Impact of Climate Change on Temperature under A2 and A1B Emission Scenarios in a Hilly Basin

Impact of Climate Change on Temperature under A2 and A1B Emission Scenarios in a Hilly Basin

Dharmaveer Singh1*, R.D. Gupta1, 2 and Sanjay K. Jain3

1. GIS Cell, Motilal Nehru National Institute of Technology, Allahabad (India) 2. Department of Civil Engineering, Motilal Nehru National Institute of Technology,

Allahabad- 211004 (India) 3. Water Resources Systems Division, National Institute of Hydrology, Roorkee

* Corresponding author’s mail:[email protected]

ABSTRACT

In the present paper, impact of climate change on maximum and minimum temperature has been assessed for future periods under A2 and A1B scenarios in a part of Sutlej river basin, located in North-Western Himalayan region, India. The future trends in maximum and minimum temperature under A2 and A1B scenarios have been discussed for five different time periods: 2001-2020, 2021-2040, 2041-2060, 2061-2080 and 2081-2100. A rise in maximum and minimum temperature has been observed. For Tmax, these are 0.12°C, 0.22°C, 0.26°C, 0.36°C and 0.40°C under A2 and 0.01°C, 0.11°C, 0.22°C, 0.28°C and 0.30°C under A1B emission scenario for the above five time periods respectively. Similarly for Tmin, these are 0.56°C, 0.54°C, 0.55°C, 0.82°C and 0.92°C under A2 and 0.05°C, 0.42°C, 0.59°C, 0.79°C and 0.73°C under A1B emission scenario. The generation of future scenarios of temperature will be useful to see the impact of climate change on snowmelt runoff computation in the study area. Keywords: Climate change, statistical downscaling, predictors, NCEP and CGCM3 1. Introduction Water is widely regarded as the most important natural resource to sustain life over the earth surface. The adequate availability of fresh water is essential for the growth of human society and development. Recently, this has been found that increased population and urbanization accelerated by rapid industrialization along with climate change have altered availability of water for storage and supply. Further, this has caused increased demand of water in present and future which in turn will lead to global fresh water crisis (Arora and Boer, 2001; Jiang et al., 2007). Around 4.8 billion people (2000) that account nearly 80% of global population are subjected to water scarcity. The regions of intensive agriculture show high level of threat because these have need of about 70% of total water extracted from surface and groundwater resources (Vorosmarty et al., 2010; Wisser et al., 2008). Several research studies carried out across the globe under different physiographic and climatic conditions have addressed the impacts of climate variability and change on water resources (Srikanthan and McMohan, 2001; Vicuna et al., 2007). Parry et al. (2007) have concluded that the overall net impact of climate change on water resource is negative however, some regions may be benefited. India has witnessed a rise in mean annual temperature of about 0.42°C over the last 100 years which is more or less consistent with the trends noticed in global mean temperatures (MoWR,

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2008). The warming is widespread and relatively more pronounced over northern parts of India. The modelled projections for future scenarios show that mean annual temperature may increase in between 3 to 5°C under different IPCC (Intergovernmental Panel on Climate Change) emission scenarios at the end of 21st century (Ravindranath et al., 2011; Sharma and Chauhan, 2011). The rise in temperature which is directly related with intensification of hydrological cycle will result in higher rates of evaporation and increase of liquid precipitation (MoWR, 2008). This will affect surface runoff, frequency and intensity of floods and droughts, soil moisture and water supplies for irrigation and hydroelectric generation (Gosian et al., 2006). The Himalayan glaciers which are source of many major river systems are exposed to climate change. The projected rise in temperature will reduce the melt water availability from glaciers. This will further bring out considerable decline in discharge of major river systems flowing through this region which in turn will affect agricultural production and developmental activities. The Brahmaputra and the Indus basins are the most susceptible to climate change (Immerzeel et al., 2010). Generally, the impacts of past climate variability and projected future climate change scenarios on hydrological processes and water resources are studied at global and continental scale and very limited case studies are available at regional and sub-regional scales. Hence, examining local imprints of changes in climate at sub-regional scale is quite impressive. This provides a prospect to estimate the degree of vulnerability of local water resources and plan appropriate adaptation measures that must be carried out ahead of time. Moreover this will provide sufficient time to consider probable future threats in all stages of water resources development projects. The motivation of the present study is to study implications of climate change on maximum and minimum daily temperature under scenarios A2 and A1B of third generation Canadian Coupled Global Climate Model (CGCM3) in a part of Sutlej basin of India. This basin is highly prone to climate change. A statistical downscaling method has been employed to downscale maximum and minimum daily temperature from large scale predictor variables of CGCM3 model at the basin scale. The remaining part of this paper has been arranged as follows; section 2 presents an overview of Global Climate Models (GCMs) and downscaling methods. The details about study area have been discussed in section 3. Data and their sources have been described in section 4. Methodology adopted for statistical downscaling has been described in section 5. Results and discussion has been shown in section 6. Finally, conclusion drawn from this study has been presented in section 7. 2. Global climate models and downscaling methods The Global Climate Models (GCMs) are physically based tools and are developed to simulate state of the present and future climate by considering increased concentration of green house gases in the atmosphere. GCMs provide a reasonable simulation accuracy of climate variables at global and continental scale. They are incapable to provide information at regional or local scales because of their coarse spatial resolution (typically of the order 50,000 km2). This restricts the direct applications of GCM’s outputs in regional climate change impact studies (Ghosh, 2010; Raghavan et al., 2012; Wilby et al., 2002). A methodology usually known as downscaling is introduced for bridging the gap between the scale of GCMs and required resolution for practical


