Bias correction of CMIP6 rainfall projection for the Lake Toba region, Indonesia, using quantile delta mapping with monthly distribution fitting

Syukri Arif Rafhida; Sri Nurdiati; Retno Budiarti; Mohamad Khoirun Najib

doi:10.15625/2615-9783/23833

Author affiliations

Authors

Syukri Arif Rafhida School of Data Science, Mathematics, and Informatics, IPB University, Bogor, Indonesia
Sri Nurdiati School of Data Science, Mathematics, and Informatics, IPB University, Bogor, Indonesia
Retno Budiarti School of Data Science, Mathematics, and Informatics, IPB University, Bogor, Indonesia
Mohamad Khoirun Najib School of Data Science, Mathematics, and Informatics, IPB University, Bogor, Indonesia

DOI:

https://doi.org/10.15625/2615-9783/23833

Keywords:

Bias correction, CMIP6, equatorial climate, Lake Toba, Quantile Delta Mapping, rainfall

Abstract

Accurate climate projections are crucial for water resource management in the Lake Toba region of Indonesia, where rainfall supports agriculture, tourism, and hydropower. However, rainfall projections from CMIP6 global climate models (GCMs) often exhibit systematic biases due to coarse spatial resolution and model assumptions. This study applies the statistical bias-correction method, Quantile Delta Mapping (QDM), to adjust CMIP6 rainfall outputs using observed data from seven weather stations around Lake Toba. The study employs ten probability distributions and compares two distribution identification approaches: monthly and full-period. Results show that monthly distribution identification (QDM1) improves model performance more consistently, reducing the Kolmogorov-Smirnov (KS) error by the raw CMIP6 data by 47.26% compared to 20.39% from the full-period approach (QDM2). Post-correction projections using a Multi-Model Ensemble Mean (MMEM) for 2015–2050 under scenarios SSP2-4.5 and SSP5-8.5 indicate moderated rainfall trends in 2050 after correction, notably reduced overestimation in wet seasons. These more conservative projections suggest slight decreases during MAM, contrasting with the uncorrected CMIP6 tendency toward wetter futures. It also suggests the future use of regional climate models (RCMs) and nonparametric or machine-learning methods to capture more complex rainfall distribution patterns in the region.

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