Regional impacts of climate change on irrigation water demands

HYDROLOGICAL PROCESSES Hydrol. Process. (2012) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.9379

Regional impacts of climate change on irrigation water demands S. Rehana1 and P. P. Mujumdar1,2* 1 2

Department of Civil Engineering, Indian Institute of Science, Bangalore, Karnataka 560 012, India Divecha Center for Climate Change, Indian Institute of Science, Bangalore, Karnataka 560 012, India

Abstract: This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoir command area. A statistical downscaling model and an evapotranspiration model are used with a general circulation model (GCM) output to predict the anticipated change in the monthly irrigation water requirement of a crop. Specifically, we quantify the likely changes in irrigation water demands at a location in the command area, as a response to the projected changes in precipitation and evapotranspiration at that location. Statistical downscaling with a canonical correlation analysis is carried out to develop the future scenarios of meteorological variables (rainfall, relative humidity (RH), wind speed (U2), radiation, maximum (Tmax) and minimum (Tmin) temperatures) starting with simulations provided by a GCM for a specified emission scenario. The medium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario, to assess the likely changes in irrigation demands for paddy, sugarcane, permanent garden and semidry crops over the command area of Bhadra reservoir, India. Results from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area. RH, Tmax and Tmin are also projected to increase with small changes in U2. Consequently, the reference evapotranspiration, modeled by the Penman–Monteith equation, is predicted to increase. The irrigation requirements are assessed on monthly scale at nine selected locations encompassing the Bhadra reservoir command area. The irrigation requirements are projected to increase, in most cases, suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due to projected increase/change in other meteorological variables (viz., Tmax and Tmin, solar radiation, RH and U2). The irrigation demand assessment study carried out at a river basin will be useful for future irrigation management systems. Copyright © 2012 John Wiley & Sons, Ltd. KEY WORDS

climate change; statistical downscaling; GCM; irrigation demands; evapotranspiration

Received 9 November 2011; Accepted 20 April 2012

INTRODUCTION The rising CO2 and climate change due to global warming directly affect both precipitation and evapotranspiration, consequently the irrigation water demands. Moreover, the irrigation water requirements of the crops change as a function of climate change. Several authors have focused on assessing the impacts of climate change on agriculture, over the past decade. Most of these studies concentrated on estimating the changes in crop productivity (e.g. Easterling et al., 1993; Rosenzweig and Parry, 1994; Singh et al., 1998; Brown and Rosenberg, 1999; Parry et al., 2004; Harmsen et al., 2009; Liu et al., 2010). Assessment studies focusing on the impacts of climate change on irrigation demands using general circulation model (GCM) outputs are becoming more accepted in recent years. GCMs are excellent tools to study the climate change impact and have been used in recent studies globally. Yano et al. (2007) studied the effects of climate change on crop growth and irrigation water demand for a wheat–maize cropping

*Correspondence to: P. P. Mujumdar, Divecha Center for Climate Change, Indian Institute of Science, Bangalore, Karnataka 560 012, India E-mail: [email protected] Copyright © 2012 John Wiley & Sons, Ltd.

sequence in a Mediterranean environment of Turkey. The climate change scenarios of temperature and precipitation were created by superimposing projected anomalies of GCMs on observed climate data of the baseline period. Elgaali et al. (2007) modeled the regional impact of climate change on irrigation water demand by considering rainfall and evapotranspiration in the Arkansas River Basin in southeastern Colorado. They assumed no change in crop phenology and found an overall increase in irrigation water demands due to climate change. The historical climate data sets of historical and projections for the continental United States are considered from Vegetation Ecosystem Modeling and Analysis Project developed by Kittel et al. (1995). Rodriguez Diaz et al. (2007) showed increase of irrigation demand between 15% and 20% in seasonal irrigation need by 2050 in the Guadalquivir river basin in Spain with perturbed climate scenarios of temperature, precipitation, solar radiation, wind speed (U2) and relative humidity (RH). Shahid (2011) estimated the changes of irrigation water demand in dry-season Boro rice field in northwest Bangladesh in the context of global climate change, with projected changes of rainfall and temperatures estimated using the modeling software SCENario GENerator (SCENGEN).

