Abstract

The agriculture sector is vulnerable to climate change and related changes in the hydrological cycle. In order to understand the changes in climatic variables and their implications for agricultural water consumption, the present study aims to analyse the temporal variability of climatic factors and water footprint (WF) of rice and wheat during the period 1986–2017 in Ludhiana, Punjab. Further, it aims to identify the dominant climatic factors that cause variation in reference evapotranspiration (ETo) and WF of rice and wheat. WF was estimated using CROPWAT, and Path analysis was used to determine the dominant climate variables. Temporal trends of climate variables were analysed using the Mann–Kendall test. The total WF of both rice and wheat shows a significant declining trend over the past 32 years. Sunshine duration and wind speed were the dominant factors influencing the variability of total WF of rice and wheat, respectively, whereas rainfall strongly influenced the green and blue WF of rice and wheat. Rainfall had a high variability, and consequently, irrigation water requirement was highly fluctuating. This indicates the significant impact of present and projected erratic pattern of precipitation on agriculture due to climate change and reiterates the importance of adaptive measures like rainwater harvesting and capacity building.

HIGHLIGHTS

  • Influence of climate variables on WF of rice and wheat was analysed for the first time for India.

  • Rainfall significantly influenced both green and blue WF of rice and wheat.

  • High variability in rainfall and irrigation water requirement highlights the urgent need for green water management.

  • Assessment of long-term trends in climate and WF is crucial for strategizing cropping patterns.

  • The study confirms the existence of ‘evaporation paradox’ and ‘solar dimming’ in Punjab, India.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

The agriculture sector is one of the most vulnerable sectors to the risks of climate change and related changes in the hydrological cycle (Smit & Skinner 2002). Large-scale changes in the hydrological cycle like increase in atmospheric water vapour, changes in precipitation, soil moisture and run-off have been linked to global warming (Bates et al. 2008). Climatic factors along with non-climatic drivers like ‘population growth, economic development, urbanization, land use changes and water management responses’ competing for water resources can have profound impacts on water availability for both rainfed and irrigated agriculture (Cisneros et al. 2014). Irrigation accounts for 70% of global water withdrawals and more than 90% of consumptive water use (IPCC 2007). With the projected expansion in irrigated area and cropping intensity, it is estimated that future irrigation water demand would surpass water availability in various regions under climate change scenario (Wada et al. 2013).

Water footprint (WF) is, in general, an indicator of direct and indirect freshwater appropriation, measured in terms of water volumes consumed (evaporated or incorporated into a product) and polluted per unit of time. The volumetric WF comprises three components: green WF refers to the consumption of rainwater; the blue WF refers to water consumed from surface and groundwater sources; while grey WF is an indicator of volume of water polluted, i.e. the freshwater volume required to assimilate the pollutant load to bring it to natural condition/ambient standards (Hoekstra et al. 2011). Temporal trends in WF reflect changes in crop water use over time for a given place (Lu et al. 2016). The WFgreen and WFblue are computed based on reference evapotranspiration (ETo) and precipitation and therefore directly associated with water availability in a given region. ETo is a measure of the ‘evaporative demand of the atmosphere’ which solely depends on climatic parameters (Allen et al. 1998). ETo is a key variable in the hydrological process and determines the availability of water for plant growth (Gao et al. 2017). Future changes in temperature, evapotranspiration and soil moisture might ultimately affect crop yields and crop water use in multiple and non-linear ways (Fader et al. 2010). Many studies have been conducted on spatial and temporal variability of evapotranspiration and the effect of climatic factors on evapotranspiration (Dinpashoh et al. 2011; Fan & Thomas 2013; Wang et al. 2015; Gao et al. 2017). But, there have been fewer studies on the temporal trends of WF and the influence of climatic factors on WF of crops (Sun et al. 2012, 2013; Lu et al. 2016; Kayatz et al. 2019).

Among crops, rice and wheat have the largest blue water footprints, together accounting for 45% of the global blue WF (Mekonnen & Hoekstra 2011). India is a major food producer, where the agriculture sector accounts for 90% fresh water use (Dhawan 2017). It is also a water-stressed country that is expected to face severe water constraints by 2050 (OECD 2012). High water stress has been found to contribute to high virtual water content values (Fader et al. 2010). The study region Punjab also known as the ‘bread basket of India’ is one of the largest producers of rice and wheat in India (Department of Food Civil Supplies & Consumer Affairs Govt. of Punjab 2019). In Punjab, 85% of water consumption is accounted for by the agriculture sector (Gulati et al. 2017), of which groundwater accounts for 90–97% of the irrigation in the Central Zone of Punjab (Sarkar et al. 2009). Punjab is facing a massive depletion in its water table at the rate of 70 cm/year (2008–2012) (Gulati et al. 2017). Climate change poses an additional threat to the availability of water. Climatic change would affect both the water consumption of crops and crop yield (Sun et al. 2012). With the projected decline in rainfall, there is a high risk of increased crop water utilization in tropical and subtropical regions (Fader et al. 2010). WF as a measure of crop water consumption can be used to assess the impact of climate change on crop water use in the long term, as well as to derive foresights for future actions.

