Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting.

Current flood early warning and risk communication approaches are often characterized by simple and/or alarmist messages, which can promote non-protective behaviours—either through overreliance on defence structures or emergency management organizations. In response, we propose and develop an early warning system (EWS) prototype aimed at fostering “flood literacy” within communities. This system seeks to empower individuals and local populations to better understand their flood risk by recognizing their personal vulnerability and the characteristics of potential floods affecting them. Such understanding enables timely and appropriate self-protective actions. The proposed EWS comprises an Internet of Things (IoT)-based camera network for monitoring rainfall, water depth, and water velocity based on Artificial Intelligence (AI) techniques. These AI algorithms have been used also to analyze and assess historical flood events in the study area, i.e., the heritage city of Matera (Basilicata Region, Italy). The monitoring system is integrated with AI-driven flood modelling to generate impact scenarios at the local scale. These forecasted scenarios can be compared with historical flood data to contextualize current measurements of rainfall and water levels and therefore the citizens can judge how significant a flood might be. The system incorporates threshold-based alerts related to flood instability for pedestrians, along with signals and symbols designed for quick interpretation and communication of self-protection measures to improve citizen resilience and response.