Regional impacts of climate change on irrigation water...

HYDROLOGICAL PROCESSESHydrol Process (2012)Published online in Wiley Online Library(wileyonlinelibrarycom) DOI 101002hyp9379

Regional impacts of climate change on irrigationwater demands

S Rehana1 and P P Mujumdar121 Department of Civil Engineering Indian Institute of Science Bangalore Karnataka 560 012 India

2 Divecha Center for Climate Change Indian Institute of Science Bangalore Karnataka 560 012 India

CIndE-m

Co

Abstract

This paper presents an approach to model the expected impacts of climate change on irrigation water demand in a reservoircommand 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 quantifythe likely changes in irrigation water demands at a location in the command area as a response to the projected changes inprecipitation and evapotranspiration at that location Statistical downscaling with a canonical correlation analysis is carried out todevelop 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 Themedium resolution Model for Interdisciplinary Research on Climate GCM is used with the A1B scenario to assess the likelychanges in irrigation demands for paddy sugarcane permanent garden and semidry crops over the command area of Bhadrareservoir IndiaResults from the downscaling model suggest that the monthly rainfall is likely to increase in the reservoir command area RHTmax and Tmin are also projected to increase with small changes in U2 Consequently the reference evapotranspirationmodeled by the PenmanndashMonteith equation is predicted to increase The irrigation requirements are assessed on monthly scale atnine selected locations encompassing the Bhadra reservoir command area The irrigation requirements are projected to increasein most cases suggesting that the effect of projected increase in rainfall on the irrigation demands is offset by the effect due toprojected increasechange in other meteorological variables (viz Tmax and Tmin solar radiation RH and U2) The irrigationdemand assessment study carried out at a river basin will be useful for future irrigation management systems Copyright copy 2012John Wiley amp 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 warmingdirectly affect both precipitation and evapotranspirationconsequently the irrigation water demands Moreover theirrigation water requirements of the crops change as afunction of climate change Several authors have focusedon assessing the impacts of climate change on agricultureover the past decade Most of these studies concentrated onestimating the changes in crop productivity (eg Easterlinget al 1993 Rosenzweig and Parry 1994 Singh et al1998 Brown and Rosenberg 1999 Parry et al 2004Harmsen et al 2009 Liu et al 2010) Assessment studiesfocusing on the impacts of climate change on irrigationdemands using general circulation model (GCM) outputsare becoming more accepted in recent years GCMs areexcellent tools to study the climate change impact and havebeen used in recent studies globally Yano et al (2007)studied the effects of climate change on crop growth andirrigation water demand for a wheatndashmaize cropping

orrespondence to P P Mujumdar Divecha Center for Climate Changeian Institute of Science Bangalore Karnataka 560 012 Indiaail pradeepciviliiscernetin

pyright copy 2012 John Wiley amp Sons Ltd

sequence in a Mediterranean environment of TurkeyThe climate change scenarios of temperature and precipi-tation were created by superimposing projected anomaliesof GCMs on observed climate data of the baseline periodElgaali et al (2007) modeled the regional impact ofclimate change on irrigation water demand by consideringrainfall and evapotranspiration in the Arkansas River Basinin southeastern Colorado They assumed no change in cropphenology and found an overall increase in irrigation waterdemands due to climate change The historical climate datasets of historical and projections for the continental UnitedStates are considered from Vegetation EcosystemModeling and Analysis Project developed by Kittel et al(1995) Rodriguez Diaz et al (2007) showed increase ofirrigation demand between 15 and 20 in seasonalirrigation need by 2050 in the Guadalquivir river basin inSpain with perturbed climate scenarios of temperatureprecipitation solar radiation wind speed (U2) and relativehumidity (RH) Shahid (2011) estimated the changes ofirrigation water demand in dry-season Boro rice field innorthwest Bangladesh in the context of global climatechange with projected changes of rainfall and tempera-tures estimated using the modeling software SCENarioGENerator (SCENGEN)

S REHANA AND P P MUJUMDAR

de Silva et al (2007) studied the impacts of climatechange on irrigation water requirements in the paddy fieldof Sri Lanka and predicted an increase of 13 to 23 ofirrigation water demand depending on climate changescenarios The climate change scenarios of temperatureradiation U2 and RH are developed by applying thepercentage changes of GCM to the baseline dataset Theproportional () changes given by a selected GCM andscenario are applied on an existing baseline climatologicaldataset to develop the future scenarios of the variablesrequired for a water balance model to estimate the paddyirrigation requirements for a single siteMost of these studies focused on evaluation of crop

water requirements based on perturbed climate changescenarios generated with GCM outputs or with availabledownscaled data sets or using modeling softwares such asSCENGEN With the development of statistical down-scaling models (SDSMs) the regional climate changeassessment studies are becoming more accepted There-fore this study uses a SDSM as the downscalingmethods are well accepted in the climate change impactassessment studies in the recent years by the researchcommunity Therefore this study emphasizes on adopt-ing such sophisticated methods to quantify the futureprojected irrigation demands This forms the basicdifference between the present work and the work donein de Silva et al (2007) A multivariable downscalingmethodology is applied at each location to develop thefuture scenarios of rainfall temperature RH and U2Further the difference between the rainfall and thepotential evapotranspiration is considered as the irriga-tion water requirement for a particular crop at a particularlocation This study stresses on climate change impactassessment of irrigation demands at a reservoir commandarea using a SDSM To obtain the projected climatechange scenarios of rainfall as well as other meteoro-logical variables which influence the evapotranspiration(viz RH U2 radiation maximum (Tmax) and minimum(Tmin) temperatures) at the scale of command area froma GCM a multivariable downscaling technique canon-ical correlation analysis (CCA) is adopted The antici-pated irrigation demands of the crops are examined forthe future scenarios by accounting for the changes inrainfall and potential evapotranspiration

STUDY AREA

The command area of the Bhadra reservoir is consideredfor the assessment of impacts of climate change onirrigation demands Bhadra is a tributary of KrishnaRiver originating from Gangamula in the Western Ghatsof Chikamagalur District in Karnataka state India Theriver flows through nearly 190 km from its origin andjoins River Tunga to form the River Tunga-Bhadra TheBhadra reservoir intercepts the river flow and provideswater for irrigation The reservoir project also generateshydropower to a minor extent The gross command areaunder the Bhadra Canal System is 162818 ha with aculturable command area of 121500 ha out of which

Copyright copy 2012 John Wiley amp Sons Ltd

105570 ha have been earmarked for irrigation Theirrigated area of 105 570 ha is considered for impactassessment in this study The irrigated area predomin-antly consists of red loamy soil except in some portionof the right canal area which has black cotton soil Theassessment of irrigation demands is carried out onpaddy sugarcane permanent garden and semi dry cropswhich are the typical crops grown in the Bhadracommand areaThemeteorological variables (Tmax andTminU2 andRH)

from 1969 to 2005 at Shimoga and high-resolutiongridded daily precipitation data from1971 to 2005 at a05 0 05 0 grid interpolated from station data areobtained from the India Meteorological Department(IMD) Pune The command area of Bhadra river spreadsover the districts of Chitradurga Shimoga Chickmagalurand Bellary Nine IMD locations are selected to evaluatethe irrigation demands in the command area The totalirrigated area of each crop in the command area isdistributed equally among these selected nine locationsThus each downscaling location represents an areaconsisting of all the crops The 05 0 05 0 IMD gridpoints falling in the districts of Chitradurga ShimogaChickmagalur and Bellary are considered as rainfalldownscaling locations as shown in Figure 1 The latitudesand longitudes of each of the nine downscaling locationsare given in Table I

