 ECHAM5 B1 ECHO A1B ECHO A2 ECHO B1 GFDL 2.0 A2 GFDL 2.0 B1 GFDL 2.1 A1B GFDL 2.1 A1FI GFDL 2.1 B1 HADCM3 A1B HADCM3 A1FI HADGEM A1B HADGEM A2 PCM A1B PCM A1FI PCM A2 PCM B1 Irrigation demand (mm) Crop yield (t/ha) 1980–1999 2020–2039 2060–2079 1980–1999 2020–2039 2060–2079 297 298 298 318 316 316 316 308 309 308 330 329 329 300 299 299 302 302 302 321 321 321 295 295 304 304 304 307 308 313 313 306 313 326 312 284 294 304 325 302 316 321 311 319 308 312 304 296 298 303 307 324 302 293 335 314 331 298 330 300 309 301 325 315 308 323 310 327 300 323 276 302 294 297 276 289 297 289 285 297 275 282 303 270 277 294 270 279 322 276 279 293 327 308 290 277 302 297 264 281 278 283 252 294 310 1.94 1.94 1.94 1.45 1.47 1.45 1.45 1.89 1.90 1.89 1.79 1.82 1.82 1.88 1.88 1.89 1.59 1.58 1.60 1.76 1.75 1.76 1.47 1.46 1.40 1.40 1.41 1.63 1.65 1.67 1.70 1.68 1.58 1.67 1.59 2.04 1.91 1.67 2.57 2.53 2.41 2.31 2.40 2.66 2.40 2.48 2.36 2.40 2.67 2.59 2.51 2.40 2.09 2.21 2.84 2.74 2.87 2.26 2.32 2.11 2.20 2.04 2.63 2.45 2.02 2.39 2.14 2.46 2.02 2.16 2.11 2.29 2.19 2.77 3.09 3.03 2.83 2.98 2.95 2.95 3.04 2.75 2.65 3.05 3.07 3.23 2.99 3.00 3.10 2.93 2.85 2.96 2.62 2.57 2.48 1.87 2.35 3.11 2.97 2.05 2.20 2.61 3.09 2.80 2.32 change by 4% on average These spatial differences in demand change indicate that water allocation patterns should change to meet new needs Although there is a distinct regional pattern in irrigation demand change crop yields decrease more uniformly in 2020–2039, by an average of 0.25 t/ha on average By 2060–2079 all subbasins experience a decrease in irrigation demand, by about 3% on average Consequently, crop yield decreases by 0.56 t/ha across the watershed, more predominantly in the eastern region of the watershed Soybean generally experiences increases in irrigation demand in 2020–2039 and decreases in 2060–2079, while crop yield increases under both time slices (Fig 9) In 2020–2039, irrigation demand increases by up to 11% at the subbasin level followed by decreases by up to 9% in 2060–2079 Yield increases are greater moving from 2020–2039 to 2060–2079 The western region is projected to experience the greatest yield increases in both time slices, with increases of almost 1.57 t/ha by 2060–2079 These large increases associate with locations in the watershed that still have irrigation demand increases in 2060–2079 3.5 Adaptation strategies Shifting the planting date (and harvest date) by up to ±30 days results in changes to corn and soybean irrigation demand, which is demonstrated in Tables and 6, respectively These values represent Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model ARTICLE IN PRESS EJRH-36; No of Pages 18 xxx.e14 S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 Fig GFDL-2.0 B1 spatial changes in corn between the control period (1980–1999) for (a) 2020–2039 irrigation demand, (b) 2060–2079 irrigation demand, (c) 2020–2039 yield, and (d) 2060–2079 yield Table Corn irrigation demand under shifted planting dates Shift (days) −30 −20 −10 No Change +10 +20 +30 Irrigation demand (mm) 2020–2039 Irrigation demand (mm) 2060–2079 Median Minimum Maximum CV Median Minimum Maximum CV 306 295 278 262 246 230 210 282 272 253 244 230 210 196 333 320 305 290 272 246 234 3.5% 3.9% 3.9% 3.7% 3.8% 3.7% 4.5% 295 285 269 257 244 225 207 270 262 246 232 215 194 179 323 307 289 277 263 255 238 3.8% 3.5% 3.8% 4.5% 5.5% 6.3% 7.3% Table Soybean irrigation demand under shifted planting dates Shift (days) −30 −20 −10 No change +10 +20 +30 Irrigation demand (mm) 2020–2039 Irrigation demand (mm) 2060–2079 Median Minimum Maximum CV Median Minimum Maximum CV 325 322 320 309 298 282 261 300 297 294 284 263 257 236 363 358 343 335 328 322 297 5.0% 4.6% 4.1% 4.0% 5.0% 5.2% 5.4% 315 306 299 289 279 266 252 274 263 258 252 249 236 217 355 344 331 327 315 292 279 6.2% 5.3% 4.6% 5.4% 5.7% 5.6% 5.8% Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e15 Fig GFDL-2.0 B1 spatial changes in soybean between the control period (1980–1999) for (a) 2020–2039 irrigation demand, (b) 2060–2079 irrigation demand, (c) 2020–2039 yield, and (d) 2060–2079 yield the median, minimum, and maximum irrigation demand along with their CVs across all models/SRES storylines in each time slice Through planting corn earlier (−10 through −30 days), irrigation demands generally increase (Table 5) As corn is planted earlier, the median, minimum, and maximum irrigation demands across all scenarios increase This indicates that corn is able to transpire more with earlier planting dates, thereby avoiding peak development in dry summer months that are often projected to experience precipitation decreases For example, early planting results in an increase in irrigation demand due to more ET and effective precipitation during June (second to third month