Hibatullah, Fausania (2020) Pemodelan Indeks Curah Hujan Ekstrem dengan Pendekatan GAMLSS (Studi Kasus : Indeks RX5day dan CDD di Provinsi Nusa Tenggara Timur). Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Provinsi NTT menjadi prioritas pertama provinsi di Indonesia yang secara signifikan dipengaruhi oleh kekeringan sehingga rentan terhadap risiko kekeringan. Pemantauan dan pengukuran yang efektif atas kejadian curah hujan ekstrem penting untuk mengevaluasi perubahan di masa depan dan dampak curah hujan ekstrem. Non-stasioneritas sering ditemukan dalam data deret waktu hidrologi, Metode Generalized Additive Model for Location, Scale and Shape (GAMLSS) menyediakan kerangka kerja pemodelan yang fleksibel yang lebih cocok untuk memodelkan perubahan iklim ekstrem. Penelitian ini menggunakan 2 indeks curah hujan ekstrem, yaitu RX5DAY (Maximum 5 Day Precipitation Total) dan CDD (Maximum Number of Consecutive Dry Days). Penelitian ini juga menganalisis pengaruh SOI dan NINO3.4 SST sebagai indeks iklim skala besar terhadap kejadian curah hujan ekstrem. Studi simulasi terhadap model GAMLSS dilakukan dan model terbaik dipilih untuk memodelkan indeks curah hujan ekstrem. Berdasarkan studi simulasi disimpulkan bahwa metode GAMLSS dapat menyesuaikan distribusi data dengan baik pada sampel besar. Kesimpulan lain diperoleh bahwa di sebagian besar stasiun di NTT, kejadian curah hujan ekstrem lebih dijelaskan oleh model non-stasioner menggunakan indeks iklim sebagai variabel prediktor daripada model stasioner dan non-stasioner dengan waktu sebagai variabel prediktor.========================================================================================================East Nusa Tenggara (denoted as NTT) is being listed on the first priority of province in Indonesia which is significantly affected by drought. The effective monitoring and measurement of extreme precipitation events are crucial for evaluating future changes and impacts of extreme precipitation. Non-stationarity is often found in hydrological time series data, The Generalized Additive Model for Location, Scale and Shape (GAMLSS) method provides a flexible modelling framework which is more suitable for modelling extreme climate change. This study uses 2 indices of extreme precipitation, the RX5DAY (Maximum 5 Day Precipitation Total) and the CDD (Maximum Number of Consecutive Dry Days), this study also analyzes the effect of SOI and NINO3.4 SST as large scale climate indices. Simulation study towards GAMLSS models is carried out and the best model is chosen to model the extreme precipitation indices. GAMLSS method can adjust data distribution well on large samples based on the simulation study. It also can be concluded that at most stations in NTT, the extreme precipitation events can be better explained by non-stationary models using climate indices as independent variables compared to stationary and non-stationary models with time as independent variables.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Curah hujan ekstrem, GAMLSS, indeks iklim, non-stasioner, climate indices, extreme precipitation, GAMLSS, non-stationary |
| Subjects: | Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) T Technology > TD Environmental technology. Sanitary engineering > TD171.75 Climate change mitigation |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis |
| Depositing User: | Fausania Hibatullah |
| Date Deposited: | 25 Aug 2020 13:11 |
| Last Modified: | 06 Nov 2023 12:15 |
| URI: | http://repository.its.ac.id/id/eprint/80960 |
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