water

Article Long-Term Perspective Changes in Crop Irrigation Requirement Caused by Climate and Agriculture Land Use Changes in Rechna ,

1,2, 3, 1, 2,4 Arfan Arshad † , Zhijie Zhang †, Wanchang Zhang * and Ishfaq Gujree 1 Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Department of Geography, Center for Environmental Sciences and Engineering (CESE), University of Connecticut, Storrs, CT 06269-4148, USA 4 Institute of Tibetan Pleatue Research, Chinese Academy of Sciences, Beijing 100094, China * Correspondence: [email protected]; Tel.: +86-10-82178131 The first two authors contributed equally to this work and should be considered as co-first authors of † this paper.

 Received: 2 July 2019; Accepted: 24 July 2019; Published: 29 July 2019 

Abstract: Climate change and agriculture land use changes in the form of cropping patterns are closely linked with crop water use. In this study the SDSM (statistical downscaling model) was used to downscale and simulate changes in meteorological parameters from 1961 to 2099 using HadCM3 General Circulation Model (GCM) data under two selected scenarios i.e., H3A2 and H3B2. Results indicated that Tmax,Tmin, and wind speed may increase while relative humidity and precipitation may decrease in the future under both H3A2 and H3B2 scenarios. Downscaled meteorological parameters were used as input in the CROPWAT model to simulate crop irrigation requirement (CIR) in the baseline (1961–1990) and the future (2020s, 2050s and 2080s). Data related to agriculture crop sown area of five major crops were collected from statistical reports for the period of 1981–2015 and forecasted using linear exponential smoothing based on the historical rate. Results indicated that the cropping patterns in the study area will vary with time and proportion of area of which sugarcane, wheat, and rice, may exhibit increasing trend, while decreasing trend with respect to the baseline scenario was found in maize and cotton. Crop sown area is then multiplied with CIR of individual crops derived from CROPWAT to simulate Net-CIR (m3) in three sub-scenarios S1, S2, and S3. Under the H3A2 scenario, total CIR in S1, S2, and S3 may increase by 3.26 BCM, 12.13 BCM, and 17.20 BCM in the 2080s compared to the baseline, while under the H3B2 scenario, Net-CIR in S1, S2, and S3 may increase by 2.98 BCM, 12.04 BCM, and 16.62 BCM in the 2080s with respect to the baseline. It was observed that under the S2 sub-scenario (with changing agriculture land-use), total CIR may increase by 12.13 BCM (H3A2) and 12.04 BCM (H3B2) in the 2080s with respect to the baseline (1961–1990) which is greater as compared to S1 (with changing climate). This study might be valuable in describing the negative effects of climate and agriculture land use changes on annual crop water supply in Rechna Doab.

Keywords: climate change; GCM; SDSM; crop irrigation requirement; effective precipitation; reference ET; CROPWAT

1. Introduction Availability of water resources has great significance for agricultural production, human settlements, industry, and natural vegetation. Nowadays available water resources are being spoiled

Water 2019, 11, 1567; doi:10.3390/w11081567 www.mdpi.com/journal/water Water 2019, 11, 1567 2 of 25 as a result of climate as well as land use changes [1]. The continuous change in climatic variables and agricultural expansion has retained new demands on available water resources every day due to changes in crop irrigation requirement [2]. Irrigated agriculture is always ranked 1st in the world according to water consumers as it consumes approximately 64% of fresh water [3–5]. Water withdrawals for agricultural production systems is much focused in space and with respect to crop types. Asian countries especially Pakistan, China, and , and the United States account for 68% of fresh water withdrawals for irrigated agriculture, out of which ~34% is consumed by India for growing rice and wheat crops covering 70% of the irrigated command area [6]. Many past studies have been conducted for understanding the implications of climate change on crop irrigation requirement (CIR) in various climatic and geographical zones of the world [7–15]. Crop irrigation requirement (CIR) has been largely affected due to changes in the trend of climatic parameters i.e., precipitation, solar radiations, relative humidity, temperature, and wind speed, as a result of climate change [5,16–19]. Studies by different researchers concluded that crop water requirement may increase in most regions of the globe due to increases in Evapotranspiration (ET) and decreases in soil moisture which have happened with warming of the climate [20–22]. Agriculture expansion inversely relates to water consumption of the irrigated command area. In recent years, continuous increment of the irrigated command area has resulted in reduction of the water supply, which will further affect the over abstraction of water resources [23]. Projected results indicated that the future irrigated agriculture area may continuously increase to fulfill food requirements of a rapidly growing population, which will ultimately consume more water regimes to satisfy growing water needs globally [24,25]. Both these issues, expanding agriculture land and changing climate, are more evident in Rechna Doab, Pakistan as it has experienced more variable weather and cropping patterns than other regions in Pakistan. Catchment areas in Indus Basin, Pakistan are accounted as the world’s most vulnerable regions to climate change. The food requirement in Pakistan has largely increased as a result of rapid population growth, from 37.5 million in 1950 to 207 million in 2017, and is expected to reach 333 million in 2050 [26]. Land and water resources in Pakistan are tremendously under pressure to fulfill the growing needs of the population [27]. In Rechna Doab, the total irrigated area has increased from 1.94 Mha in 1961 to 2.12 Mha in 1990 and cropping intensity has increased from 91% in 1961 to 131% in 1980 [28]. Indus Basin of Pakistan is allocating irrigated water among four provinces, according to the Indus Water Accord in 1991, and is considered a supply-based system rather than demand-based system. Irrigation water supply is directly affected due to changes in the trend of climatic parameters and the irrigated command area. With the expansion of the irrigated command area in Rechna Doab, irrigation water supply is reducing and end users are not satisfied with available water in the irrigated network [29,30]. Farmers always complain to water authorities (Water and Power Development Authority (WAPDA), Punjab Irrigation and Drainage Authority (PIDA) etc.) about the development of an unequal water distribution system. Therefore, it’s important to analyze the impacts of climate and agriculture land use changes on crop irrigation requirement to understand the current and projected changes in crop irrigation requirement (CIR) in Rechna Doab, Pakistan. Numerous modeling approaches have been used to study the impacts of climate on crop evapotranspiration (ET). Examples: Reference [31] generated future climate using the LARS-WG (Long Ashton Research Station-Weather Generator) in Suwon, South Korea, reference [32] used the generalized linear model (GLM) to simulate future changes in ET, reference [33] applied the Statistical Downscale Model (SDSM) to downscale ET between 2011–2099 from HadCM3 (Hadley Centre Coupled Model, version 3) climate data, reference [34] analyzed the climate change impacts on ET from 1956–2016 using the Penman–Monteith method. The above studies mainly focused on analyzing the spatio-temporal changes in ET caused by the impacts of climate but these studies pay less attention to “how the change of climate would affect the crop water use”. Anyhow, some studies analyze perspective changes in crop water requirement due to the changing climate [18,35] and agriculture expansion [36,37]. In these studies, authors analyzed the changes in crop water requirement due to agriculture expansion but they only considered agriculture expansion as one common land-use class i.e., crop land or vegetation. Water 2019, 11, 1567 3 of 25

They did not aggregate agriculture class into various crop-type classes (wheat, sugarcane, rice etc.) and their individual impacts on water requirement based on their proportion of crop sown area in current and future periods. For example, reference [36] analyzed the perspective changes in irrigation requirement caused by agriculture expansion but they did not examine the individual impacts of different agriculture crops on net crop irrigation requirement. Assessing the long-term changes in agriculture land use changes in the form of crop patterns (crop sown area), therefore, became an urgent need to improve decision making processes in water resources management. So, this study analyzed the long-term impacts of climate and agriculture land use (considering the changes in agriculture cropping patter) on crop irrigation requirement in current and future periods. General circulation models (GCMs) can simulate future changes in the climate on large scale climate studies but they have limitations to their application on small scale studies due to their low spatial resolution. GCM data can be used on the regional scale and small scale studies after downscaling it. CROPWAT developed by FAO (Food and Agriculture Organization) is a better tool to simulate crop irrigation demand in the changing climate [25]. CROPWAT has generally been applied in numerous research studies to compute crop irrigation requirement (CIR) and irrigation scheduling in different countries i.e., Taiwan, Greece, United State, Zimbabwe, Africa, Morocco, Turkey, and Pakistan [38–48]. Aims and objectives of this study were to: (1) To detect long-term trends and changes in climatic parameters (maximum temperature (Tmax), minimum temperature (Tmin), precipitation, humidity, and wind speed); (2) to analyze the long-term changes in agriculture cropping patterns based on crop sown area; and (3) to investigate the implications of climate and agriculture land use variability on total crop irrigation requirement. CROPWAT and the statistical downscale simulation model (SDSM) were coupled with each other to simulate CIR (mm) associated with climate change. Data related to the crop sown area (CSA) of five major crops (wheat, rice, sugarcane, maize, and cotton) was collected from Punjab Statistics Development (PSD) reports for the period of 1981–2015 and forecasted in future periods. Future predicted data of agriculture cropping patterns were integrated with CIR (mm) of individual crops derived from CROPWAT to simulate total CIR (m3) in three different sub scenarios: S1 (climate change), S2 (agriculture land use changes), and S3 (both climate and agriculture land use changes).

2. Material and Methods

2.1. Study Area The land exists between two rivers is defined as Doab. Rechna Doab is the part of land situated between two rivers i.e., the rivers Ravi and Chenab in Punjab. It is the most fertile region of the irrigated Indus plain of Punjab and lies in the agro-ecological zone IVa. It starts from Mirpur and and converges with the total command area (TCA) of 3.0 Mha in which 2.43 Mha area is counted in the irrigated land. It covers eight districts of Punjab, namely Hafizabad, , , T.T Singh, Faisalabad, Sheikhpura, , Sialkot, and (Figure1). It is designated as subtropical semi-arid land and climate of this area shows seasonal fluctuations in precipitation and temperature. The summer season is considered as long and hot which starts from April and ends in September with Tmax 33–48 ◦C. The winter season starts from December and ends in February, with Tmax 19–27 ◦C. Annual precipitation in the study area is about 655 mm in the upper side of Rechna, and 360 mm in lower part of Rechna Doab. Surface canal irrigation system of this doab is gravity flow and perennial canals flow at a normal period, about 340 days per year. Water 2019, 11, x FOR PEER REVIEW 4 of 26

2.2.1. Meteorological Data The Punjab Meteorological Department provided daily meteorological data including maximum temperature, minimum temperature (Tmax, Tmin, °C), relative humidity (RH, %), wind speed (WS, m s−1), sunshine hours (SH, h), and precipitation. Observed climate data was collected for five meteorological stations i.e., Faisalabad, Lahore, Sargodha, Jhelum, and Sialkot meteorological from 1980–2014Water 2019, 11 at, 1567 daily scale. These datasets were used to calibrate and validate the statistical downscale4 of 25 simulation model (SDSM).