applications. Downscaling methodology broadly can be classified into dynamic and statistical methods (Ghosh, 2010). Dynamic downscaling techniques refer to use of regional climate models (RCMs) or limited area models (LAMs) that involves nesting of GCMs. A horizontal resolution of the order of tens of kilometers is obtained from RCMs over selected area of interests. RCMs accounts use of initial boundary conditions and time dependant lateral metrological conditions derived from GCMs to provide information at high spatial and temporal scales (Giorgi, 1990; Jones et al., 1995). The complex design and computationally expensive nature of RCMs has limited their applications in climate change impact studies (Ghosh and Mishra, 2010; Hewitson and Crane 1996). A second less computationally demanding approach is statistical downscaling. The statistical downscaling methods can be classified into 3 categories; weather typing, weather generator and regression method (Wilby and Wigley, 1997). These methods have been used in several studies (Bárdossy et al., 2005; Dubrovsky et al., 2004; Fowler et al., 2000; Hua et al., 2010; Kilsby et al., 2007; Mason, 2004; Tripathi et al., 2006 Wilby et al., 1999). A review of the latest literature on statistical downscaling of predictands by using regression method has been presented in Table 1. The review describes about the various techniques which are based on regression methods. Based on this review, Statistical Downscaling Model version 4.2 (SDSM) has been found proficient and is suggested to apply in downscaling of temperature.

Table1: Literature review on statistical downscaling using regression methods

S.

No. Predictors Predictands Predictors Technique Study area References

1

mslp, p_f, p_u, p_z, p5_f, p5_v, p500, p5zh, p8_f, p8_u, p8_v, p850,

p8th, r500, r850, rhum, shum, temp, t_lag

Daily precipitation,

Tmax and Tmin

HadCM3 for A2 and B2 scenario and NCEP/NCAR

reanalysis datasets

SDSM Tunga–Bhadra

River basin, India

Meenu et al., (2012)

2 NCEP/NCAR predictors Daily precipitation


reanalysis datasets

SDSM Yangtze River basin

Huang et al., (2011)

3 NCEP/NCAR predictors Daily precipitation


reanalysis datasets

SDSM Yangtze River basin

Huang et al., (2010)

4 NCEP/NCAR predictors Daily

precipitation, Tmax and Tmin

CGCM3 for A2 and A1B scenario, and

NCEP/NCAR reanalysis data set

SDSM

Mount Makiling

forest, Philippines

Combalicer et al., (2010)

5 mslp, _2 m, 500 hPa GH, SH, 850 hPa GH, SH (850 hPa)

Daily precipitation

CGCM2 and HadCM3 for A2, scenario and

NCEP/NCAR reanalysis data set

SSVM and

ANN

Hanjiang Basin

Chen et al., (2010)

6 ta_9, ua _9, va _9, LH, SH, LWR, SWR Tmax and Tmin

CGCM3 for A1B, A2, B1 and COMMIT

scenario, and NCEP/NCAR

reanalysis data set

SVM Malaprabha reservoir,

India

Anandhi et al.,(2009)

7 NCEP/NCAR predictors Daily

temperature and precipitation

HadCM3 for A2 scenario and

NCEP/NCAR reanalysis datasets

SDSM Plastic Lake, Onterio

Aherne et al., (2008)

8 ua_5, va_5, zg _5, ua_7, va _7, zg_ 7 Daily near

surface lapse rates

NCEP/NCAR reanalysis datasets Extrapolation Canada Marshall et

al., (2007)