Therefore, it is important to understand the changes in climatic factors and crop water use over the years as well as their implications for agriculture so that relevant policy measures can be derived for future action in regions facing a similar water crisis. In this context, the present study aims to analyse the temporal variability of climatic factors and WF of rice and wheat production during the period 1986–2017 in Ludhiana, Punjab. Further, it aims to identify the dominant climatic factors that cause variation in both ETo and WF of rice and wheat.

METHODS

Study area

The state of Punjab is located in north-western India. It extends from 29° 32′ to 32° 32′ north latitude and 73° 55′ to 76° 50′ east longitude and comprises a geographical area of 5.03 million hectares (Mha), i.e. 1.54% of the total geographical area of India. Of this, 83.4% of the land (4.20 Mha) is cultivated, and rice and wheat are the major crops. The groundwater in 80% of the geographical area in Punjab is overexploited (Gulati et al. 2017). Punjab is divided into five agroclimatic zones (ACZ), of which the ‘Central plain zone’, comprising 36% of the total area of Punjab, is the largest (Rang et al. 2011). The Central plains also account for two-thirds of the total rice and wheat production in Punjab (Sarkar et al. 2009). The study district Ludhiana is located in the Central plain zone of Punjab and is therefore assumed to be representative of Punjab (Figure 1).

Figure 1

Study area (Punjab) and the representative climate station, Ludhiana. (The five agroclimatic zones have been highlighted in different colours). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2020.093.

Figure 1

Study area (Punjab) and the representative climate station, Ludhiana. (The five agroclimatic zones have been highlighted in different colours). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/wcc.2020.093.

Data collection

Meteorological data for Ludhiana for the year 1986–2018 was acquired from the Department of Agrometeorology, Punjab Agriculture University, Ludhiana. The data included maximum temperature, minimum temperature, relative humidity (RH), wind speed, sunshine hours and rainfall. Yield data for rice and wheat production of Ludhiana district for the duration 1986–2017 were derived from statistical abstracts of Punjab (Singh & Kalra 2002; Statistical abstract of Punjab 2018).

Calculation of reference evapotranspiration (ETo) and crop evapotranspiration (ETc)

The FAO-CROPWAT model was used to estimate reference evapotranspiration (ETo) and crop evapotranspiration (ETc) or crop water requirement (CWR). The CROPWAT model developed by the Land and Water Development Division of U.N. Food and Agriculture Organization (FAO) is a decision support tool that uses data on climate, soil properties and crop characteristics as input to estimate crop water requirements and irrigation requirements of a region.

ETo represents evapotranspiration from a ‘standardized vegetated surface’ which is a hypothetical reference crop (resembling grass) with a height of 0.12 m, a fixed surface resistance of 70 s m−1 and an albedo of 0.23. The reference evapotranspiration (ETo), which denotes the evaporation power of the atmosphere, is affected by the climatic parameters (Allen et al. 1998). The CROPWAT software uses the Penman–Monteith (PM) method (Allen et al. 1998) to calculate reference evapotranspiration based on the input of climate parameters.

Of the two options, i.e. ‘crop water requirement’ and ‘irrigation schedule’, offered by the CROPWAT model to calculate crop evapotranspiration, the ‘irrigation schedule option’ was used in this study since it is recommended and more accurate (Hoekstra et al. (2011). Crop evapotranspiration under standard conditions (ETc) which denotes ‘the amount of water lost through evapotranspiration’ is identical in value to CWR which is defined as ‘the amount of water required to compensate the evapotranspiration loss from the cropped field’ (Allen et al. 1998). ETc and CWR are identical in value, but since this study is focused on water resources, the term ‘crop water requirement’ has been used for further analysis.

Calculation of WF

WF of a product is expressed as water volume per unit of product (in m3/t). It generally has three components: blue water, green water and grey water. In this study, only WFgreen and WFblue, which are rainwater and irrigation water components, have been considered as these two depend on climate. WFgreen and WFblue have been computed as follows (Hoekstra et al. 2011):
formula
formula
formula
formula
formula
where CWUgreen and CWUblue are the green and blue water components, respectively, of crop water use that is equivalent to the summation of daily evapotranspiration (in mm/day) over the length (in days) of the growing period (lgp); Y is the crop yield (Y, tons/hectare or t/ha), ETgreen represents green water evapotranspiration; ETblue, i.e. the blue water evapotranspiration or field-evapotranspiration of irrigation water, also denoted as irrigation water requirement (IWR), is the difference between the total crop evapotranspiration and effective precipitation (Peff). ETblue is 0 when effective rainfall exceeds crop evapotranspiration. Crop evapotranspiration (ETc) and effective rainfall (Peff) were derived from CROPWAT output, which were further used to calculate green and blue WF. For the input in CROPWAT, Punjab-specific crop coefficients for wheat were derived from Kaur et al. (2017). The planting dates were kept constant for all years (25 June for rice transplantation and 5 November for wheat sowing). Using soil texture inputs from Singh et al. (2009), soil hydraulic properties were calculated online (http://resources.hwb.wales.gov.uk/VTC/envsci/module2/soils/soilwatr.htm) for soil inputs. The details of inputs used in CROPWAT for the estimation of evapotranspiration and IWR are presented in supplementary Tables A7 and A8. The WF of rice and wheat was calculated for each year (supplementary Table A5), followed by trend analysis using MS-Excel.