Figure 1. Map of Rechna Doab, Pakistan, configuration of irrigation network, irrigation districts, Figure 1. Map of Rechna Doab, Pakistan, configuration of irrigation network, irrigation districts, head-works, and metrological observatories located in the study area. head-works, and metrological observatories located in the study area. 2.2. Data Used in Study Table 1. List of 26 NCEP (National Centers for Environmental Prediction) predictors used in this Data used in this study comprises of meteorologicalstudy. data (temperature, humidity, wind speed, precipitation, sunshine hours, and sun radiations), GCMs data (National Center of Environmental Prediction–NationalNo Predictors Center for AtmosphericCode ResearchNo (NCEP) (TablePredictors1), and Hadley CentreCode coupled model1 (HadCM3))Mean sea andlevel crop pressure information Mslpas data. 14 850 hPa air flow strength P5zhas 2 Surface flow strength P_fas 15 850 hPa zonal velocity P8_fas 3 Table 1.SurfaceList of zonal 26 NCEP velocity (National CentersP_uas for Environmental16 850 Prediction) hPa meridian predictors velocity used in this study.P8_uas 4 Surface meridional velocity P_vas 17 850 hPa vorticity P8_vas No5 Surface Predictors vorticity P_zas Code 18 No850 hPa wing Predictors direction P8_zas Code 61 Surface Mean seawind level direction pressure P_thas Mslpas 19 14850 850 hPa hPa divergence air flow strength P850asP5zhas 72 Surface Surface divergence flow strength P_zhas P_fas 20 15500 hPa 850geopotential hPa zonal velocityheight P8thas P8_fas 3 Surface zonal velocity P_uas 16 850 hPa meridian velocity P8_uas 8 500 hPa Air flow strength P5_fas 21 850 hPa geopotential height P8zhas 4 Surface meridional velocity P_vas 17 850 hPa vorticity P8_vas 95 500 hPa Surface zonal vorticity velocity P5_uas P_zas 22 18Near surface 850 hParelative wing humidity direction R500asP8_zas 106 500 hPa Surface meridional wind direction velocity P5_vas P_thas 23 19Near surface 850 specific hPa divergence humidity R850asP850as 117 500 Surface hPa divergencevorticity P5_zas P_zhas 24 500 20 hPa specific/relative 500 hPa geopotential humidity height RhumasP8thas 128 500 500 hPa hPa wind Air flow direction strength P500as P5_fas 25 850 21 hPa specific/relative 850 hPa geopotential humidity height ShumasP8zhas 9 500 hPa zonal velocity P5_uas 22 Near surface relative humidity R500as 1310 500500 hPa hPa meridional divergence velocity P5thas P5_vas 26 23Mean Near temperature surface specific at 2 humidity m TempasR850as 11 500 hPa vorticity P5_zas 24 500 hPa specific/relative humidity Rhumas 2.2.2.12 GCM Data 500 hPa wind direction P500as 25 850 hPa specific/relative humidity Shumas 13 500 hPa divergence P5thas 26 Mean temperature at 2 m Tempas

2.2.1. Meteorological Data The Punjab Meteorological Department provided daily meteorological data including maximum temperature, minimum temperature (Tmax, Tmin, ◦C), relative humidity (RH, %), wind speed 1 (WS, m s− ), sunshine hours (SH, h), and precipitation. Observed climate data was collected for five Water 2019, 11, 1567 5 of 25 meteorological stations i.e., Faisalabad, Lahore, Sargodha, Jhelum, and Sialkot meteorological from 1980–2014 at daily scale. These datasets were used to calibrate and validate the statistical downscale simulation model (SDSM).

2.2.2. GCM Data The GCM used in this study was derived from HadCM3, the latest version model with improved ocean and atmosphere components [49]. It comprises of daily predictor variables of NCEP 1961–2000, H3A2a 1961–2099, and H3B2a 1961–2099. H3A2 and H3B2 data was used to simulate changes in future scenarios. NCEP data was utilized for calibration and validation of the SDSM model against the observed data. HadCM3 is a coupled climate model with spatial resolution of 2.50 latitude and 3.750 longitude, and freely downloadable from http://www.cics.uvic.ca/scenarios/sdsm/select.cgi. HadCM3 data under two IPPC emission scenarios i.e., H3A2 and H3B2 was collected for the period of 1961 to 2099. These emission scenarios have already developed by the IPCC (Inter-Governmental Panel on Climate Change) [50].

2.2.3. Crop Information Data For computation of CIR, CROPWAT requires multi crop parameters i.e., planting and harvesting dates of crops, crop coefficient (Kc), rooting depth, allowable depletion, and yield response. Information of these parameters was collected from relevant studies, i.e., cropping pattern information collected from paper of reference [51], planting and harvesting data was collected from past research [52,53] and Kc values, crop rooting depth, allowable depletion, and yield response factor were taken from FAO (Table2). Table2 illustrates the main information of five major crops i.e., sugarcane, maize, wheat, rice, and cotton, growing in the study area. Statistics data of the crop sown area of five major crops grown in eight districts (Faisalabad, Hafizabad, Jhang, T.T Singh, Sheikhu-Pura, Gujranwala, Sialkot, and Narowal) were collected for the period of 1981–2008 from the Government of Pakistan statistics reports [54] and 2009–2015 from Punjab Development statistics (PDS) reports (Figure2).

Table 2. Multi crop information parameters used in the CROPWAT model to compute Crop Irrigation Requirement (CIR) of five major crops grown in Rechna Doab.

Crop Date Rooting Crop Yield Response Depletion Stages Period K Types (Sowing-Harvesting) c Depth Height Factor Factor days m m Initial 30 0.40 1.5 0.50 0.65 Development 60 0.75 Sugarcane Annual Jan-Dec 3 Mid-season 180 1.25 1.20 0.65 1.5 Late season 95 0.75 0.10 0.65 Initial 20 0.4 0.30 0.20 0.55 Development 30 0.69 Wheat Nov-Mar 0.8 Mid-season 50 1.15 0.50 0.55 1.50 Late season 30 0.41 0.40 0.90 Initial 20 1.05 0.10 1.00 0.20 Development 30 1.09 Rice June-Nov 1 Mid-season 40 1.20 1.31 0.20 0.60 Late season 30 0.99 0.50 0.20 Initial 30 0.35 0.30 0.20 0.65 Development 50 0.59 Cotton May-Nov 1.30 Mid-season 60 1.15 0.50 0.65 1.40 Late season 55 0.5 0.25 0.90 Initial 20 0.30 0.30 0.40 0.55 Development 35 0.40 Maize May-Sep 1.5 Mid-season 40 1.2 1.30 0.55 1 Late season 30 0.35 0.50 0.80 Water 2019, 11, x FOR PEER REVIEW 5 of 26

The GCM used in this study was derived from HadCM3, the latest version model with improved ocean and atmosphere components [49]. It comprises of daily predictor variables of NCEP 1961–2000, H3A2a 1961–2099, and H3B2a 1961–2099. H3A2 and H3B2 data was used to simulate changes in future scenarios. NCEP data was utilized for calibration and validation of the SDSM model against the observed data. HadCM3 is a coupled climate model with spatial resolution of 2.50 latitude and 3.750 longitude, and freely downloadable from http://www.cics.uvic.ca/scenarios/sdsm/select.cgi. HadCM3 data under two IPPC emission scenarios i.e., H3A2 and H3B2 was collected for the period of 1961 to 2099. These emission scenarios have already developed by the IPCC (Inter-Governmental Panel on Climate Change) [50].

2.2.3. Crop Information Data For computation of CIR, CROPWAT requires multi crop parameters i.e., planting and harvesting dates of crops, crop coefficient (Kc), rooting depth, allowable depletion, and yield response. Information of these parameters was collected from relevant studies, i.e., cropping pattern information collected from paper of reference [51], planting and harvesting data was collected from past research [52,53] and Kc values, crop rooting depth, allowable depletion, and yield response factor were taken from FAO (Table 2). Table 2 illustrates the main information of five major crops i.e., sugarcane, maize, wheat, rice, and cotton, growing in the study area. Statistics data of the crop sown area of five major crops grown in eight districts (Faisalabad, , Jhang, T.T Singh, Sheikhu- Pura, Gujranwala, Sialkot, and Narowal) were collected for the period of 1981–2008 from the Government of Pakistan statistics reports [54] and 2009–2015 from Punjab Development statistics Water 2019, 11, 1567 6 of 25 (PDS) reports (Figure 2).

350 150 Sugarcane Maiz 140 300 130

250 120 Area Area 110 200 100

150 90

80 100 1100 240 Rice Cotton 1900 Wheat 220 1000 1800 200 900 180 1700 800

Area Area 160 Area Area 1600 140 700 1500 120 100 600 1400

1980 1985 1990 1995 2000 2005 2010 2015 2020 1980 1985 1990 1995 2000 2005 2010 2015 2020 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Year Year

Figure 2. Long-term changes in crop sown area (CSA) (1000 hectare) of five major agricultural crops grownFigure in2. RechnaLong-term Doab changes from 1981–2015.in crop sown area (CSA) (1000 hectare) of five major agricultural crops grown in Rechna Doab from 1981–2015. 2.3. Downscaling of GCM Data Using SDSM Model DownscalingTable 2. Multi crop is a information technique ofparameters improving used resolution in the CROPWAT of GCMs model data to forcomp itsute use Crop at the Irrigation local scale. DownscalingRequirement approach (CIR) of canfive major be adapted crops grown on spatio-temporal in Rechna Doab. features of the climate system [55–57]. Dynamical and statistical are two downscaling approaches which are widely usedYield for downscaling of Crop Date (Sowing- Rooting Crop Depletion GCM data [55,58–60]. The dynamicalStages downscalingPeriod Kc technique uses complexResponse algorithms to generate Types Harvesting) Depth Height Factor fine spatial resolution climatic information supplied by GCMs at coarse resolutionFactor approximately 20–50 km. Dynamical downscaling is a processdays of nesting m fine spatialm resolution data of the regional Initial 30 0.40 1.5 0.50 0.65 climateSugarcane model Annual (RCMs) Jan-Dec within low resolution data of GCMs [61]. The statistical3 downscaling approach generates future climate scenariosDevelopment by developing 60 statistical relationships between 0.75 global scale features and local variables [62–64]. The statistical downscaling approach is preferred rather than the dynamical downscaling approach because of its simple computation and is easily adapted to new spaces [55,65–67]. Therefore, the statistical downscaling approach was used in this study to downscale GCMs data under two scenarios, A2 and B2. SDSM was developed by Wilby, Dawson and Barrow [67], and it uses multi linear regression (MLR) to establish statistical relationships between large scale NCEP predictors and local-scale predictands [68]. SDSM provides two types of sub-models, (1) unconditional model for local variables distributed normally such as temperature, and (2) conditional sub model is applied when local variables are not distributed normally such as precipitation and evaporation [69]. SDSM can normalize such variables by transforming parameters using different functions i.e., natural log, fourth root, and inverse normal [64,70,71]. In this study, the unconditional sub-model with no transformation function was applied to downscale T_max, T_min, wind speed, and relative humidity while in the case of precipitation conditional sub-model, was applied with transformation function (fourth root). In order to downscale GCMs data, the following steps were adapted: (1) Quality control analysis for verification of missing data values, (2) screening of variables using NCEP predictor, (3) calibration of the model, (4) weather generation, (5) validation of SDSM, and (6) generation of future climate scenarios using GCMs.

(1) Screening of NCEP Predictors: Screening of NCEP predictor variables is a crucial step for all statistical downscaling techniques because these parameters greatly influences the output of the model [71,72]. In this study, during screening process correlation between 26 NCEP predictors (Table1) and local scale predictands (observed precipitation, temperatures, wind speed, and relative humidity) was developed in SDSM model, and then the predictors of highest correlation Water 2019, 11, 1567 7 of 25

coefficient among 26 predictors were finally selected. (Table3). Predictors with low R 2 and highest P value (greater than α (0.05)) were neglected to minimize uncertainty in future prediction. The highest value of R2 as 0.7 is satisfactory in calibration and validation of the SDSM model [73]. (2) Calibration and Validation of model: NCEP predictor variables having highest value of R2 were used in the weather generation process. Observed data of climatic parameters was divided into two halves, the first part (1980–1989) was used to calibrate the SDSM model and the second part (1990–1999) was used to validate the model. During the calibration period (1980–1989) and validation period (1990–1999), simulated results of SDSM were compared with the observed data of Tmax, Tmin, humidity, wind speed, and precipitation. (3) Scenario Generation: After successful calibration and validation of SDSM, future scenarios were generated using HadCM3 data under A2 and B2 scenarios within the time span of 1961 to 2099. Three time windows, 2020 (2010–2039), 2050 (2040–2069), and 2080 (2070–2099) were constructed to assess the patterns of climate variables in different spans.

Table 3. Screening of most appropriate NCEP predictor variables depicted good correlation with observed climate parameters.

No. Predictors Code Tmax Tmin Precp R.H WDS 1 Mean sea level pressure Mslpas √ √ √ √ √ 3 Surface zonal velocity P_uas √ √ √ √ 5 Surface vorticity P_zas √ √ √ 8 500 hPa Air flow strength P5_fas √ √ 11 500 hPa vorticity P5_zas √ √ 12 500 hPa wind direction P500as √ √ √ 14 850 hPa air flow strength P5zhas √ √ √ √ 16 850 hPa meridian velocity P8_uas √ √ √ √ 17 850 hPa vorticity P8_vas √ √ 18 850 hPa wing direction P8_zas √ √ √ √ 19 850 hPa divergence P850as √ √ √ 20 500 hPa geopotential height P8thas √ √ 21 850 hPa geopotential height P8zhas √ √ √ 23 Near surface specific humidity R850as √ √ 25 850 hPa specific/ relative humidity Shumas √ √ √ √ 26 Mean temperature at 2 m Tempas √ √ √ √ √

2.4. Prediction of Agriculture Cropping Patterns Data related to crop sown area were collected from Punjab Statistical Report (PSR) and forecasted in the future using linear and exponential forecasting methods, considering highest values of R2 based on historical rate. These methods can be used to predict future changes [74]. Agriculture land use changes in the form of cropping patterns (sown area) of different agriculture crops were observed in the baseline (1980–2015) and future time windows (2020s, 2050s, and 2080s). This data was linked with CROPWAT to check their impacts on total irrigation requirement for each crop.

