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Simulation of Seasonal Rainfall and Temperature Variation—A Case Study of Climate Change Projection in Ponnaiyar River Basin, Southern India
Chapter 12
Simulation of Seasonal Rainfall and Temperature Variation—A Case Study of Climate Change Projection in Ponnaiyar River Basin, Southern India A. Jothibasu and S. Anbazhagan Centre for Geoinformatics and Planetary Studies, Periyar University, Salem, India
Chapter Outline 12.1 Introduction 12.2 Study Area 12.3 Methodology 12.3.1 Baseline Climate 12.3.2 Projection for 2011–2040 12.3.3 Projection for 2041–2080
167 167 168 169 170 170
12.4 Results and Discussion 12.4.1 Simulation of Temperature 2011–2080 12.4.2 Rainfall Simulation (2011–2080) 12.5 Conclusions References
171 171 174 176 176
12.1 INTRODUCTION Extreme events have triggered massive consequences for human society and for the natural environment all over the world. Moreover, climate change has the potential to change the intensity and frequency of these events (Frias et al., 2012). Mahmood and Babel (2014) have explored future extreme temperature changes using the statistical downscaling model (SDSM) in the transboundary region of the Jhelum River Basin. Thoeun (2015) outlined observed and projected changes in temperature and rainfall in Cambodia. In the assessment of observed and projected changes in temperature and rainfall in Cambodia, it is noted that the PRECIS GCM output is on a monthly basis. In some cases, daily data are required to perform vulnerability and adaptation assessment. The global climate models provide a laboratory for numerical experiments on climate transitions during the past, present, and future. General circulation models (GCMs) are a class of computer-driven models for weather forecasting, understanding of climate, and for projecting climate change (IPCC, 2007). Global climate models drive the regional climate models; time-dependent large-scale lateral boundary forcing is imposed from GCM simulations ( Johns et al., 1997). The regional climate modeling (RCM) approach affords an increase of resolution over a region of the globe in comparison to the CGCMs, with regional grid-point spacing of a few tens of km in the horizontal, for operational use on climate timescales. The RCM approach could still be useful to reach a resolution of a few km for the same computational load (Laprise, 2008). A variation of this technique is to also force the large-scale component of the RCM solution throughout the entire domain (Evans et al., 2012). The main objective in the present research is to assess the simulated changes of temperature and rainfall for future predictions in the Ponnaiyar River Basin.
12.2 STUDY AREA The Ponnaiyar River Basin is an interstate river and is one of the largest rivers of the state of Tamil Nadu, often reverently called “Little Ganga of the South.” The river has supported many civilizations in peninsular India throughout history and continues to play a vital role in supplying precious water for drinking, irrigation, and industry to the people of the states of GIS and Geostatistical Techniques for Groundwater Science. https://doi.org/10.1016/B978-0-12-815413-7.00012-2 © 2019 Elsevier Inc. All rights reserved.
167
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SECTION C Groundwater Quality Assessment Using GIS and Geostatistical Aspects
Karnataka, Tamil Nadu, and Pondicherry. The study area extends over approximately of 11,595 km2 and lies between latitudes 11°350 and 12°350 000 north and longitudes 77°450 000 and 79°550 000 east (Fig. 12.1). The Ponnaiyar River originates on the south eastern slopes of the Chennakesava Hills, northwest of Nandidurg of the Kolar district in Karnataka State at an altitude of 1000 m above mean sea level (AMSL). The total length of the Ponnaiyar River is 432 km, of which 85 km lies in Karnataka State; 187 km in Dharmapuri, Krishnagiri, and Salem Districts; 54 km in Thiruvannamalai and Vellore Districts; and 106 km in Cuddalore and Villupuram Districts of Tamil Nadu. The Ponnaiyar Basin is predominantly built up with granite and gneisses rocks of the Archean period. The granite is of very good quality and extensive outcrops and masses of it are commonly found. The chief components of the rock are hornblende and feldspar. Foliation is seldom seen. In the plains of the reserve forest quartz is found commonly. Diamond granite is also found in scattered pockets in the area of the Chitteri hills in Dharmapuri and Krishnagiri sub-divisions. Charnockite rocks from the Archean period are also seen in some areas. Alluvium and sand-dunes of the Quaternary period are also seen at a few places. The 15 years’ average annual rainfall for the period 2000–2014 in the basin was 969 mm. This catchment falls under the tropical belt; the climate in general is hot with April and May being the hottest months of the year when the temperature rises to 34°C.
