A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate

Accepted Manuscript A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate Usman Khalid Awan, Ali Ismaeel PII: DOI: Reference:

S0022-1694(14)00659-3 http://dx.doi.org/10.1016/j.jhydrol.2014.08.049 HYDROL 19846

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

12 June 2014 20 August 2014 25 August 2014

Please cite this article as: Awan, U.K., Ismaeel, A., A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate, Journal of Hydrology (2014), doi: http://dx.doi.org/10.1016/j.jhydrol. 2014.08.049

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A new technique to map groundwater recharge in irrigated areas using a SWAT model under changing climate Usman Khalid Awan1*, Ali Ismaeel2 1

International Center for Agricultural Research in the Dry Areas [ICARDA]

2

International Water Management Institute [IWMI]

*Corresponding author: U.K. Awan, International Center for Agricultural Research in the Dry Areas [ICARDA], e-mail: [email protected]

Abstract The Lower Chenab canal irrigation scheme, the largest irrigation scheme of the Indus Basin irrigation system was selected for an estimate of groundwater recharge using the soil and water assessment tool (SWAT) at high spatial and temporal resolution under changing climate. Groundwater recharge was simulated using the SWAT model for representative concentration pathways (RCP) 4.5 and 8.5 climate change scenarios for the period 2012 to 2020. Actual evapotranspiration (ETa) was estimated using the SWAT model for the period 2010–2011. This was compared with the ETa determined using the surface energy balance algorithm (SEBAL) calibrated using data for the period 2005–2009... We concluded that the SWAT ETa estimates showed good agreement with those of SEBAL (coefficient of determination = 0.85±0.05, NashSutcliffe efficiency = 0.83±0.07). The total average annual groundwater recharge to the aquifer was 537 mm (+ 55 mm) with the maximum occurring during July (151 mm). The results showed that groundwater recharge would increase by 40%, as compared to the reference period, by the end of 2020 under RCP 4.5 and by 37% under RCP 8.5. The SWAT can thus be a handy tool for not only estimating the recharge at high spatial and temporal resolution, but also under changing climate.

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Keywords: Canal command area; surface energy balance algorithm (SEBAL); Indus Basin irrigation system; actual evapotranspiration; soil and water assessment tool (SWAT) Introduction No one can deny the importance of groundwater in meeting crop water demands, especially in the arid to semi-arid regions of the world. According to UNESCO [2003], 80% of the groundwater extraction in dry regions is for agricultural purposes. However, mismanagement of groundwater use in these regions is reported in many studies and this mismanagement is threatening the sustainability of this precious water resource [Döll, 2009; Wada et al., 2010]. Sustainable groundwater use depends on a comprehensive groundwater recharge policy [Awan et al., 2013]. The formulation of a groundwater recharge policy requires detailed information on groundwater recharge in time and space. The efforts of many years have failed to find a single, reliable method for measuring groundwater recharge because of the complexity of this phenomenon and the large variety of situations encountered [UNEP, 2002]. Groundwater recharge is affected by many composite elements, which themselves are regulated by many other factors. For example cropping pattern, cropping intensity, climatic parameters, hydraulic properties of the under lying soils, and irrigation practices [Freeze and Cherry, 1979] are some of the parameters which directly influence groundwater recharge. These parameters vary significantly in space and time for large irrigation schemes and, therefore, quantification of these parameters at high spatial and temporal resolution is of paramount importance. Several methods exist for quantifying groundwater recharge including a) point measurement using a lysimeter [Grasso et al., 2003; Xu and Che, 2005; Seiler and Gat, 2007], b) water balance establishment over the entire basin [Maréchal et al,. 2006; Lee et al., 2006; Wanke et al., 2

2008; Manghi et al., 2009; Mjemah et al., 2011], c) the water table fluctuation method [Meinzer and Stearns, 1929; Rasmussen and Andreasen, 1959; Gerhart, 1986; Hall and Risser, 1993] c) the Darcy method [Belan and Matlock, 1973], and d) the use of natural conservative tracers [Eriksson and Khunakasem, 1969; Wood and Sanford, 1995; Shurbaji and Campbell, 1997; Zhu, 2000; Cook et al., 2001; Wang et al., 2008]. Existing methods of estimating groundwater recharge do not consider intensively the spatial variability of factors that influence groundwater recharge [Awan et al., 2013]. Moreover these methods cannot simulate the effects of climate change on groundwater recharge. However, the SWAT [Arnold et al., 1998] is a physically based, semi-distributed model that has the capability of predicting the effects of climate change on water balance and, eventually, groundwater recharge. The Lower Chenab canal (LCC) irrigation scheme, which is the largest and oldest irrigation scheme of the Indus Basin irrigation system (IBIS), was selected for the current study. As water supplies in the LCC irrigation scheme are less by far than the crop water requirements, the use of groundwater for irrigation is indispensable. However farmers are abstracting groundwater without any regulation, which is challenging the sustainability of groundwater use. Institutions are present to monitor the groundwater levels, but the information on groundwater recharge is too sparse to regulate these. For example, the water table fluctuation (WTF) method is most commonly used in the IBIS for determining groundwater recharge. The WTF method depends upon the density of the piezometer network [Zaidi et al., 2007]. However, the piezometer network is very sparse in the IBIS. Additionally, groundwater is monitored only twice a year – pre- and post-monsoon. The difference in the two readings is used to estimate the accumulation or depletion of the groundwater resource in the area [Ahmad et al., 2009]. Moreover this method 3

does not provide any mechanism for incorporating the factors that are influencing the groundwater recharge, such as land cover, soil type, climatic parameter, etc. The SWAT model was selected to quantify the groundwater recharge on a monthly, seasonal (Rabi – October to March and Kharif – April to September), and annual basis for the entire LCC irrigation scheme, the canal command areas (CCAs) of the LCC, and the different hydrologic response units (HRUs) in the LCC irrigation scheme, for the period 2005 to 2011. Furthermore, the effect of climate change on groundwater recharge was also simulated for the period 2012 to 2020. Land use land cover (LULC) map is one of the important inputs for the SWAT model. Remote sensing technique was used to extract the LULC information at high spatial resolution. Actual evapotranspiration derived by surface energy balance algorithm (SEBAL) was used to calibrate and validate the mode. The results of the current study will provide detailed information on groundwater recharge over time and space to policy makers in the region, which they can take into account when regulating groundwater use, and enable them to formulate a sustainable policy, given the future impacts of climate change on groundwater recharge. Material and methods Study area The LCC originates at the Khanki headworks and distributes water to the eastern and western sides of the LCC through seven branch canals – Sagar, Upper Gugeera, Rakh, Mian Ali, Jhang, Lower Gugeera, and Burala (Figure 1). Figure 1 shows the CCAs of these branch canals. The average discharge rate of the LCC at the Khanki headworks is 440 m3 s-1 and the water is used to irrigate around 1.22 million ha of agricultural land. The major crops in the region are rice, wheat, sugarcane, and cotton. There are two major cropping seasons, Rabi (October to March) and Kharif (April to September). The climate in the region is arid to semi-arid. 4