S. REHANA AND P. P. MUJUMDAR

de Silva et al. (2007) studied the impacts of climate change on irrigation water requirements in the paddy field of Sri Lanka and predicted an increase of 13% to 23% of irrigation water demand depending on climate change scenarios. The climate change scenarios of temperature, radiation, U2 and RH are developed by applying the percentage changes of GCM to the baseline dataset. The proportional (%) changes given by a selected GCM and scenario are applied on an existing baseline climatological dataset to develop the future scenarios of the variables required for a water balance model to estimate the paddy irrigation requirements for a single site. Most of these studies focused on evaluation of crop water requirements based on perturbed climate change scenarios generated with GCM outputs or with available downscaled data sets or using modeling softwares such as SCENGEN. With the development of statistical downscaling models (SDSMs), the regional climate change assessment studies are becoming more accepted. Therefore, this study uses a SDSM, as the downscaling methods are well accepted in the climate change impact assessment studies in the recent years by the research community. Therefore, this study emphasizes on adopting such sophisticated methods to quantify the future projected irrigation demands. This forms the basic difference between the present work and the work done in de Silva et al. (2007). A multivariable downscaling methodology is applied at each location to develop the future scenarios of rainfall, temperature, RH and U2. Further, the difference between the rainfall and the potential evapotranspiration is considered as the irrigation water requirement for a particular crop at a particular location. This study stresses on climate change impact assessment of irrigation demands at a reservoir command area using a SDSM. To obtain the projected climate change scenarios of rainfall as well as other meteorological variables which influence the evapotranspiration (viz., RH, U2, radiation, maximum (Tmax) and minimum (Tmin) temperatures) at the scale of command area, from a GCM, a multivariable downscaling technique, canonical correlation analysis (CCA) is adopted. The anticipated irrigation demands of the crops are examined for the future scenarios by accounting for the changes in rainfall and potential evapotranspiration. STUDY AREA The command area of the Bhadra reservoir is considered for the assessment of impacts of climate change on irrigation demands. Bhadra is a tributary of Krishna River, originating from Gangamula in the Western Ghats of Chikamagalur District in Karnataka state, India. The river flows through nearly 190 km from its origin and joins River Tunga to form the River Tunga-Bhadra. The Bhadra reservoir intercepts the river flow and provides water for irrigation. The reservoir project also generates hydropower to a minor extent. The gross command area under the Bhadra Canal System is 162,818 ha with a culturable command area of 121,500 ha out of which Copyright © 2012 John Wiley & Sons, Ltd.

105,570 ha have been earmarked for irrigation. The irrigated area of 105, 570 ha is considered for impact assessment in this study. The irrigated area predominantly consists of red loamy soil except in some portion of the right canal area, which has black cotton soil. The assessment of irrigation demands is carried out on paddy, sugarcane, permanent garden and semi dry crops, which are the typical crops grown in the Bhadra command area. The meteorological variables (Tmax and Tmin, U2 and RH) from 1969 to 2005 at Shimoga and high-resolution gridded daily precipitation data from1971 to 2005 at a 0.5 0 0.5 0 grid interpolated from station data are obtained from the India Meteorological Department (IMD), Pune. The command area of Bhadra river spreads over the districts of Chitradurga, Shimoga, Chickmagalur and Bellary. Nine IMD locations are selected to evaluate the irrigation demands in the command area. The total irrigated area of each crop in the command area is distributed equally among these selected nine locations. Thus, each downscaling location represents an area consisting of all the crops. The 0.5 0 0.5 0 IMD grid points falling in the districts of Chitradurga, Shimoga, Chickmagalur and Bellary are considered as rainfall downscaling locations as shown in Figure 1. The latitudes and longitudes of each of the nine downscaling locations are given in Table I. STATISTICAL DOWNSCALING The statistical downscaling techniques are generally used to bridge the spatial and temporal resolution gaps between the coarser resolution of the GCMs and the finer resolution required in the impact assessment studies. Generally, these methods involve deriving empirical relationships that transform large-scale simulations provided by a GCM (climate variables as predictors) to regional-scale variables (surface variables as predictands). As a first step in the impact studies, the predictands to be downscaled must be selected. The hydro-meteorological variables that have a major influence on crop water requirements are the rainfall and evapotranspiration (Elgaali et al., 2007; Rodriguez Diaz et al., 2007). Evapotranspiration is mainly influenced by the air temperature, U2, RH, and solar radiation. Many impact assessment studies on reference evapotranspiration have dealt with only temperature variables of Tmax and Tmin (e.g. Harmsen et al., 2009; Lovelli et al., 2010; Maeda et al., 2011; Torres et al., 2011). However, the present study uses temperature variables as well as RH, U2 and radiation. The temperature variables (Tmax and Tmin), RH and U2 are modeled (as predictands) with a statistical downscaling technique using GCM outputs. The data used and downscaling methodology are described in the following section. Data extraction and statistical downscaling

The first step in statistical downscaling is the selection of atmospheric predictor variables to model the selected predictand variables. Following the literature (Table II) Hydrol. Process. (2012) DOI: 10.1002/hyp

CLIMATE CHANGE AND IRRIGATION DEMANDS INTEGRATION

Downscaling Locations Bhadra Reservoir Command Area

Figure 1. Downscaling locations in the Bhadra Command Area

Table I. Locations for downscaling precipitation Location

Latitude

Longitude

1 2 3 4 5 6 7 8 9

13.50N 13.50N 14.00N 14.00N 14.00N 14.00N 14.50N 14.50N 15.00N

75.50E 76.00E 75.00E 75.50E 76.00E 76.50E 76.00E 76.50E 76.00E

and the availability of the predictors from the GCM, 13 large-scale atmospheric predictors (precipitation flux, precipitable water, surface air temperature at 2 m, mean sea level pressure, geopotential height at 500 mb, surface U-wind, surface V-wind, specific humidity at 2 m, surface RH, surface latent heat flux, sensible heat flux, surface short wave radiation flux, surface long wave radiation flux) are selected. Five predictand variables are chosen to Copyright © 2012 John Wiley & Sons, Ltd.

