Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach

Marino, Pasquale; Peres, David J.; Cancelliere, Antonino; Greco, Roberto; Bogaard, Thom

doi:10.1007/s10346-020-01420-8

Cited by 92 publications

(71 citation statements)

References 92 publications

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“…Antecedent water content, soil saturation, and pore pressure play key roles in landslide instability [25,28,65], and local soil moisture content can be a precursor of landslide instability [66,67]. Even though soil moisture measurements from space are still limited in resolution and depth, satellite soil moisture information may help constrain the nowcast [68].…”

Section: Spatial Temporal Lag Note Resolution Coverage Resolution Covmentioning

confidence: 99%

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Natijne

Lindenbergh

Bogaard

2020

Sensors

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Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

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Section: Spatial Temporal Lag Note Resolution Coverage Resolution Covmentioning

confidence: 99%

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Natijne

Lindenbergh

Bogaard

2020

Sensors

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“…Data-driven method for the assessment of the temporal probability of occurrence Shallow landslides triggering occur in consequence of particular rainfall patterns coupled with particular soil hydrological conditions, related in particular to high degree of saturation in superficial soil levels (Lu and Godt 2013;Vasu et al 2016;Bordoni et al 2019). For these reasons, a data-driven model for evaluating the temporal probability of occurrence of shallow landslides in the test-sites (TEMP) was created coupling rainfall factors to soil saturation degree at the beginning of a rainfall and to the soil types, differentiated in terms of materials derived by different bedrocks.…”

Section: Assessment Of the Spatio-temporal Probability Of Occurrence mentioning

confidence: 99%

“…A second approach concerns the use of a physically based method that combines a hydrological and a slope stability model to estimate the response of the hillslope soils towards an input rainfall event (Montgomery and Dietrich 1994;Iverson 2000;Lu and Godt 2013;Montrasio et al 2014;Canli et al 2018;Kang et al 2020). These models are dynamic, allowing to analyze the change in unstable zones across a test-site during a particular rainfall event (Schilirò et al 2015;Zhuang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…This method demonstrated a good effectiveness, but it neglected the soil moisture conditions for modeling the temporal occurrence of shallow landslides. Observed soil moisture conditions at the beginning of a rainfall event do not correspond often to antecedent rainfall indexes (Brocca et al 2008), due to the complex subsurface hydrological processes which lead to landslide triggering (Lu and Godt 2013;Bordoni et al 2015;Thomas et al 2018;Picarelli et al 2020). This also may cause a significant amount of false positives and/or false negatives limiting the reliability of these models (Mirus et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

et al. 2020

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A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.

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“…In some regions of the world, the soil wetness at the beginning of the triggering rainfall has been proven to play a major role (Bogaard and van Asch, 2002) and therefore to help improve the early warning systems (Guzzetti et al, 2020;Krogli et al, 2018;Marino et al, 2020). Although in previous works in the Baguio area (Philippines) the importance of the antecedent rainfall has been shown to be key to understanding the triggering mechanisms of MORLEs during typhoons (Nolasco-Javier et al, 2015;Nolasco-Javier and Kumar, 2018), no previous work has involved the analysis of soil moisture.…”

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confidence: 99%

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Abancó¹,

Bennett²,

Matthews³

et al. 2020

Preprint

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Abstract. In 2018, Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the area of Itogon (Philippines). A landslide inventory of 1101 landslides over a 570 km2 area is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the rainfall and soil moisture conditions that led to widespread failure. Landslides mostly occurred in slopes covered by wooded grassland in clayey materials, predominantly facing East–Southeast. The analysis of both satellite rainfall (GPM IMERG) and soil moisture (SMAP-L4) finds that, in addition to rainfall from the typhoon, soil water content plays an important role in the triggering mechanism. Rainfall associated with Typhoon Mangkhut is compared with 33 high intensity rainfall events that did not trigger regional landslide events in 2018 and with previously published rainfall thresholds. Results show that: (a) it was one of the most intense rainfall events in the year but not the highest, and (b) despite satellite data tending to underestimate intense rainfall, previous published regional and global thresholds are to be too low to discriminate between landslide triggering and non-triggering rainfall events. This work highlights the potential of satellite products for hazard assessment and early warning in areas of high landslide activity where ground-based data is scarce.

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Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach

Abstract: Important note To cite this publication, please use the final published version (if applicable). Please check the document version above.

Cited by 92 publications

References 92 publications

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

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