On Thursday, July 14, 2022, a flash flood occurred in Pati Regency, Central Java, caused by heavy rainfall with an intensity of up to 147.5 mm/day on the slopes of Mount Muria. resulting several rivers in Pati Regency overflowed, causing damage to embankments and triggering a flash flood that devastated residential areas and agricultural land. In this study, a simulation was conducted to estimate the extent of the affected area by flash floods in the Sat River, Pati Regency, in the future. The study utilized HEC-RAS version 6.2, which is capable of accurately simulating non-Newtonian fluid behavior, thus enabling precise simulation of flash floods. The simulation results revealed that the flash flood in the Sat River, Pati Regency, had an impacted area of 655 hectares with an average water depth of 3.25 meters for a 2-year return period. For a 25-year return period, the impacted area increased to 1,017 hectares with an average water depth of 3.04 meters, while for a 50-year return period, the impacted area reached 1,091 hectares with an average water depth of 3.01 meters. This research provides a better understanding of the impact of flash floods in the Sat River, Pati Regency, and can serve as a reference for future disaster mitigation efforts. By knowing the extent of the affected area and the average water depth, more effective preventive and management measures can be implemented to mitigate the significant losses caused by flash floods.Keywords:       flash flood, flood mitigation, flood simulation, HEC-RAS, hydraulic analysis, Pati Regency  ABSTRAKPada Kamis, 14 Juli 2022, banjir bandang terjadi di Kabupaten Pati, Provinsi Jawa Tengah yang disebabkan oleh curah hujan dengan intensitas mencapai 147,5 mm/hari di lereng Gunung Muria. Akibatnya, sejumlah sungai di Kabupaten Pati meluap, menyebabkan kerusakan pada tanggul dan memicu banjir bandang yang merusak permukiman penduduk serta lahan persawahan. Dalam penelitian ini, dilakukan simulasi dengan tujuan untuk memperkirakan luas area yang terdampak banjir bandang di Sungai Sat, Kabupaten Pati, di masa depan. Studi ini menggunakan perangkat lunak HEC-RAS versi 6.2 yang memiliki kemampuan untuk melakukan simulasi fluida dengan sifat non-Newtonian, sehingga simulasi banjir bandang dapat dilakukan secara akurat. Hasil dari simulasi menunjukkan bahwa banjir bandang di Sungai Sat, Kabupaten Pati, memiliki luas terdampak sebesar 655 hektar dengan kedalaman air rata-rata mencapai 3,25 meter untuk periode ulang 2 tahunan. Pada periode ulang 25 tahunan, luas dampak meningkat menjadi 1.017 hektar dengan kedalaman air rata-rata sebesar 3,04 meter, sedangkan untuk periode ulang 50 tahunan, luas dampak mencapai 1.091 hektar dengan kedalaman air rata-rata sebesar 3,01 meter. Penelitian ini memberikan pemahaman yang lebih baik mengenai dampak banjir bandang di Sungai Sat, Kabupaten Pati, dan dapat digunakan sebagai acuan dalam upaya mitigasi bencana di masa depan. Dengan mengetahui luas area yang terdampak dan kedalaman air rata-ratanya, langkah-langkah pencegahan dan penanganan yang lebih baik dapat diambil untuk menghindari kerugian besar yang ditimbulkan oleh banjir bandang.Kata Kunci:            Analisis Hidrolika, Banjir Bandang , HEC-RAS, Kabupaten Pati, Mitigasi Banjir, Simulasi Banjir

Rainfall monitoring in real-time is a mandatory in tropical areas such as Indonesia. As a country with various topographical conditions ranging from low-lying urban areas, highlands, to mountainous valleys, Indonesia is prone to hydrometeorological disasters in the form of flash floods and landslides. The strategic geographical position at the equator, between the Pacific and Indian oceans, and surrounded by vast oceans, combined with various natural phenomena related to the dynamics of the atmosphere and the ocean, makes high-density rainfall observations indispensable for both disaster mitigation and climate monitoring. As a vast tropical and archipelagic country, Indonesia currently has around 1000 automatic rainfall sensors and still requires more sensors to increase the spatial resolution of the observation network. Increasing the density of the observation network using both rain gauges and weather radar poses a problem of high operational costs. Therefore, several alternative rainfall observation systems are required. In the last decade, there have been several studies related to rainfall measurements using artificial intelligence from various meteorological variables, including the exploitation of microwave signals from radio telecommunications links, both terrestrial and satellite using high frequency bands. In this survey paper, we review and discuss research articles related to rainfall estimation using state-of-the-art methods in artificial intelligence using meteorological observation data, remote sensing, terrestrial and satellite microwave communication links. In conclusion, we present several future research challenges that can be applied to increase the density of rainfall observation networks.