STATISTICAL DOWNSCALING

The statistical downscaling techniques are generally used tobridge the spatial and temporal resolution gaps between thecoarser resolution of the GCMs and the finer resolutionrequired in the impact assessment studies Generally thesemethods involve deriving empirical relationships thattransform large-scale simulations provided by a GCM(climate variables as predictors) to regional-scale variables(surface variables as predictands) As afirst step in the impactstudies the predictands to be downscaled must be selectedThe hydro-meteorological variables that have a majorinfluence on crop water requirements are the rainfall andevapotranspiration (Elgaali et al 2007 Rodriguez Diazet al 2007) Evapotranspiration is mainly influenced by theair temperature U2 RH and solar radiation Many impactassessment studies on reference evapotranspiration havedealt with only temperature variables of Tmax andTmin (egHarmsen et al 2009 Lovelli et al 2010Maeda et al 2011Torres et al 2011) However the present study usestemperature variables as well as RH U2 and radiation Thetemperature variables (Tmax and Tmin) RH and U2 aremodeled (as predictands) with a statistical downscalingtechnique using GCM outputs The data used and down-scaling methodology are described in the following section

Data extraction and statistical downscaling

The first step in statistical downscaling is the selectionof atmospheric predictor variables to model the selectedpredictand variables Following the literature (Table II)

Hydrol Process (2012)DOI 101002hyp

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 1350N 7550E2 1350N 7600E3 1400N 7500E4 1400N 7550E5 1400N 7600E6 1400N 7650E7 1450N 7600E8 1450N 7650E9 1500N 7600E

CLIMATE CHANGE AND IRRIGATION DEMANDS INTEGRATION

and the availability of the predictors from the GCM 13large-scale atmospheric predictors (precipitation fluxprecipitable water surface air temperature at 2m meansea level pressure geopotential height at 500 mb surfaceU-wind surface V-wind specific humidity at 2m surfaceRH surface latent heat flux sensible heat flux surfaceshort wave radiation flux surface long wave radiationflux) are selected Five predictand variables are chosen to


be modeled by the selected predictors These are rainfallTmax and Tmin RH and U2An area from 10 0ndash200 N to 70 0ndash800 E encompassing

the region where meteorological variables are to bedownscaled is chosen for the large-scale predictors Dataon the predictors at monthly time scale are obtained fromthe National Centers for Environmental PredictionNational Center for Atmospheric Research (NCEPNCAR) reanalysis data (Kalnay et al 1996) (availableat httpwwwcdcnoaagovcdcdatancepreanalysishtml) and are used for training the downscaling modelThe medium resolution Model for InterdisciplinaryResearch on Climate version 32 (MIROC 32) GCM(medium-resolution of 1125 1125 deg GCM from theCenter for Climate System Research Japan) is used withthe A1B scenario (IPCC 2007) for the impact assess-ment The particular GCM is used keeping in view theavailability of the projections on the predictors at themonthly scale The A1B scenario represents a balancedemission scenario with medium emission trajectories andis used here as a possible future scenarioLarge-scale monthly atmospheric variables output from

the MIROC 32 GCM for the A1B scenario (720 ppm


Table II Predictors selected for the statistical downscaling

Predictand Predictors

Rainfall Mean sea level pressure geopotentialheight at 500 mb (Ghosh andMujumdar2006) specific humidity at 500 hPaprecipitation flux surface air temperatureat 2 m maximum surface air temperatureat 2 m minimum surface air temperatureat 2 m surface U-wind and surfaceV-wind (Raje and Mujumdar 2009)

Maximum andminimumtemperatures

Air temperature zonal and meridionalwind velocities at 925 mb surface fluxvariables such as latent heat sensibleheat shortwave radiation and longwave radiation fluxes (Anandhi et al2009)

Wind variables Geopotential height air temperatureU-wind and V-wind speed relativehumidity vertical velocity absolutevorticity as multilevel quantitiesevaluated at 1000 hpa height (Davyet al 2010)

relative humiditywater vapor pressuredew-pointtemperature anddew-point deficit

Geopotential height at 500 850 and1000 hpa wind speed and vorticity at500 850 hpa temperature at 850 hpahumidity variables (relative humidityspecific humidity water vapor pressuredew-point temperature dew-pointdeficit at 850 hpa) (Huth 2005)

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


CO2 stabilization experiment) is extracted from the multi-model data set of the World Climate ResearchProgrammersquos Coupled Model Inter Comparison Project(available at httpsesgllnlgov8443aboutftpdo) Thedimension of the predictor variables set is 253042(number of NCEP grid points for surface flux surfacepressure and radiation flux variables respectively) 13(number of predictors) which is very large and workingout the model with this large number would becomputationally cumbersome Principal componentanalysis (PCA) is applied on the large data set to reducethe dimensionality and to effectively summarize thespatial information from the 253042 grid points It wasfound that 95 of the variability of original set isexplained by the first 12 PCs The eigen vectors orcoefficients obtained from NCEP data were applied to thestandardized MIROC32 data to get the projections in theprincipal directions Standard procedure of statisticaldownscaling (eg Raje and Mujumdar 2009) involvingstandardization interpolation PCA and developing astatistical relationship between predicands and predictorsis followed in this study Interpolation is performedbefore standardization to obtain the GCM output at NCEPgrid points as the location of NCEPNCAR grid pointsand MIROC grid points do not match A Mercatorprojection (conformal cylindrical map projection) suit-able for tropical regions (Mulcahy and Clarke 1995)is first performed and then a linear interpolation is


performed between the projected points Standardization(Wilby et al 2004) is performed prior to PCAand downscaling to remove systematic bias in meanand standard deviation of the GCM simulatedclimate variables

Canonical correlation analysis

In the procedure for statistical downscaling followedin this study a mathematical transfer function is to beadopted to derive predictorndashpredictand relationshipwhich can account for the multivariate predictandsThe most commonly used statistical technique withmultivariate data sets is CCA CCA can be used as adownscaling technique for relating surface-basedobservations and free-atmosphere variables when sim-ultaneous projection of predictands is of interest (egBarnett and Preisendorfer 1987 Graham et al 1987Karl et al 1990 Barnston 1994 Mpelasoka et al2001 Juneng and Tangang 2008) CCA has found wideapplication in modeling precipitation and meteorologicalvariables (eg Von Storch et al 1993 Gyalistras et al1994 Busuioc and von Storch 1996) An advantage ofthe CCA in the context of downscaling is that therelationships between climate variables and the surfacehydrologic variables are simultaneously expressed asthey in fact occur in nature by retaining the explainedvariance between the two sets CCA finds pairs of linearcombinations between the N-dimensional climate variablesX (predictors in this case) and M-dimensional surfacevariables Y (predictands in this case) which can beexpressed as follows

