Skip to main navigation Skip to search Skip to main content

Climate change impact on precipitation extremes over Indian cities: Non-stationary analysis

  • Manish Kumar Goyal
  • , Anil Kumar Gupta
  • , Srinidhi Jha
  • , Shivukumar Rakkasagi
  • , Vijay Jain

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

The phenomena of climate change and increase in warming conditions across the globe causes changes in the frequency and severity of extreme weather events. The present study analyzed extreme precipitation weather events across four Indian cities in different climatic conditions. The study uses IMD precipitation datasets from 1900 to 2004 to analyze different atmospheric influencing parameters like ENSO, AMO, and IOD on future extreme precipitation conditions. The Bayesian analysis is carried out for nonstationary analysis of extreme indices like Rx1 Day, SDII, R10, and CWD with a 10, 20, 50, and 100 years return period. Significant outcomes of the comparative study of the stationary and nonstationary analysis showed an intensification in extreme precipitation across Indian cities for all return periods using the CWD indicator. The remaining three indicators of the nonstationary study suggested intensifying extreme rainfall across all Indian cities except Guwahati.

Original languageEnglish
Article number121685
JournalTechnological Forecasting and Social Change
Volume180
DOIs
StatePublished - 1 Jul 2022
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Extreme events
  • Indian cities
  • Nonstationary
  • Precipitation
  • Return level

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation

Fingerprint

Dive into the research topics of 'Climate change impact on precipitation extremes over Indian cities: Non-stationary analysis'. Together they form a unique fingerprint.

Cite this