Figure 1 about here There are three main weather stations (Lahore, Faisalabad, and Toba Tek Singh) in the study region, which were installed and are monitored by the Pakistan Metrological Department. The average monthly rainfall, average monthly reference evapotranspiration, and average daily reference evapotranspiration for the last 30 years are shown in Figure 2. The average annual rainfall in the area is about 400 mm, 75% of which occurs during the monsoon months of June, July, and August. The difference between the amounts of rainfall and evapotranspiration dictates the need for irrigation. Figure 2 about here Estimating groundwater recharge by SWAT The SWAT [Arnold et al., 1998] is a river basin, daily time-step operated, continuous time simulated model that was developed by the Unites States Department of Agriculture-Agricultural Research Service (USDA-ARS). The SWAT has been used in many land and water resources management studies [Pikounis et al., 2003; Sun and Cornish, 2005; Schuol et al., 2008; Chung et al., 2010] to simulate the effects different management strategies (land use/land cover and reservoir, groundwater, and fertilizer management) have on local hydrology [Neitsch et al., 2005]. One of the important outputs of SWAT modeling is that it estimates groundwater recharge in unconfined (shallow) aquifers and confined (deep) ones [Arnold et al., 1993]. In SWAT, groundwater recharge to an unconfined aquifer is the water that percolates and passes to the root zone of the soil. If time goes to infinity, then this water will eventually meet the phreatic surface of the saturated zone. The water balance equation of the soil moisture that represents the hydrologic cycle simulated in SWAT can be expressed mathematically as [Neitsch et al., 2005]:

5


 =
 + ∑( – –  –  –  )

(1)

where  is the soil water content at time  (mm),  is the initial soil water content on day  (mm),  is the time (days),  is the amount of rainfall on day  (mm),  is the surface runoff on day  (mm), 

!

is the evapotranspiration on day  (mm),  is the water entering the vadose

zone from the soil profile on day  (mm), and " is the return flow on day  (mm). Detailed documentations can be found in the SWAT theoretical manual [Neitsch et al., 2005]. Analytical framework for estimating groundwater recharge using the SWAT model The analytical framework for estimating groundwater recharge using the SWAT model includes data acquisition, data pre-processing, input data preparation according to SWAT requirements, and simulating groundwater recharge [Figure 3]. Figure 3 about here Watershed delineation The LCC irrigation scheme is considered as an artificial watershed with seven sub-basins. These sub-basins are the CCAs of seven branch canals – Sagar, Upper Gugeera, Rakh, Mian Ali, Jhang, Lower Gugeera, and Burala (Figure 1). Therefore, the watershed was delineated on the basis of the pre-defined sub-basins, which are the original routings of these branch canals. Digital elevation model Digital elevation models (DEMs) are digital data files that contain terrain elevations over a specified area. The shuttle radar topography mission (SRTM) 90 m resolution DEMs were used in this study (Figure 4). The SRTM is an international project headed by the US National

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Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency. The data is made freely available by the United States Geological Survey, in agreement with NASA, and was downloaded from http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp. Figure 4 about here Land cover data SWAT uses the land use/land cover (LULC) data, together with soil data, to determine the hydrological parameters for each LULC class and soil category simulated within each sub-basin [Di Luzio et al., 2002]. As there was no detailed and up-to-date LULC map available for the study region, we used remote sensing techniques to develop one. Recent advancements in remote sensing make it possible to identify different crops on agricultural land and also to delineate nonagricultural land with high accuracy [Boletta et al., 2006; Killeen et al., 2007; Gasparri and Grau, 2009; Huang et al., 2009; Bartholomé and Belward, 2005; Friedl et al., 2010; Bicheron et al., 2008; Cheema and Bastiaanssen, 2010; de Bie et al., 2011; Nguyen et al., 2012]. For the current study we used a moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) product to generate the LULC map of the study region. Giri and Jenkins [2005] and Fisher and Mustard [2007] used MODIS with higher accuracy at the river basin scale. The combined use of aqua and terra NDVI products provided data for 8 day time steps for the LULC analysis at 250 m resolution. Kalpa et al. [2014] argue that 250 m spatial and 8 day temporal resolution is good enough to support agricultural water management in irrigation schemes. We applied a phonological approach to classify the study area into different classes. It has been found necessary to refine and improve the capabilities of the satellite imagery with secondary information, such as cropping calendars [Klein et al., 2007; Zhang et al., 2008; de Bie et al., 2011]. Clark et al. [2010] proposed a scalable approach to 7

mapping annual land cover at 250 m pixel size using MODIS time series data. The authors used the Jönsson and Eklundh [2004] program to analyze phonological signals found in time series data from satellite sensors. Recent studies using MODIS 250 m and secondary information for LULC classification at the river basin scale obtained classification accuracies of between 76% and 90% [Knight et al., 2006; Wardlow and Egbert 2008; Zhang et al., 2008; Clark et al,. 2012]. An unsupervised classification ISODATA clustering technique on MODIS imagery from November 2008 to October 2009 was applied to get just two clusters – cultivated/cropped and uncultivated (bare land or urban and industrial developments) lands. The unsupervised classification was repeated and the number of classes increased to 25 to detect the land cover classes. Several maps and signature cluster files (NDVI) were produced from the time series of MODIS with the pre-set 25 classes. The NDVI values extracted from the unsupervised classification were plotted. A three period running average is taken to smooth the resulting curves [Reed et al., 1994]. Because of this similarity in NDVI profiles, the separation-ability can be an issue [Song et al., 2009], but it can be overcome by using cropping colanders. Using the NDVI temporal curve information and expert knowledge about cropping patterns of the area, cropland clusters were separated. After this merging the number of classes was reduced from 25 to 12. Table 1 about here LULC classification depends on the accuracy of the LULC map. To check the level of accuracy and reliability of the LULC classified map, a ground truthing campaign was conducted. A total of 231 points were recorded for the ground classes during the survey. Due consideration was given so that these points were representative of all the selected classes. To show the accuracy, the error matrix approach was applied as described by Campbell [2002]. An error matrix was 8

constructed by plotting the map classes developed during the procedure of LULC classification against the ground classes which were recorded during the ground truthing survey. Soil data Soil data of the study area was obtained from the Water and Soil Investigation Division of the Government of Pakistan. The soils were classified according to different textural characteristics. The soils having the same sequence horizon to a depth of 1.83 m were categorized under the same soil textural group [CSIRO, 2003]. The spatial distribution of the different soil types, their properties, and area are given in Table 2 and the map is shown in Figure 5. Figure 5 about here Table 2 about here Climate data Climatic data were obtained from the Pakistan Metrological Department. The data covered the period 2005–2011 and included rainfall, minimum and maximum air temperatures, relative humidity, wind speed, and sunshine hours (converted to solar radiation). Canal discharge data Daily discharge data at the head of each of the seven branch canals were used in the SWAT model as an irrigation source. Data were obtained from the Programme Monitoring and Implementation Unit, Punjab Irrigation Department, Pakistan. The data cover the period 2005– 2011. Discharge data helped in understanding the depth over area ratio and in formulating the irrigation schedules for the different crops in the study area. Surface energy balance algorithm for land