Temporal variation of climatic factors and ETo

The non-parametric Mann–Kendall (MK) test (Kendall 1948; Mann 1945) was used to reveal the temporal trend in meteorological data, ETo and crop water use. The MK has been recommended by the World Meteorological Organization for the evaluation of significant trends in hydro-meteorological time series. A modified M–K test (Hamed & Rao 1998) was used to eliminate the effect of autocorrelation in data. The tests were carried out using XLSTAT software. For seasonal climate trends, the year was equally divided into two major seasons, i.e. the rice-growing season (Kharif) and wheat growing season (Rabi). For the rice-growing Kharif season, climate data from May to October was used as input; while for Rabi season, data from November to April of the following year were used to analyse the seasonal trends. The M–K test was used for analysing seasonal trends of climate variables. For annual climate trends (including ETo), daily weather data were averaged over a month and months were averaged for a single year (January–December) for the entire study duration, following which the modified M–K test was applied. Similarly, for seasonal trends, daily data were averaged for the respective months, then the average of months was considered for a single year. The standardized M–K value Z indicates the direction of trend, where a positive Z value denotes an upward trend, while a downward trend is indicated by negative Z value. The slope computed by Theil–Sen's estimator, also known as Sen's slope, is a robust indicator of the magnitude of trend, i.e. the rate of change of variables. It has been widely used in identifying the slope of the trend line in hydrological time series (Dinpashoh et al. 2011).

The standardized MK statistic, denoted by Z, was computed using the following equations (Dinpashoh et al. 2011):
formula
formula
formula
where S statistic and VAR(S) were derived from MK test output in XLSTAT.

Impact of climatic factors on WF

Path analysis and correlation analysis were used to ascertain the dominant climatic factors affecting WF of rice and wheat crops. Since the data were not normally distributed, the Spearman correlation was used. Path coefficient analysis developed by Wright (1921) is an extension of regression and provides estimates of magnitude and significance of the hypothesized relationship between two sets of variables. It can be used to separate the direct and indirect effect of independent variables on the dependent variable (Dewey & Lu 1959; Lu et al. 2016). The climatic parameters used as inputs in path analysis of rice WF were averaged for the months of May to October of a single year. Similarly, for wheat, climate parameters were averaged for the months of November to April of the following year. Path analysis was carried out in SPSS-Amos software. The simplified flow chart of the methodology is presented in Figure 2.

Figure 2

Flow diagram of methodology.

Figure 2

Flow diagram of methodology.

RESULTS

Temporal variations of climatic factors

The descriptive statistics of climatic factors and results of the MK test for annual and seasonal trends are presented in Table 1. Temporal trends of annual climatic factors are presented in Figure 3(a)–3(g). The climate variables, minimum temperature (Min. Temp), maximum temperature (Max. Temp), mean temperature (Figure 3(a)) and RH (Figure 3(c)) showed an increasing trend over the study period. Of this, the trends of minimum temperature and mean temperature were found to be statistically significant (p < 0.05) with an annual growth rate of 0.05 °C and 0.03 °C per annum (a), respectively. In contrast, rainfall (Figure 3(b)), wind speed (Figure 3(d)), sunshine duration (Figure 3(e)) and radiation (Figure 3(f)) showed a downward trend. The declining trend of sunshine duration, radiation and wind speed was found to be statistically significant. The rate of decline for wind speed, sunshine duration and radiation was found to be 0.01 kmph/a, 0.07 h/a and 0.09 MJ/m2/a, respectively (Table 1). Since sunshine duration and radiation are strongly correlated (ρ = 0.99), only sunshine duration was used in further analysis. The coefficient of variation (CV) of rainfall was found to be 34%, and the highest annual rainfall was almost 3.5 times the lowest annual rainfall. This was followed by CV of sunshine duration (10%), wind speed (7%), radiation (5%), RH (4%) and mean temperature (2%). The results of the M–K test for the seasonal data are presented in Table 1. The climate factors in Kharif season showed a pattern similar to the annual trends. Minimum and mean temperature showed a significant upward trend, whereas sunshine duration and radiation showed a significant downward trend. Rainfall in Kharif (rice-growing) season was found to have the highest Sen's slope indicating a decline at a rate of 41.3 mm/a; nearly 10 times higher than the annual rate. In the wheat growing season, minimum temperature and humidity were found to have a significant upward trend. Wind speed, sunshine duration and radiation showed a significantly decreasing trend.

