Conclusion
The impacts of climate change on agriculture and the water resources sector must be
urgently emphasized for remedial policies to be drafted by local planners and
managers. The expected changes in temperature and precipitation will hamper
water availability, especially for the rain-fed agriculture regions. In this study,
regional impact assessment has been carried out to predict future climate scenarios
through projected changes in temperature and precipitation. The projections of these
climate variables are obtained by using the change factor downscaling technique
through the climate data obtained from the CMIP5 global model for RCP scenarios.
The evaluation of CF downscaling techniques over the Bhima sub-basin indicates
satisfactory results in comparison to baseline historical data. Spatial crosscorrelations of maximum temperature and precipitation across the IMD gauge
grids were captured adequately. Further, it is observed that this downscaling satisfactorily captured cross-correlations between rainfall grids of the Upper Bhima
(K5) and Lower Bhima (K6) regions.
The comparison of historical and future downscaled maximum temperature
indicates that the average daily minimum temperature (for the historical period
1986–2005) of the Bhima sub-basin is likely to increase in response to climate
change scenarios. A marginal change of 1% is indicated for RCP 2.6; an identical
change of average daily maximum temperature for RCP 4.5 and RCP 6.0, in the
range of 6.4% to 8.0%, is also evident from the study, and the highest percentage
change, in the range of 12.6% to 14.6% in the average daily maximum temperature,
is seen for the RCP 8.5 scenario. In a similar context, downscaled projections for
precipitation are also increased under changing climate scenarios. RCP 2.6, 4.5, and
6.0 depicted changes in the range 2% to 30%, whereas the RCP 8.5 scenario
accounted for more than 35% change to the historical baseline climate
(1986–2005). However, this may be the result of rising radiative forcing pathway
RCP 8.5 scenarios, thus leading to very high greenhouse gas emissions.
References
Aggarwal PK (2008) Global climate change and Indian agriculture: impacts, adaptation, and
mitigation. Indian J Agri Sci 78:911–919
Ahmed KF, Wang G, Silander J, Wilson AM, Allen JM, Horton R, Anyah R (2013) Statistical
downscaling and bias correction of climate model outputs for climate change impact assessment
in the U.S. northeast. Global Planet Change 100:320–332. https://doi.org/10.1016/j.gloplacha.
2012.11.003
Akhtar M, Ahmad N, Booij M (2008) The impact of climate change on the water resources of
Hindukush–Karakorum–Himalaya Region under different glacier coverage scenarios. J Hydrol
355:148–163. https://doi.org/10.1016/j.jhydrol.2008.03.015
Amin MZM, Shaaban AJ, Ercan A, Ishida K, Kavvas ML, Chen ZQ, Jang S (2017) Future climate
change impact assessment of watershed-scale hydrologic processes in Peninsular Malaysia by a
regional climate model coupled with a physically-based hydrology models. Sci Total Environ
575:12–22. https://doi.org/10.1016/j.scitotenv.2016.10.009
2 Regional Assessment of Impacts of Climate Change: A Statistical Downscaling. . .
35
The impacts of climate change on agriculture and the water resources sector must be
urgently emphasized for remedial policies to be drafted by local planners and
managers. The expected changes in temperature and precipitation will hamper
water availability, especially for the rain-fed agriculture regions. In this study,
regional impact assessment has been carried out to predict future climate scenarios
through projected changes in temperature and precipitation. The projections of these
climate variables are obtained by using the change factor downscaling technique
through the climate data obtained from the CMIP5 global model for RCP scenarios.
The evaluation of CF downscaling techniques over the Bhima sub-basin indicates
satisfactory results in comparison to baseline historical data. Spatial crosscorrelations of maximum temperature and precipitation across the IMD gauge
grids were captured adequately. Further, it is observed that this downscaling satisfactorily captured cross-correlations between rainfall grids of the Upper Bhima
(K5) and Lower Bhima (K6) regions.
The comparison of historical and future downscaled maximum temperature
indicates that the average daily minimum temperature (for the historical period
1986–2005) of the Bhima sub-basin is likely to increase in response to climate
change scenarios. A marginal change of 1% is indicated for RCP 2.6; an identical
change of average daily maximum temperature for RCP 4.5 and RCP 6.0, in the
range of 6.4% to 8.0%, is also evident from the study, and the highest percentage
change, in the range of 12.6% to 14.6% in the average daily maximum temperature,
is seen for the RCP 8.5 scenario. In a similar context, downscaled projections for
precipitation are also increased under changing climate scenarios. RCP 2.6, 4.5, and
6.0 depicted changes in the range 2% to 30%, whereas the RCP 8.5 scenario
accounted for more than 35% change to the historical baseline climate
(1986–2005). However, this may be the result of rising radiative forcing pathway
RCP 8.5 scenarios, thus leading to very high greenhouse gas emissions.
References
Aggarwal PK (2008) Global climate change and Indian agriculture: impacts, adaptation, and
mitigation. Indian J Agri Sci 78:911–919
Ahmed KF, Wang G, Silander J, Wilson AM, Allen JM, Horton R, Anyah R (2013) Statistical
downscaling and bias correction of climate model outputs for climate change impact assessment
in the U.S. northeast. Global Planet Change 100:320–332. https://doi.org/10.1016/j.gloplacha.
2012.11.003
Akhtar M, Ahmad N, Booij M (2008) The impact of climate change on the water resources of
Hindukush–Karakorum–Himalaya Region under different glacier coverage scenarios. J Hydrol
355:148–163. https://doi.org/10.1016/j.jhydrol.2008.03.015
Amin MZM, Shaaban AJ, Ercan A, Ishida K, Kavvas ML, Chen ZQ, Jang S (2017) Future climate
change impact assessment of watershed-scale hydrologic processes in Peninsular Malaysia by a
regional climate model coupled with a physically-based hydrology models. Sci Total Environ
575:12–22. https://doi.org/10.1016/j.scitotenv.2016.10.009
2 Regional Assessment of Impacts of Climate Change: A Statistical Downscaling. . .
35