9 Mgeos, Mz _5, Mz _8 Daily Tmax HadCM3 and MLR USA (26 Schoof et al.,


Mrh850/Mhus850, mslp, Mzgt_ 8 _5 for downscaling Tmin

Mgeow, Mz_ 5, Mz _8 mslp, Mzgt _8_ 5 for

downscaling Tmax

and Tmin CGCM2 simulations for SRES A2 scenario, and

NCEP/NCAR and ECMWF reanalysis

data sets

stations) (2007)

10

mslp, afs _s, afs_5, afs _8, ua _s, ua _5, ua_ 8, va_ s, va _5, va _8, zg _5,

zg _8, di _s, di _8, wd _5, wd_ 8, rh _ns, hus _ns, hus _5, hus _8, ta _2m,

Z _s, Z _5, Z _8

Daily Tmax and Tmin

CGCM1 simulations for IS92a scenario, NCEP data for grid

point closest to watershed

TNN and multiple

regression based SDSM

Canada (river basin

Dibike and Coulibaly

(2006)

11 Ta_ns, mslp Daily Temperature

CSIRO/Mk2, HadCM3, PCM, and

ECHAM4 datasets for SRES A2 and B2

scenarios

Regression models Slovenia Bergant et

al., (2006)

12

Monthly Tmax for downscaling Tmax

Monthly Tmin for downscaling Tmin

Daily and monthly

Tmax and Tmin

HadCM3 projections for GHG emissions

scenario

Transfer function

USA (one station in

Oklahoma) Zhang (2005)

13 va _ns, hus _ns, hus _8, zg_5, ta _m

Daily Tmax and

Daily Tmin

CGCM1 datasets for IS92a scenario, and

NCEP/NCAR reanalysis datasets

SDSM Canada (river basin

Dibike and Coulibaly

(2005)

14 Tmean Monthly Temperature

HadCM3, ECHAM4 datasets for SRES A2

and B2 scenarios LS Sri Lanka Droogers and

Aerts (2005)

15 mslp, p_v, pzh, p500, p8_v, p8zh,

p850, s500, s850, rhum, shum, temp,

Daily Temperature

and Precipitation

CGCM1 and NCEP/NCAR

reanalysis data sets SDSM Gagnon et al.,

(2005)

16 Zg_5, zgt_ 0_ 5 Monthly

Tmean, Tmin and Tmax

NCEP/NCAR reanalysis data sets

SSA, PCA, CCA

Turkey (62 stations)

Tatli et al., (2005)

17 Mslp,ta8, prw, zg_ 0, zg _5, zgt_ 0 _5

Daily Tmin and Tmax

NCEP/NCAR reanalysis data sets,

simulations from three AOGCMs -

BMRC, CSIRO, LMD

AM France (17 stations)

Timbal et al., (2003)

18 zg _5 Winter

monthly temperature

NCEP/NCAR reanalysis data sets CCA China (147

stations) Chen and

Chen (2003)

19 Tmax and Tmin value for previous

day, Tmean _2m, hus _ns, rh _ns, mslp, ua, va, F, Z, zg 5

Daily Tmax and Tmin

NCEP/NCAR reanalysis data sets, CGCM1 dataset for greenhouse-gas-plus

sulphate- aerosols experiment

SDSM Canada (region

Toronto)

Wilby et al., (2002)

20 ta _2m, slp Monthly Tmean ECHAM4 EOF

Norway (gridded region)

Benestad (2001)

3. Study area The present study has been carried out in a part of Sutlej river basin that is confined in the hilly State of Himachal Pradesh, India. It has covered parts of Simala, Kullu, Manali, Bilaspur and Solan districts of Himachal Pradesh. This has a spread of 2457 km2 and lies between 31°05’00”N and 31°39’26’’N latitudes and 76°51’11’’E and 77°45’17’’E longitudes (Figure 1).

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National Seminar on Green Technologies for Sustainable Environmental Management, March 22-23, 2013, Doon University, Dehradun, India.