Table 1

Statistical description and results of MK test for climatic factors and ETo

Statistical descriptionMin. Temp (°C)Max. Temp (°C)Mean Temp (°C)Rainfall (mm)RH (%)Wind speed (kmph)Sunshine duration (h)Radiation (MJ/m2)ETo (mm)
Time period Annual (January–December) 
Minimum 16.00 28.20 22.45 385.10 62.00 3.29 6.60 16.40 3.56 
Maximum 18.50 30.70 24.50 1,316.80 71.00 4.58 9.10 19.50 4.22 
Mean 17.19 29.73 23.46 787.28 65.88 4.27 7.86 18.00 3.87 
Std dev 0.57 0.58 0.49 266.79 2.32 0.28 0.75 0.93 0.15 
CV(%) 34 10 3.83 
MK value (Z4.37 1.19 3.19 −0.70 1.72 −3.15 −5.82 −5.62 −0.28 
Sen's slope 0.05 0.01 0.03 −4.17 0.09 −0.01 −0.07 −0.09 −0.01 
Time period Rice-growing season (Kharif: May–October) 
Minimum 22.52 33.03 28.33 292.20 57.83 3.74 6.42 18.15 4.53 
Maximum 25.18 35.65 30.25 1,254.80 71.67 5.28 9.77 22.82 5.59 
Mean 23.89 34.81 29.35 695.20 64.56 4.81 8.17 20.59 5.03 
Std dev 0.63 0.58 0.48 284.45 3.19 0.31 0.83 1.16 0.23 
CV(%) 41 10 4.67 
MK value (Z2.70 0.07 2.50 −1.30 0.71 0.40 −0.40 −3.90 −0.16 
Sen's slope 0.24 0.07 0.20 −41.29 0.17 −0.04 −0.41 −0.57 −0.01 
Time period Wheat growing season (Rabi: November–April) 
Minimum 9.43 23.28 16.64 36.60 63.00 68.17 6.05 13.72 2.50 
Maximum 11.73 26.08 18.78 245.50 73.83 109.00 8.93 17.00 2.87 
Mean 10.57 24.75 17.66 116.64 67.20 90.13 7.53 15.41 2.71 
Std dev 0.61 0.83 0.61 51.84 2.64 8.83 0.76 0.83 0.11 
CV(%) 44 10 10 3.99 
MK value (Z2.21 −0.30 0.50 0.30 2.01 −2.21 −4.50 −4.50 −0.27 
Sen's slope 0.19 −0.02 0.09 10.70 0.68 −3.38 −0.44 −0.47 −0.05 
Statistical descriptionMin. Temp (°C)Max. Temp (°C)Mean Temp (°C)Rainfall (mm)RH (%)Wind speed (kmph)Sunshine duration (h)Radiation (MJ/m2)ETo (mm)
Time period Annual (January–December) 
Minimum 16.00 28.20 22.45 385.10 62.00 3.29 6.60 16.40 3.56 
Maximum 18.50 30.70 24.50 1,316.80 71.00 4.58 9.10 19.50 4.22 
Mean 17.19 29.73 23.46 787.28 65.88 4.27 7.86 18.00 3.87 
Std dev 0.57 0.58 0.49 266.79 2.32 0.28 0.75 0.93 0.15 
CV(%) 34 10 3.83 
MK value (Z4.37 1.19 3.19 −0.70 1.72 −3.15 −5.82 −5.62 −0.28 
Sen's slope 0.05 0.01 0.03 −4.17 0.09 −0.01 −0.07 −0.09 −0.01 
Time period Rice-growing season (Kharif: May–October) 
Minimum 22.52 33.03 28.33 292.20 57.83 3.74 6.42 18.15 4.53 
Maximum 25.18 35.65 30.25 1,254.80 71.67 5.28 9.77 22.82 5.59 
Mean 23.89 34.81 29.35 695.20 64.56 4.81 8.17 20.59 5.03 
Std dev 0.63 0.58 0.48 284.45 3.19 0.31 0.83 1.16 0.23 
CV(%) 41 10 4.67 
MK value (Z2.70 0.07 2.50 −1.30 0.71 0.40 −0.40 −3.90 −0.16 
Sen's slope 0.24 0.07 0.20 −41.29 0.17 −0.04 −0.41 −0.57 −0.01 
Time period Wheat growing season (Rabi: November–April) 
Minimum 9.43 23.28 16.64 36.60 63.00 68.17 6.05 13.72 2.50 
Maximum 11.73 26.08 18.78 245.50 73.83 109.00 8.93 17.00 2.87 
Mean 10.57 24.75 17.66 116.64 67.20 90.13 7.53 15.41 2.71 
Std dev 0.61 0.83 0.61 51.84 2.64 8.83 0.76 0.83 0.11 
CV(%) 44 10 10 3.99 
MK value (Z2.21 −0.30 0.50 0.30 2.01 −2.21 −4.50 −4.50 −0.27 
Sen's slope 0.19 −0.02 0.09 10.70 0.68 −3.38 −0.44 −0.47 −0.05 

Values in bold are significant at α = 0.05.