2.5. Modeling Crop Irrigation Requirement (CIR) CIR (crop irrigation requirement) is the quantity of water required to fulfil the evapotranspiration loss and leaching of salts from a cropped field. In this study CIRs of five major agricultural crops (wheat, sugarcane, rice, cotton, and maize) were computed using the CROPWAT model developed by FAO (Food Agriculture Organization). Meteorological variables (Tmax, Tmin, precipitation, humidity, sunshine hours, and sun radiations) downscaled from SDSM were used as input in the CROPWAT Water 2019, 11, 1567 8 of 25 model to compute CIR in the baseline (1961–1990) and future scenarios (202s, 2050s, and 2080s). In this study the following relations were used to compute CIR (mm).

ETc = Kc ETo (1) × CIR = ETc – PE (2) where CIR is crop irrigation requirement in mm, ETc represents the crop evapotranspiration (mm 1 1 day− ), PE the amount of effective precipitation (mm), ETo is the reference ET (mm day− ) and Kc is the crop coefficient. Reference ET was computed using CROPWAT which applies Penman–Monteith equation recommended by FAO [75]. ETo can be defined as amount of water transpired by well grown grass with height 0.12 m and albedo of 0.23. The following relation was used to estimate reference ET

900 0.408∆(Rn G) + u T+273 u2(es ea) ETo = − − (3) ∆ + u(1 + 0.34u2)

2 1 2 where Rn is the net amount of radiations (MJ m− day− ), G is density of heat flux in soil (MJ m− 1 1 day− ), u2 is the mean wind speed in 24 h (m s− ), es is the saturation vapor pressure (kPa), ea is actual 1 vapor pressure (kPa), ∆ is gradient of vapor pressure vs. temperature curve (kPa ◦C− ), and u is the 1 psychrometric constant (kPa ◦C− )[76]. The effective rainfall in agricultural region is defined as the amount of precipitation infiltrated into the soil, stored in root zone, and later it transpired by crops. CROPWAT computed effective precipitation (Peff) using the soil conservation service method proposed by the United States Department of Agriculture (USDA).

(P (125 0.2 3P)) P = × − × For P 250/3 mm (4) e f f 125 ≤ 125 P = + 0.1P For P > 250/3 mm (5) e f f 3 where P is amount of precipitation (mm).

2.6. CIR under Changing Climate and Agriculture Cropping Patterns CIR of individual crops derived from CROPWAT model and agriculture crop sown area were integrated in Equation (6) to compute total CIR in the baseline (1961–1990) and future scenarios (2020s, 2050s, and 2080s). Xn Total CIR = A CIR (6) i × i i=1 where CIRi stands for crop irrigation requirement of crop i, Ai is the crop sown area (CSA) of crop i. In order to assess individual and integrated impacts of climate and agriculture cropping patterns, future changes in CIR were assessed in three different sub-scenarios (1) S1: Changed climate with no change in agriculture land use, (2) S2: Changed agriculture land use with no change in climate, (3) S3: Changed climate and agriculture land use. Figure3 depicts the stepwise procedure of data analysis. Water 2019, 11, x FOR PEER REVIEW 8 of 26

𝑃 = +0.1𝑃 For P > 250/3 mm (5) Where P is amount of precipitation (mm).

2.6. CIR under Changing Climate and Agriculture Cropping Patterns CIR of individual crops derived from CROPWAT model and agriculture crop sown area were integrated in Equation (6) to compute total CIR in the baseline (1961–1990) and future scenarios (2020s, 2050s, and 2080s).

Total CIR = A ×CIR (6)

Where CIRi stands for crop irrigation requirement of crop i, Ai is the crop sown area (CSA) of crop i. In order to assess individual and integrated impacts of climate and agriculture cropping patterns, future changes in CIR were assessed in three different sub-scenarios (1) S1: Changed climate with no change in agriculture land use, (2) S2: Changed agriculture land use with no change in climate, (3) S3: Changed climate and agriculture land use. Figure 3 depicts the stepwise procedure of data analysis.Water 2019 , 11, 1567 9 of 25

GCMs Data

Crop Sown Area Observed Climate Data HadCM3 NCEP Predictors 1980-2014 Scenarios Trend Analysis Observed data Observed data (1990-1999) (1980-1989) Screening of Predictors Future Agriculture Cropping Patterns 2020, 2050, 2080

Calibration of SDSM

Total CIR = ∑ Ai × CIRi

Validation of SDSM

Downscale GCMs under Scenario Analysis H3A2 and H3B2 scenarios Final Results CROPWAT Future Scenarios Model S1, S2 and S3 Generation CIR= ETc-PE Scenarios 2020, 2050 and 2080

CIR= Crop Irrigation Requirement; S1= Changed climate with no change in agriculture land use; S2= Change agriculture land use with no change in climate; S3= Both Changing climate and agriculture land use

FigureFigure 3. 3.StepwiseStepwise procedure procedure of ofcomputing computing crop crop irriga irrigationtion requirement requirement (CIR) (CIR) using using observe observe climate climate data,data, General General Circulation Circulation Models Models (GCMs), (GCMs), and and crop crop information information data. data.

3. 3.Results Results 3.1. Screening of NCEP Predictor Variables 3.1. Screening of NCEP Predictor Variables Table3 indicates the most appropriate NCEP predictors screened out having super correlation with the observed local climate predictands (Tmax, Tmin, precipitation, relative humidity, and wind speed). It was observed that NCEP temperature (temperature at 2 m height) has super correlation with Tmax and Tmin. Therefore, this is the super predictor for downscaling Tmax and Tmin. Similar results were predicted in the study conducted by Mahmood and Babel [64], in which they used NCEP temperature (temperature at 2 m height) for downscaling Tmax and Tmin due to its significant correlation with observed metrological parameters. For precipitation, two predictors were found with super correlation i.e., 850 hPa meridian velocity and 500 hPa vorticity. In the case of relative humidity, different predictors were selected, however the most correlated super predictor was found as 500 hPa wind direction. Mean sea level pressure (pmlspas) was found as the super predictor for wind speed. The predictors selected for local climate parameters are mostly the same as used in previous studies [69,71,77].

3.2. Calibration and Validation of SDSM Model NCEP predictors having super correlation with local climate parameters were finally selected and used in weather and scenario generation. Simulated results obtained from SDSM during the Water 2019, 11, 1567 10 of 25 calibration (1980–1989) and validation (1990–1999) period were compared with observed data. During the calibration and validation period, simulated Tmax, Tmin, wind speed, relative humidity, and precipitation were found highly correlated with the observed data. During the calibration period, values of R2 for Tmax, Tmin, wind speed, relative humidity, and precipitation were 0.99, 0.99, 0.87, 0.98, and 0.98, respectively, while in the case of the validation period these were 0.99, 0.99, 0.76, 0.99, and 0.97, respectively (Figure4). Higher value of R 2 indicated that the SDSM model is reliable and very applicableWater 2019, 11 in, x RechnaFOR PEER Doab,REVIEW Pakistan for climate change prediction. 10 of 26

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Figure 4. Cont.

Water 2019, 11, 1567 11 of 25 Water 2019, 11, x FOR PEER REVIEW 11 of 26

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Month (a) (b)

FigureFigure 4. Comparison 4. Comparison of observed of observed and modeled and modeled (NCEP) mean(NCEP) monthly mean Tmaxmonthly (maximum Tmax (maximum temperature, temperature, Tmin (minimum temperature), Precp (precipitation), WDS (Wind speed), and R.H Tmin (minimum temperature), Precp (precipitation), WDS (Wind speed), and R.H (relative humidity) (relative humidity) during (a) calibration period (1980–1989) and (b) validation period (1990–1999). during (a) calibration period (1980–1989) and (b) validation period (1990–1999).

3.3. Predicted3.3. Predicted Changes Changes in Local in Lo Climatecal Climate of Rechnaof Rechna Doab Doab This paper investigated the long-term variations in climate change parameters (temperature, This paper investigated the long-term variations in climate change parameters (temperature, precipitation, relative humidity, and wind speed) within the study area in the baseline (1961–1990) precipitation,and future relative (2020s, humidity, 2050s, and and 2080s) wind under speed) two within selected the studyscenarios area i.e., in H3A2 the baseline and H3B2. (1961–1990) Figure 5 and futurepresented (2020s, 2050s, downscaled and 2080s) monthly under values two of selected climate scenarioschange parameters i.e., H3A2 under and H3B2.(a) H3A2 Figure and 5(b) presented H3B2 downscaledscenarios. monthly Table 4 values illustrated of climate the mean change values parameters of climatic parameters under (a) H3A2in the anddifferent (b) H3B2time period. scenarios. Table4Long-term illustrated changes the mean indicated values that of in climatic the H3A2 parameters scenario, the in average the di ffannualerent Tmax, time period.Tmin, and Long-term wind changesspeed indicated may increase that in from the 33.54 H3A2 to scenario,37.05 °C, 17.13 the averageto 19.74 °C, annual 97.38 Tmax,to 115.3Tmin, km h−1 andrespectively, wind speed while may 1 increaserelative from humidity 33.54 to and 37.05 precipitation◦C, 17.13 may to 19.74 decrease◦C, 97.38from 31.18% to 115.3 to 34.28%, km h− andrespectively, 262.60 to 170.59 while mm relative in humiditythe 2080s and precipitation with respect to may the decreasebaseline scenario from 31.18% (1961–1990). to 34.28%, In case and of 262.60the H3B2 to scenario, 170.59 mm the inaverage the 2080s with respectannual toTmax, the Tmin, baseline and scenario wind speed (1961–1990). may increase In from case 33.39 of the to H3B2 36.28 °C, scenario, 16.98 to the 19.39 average °C, 93.2 annual to 111.4 km h−1 respectively, while relative humidity and precipitation may decrease from 37.24% to Tmax, Tmin, and wind speed may increase from 33.39 to 36.28 ◦C, 16.98 to 19.39 ◦C, 93.2 to 111.4 km 1 35.51% and 261.95 to 163.97 mm in 2080s with respect to the baseline scenario (1961–1990). It was h− respectively,observed that while temperature relative humidityand wind speed and precipitation may exhibit an may upward decrease trend from while 37.24% precipitation to 35.51% and and 261.95humidity to 163.97 may mm exhibit in 2080s a downward with respect trend to thein the baseline study scenarioarea with (1961–1990). respect to the It baseline was observed period. that temperatureAnalysis and indicated wind speedthat the may future exhibit local anclimate upward of Rechna trend Doab, while Pakistan precipitation would and be warmer humidity and may exhibitdryer. a downward The A2 scenario trend inexhibits the study more area of an with increase respect in temperature to the baseline compared period. to Analysisthe B2 scenario. indicated that theSDSM future results local indicated climate ofthat Rechna all seasons Doab, may Pakistan exhibit woulda rise in be temperatures warmer and (Tmax, dryer. Tmin) The A2 but scenario the exhibitsmaximum more of rise an is increase found in in the temperature autumn season compared (September–November) to the B2 scenario. under SDSM both scenarios results indicatedH3A2 that alland seasons H3B2. mayPrecipitation exhibit is a expected rise in temperatures to decrease in (Tmax,all seasons Tmin) (except but winter) the maximum relative to risethe baseline, is found in the autumn season (September–November) under both scenarios H3A2 and H3B2. Precipitation is expected to decrease in all seasons (except winter) relative to the baseline, but the maximum decrease in precipitation may occur to appear in the spring season with 63 mm in H3A2 and 53 mm in H3B2. Water 2019, 11, 1567 12 of 25 Water 2019, 11, x FOR PEER REVIEW 12 of 26

H3A2The gradual and 53 risemm in in temperature H3B2. The g andradual reverse rise trendin temperature in precipitation and reverse may have trend significant in precipitation implications may haonve crop significant water demand implications in this on area. crop water demand in this area.