Um frac14 aTXm frac14 1 min NMeth THORN (1)

Vm frac14 bTYm frac14 1 min NMeth THORN (2)

where Um and Vm are called predictor and predictandcanonical variables respectively a = [a1 a2 aN] andb= [b1b2 bM] are called the canonical loadings Theobjective of canonical correlation is to identify m sets ofcanonical variables such that the correlation r between thepredictor canonical variable Um and the predictandcanonical variableVm is maximum This wayN-dimensionalpredictor set and M-dimensional predictand set is reducedto m-dimensional canonical variables which will befurther useful in developing the regression equations foreach predictand After the estimation of canonical variablesregression relation is established for each of the predictandas discussed in the following section

Linear regression using CCA

The methodology involves training the surface observedpredictands and NCEP atmospheric predictor data with theCCA analysis after data preprocessing with standardizationand PCA The PCs obtained based onNCEP data are used asreference to develop the GCM PCs A separate regression



equation is derived for each meteorological predictandvariable from the canonical variable coefficients andcorrelations computed from the observed data First fewPCs are extracted based on the percentage varianceexplained by them The selected PCs from the NCEP dataare considered as predictor set to perform CCA to fit theregression relation between the climate variables andsurface-based observations The observed predictorcanonical variable Uobs q is computed from Equation (1)with the NCEP PCs as follows

Uobsq frac14 aTXNCEPPCs (3)

In Equation (3) q represents the minimum among thenumber of PCs considered and the number of predictandsconsidered As the number of PCs considered is 12 in thiscase to account for 95 variability and the number ofpredictands considered is five CCA will yield fivepredictor and predictand canonical variables and fivecanonical correlations between them The predictandcanonical variable Vpredicted q can be evaluated from thepredictor canonical variable Uobs q obtained fromEquation (3) as follows

Vpredictedq frac14 rCq Uobsq (4)

In Equation (4) rCq is the canonical correlation coeffi-cient and represents the percent of variance in the predictandcanonical variable explained by the predictor canonicalvariable It is a diagonal matrix of size q x q The regressionequations (Equation (4)) are applied to the interpolatedNCEP gridded GCM output to model future projections ofhydro-climate predictands The downscaled scenario foreach of the predictand can be derived according to

Ypredictedq frac14 b1 Vpredictedq (5)

where Ypredicted q is the q number of predictand variables tobe evaluated from the predictand canonical variablesVpredicted q and the predictand canonical loadings bPrediction of future scenario is made using the PCs ofmonthly outputs of the atmospheric variables (predictors)from the GCM in place of NCEP PCs in Equation (3) Thecanonical correlations and the loadings are computed usingstatistical toolbox of MATLAB (2004) This downscalingmethodology is applied to downscale the rainfall and othermeteorological variables at nine downscaling locationsShimoga station meteorological parameters are used forother downscaling locations due to the availability ofobserved data only at Shimoga station A monthly timeperiod is considered for all variables The SDSM is trainedusing the past records of atmospheric and surface meteoro-logical data of 25 years (1971 to 1995) to estimate thecanonical scores and the model is tested with the remainingdata for the period 1996 to 2004 Once the modelperformance is found satisfactory in the testing period itcan be applied for obtaining the future predictions Table IIIgives the details of the statistics such as mean standarddeviation of observed and CCA downscaled results for the


testing period of 1996 to 2004 The R-value in Table IIIindicates the correlation coefficient between the observedand CCA modeled results for various variables The resultsof CCA downscaling model are used as model inputvariables to simulate the impact of climate change onirrigation demands for each crop at each downscalinglocation

ESTIMATION OF IRRIGATION DEMANDS

The total irrigation demand in the command area iscomputed based on the potential evapotranspiration of acrop and the rainfall contribution The total demand inperiod t for a particular crop c at a downscaling stations is given by

Dtcs frac14 ETct Rts

Acs if Rts lt ETc

t (6)

Dtcs frac14 0 if Rts gt ETct (7)

where ETct is the potential evapotranspiration of a crop c

in period t Rt sis the rainfall contribution in period t at adownscaling station s Ac sis the area over which the cropc is grown at station sIn the demand equations given above (Equations (6)

and (7)) the soil moisture contribution to meeting cropwater demand is neglected Further the rainfall amountconsidered in the evaluation of irrigation demands is thetotal rainfall measured from rain-gauges at each down-scaling location instead of effective rainfall The compu-tation of effective rainfall involves measured rainfallsurface runoff losses percolation losses beyond root zoneand soil moisture details

Evapotranspiration model

The reference evapotranspiration is estimated byPenmanndashMonteith (Allen et al 1998) equation givenas follows

ETtR frac14 0408Δ Rn Geth THORN thorn g 900= T thorn 273eth THORNeth THORNU2 es eaeth THORNΔthorn g 1thorn 034U2eth THORN

(8)

where ETtR is the reference evapotranspiration of eachmonth (mmmonth) Δ is the slope of the vapor pressurecurve Rn is net radiation at the surface (wm2) g ispsychrometric constant T is the average air temperatureat 2-m height U2 is wind speed at 2-m height es is thesaturated vapor pressure and ea is the actual vaporpressure (kpa)The future projections of meteorological variables

downscaled from the GCM outputs including RH U2RnTmax and Tmin are used as input to the evapotrans-piration model (PenmanndashMonteith equation (Equation (8))to evaluate the anticipated changes in the referenceevapotranspiration Among these meteorological variablessolar radiation could not be directly downscaled in thisstudy due to the nonexistence of observed solar radiationdata for the study region Most of the methods to estimatesolar radiation (eg Angstrom 1924 Hargreaves 1994)


Table V Crop duration and sowing dates

Crop Duration (days) Sowing date

Paddy 120 June 15Sugarcane 365 July 01Permanent Garden 365 June 01Semidry Crops 123 July 01

Table III Comparison of observed versus computed statistics (Testing period 1996 to 2004)

Statistic

Rainfall (mm) Downscaling Locations MaximumTemperature

(C)

MinimumTemperature

(C)

RelativeHumidity

WindSpeedkmph1 2 3 4 5 6 7 8 9

Observed Mean 17493 5910 13075 7333 7518 553 4497 4016 4222 3125 1944 7078 373Computed Mean 17196 5509 7928 6941 7541 5305 3838 3899 3168 3148 1957 6995 374ObservedStandard Deviation

23063 6850 30622 8734 8692 6460 5125 5572 5295 277 232 1003 126

ComputedStandard Deviation

18192 4718 18988 6571 6206 4376 3673 3851 3699 240 182 772 117

R-Value 087 074 058 084 082 073 078 072 077 093 089 088 096

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


include the information of cloud cover Tmax and Tminsunshine hours RH and site-specific coefficients HoweverHargreaves and Samani (1982) recommended a simpleequation to estimate the solar radiation based on Tmax andTmin As the observations of Tmax and Tmin are availablefor the study region these variables can be downscaled andthe future projections of solar radiation can be computedbased on the downscaled variables of Tmax and Tmin TheRn in the Equation (8) is estimated using Hargreavesrsquosradiation formula (Hargreaves and Samani 1982)