Figure 3

(a–g) Temporal variation of climatic factors and ETo with Sens's slope.

Figure 3

(a–g) Temporal variation of climatic factors and ETo with Sens's slope.

Temporal variations of ETo, CWR and IWR

ETo showed a statistically significant downward trend in the duration 1986–2017 (p < 0.05) (Figure 4). It was found to be decreasing at the rate of 0.012 mm/a (Table 1). The average annual ETo for the study period was found to be 3.87 mm. Thus, the trend of increasing air temperature and decreasing evapotranspiration confirms the existence of an ‘evaporation paradox’ in Ludhiana, Punjab. A significant downward trend was also found for seasonal ETo in the rice and wheat growing season, which declined at a rate of 0.16 mm/decade for rice and 0.067 mm/decade for wheat. The seasonal average ETo was 5.03 and 2.71 mm/a for rice and wheat growing season, respectively. The interannual variability in yield and seasonal ETo of rice and wheat is presented in Figure 3(g). Stepwise multiple regression was applied to identify the dominant climatic factors influencing ETo (Dinpashoh et al. 2011; Gao et al. 2017). The results of multiple regression (supplementary Table A10) revealed that sunshine duration followed by minimum temperature and wind speed were the dominant factors influencing the annual ETo in Ludhiana, Punjab.

Figure 4

Temporal trend in yield (in kg/ha) and seasonal ETo (in mm) of (a) rice and (b) wheat from 1986 to 2017.

Figure 4

Temporal trend in yield (in kg/ha) and seasonal ETo (in mm) of (a) rice and (b) wheat from 1986 to 2017.

Figures 5 and 6 show the interannual variability of CWR and IWR of rice and wheat in the duration 1986–2017. The CWR of both rice and wheat showed a significant declining trend at 2.6 and 1 mm/a, respectively (p < 0.05). The IWR showed a non-significant declining trend at the rate of 2.05 and 0.93 mm/a for rice and wheat, respectively. As compared to CWR, IWR showed a greater fluctuation because of variability in rainfall. If rainfall is less, the same amount of water is compensated by irrigation; therefore, a high variability in rainfall consequently leads to high variability in IWR of crops. IWR (CV = 294%) for rice was found to have a greater variation than wheat IWR (CV = 29%) (Table A6), which could be because rice is grown in the monsoon season and rainfall was found to have CV of 41%. Further, correlation analysis was used to determine the relationship between CWR (ETc) and climatic factors. In the case of rice, a significant positive relationship was found between CWR and sunshine hours (ρ = 0.68) (Table 2); CWR and RH shared a significant negative relation (ρ = −0.37). For wheat, significant positive relationship was found between CWR and sunshine duration (ρ = 0.62), followed by CWR and wind speed (ρ = 0.43). CWR, IWR and yield data for each year are presented in supplementary Table A4. Yield for both rice and wheat showed a significant increasing trend over the period 1986–2017. Rice and wheat yield were found to increase at the rate of 35 and 31 kg/ha/a, respectively (Figure 4).

Table 2

Correlation matrix of CWR, WF and climatic factors

FactorsTemperatureRelative humidityWind speedSunshine durationRainfall
Rice 
CWR (R) −0.097 −0.374* 0.186 0.680* −0.262 
WFgreen (R) −0.368* 0.355* −0.314 0.256 0.714* 
WFblue (R) 0.223 −0.491* 0.319 0.290 −0.720* 
WF (R) −0.341 −0.127 0.208 0.796* 0.063 
Wheat 
CWR (W) −0.161 −0.084 0.431* 0.623* −0.103 
WFgreen (W) −0.439* 0.490* 0.347 0.025 0.907* 
WFblue (W) 0.296 −0.613* 0.152 0.461* −0.786* 
WF(W) −0.196 −0.285 0.608* 0.733* −0.066 
FactorsTemperatureRelative humidityWind speedSunshine durationRainfall
Rice 
CWR (R) −0.097 −0.374* 0.186 0.680* −0.262 
WFgreen (R) −0.368* 0.355* −0.314 0.256 0.714* 
WFblue (R) 0.223 −0.491* 0.319 0.290 −0.720* 
WF (R) −0.341 −0.127 0.208 0.796* 0.063 
Wheat 
CWR (W) −0.161 −0.084 0.431* 0.623* −0.103 
WFgreen (W) −0.439* 0.490* 0.347 0.025 0.907* 
WFblue (W) 0.296 −0.613* 0.152 0.461* −0.786* 
WF(W) −0.196 −0.285 0.608* 0.733* −0.066 

*Values in bold are different from 0 with a significance level alpha = 0.05. R: rice; W: wheat; ETc: crop evapotranspiration; WF: water footprint.

Figure 5

Interannual variability in CWR, IWR and total rainfall (May–October) in the rice-growing season during 1986–2017.

Figure 5

Interannual variability in CWR, IWR and total rainfall (May–October) in the rice-growing season during 1986–2017.

