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All Journal Jurnal Mahasiswa Jurusan Teknik Pengairan Jurnal Talenta Sipil
Almira, Aufa Hanan
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Civil Engineering, Building, Construction & Architecture Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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TRANSFORMASI DATA HUJAN MENJADI DEBIT MENGGUNAKAN METODE FJ MOCK DAN THORNTHWAITE MATHER DI SUB DAS KALI GUNTING KABUPATEN JOMBANG. Almira, Aufa Hanan; Harisuseno, Donny; Soetopo, Widandi
Jurnal Mahasiswa Jurusan Teknik Pengairan Vol 3, No 2 (2020)
Publisher : Jurusan Teknik Pengairan, Fakultas Teknik, Universitas Brawijaya

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Abstract

Transformasi data hujan menjadi debit adalah mengolah data hujan di lapangan menjadi data debit dengan pemodelan hidrologi. Metode-metode ini digunakan untuk menghitung besarnya nilai debit aliran sungai. Metode yang digunakan dalam studi ini adalah Metode FJ Mock dan Thornthwaite Mather. Hasil dari kedua metode ini akan dibandingkan dengan debit pengamatan AWLR (Automatic Water Level Recorder) Karangwinongan untuk mengetahui tingkat kesesuaian metode pada daerah studi. Berdasarkan uji kesesuaian metode dari kedua metode yang digunakan, metode FJ Mock dipilih menjadi metode yang terbaik untuk Sub DAS Kali Gunting. Berdasarkan uji kesesuaian metode ini dihasilkan Nash Sutcliffe Efficiency (NSE) = 0,42 , Koefisien Korelasi (R) = 0,75 , Root Mean Squared Error (RMSE) = 0,25, dan Kesalahan Relatif (KR) = 0,14 %. Perhitungan dengan metode FJ Mock menghasilkan debit maksimum = 14,31 m3/dt dan minimum = 0,54 m3/dt. The transformation of rainfall data into discharge is to process the rainfall data on the field into a discharge data of hydrological modeling. These methods are used to assess the amount value of river flow discharge. The methods that used in this study are FJ Mock and Thornthwaite Mather methods. The result of these two methods will be compared to the discharge observation AWLR (Automatic Water Level Recorder) Karangwinongan to know the level of conformity of methods in the area of study. Based on the conformity test method of both methods used, the FJ Mock method was chosen to be the best method for Kali Gunting Sub Watershed. Based on the conformity test this method was produced by Nash Sutcliffe Efficiency (NSE) = 0,42, correlation coefficient (R) = 0,75, Root Mean Squared Error (RMSE) = 0,25, and relative error (KR) = 0,14%. Calculations by FJ Mock methods produce a maximum discharge = 14.31 m3/dt and a minimum = 0,54 m3/dt.   
Koreksi Bias Data Hujan Satelit Persiann Sebagai Alternatif Pos Stasiun Hujan dengan Pendekatan Quantile Mapping di Sub Das Brantas Hulu Almira, Aufa Hanan; Damarnegara, Anak Agung Ngurah Satria
Jurnal Talenta Sipil Vol 7, No 2 (2024): Agustus
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/talentasipil.v7i2.515

Abstract

The rainfall data is usually obtained from meteorological stations, which many sill observed manually in Indonesia and can lead to an observation error. Remote sensing rainfall data observed by satellite offer interesting alternatives to overcome the limitation of existing meteorological stations in Indonesia. It offers accuracy, spatial coverage, timeliness and cost effectiveness. However it contains bias if compared to land based station. This research will evaluate rainfall data observed in PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) in Brantas Hulu Watershed. The rainfall data of 13 years from 12 stations is evaluated to obtain area averaged rainfall using Thiessen Polygon method. Three datasets from PERSIANN, PERSIANN CCS and PERSIANN CDR are compared statistically with the area averaged area for daily, monthly and yearly rainfall. Bias correction is performed using Quantile Mapping to match the cumulative distribution curve from the area averaged rainfall. Verification of bias correction is performed using 2 years data from 2019 to 2020. Finally, the extreme rainfall analysis is performed to test the corrected data for design rainfall calculations. The correlation of daily data shows poor agreement for all three datasets with correlation coefficient below 0,40. The monthly and yearly data gives better correlation with coefficient above 0,80. It shows that for time series event, the satellite data gives poor correlation with the area averaged rainfall. However, quantile mapping correction gives consistent cumulative distribution agreement for correction and model verification. The verification analysis shows an increase in coefficient correlation from 0.387 to 0.43 (daily) and 0.887 to 0.915 (monthly). In addition, there is a decrease in normalized root mean square error (NRMSE) from 31.03% to 16.91% (daily) and 46.03% to 14.03% (monthly). A decrease in normalized mean absolute error (NMAE) occurred from 16.12% to 9.74% (daily) and 31.67% to 10.07% (monthly). Beside that, the results of extreme rainfall analysis produce values that are very close to the rain station. So it is found that the PERSIANN CCS quantile mapping regression model is the best in improving data quality.
Co-Authors Damarnegara, Anak Agung Ngurah Satria Donny Harisuseno Widandi Soetopo