Scenario-H3A2 50 50 Scenario-H3B2

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Figure 5. Cont.

Water 2019, 11, 1567 13 of 25 Water 2019, 11, x FOR PEER REVIEW 13 of 26

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(a) (b)

FigureFigure 5. 5. DownscaledDownscaled mean mean monthly monthly values values of of climatic climatic parameters parameters (mean (mean monthly monthly Tmax Tmax (maximum (maximum temperature,temperature, TminTmin (minimum (minimum temperature), temperature), Precp Precp (precipitation), (precipitation WDS), WDS (wind (wind speed), speed), and R.H and (relative R.H (relativehumidity)) humidity)) in the baseline in the baseline (1961–1990) (1961 and–1990) future and (2020s, future 2050s,(2020s, and 2050s, 2080s) and under2080s) ( aunder) H3A2 (a) and H3A2 (b) andH3B2, (b) scenarios. H3B2, scenarios.

Table 4. Relative changeschanges inin averageaverage values values of of climatic climatic parameters parameters in in future future periods periods with with respect respect to theto thebaseline baseline period period (1961–1990). (1961–1990).

1 Scenarios Tome Period Tmax ◦C TminTmin◦C RH % Precp mm Wind Speed km h− Scenarios Tome Period Tmax °C RH % Precp mm Wind Speed km h−1 Baseline (1961–1990) 35.54 17.13°C 31.18 262.60 97.38 2020 34.65 18.03 36.33 242.22 99.18 H3A2 Baseline 205035.54 35.54 17.13 18.89 31.18 35.65 262.60 214.85 97.38 107.2 (19612080–1990) 37.05 19.74 34.28 170.59 115.3 H3A2 Baseline2020 (1961–1990) 34.65 33.39 18.03 16.98 36.33 37.24 242.22 261.50 99.18 93.2 20502020 35.54 34.32 18.89 17.75 35.65 36.87 214.85 236.95 107.2 94.59 H3B2 20802050 37.05 35.08 19.74 18.60 34.28 36.14 170.59 223.27 115.3 102 2080 36.28 19.39 35.51 163.97 111.4 Baseline 33.39 16.98 37.24 261.50 93.2 (1961–1990) 3.4. Predicted Changes in Reference ET H3B2 2020 34.32 17.75 36.87 236.95 94.59 Future changes2050 in meteorological 35.08 parameters 18.60 may36.14 have greater223.27 implications for102 reference ET (ETo). Figure6 describes2080 variations 36.28 in monthly 19.39ETo in baseline35.51 (1961–1990)163.97 and future111.4 (2020s, 2050s, and 2080s) under two selected scenarios (a) H3A2 and (b) H3B2. Under both selected scenarios, ETo is 3.4.increasing Predicted in allChanges months in fromReference 2020 ET to 2080, however, maximum increment was found from May to September.Future Monthlychanges in variations meteorological of ETo indicated parameters that, may in the have H3A2 greater scenario, implications it may increase for reference gradually ET (ETo). Figure 6 describes variations in monthly ETo in baseline (1961–1990) and future (2020s, 2050s, and 2080s) under two selected scenarios (a) H3A2 and (b) H3B2. Under both selected scenarios, ETo

Water 2019, 11, x FOR PEER REVIEW 14 of 26 is increasing in all months from 2020 to 2080, however, maximum increment was found from May to Water 2019, 11, 1567 14 of 25 September. Monthly variations of ETo indicated that, in the H3A2 scenario, it may increase gradually from January as 1.67 (baseline), 1.78 (2020s), 1.85 (2050s), and 1.95 (2080s) mm day−1 to peak value of −1 1 aboutfrom January7.29 (baseline), as 1.67 (baseline),7.42 (2020s), 1.78 7.82 (2020s), (2050s), 1.85 8.01 (2050s), (2080s) and mm 1.95 day (2080s) in mmJuly dayand− thento peak it decreases value of 1 −1 graduallyabout 7.29 to (baseline), 1.64 (baseline), 7.42 (2020s), 1.73 (2020s), 7.82 (2050s), 1.9 (2050s), 8.01 (2080s) and 2.09 mm (2080s) day− mmin July day and in then December. it decreases The 1 samegradually variation to 1.64 trend (baseline), was found 1.73 in (2020s), the H3B2 1.9 scenario. (2050s), andUnder 2.09 both (2080s) scenarios, mm day monthly− in December. ETo increased The graduallysame variation from January trend was to the found peak in value the H3B2 in June, scenario. July and Under then both decreased scenarios, gradually monthly up ETto December.o increased Undergradually the fromH3A2 January scenario, to average the peak annual value inET June,o in the July baseline and then period decreased (1961–1990) gradually was up 4.55 to mm December. day−1 −1 −1 1 andUnder in the the future H3A2 it scenario, may reach average up to annual 4.70 mmET dayo in the in baselinethe 2020s, period 4.90 mm (1961–1990) day in wasthe 2050s, 4.55 mm and day 5.17− −1 1 1 mmand day in the in future the 2080s, it may while reach under up to the 4.70 H3B2 mm scenario day− in the the average 2020s, 4.90 annual mm ET dayo in− thein the baseline 2050s, was and 4.43 5.17 −1 1 −1 −1 mmmm day day− andin thein the 2080s, future while it may under reach the up H3B2 to 4.57 scenario mm day the in average the 2020s, annual 4.76ET mmo in day the baselinein the 2050s, was 1 −1 1 1 and4.43 5.03 mm mm day− dayand in in the future2080s. itETO may would reach upgradually to 4.57 mmincrea dayse− byin 3.1% the 2020s, in the 4.76 2020s, mm 7.14% day− inin the the 1 2050s,2050s, and and 12% 5.03 in mm the day 2080s− in in thethe 2080s.H3A2 ETOscenario would wh graduallyile in the H3B2 increase it would by 3.1% increase in the by 2020s, 3.12% 7.14% in the in 2020s,the 2050s, 6.96% and in 12% the in2050s, the 2080s and 11.92% in the H3A2 in the scenario 2080s with while respect in the H3B2to the itbaseline would increaseperiod. byThe 3.12% H3A2 in scenariothe 2020s, may 6.96% exhibit in the greater 2050s, rise and in 11.92% reference in theET 2080scompared with to respect the H3B2 to the scenario baseline due period. to a greater The H3A2 rise inscenario temperature may exhibit in the greaterH3A2 scenario. rise in reference Seasonal ET chan comparedges indicate to the that H3B2 under scenario both duescenarios, to a greater autumn rise mayin temperature exhibit maximum in the H3A2 increase scenario. in ET0 Seasonalin the future changes periods indicate relative that to under the baseline, both scenarios, which is autumn due to amay greater exhibit rise maximum in temperature increase and in low ET0 rainfall in the future in this periods season. relative Overall, to thewith baseline, the changes which of is climatic due to a factors,greater ET riseo in in Rechna temperature Doaband may low continue rainfall to in increase this season. in the Overall, future, withwhich the would changes cause of climatic a gradual factors, rise inET CIR.o in Rechna Doab may continue to increase in the future, which would cause a gradual rise in CIR.

Scenario-H3A2 Scenario-H3B2 8 8

7 7

6 6

5 5 ET (mm) ET 4 4 1961-1990 1961-1990 2020 3 2020 3 2050 2050 2080 2 2080 2

1 Jan feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1 Jan feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (a) (b)

1 −1 FigureFigure 6. PredictedPredicted changeschanges inin mean mean monthly monthly evapotranspiration evapotranspiration (mm (mm day day− ) in) thein the baseline baseline (1961–1990) (1961– 1990)and the and future the future (2020s, (2020s, 2050s, 2050s, and 2080s) and 2080s) under under (a) H3A2 (a) H3A2 and ( band) H3B2 (b) H3B2 scenarios. scenarios.

3.5.3.5. Predicted Predicted Changes Changes in in Effective Effective Precipitation Precipitation (Pe) (Pe) ItIt can can be be seen seen from from Figure Figure 77 thatthat thethe monthlymonthly eeffectiveffective precipitation precipitation (Pe) (Pe) under under both both scenarios scenarios (a)(a) H3A2 H3A2 and and (b) (b) H3B2 H3B2 show show downward downward trends trends in in fu futureture periods. periods. The The effect effectiveive precipitation precipitation showed showed considerableconsiderable monthly variation.variation. TheThe maximum maximum e ffeffectiveective precipitation precipitation was was in July,in July, whereas whereas it was it closewas closeto zero to inzero October–December. in October–December. Results Results indicated indicated that that average average annual annual Pe may Pe may decrease decrease from from 210 mm210 mmin the in the baseline baseline (1961–1990) (1961–1990) to 193to 193 mm mm in in the the 2020s, 2020s, 172 172 mm mm in in the the 2050s, 2050s, andand 136136 mm in the 2080s 2080s underunder H3A2 H3A2 scenario. scenario. While While under under the the H3B2 H3B2 scenario scenario it it may may decreased decreased from from 209 209 mm mm in in the the baseline baseline (1961–1990)(1961–1990) to to 189 189 mm mm in in the the 2020s, 2020s, 181 181 mm mm in in the the 2050s, 2050s, and and 131 131 mm mm in in the the 2080s. 2080s. Annual Annual Pe Pe may may decreasedecrease by by 7.76% 7.76% in in the the 2020s, 2020s, 18.18% 18.18% in in the the 205 2050s,0s, and and 35.03% 35.03% in in the the 2080s 2080s in in the the H3A2 H3A2 scenario scenario whilewhile in in H3B2 H3B2 it it would would decrease decrease by by 9.38% 9.38% in in the the 2020s, 2020s, 13.07% 13.07% in in the the 2050s, 2050s, and and 37.29% 37.29% in in the the 2080s 2080s withwith respect respect to to the the baseline period. Seasonal Seasonal vari variationsations indicated indicated that that Pe Pe may may exhibit exhibit a a downward downward trendtrend in in the the future future except except winter winter which which may may exhibit exhibit an an increment increment in in Pe Pe which which is is due due to to the the rise rise in in precipitationprecipitation in in this this season. season. Due Due to to combined combined impact, impact, an an increase increase in in ET ET and and decrease decrease in in PE PE may may have have greatergreater implications implications for for irrigation irrigation requir requirementement and it could rise in the future.

Water 2019, 11, 1567 15 of 25 Water 2019, 11, x FOR PEER REVIEW 16 of 26

Scenario-H3A2 80 1961-1990 Scenario-H3B2 1961-1990 80 2020 2020 70 2050 2050 2080 60 2080 60 50

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Pe (mm) 40 30

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0 0 -10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(a) (b)

Figure 7. Mean monthly changes in eeffectiveffective precipitation (mm) in the baseline (1961–1990) and the future (2020s,(2020s, 2050s,2050s, andand 2080s)2080s) underunder ((aa)) H3A2H3A2 andand ((bb)) H3B2H3B2 scenarios.scenarios.

3.6. Predicted Changes in Agriculture Cropping Patterns Table5 5 depicted depicted changes changes inin agricultureagriculture landland useuse changeschanges inin thethe formform ofof croppingcropping patternspatterns (crop(crop sown area) of fivefive majormajor crops, wheat, sugarcane, rice, cotton, and maize grown on a large area of Rechna Doab. Results Results indicated indicated that that in infuture future scenarios, scenarios, crop crop sown sown area area of sugarcane, of sugarcane, wheat wheat and andrice ricemay may exhibit exhibit an increasing an increasing trend trend while while cotton cotton and and ma maizeize exhibit exhibit a areducing reducing trend. trend. In In the the baseline 3 scenario (1981–2015), totaltotal cropcrop sownsown areaarea ofof fivefive majormajor agricultureagriculture cropscrops waswas 3011.233011.23 × 10103 ha and in 3 3 × 3 the futurefuture itit maymay reachreach toto 3274.53274.5 × 10103 haha (2020s),(2020s), 3693.53693.5 × 103 ha (2050s), and 4220 × 103 haha (2080s). × × × 3 Wheat cropcrop maymay exhibitexhibit a a rapid rapid expansion expansion in in the the cultivated cultivated area area as as it wasit was grown grown on on 1648.68 1648.6810 × 10ha3 ha in 3 × 3 thein the baseline baseline period period (1981–2015) (1981–2015) and and in the in the future future it may it may reach reach up toup 1900 to 190010 × 10ha3 (2020),ha (2020), 2250 225010 × 10ha3 3 × × (2050), and 2448 10 ha3 (2080s). Wheat grown on a large area of Rechna Doab is followed by rice ha (2050), and 2448× × 10 ha (2080s). Wheat grown on a large area of Rechna Doab is followed by rice and sugarcane.

