Analysis of spatio-temporal variation of hydroclimatic variables of the Krishna river basin under future scenarios

ABSTRACT

Climate change and intensified human activities are expected to alter the natural hydrological cycle would cause changes in the distribution of water resources and their availability across space and time. Therefore, the present study focused on understanding the spatiotemporal variations of hydroclimatic variables namely rainfall, surface runoff, water yield, and the Aridity Index (AI) of the Krishna River Basin (KRB) under combined impact of climate and Land Use/Land Cover (LULC) change scenarios. From trend analysis, the best model (CNRM-CM5 driven RCM) projected an increasing trend in rainfall under both RCP 4.5 and 8.5 scenarios for the entire century. Results indicated that, except for the central part, most of KRB experiences high runoff and water yield conditions under both RCP 4.5 and 8.5 scenarios in the mid and end centuries, conversely it may face low runoff and water yield conditions in the early century. Most of the central part is in arid and semi-arid conditions and, the eastern part of the basin is in dry sub-humid conditions, while the Western Ghats, Palleru, and Munneru regions of the basin fall under humid and hyper humid regions under RCP 4.5 scenario. Whereas the majority of the basin falls under humid and hyper humid regions under RCP 8.5 scenario except for the central and few other parts of the basin. In light of this, it was essential to review the current water management strategies and plan future projects to provide efficient and effective ways to mitigate the adverse impacts resulting from climate variability.

KEYWORDS:

• Aridity index
• climate change
• hydroclimatic variables
• Krishna river basin
• spatiotemporal variations
• trend analysis

Acknowledgements

The authors also like to acknowledge the India Meteorological Department (IMD), Central Water Commission (CWC), and National Remote Sensing Centre (NRSC) (AWiFS LULC data from Bhuvan), Hyderabad for their support in sharing the data and also thanks to Earth System Grid Federation (ESGF) for their (climate model) data support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors like to acknowledge the support from Frontier Areas of Science and Technology - Centre of Excellence (FAST-CoE) in Sustainable Development at I.I.T. Hyderabad, funded by the Ministry of Human Resource Development Government of India.