Abstract

AbstractSynoptic fingerprinting of flood driving rainfall events over the Hyderabad city, India were assessed through an unsupervised machine learning technique—self‐organizing maps (SOM) for the period 1979–2020. Flood dates were identified using nonscientific data sources, and then large‐scale climate variables, that is, integrated vapour transport (IVT) and geopotential height at 500 hPa (Z500) along with rainfall at daily scale were considered. SOM was applied on IVT and results indicated large spatial scale‐ and season‐specific mechanisms in addition to local circulation systems—this highlights SOM's ability to cluster circulation mechanisms as per the relevance. The rainy seasons, that is, monsoon and postmonsoon showed well‐known moisture pathways. While synoptic systems can be easily linked with monsoon and postmonsoon mechanisms, it is interesting to note that the local systems exist even during the monsoon and postmonsoon season in addition to the summer season. Spatial patterns of Z500 corresponding to SOM nodes of IVT showed the presence of low‐pressure systems (LPSs), tropical cyclones and trough systems. Further analysis was performed, and a total of 35 atmospheric rivers (ARs) were identified. Plots of back trajectories for the selected AR events indicated that Arabian Sea is the main source of moisture in the monsoon season followed by Bay of Bengal for postmonsoon and tropical cyclones in summer season events. Additionally, moisture from land is also observed as another source of moisture. Back‐trajectory analysis further indicated AR‐LPS interaction during the monsoon and ARs feeding moisture to LPSs. Overall, well‐known and season‐specific synoptic systems that brought significant rainfall to the study region were identified by the SOM. The proposed framework is adaptable for different locations, and as floods in cities across the globe are on the rise, these findings will have a prominent role in urban flood forecasting and management.

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