12.3
METHODOLOGY
In the present study, the future climate conditions forced with the Special Report on Emission Scenarios (SRES) of AR4 of the IPCC at regional scale obtained throughout a dynamical downscaling from GCMs are available on the CCAFS website. The datasets contain a large range of RCMs developed by different countries and climate modeling communities with different spatial resolutions. The datasets are available in ARC GRID and ARC ASCII format, in decimal degrees and datum WGS84. This data format facilitates their integration into a geographical information system (GIS) environment for processing. The climate model downscaling data of the bccr_bcm2_0 was selected for this investigation due to its fine space scale (30 s) (Table 12.1). The fine space resolution is very important for this investigation due to the complex topography of the study area. The climatic baseline over the period 1961–1990 was used to compare and calculate projected changes for
FIG. 12.1 The location of the Ponnaiyar River Basin shows the shuttle radar topography mission digital elevation model.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
12
169
TABLE 12.1 Particulars of SRES A1B Global Circulation madel File set
Delta method IPCC AR4
Spatial interpolation of anomalies (deltas) of original GCM outputs from IPCC CMIP2 applied to a Worldclim high-resolution baseline climate. Data were processed by CIAT
Scenario
SRES A1b
A very heterogeneous world with a continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than in other storylines
Model
bccr_bcm2_0
sres_a1b_2040s_prec_30s_tile_a5_asc.zip
Extent
Global (A5 and B5)
Format
ASCII grid format
Period
2020–2080
Variables
Bioclimatic
Derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. See http://www.worldclim.org/bioclim
Maximum temperature
Monthly mean maximum temperature. Unit: °C * 10
Mean temperature
Monthly mean average temperature. Unit: °C * 10
Minimum temperature
Monthly mean minimum temperature. Unit: °C * 10
Precipitation
Monthly accumulated rainfall. Unit: mm/month
Resolution
ARC ASCII GRID refers to a specific interchange format developed for ARC/INFO rasters in ASCII format. The format consists of a header that specifies the geographic domain and resolution, followed by the actual grid cell values
30 s
the average annual maximum temperature, average annual minimum temperature, and seasonal precipitation. The pattern of temperature and rainfall anomalies was calculated for each raster cell grid using ArcGIS software for short term (2011– 2040) and medium term (2041–2080). Future climate simulation related to the IPCC’s A1B greenhouse gas (GHG) emissions scenario was chosen to assess future temperature and rainfall projections. This scenario predicts an intermediate level of warming by the end of the century and a future where technology is shared between developed and developing nations in order to reduce regional economic disparities.
12.3.1
Baseline Climate
The hottest month of the year in the Ponnaiyar River Basin is May. During this month average maximum temperatures are between 28°C and 38°C. Coastal regions are hot and humid in summer with maximum temperatures around 38°C and humidity levels exceeding 90%. In the interior plains high temperatures in summer can exceed 35°C. The coldest month of the year in the study area is January. During this month average minimum temperatures are between 11°C and 21°C (Fig. 12.2). The coldest temperatures are encountered in highland and mountain areas in the northern and southern part of the study area. Rainfall is caused by four principal mechanisms—convection, cold frontal troughs, monsoons, and tropical storms/ cyclones—and their interactions with local topography and other meteorological conditions (Kwarteng et al., 2008). During the winter months of January and February, average rainfall is between 3 and 47 mm in the study area. Physiographic conditions significantly affect average winter rainfall (Charabi and Al-Hatrushi, 2010). In the summer months of March to May the average rainfall is between 61 and 242 mm for the overwhelming majority of the study area. During the summer months some parts of the study region are transformed into lush landscapes of green field and verdant vegetation (Charabi, 2009). During the northeast monsoon season the average baseline rainfall is between 278 and 774 mm. The hilly region and central part of the study area received the higher rainfall in this season. The baseline southwest monsoon average annual rainfall indicated that the eastern part of the coastal regions had higher rainfall (608–761 mm) (Fig. 12.3).
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FIG. 12.2 Temperature patterns of the study area for the period 1961–1990: (A) average minimum temperature and (B) average maximum temperature.