Table 5. Future changes in agriculture crop sown area of five major crops (unit: 1000 ha). Table 5. Future changes in agriculture crop sown area of five major crops (unit: 1000 ha).

Crop TypeCrop BaselineType Baseline (1981–2015) (1981–2015) 20202020 2050 2050 2080 2080 SugarcaneSugarcane 241.55241.55 270270 308.5 308.5 351 351 WheatWheat 1648.681648.68 19001900 2250 2250 2448 2448 Rice 852 890 1400 1710 CottonRice 156852 120890 1400 80 1710 60 MaizeCotton 113156 64120 80 6660 47 TotalMaize 3011.23113 3274.564 66 3693.5 47 4220 Total 3011.23 3274.5 3693.5 4220 3.7. Predicted Changes in CIR 3.7. Predicted Changes in CIR ETc is greatly correlated with ETo, and represents the amount of water to irrigate the crops (i.e., CIR inETc this is greatly case). Summercorrelated and with spring ETo, and seasons represents exhibit the maximum amount of rate water of ET toO irrigatewhich the indicates crops (i.e., that cropsCIR in grown this case). in these Summer seasons and mayspring consume seasons moreexhibit water maximum than other rate of seasons. ETO which Moreover, indicates some that crops maygrown have in these their developmentseasons may stageconsume during more these water seasons than which other mayseasons. have Moreover, high value some of Kc crops and result may inhave consumption their development of more stage water. during Figure these8 depicts seasons changes which inmay the have monthly high CIRvalue (mm) of Kc of and five result major in agriculturalconsumption crops of more under water. two selected Figure scenarios8 depicts (a) ch H3A2anges and in the (b) H3B2.monthly CIR CIR of each (mm) crop of mayfive exhibitmajor anagricultural increasing crops trend under in each two month selected from scenarios the baseline (a) H3A2 period and (1961–1990) (b) H3B2. to CIR the of future each (2020s,crop may 2050s, exhibit and 2080)an increasing under both trend selected in each scenarios month from i.e., A2 the and baseline B2, but period with di (1961–1990)fferent magnitudes. to the future These (2020s, changes 2050s, are cross-pondingand 2080) under to both rise inselected temperature scenarios and i.e., decrease A2 and in B2, precipitation. but with different Figure magnitudes.9 explains the These variations changes of monthlyare cross-ponding total CIR of to five rise crops in undertemperature both scenarios. and decrease Under thein precipitation. H3A2 scenario, Figure the monthly 9 explains total CIRthe ofvariations five agricultural of monthly crops total was CIR highest of five in Augustcrops un withder 911both mm scenarios. in the baseline Under the (1961–1990), H3A2 scenario, 939 mm the in themonthly 2020s, total 990 CIR mm of in five the 2050s,agricult andural 1064 crops mm was in highest the 2080s, in August while the with lowest 911 aremm in in January the baseline as 70.3 (1961– mm 1990), 939 mm in the 2020s, 990 mm in the 2050s, and 1064 mm in the 2080s, while the lowest are in January as 70.3 mm in the baseline (1961–1990), 74.4 mm in the 2020s, 76.5 mm in the 2050s, and 79.1

Water 2019, 11, 1567 16 of 25 Water 2019, 11, x FOR PEER REVIEW 17 of 26 inmm the in baseline the 2080s. (1961–1990), Under the 74.4H3B2 mm scenario, in the 2020s,monthly 76.5 total mm CIR in theof five 2050s, agricultural and 79.1 crops mm in was the highest 2080s. Underin August the H3B2 as 905 scenario, mm in the monthly baseline total (1961–1990), CIR of five agricultural962 mm in the crops 2020s, was 967 highest mm inin Augustthe 2050, as and 905 mm1061 inmm the in baseline the 2080s, (1961–1990), while the 962 lowest mm water in the demands 2020s, 967 were mm inin theJanuary 2050, as and 68.6 1061 mm mm in inthe the baseline 2080s, (1961– while the1990), lowest 68.3 watermm in demands the 2020s, were 74.2 in mm January in the as2050s, 68.6 and mm 79.4 in the mm baseline in the 2080s. (1961–1990), Figure 68.310 depicted mm in thethe 2020s,seasonal 74.2 crop mm irrigation in the 2050s, requiremen and 79.4 mmt of each in the individual 2080s. Figure crop 10 in depicted both selected the seasonal scenarios. crop Under irrigation the requirementH3A2 scenario, of each total individual CIR to produce crop in sugarcan both selectede is likely scenarios. the highest Under as the1879.8 H3A2 mm scenario, year−1 in totalthe baseline CIR to −1 1 −1 −1 produce(1961–1990), sugarcane 1934.6 is mm likely year the in highest the 2020s, as 1879.8 2061.1 mm mm year year− in in the the baseline 2050s, (1961–1990),and 2219.3 mm 1934.6 year mm in 1 1 1 −1 yearthe −2080s.in the While 2020s, under 2061.1 the mm H3B2 year scenario− in the 2050s,the highest and 2219.3 rate of mm it might year− bein 1842.6 the 2080s. mm Whileyear underin the −1 1 −1 thebaseline H3B2 (1961–1990), scenario the highest1896.5 mm rate year of it might in the be 2020s, 1842.6 1879.8 mm year mm− yearin the in baseline the 2050s, (1961–1990), and 2162.4 1896.5 mm −1 1 1 1 mmyear year in− thein 2080s. the 2020s, The 1879.8rank of mm water year consumption− in the 2050s, rate and of 2162.4rice was mm 2nd yearas− it inconsumed the 2080s. 1118.7 The rank mm −1 −1 −1 ofyear waterin consumptionthe baseline and rate ofin ricethe wasfuture 2nd it asmay it consumed reach to 1143.5 1118.7 mm yearyear− in(2020), the baseline 1189.5 andmm inyear the −1 1 1 1 future(2050), it and may 1268.9 reach tomm 1143.5 year mm (2080) year −under(2020), the 1189.5 H3A2 mm scenario. year− In(2050), case andof the 1268.9 H3B2 mm scenario, year− (2080) water underconsumption the H3A2 rate scenario. of rice Inwas case 1111.5 of the mm H3B2 year scenario,−1 in thewater baseline consumption (1961–1190) rate and of ricein the was future 1111.5 it mmmay 1 −1 −1 −1 1 yearreach− upin theto 1140 baseline mm (1961–1190)year (2020), and 1177 in themm future year it(2050), may reach 1251.1 up mm to 1140year mm (2080). year −All(2020), crops show 1177 1 1 mmsignificant year− (2050),rise in 1251.1the water mm consumption year− (2080). rate All cropsdue to show rise significantin temperatures rise in and the waterdecrease consumption in rainfall. rateUnder due the to riseH3A2 in temperaturesscenario, the average and decrease annual in CIR rainfall. of sugarcane, Under the maize, H3A2 cotton, scenario, rice, the and average wheat annual could CIRbe increased of sugarcane, by 339 maize, mm, cotton, 123 mm, rice, 181 and mm, wheat 150 couldmm, an bed increased 45 mm, respectively by 339 mm, in 123 2080 mm, with 181 respected mm, 150 mm,to the and baseline. 45 mm, respectively in 2080 with respected to the baseline.

Baseline (1961-1990) Baseline (1961-1990) Sugarcane H3A2-2020 350 350 Sugarcane H3B2-2020 H3A2-2050 H3B2-2050 H3A2-2080 H3B2-2080 300 300

250 250

200 200

150 (mm) CIR 150

100 100

50 50

0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

300 300 Baseline (1961-1990) Maiz Baseline (1961-1990) H3A2-2020 H3B2-2020 Maiz H3A2-2050 H3B2-2050 H3A2-2080 250 250 H3B2-2080

200 200

150 150 CIR (mm)

100 100

50 50

0 0 May Jun July August Sep May Jun July August Sep Figure 8. Cont.

Water 2019, 11, 1567 17 of 25 Water 2019, 11, x FOR PEER REVIEW 18 of 26

300 Baseline (1961-1990) 300 H3A2-2020 Cotton Baseline (1961-1990) Cotton H3A2-2050 H3B2-2020 H3B2-2050 250 H3A2-2080 250 H3B2-2080

200 200

150 150 CIR (mm)

100 100

50 50

0 0 May Jun July August Sep Oct Nov May Jun July August Sep Oct Nov

Baseline (1961-1990) Baseline (1961-1990) Rice Rice H3B2-2020 H3A2-2020 300 H3A2-2050 300 H3B2-2050 H3A2-2080 H3B2-2080

250 250

200 200

150 150 CIR (mm) CIR

100 100

50 50

0 Jun July aug sep Oct Nov 0 Jun July aug sep Oct Nov 100 Baseline (1961-1990) Wheat H3A2-2020 Baseline (1961-1990) Wheat H3A2-2050 100 H3A2-2020 H3A2-2080 H3A2-2050 80 H3A2-2080

80

60 60

CIR (mm) CIR 40 40

20 20

0 Dec Jan Feb Mar Apr 0 Dec Jan Feb Mar Apr

(a) (b)

FigureFigure 8. MonthlyMonthly changes changes in in crop crop irrigation irrigation requirement requirement (CIR) (CIR) of of five five agricultural agricultural crops, crops, sugarcane, sugarcane, maize,maize, cotton, cotton, rice, and wheat under ( a) H3A2 and ( b) H3B2 scenarios in Rechna Doab.

Water 2019, 11, 1567 18 of 25 Water 2019, 11, x FOR PEER REVIEW 19 of 26 Water 2019, 11, x FOR PEER REVIEW 19 of 26

Figure 9. Monthly summation of CIR of five agricultural crops under (a) H3A2 and (b) H3B2 scenarios FigureFigure 9. 9.Monthly Monthly summation summation of CIR CIR of of fi fiveve agricultural agricultural crops crops under under (a) H3A2 (a) H3A2 and (b and) H3B2 (b) scenarios H3B2 scenarios in Rechna Doab. in Rechnain Rechna Doab. Doab.