Rn frac14 krs Tmax Tmineth THORN1=2Ra (9)

where krs is an adjustment factor equal to 016 for interiorlocations and 019 for coastal locations Tmax and Tmin arethemeanmonthlymaximum andminimum air temperaturesrespectively in 0C Ra is extraterrestrial radiation (wm

2) andis computed from expressions given in Allen et al (1998)The reference evapotranspiration (ETt R) obtained

(Equation (8)) needs to be adjusted to obtain the potentialcrop evapotranspiration (ETc

tp) with crop coefficients foreach period t for a crop c (kt c) Thus ETc

tp is given by

ETctp frac14 ETtR X ktc (10)

The potential evapotranspiration for each crop (Equation(10)) and the rainfall in each period t downscaled fromCCA downscaling are used to compute future projectionsof irrigation demands for each crop in each period t Theirrigated area for different crops under left and right bankcanal commands (Table IV) and duration of the crops withtheir sowing dates (Table V) are used in the computation ofirrigation demands The crop factors used for paddysugarcane permanent garden and semidry crops corre-sponds to Rice Sugarcane Group E crops (Citrus) andMaize respectively from Michael (1978) as given in

Table IV Crop distribution in the command area

CanalPaddy(ha)

Sugarcane(ha)

Permanentgarden (ha)

SemidryCrops (ha)

Total area(ha)

LBC 3484 1713 303 867 6367RBC 34 720 24 800 18 849 20 834 99 203Total 38 204 26 513 19 152 21 701 105 570

RBC Right Bank Canal LBC Left Bank Canal


Table VI The total irrigation requirement (includingleft bank and right bank canal) at the field level for eachcrop in each month is estimated as per the cropping patternin Table V

RESULTS AND DISCUSSION

Impact of climate change on rainfall andreference evapotranspiration

Simulated rainfall refers to the rainfall obtained from theNCEP data and the predicted rainfall results from use ofCCA downscaling model with MIROC 32 GCM for theA1B scenario The CCA model is able to well simulatethe observed data (Figure 2(a) for Locations 1 to 9) forthe training period of 1971 to 1995 with both NCEP andGCM TheGCMpredicted rainfall as shown in Figure 2 (a)for Locations 1 to 9 for the training period of 1971ndash1995are modeled with the monthly predictors in the MIROC32 GCM for the current climate with 20c3m experimentAll future projections are for the A1B scenario for25 years time slices of 2020ndash2044 2045ndash2069 and2070ndash2095 (Figure 2 (b) for Locations 1 to 9) The greenbox plots are for the period of 2020 to 2044 the blue boxplots are for the period of 2045 to 2069 and the red boxplots are for the period of 2070 to 2095 The projectedmonthly rainfall shows an increasing trend in all monthsat all nine downscaling locations The expected rainfallincrease is determined by the change in the large-scaleatmospheric variables (air temperature mean sea levelpressure geopotential height humidity and windvariables) considered as predictors (Table II) in the studyregion Such an increase in rainfall is also observed inthe study of Meenu et al (2011) for the same casestudy of Bhadra command area with SDSM and alsowith support vector machine


Table VI Monthly crop coefficients (Source Michael 1978)

Crop

Months

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

Paddy (Rice) 085 100 115 130 125 110 090Sugarcane 075 080 085 085 090 095 100 100 095 090 085 075Permanent Garden (Citrus) 050 055 055 060 060 065 070 070 065 060 060 055Semidry crops (Maize) 085 100 115 130 125 110 090

1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

200

400

600Mon

thly

Rai

nfal

l (m

m)

Observed NCEP Simulated Predicted from Miroc 32 GCM (20c3m)

1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)

(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 NCEPdata and predicted from MIROC 32 GCM with 20c3m experiment for the training period of 1971 to 1995 (b) represents the future projections fromMIROC 32 GCM with A1B scenario for each month with green box plots for period 2020ndash2044 blue box plots are for period 2045ndash2069 and the red

box plots are for period 2070ndash2095


Figure 3 shows similar results of other meteorologicalvariables RH U2 Tmax and Tmin All the meteoro-logical variables are well simulated by CCA downscaling(Figure 3 (a)) for the training period of 1971 to 1995 Theprojections of Tmax and Tmin and RH also show an


increasing trend for all the months The U2 projections donot show any particular trendThe reference evapotranspiration estimated from the

projections of Tmax and Tmin RH and U2 using theevapotranspiration model (Equation (8)) is shown in


1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)


Figure 4 The observed evapotranspiration for each monthshown in the Figure 4 is computed from the evapotrans-piration model (Equation (8)) with observed meteoro-logical data for the period 1971 to 1995 The futureprojections of reference evapotranspiration predicted toincrease for all months Particularly the change ofevapotranspiration is more in the months of April andMay due to the large projected changes of Tmax andTmin variables

Impact of climate change on irrigation water demands

The irrigation water requirements are computed forpaddy sugarcane permanent garden and semidry crops atLocations 1 to 9 The monthly reference evapotranspir-ation is corrected with crop coefficients for each crop tocompute the potential evapotranspiration which in turncan be used to compute the irrigation water demand of thecrop The monthly irrigation water demands are estimatedfrom the projections of rainfall at each of the locationdownscaled from CCA model and potential evapotrans-piration projections from Equation (10) The monthlyprojected variation of irrigation water requirements forLocations 1 to 9 are shown in Figures 5ndash7 and 8


respectively for paddy sugarcane permanent garden andsemidry crops The annual irrigarion demands for thecrops at the nine locations are shown in Figure 9 Thepredicted change of irrigation water demands at eachlocation is a function of rainfall at that location and thereference evapotranspiration

Irrigation water requirement - paddy

The crop growing period of paddy spans from April toOctober The irrigation demands of paddy are computedfor these months as shown in Figure 5 However atLocations 1 and 3 paddy demands are only in the monthsof April and May while for the other months the rainfallis sufficient to fulfill the water requirements of paddy Themonths showing the demands as zero indicates the waterneeded for optimal growth of the crop is provided byrainfall and irrigation is not required in those particularmonths For remaining locations the demands are presentfor all the months starting from April to September exceptin the month of October (Figure 5) At Locations 7 8 and9 in September month where the current demands arezero significant increase in the projected irrigation waterrequirements are observed due to the increase in the