12.3.2
Projection for 2011–2040
The simulated average maximum and minimum temperature changes during the period 2011–2040 are shown in Fig. 12.4. The A1B scenario clearly shows an increase in the future maximum temperature in the range of 1–2°C for the entire study area through 2040. The projected average annual rainfall pattern for all four seasons is shown in Fig. 12.5. The winter season rainfall pattern changes from 8 to 49 mm in the study area. In the period of 2011–2040 the summer rainfall pattern deviated from 42 to 207 mm. The rainfall trends for northeast and southwest monsoons also increased in the projected period of 2011–2040.
12.3.3
Projection for 2041–2080
The average maximum and minimum temperatures are projected to increase in the range of 2–3°C, with the geographic distribution of these changes following similar patterns as in the earlier period by 2080. Fig. 12.6 shows the simulated maximum and minimum temperature changes during the period 2041–2080. The A1B scenario also clearly shows future
Simulation of Seasonal Rainfall and Temperature Variation Chapter
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171
FIG. 12.3 Baseline rainfall patterns of study area for the period 1961–1990: (A) winter, (B) summer, (C) northeast, and (D) southwest rainfall.
minimum temperature increases that are similar to the results shown for maximum temperature changes. For both time periods there is a clear expansion compared to the results for maximum temperature change, suggesting that minimum temperatures will experience the greatest impact from climate change. Fig. 12.7 shows that the simulated average annual rainfall changes during the period 2041–2080. It indicates that the A1B scenario clearly shows that most of study area will become drier in the summer and winter seasons, with large portions of the study area receiving up to 40 mm less in annual rainfall throughout the projection period. On the other hand, the model results indicate that the southwest monsoons are likely to intensify, leading to increased rainfall in the southwestern parts of the country.
12.4 RESULTS AND DISCUSSION GCMs lack the regional detail that impacts assessments on climate change. An RCM adds small-scale detailed information of future climate change to the large-scale projections of a GCM. Coarse resolution information from a GCM can be used to develop temporally and spatially fine scale information. The main advantage of an RCM is that it can provide highresolution information on a large physically consistent set of climate variables and therefore a better representation of extreme events. Comparing the baseline (1961–1990) simulation output with the observed data for the period of 2011– 2080, the RCM simulation was validated.
12.4.1
Simulation of Temperature 2011–2080
During the projected period the study area seems to show continually increasing temperatures from 1°C to 3.5°C. Significant annual average temperature increases of record high temperatures for around 2040 and 2080 indicate strong interdecadel variability. Nevertheless, the significant increase recorded for 2080 is extremely severe compared to the rest. Highest (3.7ºC) and lowest (1.3ºC) temperature increases for the study area is obtained during the period of 2011–2080 (Fig. 12.8).
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SECTION C Groundwater Quality Assessment Using GIS and Geostatistical Aspects
FIG. 12.4 Projected temperature pattern (2011–2040): (A) minimum temperature and (B) maximum temperature.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
FIG. 12.5 Rainfall patterns of study area for the period 2011–2040.
12
173
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FIG. 12.6 Projected temperature pattern (2041–2080): (A) minimum temperature and (B) maximum temperature.
12.4.2
Rainfall Simulation (2011–2080)
In a region where the historic average annual rainfall levels are between 50 and 100 mm, climate change is expected to lead to between 20 and 40 mm less rainfall by 2080. This is equivalent to a reduction in the average annual rainfall of about 40%. With less future rainfall in northern areas, groundwater recharge and surface water flow are expected to also decrease.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
FIG. 12.7 Rainfall patterns of study area for the period 2041–2080.
FIG. 12.8 Simulated temperature fluctuations between 2011 and 2080.
12
175
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SW
Seasons
NE
summer 2010–80 2040–80 2010–40 Winter
–60
–40
–20
0
20
40 60 Rainfall in mm FIG. 12.9 Simulated seasonal average rainfall patterns between 2011 and 2080.
80
100
120
140
The SRES A1B scenario indicates that a slackening of the winter monsoon. The rainfall during the summer monsoon in 2080 is projected to be the worst over the study area. The negative annual rainfall trend is extremely evident in relation to climate change. Nevertheless, all of the regions seem to experience increased rainfall in the monsoons of the southwest toward the end of the century (2080). The significant increase in the annual temperatures simulated for 2080 corresponds to a significant reduction in the annual summer rainfall simulated for the same years (Fig. 12.9). Of these years, the highest rainfall reduction was simulated for 2080, which corresponds to the highest temperature increase simulated for annual temperature anomalies. This indication of significant reduction of rainfall together with significant increases in temperature is generally exhibited during El Nino events.
