Various stakeholders seek effective methods to communicate the potential impacts of tropical cyclone (TC) rainfall and subsequent flood hazards. While current methods, such as Intensity–Duration–Frequency curves, offer insights, they do not fully capture TC rainfall complexity and variability. This research introduces an innovative workflow utilizing GPM-IMERG satellite precipitation estimates to cluster TC rainfall spatial–temporal patterns, thereby illustrating their potential for flood hazard assessment by simulating associated flood responses. The methodology is tested using rainfall time series from a single TC as it traversed a 500 km diameter buffer zone around Dominica. Spatial partitional clustering with K-means identified the spatial clusters of rainfall time series with similar temporal patterns. The optimal value of K = 4 was most suitable for grouping the rainfall time series of the tested TC. Representative precipitation signals (RPSs) from the quantile analysis generalized the cluster temporal patterns. RPSs served as the rainfall input for the openLISEM, an event-based hydrological model simulating related flood characteristics. The tested TC exhibited three spatially distinct levels of rainfall magnitude, i.e., extreme, intermediate, and least intense, each resulting in different flood responses. Therefore, TC rainfall varies in space and time, affecting local flood hazards; flood assessments should incorporate variability to improve response and recovery.

Tropical cyclones (TCs) significantly impact the Caribbean Lesser Antilles, often causing severe wind and water damage. Traditional flood hazard assessments simplify TC rainfall as single-peak, short-duration events tied to specific return periods, overlooking the spatial–temporal variability in rainfall that TCs introduce. To address this limitation, a new user-friendly tool incorporates spatial–temporal rainfall variability into TC-related flood hazard assessments. The tool utilizes satellite precipitation data to break down TC-associated rainfall into distinct pathways/scenarios, mapping them to ground locations and linking them to specific sections of the storm’s rainfall footprint. This approach demonstrates how different areas can be affected differently by the same TC. In this study, we apply the tool to evaluate rainfall patterns and flood hazards in St. George’s, Grenada, during Hurricane Beryl in 2024. The scenario representing the 75th quantile in Spatial Region 2 (S2-Q_0.75) closely matched the actual rainfall observed in the study area. By generating multiple hazard maps based on various rainfall scenarios, the tool provides decision-makers with valuable insights into the multifaced flood hazard risks posed by a single TC. Ultimately, island communities can enhance their early warning and mitigation strategies for TC impacts.

Different stakeholders are now looking for methods suitable for communicating the potential impacts of tropical cyclone (TC) associated rainfall and the subsequent flood hazard. We developed a new solution that utilizes GPM-IMERG satellite precipitation estimates to characterize TC rainfall spatial-temporal patterns and derives precipitation curves appropriate for TC-related flood hazard assessment. The method was demonstrated when modeling floods on Dominica due to the rainfall of Tropical Storm Erika in 2015. We performed three experiments on the procedure, starting with a time-series clustering analysis using the K-means algorithm for optimal clusters K = 5, 4, and 3. For each experiment, we excluded pixels associated with very low precipitation intensities and amounts likely disparate from the TC. We then introduced an intensity threshold of 10mm/hr to define the onset of the storm’s rain and align the pixel time series before deriving cluster representative precipitation signals (RPS) based on time step quantiles. The RPSs were used as precipitation inputs for the openLISEM, an event-based hydrological model, to simulate the resultant flood characteristics. RPSs from K = 4 were selected as Tropical Storm Erika’s final rainfall scenarios, which resulted in different flood scenarios. These results demonstrate the underlying variability in the rainfall of a single TC which should be accounted for to achieve a reliable flood hazard assessment.