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The actual evapotranspiration (ETa) estimated with the SWAT model was compared with that calculated using the surface energy balance algorithm for land (SEBAL) [Bastiaanssen et al., 1998] to calibrate and validate the SWAT model. Although different algorithms exist to determine a satellite-based energy balance [Norman et al., 1995; Menenti and Choudhury, 1993; Kustas and Norman, 1996; Roerink et al., 2000; Su, 2002; Allen et al., 2005, 2007a], SEBAL is well established and has been validated in different regions of the world, including the study area [Bastiaanssen et al., 1998; Bastiaanssen et al., 2002; Bastiaanssen et al., 2005; Hafeez et al., 2007; Teixeira et al., 2009; Kongo et al., 2011; Awan et al., 2011]. The key inputs to the SEBAL model include satellite-based images and very small amounts of ground-based meteorological data. MODIS data was selected for this study because of its high temporal resolution. In the current study, MODIS Level 1B image products, including surface albedo MOD 11 [Liang, et al., 2002], vegetation index MOD 13, emissivity [Van de Griend and Owe, 1993], and surface temperature MOD 09 were downloaded. Approximately 479 products were downloaded free of cost from http://glovis.usgs.gov/ between 2005 and 2011. In addition to image data, SEBAL also requires weather data from some well-spread meteorological stations. For this purpose, wind speed, humidity, solar radiation, and air temperature, recorded on an hourly and daily basis, were collated from the Pakistan Meteorological Department. The detailed formulation of the SEBAL algorithm is presented by Bastiaanssen et al. [1998] and we used this for the current study. The information on the algorithm is also reported in different research studies [e.g. Conrad et al., 2007; Hafeez et al., 2007; Hellegers et al., 2009; Karatas et al., 2009; Awan et al., 2011]. Briefly the model is based on the surface energy budget with actual evapotranspiration as a residual product and is expressed as

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#$ = %& + ' + (

(2)

where Rn is the net radiation absorbed [W/m2], Go is soil heat flux [W/m2], H is the sensible heat flux [W/m2] LE is the latent heat of vaporization [W/m2]. Figure 6 shows the different steps adopted for estimating actual ETa. Figure 6 about here Model calibration and performance evaluation The SWAT model was calibrated with data from 2005 to 2009 and validated for the period 2010 to 2011. The manual calibration of SWAT is based on a trial and error analysis and consists of changing one parameter at a time and re-running the model to obtain an output that is within acceptable limits to the reference data. Santhi et al. [2001] proposed a procedure for manually calibrating the SWAT model. The authors suggest that the results of the SWAT calibration are acceptable if (i) the coefficient of determination (R2) is greater than 0.60 and (ii) the NashSutcliffe model efficiency (NSE) is greater than 0.50. R2 and NSE are used by many researchers as statistical measures for evaluating the predictive performance of SWAT [Santhi et al., 2001; Cotter, 2002; Grizzetti et al., 2003; Sintondji, 2005; Wu and Xu, 2006; Chekol, 2006; Abraham et al., 2007; Parajuli et al., 2009]. In the current study we also used these two statistical parameters for evaluating the performance of the SWAT model. The R2 and NSE can be calculated as:

#) =

∑(*+*, )(-+-, ) / 1.3

.∑(*+*, ) 0

{∑(-+-, )/}1.3

11

(3)

∑(*+-)/

6 = 1 − ∑(*+*

,)

(4)

/

where: # is the reported data; #9 is the mean of the reported data;  is estimated data and 9 is the mean of the estimated data. Figure 7 about here Climate change scenario Groundwater recharge from large irrigation schemes depends on rainfall and the amount of irrigation water supplied. This amount is not likely to change over the next few decades because of system design. However changes in rainfall and the minimum and maximum temperatures can affect groundwater recharge significantly in large irrigation schemes. Therefore, for the current study, we used the scenarios of representative concentration pathways (RCP) 4.5 and 8.5 to simulate the consequences of changes in rainfall and the minimum and maximum temperatures on groundwater recharge using the SWAT model. The SWAT model has the capacity to simulate the effects of climate change on groundwater recharge. As the simulation period should not exceed the calibration and validation period, the impact of climate change on groundwater recharge was simulated from 2012 to 2020. The average monthly variation in rainfall and the minimum and maximum temperatures for the simulation period (2012 to 2020) expected under RCP 4.5 are shown in Figure 8. Under the RCP 4.5 scenario, it is expected that there would be a 70% increase in rainfall by the end of 2020 compared with the reference period (2005–2011). The mean annual minimum temperature for the simulation period for the stations in the study area would range from 14.29 to 16.10oC. This represents a decrease of 11.38% in comparison with the reference period. The mean annual maximum temperature for the same period would 12

range from 29.84 to 33.10oC – a decrease of 0.67%. The annual rainfall amount for these stations would be in the range 405–1190 mm. Figure 8 about here Similarly, monthly variations of rainfall and the minimum and maximum temperatures under the RCP 8.5 scenario are shown in Figure 9. The mean annual minimum temperature for the simulation period for stations in the study area would range from 15.07 to 25.34oC, an increase of 3.10% as compared to the reference period. For the same period, the mean annual maximum temperature would range from 29.31 to 41.67oC, an increase of 0.29% relative to the reference period. The annual rainfall amount for these stations would be in the range 407–1287 mm, an increase of 75%. Figure 9 about here Results and Discussion Land use land cover classification for the study region The resulting mean NDVI time profiles of the final 12 agricultural classes are shown in Figure 10. The growing season, sowing and harvesting times, growth period, and different phonological behavior can easily be identified in these 12 classes. The two peaks of the NDVI values clearly indicate that there are two different growing seasons – Kharif and Rabi – in the region. Figure 10 about here The error matrix shows an overall accuracy of 79.65% for the LULC classification of the study area (Tables 3a and 3b). The average user accuracy is 72.85% and the average producer accuracy is 74.94%. Thunnissen and Noordman [1997] reported that an accuracy of around 70% is acceptable at a regional scale. However, depending upon the spatial resolution of the image and 13

field sizes, the level of accuracy may vary from 49% to 96%. Wardlow and Egbert [2008] attained accuracies of 84% from MODIS 250 m spatial resolution imagery. Considering that the field sizes in the study region are small (0.4–10 ha), an accuracy of 79.65% is rather satisfactory. Tables 3a, 3b about here Figure 11 shows the LULC map of the LCC irrigation scheme. Wheat-rice cultivation is dominant in the Sagar and Upper Gugeera CCAs. Jhang CCA has sugarcane and fodder cultivation. Although the wheat-rice rotation is dominant at the head of the Rakh and Mian Ali CCAs, the tail end areas have sparse or no vegetation because of soil salinity problems. Lower Gugeera and Burala CCAs have cotton as the dominant crop. Figure 11 about here Table 4 shows the codes used for the LULC classes in the SWAT model and the share of each LULC in the irrigation scheme. The results show that wheat-fodder is a dominant cropping pattern in the LCC area. Fodder is the most frequently grown single crop in the region. Cotton is a Kharif crop while wheat is the dominant Rabi crop. Sugarcane is also grown in some of the irrigated areas in the region. Table 4 about here Actual evapotranspiration in different CCAs of LCC irrigation scheme Figure 12 shows the actual evapotranspiration (ETa) in different CCAs during the last seven years (2005–2011). The average annual ETa for the entire LCC irrigation scheme is 853 mm (+ 43 mm) (Figure 12). The time series variation of ETa for the entire LCC irrigation scheme is given in the next section. Figure 12 shows that there are no significant temporal variations between different CCAs. For example, the average ETa for Burala CCA is 902 mm with a 14