1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)


evapotranspiration demand of crops For example themonthly mean rainfall of May is increasing at Location 7from 2875 mm to 3264 mm for the period of 2020ndash2044to 3837 mm for the period of 2045ndash2069 and to 4261 mmfor the period of 2070ndash2095 At the same time the increasein Tmax and Tmin are also increasing For examplemonthly Tmax temperature for May is increasing fromobserved 3364 C to 3626 C for 2020ndash2044 to 3751C for 2045ndash2069 to 3831 C for 2070ndash2095 Similarlymonthly minimum temperature of May is also increasingwith observed 2149 C to 2146 C for 2020ndash2044 to2233 C for 2045ndash2069 and 2301 C for 2070ndash2095 Asignificant increase in RH from observed 6702 to7097 for 2020ndash2044 to 7173 for 2045ndash2069 to7240 for 2070ndash2095 is also seen from the results Theminor changes in U2 are from observed 4089 ms to 425ms for 2020ndash2044 to 426 ms for 2045ndash2069 and to 438ms for 2070ndash2095 Such increase in RH U2 temperaturevariables results in net increase in evapotranspiration forexample at Location 7 in the month of May That is theincrease in evapotranspiration offsets the increasing effectof rainfall at Location 7 indicating increased irrigationdemand in future for paddy (Figure 5) However at some


locations paddy demands are predicted to decrease atmonthly scale eg at Location 2 in August month(Figure 5) due to the relative increase in rainfall comparedto the evapotranspiration at that location Overall irrigationrequirements of paddy are predicted to increase at all ninelocations at monthly scale (Figure 5) and at annual scale(Figure 9) The maximum annual paddy demand ispredicted to occur at Location 8 (Figure 1) with currentdemand as 1400 Mm3 with increasing demands as 2697Mm3 for the period of 2020ndash2044 with 2735 Mm3 for theperiod of 2045ndash2069 with 278 Mm3 for the period of2070ndash2095

Irrigation water requirement - sugarcane

Sugarcane crop is growing in all 365 days of a yearand the crop water demand exists in all 12 monthsSugarcane demands are more in the months of April andMay for all nine locations (Figure 6) due to lower rainfalland higher temperatures in these months For the monthof January the demand is predicted to decrease atLocations 1 2 4 5 and 6 compared to the currentdemands depending on the projections of rainfall and


1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)


evapotranspiration Even though small reductions ofdemands in the monthly scale are observed the annualirrigation water demands are predicted to increase forsugarcane over the Bhadra command area (Figure 9) The


maximum annual irrigation demands occur at Location 8(Figure 1) with current demand being 1529 Mm3 andprojected demands of 2312 Mm3 for 2020ndash2044 2316Mm3 for 2045ndash2069 235 Mm3 for 2070ndash2095


Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)

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

62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)

(a)Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec2

3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)

Figure 3 Downscaling results of (i) relative humidity (ii) wind speed (iii) maximum temperature and (iv) minimum temperature from the CCA modelIn above figures (a) denote annual scale observed simulated from NCEP and simulated from MIROC 32 GCM with 20c3m experiment for the trainingperiod of 1971 to 1995 (b) denotes monthly scale projections with the green box plots are for 2020ndash2044 blue box plots are for 2045ndash2065 and red box

plots are for 2070ndash2095


Copyright copy 2012 John Wiley amp Sons Ltd Hydrol Process (2012)DOI 101002hyp

Figure 4 Monthly reference evapotranspiration for Bhadra Commandarea estimated from MIROC 32 GCM output with A1B scenario


Irrigation water requirement - permanent garden

The crop water requirement of permanent garden spansfor the entire year and the irrigation demands areestimated for the all 12 months The annual demands ofpermanent garden are predicted to increase (Figure 9)even though the decreases in demands are small for themonthly scale (Figure 7) The maximum annual demandoccur at Location 3 with 289 Mm3 of current demandincreasing to 695 Mm3 for period of 2020ndash2044 879Mm3 for a period of 2045ndash2069 1026 Mm3 for a periodof 2070ndash2095

Irrigation water requirement - semidry crops

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

A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2

Figure 5 Monthly (April to October) irrigation water requirem


are quantified as shown in Figure 8 Water requirementsfor the semidry crops are predicted to increase at monthlyscale (Figure 8) as well as at annual scale (Figure 9) Atmost of the locations the estimated current irrigationdemands are zero but the projected demands areincreasing The maximum increase in annual demandoccurs at Location 7 with current demand being 264Mm3 and increasing to 1526 Mm3 for the period of2020ndash2044 1712 Mm3 for the period of 2045ndash20691968 Mm3 for the period of 2070ndash2095 Annualirrigation demands are less for semidry crops comparedto the other crops as the command area is small and alsothe crop growing period is small being restricted to themonths of April to October onlyDue to their cropping pattern and the command area

water requirements of Paddy and Sugarcane are highercompared to those of permanent garden and semi drycrops For all crops at all nine locations the projectedirrigation demands are higher compared to the currentdemands Even though the projected demands are highercompared to observed ones the relative difference in thefuture demands for the periods of 2020ndash2044 2045ndash2069and 2070ndash2095 are small due to the projected increasein the rainfall in the Bhadra command area The annualirrigation demand assessment carried out in thisstudy will give an overall idea about the changes indemands for each particular crop at each downscalinglocation Moreover the monthly analysis of demandsfor each crop at a particular location will be usefulfor the decision makers for better management ofirrigation systems

J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095

ent for paddy at Locations 1ndash9 for Bhadra Command Area


J F M A M J J A S O N D0

2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095

Figure 6 Monthly irrigation water requirement for sugarcane at Locations 1ndash9 for Bhadra Command Area


1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095

Figure 7 Monthly irrigation water requirement for permanent garden at Locations 1ndash9 for Bhadra Command Area


CONCLUSIONS

A methodology is developed in the present study forpredicting the future irrigation water demands in thecommand area of a river The expected changes of rainfallRH U2 Tmax and Tmin are modeled by using a SDSM


CCA with MIROC 32 GCM output for the A1B scenarioThe potential evapotranspiration projections are modeledwith an evapotranspiration model (PenmanndashMonteithequation) accounting for the projected changes intemperature RH solar radiation and U2 The need tocalculate the evapotranspiration using the temperature


A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095

Figure 8 Monthly semidry irrigation water requirements for Locations 1ndash9 for Bhadra Command Area

1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 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 onlytemperature variables has therefore been stressed Theirrigation water requirements are quantified by accountingfor projected rainfall and potential evapotranspiration Themonthly irrigation water demands of paddy sugarcanepermanent garden and semidry crops are quantified at nine


downscaling locations covering the entire command area ofBhadra river basin The annual irrigationwater requirementsfor paddy sugarcane permanent garden and semidry cropsare predicted to increase in the Bhadra command area Theprojected changes in irrigation demands will be helpful indeveloping adaptive policies for reservoir operations



In this study the soil moisture contribution to meetingcrop water demand is neglected However for an accuraterepresentation of the crop water demands the soilmoisture dynamics of individual crops must be consid-ered in the impact assessment studies Further the rainfallamount considered in the estimation of irrigationdemands is the actual rainfall instead of effective rainfallThe effective rainfall is the fraction of actual amount ofrainwater useful for meeting the water need of the cropsThe effective rainfall calculation includes the soil waterretention and percolation the key aspects which shouldbe included in further studies in order to develop moreuseful projected demands accounting for climate changeFurther the future projected irrigation demands are due

to a single GCM using a single scenario It is widelyacknowledged that the mismatch between different GCMsover regional climate change projections represents asignificant source of uncertainty (eg New and Hulme2000 Simonovic and Li 2003 Simonovic and Davies2006 Wilby and Harris 2006 Ghosh and Mujumdar2007) Further studies are necessary to evaluate the futureirrigation demands for different GCMs with scenarios tomodel the underlying GCM and scenario uncertainty Theresults will serve as guidelines for the decision makers toaccommodate sufficient water in those months whererainfall only will not be sufficient to fulfill the crop waterrequirements Further the results will be useful inexamining different cropping patterns in the commandarea keeping in view the increased crop water demandsand possible decrease in streamflow