Figure 1: Location map of the study region The basin is drained by the Sutlej river that is the largest North-West flowing river of the Indus river systems. The topography of the study area is very rugged with an altitude ranging from 500 m to 5200 m. The large disparities in the topographical relief have resulted in variety of climate in the Sutlej basin. The important factors which control the climate in the basin are the effect of mountain barrier and sharp variations in altitude and local relief features over a short horizontal distance. Largely due to variations in altitude, the climate varies from hot and moist tropical climate in lower valleys, to cool temperate climate at about 2000 m and tends towards polar as the altitude increases beyond 2000 m. Altitude controls not only temperature but rainfall also. The second factor controlling the climate is aspect of slopes. Usually the south facing slopes are sunnier and also get more rain. The mean annual temperature and precipitation has been recorded 21.23°C and 103cm respectively. 4. Data sets The daily observed minimum and maximum temperature data have been selected as predictands variables for the downscaling. The observed data has been collected and supplied by Bhakra Beas Management Board (BBMB) for three hydro-metrological stations namely Rampur, Sunni and Kasol within the Sutlej basin. The minimum and maximum temperature data are available for the period 1970-2007 (Rampur and Sunni) and 1963-2007 (Kasol) respectively. The large scale atmospheric variables called predictors are grouped into two category; observed predictors (NCEP/NCAR reanalysis data sets) and modelled predictors (GCMs simulated data). The predictor variables for NCEP and CGCM3 are directly downloaded from the Data Access Integration (DAI) website (http://loki.qc.ec.gc.ca/DAI/predictors-e.html). The data is supplied on a grid box by grid box basis in a zip file. The total number of 26 predictor variables is provided within each file at a daily time step (Table 2). The NCEP/NCAR reanalysis data is available from 1961 to 2003 (43 years) and this data has been interpolated to CGCM3 grid resolution (3.75°latitude × 3.75°longitude). The predictor variables of CGCM3 are available for period 1961-2100 (A2 scenario) and 2001-2100 (A1B scenario).


Table 2: List of available predictors for each grid box

Predictors Description Predictors Description

mslp Mean sea level pressure p5zh 500hPa Divergence p__f 1000hPa Wind Speed p8_f 850hPa Wind Speed p__u 1000hPa U-component p8_u 850hPa U-component p__v 1000hPa V-component p8_v 850hPa V-component p__z 1000hPa Vorticity p8_z 850hPa Vorticity p_th 1000hPa Wind Direction p850 850hPa Geopotential height p_zh 1000hPa Divergence p8th 850hPa Wind Direction 5_f 500hPa Wind Speed p8zh 850hPa Divergence

p5_u 500hPa U-component s500 500hPa Specific Humidity p5_v 500hPa V-component s850 850hPa Specific Humidity p5_z 500hPa Vorticity shum 1000hPa Specific Humidity p500 500hPa Geopotential height temp Temperature at 2m p5th 500hPa Wind Direction prcp Accumulated precipitation

5. Methodology The Statistical Downscaling Model version 4.2 (SDSM 4.2) has been selected for this study because of its worldwide use and validation (Aherne et al., 2008; Dibike and Coulibaly, 2005; Huang et al., 2010; Meenu et al., 2012; Wilby et al., 2002). This is a window based decision tool which can be freely downloaded (http://co-public.lboro.ac.uk/cocwd/SDSM/main.html). The SDSM which a combination of multiple regression method and stochastic weather generator requires two types of daily data for downscaling. The first type corresponds to station scale predictands such as daily temperature and the second refers to large scale predictors of NCEP/NCAR and GCM of a grid box closest to study area. The methodology adopted in the present work has been shown in Figure 2 with the help of flow chart. 6. Results and discussions In this section, selection of predictors from NCEP/NCAR reanalysis data sets has been done. Further, the SDSM 4.2 model has been calibrated and validated for downscaling and generation of maximum and minimum temperature for the future periods. The future period is divided into three time slices; 2020s (2011-40), 2050s (2041-2070) and 2080 (2071-2100). 6.1. Choice of predictors The choice of predictors is made on the basis of explained variance, correlation analysis, partial correlation analysis and scatter plots. The physical sensitivity between selected predictors and predictand is also taken into account for the site (Khan et al. 2006). In this study, the most appropriate sets of predictor variables have been selected on the basis of partial correlation and percentage of explained variance (E) analysis among the predictands and the individual predictors. The predictor variables selected for downscaling temperature used in this study have been shown in bold text in the Table 3. The sets of predictors chosen vary significantly from one station to another for maximum and minimum temperature.


Figure 2: Downscaling and generation of scenarios for maximum and minimum temperature (modified after Wilby and Dawson, 2007)

6.2. Calibration and validation of SDSM The developed model has been calibrated with NCEP/NCAR reanalysis data sets and daily predictands (maximum and minimum temperature). A statistical relationship has been developed between selected NCEP/NCAR predictor variables and observed predictands (Tmax and Tmin). The model has also been validated with independent time series data of observed daily maximum and minimum temperature against simulated values downscaled from selected large scale predictors of NCEP/NCAR reanalysis data sets and scenario A2 of CGCM3 model.


