DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds

Althuwaynee, Omar F.; Melillo, Massimo; Gariano, Stefano Luigi; Park, Hyuck‐Jin; Kim, Sang Wan; Lombardo, Luigi; Hader, Paulo Rodolpho Pereira; Mohajane, Meriame; Quevedo, Renata Pacheco; Catani, Filippo; Aydda, Ali

doi:10.1016/j.envsoft.2023.105657

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Physically-based methods can also be used for regional-scale landslide susceptibility mapping and analysis can be done within a short time. Some “plugins” of physically-based models are also available in different GIS software such as QGIS [ [34] , [35] , [36] ].…”

Section: Resultsmentioning

confidence: 99%

“…Physically-based methods use failure mechanisms and the underlying process of landslides [ [30] , [31] , [32] , [33] ]. In most cases, physically-based methods are costly, but, now different methods are used in landslide susceptibility mapping with good accuracy at the regional scale and with short analysis time [ [34] , [35] , [36] ].…”

Section: Introductionmentioning

confidence: 99%

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A review on landslide susceptibility mapping research in Bangladesh

Chowdhury

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on landslide susceptibility mapping research in Bangladesh

Chowdhury

2023

Heliyon

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“…Traditional threshold analysis results in great uncertainty regarding landslide occurrence predictions. The accuracy of both the I-D and E-D models depends on multiple factors, each carrying its own weight in terms of influencing the outcomes, such as the resolution of rainfall and geo-hazard data [29], the relative locations of nearby rain gauges and landslides [30], and the criterion used to define the lower bound of the threshold. To enhance the prediction accuracy of the rainfall thresholds with regard to landslide occurrence, more advanced monitoring techniques and more complex statistical models were developed in the last decade, including the utilization of higher-resolution rainfall and geospatial data [5,31], the integration of landslide-related hydrologic models [32,33], the incorporation of detailed geologic and geomorphological information on the study area [34,35], the utilization of black box models for predictive analytics [36,37], and the use of non-triggering rainfall data in the analysis [38].…”

Section: Introductionmentioning

confidence: 99%

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Xu,

Xiao,

Zhao

et al. 2024

Sustainability

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Deriving rainfall thresholds is one of the most convenient and effective empirical methods for formulating landslide warnings. The previous rainfall threshold models only considered the threshold values for areas with landslide data. This study focuses on obtaining a threshold for each single landslide via the geostatistical interpolation of historical landslide–rainfall data. We collect the occurrence times and locations of landslides, along with the hourly rainfall data, for Dazhou. We integrate the short-term and long-term rainfall data preceding the landslide occurrences, categorizing them into four groups for analysis: 1 h–7 days (H1–7), 12 h–7 days (H12–D7), 24 h–7 days (H24–D7), and 72 h–7 days (H72–D7). Then, we construct a rainfall threshold distribution map based on the 2014–2020 data by means of Kriging interpolation. This process involves applying different splitting coefficients to distinguish the landslides triggered by short-term versus long-term rainfall. Subsequently, we validate these thresholds and splitting coefficients using the dataset for 2021. The results show that the best splitting coefficients for H1–D7, H12–D7, H24–D7, and H72–D7 are around 0.19, 0.52, 0.55, and 0.80, respectively. The accuracy of the predictions increases with the duration of the short-term rainfall, from 48% for H1–D7 to 67% for H72–D7. The performance of these threshold models indicates their potential for practical application in the sustainable development of geo-hazard prevention. Finally, we discuss the reliability and applicability of this method by considering various factors, including the influence of the interpolation techniques, data quality, weather forecast, and human activities.

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“…These systems can be developed over large regions as well as at the scale of single landslides (Guzzetti et al, 2020). In the first case, rainfall data is usually accessed from national rain gauge networks (Al-Thuwaynee et al, 2023), or even from satellite data (Wang et al, 2021) to estimate potentially unstable areas and their temporal aspect. As for localized early warnings, they can usually rely on a rich hydrological and geotechnical information gathered via landslide-specific installations, from which rainfall thresholds are still derived to understand possible slope failures timing (Segoni et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system

Ahmed¹,

Tanyaş²,

Huser³

et al. 2023

Preprint

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The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions. This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.

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DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds

Cited by 9 publications

References 36 publications

A review on landslide susceptibility mapping research in Bangladesh

A review on landslide susceptibility mapping research in Bangladesh

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system

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