FigureFigure 10. 10.Changes Changes inin cropcrop irrigation requirement requirement (CIR) (CIR) of different of different agricultural agricultural crops cropsunder under(a) H3A2 (a ) H3A2 Figure 10. Changes in crop irrigation requirement (CIR) of different agricultural crops under (a) H3A2 andand (b )(b H3B2) H3B2 scenarios scenarios in in RechnaRechna Doab. Doab. and (b) H3B2 scenarios in Rechna Doab. 3.8.3.8. CIR CIR under under Changing Changing ClimateClimate and Agriculture Agriculture Cropping Cropping Patterns Patterns 3.8. CIR under Changing Climate and Agriculture Cropping Patterns In orderIn order to assessto assess the the impacts impacts of of both both climate climate and and agriculture agriculture plantingplanting areaarea changeschanges on CIR, future In order to assess the impacts of both climate and agriculture planting area changes on CIR, future changes in total CIR were projected in three different sub-scenarios (1) S1: Changed climate changesfuture inchanges total CIR in total were CIR projected were projected in three in dithreefferent different sub-scenarios sub-scenarios (1) S1:(1) S1: Changed Changed climate climate with no with no change in agriculture land uses, (2) S2: Changed agriculture land use with no change in changewith inno agriculturechange in agriculture land uses, land (2) S2:uses, Changed (2) S2: Changed agriculture agriculture land use land with use nowith change no change in climate, in (3) climate, (3) S3: Changed climate and agriculture land uses. Tables 6 and 7 indicate the changes in S3:climate, Changed (3) climateS3: Changed and climate agriculture and agriculture land uses. land Tables uses.6 andTables7 indicate 6 and 7 theindicate changes the changes in total in CIR in total CIR in H2A2 and under the H3B2 scenario under three sub-scenarios. Assuming the changing H2A2total and CIR under in H2A2 the and H3B2 under scenario the H3B2 under scenario three un sub-scenarios.der three sub-scenarios. Assuming Assuming the changing the changing climate in the climate in the future (S1), it was observed that all crops may exhibit an increasing trend in CIR under futureclimate (S1), in it the was future observed (S1), it was that observed all crops that may all exhibit crops may an increasingexhibit an increasing trend in trend CIR underin CIR allunder scenarios all scenarios but with different magnitudes. Under the H3A2 scenario with sub-scenario (S1), CIR of all scenarios but with different magnitudes. Under the H3A2 scenario with sub-scenario (S1), CIR of butsugarcane, with different cotton, magnitudes. wheat, rice, Underand maize the H3A2may in scenariocrease by with0.82, sub-scenario0.28, 0.74, 1.27, (S1), and CIR 0.13 of BCM sugarcane, sugarcane, cotton, wheat, rice, and maize may increase by 0.82, 0.28, 0.74, 1.27, and 0.13 BCM cotton,respectively wheat, in rice, the and2080s maize relative may to the increase baseline by (1 0.82,961–1990), 0.28, 0.74,and in 1.27, the H3B2 and 0.13scenario BCM it may respectively increase in the respectively in the 2080s relative to the baseline (1961–1990), and in the H3B2 scenario it may increase 2080sby 0.77, relative 0.26, to 0.61, the 1.18, baseline and 0.13 (1961–1990), BCM, respectively and in thein the H3B2 2080s. scenario Total CIR it required may increase to irrigate by 0.77, the five 0.26, 0.61, by 0.77, 0.26, 0.61, 1.18, and 0.13 BCM, respectively in the 2080s. Total CIR required to irrigate the five 1.18,major and crops 0.13 BCM,may increase respectively by 3.26 in (A2) the and 2080s. 2.98 Total BCM CIR (B2) requiredin the 2080s to irrigatewith respect the fiveto the major baseline crops may major crops may increase by 3.26 (A2) and 2.98 BCM (B2) in the 2080s with respect to the baseline scenario (1961–1990). Gradual rise in CIR may due to the rise in temperature and ET rate in the future. increasescenario by (1961–1990). 3.26 (A2) and Gradual 2.98 BCMrise in (B2) CIR inmay the due 2080s to th withe rise respectin temperature to the baselineand ET rate scenario in the future. (1961–1990). Assuming the changing planting area of crops in the future (S2), it was seen that sugarcane, wheat, GradualAssuming rise the in CIRchanging may planting due to area the riseof crops in temperature in the future (S2), and it ET was rate seen in that the sugarcane, future. Assuming wheat, the and rice may consume more water due to the gradual rise in the planting area in the future while changingand rice planting may consume area ofmore crops water in thedue futureto the gradual (S2), it wasrise in seen the thatplanting sugarcane, area in the wheat, future and while rice may water application for cotton and maize may reduce because in the future their planting area may consumewater application more water for due cotton to the and gradual maize may rise inreduce the planting because in area the in future the future their planting while water area applicationmay reduce. Under the H3A2 scenario with sub-scenario (S2), CIR of sugarcane, wheat, and rice may forreduce. cotton andUnder maize the H3A2 may scenario reduce becausewith sub-scenario in the future (S2), theirCIR of planting sugarcane, area wheat, may and reduce. rice may Under the increase by 2.05, 1.73, and 9.59 BCM, respectively in the 2080s, and in the H3B2 scenario it may increase by 2.05, 1.73, and 9.59 BCM, respectively in the 2080s, and in the H3B2 scenario it may H3A2increase scenario by 2.01, with 1.73, sub-scenario and 9.53 BCM, (S2), respectively CIR of sugarcane, in the 2080s wheat, with andrespect rice to may the baseline increase period. by 2.05, 1.73, increase by 2.01, 1.73, and 9.53 BCM, respectively in the 2080s with respect to the baseline period. andTotal 9.59 CIR BCM, required respectively to irrigate in the the five 2080s, major and crops in the may H3B2 increase scenario by 12.12 it mayBCM increase(A2) and by12.04 2.01, BCM 1.73, and Total CIR required to irrigate the five major crops may increase by 12.12 BCM (A2) and 12.04 BCM 9.53(B2) BCM, in the respectively 2080s with inrespect the 2080s to the with baseline respect scenario to the (1961–1990). baseline period. Assuming Total with CIR both required changing to irrigate (B2) in the 2080s with respect to the baseline scenario (1961–1990). Assuming with both changing climate and agriculture land use (S3), it was observed that the net volume of water to irrigate the theclimate five major and agriculture crops may land increase use (S3), by 12.12it was BCM observ (A2)ed that and the 12.04 net BCMvolume (B2) of water in the to 2080s irrigate with the respect to the baseline scenario (1961–1990). Assuming with both changing climate and agriculture land use

(S3), it was observed that the net volume of water to irrigate the crops may increase in future periods relative to the baseline. In the S3 scenario, total CIR required to irrigate the five major crops may Water 2019, 11, 1567 19 of 25 increase by 17.15 BCM (A2) and 16.62 BCM (B2) in the 2080s with respect to the baseline scenario (1961–1990). The S1, S2, and S3 scenarios clearly explain the individual impacts of both parameters (climate and agriculture land use) on net volumetric application of water for agriculture crops in the future. It can be seen that the S2 scenario may affect the CIR with greater magnitude compared to the S2 scenario. However, combined affects (S3) of both parameters have great implications for future CIR in Rechna Doab.

Table 6. Total CIR for different crops under the H3A2 scenario with three different sub-scenarios S1, S2, and S3. * Note: CSA indicated crop sown area.

S1 S2 S3 Total CIR = Total CIR = Total CIR = CIR CSA CIR CSA CIR CSA CIR*CSA CIR*CSA CIR*CSA mm 1000 mm 1000 mm 1000 Crops 1 BCM 1 BCM 1 BCM year− ha year− ha year− ha Sugarcane 1934.6 241.55 4.6730263 1879.8 271 5.094258 1934.6 271 5.242766 cotton 904.7 156 1.411332 867.7 120 1.04124 904.7 120 1.08564 2020s Wheat 232.4 1648.68 3.8315323 216.9 1900 4.1211 232.4 1900 4.4156 Rice 1143.5 852 9.74262 1118.7 890 9.95643 1143.5 890 10.17715 Maize 665.7 113 0.752241 646.4 87 0.562368 665.7 87 0.579159 Total 4880.9 3011.23 20.410752 4729.5 3268 20.775396 4880.9 3268 21.500315 Sugarcane 2061.1 241.55 4.9785871 1879.8 308.5 5.799183 2061.1 308.5 6.3584935 cotton 960.1 156 1.497756 867.7 180 1.56186 960.1 180 1.72818 2050s Wheat 246.9 1648.68 4.0705909 216.9 2250 4.88025 246.9 2250 5.55525 Rice 1189.5 852 10.13454 1118.7 1400 15.6618 1189.5 1400 16.653 Maize 704.5 113 0.796085 646.4 66 0.426624 704.5 66 0.46497 Total 5162.1 3011.23 21.477559 4729.5 4204.5 28.329717 5162.1 4204.5 30.7598935 Sugarcane 2219.3 241.55 5.3607192 1879.8 351 6.598098 2219.3 351 7.789743 cotton 1048.9 156 1.636284 867.7 60 0.52062 1048.9 60 0.62934 2080s Wheat 261.9 1648.68 4.3178929 216.9 2448 5.309712 261.9 2448 6.411312 Rice 1268.9 852 10.811028 1118.7 1710 19.12977 1268.9 1710 21.69819 Maize 769.4 113 0.869422 646.4 47 0.303808 769.4 47 0.361618 Total 5568.4 3011.23 22.995346 4729.5 4616 31.862008 5568.4 4616 36.890203

Table 7. Total CIR for different crops under H3B2 scenario with three different sub-scenarios S1, S2, and S3. * Note: CSA indicated crop sown area.

S1 S2 S3 Total CIR = Total CIR = Total CIR = CIR CSA CIR CSA CIR CSA CIR*CSA CIR*CSA CIR*CSA mm 1000 mm 1000 mm 1000 Crops 1 BCM 1 BCM 1 BCM year− ha year− ha year− ha Sugarcane 1896.5 241.55 4.580996 1842.6 271 4.993446 1896.5 271 5.139515 cotton 897.3 156 1.399788 854.1 120 1.02492 897.3 120 1.07676 2020s Wheat 227.5 1648.68 3.750747 216.7 1900 4.1173 227.5 1900 4.3225 Rice 1140 852 9.7128 1111.5 890 9.89235 1140 890 10.146 Maize 667.8 113 0.754614 641.5 87 0.558105 667.8 87 0.580986 Total 4829.1 3011.23 20.19894 4666.4 3268 20.58612 4829.1 3268 21.26576 Sugarcane 1962.1 241.55 4.739453 1842.6 308.5 5.684421 1962.1 308.5 6.053079 cotton 935.6 156 1.459536 854.1 180 1.53738 935.6 180 1.68408 2050s Wheat 237.9 1648.68 3.92221 216.7 2250 4.87575 237.9 2250 5.35275 Rice 1177 852 10.02804 1111.5 1400 15.561 1177 1400 16.478 Maize 688.8 113 0.778344 641.5 66 0.42339 688.8 66 0.454608 Total 5001.4 3011.23 20.96287 4666.4 4204.5 28.08194 5001.4 4204.5 30.02252 Sugarcane 2162.4 241.55 5.223277 1842.6 351 6.467526 2162.4 351 7.590024 cotton 1027 156 1.60212 854.1 60 0.51246 1027 60 0.6162 2080s Wheat 254.2 1648.68 4.190945 216.7 2448 5.304816 254.2 2448 6.222816 Rice 1251.1 852 10.65937 1111.5 1710 19.00665 1251.1 1710 21.39381 Maize 757.2 113 0.855636 641.5 47 0.301505 757.2 47 0.355884 Total 5451.9 3011.23 22.53135 4666.4 4616 31.59296 5451.9 4616 36.17873

4. Discussion Changing climate and agriculture expansion in the form of growing plantation area have greater implications for irrigated water supply [36]. This study analyzed the long-term changes in CIR (crop Water 2019, 11, 1567 20 of 25 irrigation requirement) caused by climate and agriculture land use changes. Past research has analyzed that minimum and maximum temperatures have increased in both seasons (winter and summer) throughout Pakistan, specifically central regions of Pakistan (Punjab) are becoming more warmer and dryer [78–80]. GCMs (general circulation models) can simulate future changes in climate. HadCM3 under two scenarios (A2 and B2) was used to capture climate change characteristics in the study region. Results of this paper indicate that under both scenarios (A2 and B2), temperatures (Tmax, Tmin) would increase in future periods (2020s, 2050s, and 2080s) while precipitation will decrease with respect the baseline scenario (1961–1990). These results indicate that climate in this region may likely be warmer and dryer in the future which may have greater implications for crop evapotranspiration and irrigation requirement. Reference ET is highly dependence on change in weather conditions, especially air temperatures. Various studies conducted in different regions have also indicated that reference ET has continuously increased due to gradual rise in temperatures [81–83]. Results of this study have shown that, in both climate change scenarios, monthly ETo is increasing gradually from January to the peak in July and then decreasing gradually up to December. The highest increase in reference ET is from April to September which is due to a hot and dry summer and low rainfall in these months as well as growth period of crops [84]. Long-term future changes in ETo have indicated that it would increase in future periods due to an increase in temperature, which would cause a gradual rise in irrigation requirement. This study predicted that the crop irrigation requirement of fiver major crops (rice, sugarcane, maize, wheat, and cotton) would increase in the future due to a gradual rise in ET in different months with different magnitudes. Monthly sum of CIR of five crops is found to be the maximum in August and minimum in January, which is related to ET and the growing stage. The reason why maximum water demand takes place in August is mainly due to the fact that three crops (sugarcane, cotton, and rice) out of five total crops have mid-season stage in August, resulting in more consumption of water. Sugarcane and rice consume more water as compared to other crops [85]. References [84,86] have analyzed impacts of climate change on crop water requirement of rice, sugarcane, maize, wheat, and cotton and their analysis also indicated that crop water demand will increase due to the rise in temperature in future scenarios. Results of agriculture cropping patterns indicated that in future scenarios crop sown area of sugarcane, wheat, and rice may exhibit an increasing trend while cotton and maize may exhibit a reducing trend. This is because of the growing population demanding more food requirement to meet their needs. Past studies indicated that food and fiber requirement in Pakistan has largely increased as a result of rapid population growth, from 37.5 million in 1950 to 207 million in 2017 and is expected to reach 333 million in 2050 [26]. Wheat, sugarcane, maize, rice, and cotton are major food and fiber crops in Pakistan. In past years, the crops sown area of wheat, rice, and sugarcane has increased at a greater rate because these are staple food crops and extensively used in daily life, also the product of these crops are exported in other countries. Farmers are growing more crops on more land area to meet the demand of the population, therefore, land and water resources of Pakistan are tremendously under pressure to fulfill the growing needs of the population [27]. The study conducted by [87] indicated that the area under major crops (fooder, wheat, rice, sugarcane, cotton) has been increased in the past few years and will also increase more in the future to meet the food demand of a rapidly growing population in Pakistan. In Rechna Doab, the total irrigated area from 1.94 Mha in 1961 to 2.12 Mha in 1990 and cropping intensity has increased from 91% in 1961 to 131% in 1980 [28]. In order to analyze the net volume of water (Total CIR (m3) = CIR A) consumed by individual crops, the sown area of × each crop was multiplied with the crop irrigation requirement of that crop. Assessment of individual impacts of both climate and agriculture cropping patterns on CIR is crucial, that is why future CIR is subjected to three further sub-scenarios S1 (changed climate), S2 (changed agriculture land uses), and S3 (changed climate and agriculture land uses). The climate change scenarios indicate that future CIR is likely to increase in the study area but at a less rate when compared with agricultural land use change scenario. However combined affects (S3) of both parameters have much severe implications for future CIR in Rechna Doab. This study serves as a first exploration on how the potential impacts Water 2019, 11, 1567 21 of 25 on environmental sustainability of agricultural expansion and intensification can be expressed over time, using a combination of approaches (land use and climate change scenarios). In order to gain a deeper insight into the impacts at a regional scale, future studies need to include spatial analysis and analyze additional climate change scenarios. Anyhow, this study has some limitations which may be the subject of future investigations. Firstly, Kc values of different agricultures crops were taken from FAO tables and relevant studies and these values were kept constant in the future periods to allow for a less complicated process and more comprehensive analysis. Secondly, agriculture land use changes in the form of crop sown area in future periods was just simulated using trend analysis based on the historical rate. In practice, agriculture expansion may fluctuate because of many factors ranging from population growth to socio-economic factors. Further attempts are required to simulate the crop sown area in the future based on socio-economic factors, foods demands, as well as their value addition in the market.

