Analisis Limpasan Langsung Metode SCS Menggunakan Data Hujan TRMM Studi Kasus Subdas Code Hulu
Authors: Harsanto Puji , Tsaury Muhammad Sufyan 
Published: October 2021

Ketersediaan data menjadi hal yang krusial dalam analisis hidrologi. Banyak permasalahan menyangkut ketersediaan data yang seringkali ditemui di lapangan, seperti minimnya data, data yang tidak kontinyu, atau sebaran stasiun yang tidak merata. Seiring berkembangnya teknologi, permasalahan tersebut dapat diselesaikan dengan memanfaatkan data pengamatan satelit yang memiliki resolusi spasial dan temporal tinggi, cakupan luas, akses cepat, dan ekonomis. Akan tetapi, data satelit perlu divalidasi dengan data pengamatan nyata di lapangan. Penelitian ini dilakukan untuk validasi data satelit TRMM terhadap data observasi berbasis darat dengan membandingkan debit limpasan dari data hujan terukur di darat atau ARR (Automatic Rainfall Recorder) dengan data hujan TRMM,  lalu dikoreksi dengan debit limpasan terukur di stasiun AWLR (Automatic Water Level Recorder) Gemawang. Debit limpasan dari hujan dihitung dengan menggunakan Metode SCS. Hasil penelitian menunjukan jeda waktu rata-rata pengukuran hujan TRMM dan ARR sekitar 8,5 jam. Ditemukan perbedaan bentuk hidrograf limpasanTRMM. Pada data 18 Januari 2018, terdapat kesalahan bentuk gelombang hidrograf (Ew) sebesar 11.843. Dari analisis indeks kesesuaian dan efisiensi, data satelit TRMM mendapat hasil koefisien korelasi rata-rata debit ARR-AWLR dan TRMM-AWLR tergolong rendah yaitu masing-masing sebesar 0,2416 dan 0,1041, sedangkan koefisien efisiensinya 1,67 yang dikategorikan sebagai data yang efisien. Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.