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Abstract

Flash floods and landslides are severe natural hazards caused by heavy rainfall. When aiming for a robust real-time early warning system, rain radar products are key for tracking heavy storms and quantifying hydrological behavior in mountainous catchments. The precision of radar rainfall estimates remains an essential concerns in the application of radar-based observation. Finally, composite radar products of 2 or more rain radar observation are required to improve the quality of the radar product to visualize the distribution and movement of precipitation over a large area. In our research, we aim to build a near-real time flash flood and landslide EWS for a hazard-prone Khao Yai NP in northeastern Thailand. Specifically, we evaluate low-cost sensors, linked to high resolution radar rainfall observations. These sensors measure a.o. soil moisture, precipitation, water and air pressure, and transmit real-time data via NB-IoT mobile signals. The rain gauge rainfall data will be merged with weather radar data to compute radar rainfall bias adjustment for preparing high-quality gridded rainfall over the study area. Moreover, we investigate the controlling factors influencing the quality of radar composites over and quantify the rain radar composites. The results indicated that specific quality indexes could be used to identify areas with inaccurate or unreliable raw data. The low-cost sensors and radar composites are subsequently used in spatially distributed models that are the basis of an early warning systems that is under development in this hazard-prone mountainous region.