12.5
CONCLUSIONS
The Ponnaiyar River Basin baseline climate was evaluated relative to temperatures and rainfall patterns using proxy data. With climate change these baseline conditions are expected to change. Projected changes in temperatures and rainfall were assessed and the results of this assessment formed the basis by which the vulnerability of the study area to climate change could be understood. Water resources are already facing considerable threat in south India. With climate change it is expected that the prevention of groundwater degradation and balancing supply and demand will become even greater challenges. As discussed previously, northern Oman is expected to face decreasing rainfall in the coming decades. When combined with continued socioeconomic growth, the current challenges in balancing water supply and demand will increase, as will the difficulty in maintaining water-quality standards. Moreover, greater rainfall variability and longer drought episodes may adversely impact the already fragile and vulnerable mountain ecosystems of the region. Integrated water resource management (IWRM) is considered a fundamental organizing framework to identify and evaluate potential adaptation strategies for water resources.
REFERENCES Charabi, Y., 2009. Arabian summer monsoon variability: teleconexion to ENSO and IOD. Atmos. Res. 91, 105–117. Charabi, Y., Al-Hatrushi, S., 2010. Synoptic aspects of winter rainfall variability in Oman. Atmos. Res. 95, 470–486. Evans, J., McGregor, J., McGuffie, K., 2012. Future regional climates. In: Henderson-Sellers, A., McGuffie, K. (Eds.), The Future of the World’s Climate, pp. 223–250. https://doi.org/10.1016/B978-0-12-386917-3.00009-9.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
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Frias, M.D., Minguez, R., Gutierrez, J.M., Mendez, F.J., 2012. Future regional projections of extreme temperatures in Europe: a nonstationary seasonal approach. Clim. Change 113 (2), 371–392. IPCC, 2007. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L. (Eds.), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom/New York, NY. Johns, T.C., Carnell, R.E., Crossley, J.F., Gregory, J.M., Mitchell, J.F.B., Senior, C.A., Tett, S.F.B., Wood, R.A., 1997. The second Hadley centre coupled ocean-atmosphere GCM: model description, spinup and validation. Clim. Dyn. 13, 103–134. Kwarteng, A.Y., Dorvlo, A.S., Ganiga, T.K., 2008. Analysis of a 27-year rainfall data (1977-2003) in the Sultanate of Oman. Int. J. Climatol. 29, 605–617. Laprise, R., 2008. Regional climate modeling. J. Comput. Phys. 227, 3641–3666. Mahmood, R., Babel, M.S., 2014. Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum river basin. Weather Clim. Extrem. 5–6, 56–66. Thoeun, H.C., 2015. Observed and projected changes in temperature and rainfall in Cambodia. Weather Clim. Extrem. 7, 61–71.
Simulation of Seasonal Rainfall and Temperature Variation—A Case Study of Climate Change Projection in Ponnaiyar River Basin, Southern India A. Jothibasu and S. Anbazhagan Centre for Geoinformatics and Planetary Studies, Periyar University, Salem, India
Chapter Outline 12.1 Introduction 12.2 Study Area 12.3 Methodology 12.3.1 Baseline Climate 12.3.2 Projection for 2011–2040 12.3.3 Projection for 2041–2080
167 167 168 169 170 170
12.4 Results and Discussion 12.4.1 Simulation of Temperature 2011–2080 12.4.2 Rainfall Simulation (2011–2080) 12.5 Conclusions References
171 171 174 176 176
12.1 INTRODUCTION Extreme events have triggered massive consequences for human society and for the natural environment all over the world. Moreover, climate change has the potential to change the intensity and frequency of these events (Frias et al., 2012). Mahmood and Babel (2014) have explored future extreme temperature changes using the statistical downscaling model (SDSM) in the transboundary region of the Jhelum River Basin. Thoeun (2015) outlined observed and projected changes in temperature and rainfall in Cambodia. In the assessment of observed and projected changes in temperature and rainfall in Cambodia, it is noted that the PRECIS GCM output is on a monthly basis. In some cases, daily data are required to perform vulnerability and adaptation assessment. The global climate models provide a laboratory for numerical experiments on climate transitions during the past, present, and future. General circulation models (GCMs) are a class of computer-driven models for weather forecasting, understanding of climate, and for projecting climate change (IPCC, 2007). Global climate models drive the regional climate models; time-dependent large-scale lateral boundary forcing is imposed from GCM simulations ( Johns et al., 1997). The regional climate modeling (RCM) approach affords an increase of resolution over a region of the globe in comparison to the CGCMs, with regional grid-point spacing of a few tens of km in the horizontal, for operational use on climate timescales. The RCM approach could still be useful to reach a resolution of a few km for the same computational load (Laprise, 2008). A variation of this technique is to also force the large-scale component of the RCM solution throughout the entire domain (Evans et al., 2012). The main objective in the present research is to assess the simulated changes of temperature and rainfall for future predictions in the Ponnaiyar River Basin.