of growth) This is desirable, because at this point in corn development there is a need for more water for transpiration, while the crop biomass is greater Meanwhile at this stage, more rainfall is intercepted and more transpiration occurs, leading to less percolation than in late planting strategies In addition, median irrigation demand decreases by 10–15 mm moving from 2020–2039 to 2060–2079, likely due to hotter temperatures, more variable precipitation, and increasing atmospheric CO2 concentration impacting corn stomata In terms of corn yield, most scenarios project decreases under earlier plantings for 2020–2039 For example, 71% of scenarios project yield decreases on average of 0.1 t/ha when the planting date is 30 days earlier than the original planting date The exception is the −10 day shift, where only 40% of the scenarios project decreases (although the average yield increase is less than 0.06 t/ha) At 2060–2079 the results are similar; although when planting 10 days earlier 69% of the scenarios project yield increases on average of 0.09 t/ha from the original planting date The general lack of increase in yield by planting a month earlier may be due to temperatures still being too cold early in the growth process even though the corn is able to utilize more water than under the normal planting date If the planting date of corn Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 xxx.e16 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 is to be moved earlier in the season the only real benefit would occur when planting ten days early every year This gives the corn access to more precipitation and avoids daily minimum temperatures that are still too low in early April Late planting (+10 through +30 days) leads to decreases in corn irrigation demand (Table 5), which may be due to the critical growth period being concurrent with drier and hotter months For example, late planting results in greater irrigation demand and effective precipitation in August (second to third month of growth) compared to early planting Lower ET values during the month of August (second to third month of growth) can be associated with smaller crop biomass, lower precipitation, and more temperature stress compared to the second to third month of growth (June) under early planting The irrigation demand decreases become greater moving from 2020–2039 to 2060–2079 Lower irrigation demands under later planting dates lead to most scenarios projecting yield decreases in both time periods About 89% project yield decreases under +30 for 2020–2039, although the average decrease is about 0.38 t/ha The results are similar for 2060–2079 in terms of yield changes Therefore, it is recommended to plant corn earlier in the season rather than delaying planting until June due to possible temperature stress, lower irrigation demands, and reductions in projected yield While soybean irrigation demand experiences trends similar to that of corn under adaptation strategies (Table 6), soybean yield generally increases Indicated by the CVs, irrigation demand uncertainty is slightly greater for soybean than corn In 2020–2039, the majority of scenarios project increases in soybean yield for all shifted planting dates compared to the original planting date All scenarios project yield increases for -30 days (average increase of 0.59 t/ha), while 97% of scenarios project increases averaging 0.77 t/ha under +30 days Fewer scenarios project increases moving closer to the original planting date, while the magnitude of increase is also projected to be smaller (only 0.12 and 0.15 t/ha for −10 and +10 compared to the original date, respectively) Soybean yield trends with respect to planting date are similar in 2060–2079, although slightly fewer scenarios project increases than in 2020–2039 In addition, the magnitudes of increases from the original planting date are smaller (0.39 t/ha for −30 and 0.57 t/ha for +30) These increases in demand indicate that more available water in the earlier plantings benefit yield, while planting in April rather than May likely results in less possibility of temperature stress Late planting does reduce irrigation demand, but the warmer temperatures seem to be beneficial to the soybean yields, especially in 2020–2039 This effect is lessened in 2060–2079, possibly due to peak summer temperature increases that may cause temperature stress and less ability to transpire Yield increases occurring regardless of direction of planting date shift indicates that increases in atmospheric CO2 , decreasing ET, and crop phenology may play a significant role in influencing soybean yields Conclusion This study examined the impacts of climate change on corn and soybean irrigation demand in the Kalamazoo River Watershed of Michigan Ten bias-corrected