be modeled by the selected predictors. These are rainfall, Tmax and Tmin, RH and U2. An area from 10 0–200 N to 70 0–800 E, encompassing the region where meteorological variables are to be downscaled, is chosen for the large-scale predictors. Data on the predictors at monthly time scale are obtained from the National Centers for Environmental Prediction/ National Center for Atmospheric Research (NCEP/ NCAR) reanalysis data (Kalnay et al., 1996) (available at http://www.cdc.noaa.gov/cdc/data.ncep.reanalysis. html) and are used for training the downscaling model. The medium resolution Model for Interdisciplinary Research on Climate version 3.2 (MIROC 3.2) GCM (medium-resolution of 1.125 1.125 deg, GCM from the Center for Climate System Research, Japan) is used with the A1B scenario (IPCC, 2007), for the impact assessment. The particular GCM is used keeping in view the availability of the projections on the predictors at the monthly scale. The A1B scenario represents a balanced emission scenario with medium emission trajectories, and is used here as a possible future scenario. Large-scale monthly atmospheric variables output from the MIROC 3.2 GCM for the A1B scenario (720 ppm Hydrol. Process. (2012) DOI: 10.1002/hyp


Table II. Predictors selected for the statistical downscaling Predictand Rainfall

Maximum and minimum temperatures

Wind variables

relative humidity, water vapor pressure, dew-point temperature, and dew-point deficit

Predictors Mean sea level pressure, geopotential height at 500 mb (Ghosh and Mujumdar, 2006); specific humidity at 500 hPa, precipitation flux, surface air temperature at 2 m, maximum surface air temperature at 2 m, minimum surface air temperature at 2 m, surface U-wind and surface V-wind (Raje and Mujumdar, 2009). Air temperature, zonal and meridional wind velocities at 925 mb, surface flux variables such as latent heat, sensible heat, shortwave radiation and long wave radiation fluxes (Anandhi et al., 2009). Geopotential height, air temperature, U-wind and V-wind speed, relative humidity, vertical velocity, absolute vorticity as multilevel quantities evaluated at 1000 hpa height (Davy et al., 2010) Geopotential height at 500, 850 and 1000 hpa, wind speed and vorticity at 500, 850 hpa, temperature at 850 hpa, humidity variables (relative humidity, specific humidity, water vapor pressure, dew-point temperature, dew-point deficit at 850 hpa) (Huth, 2005)

The references cited in the table indicate the earlier studies in which the predictors are used for the specified predictands

CO2 stabilization experiment) is extracted from the multimodel data set of the World Climate Research Programme’s Coupled Model Inter Comparison Project (available at https://esg.llnl.gov:8443/about/ftp.do). The dimension of the predictor variables set is 25/30/42 (number of NCEP grid points for surface flux, surface/ pressure and radiation flux variables, respectively) 13 (number of predictors), which is very large, and working out the model with this large number would be computationally cumbersome. Principal component analysis (PCA) is applied on the large data set to reduce the dimensionality and to effectively summarize the spatial information from the 25/30/42 grid points. It was found that 95% of the variability of original set is explained by the first 12 PCs. The eigen vectors or coefficients obtained from NCEP data were applied to the standardized MIROC3.2 data to get the projections in the principal directions. Standard procedure of statistical downscaling (e.g. Raje and Mujumdar, 2009) involving standardization, interpolation, PCA and developing a statistical relationship between predicands and predictors is followed in this study. Interpolation is performed before standardization to obtain the GCM output at NCEP grid points as the location of NCEP/NCAR grid points and MIROC grid points do not match. A Mercator projection (conformal cylindrical map projection), suitable for tropical regions (Mulcahy and Clarke, 1995), is first performed, and then a linear interpolation is Copyright © 2012 John Wiley & Sons, Ltd.

performed between the projected points. Standardization (Wilby et al., 2004) is performed prior to PCA and downscaling to remove systematic bias in mean and standard deviation of the GCM simulated climate variables. Canonical correlation analysis

In the procedure for statistical downscaling followed in this study, a mathematical transfer function is to be adopted to derive predictor–predictand relationship which can account for the multivariate predictands. The most commonly used statistical technique with multivariate data sets is CCA. CCA can be used as a downscaling technique for relating surface-based observations and free-atmosphere variables when simultaneous projection of predictands is of interest (e.g. Barnett and Preisendorfer, 1987; Graham et al., 1987; Karl et al., 1990; Barnston, 1994; Mpelasoka et al., 2001; Juneng and Tangang, 2008). CCA has found wide application in modeling precipitation and meteorological variables (e.g. Von Storch et al., 1993; Gyalistras et al., 1994; Busuioc and von Storch, 1996). An advantage of the CCA in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, as they in fact occur in nature, by retaining the explained variance between the two sets. CCA finds pairs of linear combinations between the N-dimensional climate variables, X, (predictors, in this case) and M-dimensional surface variables, Y, (predictands, in this case) which can be expressed as follows: Um ¼ aT X; m ¼ 1; ::::::: minðN; M Þ

(1)

Vm ¼ bT Y; m ¼ 1; ::::::: minðN; M Þ

(2)

where Um and Vm are called predictor and predictand canonical variables respectively, a = [a1, a2,. . ...aN] and b = [b1,b2, . . ...bM] are called the canonical loadings. The objective of canonical correlation is to identify m sets of canonical variables such that the correlation, r, between the predictor canonical variable, Um, and the predictand canonical variable, Vm, is maximum. This way N-dimensional predictor set and M-dimensional predictand set is reduced to m-dimensional canonical variables which will be further useful in developing the regression equations for each predictand. After the estimation of canonical variables, regression relation is established for each of the predictand as discussed in the following section. Linear regression using CCA