5. Conclusions Irrigated agriculture is the largest water consuming sector in Rechna Doab. Growth of irrigated surfaces and the changing climate has placed new demands on the irrigated water supply. This study attempted to investigate current and projected changes in CIR under changing climate and agriculture expansion. This study predicted that in the future, the climate of Rechna Doab, Pakistan, will become warmer and dryer due to increasing temperatures and a decrease in rainfall and humidity. During calibration and validation of the SDSM model, high consistency of results with observed data indicated that the arid region of Rechna Doab is reliable for climate change prediction. The SDSM-HadCM3 A2 scenario had the greatest changes, namely an increase of 1.10 to 3.51 ◦C and 0.9 to 3.02 ◦C in the monthly means of Tmax and Tmin, respectively while in the B2 scenario it could be increased by 0.85–2.89 ◦C and 0.85–2.94 ◦C respectively in 2080 relative to the baseline (1961–1990). The gradual rise in temperature may have significant implications on crop water demand. When these increasing trends of temperatures were used to estimate future potential evapotranspiration using CROPWAT, increases in ET of the crops were found leading to elevated CIR. Among all agriculture crops, sugarcane was accounted as the major consumer of water, followed by rice which is at the 2nd rank. Similarly, projected changes in agriculture land use changes in the form of cropping patterns (crop sown area) depicted an increasing trend in the plantation area of sugarcane, wheat, and rice, while maize and cotton predicted a decreasing trend with respect to the baseline scenario. Overall, analysis presented that CIR is likely to increase under changing climate but at a less rate when predicted with the agricultural land use change scenario. However, combined impacts of both parameters (climate and agriculture land use changes) were found to be more severe for elevating CIR in the future period. In this study, simulated future irrigation requirement is an important asset to identify the emerging trends and support water management strategies.

Author Contributions: A.A. and W.Z. conceived this research. A.A. and Z.Z. performed the experiments, analyzed the results and wrote the paper under the guidance of W.Z. W.Z., Z.Z., and I.G. gave comments and modified the manuscript. Funding: This research was financially supported by the National Key Research and Development Program of China (Project Nos. 2016YFA0602302 and 2016YFB0502502) and the National Natural Science Foundation of China (Project No. 41175088). Great thanks should be given to the Hadley Centre for making the Hadley Centre coupled model (HadCM3) available for public uses. Conflicts of Interest: Authors declare no conflicts of interest.

References

1. Wang, R.; Kalin, L.; Kuang, W.; Tian, H. Individual and combined effects of land use/cover and climate change on Wolf Bay watershed streamflow in southern Alabama. Hydrol. Process. 2014, 22, 5530–5546. [CrossRef] Water 2019, 11, 1567 22 of 25

2. Sun, S.; Zhou, T.; Wu, P.; Wang, Y.; Zhao, X.; Yin, Y. Impacts of future climate and agricultural land-use changes on regional agricultural water use in a large irrigation district of northwest China. Land Degrad. Dev. 2019.[CrossRef] 3. Hoekstra, A.; Mekonnen, M. The water footprint of humanity. Proc. Natl. Acad. Sci. USA 2012, 109, 3232–3237. [CrossRef][PubMed] 4. Diffenbaugh, N.S.; Singh, D.; Mankin, J.S.; Horton, D.E.; Swain, D.L.; Touma, D.; Charland, A.; Liu, Y.; Haugen, M.; Tsiang, M.; et al. Quantifying the Influence of Global Warming on Unprecedented Extreme Climate Events. Proc. Natl. Acad. Sci. USA 2014, 114, 4881–4886. 5. Shiklomanov, I.; Rodda, J. World Water Resources at the Beginning of the Twenty-First Century; UNESCO International Hydrology Series; Cambridge University Press: Cambridge, UK, 2004. 6. West, P.C.; Gerber, J.S.; Engstrom, P.M.; Mueller, N.D.; Brauman, K.A.; Carlson, K.M.; Cassidy, E.S.; Johnston, M.; MacDonald, G.K.; Ray, D.K.; et al. Leverage points for improving global food security and the environment. Science 2014, 345, 325–328. [CrossRef][PubMed] 7. Döll, P. Impact of climate change and variability on irrigation requirements: A global perspective. Clim. Chang. 2002, 54, 269–293. [CrossRef] 8. Yano, T.; Koriyama, M.; Haraguchi, T.; Aydin, M. Prediction of future change of water demand following global warming in the Cukurova region of Turkey. In Proceedings of the International Conference on Water, Land and Food Security in Arid and Semi-Arid Regions, Mediterranean Agronomic Institute Valenzano (CIHEAM-MAIB), Bari, Italy, 6–11 September 2005. 9. Diaz, J.R.; Weatherhead, E.K.; Knox, J.W.; Camacho, E. Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg. Environ. Chang. 2007, 7, 149–159. [CrossRef] 10. De Silva, C.; Weatherhead, E.K.; Knox, J.W.; Rodriguez-Diaz, J.A. Predicting the impacts of climate change—A case study of paddy irrigation water requirements in Sri Lanka. Agric. Water Manag. 2007, 93, 19–29. [CrossRef] 11. Shahid, S. Impact of climate change on irrigation water demand of dry season Boro rice in northwest Bangladesh. Clim. Chang. 2011, 105, 433–453. [CrossRef] 12. Safeeq, M.; Fares, A. Hydrologic response of a Hawaiian watershed to future climate change scenarios. Hydrol. Process. 2012, 26, 2745–2764. 13. Ashour, E.; Al-Najar, H. The impact of climate change and soil salinity in irrigation water demand in the Gaza strip. J. Earth Sci. Clim. Chang. 2012, 3, 120. 14. Downing, T.; Butterfield, R.; Edmonds, B.; Knox, J.W.; Moss, S.; Piper, B.; Weatherhead, E. CCDeW: Climate Change and Demand for Water. 2003. Available online: http://dspace.lib.cranfield.ac.uk/handle/1826/3576 (accessed on 29 July 2019). 15. Wada, Y.; Wisser, D.; Eisner, S.; Flörke, M.; Gerten, D.; Haddeland, I.; Hanasaki, N.; Masaki, Y.; Portmann, F.T.; Stacke, T.; et al. Multimodel projections and uncertainties of irrigation water demand under climate change. Geophys. Res. Lett. 2013, 40, 4626–4632. [CrossRef] 16. Thomas, A. Agricultural irrigation demand under present and future climate scenarios in China. Glob. Planet. Chang. 2008, 60, 306–326. [CrossRef] 17. Wang, X.J.; Zhamg, J.Y.; Shahid, S.; ElMahdi, A.; He, R.M.; Bao, Z.X.; Ali, M. Water resources management strategy for adaptation to droughts in China. Mitig. Adapt. Strateg. Glob. Chang. 2012, 17, 923–937. 18. Ding, Y.; Wang, W.; Song, R.; Shao, Q.; Jiao, X.; Xing, W. Modeling spatial and temporal variability of the impact of climate change on rice irrigation water requirements in the middle and lower reaches of the Yangtze River, China. Agric. Water Manag. 2017, 193, 89–101. [CrossRef] 19. Rehana, S.; Mujumdar, P. Regional impacts of climate change on irrigation water demands. Hydrol. Process. 2013, 27, 2918–2933. [CrossRef] 20. Brumbelow, K.; Georgakakos, A. An assessment of irrigation needs and crop yield for the United States under potential climate changes. J. Geophys. Res. Atmos. 2001, 106, 27383–27405. [CrossRef] 21. IPCC. The Physical Science Basis. In Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2007. 22. Pereira, A.R.; Green, S.; Nova, N.A.V. Penman–Monteith reference evapotranspiration adapted to estimate irrigated tree transpiration. Agric. Water Manag. 2006, 83, 153–161. [CrossRef] 23. Poyato, E.C. Análisis de la eficiencia y el ahorro del agua en el regadío de la cuenca del Guadalquivir. Inversiones en la modernización de regadíos. Agric. Rev. Agropecu. 2005, 880, 880–887. Water 2019, 11, 1567 23 of 25