12.2 STUDY AREA The Ponnaiyar River Basin is an interstate river and is one of the largest rivers of the state of Tamil Nadu, often reverently called “Little Ganga of the South.” The river has supported many civilizations in peninsular India throughout history and continues to play a vital role in supplying precious water for drinking, irrigation, and industry to the people of the states of GIS and Geostatistical Techniques for Groundwater Science. https://doi.org/10.1016/B978-0-12-815413-7.00012-2 © 2019 Elsevier Inc. All rights reserved.
167
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SECTION C Groundwater Quality Assessment Using GIS and Geostatistical Aspects
Karnataka, Tamil Nadu, and Pondicherry. The study area extends over approximately of 11,595 km2 and lies between latitudes 11°350 and 12°350 000 north and longitudes 77°450 000 and 79°550 000 east (Fig. 12.1). The Ponnaiyar River originates on the south eastern slopes of the Chennakesava Hills, northwest of Nandidurg of the Kolar district in Karnataka State at an altitude of 1000 m above mean sea level (AMSL). The total length of the Ponnaiyar River is 432 km, of which 85 km lies in Karnataka State; 187 km in Dharmapuri, Krishnagiri, and Salem Districts; 54 km in Thiruvannamalai and Vellore Districts; and 106 km in Cuddalore and Villupuram Districts of Tamil Nadu. The Ponnaiyar Basin is predominantly built up with granite and gneisses rocks of the Archean period. The granite is of very good quality and extensive outcrops and masses of it are commonly found. The chief components of the rock are hornblende and feldspar. Foliation is seldom seen. In the plains of the reserve forest quartz is found commonly. Diamond granite is also found in scattered pockets in the area of the Chitteri hills in Dharmapuri and Krishnagiri sub-divisions. Charnockite rocks from the Archean period are also seen in some areas. Alluvium and sand-dunes of the Quaternary period are also seen at a few places. The 15 years’ average annual rainfall for the period 2000–2014 in the basin was 969 mm. This catchment falls under the tropical belt; the climate in general is hot with April and May being the hottest months of the year when the temperature rises to 34°C.