The methodology involves training the surface observed predictands and NCEP atmospheric predictor data with the CCA analysis after data preprocessing with standardization and PCA. The PCs obtained based on NCEP data are used as reference to develop the GCM PCs. A separate regression Hydrol. Process. (2012) DOI: 10.1002/hyp


equation is derived for each meteorological predictand variable from the canonical variable coefficients and correlations computed from the observed data. First few PCs are extracted based on the percentage variance explained by them. The selected PCs from the NCEP data are considered as predictor set to perform CCA to fit the regression relation between the climate variables and surface-based observations. The observed predictor canonical variable, Uobs, q, is computed from Equation (1) with the NCEP PCs as follows: Uobs;q ¼ aT XNCEP;PCs

(3)

In Equation (3), q represents the minimum among the number of PCs considered and the number of predictands considered. As the number of PCs considered is 12 in this case, to account for 95% variability, and the number of predictands considered is five, CCA will yield five predictor and predictand canonical variables and five canonical correlations between them. The predictand canonical variable, Vpredicted, q, can be evaluated from the predictor canonical variable, Uobs, q, obtained from Equation (3) as follows: Vpredicted;q ¼ rCq Uobs;q

(4)

In Equation (4), rCq is the canonical correlation coefficient and represents the percent of variance in the predictand canonical variable explained by the predictor canonical variable. It is a diagonal matrix of size q x q. The regression equations (Equation (4)) are applied to the interpolated NCEP gridded GCM output to model future projections of hydro-climate predictands. The downscaled scenario for each of the predictand can be derived according to: Ypredicted;q

¼ b1 Vpredicted;q

(5)

where Ypredicted, q is the q number of predictand variables to be evaluated from the predictand canonical variables Vpredicted, q and the predictand canonical loadings b. Prediction of future scenario is made using the PCs of monthly outputs of the atmospheric variables (predictors) from the GCM in place of NCEP PCs in Equation (3). The canonical correlations and the loadings are computed using statistical toolbox of MATLAB (2004). This downscaling methodology is applied to downscale the rainfall and other meteorological variables at nine downscaling locations. Shimoga station meteorological parameters are used for other downscaling locations due to the availability of observed data only at Shimoga station. A monthly time period is considered for all variables. The SDSM is trained using the past records of atmospheric and surface meteorological data of 25 years (1971 to 1995) to estimate the canonical scores, and the model is tested with the remaining data, for the period 1996 to 2004. Once the model performance is found satisfactory in the testing period, it can be applied for obtaining the future predictions. Table III gives the details of the statistics such as mean, standard deviation of observed and CCA downscaled results for the Copyright © 2012 John Wiley & Sons, Ltd.

testing period of 1996 to 2004. The R-value in Table III indicates the correlation coefficient between the observed and CCA modeled results for various variables. The results of CCA downscaling model are used as model input variables to simulate the impact of climate change on irrigation demands for each crop at each downscaling location. ESTIMATION OF IRRIGATION DEMANDS The total irrigation demand in the command area is computed based on the potential evapotranspiration of a crop and the rainfall contribution. The total demand in period t, for a particular crop, c, at a downscaling station, s, is given by: (6) Dt;c;s ¼ ETtc Rt;s Ac;s if Rt;s < ETtc Dt;c;s ¼ 0 if Rt;s > ETtc

(7)

where ETtc is the potential evapotranspiration of a crop, c in period t; Rt, sis the rainfall contribution in period t, at a downscaling station, s; Ac, sis the area over which the crop c is grown at station s. In the demand equations given above (Equations (6) and (7)), the soil moisture contribution to meeting crop water demand is neglected. Further, the rainfall amount considered in the evaluation of irrigation demands is the total rainfall measured from rain-gauges at each downscaling location instead of effective rainfall. The computation of effective rainfall involves measured rainfall, surface runoff losses, percolation losses beyond root zone and soil moisture details. Evapotranspiration model

The reference evapotranspiration is estimated by Penman–Monteith (Allen et al., 1998) equation, given as follows: ETt;R ¼

0:408ΔðRn GÞ þ gð900=ðT þ 273ÞÞU2 ðes ea Þ Δ þ gð1 þ 0:34U2 Þ (8)

where ETt, R is the reference evapotranspiration of each month (mm/month), Δ is the slope of the vapor pressure curve, Rn is net radiation at the surface (w/m2), g is psychrometric constant, T is the average air temperature at 2-m height, U2 is wind speed at 2-m height, es is the saturated vapor pressure and ea is the actual vapor pressure (kpa). The future projections of meteorological variables downscaled from the GCM outputs, including RH, U2, Rn,Tmax and Tmin, are used as input to the evapotranspiration model (Penman–Monteith equation (Equation (8)) to evaluate the anticipated changes in the reference evapotranspiration. Among these meteorological variables, solar radiation could not be directly downscaled in this study due to the nonexistence of observed solar radiation data for the study region. Most of the methods to estimate solar radiation (e.g. Angstrom, 1924; Hargreaves, 1994) Hydrol. Process. (2012) DOI: 10.1002/hyp


