The use of INANWP model for a landslide early warning system: a prototype from the Kulon Progo Region in Yogyakarta, Indonesia Danang Eko Nuryanto1, Guruh Samodra2, Erwin Eko Wahyudi3, Nanang Susyanto4, Muhammad Auzan3, Andi Dharmawan3, Danang Sri Hadmoko2, Wido Hanggoro1, Donaldi Sukma Permana1, and Dwikorita Karnawati1
1 Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jakarta, Indonesia
2 Department of Environmental Geography, Faculty of Geography Universitas Gadjah Mada, Yogyakarta, Indonesia
3 Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada, Yogyakarta, Indonesia
4 Department of Mathematics, Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada, Yogyakarta, Indonesia
Abstract
This study uses Indonesian Numerical Weather Prediction (INANWP) modeling to assist weather conditions for a landslide early warning system (LEWS) in the Kulon Progo region in Yogyakarta, Indonesia. The INANWP is an advanced numerical model with observation data assimilation. Rainfall-induced landslides pose a significant threat to communities living in mountainous areas in Indonesia, one of the most landslide-affected countries in the world. Due to its steep topography, volcanic soils, and very high population density, Java Island is the most affected region. Landslide risk is often mitigated by slope stabilization and drainage methods in sites where landslides have occurred and by reducing exposure of structures through proper land use planning. The main purpose of this study is to describe the development of a geographical LEWS WebGIS prototype using comprehensive landslide inventory data, rainfall satellite data, and rainfall data from INANWP. Based on the landslide inventory and IMERG rainfall data, the rainfall threshold for landslide occurrence was computed using the cumulated event rainfall (E) and the length of the event (D). The deployment of the landslide threshold on rainfall data from INANWP was used to predict the chance of spatiotemporal landslides in the future. Landslide inventory data was divided into 647 landslides (January 2018 to July 2021) for rainfall threshold establishment and 137 landslides (September 2021 to March 2022). Developing the LEWS WebGIS prototype based on rainfall threshold for landslide occurrence provides new possibilities for better awareness, communication strategies, and warning of landslide hazards.
Keywords: INANWP, Kulon Progo region, landslide early warning system, rainfall threshold
