Multi-storm analysis of satellite precipitation informed streamflow for landfalling tropical cyclones.

After the historically active 2020 hurricane season, the past two hurricane seasons have been climatologically average (1991 – 2020 climatology) in the Atlantic basin but have still managed to set records for landfalling tropical cyclones (TCs). These TCs have brought extensive damage in the form of death and destruction to coastal and even inland communities. Tropical cyclones have contributed the most damage by dollar amount based on the 310 billion dollar damages reported by the weather disasters between 1980 and 2021. TCs contributed to over $1.1 trillion total damage costs, with an average cost of $20.5 billion per event. They are also responsible for the highest number of deaths: 6,697 between 1980 and 2021. TC related inland flooding contributed $177.9 billion dollars in damage during the same period. These numbers only reflect the occurrences in the Atlantic basin, and tropical cyclones are a global phenomenon. Many regions globally suffer from a scarcity of real-time precipitation estimates and flood prediction systems.

While TC damage from wind hazards has been the focus of research for many years, TC induced flood vulnerability is emerging as a substantial problem in need of greater study. As a result, communities and infrastructure are arguably better prepared to withstand wind hazards than from TC induced flooding. Next generation efforts now underway are focused on understanding the feedback between flooding and localized rain from TCs. These hydrology-based studies are important as communities develop regional infrastructure and vulnerability assessments. Since all flood events and associated socioeconomic impacts are not equal, there is a need to assess location-based flooding and rainfall impacts using globally available datasets. However, it is still not clear which rainfall products should be used or what that choice means for the flood impact assessment. In this presentation, we summarize our experience, building on a series of studies to investigate answers to the questions (i) Are satellite quantitative precipitation estimates (QPEs) reliable in the TCs scenarios? (ii) How variable are the gridded precipitation data products from each other? and (iii) What is the implication of the difference in gridded precipitation data products to surface hydrology? The final question helps us address whether satellite precipitation informed streamflow is reliable enough to be used to guide responses to TC induced inland flooding events.

Multiple storms, including Hurricane Charley 2004, Hurricane Frances 2004, Hurricane Jeanne 2004, Tropical Storm Fay 2008, Tropical Storm Beryl 2012, Hurricane Irma 2017, Hurricane Michael 2018 have been selected to represent the diversity of storm strength and its impact on inland watersheds. This starts by assessing how well storm structure is captured using various gridded precipitation estimates. Each of these storms have unique characteristics in terms of its origin, formation, accumulated cyclone energy, track and duration over land and ocean, the atmospheric and land conditions that drive the spatial and quantitative distribution of precipitation from such a system of rain. For example, the maximum rain observed by NCEP during the storms named here were 9.88, 23.57, 11.97, 27.65, 15, 21.66, 13.01 inches respectively. The only common aspect is the study region – South Atlantic Gulf region which was impacted by all of these storms. Gridded precipitation products used for this study are NCEP Stage-IV, GPM IMERG and interpolated gauge observation from NWS Observer Program (COOP network data) over CONUS (G-COOP).

Our previous study involving Hurricane Florence 2018, which caused widespread flooding, noted the different quantitative precipitation estimates (QPEs) highlighted disparity in the assessments for the time period Sep 13 to Sep 17 2018. The comparison analysis shows that GPM IMERG (RMSE = 11.97 mm/day, r² = ~0.8) followed by StageIV (RMSE = 20.84 mm/day, r² = ~0.71) were in close agreement with the in-situ Automated Surface Observing Sites (ASOS) observations.

Analysis in the case of Tropical Storm Beryl 2012 highlighted the differences between GPM IMERG, Stage IV and G-COOP precipitation estimates. The spatiotemporal distribution of these products were analyzed using the MODE analysis that quantifies more nuanced characteristics of rainfall between data products compared to traditional statistics of RMSE, correlation etc. The MMI score derived from MODE analysis established that IMERG has good match with other gridded products (Stage IV and G-COOP) for rainfall accumulation < 65 mm which is consistent with the findings of qualitative analysis of spatial patterns. The area ratio of the objects was formed by the convolution precipitation and radius threshold, is ~0.86, while the 90th and 50th percentile intensity ratios are 0.77 and 0.76, between IMERG and G-COOP (meaning 77 percent area has similar 90th percentile IMERG derived precipitation accumulation as G-COOP).

To quantify how differences in rainfall intensity and location derived from these gridded precipitation datasets impact surface hydrology simulations, the Variable Infiltration Capacity (VIC) model has been used to assess the propagation of variability in daily rainfall rate to the time of peak streamflow as well as the total volume of surface runoff leading to flooding. Surface runoff and baseflow are passed to a GIS-based routing model to estimate streamflow at established USGS gauging stations in watersheds affected by these tropical storms. Results from hydrologic modeling will be used to assess changes by quantifying the error metrics to the timing of surface runoff and peak discharge as well as the quantity of flow in the river. Such an assessment is important to evaluate whether each of the gridded precipitation data products will be suitable for flood warning despite differences in total precipitation flux observed for each storm. Testing for differences in the hydrologic response in multiple storm cases, will help attest the value of IMERG and other globally available precipitation datasets as a resource for estimating the short-term hydrologic effects of TC storms worldwide.