Figure 6

Interannual variability in CWR, IWR and total rainfall (November–April) in the wheat growing season during 1986–2017.

Figure 6

Interannual variability in CWR, IWR and total rainfall (November–April) in the wheat growing season during 1986–2017.

Interannual variability in WF of rice and wheat

Interannual variability in green WF

Figure 7 shows the interannual variability of green WF of rice and wheat during 1986–2017. The green WF of both rice and wheat does not show a statistically significant trend with time. This implies no change in the green WF of rice and wheat over the past 32 years. The average green WF of rice and wheat was found to be 1,239 and 206 m3/t, respectively (Table A6). Correlation analysis indicated a significant positive effect between WFgreen of rice and rainfall (ρ = 0.71) followed by WFgreen of rice and RH (ρ = 0.36). There was a negative correlation of temperature (ρ = −0.37) with the WFgreen of rice (p < 0.05) (Table 2). This was consistent with the results of path analysis that showed that rainfall and RH significantly influenced WFgreen during the rice-growing season (p < 0.05) (Table 3). Additionally, sunshine duration was also found to significantly influence WFgreen. Similar to the WFgreen of rice, the correlation analysis of WFgreen of wheat revealed a significant positive effect of rainfall (ρ = 0.91) followed by RH (ρ = 0.49) and a negative effect of temperature (ρ = −0.44) (p < 0.05). According to path analysis results, the WFgreen of wheat in the Rabi (winter) season was significantly influenced (p < 0.05) by rainfall only.

Table 3

Path coefficient analysis of green WF, blue WF and total WF of rice and wheat

Rice
Wheat
Green WFBlue WFTotal WFGreen WFBlue WFTotal WF
Temperature 0.03 0.108 0.188 0.116 0.001 0.137 
RH 0.512−0.328* 0.232 0.015 −0.161 −0.215 
Rainfall 0.479* −0.4* 0.088 0.926* −0.678* 0.097 
Wind speed −0.326* 0.351* 0.049 0.125 0.195 0.428* 
Sunshine duration 0.482* 0.169 0.879* 0.115 0.154 0.357 
Rice
Wheat
Green WFBlue WFTotal WFGreen WFBlue WFTotal WF
Temperature 0.03 0.108 0.188 0.116 0.001 0.137 
RH 0.512−0.328* 0.232 0.015 −0.161 −0.215 
Rainfall 0.479* −0.4* 0.088 0.926* −0.678* 0.097 
Wind speed −0.326* 0.351* 0.049 0.125 0.195 0.428* 
Sunshine duration 0.482* 0.169 0.879* 0.115 0.154 0.357 

*Significance at α = 0.05 level.

Figure 7

Interannual variability in green WF of rice and wheat during 1986–2017.

Figure 7

Interannual variability in green WF of rice and wheat during 1986–2017.

Interannual variability in blue WF

Similar to the trend of WFgreen, the blue WF of both rice and wheat did not show a significant trend over the duration 1986–2017 (Figure 8). The average blue WF for rice and wheat, respectively, for 32 years was found to be 296 and 334 m3/t (Table A6). Similar to the IWR of rice, the blue WF of rice was found to be highly fluctuating varying from 0 m3/t (in several years) to as high as 1,448 m3/t in 1987 (Table A5). This is because of variability in rainfall. In the case of rice, correlation analysis indicated a significant negative effect of rainfall (ρ = −0.72) and RH (ρ = −0.49) on the WFblue of rice (Table 2), thus indicating high rainfall led to low blue WF. Path analysis showed that RH, rainfall and wind speed influenced WFblue during the rice-growing season (p < 0.05) (Table 3). Deficit of rainfall is compensated by irrigation, i.e. the blue WF. A high rainfall year translates to less IWR (WFblue). Therefore, a negative correlation exists between WFblue and rainfall. The correlation analysis of WFblue of wheat revealed a significant positive effect of sunshine duration (ρ = 0.461) and a negative effect of rainfall (ρ = −0.786) and RH (ρ = −0.613) (p < 0.05). Similar to WFgreen of wheat, the WFblue of wheat was also found to be significantly influenced (p < 0.05) by rainfall according to the results of path coefficient analysis (Table 3).

Figure 8

Interannual variability in blue WF of rice and wheat during 1986–2017.

Figure 8

Interannual variability in blue WF of rice and wheat during 1986–2017.