Table III. Comparison of observed versus computed statistics (Testing period, 1996 to 2004) Rainfall (mm) Downscaling Locations Statistic

1

Observed Mean Computed Mean Observed Standard Deviation Computed Standard Deviation R-Value

2

3

4

5

6

7

8

9

Maximum Minimum Relative Wind Temperature Temperature Humidity Speed ( C) ( C) % kmph

174.93 59.10 130.75 73.33 75.18 55.3 44.97 40.16 42.22 171.96 55.09 79.28 69.41 75.41 53.05 38.38 38.99 31.68 230.63 68.50 306.22 87.34 86.92 64.60 51.25 55.72 52.95

31.25 31.48 2.77

19.44 19.57 2.32

70.78 69.95 10.03

3.73 3.74 1.26

181.92 47.18 189.88 65.71 62.06 43.76 36.73 38.51 36.99

2.40

1.82

7.72

1.17

0.93

0.89

0.88

0.96

0.87

0.74

0.58

0.84

0.82

0.73

0.78

0.72

0.77

The relative humidity, wind speed, maximum and minimum temperatures in the table are at station Shimoga.

include the information of cloud cover, Tmax and Tmin, sunshine hours, RH and site-specific coefficients. However, Hargreaves and Samani (1982) recommended a simple equation to estimate the solar radiation based on Tmax and Tmin. As the observations of Tmax and Tmin are available for the study region, these variables can be downscaled, and the future projections of solar radiation can be computed based on the downscaled variables of Tmax and Tmin. The Rn in the Equation (8) is estimated using Hargreaves’s radiation formula (Hargreaves and Samani, 1982): Rn ¼ krs ðT max T min Þ1=2 Ra

(9)

where krs is an adjustment factor equal to 0.16 for interior locations and 0.19 for coastal locations; Tmax and Tmin are the mean monthly maximum and minimum air temperatures respectively in 0C; Ra is extraterrestrial radiation (w/m2) and is computed from expressions given in Allen et al. (1998). The reference evapotranspiration (ETt, R) obtained (Equation (8)) needs to be adjusted to obtain the potential c ) with crop coefficients for crop evapotranspiration (ETt;p c each period, t for a crop c (kt, c) Thus, ETt;p is given by: c ETt;p ¼ ETt;R X kt;c

(10)

The potential evapotranspiration for each crop (Equation (10)) and the rainfall in each period, t downscaled from CCA downscaling, are used to compute future projections of irrigation demands for each crop in each period, t. The irrigated area for different crops under left and right bank canal commands (Table IV) and duration of the crops with their sowing dates (Table V) are used in the computation of irrigation demands. The crop factors used for paddy, sugarcane, permanent garden and semidry crops corresponds to Rice, Sugarcane, Group E crops (Citrus) and Maize, respectively, from Michael (1978) as given in Table IV. Crop distribution in the command area Paddy Sugarcane Canal (ha) (ha) LBC 3484 RBC 34 720 Total 38 204

1713 24 800 26 513

Permanent garden (ha)

Semidry Total area Crops (ha) (ha)

303 18 849 19 152

RBC: Right Bank Canal; LBC: Left Bank Canal Copyright © 2012 John Wiley & Sons, Ltd.

867 20 834 21 701

6367 99 203 105 570

Table V. Crop duration and sowing dates Crop Paddy Sugarcane Permanent Garden Semidry Crops

Duration (days)

Sowing date

120 365 365 123

June 15 July 01 June 01 July 01

Table VI. The total irrigation requirement (including left bank and right bank canal) at the field level for each crop in each month is estimated as per the cropping pattern in Table V. RESULTS AND DISCUSSION Impact of climate change on rainfall and reference evapotranspiration

Simulated rainfall refers to the rainfall obtained from the NCEP data and the predicted rainfall results from use of CCA downscaling model with MIROC 3.2 GCM for the A1B scenario. The CCA model is able to well simulate the observed data (Figure 2(a) for Locations 1 to 9) for the training period of 1971 to 1995 with both NCEP and GCM. The GCM predicted rainfall as shown in Figure 2 (a) for Locations 1 to 9 for the training period of 1971–1995 are modeled with the monthly predictors in the MIROC 3.2 GCM for the current climate with 20c3m experiment. All future projections are for the A1B scenario for 25 years time slices of 2020–2044, 2045–2069 and 2070–2095 (Figure 2 (b) for Locations 1 to 9). The green box plots are for the period of 2020 to 2044, the blue box plots are for the period of 2045 to 2069 and the red box plots are for the period of 2070 to 2095. The projected monthly rainfall shows an increasing trend in all months at all nine downscaling locations. The expected rainfall increase is determined by the change in the large-scale atmospheric variables (air temperature, mean sea level pressure, geopotential height, humidity and wind variables) considered as predictors (Table II) in the study region. Such an increase in rainfall is also observed in the study of Meenu et al. (2011) for the same case study of Bhadra command area with SDSM and also with support vector machine. Hydrol. Process. (2012) DOI: 10.1002/hyp


Table VI. Monthly crop coefficients (Source: Michael, 1978) Months Crop

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Paddy (Rice) Sugarcane Permanent Garden (Citrus) Semidry crops (Maize)