This study utilizes GPM-IMERG satellite rainfall estimates to assess the asymmetric rainfall patterns in 27 tropical cyclones (TCs) across the Lesser Antilles region from 2000 to 2020. The aim is to evaluate whether there is a persistent relationship between precipitation and wind characteristics, which could support improved TC-related flood risk assessment for these islands. With a focus on hurricane and tropical storm categories, the 30-minute precipitation variability was assessed within a radius of 500 km from the TC’s eye during its path in the study area. In addition, TC’s forward speed and wind characteristics, like TC’s category and the extent of 34-knot winds (R34), are included. The analysis reveals temporal trends, indicating increased TC rainfall events in the study area during the second decade. Correlations show positive relationships between rainfall total (RT), rainfall area (RA), and rainfall intensity at the 90th percentile (RI0.9), with RT and RI0.9 showing the strongest link in the majority of the observations. Contrary to conventional assumptions, this research challenges the idea that highest category TCs in the wind intensity always produce higher rainfall, as we see that higher-category hurricanes such as H4 (209-251km/hr) and H5 (>=252km/hr) were often associated with lower rainfall values in RT and RI0.9 compared to tropical storms (63 - 118 km/hr). Tropical storms, like higher-category hurricanes, can display large rainfall areas. In addition, quadrant analysis of rainfall zones around the TC eye highlights that the NE and SE quadrants in TC have significantly more rainfall impact. However, it also reveals the danger posed by weaker quadrants in wind characteristics such as SW and NW, as they can exhibit high rainfall values in RA and RT. The study indicates complex, non-linear relationships between TC’s wind and precipitation characteristics in the Lesser Antilles region. Incorporating the rainfall variability observed in TC dynamics into early warning systems and risk assessment is essential for a more effective emergency response and mitigation planning.General methodologyThe satellite rainfall estimates were obtained within a defined buffer of a diameter of 500km around the TC eye while following the TC trajectory. The buffer was further dissected into quadrant spatial zones (NE, SE, SW and NW) to provide a detailed perspective on rainfall distribution in different parts of the TC impact area. For each eye position, rainfall characteristics (RT, RA and RI0.9) were computed for the whole buffer and later individual quadrant partitions. The computed rainfall characteristics were then investigated for potential correlation relationships with the TC wind intensity. In addition, quadrant rainfall patterns were analyzed for persistence throughout the TC duration.

Across the chapters of this volume, a clear picture emerges of a disaster-management landscape undergoing rapid transformation. Geospatial technologies, which were once the domain of specialists, have become mainstream and are indispensable across all stages of the disaster cycle. They are reshaping how hazards are detected and forecasted, how exposed populations and assets are characterised, how impacts are assessed, and how recovery is monitored.

This study utilises GPM‐IMERG satellite rainfall estimates to analyse the asymmetric rainfall patterns of 35 tropical cyclones (TCs) that passed over the Caribbean Lesser Antilles (CLA) islands between 2000 and 2020. The aim is to evaluate whether TC rainfall spatial–temporal patterns vary systematically across Saffir‐Simpson (SS) categories to strengthen early warning protocols. The widely used SS scale focuses on wind intensity, overlooking the significant role of TC rainfall in flooding. In the CLA, landfalling and offshore TCs cause significant rainfall impacts across multiple islands and storms exhibit high variability in trajectories, durations and SS category transitions. The study examines the relationship between SS categories and TC rainfall characteristics, using maximum sustained winds (MSW) to classify storms into categories. Linear and monotonic correlation analyses are performed using continuous MSW values to explore overall associations with rainfall; however, reporting and interpretation focus on discrete SS categories, which represent the operationally relevant storm intensity classes for hazard analysis. Although SS categories represent linearly increasing storm intensity, rainfall exhibits nonlinear patterns with significant variability within and across categories. Contrary to common expectations, TCs in the highest SS categories (H4 and H5) do not consistently produce the highest rainfall. Tropical storms can generate rainfall amounts comparable to or exceeding those of hurricanes and hurricanes in categories H1–H3 sometimes produce more intense rainfall than those in H4 and H5. The study also examines rainfall asymmetry in the four quadrants (NE, SE, SW, NW) around the TC eye by SS category. Rainfall is generally concentrated in the NE and SE quadrants, but the SW and NW quadrants also contribute substantially, reflecting the spiralling nature of TCs, which continuously refuel rainfall in all quadrants throughout the storm's lifecycle. These findings underscore the complexity of TC rainfall patterns and the need to integrate such variability into early warning systems to enhance disaster preparedness and mitigation strategies.