Intraseasonal variability of vegetation water content in India driven by monsoon hydrometeorology

Abstract Understanding how Indian vegetation during the summer monsoon (June–September) responds to intraseasonal variability, with hydrometeorological stressors like vapor pressure deficit (VPD) and soil moisture (SM), is crucial and yet it is not well-explored. Existing studies often rely on correlation analysis and occasionally use causal inference, but they tend to overlook the intraseasonal scales of monsoon variability and their impact on vegetation water content. Traditional methods struggle to reveal these dependencies due to the tight coupling between SM and VPD, making it difficult to isolate their individual effects. This study fills this gap by analyzing the interactions between SM, vegetation water content, and VPD at the two dominant intraseasonal modes of the Southwest Monsoon, i.e. the 10–20 d and 30–60 d timescales, using multi-satellite observations from 1998 to 2018. We found a counterintuitive phase relationship in central India’s core monsoon zone, where vegetation optical depth (VOD), a measure of vegetation water content, is out of phase with SM but in phase with VPD. Our analysis indicates that VPD enhances vegetation’s water uptake, increasing VOD when SM is not limiting, extending even into monsoon breaks. The delayed impact of prior rainfall contributes to the soil-water availability during the break and subsequently, to the out-of-phase relationship between SM and VOD. While both intraseasonal variability modes are evident in SM, VPD, and VOD, the out-of-phase relationship between SM and VOD and the in-phase relationship between VPD and VOD are more pronounced at the 30–60 d scale. Given that intraseasonal variability is a key characteristic of the monsoon, shaping its seasonal totals and impacts, the findings from this study offer crucial insights into vegetation responses to climate variability in the Indian subcontinent. These results can be expected to have implications for atmosphere-land interactions and climate predictions.