REFERENCES

Allen RG Pereira LS Raes D Smith M 1998 Crop EvapotranspirationGuidelines for Computing Crop Water Requirements FAO Irrigationand Drainage Paper 56 ISBN 92-5-104219-5 Food and AgricultureOrganization of the United Nations Rome

Anandhi A Srinivas VV Kumar DN Nanjundiah RS 2009 Role ofpredictors in downscaling surface temperature to river basin in India forIPCC SRES scenarios using support vector machine InternationalJournal of Climatology 29(4) 583ndash603

Angstrom A 1924 Solar and terrestrial radiation Quarterly Journal of theRoyal Meteorological Society 50 121ndash126

Barnett TP Preisendorfer RW 1987 Origins and levels of monthly andseasonal forecast skill for United States air temperature determinedby canonical correlation analysis Monthly Weather Review 1151825ndash1850

Barnston AG 1994 Linear statistical short-term climate predictive skill inthe Northern Hemisphere Journal of Climate 7 1513ndash1564

Brown RA Rosenberg NJ 1999 Climate change impacts on the potentialproductivity of corn and winter wheat in their primary United Statesgrowing regions Climate Change 41 73ndash107

Busuioc A Von Storch H 1996 Changes in the winter precipitation inRomania and its relation to the large-scale circulation Tellus Series ADynamic Meteorology and Oceanography 48(4) 538ndash552

Davy RJ Woods MJ Russell CJ Coppin PA 2010 StatisticalDownscaling of Wind Variability from Meteorological FieldsBoundary-Layer Meteorology DOI 101007s10546-009-9462-7

De Silva CS Weatherhead EK Knox JW Rodriguez-Diaz JA 2007Predicting the impacts of climate changemdasha case study on paddyirrigation water requirements in Sri Lanka Agricultural WaterManagement 93(1ndash2) 19ndash29

Easterling WE Crosson PR Rosenberg NJ McKenney MS Katz LALemon KM 1993 Agricultural impacts of and response to climatechange in the Missouri-Iowa-Nebraska- Kansas (MINK) regionClimate Change 24 23ndash61


Elgaali E Garcia LA Ojima DS 2007 High resolution modeling of theregional impacts of climate change on irrigation water demand ClimateChange 84 441ndash461

Ghosh S Mujumdar PP 2006 Future Rainfall Scenario over Orissa withGCM Projections by Statistical Downscaling Current Science 90(3)396ndash404

Ghosh S Mujumdar PP 2007 Nonparametric methods for modelingGCM and scenario uncertainty in drought assessment Water ResourcesResearch 43 W07405 DOI 1010292006WR005351

Graham NE Michaelsen J Barnett TP 1987 An investigation of the ElNino-Southern Oscillation cycle with statistical models 1 Predictor fieldcharacteristics Journal of Geophysical Research 92 14 251ndash14 270

Gyalistras D von Storch H Fischlin A Beniston M 1994 Linking GCM-simulated climatic changes to ecosystem models case studies ofstatistical downscaling in the Alps Climate Research 4(3) 167ndash189

Hargreaves GH 1994 Simplified coefficients for estimating monthly solarradiation in North America and Europe Dept Paper Dept Biol andImg Engrg Utah State Univ Logan Utah

Hargreaves GH Samani ZA 1982 Estimating potential evapotranspirationJournal of Irrigation Drainage Engineering ASCE 108(3) 25ndash230

Harmsen EW Miller NL Schlegel NJ Gonzalez JE 2009 Seasonal climatechange impacts on evapotranspiration precipitation deficit and crop yieldin Puerto Rico Agricultural Water Management 96 1085ndash1095

Huth R 2005 Downscaling humidity variables International Journal ofClimatology 25 243ndash250

Intergovernmental Panel on Climate Change (IPCC) 2007 Climate ChangeThe Physical Science BasismdashContribution of Working Group I to theFourth Assessment Report of the Intergovernmental Panel on ClimateChange Solomon S et al (ed) Cambridge Univ Press New York 2007

Juneng LTangang FT 2008 Level and source of predictability ofseasonal rainfall anomalies in Malaysia using canonical correlationanalysis International Journal of Climatology 28 1255ndash1267

Kalnay E Kanamitsu M Kistler R Collins W Deaven D Gandin LIredell M Saha S White G Woollen J Zhu Y Leetmaa A Reynolds RChelliahM EbisuzakiW HigginsW Janowiak J MoKC Ropelewski CWang J Jenne R Joseph D 1996 The NCEPNCAR 40-year reanalysisproject Bulletin of the American Meteorological Society 77(3) 437ndash471

Karl TR Wang WC Schlesinger ME Knight RW Portman D 1990 Amethod of relating general circulation model simulation climate tothe observed local climate Part I Seasonal Statistics Journal ofClimate 3 1053ndash1079

Kittel TGF Rosenbloom NA Painter TH Schimel DS 1995 VEMAPModeling Participants The VEMAP integrated database for modelingUnited States ecosystemvegetation sensitivity to climate changeJournal of Biogeography 22 857ndash862

Liu S Mo X Lin Z Xu Y Ji J Wen G Richey J 2010 Crop yieldresponse to climate change in the Huang-Huai-Hai plain of ChinaAgricultural Water Management 97(8) 1195ndash1209

Lovelli S Perniola M Di Tommaso T Ventrella D Moriondo MAmato M 2010 Effects of raising atmospheric CO2 on cropevapotranspiration in a Mediterranean area Agricultural WaterManagement 97(9) 1287ndash1292

Maeda EE Pellikka PKE Clark BJF Siljander M 2011 Prospectivechanges in irrigation water requirements caused by agriculturalexpansion and climate changes in the eastern arc mountains of KenyaJournal of Environmental Management 92(3) 982ndash993

MATLAB 2004 Statistics Toolbox The Math Works Inc httpwwwmathworksin

Meenu R Rehana S Mujumdar PP 2011 Assessment of hydrologicimpacts of climate change in Tunga-Bhadra basin India with HEC-HMS and SDSM Hydrological Processes Accepted Article DOI101002hyp9220

Michael AM 1978 Irrigation Theory and Practice Vikas PublishingHouse Pvt Ltd New Delhi

Mpelasoka FS Mullan AB Heerdegen RG 2001 New Zealand climatechange information derived by multivariate statistical and artificialneural network approaches International Journal of Climatology21 1415ndash1433

Mulcahy KA Clarke KC 1995 What shape are we in The display ofmap projection distortion for global change research In Proceedings ofGISLIS rsquo95 American Society of Photogrammetry and RemoteSensing Bethesda Md 175ndash181

New M Hulme M 2000 Representing uncertainty in climate changescenarios A Monte Carlo approach Integrated Assessment 1 203ndash213

Parry ML Rosenzweig C Iglesias A Livermore M Fischer G 2004Effects of climate change on global food production under SRESemissions and socio-economic scenarios Global EnvironmentalChange 14(1) 53ndash67



