## 5 Discussion
Our analysis similarly indicates widespread coastal inundation in these districts during the same period. The resulting inundation patterns along the coastline are strongly influenced by the track and landfall location of a tropical cyclone, in addition to hydro-meteorological factors such as wind speed, storm surge, and tidal conditions. This study's findings align with prior events documented in the literature. For instance, Cyclone Mora (2017) severely impacted eight districts--Bandarban, Rangamati, Khagrachari, Cox's Bazar, Chittagong, Noakhali, Bhola, and Feni [73]--and our analysis corroborates similar patterns of inundation in these areas. Cyclone Amphan, categorized as a super cyclone, severely affected Sattkhira and Khulna in 2020. Other districts, such as Barguna and Patuakhali, also experienced flooding [74]. We found severely inundated locations, including Morrelganj, Kaliganj, and Kalapara, with localized impacts observed in Chakaria. Similarly, cyclone Yaas led to widespread inundation in Sattkhira, Piroipur, and Patuakhali, primarily due to elevated water levels from storm surges coinciding with astronomical high tides. The low-lying areas in Khulna, Sattkhira, Bagerhat, Barguna, Patuakhali, and Bhola were submerged due to the tidal surge associated with Cyclone Yaas [75]. These observations are further substantiated by our inundation analysis (Figure 10), which also identifies extensive flooding in Sattkhira, Piroipur, Patuakhali, Khulna, Bagerhat, Barguna, and Bhola. The spatial patterns of inundation observed during cyclones Titli, Fani, Mitrang, Midhili, and Remal also display a high degree of similarity with previously reported findings [76]. Notably, for all the cyclones studied, the associated depressions persisted inland for durations ranging from 9 to 30 h post-landfall, contributing to prolonged and extensive inundation in the affected regions [69]. The frequency-based inundation mapping (Figure 10) reveals that the majority of flood-affected areas fall within the low inundation category for both monsoonal and cyclonic events. Notably, cyclones impact a much larger area (19,085 km\({}^{2}\)) compared to monsoon rainfall events (12,737 km\({}^{2}\)), indicating the impact of cyclones to be more spatially widespread. In the moderate inundation category, the affected areas are comparable between the two types of events, 4054 km\({}^{2}\) for monsoonal floods and 3459 km\({}^{2}\) for cyclonic events. This suggests that although cyclones are less frequent, they are capable of producing flood intensities on par with seasonal monsoon systems. In the case of High Inundation Category, the extent of severe flooding is considerably greater during monsoonal periods (1626 km\({}^{2}\)) than during cyclones (255 km\({}^{2}\)). This trend underscores the heightened risk of intense inland flooding during prolonged or extreme monsoonal rainfall, which is likely a result of sustained precipitation over larger catchment areas. This highlights the need for distinct response strategies for each type of hydrometeorological event when managing flood risks along the GBM coastline. Cyclone-induced flooding typically includes both storm surge and intense rainfall, which often occur simultaneously and are difficult to distinguish from one another. The observation that areas like Bagerhat and Morrelganj experience severe flooding during cyclones, while Kalapara faces high flood levels primarily during the monsoon, may indicate inadequate drainage, likely a consequence of the polder system in those regions. While the flood extent maps derived from SAR imagery provide critical insights into the spatiotemporal dynamics of inundation across the coastal region, several sources of uncertainty may affect the accuracy and interpretation of the results. One major limitation arises from the reliance on backscatter intensity for inundation classification, which can be influenced by surface conditions such as vegetation cover, soil moisture, and built-up infrastructure.