12.3
METHODOLOGY
In the present study, the future climate conditions forced with the Special Report on Emission Scenarios (SRES) of AR4 of the IPCC at regional scale obtained throughout a dynamical downscaling from GCMs are available on the CCAFS website. The datasets contain a large range of RCMs developed by different countries and climate modeling communities with different spatial resolutions. The datasets are available in ARC GRID and ARC ASCII format, in decimal degrees and datum WGS84. This data format facilitates their integration into a geographical information system (GIS) environment for processing. The climate model downscaling data of the bccr_bcm2_0 was selected for this investigation due to its fine space scale (30 s) (Table 12.1). The fine space resolution is very important for this investigation due to the complex topography of the study area. The climatic baseline over the period 1961–1990 was used to compare and calculate projected changes for
FIG. 12.1 The location of the Ponnaiyar River Basin shows the shuttle radar topography mission digital elevation model.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
12
169
TABLE 12.1 Particulars of SRES A1B Global Circulation madel File set
Delta method IPCC AR4
Spatial interpolation of anomalies (deltas) of original GCM outputs from IPCC CMIP2 applied to a Worldclim high-resolution baseline climate. Data were processed by CIAT
Scenario
SRES A1b
A very heterogeneous world with a continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than in other storylines
Model
bccr_bcm2_0
sres_a1b_2040s_prec_30s_tile_a5_asc.zip
Extent
Global (A5 and B5)
Format
ASCII grid format
Period
2020–2080
Variables
Bioclimatic
Derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. See http://www.worldclim.org/bioclim
Maximum temperature
Monthly mean maximum temperature. Unit: °C * 10
Mean temperature
Monthly mean average temperature. Unit: °C * 10
Minimum temperature
Monthly mean minimum temperature. Unit: °C * 10
Precipitation
Monthly accumulated rainfall. Unit: mm/month
Resolution
ARC ASCII GRID refers to a specific interchange format developed for ARC/INFO rasters in ASCII format. The format consists of a header that specifies the geographic domain and resolution, followed by the actual grid cell values
30 s
the average annual maximum temperature, average annual minimum temperature, and seasonal precipitation. The pattern of temperature and rainfall anomalies was calculated for each raster cell grid using ArcGIS software for short term (2011– 2040) and medium term (2041–2080). Future climate simulation related to the IPCC’s A1B greenhouse gas (GHG) emissions scenario was chosen to assess future temperature and rainfall projections. This scenario predicts an intermediate level of warming by the end of the century and a future where technology is shared between developed and developing nations in order to reduce regional economic disparities.
12.3.1
Baseline Climate
The hottest month of the year in the Ponnaiyar River Basin is May. During this month average maximum temperatures are between 28°C and 38°C. Coastal regions are hot and humid in summer with maximum temperatures around 38°C and humidity levels exceeding 90%. In the interior plains high temperatures in summer can exceed 35°C. The coldest month of the year in the study area is January. During this month average minimum temperatures are between 11°C and 21°C (Fig. 12.2). The coldest temperatures are encountered in highland and mountain areas in the northern and southern part of the study area. Rainfall is caused by four principal mechanisms—convection, cold frontal troughs, monsoons, and tropical storms/ cyclones—and their interactions with local topography and other meteorological conditions (Kwarteng et al., 2008). During the winter months of January and February, average rainfall is between 3 and 47 mm in the study area. Physiographic conditions significantly affect average winter rainfall (Charabi and Al-Hatrushi, 2010). In the summer months of March to May the average rainfall is between 61 and 242 mm for the overwhelming majority of the study area. During the summer months some parts of the study region are transformed into lush landscapes of green field and verdant vegetation (Charabi, 2009). During the northeast monsoon season the average baseline rainfall is between 278 and 774 mm. The hilly region and central part of the study area received the higher rainfall in this season. The baseline southwest monsoon average annual rainfall indicated that the eastern part of the coastal regions had higher rainfall (608–761 mm) (Fig. 12.3).
170
SECTION C Groundwater Quality Assessment Using GIS and Geostatistical Aspects
FIG. 12.2 Temperature patterns of the study area for the period 1961–1990: (A) average minimum temperature and (B) average maximum temperature.
12.3.2
Projection for 2011–2040
The simulated average maximum and minimum temperature changes during the period 2011–2040 are shown in Fig. 12.4. The A1B scenario clearly shows an increase in the future maximum temperature in the range of 1–2°C for the entire study area through 2040. The projected average annual rainfall pattern for all four seasons is shown in Fig. 12.5. The winter season rainfall pattern changes from 8 to 49 mm in the study area. In the period of 2011–2040 the summer rainfall pattern deviated from 42 to 207 mm. The rainfall trends for northeast and southwest monsoons also increased in the projected period of 2011–2040.
12.3.3
Projection for 2041–2080
The average maximum and minimum temperatures are projected to increase in the range of 2–3°C, with the geographic distribution of these changes following similar patterns as in the earlier period by 2080. Fig. 12.6 shows the simulated maximum and minimum temperature changes during the period 2041–2080. The A1B scenario also clearly shows future
Simulation of Seasonal Rainfall and Temperature Variation Chapter
12
171
FIG. 12.3 Baseline rainfall patterns of study area for the period 1961–1990: (A) winter, (B) summer, (C) northeast, and (D) southwest rainfall.
minimum temperature increases that are similar to the results shown for maximum temperature changes. For both time periods there is a clear expansion compared to the results for maximum temperature change, suggesting that minimum temperatures will experience the greatest impact from climate change. Fig. 12.7 shows that the simulated average annual rainfall changes during the period 2041–2080. It indicates that the A1B scenario clearly shows that most of study area will become drier in the summer and winter seasons, with large portions of the study area receiving up to 40 mm less in annual rainfall throughout the projection period. On the other hand, the model results indicate that the southwest monsoons are likely to intensify, leading to increased rainfall in the southwestern parts of the country.