Interannual variability in total WF

Annual variability of WF for rice and wheat is presented in Figure 9. The total WF of both rice and wheat showed a significant decrease over the past 32 years (p < 0.05) declining at the rate of 19 and 6 m3/t/a, respectively. The average total WF of rice and wheat for the period 1986–2017 was found to be 1,535 and 540 m3/t, respectively. If the 32-year period is divided into three periods, period I (1986–1995), period II (1996–2005) and period III (2006–2017): the average WF of rice for each period was 1,721, 1,641 and 1,293 m3/t, respectively, with a percentage decrease of 5% between period I and II and 21% between period III and II. The average WF for wheat was 629, 507, 493 m3/t, respectively, for the corresponding periods. In terms of percentage, the wheat WF decreased by 19% between the periods I and II, and 3% between the periods II and III. The contribution of the WFgreen and WFblue, respectively, to the total WF was 80.7 and 19.3% for rice, and 38.2 and 61.8% for wheat. The annual total WF of rice was found to be positively correlated with sunshine duration (ρ = 0.79) (p < 0.05). The WFtotal of wheat was found to be positively correlated with sunshine duration (ρ = 0.73) followed by wind speed (ρ = 0.61) (p < 0.05). Results of path analysis indicated that the total WF of rice grown in Kharif (monsoon/autumn) season in Ludhiana was mainly influenced by sunshine duration (p < 0.05), while wind speed influenced the WF of wheat. The details of regression weights for the relationship between WF of rice and wheat and climatic factors are presented in supplementary Table A9.

Figure 9

Interannual variability in total WF of rice and wheat during 1986–2017.

Figure 9

Interannual variability in total WF of rice and wheat during 1986–2017.

DISCUSSION

Among the climatic factors, temperature showed an increasing trend, which is in agreement with the global trend. The increase in temperature despite an observed decrease in sunshine duration indicates an increased warming due to heat trapping by greenhouse gases. Additionally, an increase in temperature did not translate to an increase in ETo and CWR, thus indicating the role of other climatic factors in determining WF. The declining trend of sunshine duration and solar radiation found in this study is in agreement with the reduction in solar radiation, also known as ‘solar dimming’, observed globally (Stanhill & Cohen 2001). Increase in the amount of aerosols and other air pollutants was found to be the major reason behind this phenomenon (Stanhill & Cohen 2001), while in South Asia, it was found to be primarily driven by cloud cover (Kambezidis et al. 2012). Wind speed is another factor that has been shown to affect aerosol concentration (Moorthy et al. 1998). According to the findings of our study, wind speed shows a declining trend, which could have affected the aerosol concentrations and cloud cover that are less likely to be blown away with decreasing wind speed. Rainfall showed an insignificant decreasing trend. Similarly, global mean precipitation trends between 1901 and 2005 were found to be statistically non-significant (Bates et al. 2008). But, the decreasing trend is in agreement with the observed decrease in rainfall over the tropic and subtropic zone in the past 30–40 years (Bates et al. 2008). Among all climate variables, rainfall was found to have the highest CV both annually (34%) and seasonally (41% in Kharif season and 44% in Rabi season). CV indicates the deviation of rainfall from the mean value and illustrates the erratic nature of rainfall in Ludhiana during the study period. A CV > 30% for rainfall indicates high variability in rainfall amount and distribution patterns (Kisaka et al. 2015). Rainfall is projected to become more erratic and unpredictable in the future (IPCC 2013). The predicted increase in precipitation intensity due to a decrease in even spread of precipitation interspersed with longer dry spells in the subtropical region (Bates et al. 2008) might do more harm to the crop even if the total amount of precipitation matches the crop water requirements. This would have significant implications for agriculture. Water availability in such long dry spells can be ensured through rain harvesting and storage during the wet spells.

Consistent with the findings of this study, ETo was found to show a decreasing trend in most of the studies worldwide (Dinpashoh et al. 2011; Fan & Thomas 2013; Wang et al. 2015; Gao et al. 2017). Decrease in net radiation (Wang et al. 2015) or sunshine duration (Fan & Thomas 2013) was found to be the major cause of decreasing ETo. In this study, sunshine duration, the dominant factor influencing ETo, also showed a significant decreasing trend. This could be a possible reason for the downward trend of ETo. Similarly, crop evapotranspiration (ETc) was also found to decrease significantly along with a decrease in total WF of both rice and wheat. Therefore, both the factors, i.e. increase in yields and decrease in (ETc) or CWR contributed to the decrease in total WF across the years. This is in agreement with the reported decrease in WF of wheat due to both yield increase and decline in ETc in China over 1980–2009 (Sun et al. 2012). Likewise, a reduction in WF of cereals over 2005–2014 in India is reported to be primarily driven by an increase in yields along with decreased evapotranspiration (Kayatz et al. 2019). Several studies have reported a decline in long-term WF. A decreasing trend in WF was reported for winter wheat and summer maize over 35 years in North China (Lu et al. 2016), spring wheat and maize in Hetao irrigation district, China between 1960 and 2008 (Sun et al. 2013), barley and spring wheat in Canada between 1965 and 2014 (Zhao et al. 2019) and all cereals in India for the period 2005–2014 (Kayatz et al. 2019). On the contrary, an increasing trend in WF for various crops in Lake Dianchi Basin, China (1981–2011) (Zhang et al. 2015) was reported.