24. Nations, U. World Population Prospects: The 2012 Revision, Highlights and Advance Tables (Working Paper No. ESA/P/WP. 228); United Nations Publications: New York, NY, USA, 2013. 25. FAO. How to feed the world in 2050. In Proceedings of the Expert Meeting on How to Feed the World in 2050, Rome, Italy, 24–26 June 2009. 26. GOP. Pakistan Economy Survey; Govt. of Pakistan, Finance Division, Economic Advisor’s Wing: Islamabad, Pakistan, 2017. 27. Ali, A.; Rehman, F.; Nasir, M.; Ranjha, M.Z. Agricultural policy and wheat production: A case study of Pakistan. Sarhad J. Agric. 2011, 27, 201–211. 28. PDS. Punjab Development Statistics; Bureau of Statistics, Government of Punjab: Lahore, Pakistan, 2015. 29. Usman, M.; Liedl, R.; Shahid, M.A. Managing irrigation water by yield and water productivity assessment of a rice-wheat system using remote sensing. J. Irrig. Drain. Eng. 2014, 140, 04014022. [CrossRef] 30. Sheikh, A.; Abbas, A. Barriers in efficient crop management in rice-wheat cropping system of Punjab. Pak. J. Agric. Sci. 2007, 44, 341–349. 31. Lee, E.J.; Kang, M.S.; Park, S.W.; Kim, H.K. Estimation of Future Reference Evapotranspiration Using Artificial Neural Network and Climate Change Scenar; American Society of Agricultural and Biological Engineers: Pittsburgh, PA, USA, 2010. 32. Chun, K.P.; Wheater, H.S.; Onof, C. Projecting and hindcasting potential evaporation for the UK between 1950 and 2099. Clim. Chang. 2012, 113, 639–661. [CrossRef] 33. Li, Z.; Zheng, F.L.; Liu, W.Z. Spatiotemporal characteristics of reference evapotranspiration during 1961–2009 and its projected changes during 2011–2099 on the Loess Plateau of China. Agric. For. Meteorol. 2012, 154, 147–155. [CrossRef] 34. Fan, J.; Wu, L.; Zhang, F.; Xiang, Y.; Zheng, J. Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. J. Hydrol. 2016, 542, 923–937. [CrossRef] 35. Fuhrer, J.; Smith, P.; Gobiet, A. Implications of climate change scenarios for agriculture in alpine regions—A case study in the Swiss Rhone catchment. Sci. Total Environ. 2014, 493, 1232–1241. [CrossRef] 36. Maeda, E.E.; Pellikka, P.K.; Clark, B.J.; Siljander, M. Prospective changes in irrigation water requirements caused by agricultural expansion and climate changes in the eastern arc mountains of Kenya. J. Environ. Manag. 2011, 92, 982–993. [CrossRef] 37. Ayala, L.M.; van Eupen, M.; Zhang, G.; Pérez-Soba, M.; Martorano, L.G.; Lisboa, L.S.; Beltrao, N.E. Impact of agricultural expansion onwater footprint in the Amazon under climate change scenarios. Sci. Total Environ. 2016, 569, 1159–1173. [CrossRef] 38. Smith, M. CROPWAT: Manual and Guidelines; FAO of UN: Rome, Italy, 1991. 39. Smith, M.; Kivumbi, D.; Heng, L. Use of the FAO CROPWAT model in deficit irrigation studies. 2002. Available online: http://www.fao.org/DOCREP/004/Y3655E/Y3655E00.HTM (accessed on 29 July 2019). 40. George, B.; Shende, S.; Raghuwanshi, N. Development and testing of an irrigation scheduling model. Agric. Water Manag. 2000, 46, 121–136. [CrossRef] 41. Anadranistakis, M.; Liakatas, A.; Kerkides, P.; Rizos, S.; Gavanosis, J.; Poulovassilis, A. Crop water requirements model tested for crops grown in Greece. Agric. Water Manag. 2000, 45, 297–316. [CrossRef] 42. Munoz, G.; Maraux, F.; Wahaj, R. Actual Crop Water Use in Project Countries a Synthesis at the Regional Level. The World Bank, 2007. Available online: https://doi.org/10.1596/1813-9450-4288 (accessed on 29 July 2019). 43. Kuo, S.F.; Ho, S.S.; Liu, C.W. Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in ChiaNan Irrigation Association, Taiwan. Agric. Water Manag. 2006, 82, 433–451. [CrossRef] 44. Kang, S.; Payne, W.A.; Evett, S.R.; Robinson, C.A.; Stewart, B.A. Simulation of winter wheat evapotranspiration in Texas and Henan using three models of differing complexity. Agric. Water Manag. 2009, 96, 167–178. [CrossRef] 45. Muhammad, N. Simulation of maize crop under irrigated and rainfed conditions with CROPWAT model. J. Agric. Biol. Sci. 2009, 4, 68–73. 46. Mimi, Z.A.; Jamous, S.A. Climate change and agricultural water demand: Impacts and adaptations. Afr. J. Environ. Sci. Technol. 2010, 4. 47. Stancalie, G.; Marica, A.; Toulios, L. Using earth observation data and CROPWAT model to estimate the actual crop evapotranspiration. Phys. Chem. Earth Parts A/B/C 2010, 35, 25–30. [CrossRef] Water 2019, 11, 1567 24 of 25

48. Mhashu, S. Yield Response to Water Function and Simulation of Deficit Irrigation Scheduling of Sugarcane at Estate in Zimbabwe Using CROPWAT 8.0 and CLIMWAT 2.0. Master’s Thesis, Universitadegli Studi di Firenze Facolta di Agraria (University of Florence, Faculty of Agriculture), Florence, Italy, 2007. 49. Gordon, C.; Cooper, C.; Senior, C.A.; Banks, H.; Gregory, J.M.; Johns, T.C.; Mitchell, J.F.; Wood, R.A. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn. 2000, 16, 147–168. [CrossRef] 50. Nakicenovic, N.; Alcamo, J.; Davis, G.; de Vries, B.; Fenhann, J.; Gaffin, S.; Gregory, K.; Gru¨bler, A.; Jung, T.Y.; Kram, T.; et al. IPCC Special Report on Emissions Scenarios; Cambridge University Press: Amsterdam, The Netherlands, 2000; Available online: https://www.ipcc.ch/report/emissions-scenarios/ (accessed on 29 July 2019). 51. Razandi, Y.; Pourghasemi, H.R.; Neisani, N.S.; Rahmati, O. Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci. Inform. 2015, 8, 867–883. [CrossRef] 52. Babu, R.G.; Veeranna, J.; Kumar, K.R.; Rao, I.B. Estimation of water requirement for different crops using CROPWAT model in Anantapur region. J. Environ. Sci. 2014, 9, 75–79. [CrossRef] 53. Mehta, R.; Pandey, V. Crop water requirement (ETc) of different crops of middle Gujarat. J. Agrometeorol. 2016, 18, 8354. 54. GPS. Our Data Citations; Division Federal Bureau of Statistics Economic Wing: Islamabad, Pakistan, 2008. 55. Daniels, A.E.; Morrison, J.F.; Joyce, L.A.; Crookston, N.L.; Chen, S.C.; McNully, S.G. Climate projections FAQ; Department of Agriculture, Forest -Service, Rocky Mountain Research Station: Fort Collins, CO, USA, 2012; pp. 1–31. 56. Ray, A.J.; Barsugli, J.J.; Averyt, K.B.; Wolter, K.; Hoerling, M.; Doesken, N.; Udall, B.; Webb, R.S. Climate Change in Colorado: A Synthesis to Support Water Resources Management and Adaptation. 2008. Available online: http://wwa.colorado.edu (accessed on 29 July 2019). 57. Jones, P.G.; Thornton, P.K.; Heinke, J. Generating Characteristic Daily Weather Data Using Downscaled Climate Model Data from the IPCC’s Fourth Assessment; International Livestock Research Institute: Nairobi, Kenya, 2009. 58. STARDEX, Downscaling Climate Extremes. Available online: https://crudata.uea.ac.uk/projects/stardex/ (accessed on 29 July 2019). 59. Fowler, H.; Archer, D. Conflicting signals of climatic change in the Upper Indus Basin. J. Clim. 2006, 19, 4276–4293. [CrossRef] 60. Wilby, R.L.; Troni, J.; Biot, Y.; Tedd, L.; Hewitson, B.C.; Smith, D.M.; Sutton, R.T. A review of climate risk information for adaptation and development planning. Int. J. Climatol. A J. R. Meteorol. Soc. 2009, 29, 1193–1215. 61. Seaby, L.P.; Refsgaard, J.C.; Sonnenborg, T.O.; Stisen, S.; Christensen, J.H.; Jensen, K.H. Assessment of robustness and significance of climate change signals for an ensemble of distribution-based scaled climate projections. J. Hydrol. 2013, 486, 479–493. [CrossRef] 62. Wilby, R.L.; Wigley, M.L.; Conway, D.; Jones, P.D.; Hewitson, B.C.; Main, J.; Wilks, D.S. Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res. 1998, 34, 2995–3008. [CrossRef] 63. Zorita, E.; Von Storch, H. The analog method as a simple statistical downscaling technique: Comparison with more complicated methods. J. Clim. 1999, 12, 2474–2489. [CrossRef] 64. Mahmood, R.; Babel, M.S. Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor. Appl. Climatol. 2013, 113, 27–44. [CrossRef] 65. Fowler, H.J.; Blenkinsop, S.; Tebaldi, C. Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol. 2007, 27, 1547–1578. [CrossRef] 66. Development, United States Armed Forces Institute. A Review of Downscaling Methods for Climate Change Projections. 2014. Available online: https://www.climatelinks.org/resources/ (accessed on 29 July 2019). 67. Wilby, R.L.; Dawson, C.W.; Barrow, E.M. SDSM—A decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw. 2002, 17, 145–157. [CrossRef] Water 2019, 11, 1567 25 of 25

68. Liu, J.; Williams, J.R.; Wang, X.; Yang, H. Using MODAWEC to generate daily weather data for the EPIC model. Environ. Model. Softw. 2009, 24, 655–664. [CrossRef] 69. Chu, J.; Xia, J.; Xu, C.Y.; Singh, V.P. Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor. Appl. Climatol. 2010, 99, 149–161. [CrossRef] 70. Khan, M.S.; Coulibaly, P.; Dibike, Y. Uncertainty analysis of statistical downscaling methods. J. Hydrol. 2006, 319, 357–382. [CrossRef] 71. Huang, J.; Zhang, J.; Zhang, Z.; Xu, C.; Wang, B.; Yao, J. Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch. Environ. Res. Risk Assess. 2011, 25, 781–79272. [CrossRef] 72. Wilby, R.L.; Dawson, C.W. SDSM 4.2—A decision support tool for the assessment of regional climate change impacts. 2007. Available online: https://sdsm.org.uk/sdsmmain.html (accessed on 29 July 2019). 73. Pallant, J. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS for Windows; Allen & Unwin Pty: Crows Nest, Australia, 2005. 74. Faria, A.E., Jr. Forecasting: Methods and Applications; Makridakis, S., Wheelwright, S.C., Hyndman, R.J., Eds.; John Wiley and Sons: New York, NY, USA, 1998; p. 642. ISBN 0-471-53233-9. 75. Allen, R.; Pereira, L.S.; Raes, D.; Smith, M. Crop evapotranspiration: Guidelinesfor computing crop water requirements—FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998. 76. Er-Raki, S.; Chehbouni, A.; Guemouria, N.; Duchemin, B.; Ezzahar, J. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agric. Water Manag. 2007, 84, 44–56. [CrossRef] 77. Hashmi, M.Z.; Shamseldin, A.Y.; Melville, B.W. Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch. Environ. Res. Risk Assess. 2011, 25, 475–484. [CrossRef] 78. Klein Tank, A.M.; Peterson, T.C.; Quadir, D.A.; Dorji, S.; Zou, X.; Tang, H.; Santhosh, K.; Joshi, U.R.; Jaswal, A.K.; Kolli, R.K.; et al. Changes in daily temperature and precipitation extremes in central and south Asia. J. Geophys. Res. Atmos. 2006, 111.[CrossRef] 79. Afzaal, M.; Haroon, M.A.; Zaman, Q. Interdecadal Oscillations and the Warming Trend in the Area-Weighted Annual Mean Temperature of Pakistan. Pak. J. Meteorol. 2009, 6, 13–19. 80. Arfan, A.; Zhang, W.; Zaman, M.A.; Dilawar, A.; Sajid, Z. Monitoring the impacts of spatio-temporal land-use changes on the regional climate of city Faisalabad, Pakistan. Ann. GIS. 2019, 25, 57–70. 81. Yu, P.S.; Yang, T.C.; Chou, C.C. Effects of climate change on evapotranspiration from paddy fields in southern Taiwan. Clim. Chang. 2002, 54, 165–179. [CrossRef] 82. Tabari, H.; Marofi, S.; Aeini, A.; Talaee, P.H.; Mohammadi, K. Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol. 2011, 151, 128–136. [CrossRef] 83. Piticar, A.; Mihăilă, D.; Lazurca, L.G.; Bistricean, P.I.; Putuntica, A.; Briciu, A.E. Spatiotemporal distribution of reference evapotranspiration in the Republic of Moldova. Theor. Appl. Climatol. 2016, 124, 1133–1144. [CrossRef] 84. Chowdhury, S.; Al-Zahrani, M.; Abbas, A. Implications of climate change on crop water requirements in arid region: An example of Al-Jouf, Saudi Arabia. J. King Saud Univ. Eng. Sci. 2016, 28, 21–31. 85. Zhou, T.; Wu, P.; Sun, S.; Li, X.; Wang, Y.; Luan, X. Impact of Future Climate Change on Regional Crop Water Requirement—A Case Study of Hetao Irrigation District, China. Water 2017, 9, 429. [CrossRef] 86. Parekh, F.; Prajapati, K.P. Climate Change Impacts on Crop Water Requirement for Sukhi Reservoir Project. Int. J. Innov. Res. Sci. Eng. Technol. 2013, 2, 2–10. 87. Kirby, M.; Mainuddin, M.; Khaliq, T.; Cheema, M.J. Agricultural production, water use and food availability in Pakistan: Historical trends, and projections to 2050. Agric. Water Manag. 2016, 179, 34–46. [CrossRef]

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