0.75 0.50

0.80 0.55

0.85 0.55

0.85 0.85 0.60 0.85

1.00 0.90 0.60 1.00

1.15 0.95 0.65 1.15

1.30 1.00 0.70 1.30

1.25 1.00 0.70 1.25

1.10 0.95 0.65 1.10

0.90 0.90 0.60 0.90

0.85 0.60

0.75 0.55

Location 1 Observed

NCEP Simulated

Predicted from Miroc 3.2 GCM (20c3m)

1000

Monthly Rainfall (mm)

500

0 1971

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(a) 600 400 200 0 Jan

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NCEP Simulated



200

0 1971

1975

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1983

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1991

1995

(a) 200 150 100 50 0 Jan

Feb

Mar

Apr

May

Jun

Jul

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Sep

Oct

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Dec

(b) Figure 2. Downscaling results of rainfall from the CCA model from Locations 1 to 9. In above figures, (a) shows the observed, simulated from NCEP data and predicted from MIROC 3.2 GCM with 20c3m experiment for the training period of 1971 to 1995, (b) represents the future projections from MIROC 3.2 GCM with A1B scenario for each month with green box plots for period 2020–2044, blue box plots are for period 2045–2069 and the red box plots are for period 2070–2095

Figure 3 shows similar results of other meteorological variables, RH, U2, Tmax and Tmin. All the meteorological variables are well simulated by CCA downscaling (Figure 3 (a)) for the training period of 1971 to 1995. The projections of Tmax and Tmin and RH also show an Copyright © 2012 John Wiley & Sons, Ltd.

increasing trend for all the months. The U2 projections do not show any particular trend. The reference evapotranspiration estimated from the projections of Tmax and Tmin, RH and U2 using the evapotranspiration model (Equation (8)) is shown in Hydrol. Process. (2012) DOI: 10.1002/hyp


Location 3 Observed

2000

NCEP Simulated



1000

0 1971

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(a) 1000

500

0 Jan

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(b) Location 4 400

Observed

NCEP Simulated



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(a)

200 100 0 Jan

Feb

Mar

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(b)

Figure 4. The observed evapotranspiration for each month shown in the Figure 4 is computed from the evapotranspiration model (Equation (8)) with observed meteorological data for the period 1971 to 1995. The future projections of reference evapotranspiration predicted to increase for all months. Particularly, the change of evapotranspiration is more in the months of April and May due to the large projected changes of Tmax and Tmin variables. Impact of climate change on irrigation water demands

The irrigation water requirements are computed for paddy, sugarcane, permanent garden and semidry crops at Locations 1 to 9. The monthly reference evapotranspiration is corrected with crop coefficients for each crop to compute the potential evapotranspiration which in turn can be used to compute the irrigation water demand of the crop. The monthly irrigation water demands are estimated from the projections of rainfall at each of the location downscaled from CCA model and potential evapotranspiration projections from Equation (10). The monthly projected variation of irrigation water requirements for Locations 1 to 9 are shown in Figures 5–7 and 8, Copyright © 2012 John Wiley & Sons, Ltd.

respectively, for paddy, sugarcane, permanent garden and semidry crops. The annual irrigarion demands for the crops at the nine locations are shown in Figure 9. The predicted change of irrigation water demands at each location is a function of rainfall at that location and the reference evapotranspiration. Irrigation water requirement - paddy

The crop growing period of paddy spans from April to October. The irrigation demands of paddy are computed for these months as shown in Figure 5. However, at Locations 1 and 3, paddy demands are only in the months of April and May, while for the other months, the rainfall is sufficient to fulfill the water requirements of paddy. The months showing the demands as zero indicates the water needed for optimal growth of the crop is provided by rainfall and irrigation is not required in those particular months. For remaining locations, the demands are present for all the months starting from April to September except in the month of October (Figure 5). At Locations 7, 8 and 9 in September month, where the current demands are zero, significant increase in the projected irrigation water requirements are observed due to the increase in the Hydrol. Process. (2012) DOI: 10.1002/hyp


Location 5 400

Observed

NCEP Simulated



200

0 1971

1975

1979

1983

1987

1991

1995

(a) 200 150 100 50 0 Jan

Feb

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Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

(b) Location 6 Observed

NCEP Simulated



200

0 1971

1975

1979

1983

1987

1991

1995

(a) 150 100 50 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

(b)

evapotranspiration demand of crops. For example, the monthly mean rainfall of May is increasing at Location 7 from 28.75 mm to 32.64 mm for the period of 2020–2044, to 38.37 mm for the period of 2045–2069 and to 42.61 mm for the period of 2070–2095. At the same time, the increase in Tmax and Tmin are also increasing. For example, monthly Tmax temperature for May is increasing from observed 33.64 C to 36.26 C for 2020–2044, to 37.51 C for 2045–2069, to 38.31 C for 2070–2095. Similarly, monthly minimum temperature of May is also increasing with observed 21.49 C to 21.46 C for 2020–2044, to 22.33 C for 2045–2069 and 23.01 C for 2070–2095. A significant increase in RH from observed 67.02% to 70.97% for 2020–2044, to 71.73% for 2045–2069, to 72.40% for 2070–2095 is also seen from the results. The minor changes in U2 are from observed 4.089 m/s to 4.25 m/s for 2020–2044, to 4.26 m/s for 2045–2069 and to 4.38 m/s for 2070–2095. Such increase in RH, U2, temperature variables results in net increase in evapotranspiration, for example, at Location 7 in the month of May. That is, the increase in evapotranspiration offsets the increasing effect of rainfall at Location 7 indicating increased irrigation demand in future for paddy (Figure 5). However, at some Copyright © 2012 John Wiley & Sons, Ltd.

