Spatiotemporal Variations of Precipitation and Temperatures Under CORDEX Climate Change Projections: A Case Study of Krishna River Basin, India
The Earth’s climate is not static; it changes according to the natural and anthropogenic climate variability. Anthropogenic forcing due to increase of greenhouse gases in the atmosphere has driven changes in climate variables globally. Changes in climatological variables have severe impact on global hydrological cycle affecting the severity and occurrence of natural hazards such as floods and droughts. Estimation of projections under climate signals with statistical and dynamic downscaling models and integration with water resource management models for the impact assessment have gained much attention. The fine-resolution climate change predictions of dynamic regional climate model (RCM) outputs, which include regional parameterization, have been widely applied in the hydrological impact assessment studies. Advancement of the Coordinated Regional Downscaling Experiment (CORDEX) program has enabled the use of RCMs in regional impact assessment which has progressed in recent years. CORDEX model outputs were considered to be valuable in terms of establishing large ensembles of climate projections based on regional climate downscaling all over the world. However, the simulations of RCM outputs have to be evaluated to check the reliability in reproducing the observed climate variability over a region. The present study demonstrates the use of bias-corrected CORDEX model simulation in analyzing the regional-scale climatology at river basin scale, Krishna river basin (KRB), India. The precipitation and temperature simulations from CORDEX models with RCP 4.5 were evaluated for the historical data for the period of 1965 to 2014 with India Meteorological Department (IMD) gridded rainfall and temperature data sets cropped over the basin. The projected increase of precipitation under climate signals was predicted to be from 74.4 to 136.7 mm over KRB for the future time period of 2041–2060 compared to the observed periods of 1966–2003. About 1.06 °C to 1.35 °C of increase in temperatures was predicted for the periods of 2021–2040 and 2041–2060, respectively, compared to the observed period of 1966–2014 over KRB. The climate variable projections obtained based on RCM outputs can provide insights toward the variations of water-energy variables and consequent impact on basin yields and losses in river basin management.
KeywordsBias correction Dynamic downscaling Hydrology Regional circulation model (RCM) General circulation model (GCM)
The research work presented in the manuscript is funded by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India through Start-up Grant for Young Scientists (YSS) Project no. YSS/2015/002111.
- Ehret U (2012) Should we apply bias correction to global and regional climate model data? Hydrol Earth Syst Sci Discuss 9:5355–5387. https://www.hydrol-earth-syst-sci.net/16/3391/2012/
- IPCC (2007) Climate change 2007: impacts, adaptation, and vulnerability. In: Parry ML et al (eds) Contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- IPCC (2014) In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: Global and sectoral aspects. Contribution of Working Group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York. 1132 ppGoogle Scholar
- Mcguffie K, Henderson-Sellers (1997) A climate modeling primer. John Wiley and Sons, ChichesterGoogle Scholar
- Rajeevan M, Bhate J (2009) A high resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies. Curr Sci 96(4):558–562Google Scholar
- Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) “The guidelines for use of climate scenarios developed from statistical downscaling methods.” In: Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA). Available at: http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf