<p dir="ltr">As a new spaceborne laser remote sensing system, the Global Ecosystem Dynamics Investigation, or GEDI, is being widely used for monitoring forest ecosystems. However, its measurements are subject to uncertainties that will affect the calculation of ground elevation and vegetation height. This research intends to investigate the quality of the GEDI elevation data and its relevance to topography and land cover.</p><p dir="ltr">In this study, the elevation of the GEDI data is compared to 3DEP DEM, which has a higher resolution and accuracy. All the experiments in this study are conducted for two locations with vastly different terrain and land cover conditions, namely Tippecanoe County in Indiana and Mendocino County in California. Through this investigation we expect to gain a comprehensive understanding of GEDI’s elevation quality in various terrain and land cover conditions.</p><p dir="ltr">The results show that GEDI data in Tippecanoe County has better elevation accuracy than the GEDI data in Mendocino County. GEDI in Tippecanoe County is almost four times more accurate than in Mendocino County. Regarding land cover, GEDI have better accuracy in low vegetation areas than in forest areas. The ratio can be around three times better in Tippecanoe County and around one and half times better in Mendocino County. In terms of slope, GEDI data shows a clear positive correlation between RMSE and slope. The trend indicates as slope increases, the RMSE increases concurrently. In other words, slope and GEDI elevation accuracy are inversely related. In the experiment involving slope and land cover, the results show that slope is the most influential factor to GEDI elevation accuracy.</p><p dir="ltr">This study informs GEDI users of the factors they must consider for forest biomass calculation and topographic mapping applications. When high terrain slope and/or high vegetation is present, the GEDI data should be checked with other data sources like 3DEP DEM or any ground truth measurements to assure its quality. We expect these findings can help worldwide users understand that the quality of GEDI data is variable and dependent on terrain relief and land cover.</p>

<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>