locations, paddy demands are predicted to decrease at monthly scale, e.g. at Location 2 in August month (Figure 5) due to the relative increase in rainfall compared to the evapotranspiration at that location. Overall irrigation requirements of paddy are predicted to increase at all nine locations at monthly scale (Figure 5) and at annual scale (Figure 9). The maximum annual paddy demand is predicted to occur at Location 8 (Figure 1) with current demand as 14.00 Mm3 with increasing demands as 26.97 Mm3 for the period of 2020–2044, with 27.35 Mm3 for the period of 2045–2069, with 27.8 Mm3 for the period of 2070–2095. Irrigation water requirement - sugarcane

Sugarcane crop is growing in all 365 days of a year, and the crop water demand exists in all 12 months. Sugarcane demands are more in the months of April and May for all nine locations (Figure 6) due to lower rainfall and higher temperatures in these months. For the month of January, the demand is predicted to decrease at Locations 1, 2, 4, 5 and 6 compared to the current demands depending on the projections of rainfall and Hydrol. Process. (2012) DOI: 10.1002/hyp


Location 7 Observed

200

NCEP Simulated



100

0 1971

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NCEP Simulated



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100 50 0 Jan

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Location 9 Observed

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(b)

evapotranspiration. Even though small reductions of demands in the monthly scale are observed, the annual irrigation water demands are predicted to increase for sugarcane over the Bhadra command area (Figure 9). The Copyright © 2012 John Wiley & Sons, Ltd.

maximum annual irrigation demands occur at Location 8 (Figure 1) with current demand being 15.29 Mm3 and projected demands of 23.12 Mm3 for 2020–2044, 23.16 Mm3 for 2045–2069, 23.5 Mm3 for 2070–2095. Hydrol. Process. (2012) DOI: 10.1002/hyp

CLIMATE CHANGE AND IRRIGATION DEMANDS INTEGRATION 80

Relative Humidity (%)

80

78 76 74

75

72 70 68

70

66 64 65 Observed NCEP

GCM

62 Jan

Feb

Mar

Apr

May

Jun

(a)

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Wind Speed (kmph)

(i) 4.2

6

4

5

3.8

4

3.6 3 3.4 Observed

NCEP

GCM

2 Jan

Feb

Mar

Apr

May

Jun

(a)

Jul

Aug

Sep

Oct

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Maximum Temperature (Deg C)

40 39

32.5

38 32

37 36

31.5

35 31

34 33

30.5

32 31

30

30 29.5 Observed NCEP

GCM

29 Jan

Feb

Mar

Apr

May

Jun

(a)

Jul

Aug

Sep

Oct

Oct

Nov

Nov

Dec

(b) (iii)

Minimum Temperature (Deg C)

24 20.5

23

20

22

19.5

21

19

20

18.5

19 18

18

Observed NCEP

GCM

17 Jan

Feb

Mar

Apr

(a)

May

Jun

Jul

Aug

Sep

Dec

(b) (iv)

Figure 3. Downscaling results of (i) relative humidity, (ii) wind speed, (iii) maximum temperature and (iv) minimum temperature from the CCA model. In above figures, (a) denote annual scale observed, simulated from NCEP and simulated from MIROC 3.2 GCM with 20c3m experiment for the training period of 1971 to 1995. (b) denotes monthly scale projections with the green box plots are for 2020–2044, blue box plots are for 2045–2065 and red box plots are for 2070–2095

Copyright © 2012 John Wiley & Sons, Ltd.

Hydrol. Process. (2012) DOI: 10.1002/hyp


Figure 4. Monthly reference evapotranspiration for Bhadra Command area estimated from MIROC 3.2 GCM output with A1B scenario

Irrigation water requirement - permanent garden

The crop water requirement of permanent garden spans for the entire year, and the irrigation demands are estimated for the all 12 months. The annual demands of permanent garden are predicted to increase (Figure 9) even though the decreases in demands are small for the monthly scale (Figure 7). The maximum annual demand occur at Location 3 with 2.89 Mm3 of current demand increasing to 6.95 Mm3 for period of 2020–2044, 8.79 Mm3 for a period of 2045–2069, 10.26 Mm3 for a period of 2070–2095. Irrigation water requirement - semidry crops

The growing period for semidry crops spans from April to October and the demands for the corresponding months