standard variation of only 19 mm. The maximum variation, 30 mm, is in the Mian Ali CCA while the minimum variation, 14 mm, is in the Jhang CCA. Normally the amount of irrigation water supplied, the irrigation practices, and cropping patterns did not change significantly over time in the study region. The average ETa values in Rakh CCA and Mian Ali CCA are lower than those in other CCAs and below the average ETa for the entire LCC irrigation scheme. The low ETa values in these regions are a result of the cultivation of crops which do not have high crop water requirements (such as vegetables). Moreover these areas have high soil salinity and poor groundwater quality. A higher ETa value for Sagar CCA is a consequence of rice cultivation and its location at the head of the LCC irrigation scheme. Figure 12 about here Figure 13 about here SWAT model calibration and validation Comparison of the SWAT ETa with the SEBAL ETa shows good agreement. The calibration process showed that the value of R2 is around 0.81 and the NSE value is around 0.76. These values are within the acceptable limits (R2 = 0.60 and NSE = 0.50) as recommended by Santhi et al. [2001]. The difference between the simulated and reference mean monthly ETa was only 5.2%. The standard deviation for the SWAT model was 32.59 and that of the SEBAL model was 30.44, as shown in Figure 14. Figure 14 about here The R2 value for the validation period is around 0.91 and the NSE value is around 0.89. This shows that model is well calibrated and validated. The mean of the modeled ETa for the validation period was around 74.94 while that of the referenced ETa was 74.69. The standard 15

deviation for the SWAT ETa was 39.54 and that of the SEBAL ETa was 36.53, as shown in Figure 15. Figure 15 about here Groundwater recharge at different spatial scales Groundwater recharge for the entire LCC irrigation scheme Figure 16 shows the average monthly groundwater recharge values (2005–2011) for the entire LCC irrigation scheme. The total average annual groundwater recharge to the aquifer was 537 mm (+ 55 mm). Maximum groundwater recharge occurs during July (151 mm), while there is no groundwater recharge from September to November. In general, the canals are closed during this period and the rainfall is not enough to recharge the groundwater. Hence, the groundwater recharge during the Rabi season is only 118 mm (+ 53 mm) which is just 22% of the total average annual groundwater recharge. February receives 63% of the seasonal (Rabi) groundwater recharge. Hence during the Rabi season groundwater recharge to the aquifer is very low and also confined to just January and February as shown in Figure 16. In contrast, the Kharif season has intensive irrigation and monsoon rainfalls. The total groundwater recharge to the aquifer is 418 mm, which is 78% of the total groundwater recharge received by the entire irrigation scheme. In the Kharif season, the average groundwater recharge during July is around 151 mm which is 28% of the yearly and 36% of the seasonal average groundwater recharges. Despite the high intensity of the monsoon rainfalls, there is hardly any runoff because of the raised field boundaries (bunds) and gentle slope of the area. Figure 16 about here Groundwater recharge in different CCAs of the LCC irrigation scheme 16

Figures 17 and 18 show average annual groundwater recharges (2005–2011) in the different CCAs of the LCC irrigation scheme. The maximum groundwater recharge (651 + 65 mm) is received by Sagar CCA. Sagar CCA is located at the head of the irrigation scheme. Basharat and Tariq [2010] reported that the CCAs which are located at the heads of irrigation systems received higher amounts of irrigation water and consequently have higher groundwater recharges. Moreover, Sagar CCA is an intensively cropped area and receives higher rainfall. Figure 17 about here Sagar CCA receives a groundwater recharge of around 651 + 65 mm and Burala CCA, 630 + 63 mm. Lower Gugeera (580 + 59 mm), Upper Gugeera (559 + 56 mm), and Jhang CCA (523 + 59 mm) receive almost same amounts of groundwater recharge. Rakh (399 + 39 mm) and Mian Ali (368 + 36 mm) receive the lowest groundwater recharges. These two areas are located at the tail of the LCC irrigation scheme. Also, their soils are saline and, therefore, cropping intensities are quite low in these areas. Mian Ali CCA is cultivated for just one season (Rabi). The water demand of Rabi crops are less than those of Kharif crops. Figure 18 about here Groundwater recharge in different HRUs of the LCC irrigation scheme Figure 19 shows the annual average groundwater recharge (2005–2011) at the hydrological response unit (HRU) level. The maximum groundwater recharge at the HRU level is 687 + 63 mm. This high groundwater recharge is for those HRUs which have a wheat-rice or wheatsugarcane cropping rotation. HRUs which have built-up settlements and are part of large cities have no groundwater recharge. Groundwater recharge for HRUs having uncultivated or bare soils is 43 + 15 mm.

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The other factor which affects groundwater recharge at the HRU level is soil type. Buchiana types of soil have sandy loam to fine sandy loam soil textures. These soils have the largest share of groundwater recharge (24%) as compared to other types of soil in the area. Figure 19 about here Groundwater recharge in LCC irrigation scheme under changing climate Average monthly groundwater recharge by the end of 2020 Figure 20 shows the average monthly groundwater recharge for the entire LCC irrigation scheme under the RCP 4.5 and RCP 8.5 scenarios to the end of 2020. The results show that groundwater recharge would increase by 40% under RCP 4.5 and 37 % under RCP 8.5 as compared to the business-as-usual (BAU) scenario. The average groundwater recharge for the Kharif season would increase by 45% as compared to the BAU scenario for both RCP 4.5 and RCP 8.5 scenarios. The highest groundwater recharge during August would be 189 mm for RCP 4.5 and 180 mm for RCP 8.5; the groundwater recharge during this month would increase by 160 %. Average groundwater recharge for the Rabi season would increase by 18% for the RCP 4.5 scenario and 45% for the RCP 8.5 scenario as compared to the BAU one. The highest groundwater recharge would occur during February and would be 86 mm for the RCP 4.5 scenario and 71 mm for RCP 8.5 one. Thus the groundwater recharge during this month would increase by 22% for RCP 4.5, but would decrease by 8% for the RCP 8.5 scenario in comparison to the BAU scenarios. Figure 20 shows that the average monthly groundwater recharge for the entire LCC irrigation scheme would be 60 mm, which would be 47% higher than the BAU scenario. Figure 20 about here 18