12.4 RESULTS AND DISCUSSION GCMs lack the regional detail that impacts assessments on climate change. An RCM adds small-scale detailed information of future climate change to the large-scale projections of a GCM. Coarse resolution information from a GCM can be used to develop temporally and spatially fine scale information. The main advantage of an RCM is that it can provide highresolution information on a large physically consistent set of climate variables and therefore a better representation of extreme events. Comparing the baseline (1961–1990) simulation output with the observed data for the period of 2011– 2080, the RCM simulation was validated.
12.4.1
Simulation of Temperature 2011–2080
During the projected period the study area seems to show continually increasing temperatures from 1°C to 3.5°C. Significant annual average temperature increases of record high temperatures for around 2040 and 2080 indicate strong interdecadel variability. Nevertheless, the significant increase recorded for 2080 is extremely severe compared to the rest. Highest (3.7ºC) and lowest (1.3ºC) temperature increases for the study area is obtained during the period of 2011–2080 (Fig. 12.8).
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SECTION C Groundwater Quality Assessment Using GIS and Geostatistical Aspects
FIG. 12.4 Projected temperature pattern (2011–2040): (A) minimum temperature and (B) maximum temperature.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
FIG. 12.5 Rainfall patterns of study area for the period 2011–2040.
12
173
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FIG. 12.6 Projected temperature pattern (2041–2080): (A) minimum temperature and (B) maximum temperature.
12.4.2
Rainfall Simulation (2011–2080)
In a region where the historic average annual rainfall levels are between 50 and 100 mm, climate change is expected to lead to between 20 and 40 mm less rainfall by 2080. This is equivalent to a reduction in the average annual rainfall of about 40%. With less future rainfall in northern areas, groundwater recharge and surface water flow are expected to also decrease.
Simulation of Seasonal Rainfall and Temperature Variation Chapter
FIG. 12.7 Rainfall patterns of study area for the period 2041–2080.
FIG. 12.8 Simulated temperature fluctuations between 2011 and 2080.
12
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SW
Seasons
NE
summer 2010–80 2040–80 2010–40 Winter
–60
–40
–20
0
20
40 60 Rainfall in mm FIG. 12.9 Simulated seasonal average rainfall patterns between 2011 and 2080.
80
100
120
140
The SRES A1B scenario indicates that a slackening of the winter monsoon. The rainfall during the summer monsoon in 2080 is projected to be the worst over the study area. The negative annual rainfall trend is extremely evident in relation to climate change. Nevertheless, all of the regions seem to experience increased rainfall in the monsoons of the southwest toward the end of the century (2080). The significant increase in the annual temperatures simulated for 2080 corresponds to a significant reduction in the annual summer rainfall simulated for the same years (Fig. 12.9). Of these years, the highest rainfall reduction was simulated for 2080, which corresponds to the highest temperature increase simulated for annual temperature anomalies. This indication of significant reduction of rainfall together with significant increases in temperature is generally exhibited during El Nino events.
12.5
CONCLUSIONS
The Ponnaiyar River Basin baseline climate was evaluated relative to temperatures and rainfall patterns using proxy data. With climate change these baseline conditions are expected to change. Projected changes in temperatures and rainfall were assessed and the results of this assessment formed the basis by which the vulnerability of the study area to climate change could be understood. Water resources are already facing considerable threat in south India. With climate change it is expected that the prevention of groundwater degradation and balancing supply and demand will become even greater challenges. As discussed previously, northern Oman is expected to face decreasing rainfall in the coming decades. When combined with continued socioeconomic growth, the current challenges in balancing water supply and demand will increase, as will the difficulty in maintaining water-quality standards. Moreover, greater rainfall variability and longer drought episodes may adversely impact the already fragile and vulnerable mountain ecosystems of the region. Integrated water resource management (IWRM) is considered a fundamental organizing framework to identify and evaluate potential adaptation strategies for water resources.
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