In this study, 20 years (1963-1982) maximum and minimum daily temperature data for Kasol and 16 years (1970-1985) for Sunni and Rampur have been used for calibration process respectively. The model has been designed for monthly run and unconditional process option has been selected for downscaling of maximum and minimum daily temperature. The statistical indicators such as the monthly average percentage of explained variance (E) and the monthly average standard error (SE) have been used to reflect downscaling results of daily maximum and minimum temperature at each site in the basin. These have been described in Table 4.

Table 3: Selection of predictors using partial correlations of NCEP/NCAR

Hydro-metrological Station Tmax. Tmin Predictors Partial P value Predictors Partial P value Correlation (r) Correlation (r)

Kasol

p__z 0.228 0.0000 p500 0.101 0.0000 p500 0.304 0.0000 p8th 0.101 0.0000 p8_z -0.267 0.0000 shum_lag 0.221 0.0000 s850 -0.280 0.0000 t_lag 0.607 0.0000 shum 0.223 0.0000

t_lag 0.592 0.0000

Sunni

p__f 0.212 0.0000 p__f 0.189 0.0000 p__z 0.102 0.0000 p__z 0.145 0.0000 p500 0.104 0.0000 p500 0.121 0.0000 shum -0.128 0.0000 shum_lag 0.356 0.0000 t_lag 0.320 0.0000 t_lag 0.341 0.0000

Rampur

p__v 0.072 0.0000 p__f 0.203 0.0000 mslp_lag -0.279 0.0000 p8_z 0.082 0.0000

p500 0.253 0.0000 shum_lag 0.273 0.0000 p8_z 0.133 0.0000 temp 0.187 0.0000 s500 -0.236 0.01605 t_lag 0.114 0.0000 t_lag 0.160 0.0000

Table 4: Performance assessment of the SDSM model during calibration period

Station

Tmax Tmin

E (%) SE (°C) R2

RMSE (°C) E (%) SE (°C) R2 RMSE

(°C)

Kasol (1963-82) 53.7 2.01 0.89 1.85 52.4 1.55 0.92 1.96

Sunni (1970-85) 43.7 2.58 0.79 2.65 50.8 1.76 0.94 1.84

Rampur (1970-85) 39.2 2.73 0.87 2.10 52 1.54 0.90 2.19

The statistical parameters such as coefficient of determination (R2) and root mean square error (RMSE) have been used to test the SDSM performance during the calibration period (Table 4).


The results show that maximum and minimum daily temperature is highly correlated during the calibration period. The graphs (Figure 3) have been plotted against observed and simulated values of maximum and minimum temperatures for each site. Figure 3 shows that a good agreement exits between observed and simulated maximum and minimum temperatures.

Figure 3: Comparing observed and simulated values of maximum and minimum temperature for all three stations during calibration period

The SDSM model has been validated using 18 years (1983-2000) data for Kasol and 15 years (1986-2000) for Sunni and Rampur respectively. The coefficient of determination (R2) and root mean square error (RMSE) have been used to test the simulation results of mean daily maximum and minimum temperatures (Table 5). The results show that significant variation exist in different stations. The R2 value for the maximum and minimum temperature simulated from NCEP and A2 ranges from 0.58 to 0.93 and RMSE (°C) value from 1.55°C to 4.41°C. Figure 4 explains that the simulated properties acceptably describe the observed statistics. But deviation of amount is noticed between them.

Table 5: Performance assessment of the SDSM model during validation period

Station Tmax Tmin

Predictors R2 RMSE (°C) Predictors R2 RMSE

(°C)

Kasol (1983-2000) NCEP 0.86 1.96 NCEP 0.93 1.55

A2 0.85 2.47 A2 0.88 2.04

Sunni (1986-2000) NCEP 0.74 3.19 NCEP 0.90 2.47

A2 0.58 4.41 A2 0.86 3.04

Rampur (1986-2000) NCEP 0.82 2.49 NCEP 0.92 1.81

A2 0.88 1.94 A2 0.88 2.20


Figure 4: Comparison of monthly mean maximum and minimum temperatures among observed and downscaled from NCEP data and CGCM3 data during validation period in

Sutlej river basin

6.3. Projection of Tmax and Tmin under A1B and A2 emission scenarios The calibrated SDSM model has been used to downscale and generate daily time series of maximum and minimum temperature from large scale predictor variables of CGCM3 model under scenarios A2 and A1B. The period of 1970–2000 has been taken as a base period and the future time period is divided into five different time series; 2001-2020, 2021-2040, 2041-2060, 2061-2080 and 2081-2100. The future scenarios of daily maximum (Tmax) and minimum temperature (Tmin) have been compared to base line period to critically analyze the patterns of change in both the maximum and minimum temperature in the study region. The future annual trend of maximum and minimum temperature along with inter annual variability has been examined for all stations in the study area (Figure 5). The increasing trend has been observed for maximum temperature at Kasol and Rampur whereas for minimum temperature at Sunni and Rampur in the end of 21st century.