The proportion of green WF (81%) in total water consumption for rice was relatively high as compared to blue WF as rice is grown in the monsoon season. On the other hand, blue WF contributed a higher proportion (61%) to the total WF of wheat which largely depends on irrigation water as the winter (Rabi) season in the study area receives less rainfall. Under climate change, future irrigation water demand is projected to exceed water availability (Wada et al. 2013). The study region, where 98.5% of the gross sown area is irrigated, is heavily dependent on groundwater for irrigation, and the situation is made worse due to groundwater abstractions. The net volume of groundwater available for irrigation use in 2025 was projected to be greater than 4318 ha m in the majority of the blocks in the state of Punjab, indicating the negative availability of groundwater for irrigation in the future (Shiao et al. 2015). Further, the normalized deficit index (NDI) in Punjab falls in the category ‘2–5’ and ‘ > 5’, indicating extreme overdraft (Shiao et al. 2015). The NDI denotes the amount of water that needs to be drawn from external storage to meet current demand annually. An NDI value >1 indicates storage required to meet deficit is less than average annual rainfall (Shiao et al. 2015). This is strong evidence of the fact that the study area is lacking in water storage practices and infrastructure, despite the severe water crisis. Previously, studies have focused more on the reduction of blue and grey WF, since green WF is completely dependent on climatic conditions. In agreement, the findings of this study highlight the importance of rainfall and its erratic nature, and therefore, the need for better management of green water in the face of the climate crisis. Green water can be converted to blue water and used for irrigation through storage. Rainfall was also found to be the most significant and common climatic factor influencing the blue–green WF of rice and wheat. The WF components taken separately, i.e. green and blue WF of both rice and wheat, did not show a significant trend despite the tremendous increase in yields over the years, which was due to the high variation in CWR and IWR as a result of variation in rainfall. Such variability in IWR leads to the vulnerability of regions that have deficit water reserves but still grow crops that have a high WF like rice. Thus, green water or rain water must be stored to compensate for the variability in rainfall and IWR. The impact of the high variability in rainfall is expected to increase by 50% if groundwater depletion continues in India (Fishman 2018). Moreover, it has been found that sustainable use of irrigation water in India could only mitigate less than 10% of the climate change impact (Fishman 2018). Water storage is a recommended key strategy in climate change adaptation to ensure water availability throughout the year (McCartney & Smakhtin 2010). Water-saving technologies, collection and storage of rainwater have been suggested as an active adaptation measure to the spatiotemporal variations in the distributions of precipitation (Zhao et al. 2019). Therefore, it is imperative that steps should be taken in the direction of developing water storage infrastructure for agriculture in the state. This is a generalized conclusion for any region facing similar crisis.

In the study area, it was also observed that even though farmers were facing water issues, they were not driven or aware about rain water conservation. Therefore, apart from institutional investments in water harvesting infrastructure, it is also crucial that awareness and capacity building for green water conservation are simultaneously implemented. In addition, for decentralized rainwater harvesting, investments in local institutions and small credit schemes are important so that the initial costs can be made affordable for small-scale water harvesting by farm households (Fox et al. 2005).

CONCLUSION

The present study analysed temporal trends in climatic factors, ETo and WF of rice and wheat for the period 1986–2017 in Ludhiana, Punjab, along with impacts of changes in climatic factors on WF. Sunshine duration had a declining trend, and consequently, ETo also showed a downward trend, which in turn led to a decline in CWR. However, IWR was found to be highly fluctuating because of high variability in rainfall over the study period. Temperature showed an increasing trend, thus establishing ‘evaporation paradox’ in the study region. The total WF of both rice and wheat, influenced mainly by sunshine duration, was found to decrease over the years because of significant increase in yields and a decline in CWR. However, green and blue WF of rice and wheat, which were most significantly influenced by rainfall, showed no significant trend. This study demonstrated the implications of varying rainfall on WF. Therefore, long-term trends in WF in relation to climatic factors, particularly variability in precipitation, should be accounted for while strategizing future cropping patterns.

Most importantly, the study area is facing a water crisis with decreasing and fluctuating levels of rainfall and already depleting groundwater resources. Even though the model estimates of CWR (ETc) for rice and wheat showed a declining trend in Punjab, water availability for agriculture could be a tough challenge in the future, considering climate change. Apart from mitigation measures to reduce water use, adaptive measures like water storage could prove to be useful to fight the challenge of water availability for agriculture in the future. This is true for all regions facing a similar crisis. Awareness building and investments in rain water harvesting infrastructure are particularly important measures that need to be urgently implemented.

This study was limited to understanding the role of climatic factors in WF variation, which was computed theoretically based on climate data. It is certain that the trend of WF based on actual irrigation water use (including water loses during transmission) would be different and more accurate than the findings of this study. Furthermore, the computation of WF in CROPWAT was also undermined by the use of constant planting dates and growing period (irrespective of variety) over the years since data for crop planting dates for each year was not available. Particularly for rice, a crop transplanting date of 25 June was taken as per recent government regulations. Therefore, rice WF calculated in this study reflects the best possible scenario, as ET reduces by ∼75 mm when the transplantation date is shifted from late May to late June (Humphreys et al. 2010).

ACKNOWLEDGEMENT

One of the authors acknowledges the fellowship received from the host institute.

DECLARATION OF INTEREST

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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Supplementary data