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.

Researchers dealing with flood hazard and risk assessment typically refer to available in-situ gauging stations for the calibration and validation of hydrological and hydrodynamic (HD) models. However, lack of dense gauging data such as stage-discharge relationship (rating curves) results in higher uncertainty in hydrological studies. The multi-mission satellite altimetry observations are capable since long time to monitor continental water bodies at regular interval. In this study, combined use of hydrodynamic model and satellite altimetry data has been exploited for establishing virtual gauging network in flood-prone sparsely gauged river basin. The virtual gauging stations were established at locations where ground tracks of various altimeters (Jason-2/3, SARAL/AltiKa, and Sentinel-3A/3B) cross the river channel. Rating curves were generated at these virtual stations using calibrated and validated HD model. High agreement between the simulated and altimetry-based water levels at virtual stations showed the potential of satellite altimetry data for the multi-site validation of HD model and constructed rating curves. The availability of water level time series and rating curves at multiple virtual locations in addition to existing in-situ physical gauging stations provides an opportunity to expand gauging network in sparsely gauged basin. Thus, the proposed framework may open new perspectives for enhancing river flow dynamic studies considering upcoming satellite missions like SWOT which will ensure more observations in future.