Seasonal drought severity identification using a modified multivariate index: a case study of Indo-Gangetic Plains in India

Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India.