<p dir="ltr">Tropical Cyclones (TCs) are intense low-pressure weather systems that acts as a meteorological monster causing severe rainfall and widespread freshwater flooding, leading to extensive damage and disruption. Quantitative precipitation estimates (QPEs) are crucial for accurately understanding and evaluating the impacts of TCs. However, QPEs derived from various modalities, such as rain gauges, ground-based merged radars, and satellites, can differ significantly and require thorough comparison. Understanding the limitations/advantages of using each QPE is essential to simulate a hydrological model especially to estimate extreme events like TCs. The objective of the dissertation is to 1) characterize the tropical cyclone precipitation (TCP) using three gridded products, 2) characterize the impact of using different QPEs in estimation of hydrological variables using a hydrology model, and 3) understand the usability of satellite-derived QPEs for eight cases of TC and its impact on the estimate of hydrological variables. The QPEs include near real-time and post-processed satellite data from NASA’s Global Precipitation Mission-Integrated Multi-sensor Retrievals for GPM Rainfall Product (IMERG), merged ground radar observations (Stage IV) from the National Centers for Environmental Prediction (NCEP), and interpolated gauge observations from the National Weather Service Cooperative Observer Program (GCOOP). The study quantifies how differences in rainfall intensity and location, as derived from these gridded precipitation datasets, impact surface hydrology. The Variable Infiltration Capacity (VIC) model and the geographic information system (GIS) routing assess the propagation of bias in the daily rainfall rate to total runoff, evapotranspiration, and flooding. The analysis covers eight tropical cyclones, including Hurricane Charley (2004), Hurricane Frances (2004), Hurricane Jeanne (2004), Tropical Storm Fay (2008), Tropical Storm Beryl (2012), Tropical Storm Debby (2012), Hurricane Irma (2017) and Hurricane Michael (2018) focusing on different regions in South-Atlantic Gulf region and land uses. The findings indicate that IMERG underpredicts precipitation at higher quantiles but aligns closely with ground-based and radar-based products at lower quantiles. IMERG reliably estimates total runoff and evapotranspiration in 90% of TC scenarios along the track and in agricultural and forested regions. There is substantial overlap ~ 70% between IMERG and GCOOP/Stage IV for the 90th percentile rainfall spatially for the case of TC Beryl 2012. Despite previous perceptions of underestimation, the study suggests that satellite-derived rainfall products can be valuable in simulating streamflow, particularly in data-scarce regions where ground estimates are lacking. The relative error in estimation is 12% and 22% when using IMERG instead of Stage IV and GCOOP rainfall data. The findings contribute to a broader perspective on usability of IMERG in estimating near real-time hydrological characteristics, paving the way for further research in this area. This analysis demonstrates that IMERG can be a reliable data product for hydrological studies even in the extreme events like landfalling TCs. This will be helpful in improving the preparedness of vulnerable communities and infrastructure against TC-induced flooding in data scare regions.</p>