Grid Satellite Rainfall Products Potential Application for Developing I-D and E-D Thresholds for Landslide Early Alert System over Bali Island

Abstract

Bali has been one of the most popular tourist destinations in Indonesia. However, on the other hand, Bali has a high risk of natural disaster vulnerability. The number of landslides in Bali took the first position compared to other natural disasters. Currently, remote sensing platforms can present Grid Satellite Rainfall Products (GSRPs), which provide rainfall information that can identify rainfall conditions for landslide events. This study aims to analyze the potential GSRPs application of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), and Global Satellite Mapping of Precipitation (GSMaP) in determining the mean rainfall intensities and duration (I-D); accumulated rainfall and duration (E-D) thresholds for landslide occurrences over Bali Island. The method used to develop I-D and E-D thresholds is the power-law equation and frequentist sampling method in various probability levels (5%, 10%, 20%, 30%, 40%, and 50%). The result shows that I-D and E-D thresholds established by GSRPs are generally lower than the threshold defined by rain gauge observations. Among the three GSRPs, IMERG is performing the best in establishing the I-D and E-D thresholds for landslide phenomena. The level of potential that IMERG can use in developing the I-D and E-D thresholds is 59.16% and 84.06%, respectively. The E-D threshold derived from the IMERG product can be used to establish the landslide early alert system over Bali Island because it has a high spatial-temporal resolution, word-wide coverage, and near-real-time observation.

Performance of high-resolution satellite rainfall datasets in developing rainfall-duration threshold for landslide incidents over Badung Regency

“…Cumulative rainfall estimation by PERSIANN yields a robust correlation with rainfall stations, whereas IMERG and GSMaP exhibit instances of overestimation and underestimation. Notably, IMERG presents minimal deviation when gauging accumulated rainfall that triggers landslide events in comparison to other datasets [21]. Further analysis reveals IMERG's proficiency across daily, decadal, and seasonal time scales, while GSMaP displays a negative bias across all observed scales (daily, decadal, monthly, and seasonal) [17].…”

“…A number of researchers have tried to establish rainfall thresholds in accurately predicting slope collapse/landslide using the parameters of average rainfall, duration of the rainfall event, a ratio of rainfall to daily rainfall, previous rainfall to annual average rainfall, and daily rainfall to maximum previous rainfall ratio [2], [23]- [28]. The utilization of satellite rainfall products in determining the rainfall threshold for landslide occurrence is still little done especially in Bali Province [21]. Previous researchers have analyzed many landslide-triggering rainfall events for the determination of rainfall threshold values using daily, dasarian, and monthly rainfall data [6].…”

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

“…Numerous scholars have conducted prior research to investigate the utilization of satellite-derived precipitation data in the determination of rainfall thresholds that trigger landslides. Prominent studies have examined the contributions of TRMM [15], [18]- [20], GSMaP [21], [22], IMERG [11], [18], [22]- [24], PERSIANN [12], [22], and CMOPRH [9]. Previous studies have identified variations in the effectiveness of SRGDs, which can be attributed to regional factors.…”

“…This has been achieved by considering parameters such as average rainfall, the duration of rainfall events, the ratio of rainfall to daily rainfall, previous rainfall to average annual rainfall, and daily rainfall to the maximum ratio of previous rainfall [1], [4]- [9], [27]. The utilization of SRGDs in determining rain thresholds for landslide events is still limited, especially in Bali Province [22]. Moreover, previous studies have not analyzed rainfall thresholds based on the integration of high temporal-spatial resolution of SRGDs.…”