Location 1

Location 3

Location 2

8

8

8

6

6

6

4

4

4

2

2

2

0

Paddy Irrigation Water Requirement (Mm3)

are quantified as shown in Figure 8. Water requirements for the semidry crops are predicted to increase at monthly scale (Figure 8) as well as at annual scale (Figure 9). At most of the locations, the estimated current irrigation demands are zero, but the projected demands are increasing. The maximum increase in annual demand occurs at Location 7 with current demand being 2.64 Mm3 and increasing to 15.26 Mm3 for the period of 2020–2044, 17.12 Mm3 for the period of 2045–2069, 19.68 Mm3 for the period of 2070–2095. Annual irrigation demands are less for semidry crops compared to the other crops as the command area is small and also the crop growing period is small, being restricted to the months of April to October only. Due to their cropping pattern and the command area, water requirements of Paddy and Sugarcane are higher compared to those of permanent garden and semi dry crops. For all crops at all nine locations, the projected irrigation demands are higher compared to the current demands. Even though the projected demands are higher compared to observed ones, the relative difference in the future demands for the periods of 2020–2044, 2045–2069 and 2070–2095 are small, due to the projected increase in the rainfall in the Bhadra command area. The annual irrigation demand assessment carried out in this study will give an overall idea about the changes in demands for each particular crop at each downscaling location. Moreover, the monthly analysis of demands for each crop at a particular location will be useful for the decision makers for better management of irrigation systems.

A M J J A S O

0

Location 4

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0

Location 5

Location 6

8

8

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0

A M J J A S O 2020-2044

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8

0

A M J J A S O

2045-2069

0

A M J J A S O 2070-2095

Figure 5. Monthly (April to October) irrigation water requirement for paddy at Locations 1–9 for Bhadra Command Area Copyright © 2012 John Wiley & Sons, Ltd.



Location 1

Location 3

6

6

4

4

4

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2

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0

Sugarcane Irrigation Water Requirement (Mm3)

Location 2

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J FMAMJ J A S ON D

0

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0

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Present

J FMAMJ J A S ON D 2020-2044

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Location 9

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2045-2069


Figure 6. Monthly irrigation water requirement for sugarcane at Locations 1–9 for Bhadra Command Area

Location 2

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3

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1

1

1

0

Permanent Garden Irrigation Water Requirement (Mm3)

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0


J FMAMJ J A S ON D

Location 9

3

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2045-2069

0


Figure 7. Monthly irrigation water requirement for permanent garden at Locations 1–9 for Bhadra Command Area

CONCLUSIONS A methodology is developed in the present study for predicting the future irrigation water demands in the command area of a river. The expected changes of rainfall, RH, U2, Tmax and Tmin are modeled by using a SDSM, Copyright © 2012 John Wiley & Sons, Ltd.

CCA, with MIROC 3.2 GCM output for the A1B scenario. The potential evapotranspiration projections are modeled with an evapotranspiration model (Penman–Monteith equation) accounting for the projected changes in temperature, RH, solar radiation and U2. The need to calculate the evapotranspiration using the temperature Hydrol. Process. (2012) DOI: 10.1002/hyp


Location 1

Location 3

2.5

2.5

1.5

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1.5

0

Semidry Irrigation Water Requirement (Mm3)

Location 2

2.5

0

A M J J A S O

0

A M J J A S O

Location 4

Location 5

Location 6

2.5

2.5

2.5

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1.5

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0

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A M J J A S O

Location 7

A M J J A S O

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Location 9

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2.5

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0

A M J J A S O

0

A M J J A S O Present

0

A M J J A S O 2020-2044

A M J J A S O

2045-2069

2070-2095

Figure 8. Monthly semidry irrigation water requirements for Locations 1–9 for Bhadra Command Area

Sugarcane 30

Paddy Irrigation Water Requirement (Mm3)

Irrigation Water Requirement (Mm3)

30

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Location 2020-2044

2045-2069

2070-2095

Figure 9. Projected annual irrigation water requirements at each location for each crop for Bhadra Command Area

variables, humidity, solar radiation and U2 rather than only temperature variables has therefore been stressed. The irrigation water requirements are quantified by accounting for projected rainfall and potential evapotranspiration. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops are quantified at nine Copyright © 2012 John Wiley & Sons, Ltd.

downscaling locations covering the entire command area of Bhadra river basin. The annual irrigation water requirements for paddy, sugarcane, permanent garden and semidry crops are predicted to increase in the Bhadra command area. The projected changes in irrigation demands will be helpful in developing adaptive policies for reservoir operations. Hydrol. Process. (2012) DOI: 10.1002/hyp


In this study, the soil moisture contribution to meeting crop water demand is neglected. However, for an accurate representation of the crop water demands, the soil moisture dynamics of individual crops must be considered in the impact assessment studies. Further, the rainfall amount considered in the estimation of irrigation demands is the actual rainfall instead of effective rainfall. The effective rainfall is the fraction of actual amount of rainwater useful for meeting the water need of the crops. The effective rainfall calculation includes the soil water retention and percolation, the key aspects which should be included in further studies in order to develop more useful projected demands accounting for climate change. Further, the future projected irrigation demands are due to a single GCM using a single scenario. It is widely acknowledged that the mismatch between different GCMs over regional climate change projections represents a significant source of uncertainty (e.g. New and Hulme, 2000; Simonovic and Li, 2003; Simonovic and Davies, 2006; Wilby and Harris, 2006; Ghosh and Mujumdar, 2007). Further studies are necessary to evaluate the future irrigation demands for different GCMs with scenarios to model the underlying GCM and scenario uncertainty. The results will serve as guidelines for the decision makers to accommodate sufficient water in those months where rainfall only will not be sufficient to fulfill the crop water requirements. Further, the results will be useful in examining different cropping patterns in the command area keeping in view the increased crop water demands and possible decrease in streamflow.

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