statistically downscaled GCMs were used in combination with four SRES storylines to compare 1980–1999 with 2020–2039 and 2060–2079 Alterations to the watershed scale annual water balance were found to be largely dependent on the magnitude and sign of precipitation and temperature changes As the climate models and various SRES storylines project varying temperature increases and precipitation changes, uncertainty increased moving further into the future Precipitation generally increases in the watershed resulting in greater percolation, although surface runoff changes were statistically insignificant Evapotranspiration slightly decreased, likely from increasing atmospheric CO2 concentrations Snowmelt was also projected to decrease moving further into the future due to rising winter temperatures There were some apparent trends in the watershed water balance, but the increase in uncertainty moving further into the future is an important consideration for water supply management Corn and soybean irrigation demand were found to vary within the growing season Uncertainty generally increased moving from 1980–1999 to 2060–2079, with uncertainty also increasing in months with greater irrigation demand Most climate scenarios projected decreases in irrigation demand, indicating that there will likely be less water available during the growing season in the future, or evapotranspiration will be hindered due to temperature stress in peak development periods for corn This results in most scenarios projecting decreases in corn yield However, soybean was Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 G Model EJRH-36; No of Pages 18 ARTICLE IN PRESS S.A Woznicki et al / Journal of Hydrology: Regional Studies xxx (2015) xxx.e1–xxx.e18 xxx.e17 projected to experience yield increases, possibly suggesting that greater atmospheric CO2 concentrations along with decreases in ET and crop phenology will be beneficial to growth even under the higher temperatures and the possibility of a drier growing season Spatial variations in irrigation demand and yield across the watershed were also revealed Locations experiencing irrigation demand increases correlated with increases in yield, indicating that the ability to use more water aided in greater crop growth By identifying locations with lower irrigation demand, water managers can allocate water more precisely in regions to enhance crop growth Shifting the planting date was in an attempt to utilize more available water or avoid peak crop development occurring in summer months projected to experience large future temperature increases Corn irrigation demand increased through planting earlier, which resulted in yields similar to 1980–1999 However, late planting resulted in decreases in future irrigation demand, indicating less water use by corn and consequently, yields suffered Soybeans generally performed well in terms of planting date shifts and yield increases although the irrigation demand trends were similar to that of corn This study found that corn and soybean irrigation demand and yields in cold regions such as Michigan are likely to be affected by climate change, although there is still uncertainty in the magnitude of this impact As temperature and precipitation patterns change more significantly, the availability of water for crop growth will change, along with the locations that may need water the most Therefore, this study is an important resource for watershed managers, stakeholders, and agricultural producers in allocating future irrigation water supplies to benefit crop growth and improve yield Conflict of 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the Upper Mississippi River Basin Trans ASABE 53 (5), 1533–1546 Thomas, A., 2008 Agricultural irrigation demand under present and future climate scenarios in China Global Planet Change 60 (3), 306–326 Xiong, W., Holman, I., Lin, E., Conway, D., Jiang, J., Xu, Y., Li, Y., 2010 Climate change, water availability and future cereal production in China Agric Ecosyst Environ 135 (1–2), 58–69 Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation J Hydrol.: Reg Stud (2015), http://dx.doi.org/10.1016/j.ejrh.2014.12.003 ... in reducing ET and subsequently irrigation demand Soybean irrigation demand peaks in July, while demand is lowest in May and September (Fig 7b) Seasonal patterns of irrigation demand generally... precipitation change and resulting ET, soil water holding capacity, and percolation Please cite this article in press as: Woznicki, S.A., et al., Climate change and irrigation demand: Uncertainty and adaptation. .. average irrigation demand for (a) corn and (b) soybean predict decreases between 0.27 and 0.82 t/ha by 2060–2079 Decreases in irrigation demand are likely due to changes in July and August demand