Modeling of Heavy Rainfall Triggering Landslide Using WRF Model

Abstract

This study revealed the behavior of heavy rainfall before landslide event based on the Weather Research Forecasting (WRF) model. Simulations were carried out to capture the heavy rainfall patterns on 27 November 2018 in Kulonprogo, Yogyakarta. The modeling was performed with three different planetary boundary layer schemes, namely: Yonsei University (YSU), Sin-Hong (SH) and Bougeault and Lacarrere (BL). Our results indicated that the variation of rainfall distribution were small among schemes. The finding revealed that the model was able to capture the radar’s rainfall pattern. Based on statistical metric, WRF-YSU scheme was the best outperforming to predict a temporal pattern. Further, the study showed a pattern of rainfall development coming from the southern coastal of Java before 13:00 LT (Local Time=WIB=UTC+7) and continued to inland after 13:00 LT. During these periods, the new clouds were developed. Based on our analysis, the cloud formation that generated rainfall started at 10:00 LT, and hit a peak at 13:00 LT. A starting time of cloud generating rainfall may be an early indicator of landslide.

Prediksi Curah Hujan Bulanan Untuk Peringatan Dini Longsor Di Banjarnegara Bagian Selatan Dengan Statistical Downscaling Dan Ensemble

“…Pentingnya pemodelan prediksi curah hujan yang mampu menjadi peringatan dini longsor sudah dilakukan oleh peneliti sebelumnya. Pada umumnya menyampaikan bagaimana parameter planetary boundary layer (PBL) mempunyai peran dalam pembentukan awan hujan [12,13]. Namun bagaimana caranya memanfaatkan keluaran model tersebut sehingga dapat di aplikasikan dalam system peringatan dini longsor belum secara jelas diuraikan.…”

A Preliminary Study on the Parameter Configuration of Weather Research Forecasting in Tropical Peatland, Central Kalimantan

“…Weather research and forecasting (WRF) model is a state-of-the art of numerical weather prediction that has been widely tested and applied worldwide to study the atmospheric condition. Now its application is not limited to operational weather forecasting only, but expands to weather impact studies such as flood forecast (Patel et al, 2019;Zhou et al, 2018), susceptible to landslide events (Nuryanto et al, 2020), and forest fires prediction (Lisnawati et al, 2022;Shamsaei et al, 2023;Taufik et al, 2023).…”

Statistical Assessment of High-Resolution Climate Model Rainfall Data in the Ciliwung Watershed, Indonesia

“…Rainfall is an essential variable in the water supply-demand calculations. Thus, it is crucial for water resource management (Sharafatmandrad and Mashizi, 2021;Yu et al, 2022) and disaster mitigation (Dhanesh et al, 2020;Nuryanto et al, 2020), especially to predict the occurrence of flood and drought events. The accuracy of water resource assessment is affected by the limited monitoring of rainfall at a finer spatial resolution, as it is mostly not measured except in a few stations.…”