Average annual groundwater recharge by the end of 2020 in different CCAs of the LCC irrigation scheme Figure 21 shows the average annual groundwater recharge for the RCP 4.5 and RCP 8.5 scenarios in different CCAs of the LCC irrigation scheme. The maximum groundwater recharge (839 mm under the RCP 4.5 scenario) would occur in Sagar CCA. Sagar and Burala CCAs receive similar groundwater recharges of around 839 mm under the RCP 4.5 scenario, but receive 827 mm and 822 mm respectively for the RCP 8.5 one. The average groundwater recharge for these CCAs would be 30% higher than the BAU scenario. Under the RCP 4.5 scenario Lower Gugeera CCA would receive a groundwater recharge of 808 mm, Upper Gugeera CCA 780 mm, and Jhang CCA 693 mm. Under the RCP 8.5 scenario the figures would be Lower Gugeera CCA 791 mm, Upper Gugeera CCA 766 mm, and Jhang CCA 679 mm. On average Upper Gugeera CCA would be 38% higher, Lower Gugeera CCA 38% higher, and Jhang CCA 31% higher than the BAU scenarios. Rakh and Mian Ali would receive the lowest groundwater recharge of 612 mm, which would be for Rakh CCA 54% and for Mian Ali 65 % higher than their BAU scenarios. Figure 21 about here Conclusion This study demonstrates that, using remote sensing data, a SWAT model can estimate groundwater recharge at high spatial and temporal resolution. This detailed information on groundwater recharge in space and time can provide several policy options for the policy makers to control and regulate groundwater recharge for sustainable groundwater use. The added advantage of this technique is that it can be used to simulate groundwater recharge under changing climate. The policy makers can thus forecast how the change in maximum and 19

minimum temperatures and rainfall are likely to affect groundwater recharge for the next decade. Ultimately they can take timely decisions for the best possible options to mitigate the impact of climate change. The results for the LCC irrigation scheme revealed that the Kharif season is the main contributor to groundwater recharge because of the high rainfall and intensive irrigation. Acknowledgements This research was conducted within the project ‘Revitalizing irrigation in Pakistan’ funded by the Embassy of the Kingdom of Netherlands, Islamabad,-Pakistan through Grant #22294. We are grateful to Mr. Habib Ullah Bodla, Chief, Punjab Monitoring and Implementation Unit, for providing us with technical support and discharge data for the different canals in the study region. Thanks are also due to our fellow researchers, senior staff members, and the reviewers whose comments and questions helped us improve this paper. References Abraham, L. Z., J. Roehrig, and D. A. Chekol (2007), Calibration and validation of SWAT hydrologic model for Meki watershed, Ethiopia, paper presented at Conference on International Agricultural Research for Development, University of Kassel-Witzenhausen and University of Gottingen. Ahmad, M. D., W.G.M. Bastiaanssen, and R.A, Feddes (2002), Sustainable use of groundwater for irrigation: a numerical analysis of the subsoil water fluxes, Irrig. Drain., 51, 227–241. Ahmad, M. D., H. Turral, and A. Nazeer (2009), Diagnosing irrigation performance and water productivity through satellite remote sensing and secondary data in a large irrigation system of Pakistan, Agr. Water Manage., 96, 551–564.

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Akhtar, M., N. Ahmad, and M. J. Booij (2008), The impact of climate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios, J. Hydrol., 355, 148–163. Allen, R. G., M. Tasumi, A. Morse, and R. Trezza (2005), A Landsat based energy balance and evapotranspiration model in Western US rights regulation and planning, Irrigation Drainage System, 19, 251–268. Allen, R. G., M. Tasumi, and R. Trezza (2007), Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model, Irrigation and Drainage Engineering, 133(4), 380–394. Arnold, J. G., P. M. Allen, and G. Bernhardt (1993), A comprehensive surface-groundwater flow model, J. Hydrol., 142(1-4), 47-69. Arnold, J. G. R. Srinivasan, R. S.., Muttiah, and J. R. Williams (1998), Large area hydrologic modeling and assessment: Part I. Model development, Am. Wat. Res., 34, 73–89. Awan, U. K., B. Tischbein, and C. Martius (2013), Combining hydrological modeling and GIS approaches to determine the spatial distribution of groundwater recharge in an arid irrigation scheme, Irrigation Sc., 31(4), 793–806. Awan, U. K., B. Tischbein, C. Conrad, C. Martius, and M. Hafeez (2011), Remote sensing and hydrological measurements for irrigation performance assessments in a water user association in the Lower Amu Darya River Basin, Water Resour. Manag., 25(10), 2467– 2485. Bartholomé, E. and A. S. Belward (2005), GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26(9), 1959–1977. 21

Basharat, M. and A. R. Tariq (2010), Equity considerations in irrigation design and inequitable distribution of groundwater in LBDC command, Mehran University Research Journal of Engineering and Technology, 31(4) Bastiaanssen, W. G. M., M. D. Ahmad, and Y. Chemin (2002), Satellite surveillance of evaporative depletion across the Indus Basin, Water Resour. Res., 38(2), 1273. Bastiaanssen, W. G. M., M. Menenti, R. A. Feddes, and A. A. M. Holtslag (1998), A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation, J. Hydrol., 212–213, 198–212. Bastiaanssen, W. G. M., E. J. M. Noordman, H. Pelgrum, G. Davids, and R. G. Allen (2005), SEBAL for spatially distributed ET under actual management and growing conditions, Irrigation and Drainage Engineering, 131(1), 85–93. Belan, R. A. and W. G. Matlock (1973), Groundwater recharge from a portion of the Santa Catalina Mountains, Hydrology and Water Resources in Arizona and the Southwest, 3, 33–40. Bicheron, P., P. Defourny, C. Brockmann, .L. Schouten, C. Vancutsem, M. Huc, S. Bontemps, M. Leroy, F. Achard, M. Herold, F. Ranera, and O. Arino (2008), GlobCover 2005 – products description and validation report, Version 2.1. Available on the ESA IONIA website (http://ionia1.esrin.esa.int/). Boletta, P. E., A. C. Ravelo, A. M. Planchuelo, and M. Grilli (2006), Assessing deforestation in the Argentine Chaco, Forest Ecol. Manag., 228, 108–114. Campbell, J.B. (2002), Introduction to Remote Sensing. 3rd Edition, Guilford Press, New York, NY. 22

Cheema, M. J. M. and W. G. M. Bastiaanssen (2010), Land use and land cover classification in the irrigated Indus Basin using growth phenology information from satellite data to support water management analysis, Agr. Water Manage., 97, 1541–1552. Chekol, A. D. (2006), Modeling of hydrology and soil erosion of upper Awash River basin, Ethiopia, PhD Thesis. University of Bonn, Germany. Chung, I., N. Kim, J. Lee, and M. Sophoceous (2010), Assessing distributed groundwater recharge rate using integrated surface water-groundwater modelling: Application to Mihocheon watershed, South Korea, Hydrogeol. J., 18(5), 1253–1264. Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2003), Investigating conjunctive water management options using a dynamic surface-groundwater modelling approach: a case study of Rechna Doab. Technical Report 35/03, CSIRO Land and Water, Griffiths, Australia. Clark, L. M., T. M. Aide, and G, Riner (2012), Land change for all municipalities in Latin America and the Caribbean assessed from 250 m MODIS imagery (2001–2010), Remote Sens. Environ., 126, 84–103. Clark, M. L., T. M. Aide, H. R. Grau, and G. Riner (2010), A scalable approach to mapping annual land-cover at 250 m using MODIS time-series data: a case study in the dry Chaco ecoregion of South America, Remote Sens. Environ., 114, 2816–2832. Conrad, C., S. W. Dech, M. Hafeez, J. Lamers, C. Martius, and G. Strunz (2007), Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products, Irrigation and Drainage System, 21(3‐4), 197–218.