Figure 5: Annual trend of 100 year (2001-2100) scenario statistics of maximum and minimum temperature for Kasol, Sunni and Rampur stations in the Sutlej basin


On the whole net increase in maximum and minimum temperature has been noticed in the study region for five different time period: 2001-2020, 2021-2040, 2041-2060, 2061-2080 and 2081-2100 under A2 and A1B scenario. The rise in maximum and minimum temperature has been found more prominent under A2 scenario in comparison with A1B scenario. For Tmax, these are 0.12°C, 0.22°C, 0.26°C, 0.36°C and 0.40°C under A2 and 0.01°C, 0.11°C, 0.22°C, 0.28°C and 0.30°C under A1B emission scenario for the above five time periods respectively. Similarly for Tmin, these are 0.56°C, 0.54°C, 0.55°C, 0.82°C and 0.92°C under A2 and 0.05°C, 0.42°C, 0.59°C, 0.79°C and 0.73°C under A1B emission scenario.

The monthly variations in maximum and minimum temperature have been also investigated in the study area under scenarios A2 and A1B of CGCM3 model for future periods (Figure 6 and Figure 7). The increase in maximum daily temperature has been found from January to May and from October to December under both the scenarios at Kasol and Sunni. At Rampur, rise in temperature has been noticed from January to April under A1B and A2 scenario. Significant decrease in maximum daily temperature has been observed in the months of May, June, July, August and September. This is more prominent at Sunni. Similarly, the overall increase in daily minimum temperature has been found throughout year under scenarios A1B and A2 of CGCM3 model at Sunni and Rampur. At Kasol, fall in daily minimum temperature has been recorded in the months of January, February, March, November and December under both the scenarios.

Figure 6: Mean monthly variation in maximum temperature under scenario A2 and A1B



Figure 7: Mean monthly variation in minimum temperature under scenario A2 and A1B

7. Conclusion In the present study spatial and temporal patterns of maximum and minimum daily surface temperature have been investigated in middle catchment of the Sutlej basin for the future periods. The changes in maximum and minimum temperature have been studied under scenario A2 and A1B. A statistical downscaling approach has been adopted for downscaling and generation of future scenarios from large scale predictors of CGCM3 model. The projected results show net increase in maximum and minimum temperature for five different time period: 2001-2020, 2021-2040, 2041-2060, 2061-2080 and 2081-2100 under A2 and A1B scenario. The rise in minimum temperature has been found slightly higher in comparison to maximum temperature. Monthly variation is more pronounced and this significantly differs from one station to another station within the study region. These kinds of monthly variation in maximum and minimum daily temperature may cause decline of snowlines and alter the behaviour of the river discharge. Therefore, the present study will provide useful insight to devise better strategy for the management of water resources in the Sutlej basin. References 1. Aherne, J., Futter, M. N., Dillion, P. J., 2008. The impacts of future climate change and

sulphur emission reductions on acidification recovery at Plastic Lake, Ontario. Hydrological and Earth System Sciences 12, 383–392.

2. Anandhi, A., Shrinivas, V. V., Kumar, D. N., Nanjundiah, R. S., 2009. Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. International Journal of Climatology 29 (4), 583-603, DOI: 10.1002/joc.1719.



3. Arora, V. K. and Boer, G. J., 2001. Effects of simulated climate change on the hydrology of major river basins. Journal of Geophysical Research, 106(D4), 3335–3348, DOI: 10.1029/2000JD900620.

4. Bardossy, A., Bogardi, I., Matyasovszky, I., 2005. Fuzzy rule-based downscaling of precipitation. Theoretical and Applied Climatology 82 (1-2), 119–129.

5. Benestad, R. E., 2001. A comparison between two empirical downscaling strategies. International Journal of Climatology 21 (13), 1645–1668, DOI: 10.1002/joc.703.

6. Bergant, K., Kajfez-Bogataj, L., Trdan, S., 2006. Uncertainties in modelling of climate change impact in future: An example of onion thrips (Thrips Tabaci indeman) in Slovenia. Ecological Modelling 194, 244–255.

7. Chen, D. and Chen, Y., 2003. Association between winter temperature in China and upper air circulation over East Asia revealed by canonical correlation analysis. Global and Planetary Change 37, 315–325.

