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All Journal Jurnal Sains Teknologi dan Lingkungan (JSTL)
Lilik Hanifah
Jurusan Teknik Sipil Fakultas Teknik, Universitas Mataram
Agriculture, Biological Sciences & Forestry Computer Science & IT Public Health

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Koreksi Bias Data Hujan Luaran GCM ECHAM5 Untuk Prediksi Curah Hujan Bulanan dan Musiman Pulau Lombok: Bias Correction for GCM ECHAM5 Model Rainfall Data Output in Estimating Monthly and Seasonally Rainfall for Lombok Island Humairo Saidah; Agustono Setiawan; Lilik Hanifah; Eko Pradjoko; Agus Suroso
JURNAL SAINS TEKNOLOGI & LINGKUNGAN Vol. 7 No. 2 (2021): JURNAL SAINS TEKNOLOGI & LINGKUNGAN
Publisher : LPPM Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jstl.v7i2.289

Abstract

This study aims to evaluate the ability of the ECHAM5 GCM model output data in estimating monthly rainfall on the island of Lombok. The data used in this study are ECHAM5 monthly rainfall data and automatic rainfall recorder (ARR) measurement rain data for 2000-2018 obtained from ARR Gunung Sari. Correction of bias is conducted by using the mean ratio method and the regression method. The method that produces the best approach is then used to obtain rain data projections and a simple regression method. Evaluation and validation used the Pearson correlation coefficient (r), Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE) values. The results obtained are that the daily and monthly rainfall data from the ECHAM5 model cannot be directly used to replace the rain measurement data because of its very low accuracy. The downscaling technique performed on daily and monthly rainfall data using the average ratio method does not show satisfactory performance where the efficiency figures produced are still low even gave a slight increasing number. However, the ECHAM5 model data can be used to obtain rainfall projections on a monthly and seasonal scale with a good and satisfactory correlation.  Key words: mean ratio method; global climate model; ECHAM5; monthly rainfall.
Co-Authors Agus Suroso Agustono Setiawan Eko Pradjoko Humairo Saidah