23

Cook, P. G., F. W. Leaney, and I. D. Jolly (2001), Groundwater recharge in the Mallee region, and salinity implications for the Murray River: A Review, Technical Report 45/01, CSIRO Land and Water, Glen Osmond, Australia. Cotter, A. (2002), Critical evaluation of TMDL data requirements for agricultural watersheds, M.S. thesis, University of Arkansas, Fayetteville, Arkansas. Döll, P. (2009), Vulnerability to the impact of climate change on renewable groundwater resources: a global-scale assessment, Environ. Res. Lett., 4, 036006. de Bie, C. A. J. M., M. R. Khan, V. U. Smakhtin, V. Venus, M. J. C. Weir, and E. M. A Smaling (2011), Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping, Int. J. Remote Sen., 32(2011)21, 6673–6693. Di Luzio, M., R. Srinivasan, and J. G. Arnold (2002), Integration of watershed tools and SWAT model into BASINS, Am. Wat. Res., 38(4), 1127–1141 Eriksson, E. and V. Khunakasem (1969), Chloride concentration in groundwater, recharge rate and rate of deposition of chloride in the Israel coastal plain, J. Hydrol. 7, 178–197 Fisher, J. I. and J. F. Mustard (2007), Cross-scalar satellite phenology from ground, Landsat, and MODIS data, Remote Sens. Environ., 109, 261–273. Freeze, R. A. and J. A. Cherry (1979), Groundwater, Prentice-Hall Inc., Englewood Cliffs, NJ. Friedl, M. A. D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang (2010), MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114(1), 168–182. Gasparri, N. I. and H. R. Grau (2009), Deforestation and fragmentation of Chaco dry forest in NW Argentina (1972–2007), Forest Ecol. Manag., 258, 913–921. 24

Gerhart, J. M. (1986), Ground-water recharge and its effect on nitrate concentrations beneath a manured field site in Pennsylvania, Groundwater, 24, 483–389. Giri, C. and C. Jenkins (2005), Land cover mapping of Greater Mesoamerica using MODIS data. Can. J. Remote Sens., 31, 274–282. Grasso, D. A., P. Y. Jeannin, and F. Zwahlen (2003), A deterministic approach to the coupled analysis of karst springs hydrographs and chemographs, J. Hydrol., 271, 65–76. Grizzetti, B., F. Bouraoui, K. Granlund, S. Rekolainen, and G. Bidoglio (2003), Modelling diffuse emission and retention of nutrients in the Vantaanjoki watershed (Finland) using the SWAT model, Ecol. Model., 169(1), 25–38. Hafeez, M. and S. Khan (2007), Spatial mapping of actual crop water use in ground water dominant irrigation system. Aust. J. Agr. Res.. Hall, D. W. and D. W. Risser (1993), Effects of agricultural nutrient management on nitrogen fate and transport in Lancaster county, Pennsylvania, Water Resour. Bull., 29, 55–76. Hellegers, P. J. G. J., R. Soppe, C. J. Perry, and W. G. M. Bastiaanssen (2009), Combining remote sensing and economic analysis to support policy decisions that affect water productivity, Irrigation Sci., 27(3), 243–251. Huang, F., R. Parker, R. Leonard, Y. Yong, and J. Liu (2009), Frequency of resistance alleles to Bacillus thuringiensis corn in Texas populations of sugarcane borer, Diatraea saccharalis (F.) (Lepidoptera: Crambidae), Crop Prot., 28, 174–180. Jonsson, P. and L. Eklundh (2004), TIMESAT – a program for analyzing time-series of satellite sensor data, Computers and Geosciences, 30, 833–845.

25

Karatas, B. S., E. Akkuzu, H. B. Unal, S. Asik, and M. Avci (2009), Using satellite remote sensing to assess irrigation performance in water user associations in the Lower Gediz Basin, Turkey, Agr. Water Manage., 96, 982–990. Killeen, T. J., V. Calderon, L. Soria, B. Quesada, M. Steininger, G. Harper, L.A. Solorzano, and C. J. Tucker (2007), Thirty years of land-cover change in Bolivia, AMBIO 36, 600–606. Klein, A. M, B. E. Vaissiere, J. H. Cane, I. Steffan-Dewenter, S. A. Cunningham, C. Kremen, and T. Tscharntke (2007), Importance of pollinators in changing landscapes for world crops, P. Roy. Soc. B Bio., 274, 303–313. Knight, J. F., R. S. Lunetta, J. Ediriwickrema, and S. Khorram (2006), Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: a phenology-based approach, GISci. Remote Sen., 43, 1548–1603. Kongo, M. V., G. W. P. Jewitt, and S. A. Lorentz (2011), Evaporative water use of different land uses in the upper-Thukela river basin assessed from satellite imagery, Agr. Water Manage., 98(11), 1727–1739. Kustas, W. P. and J. M., Norman (1996), Use of remote sensing for evapotranspiration monitoring over land surfaces, Hydrolog. Sci. J., 41, 495–516. Lee, L. J. E., D. S. L. Lawrence, and M. Price (2006), Analysis of water level response to rainfall and implications for recharge pathways in the chalk aquifer, SE England, J. Hydrol., 330(3-4), 604–620. Liang, S., C. Shuey, A. Russ, H. Fang, C. Walthall, and C. Daughtry (2002), Narrowband to broadband conversions of land surface albedo: II. Validation, Remote Sens. Environ.

26

Manghi, F., B. Mortazavi, C. Crother, and M. R. Hamdi (2009), Estimating regional groundwater recharge using a hydrological budget method, Water Res. Manage Maréchal, J. C., B. Dewandel, S. Ahmed, L. Galeazzi, and F. K. Zaidi (2006), Combined estimation of specific yield and natural recharge in semi-arid groundwater basin with irrigated agriculture, J. Hydrol., 329, 281–293. Meinzer, O. E. and N. D. Stearns (1929), A study of groundwater in Pomperaug basin, Connecticut, with special reference to intake and discharge. US. Geol. Survey WaterSupply paper 567(B), 73–146. Menenti, M. and B. J. Choudhury (1993), Parameterization of land surface evapotranspiration using a location dependent potential evapotranspiration and surface temperature range, in Proc. Exchange Processes at the Land Surface for a Range of Space and Time Scales, edited by H. J. Bolle et al., pp. 561–568, IAHS Press, Wallingford, UK. Mjemah, I. C., M. Van Camp, K. Martens, and K. Walraevens (2011), Groundwater exploitation and recharge rate estimation of a quaternary sand aquifer in Dar-es-Salaam area, Tanzania, Environmental Earth Sciences, 63(3), 559–569. Neitsch, S. L., J. G. Arnold, J. R. Kiniry, R. Srinivasan, and J. R. Williams (2005), Soil and water assessment tool input/output file documentation, Version 2005. Temple, Tex.: USDA-ARS

Grassland,

Soil and

Water

Research Laboratory.