8. Chen, S.-T., Yu, P.-S., Tang, Y.-H., 2010. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology 385 (1-4), 13-22, Doi:10.1016/j.jhydrol.2010.01.021.

9. Combalicer, E. A., Cruz, R.V.O., Lee, S., IM, S., 2010. Assessing climate change impacts on water balance in the Mount Makiling forest, Philippines. Journal of Earth System Sciences.119 (3), 265–283.

10. Dibike, Y. B. and Coulibaly, P., 2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology 307, 145–163.

11. Dibike, Y. B. and Coulibaly, P., 2006. Temporal neural networks for downscaling climate variability and extremes. Neural Networks 19(2), 135–144.

12. Droogers, P. and Aerts, J., 2005. Adaptation strategies to climate change and climate variability: A comparative study between seven contrasting river basins. Physics and Chemistry of the Earth 30, 339–346.

13. Dubrovsky, M., Buchtele, J., Zalud, Z., 2004. High-frequency and low frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Climatic Change 63(1-3), 145–179.

14. Fowler, H. J., Kilsby, C. G., O’Connell, P. E., 2000. A stochastic rainfall model for the assessment of regional water resource systems under changed climatic conditions. Hydrology and Earth System Sciences 4(2), 261–280.

15. Gagnon, S., Singh, B., Roussselle, J., Roy, L., 2005. An Application of the Statistical DownScaling Model (SDSM) to Simulate Climatic Data for Stream flow Modelling in Québec. Canadian Water Resources Journal 30(4), 297–314.

16. Ghosh, S., 2010. SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. Journal of Geophysical Research 115, D22102, DOI: 10.1029/2009JD013548.



17. Ghosh, S. and Mishra, C., 2010. Assessing Hydrological Impacts of Climate Change: Modeling Techniques and Challenges. The Open Hydrology Journal 4, 115-121.

18. Giorgi, F., 1990. Simulation of regional climate using a limited area model nested in a general circulation model. Journal of Climate 3(9), 941–63.

19. Gosain, A. K., Rao, S., Basuray, D., 2006. Climate change impact assessment on hydrology of Indian river basins. Current Science, 90(3), 346–353.

20. Hewitson, B. C. and Crane, R. G., 1996. Climate downscaling: techniques and application. Climate Change 7(2), 85–95.

21. Hua, C., Jing, G., Wei, X., Guo, S., Xu, C.-Y., 2010. Downscaling GCMs using the Smooth Support Vector Machine Method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 27( 2), 274–284.

22. Huang, J., Zhang, J. Zhang, Z., Xu, C. Y., Wang, B., Yao, J., 2010. Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch Environ Res Risk Assess, 25(6),781-792, DOI 10.1007/s00477-010-0441-9.

23. Huang, J., Zhang, J. Zhang, Z., Sun, S., Yao, J., 2011. Simulation of extreme precipitation indices in the Yangtze River basin by using statistical downscaling method (SDSM). Theoretical and Applied Climatology, DOI 10.1007/s00704-011-0536-3.

24. Immerzeel, W.W., van Beek, L.P.H. Bierkens, M.F.P., 2010. Climate Change Will Affect the Asian Water Towers, Science.328 (5984), 1382–1385, DOI:10.1126/science.1183188.

25. Intergovernmental Panel on Climate Change (IPCC), 2007. Climate change (2007). Impacts, adaptation and vulnerability. Contribution of working group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by M. Parry et al., Cambridge University Press, UK.

26. Jain, S. K., Goswami, A., Saraf, A. K., 2009. Assessment of Snowmelt Runoff Using Remote Sensing and Effect of Climate Change on Runoff. Water Resource Management 24(9), 1763–1777.

27. Jiang, T., Chen, Y. D., Xu, C-yu., Chen, X., Chen, X., Singh, V. P., 2007. Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China. Journal of Hydrology, 336, 316-333, DOI: 10.1016/j.jhydrol.2007.01.010.

28. Jones, R. G., Murphy, J. M., Noguer, M., 1995. Simulation of climate change over Europe using a nested regional climate model. I. Assessment of control climate, including sensitivity to location of lateral boundaries. Quarterly Journal of the Royal Meteorological Society 121(526), 1413–50.

29. http://co-public.lboro.ac.uk/cocwd/SDSM/main.html[ Last accessed date 1/02/2012]

30. http://loki.qc.ec.gc.ca/DAI/predictors-e.html [ Last accessed date 3/02/2012]

31. Khan, M. S., Coulibaly, P., Dibike, Y., 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319(1-4), 357–382.



32. Kilsby, C. G., Jones, P. D., Burton, A., Ford, A. C., Fowler, H. J., Harpham, C., James, P., Smith, A., Wilby, R. L., 2007. A daily weather generator for use in climate change studies. Environmental Modelling and Software 22(12), 1705–1719.

