Incorporating the climate oscillations in the computation of meteorological drought over India

• Published in: Natural hazards (Dordrecht) Vol. 117; no. 3; pp. 2617 - 2646
• Main Authors: Chiru Naik, D., Chavan, Sagar Rohidas, Sonali, P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-07-2023; Springer Nature B.V
• Subjects: Civil Engineering; Climate; Climate change; Computation; Datasets; Drought; Drought characteristics; Drought index; Earth and Environmental Science; Earth Sciences; Environmental impact; Environmental Management; Extreme drought; Frequency analysis; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Natural Hazards; Original Paper; Oscillations; Precipitation; Standardized precipitation index; India; Large-scale climate oscillations; Standardized precipitation index; Precipitation; Meteorological drought index; India
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-023-05958-3

Large-scale climate oscillations (e.g. Nino 3.4, SOI, MEI and IOD) govern the occurrence of meteorological droughts. The present study is envisaged to model non-stationary meteorological drought index, at the grid scale covering entire India by considering the large-scale climate oscillations affecting the phenomenon of precipitation. The non-stationary framework is considered to estimate the Standardized Precipitation Index (SPI), which in turn is used for analysing the frequency of severe and extreme drought events for two reference time periods (i.e. 1901–1950 and 1951–2018) at various timescales, viz. 3, 6, 9 and 12 months. The efficiency of non-stationary SPI (NSPI) approach over stationary SPI (SSPI) approach in characterizing the drought events is evaluated based on two precipitation datasets from the India Meteorological Department (IMD) and Climate Research Unit (CRU). The NSPI is observed to outperform the SSPI in assessing the meteorological droughts at majority of the grids over India as the timescale increases from 3 to 12 months. Different sets of climate oscillations with significant correlation with precipitation are identified for IMD and CRU datasets as well as for different reference periods, indicating that the drought characterization is sensitive to the chosen reference time period as well as the precipitation datasets considered for the analysis. Overall the NSPI approach is shown to provide reliable and robust determination of drought characteristics by incorporating the climate oscillations under changing climate. The findings from this study can be used to devise adaptation strategies to mitigate adverse impacts of droughts.