Available at:

www.brc.tamus.edu/swat/doc.html. Nguyen Thi Thu Ha, C. A. J. M. de Bie, A. Ali, E.M. A. Smaling, and Thai Hoanh Chu (2012), Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis, Int. J. Remote Sens., 33(2), 415–434. 27

Norman, J. M., W. P.Kustas, and K. S. Humes (1995), A two-source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agr. Forest Meteorol., 77, 263–293. Parajuli, P. B., K. R. Mankin, and P. L. Barnes (2009), Source specific fecal bacteria modeling using soil and water assessment tool model, Bioresource Technol., 100, 953–963. Pikounis, M., E. Varanou, E. Baltas, A. Dassaklis, and M. Mimikou (2003), Application of the SWAT model in the Pinios River basin under different land-use scenarios, Global Nest J., 5(2), 71–79. Pope, V. D., M. L. Gallani, P. R. Rowntree, and R. A. Stratton (2000), The impact of new physical parametrizations in the Hadley Centre climate model – HadAM3, Clim. Dynam., 16, 123–146. Qureshi, A. S. and M. Akhtar (2003), Impact of utilization factor on the estimation of groundwater pumpage, Pakistan Journal of Water Resources, 7(1), 17–27. Rasmussen, W. C. and G. E. Andreasen (1959), Hydrologic budget of the Beaverdam Creek Basin, Maryland. U.S. Geological Survey Water-Supply Paper 1472, 106. Reed, B. C., J. F. Brown, and D. Vander Zee (1994), Measuring phenological variability from satellite imagery, Vegetation Sciences, 5, 703–714. Roerink, G. J., Z. Su, and M. Menenti (2000), S-SEBI: a simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth. Pt. B. 25(2), 147−157. Santhi, C., J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan, and L. M. Hauck (2001), Validation of the SWAT model on a large river basin with point and nonpoint sources, Am. Wat. Res., 37(5), 1169–1188. 28

Schuol, J., K. C. Abbaspour, R, Srinivasan, and H. Yang (2008), Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model, J. Hydrol., 352(1-2), 30–49. Seiler, K. and J. Gat (2007), Groundwater recharge from run-off, infiltration and percolation. Water Trans., 55, 244. Shurbaji, A. R. and A. R. Campbell (1997), Study of evaporation and recharge in desert soil using environmental tracers, New Mexico, USA, Environmental Geology, 29(3-4), 147– 151. Sintondji, L. (2005), Modelling the rainfall-runoff process in the Upper Ouémé catchment (Terou in Benin republic) in a context of global change: extrapolation from the local to the regional scale. PhD thesis, Faculty of Mathematics and the Natural Sciences, University of Bonn. Aachen Germany. Song, K., M. Jia, M. Hafeez, Z. Wang, D. Lu, L. Zeng, D. Liu, B. Zhang, J. Du, and Q. Liu (2009), Land use/cover characterization with MODIS time series data with hybrid classification mothed over Australia for 2001 and 2003, paper presented at the IEEE Geoscience and Remote Sensing Society’s IGARSS 2009 Conference, University of Cape Town, South Africa.

Su, Z. (2002), The surface energy balance system (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sc., 6, 85–99. Sun, H. and P. S. Cornish (2005), Estimating shallow groundwater recharge in the headwaters of the Liverpool plains using SWAT, Hydrol. Process., 19(3), 795–807.

29

Teixeira, A. H. C., W. G. M. Bastiaanssen, M.D. Ahmad, and M. G. Bos (2009), Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the low-middle São Francisco River basin, Brazil Part A: calibration and validation, Agr. Forest Meteorol., 149, 462–476. Thunnissen, H. A. M. and E. Noordman (1997), National Land Cover Database of the Netherlands: Classification, Methodology and Operational Implementation. Netherlands Remote Sensing Board, Delft, Netherlands. United Nations Environmental Programme (UNEP) (2002), A Survey of Methods for Analyzing Groundwater Recharge in Arid and Semi-arid Regions. UNEP, Division of Early Warning and Assessment. Nairobi, Kenya. United Nations Educational, Scientific and Cultural Organization (UNESCO) (2003), Water for people, water for life, United Nations World Water Development Report, Part II: a look at the world’s freshwater resources. Available at; www.unesco.org. Van de Griend, A. A. and M. Owe (1993), On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces, Int. J. Remote Sens., 14, 1119–1131. Wada, Y., L. P. H. van Beek, C. M. van Kempen, J. W. T. M. Reckman, S. Vasak, and M. F. P. Bierkens (2010), Global depletion of groundwater resources, Geophys. Res. Lett., 37, 20402. Wang, Y. J., H. Cheng, R.L. Edwards, X. Kong, X. Shao, S. Chen, J. Wu, X. An Jiang, and X. Z. Wang (2008), Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years, Nature, 451, 1090–1093. 30

Wanke, H., A. Dünkeloh, and P. Udluft (2008), Groundwater recharge assessment for the Kalahari catchment of north-eastern and north-western Botswana with a regional-scale water balance model, Water Res. Manage., 22, 1143–1158. Wardlow, B. D. and S. L. Egbert (2008), Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains, Remote Sens. Environ., 112(3), 1096–1116. Wood, W. W. and W. E. Sanford (1995), Chemical and isotopic methods for quantifying groundwater recharge in a regional semiarid environment, Groundwater, 33(3), 458. Wu, K. and Y.J. Xu (2006), Evaluation of the applicability of the SWAT model for coastal watersheds in southeastern Louisiana, J. Am. Water Resour. As., 42(5), 1247–1260. Xu, C. Y. and D. Chen (2005), Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany, Hydrol. Process., 19, 3717–3734. Zaidi, F. K., S. Ahmed, J. C. Maréchaland B. Dewandel (2007), Optimizing a piezometric network in the estimation of the groundwater budget: a case study from a crystalline-rock watershed in southern India, Hydrogeol. J., 15, 1131–1145. Zhang, L., W. Van Der Werf, W. Cao, B. Li, X. Pan, and J. H. J. Spiertz (2008),. Development and validation of SUCROSCotton: a potential crop growth simulation model for cotton, NJAS–Wagen. J. Life Sc., 56(1–2), 59–83. Zhu, C., (2000), Estimate of recharge from radiocarbon dating of groundwater and numerical flow and transport modeling, Water Resour. Res., 36, 2607–2620.

31

CCA : 1.59 Mha No. of Distributaries: 7

Climate: Semi - arid

Fig. 1

Location of the study area in the Punjab province of Pakistan

1

Monthly Average ETo

Fig. 2

250 225 200 175 150 125 100 75 50 25 0

Toba Tek Singh

January February March April May June July August Septemebr October November December

Rainfall and Monthly Average ETo, mm

20 18 16 14 12 10 8 6 4 2 0

Faisalabad

January February March April May June July August Septemebr October November December

Rainfall and Monthly Average ETo, mm

January February March April May June July August Septemebr October November December

250 225 200 175 150 125 100 75 50 25 0

Mean monthly average and daily ETo, and rainfall (mm) at three locations of Rechna Doab: Lahore, Faisalabad and Toba Tek Singh

2

20 18 16 14 12 10 8 6 4 2 0

Monthly Daily Average ETo, mm

20 18 16 14 12 10 8 6 4 2 0

Lahore

Monthly Daily Average ETo

Monthly Daily Average ETo, mm

250 225 200 175 150 125 100 75 50 25 0

Monthly Daily Average ETo, mm

Rainfall and Monthly Average ETo, mm

Monthly Average Rainfall

Data Collection

Datasets

CCA & reaches

Slope

DEM

Soil

Land use

Watershed delineation Overlay Calculation of subbasin parameter HRUs definition Weather data

SWAT Database

Irrigation practices Writing input table

Edit input parameters

Rewriting files

No

Simulated ≈ Observed

CCA= Canal Command Area DEM= Digital Elevation Model HRUs=Hydrological Response Units GW=Groundwater

Yes

GW Recharge Map

Fig.3

Detailed methodological framework of SWAT modelling

3

Fig.4

Digital elevation model (DEM) with 90 m spatial resolution for the study area.