33. Marshall, S. J., Sharp, M. J., Burgess, D. O., Anslow, F. S., 2007. Near-surface temperature lapse rates on the Prince of Wales Ice field, Ellesmere island, Canada: implications for regional downscaling of temperature. International Journal of Climatology 27(3), 385–398, DOI: 10.1002/joc.1396.

34. Mason, S. J., 2004. Simulating Climate over Western North America Using Stochastic Weather Generators. Climatic Change, 62(1-3),155–187.

35. Meenu, R., Rehana, S., Mujumdar, P. P., 2012. Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin, India with HEC-HMS and SDSM. Hydrological Processes, DOI: 10.1002/hyp.9220.

36. Ministry of Water Resources (2008). ‘Preliminary Consolidated Report on Effect of Climate Change on Water Resources:[http://cwc.gov.in/main/downloads/Preliminary_Report_final.pdf. [Last accessed date 12.01.2012].

37. Parry, M., Canziani, O., Palutikof, J., van der Linden, P., Hanson, C., 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. 1st edition. Cambridge University Press.

38. Raghavan, S. V., Vu, M. T., Liong, S. Y., 2012. Assessment of future stream flow over the Sesan catchment of the Lower Mekong Basin in Vietnam. Hydrological Process 26(4), 3661-3668, DOI: 10.1002/hyp.8452.

39. Ravindranath, N .H., Rao, S., Sharma, N., Nair, M., Gopalakrishnan, R., Rao, A. S., Malaviya, S., Tiwari, R., Sagadevan, A., Munsi, M., Krishana, N., Bala,G., 2011. Climate change vulnerability profiles for North East India. Current Science 101(3),384-394.

40. Schoof, J. T., Pryor, S. C., Robeson, S. M., 2007. Downscaling daily maximum and minimum temperatures in the midwestern USA: a hybrid empirical approach. International Journal of Climatology 27(4), 439–454, DOI: 10.1002/joc.1412.

41. Sharma, S. K. and Chauhan, R., 2011. Climate change research initiative: Indian Network for Climate Change Assessment. Current Science, 101(3). 308-311.

42. Srikanthan, R. and McMahon, T. A., 2001. Stochastic generation of annual, monthly and daily climate data: a review. Hydrology and Earth Systems Sciences 5 (4), 653– 670.

43. Tatli, H., Dalfes, H. N., Mentes, S., 2005. Surface air temperature variability over Turkey and its connection to large-scale upper air circulation via multivariate techniques. International Journal of Climatology 25, 161–180.

44. Timbal, B., Dufour, A., McAvaney, A., 2003. An estimate of future climate change for western France using a statistical downscaling technique. Climate Dynamics, 20(7-8), 807–823.

45. Tripathi, S., Srinivas, V. V., Nanjundiah, R. S., 2006. Downscaling of precipitation for climate change scenarios: a support vector machine approach. Journal of Hydrology 330(3–4): 621–640, DOI:10.1016/j.jhydrol.2006.04.030.



46. Vicuna, S. and Dracup, J. A., 2007. The evolution of climate change impact studies on hydrology and water resources in California. Climatic Change 82, 327–350.

47. Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Liermann, C. R., Davies, P. M., 2010. Global threats to human water security and river biodiversity.Nature, 467(7315), 555–561, DOI: 10.1038/nature09440.

48. Wilby, R. L., Dawson, C. W., Barrow, E. M., 2002. SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2), 147-159.

49. Wilby, R. L. and Dawson, C. W., 2007. SDSM User Manual- A Decision Support Tool for the Assessment of Regional Climate Change Impacts. https://copublic.lboro.ac.uk/cocwd/SDSM/main.html [Last accessed date 17.11.2011].

50. Wilby, R. L., Hay, L. E., Leavesly, G. H., 1999. A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. Journal of Hydrology 225(1-2), 67–91.

51. Wilby, R. L. and Wigley, T. M. L., 1997. Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography 21(2), 530–548.

52. Wisser, D., Frolking, S., Douglas, E. M., Fekete, B. M., Vörösmarty, C. J., Schumann, A. H., 2008. Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets. Geophysical Research Letters, 35(24), L24408, DOI: 10.1029/2008GL035296.

53. Zhang, X. C., 2005. Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agricultural and Forest Meteorology 135, 215–229.



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