Raje D Mujumdar PP 2009 A conditional random field baseddownscaling method for assessment of climate change impact onmultisite daily precipitation in the Mahanadi basin Water ResourcesResearch 45(10) W10404 DOI 1010292008WR007487

Rodriguez Diaz JA Weatherhead EK Knox JW Camacho E 2007Climate change impacts on irrigation water requirements in theGuadalquivir river basin in Spain Regional Environmental Change 7149ndash159

Rosenzweig C Parry ML 1994 Potential impact of climate change onworld food supply Nature 367 133ndash138

Shahid S 2011 Impact of climate change on irrigation water demandof dry season Boro rice in northwest Bangladesh Climate Change105 433ndash453

Simonovic SP Davies EGR 2006 Are we modeling impacts of climatechange properly Hydrological Processes 20 431ndash433

Simonovic SP Li L 2003 Methodology for assessment of climate changeimpacts on large-scale flood protection system Journal of WaterResources Planning and Management 129(5) 361ndash371

Singh B Maayar ME Andreacute P Bryant CR Thouez JP 1998 Impacts of aGHG-induced climate change on crop yields Effects of acceleration in


maturation moisture stress and optimal temperature Climate Change38 51ndash86

Torres AF Walker WR McKee M 2011 Forecasting daily potentialevapotranspiration using machine learning and limited climatic dataAgricultural Water Management 98(4) 553ndash562

Von Storch H Zorita E Cubasch U 1993 Downscaling of global climatechange estimates to regional scales an application to Iberian rainfall inwintertime Journal of Climate 6 1161ndash1171

Wilby RL Harris IA 2006 A framework for assessing uncertainties inclimate change impacts low-flow scenarios for the river Thames UKWater Resources Research 42 W02419 DOI 1010292005WR004065

Wilby RL Charles SP Zorita E Timbal B Whetton P Mearns LO 2004The guidelines for use of climate scenarios developed from statisticaldownscaling methods Supporting material of the IntergovernmentalPanel on Climate Change (IPCC) prepared on behalf of Task Group onData and Scenario Support for Impacts and Climate Analysis (TGICA)(lthttpipccddccruueaacukguidelinesStatDown Guidepdfgt)

Yano T Aydin M Haraguchi T 2007 Impact of climate change onirrigation demand and crop growth in a Mediterranean environment ofTurkey Sensors 7 2297ndash2315



de Silva et al (2007) studied the impacts of climatechange on irrigation water requirements in the paddy fieldof Sri Lanka and predicted an increase of 13 to 23 ofirrigation water demand depending on climate changescenarios The climate change scenarios of temperatureradiation U2 and RH are developed by applying thepercentage changes of GCM to the baseline dataset Theproportional () changes given by a selected GCM andscenario are applied on an existing baseline climatologicaldataset to develop the future scenarios of the variablesrequired for a water balance model to estimate the paddyirrigation requirements for a single siteMost of these studies focused on evaluation of crop

water requirements based on perturbed climate changescenarios generated with GCM outputs or with availabledownscaled data sets or using modeling softwares such asSCENGEN With the development of statistical down-scaling models (SDSMs) the regional climate changeassessment studies are becoming more accepted There-fore this study uses a SDSM as the downscalingmethods are well accepted in the climate change impactassessment studies in the recent years by the researchcommunity Therefore this study emphasizes on adopt-ing such sophisticated methods to quantify the futureprojected irrigation demands This forms the basicdifference between the present work and the work donein de Silva et al (2007) A multivariable downscalingmethodology is applied at each location to develop thefuture scenarios of rainfall temperature RH and U2Further the difference between the rainfall and thepotential evapotranspiration is considered as the irriga-tion water requirement for a particular crop at a particularlocation This study stresses on climate change impactassessment of irrigation demands at a reservoir commandarea using a SDSM To obtain the projected climatechange scenarios of rainfall as well as other meteoro-logical variables which influence the evapotranspiration(viz RH U2 radiation maximum (Tmax) and minimum(Tmin) temperatures) at the scale of command area froma GCM a multivariable downscaling technique canon-ical correlation analysis (CCA) is adopted The antici-pated irrigation demands of the crops are examined forthe future scenarios by accounting for the changes inrainfall and potential evapotranspiration

STUDY AREA

The command area of the Bhadra reservoir is consideredfor the assessment of impacts of climate change onirrigation demands Bhadra is a tributary of KrishnaRiver originating from Gangamula in the Western Ghatsof Chikamagalur District in Karnataka state India Theriver flows through nearly 190 km from its origin andjoins River Tunga to form the River Tunga-Bhadra TheBhadra reservoir intercepts the river flow and provideswater for irrigation The reservoir project also generateshydropower to a minor extent The gross command areaunder the Bhadra Canal System is 162818 ha with aculturable command area of 121500 ha out of which


105570 ha have been earmarked for irrigation Theirrigated area of 105 570 ha is considered for impactassessment in this study The irrigated area predomin-antly consists of red loamy soil except in some portionof the right canal area which has black cotton soil Theassessment of irrigation demands is carried out onpaddy sugarcane permanent garden and semi dry cropswhich are the typical crops grown in the Bhadracommand areaThemeteorological variables (Tmax andTminU2 andRH)

from 1969 to 2005 at Shimoga and high-resolutiongridded daily precipitation data from1971 to 2005 at a05 0 05 0 grid interpolated from station data areobtained from the India Meteorological Department(IMD) Pune The command area of Bhadra river spreadsover the districts of Chitradurga Shimoga Chickmagalurand Bellary Nine IMD locations are selected to evaluatethe irrigation demands in the command area The totalirrigated area of each crop in the command area isdistributed equally among these selected nine locationsThus each downscaling location represents an areaconsisting of all the crops The 05 0 05 0 IMD gridpoints falling in the districts of Chitradurga ShimogaChickmagalur and Bellary are considered as rainfalldownscaling locations as shown in Figure 1 The latitudesand longitudes of each of the nine downscaling locationsare given in Table I

STATISTICAL DOWNSCALING

The statistical downscaling techniques are generally used tobridge the spatial and temporal resolution gaps between thecoarser resolution of the GCMs and the finer resolutionrequired in the impact assessment studies Generally thesemethods involve deriving empirical relationships thattransform large-scale simulations provided by a GCM(climate variables as predictors) to regional-scale variables(surface variables as predictands) As afirst step in the impactstudies the predictands to be downscaled must be selectedThe hydro-meteorological variables that have a majorinfluence on crop water requirements are the rainfall andevapotranspiration (Elgaali et al 2007 Rodriguez Diazet al 2007) Evapotranspiration is mainly influenced by theair temperature U2 RH and solar radiation Many impactassessment studies on reference evapotranspiration havedealt with only temperature variables of Tmax andTmin (egHarmsen et al 2009 Lovelli et al 2010Maeda et al 2011Torres et al 2011) However the present study usestemperature variables as well as RH U2 and radiation Thetemperature variables (Tmax and Tmin) RH and U2 aremodeled (as predictands) with a statistical downscalingtechnique using GCM outputs The data used and down-scaling methodology are described in the following section

Data extraction and statistical downscaling

The first step in statistical downscaling is the selectionof atmospheric predictor variables to model the selectedpredictand variables Following the literature (Table II)