4

Fig. 5

Spatial distribution of different soil series (local names) in the study area

5

Fig. 6

Conceptual scheme and flow line diagram for SEBAL

6

Run

If simulated ETa is ±15 % of reported ETa and NSE ≥ 0.5, R2 ≥ 0.6

Adjust SOL_Z

If simulated ETa is ±15 % of reported ETa and NSE ≥ 0.5, R2 ≥ 0.6

Adjust ESCO

Run If simulated ETa is ±15 % of reported ETa and NSE ≥ 0.5, R2 ≥ 0.6

Calibration completed

Fig. 7

Manual calibration procedure for Actual Evapotranspiration (ETa) in SWAT model (modified after Santhi et al., 2001)

7

Fig. 8

Average monthly variation of rainfall, and maximum and minimum temperature (2012-2020) for RCP4.5 scenario

8

Fig. 9

Average monthly variation of rainfall, and maximum and minimum temperature (2012-2020) for RCP8.5 scenario

9

Fig. 10

Normalized difference vegetation index (NDVI) profiles for different land use land cover classes

10

Fig. 11

Land use land cover classification map for year 2008-09

11

Fig. 12

Spatial (different canal command areas) and temporal (2005 to 2011) variation of actual evapotranspiration in Lower Chenab canal irrigation scheme

12

Fig. 13

Spatial variation of average actual evapotranspiration (2005 to 2011) in Lower Chenab canal irrigation scheme

13

Mean

Standard Deviation

Goodness-of-fit measures

SWAT ETa

SEBAL ETa

SWAT ETa

SEBAL ETa

R2

NSE

74.81

71.10

32.59

30.44

0.81

0.76

Fig. 14

Calibration results of SWAT model

14

Mean

Standard Deviation

Goodness-of-fit measures

SWAT ETa

SEBAL ETa

SWAT ETa

SEBAL ETa

R2

NSE

74.94

74.69

39.54

36.53

0.91

0.89

Fig. 15

Validation results of SWAT model

15

Fig. 16

Average monthly variation of groundwater recharge in Lower Chenab canal irrigation scheme

16

Fig. 17

Mean annual groundwater recharge in different canal command areas of Lower Chenab canal irrigation scheme

17

Fig. 18

Mean annual distribution of groundwater recharge in different canal command areas of Lower Chenab canal irrigation scheme

18

Fig.19

Mean annual groundwater recharge at hydrological response unit (HRU) level

19

Figure 20

Mean monthly groundwater recharge under BAU, RCP4.5 and RCP 8.5 scenarios for entire Lower Chenab canal irrigation scheme

20

Fig. 21

Mean annual groundwater recharge for BAU, RCP 4.5 and RCP 8.5 scenarios for different canal command areas of Lower Chenab canal irrigation scheme

21

Table 1

Cropping calendar for different crops being grown in the study area Crop Calendar for Punjab Pakistan year 1

Sr. No

Crop

1

wheat

2

Sugarcane

3

Cotton

Sowing

4

Rice

Sow

5

Jan Feb Mar

Maiz

Spring

Fodder

May

Jun

Jul

Aug

Sep

Oct

Sowing

Sow

Initial Growth

Dec

Growth

Transplant

Feb Mar

Growth Harvest

Harvest

Growth

Growth

Harvest

Harvest Start

Multi Cut Growth

34

End

Multi Cut Growth

Apr Harvest

Harvest Sow

Start

Jan

Vegetative growth to maturity Growing & Fruiting

Rabi Kharif

Nov Sowing

Normal 6

Apr

year 2

End

Table 2

Soil series with local name, texture and texture classes in the study area Series (local name)

Texture Class

Texture

Jhang

Coarse

Sandy Loam, Sand

Farida

Moderately Coarse

Sandy Loam, Fine Sandy Loam

Butchiana

Medium

Sandy Loam, Fine Sandy Loam

Chuharkana

Moderately Fine

Nokhar

Fine

Sandy Clay Loam, Clay Loam, Silty Clay Loam Sandy Clay, Silty Clay, Clay.

35

1

Table 3a Class /Reference Settlements/ Bare Natural Grass Orchards Fodder-Fallow Fodder-Fodder Fodder-Maize Wheat-Fodder Sugarcane Forest Fodder-Cotton Wheat-Cotton Wheat-Rice Column Total

Error matrix for accuracy assessment of land use land cover classification in the study area Settlements / Bare 20 1 0 0 1 0 0 0 0 0 0 0 22

Natural Grass 2 3 2 0 4 0 0 0 0 0 0 0 11

Orchards 0 0 1 0 1 0 0 0 0 0 0 1 3

FodderFallow 0 0 0 8 0 0 0 0 0 0 1 6 15

FodderFodder 0 1 0 0 17 0 0 0 0 0 1 2 21

FodderMaize 0 0 0 1 0 14 0 0 0 0 0 0 16

WheatFodder 0 0 0 0 0 0 22 0 0 0 0 1 23

Sugarc ane 0 0 0 0 1 2 3 20 0 0 1 0 27

Forest 1 0 1 0 0 1 0 0 7 0 0 0 10

FodderCotton 0 0 0 0 0 0 0 0 0 20 3 0 23

2 3 4

Table 3b

Producers and users accuracy assessment for different land use land covers in the study area Classified/Referanced

Producer's Accuracy

User's Accuracy

5

Water-Sattlemen Natural Grass

90.91 27.27

71.43 60.00

6

Orchards Fodder-Fallow

33.33 53.33

20.00 88.89

7

Fodder-Fodder Fodder-Maize Wheat-Fodder Sugarcane Forest Fodder-Cotton Wheat-Cotton Wheat-Rice

80.95 93.33 95.65 70.37 70.00 86.96 86.67 86.96

70.83 77.78 84.62 100.00 87.50 100.00 68.42 80.00

8 9 10 11

36

WheatCotton 0 0 0 0 0 0 0 0 0 0 15 0 15

WheatRice 5 0 1 0 0 2 0 0 1 0 0 37 46

Total Row 28 5 5 9 24 18 26 19 8 20 19 50 231

Table 4

Distributions of land use land cover in Lower Chenab canal irrigation scheme with different codes in the SWAT model CODE

Area Covered (Km2)

% Coverage in Basin

Water-Settlement-Bare

WSTB

587.23

3.67

Natural Grass

GRAS

333.91

2.08

Forest-orchard

FSOR

100.96

0.63

Fodder-Fallow

FOFA

2397.83

14.99

Fodder-Fodder

FOFO

1388.11

8.68

Wheat-Fodder

WHFO

4490.26

28.08

Fodder-Maize

FOMA

1776.90

11.11

Sugarcane

SUGR

730.64

4.57

Forest

FORS

140.26

0.87

Fodder-Cotton

FOCO

1390.05

8.69

Wheat-Cotton

WHCO

825.84

5.16

Wheat-Rice

WHRI

1826.32

11.42

Land Cover type

37

Highlights for the manuscript entitled A new technique to map groundwater recharge in irrigated areas by SWAT model under changing climate


Mapping GW recharge in irrigated areas by SWAT model at high spatial and temporal resolution




Impact of climate change scenarios on GW recharge by SWAT model




GW recharge is higher for those canal command areas which are closer to main canal




GW recharge is higher for those HRUs which have wheat crop and light soils




GW recharge would increase by 37-40 % under changing climate

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