Investigating Spatiotemporal Patterns of Soil Moisture - Precipitation
Dependence over India
Compound event research has gained a lot of momentum over the past few
years. Traditionally risk assessment studies used to consider only one
climatic driver/ process at a time. However, it was then recognized that
it is the combination of multiple drivers and their statistical
dependencies that lead to aggravated, non-linear impacts. The present
study investigated and quantified the preconditioning of precipitation
extremes (P) by existing soil moisture (SM) anomalies. Event coincidence
analysis (ECA) was employed to investigate the coupling nature between
SM & P event series. The datasets used include GLDAS-2.2 CLSM model
products for soil moisture and GPM IMERG V06 for gridded rainfall data.
Using SM and P data from 2004-2020, we identified hot-spots of SM-P
coupling over India. A statistical significance test (α = 0.05) was
carried out to ensure that the observed coincidences are not by chance.
Our observed results agree with the widely regarded hypothesis of
stronger SM-P coupling in transitional regions between wet and dry
climates. The temporal evolution of SM-P dependence over the past two
decades is also investigated. Results obtained provides critical
insights on the complex dynamical relationship between soil moisture and
precipitation. The dependence nature unraveled has vast implications for
directing future research on coupled hydro-meteorological phenomena.