Bhadra Reservoir

Command Area














































Acs if Rts lt ETc

t (6)








(8)








Statistic


(C)

MinimumTemperature

(C)

RelativeHumidity



23063 6850 30622 8734 8692 6460 5125 5572 5295 277 232 1003 126


18192 4718 18988 6571 6206 4376 3673 3851 3699 240 182 772 117

R-Value 087 074 058 084 082 073 078 072 077 093 089 088 096









tp is given by




CanalPaddy(ha)

Sugarcane(ha)


SemidryCrops (ha)

Total area(ha)

LBC 3484 1713 303 867 6367RBC 34 720 24 800 18 849 20 834 99 203Total 38 204 26 513 19 152 21 701 105 570









Crop

Months



1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1


200

400

600Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)









1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES






















































Bhadra Reservoir

Command Area














































Acs if Rts lt ETc

t (6)








(8)








Statistic


(C)

MinimumTemperature

(C)

RelativeHumidity



23063 6850 30622 8734 8692 6460 5125 5572 5295 277 232 1003 126


18192 4718 18988 6571 6206 4376 3673 3851 3699 240 182 772 117

R-Value 087 074 058 084 082 073 078 072 077 093 089 088 096









tp is given by




CanalPaddy(ha)

Sugarcane(ha)


SemidryCrops (ha)

Total area(ha)

LBC 3484 1713 303 867 6367RBC 34 720 24 800 18 849 20 834 99 203Total 38 204 26 513 19 152 21 701 105 570









Crop

Months



1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1


200

400

600Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)









1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES























































































Acs if Rts lt ETc

t (6)








(8)








Statistic


(C)

MinimumTemperature

(C)

RelativeHumidity



23063 6850 30622 8734 8692 6460 5125 5572 5295 277 232 1003 126


18192 4718 18988 6571 6206 4376 3673 3851 3699 240 182 772 117

R-Value 087 074 058 084 082 073 078 072 077 093 089 088 096









tp is given by




CanalPaddy(ha)

Sugarcane(ha)


SemidryCrops (ha)

Total area(ha)

LBC 3484 1713 303 867 6367RBC 34 720 24 800 18 849 20 834 99 203Total 38 204 26 513 19 152 21 701 105 570









Crop

Months



1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1


200

400

600Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)









1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES

























































Statistic


(C)

MinimumTemperature

(C)

RelativeHumidity



23063 6850 30622 8734 8692 6460 5125 5572 5295 277 232 1003 126


18192 4718 18988 6571 6206 4376 3673 3851 3699 240 182 772 117

R-Value 087 074 058 084 082 073 078 072 077 093 089 088 096









tp is given by




CanalPaddy(ha)

Sugarcane(ha)


SemidryCrops (ha)

Total area(ha)

LBC 3484 1713 303 867 6367RBC 34 720 24 800 18 849 20 834 99 203Total 38 204 26 513 19 152 21 701 105 570









Crop

Months



1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1


200

400

600Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)









1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES






















































Crop

Months



1971 1975 1979 1983 1987 1991 19950

500

1000

Location 1


200

400

600Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 2


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)

(a)









1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES





















































1971 1975 1979 1983 1987 1991 19950

1000

2000

Location 3


500

1000

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

400

Location 4


100

200


(a)

(b)

(a)

(b)










1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES





















































1971 1975 1979 1983 1987 1991 19950

200

400

Location 5


50

100

150

200

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

200

Location 6


50

100

150


(a)

(b)

(a)

(b)








1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES





















































1971 1975 1979 1983 1987 1991 19950

100

200

Location 7


50

100

150

Mon

thly

Rai

nfal

l (m

m)

Mon

thly

Rai

nfal

l (m

m)


1971 1975 1979 1983 1987 1991 19950

100

200Location 8


50

100


(a)

(b)

(a)

(b)

1971 1975 1979 1983 1987 1991 19950

200

Location 9


0

50

100

150

Mon

thly

Rai

nfal

l (m

m)


(a)

(b)






Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES





















































Observed NCEP GCM65

70

75

80

Rel

ativ

e H

um

idit

y (

)


62

64

66

68

70

72

74

76

78

80

(b)(i)

Observed NCEP GCM

34

36

38

4

42

Win

d S

pee

d (

kmp

h)


3

4

5

6

(b)(ii)

Observed NCEP GCM

295

30

305

31

315

32

325

Max

imu

m T

emp

erat

ure

(D

eg C

)


29

30

31

32

33

34

35

36

37

38

39

40

(b)(iii)

Observed NCEP GCM

18

185

19

195

20

205

Min

imu

m T

emp

erat

ure

(D

eg C

)


17

18

19

20

21

22

23

24

(b)(iv)











A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES



























































A M J J A S O0

2

4

6

8

Pad

dy

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 4

A M J0

2

4

6

8Lo

A M J J A S O0

2

4

6

8Location 7

A M J0

2

4

6

8Lo

Present 2020-2





J A S O

cation 2

A M J J A S O0

2

4

6

8Location 3

J A S O

cation 5

A M J J A S O0

2

4

6

8Location 6

J A S O

cation 8

A M J J A S O0

2

4

6

8Location 9

044 2045-2069 2070-2095




2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES






















































2

4

6

Su

gar

can

e Ir

rig

atio

nW

ater

Req

uir

emen

t (M

m3 )

Location 1


2

4

6Location 2


2

4

6Location 3


2

4

6Location 4


2

4

6Location 5


2

4

6Location 6


2

4

6Location 7


2

4

6Location 8


2

4

6Location 9

Present 2020-2044 2045-2069 2070-2095



1

2

3

Per

man

ent

Gar

den

Irri

gat

ion

Wat

er R

equ

irem

ent

(Mm

3 )

Location 1


1

2

3Location 2


1

2

3Location 3


1

2

3Location 4


1

2

3Location 5


1

2

3Location 6


1

2

3Location 7


1

2

3Location 8


1

2

3Location 9

Present 2020-2044 2045-2069 2070-2095



CONCLUSIONS





A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES





















































A M J J A S O0

15

25

Sem

idry

Irri

gat

ion

W

ater

Req

uir

emen

t (M

m3 )

Location 1

A M J J A S O0

15

25Location 2

A M J J A S O0

15

25Location 3

A M J J A S O0

15

25Location 4

A M J J A S O0

15

25Location 5

A M J J A S O0

15

25Location 6

A M J J A S O0

15

25Location 7

A M J J A S O0

15

25Location 8

A M J J A S O0

15

25Location 9

Present 2020-2044 2045-2069 2070-2095


1 2 3 4 5 6 7 8 90

10

20

30

Location

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Irri

gat

ion

Wat

er

R

equ

irem

ent

(Mm

3 )Ir

rig

atio

n W

ater

Req

uir

emen

t (M

m3 )

Paddy

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Sugarcane

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Permanent Garden

1 2 3 4 5 6 7 8 90

5

10

15

20

25

30

Location

Semidry Crops

Present 2020-2044 2045-2069 2070-2095










REFERENCES
























































REFERENCES





















































Regional impacts of climate change on irrigation water...

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