Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

Abraham, Minu Treesa; Satyam, Neelima; Rosi, Ascanio; Pradhan, Biswajeet; Segoni, Samuele

doi:10.1016/j.catena.2021.105147

Cited by 50 publications

(22 citation statements)

References 62 publications

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“…Analysis of hydrogeological conditions during past landslide events using available soil hydrological information indicated that critical rainfall amounts for slope failure may be reduced if soil moisture levels are elevated, as was shown for example for several landslide events in 2005 in the Seattle area, USA (Baum & Godt, 2010), or a series of events in 2018 in Pittsburgh, PA (Ashland, 2021). With a growing availability of soil wetness information, the performance of existing landslide forecast models could be improved after inclusion of soil hydrological data, for example, from in situ soil moisture measurements (Mirus, Becker, et al, 2018;Mirus, Morphew, & Smith, 2018;Thomas et al, 2020), from satellite observations (Abraham et al, 2021;Bordoni et al, 2021;Brocca et al, 2016), or from soil moisture simulations (Ponziani et al, 2012;Segoni, Rosi, et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

Wicki

Hauck

2022

Vadose Zone Journal

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Soil wetness is an important property in determining the variable disposition of hillslopes to shallow landslides. Recent studies have demonstrated the potential of in situ soil wetness information for landslide early warning. However, the spatial representativeness of in situ sensors may be affected by local heterogeneities of soil properties and hydrological processes, and their installation may be destructive. Electrical resistivity tomography (ERT) has been used in the past to estimate plot‐scale soil moisture variation and may overcome these limitations. In this study, we installed and operated an automated ERT monitoring system on a landslide‐prone hillslope in the Napf region (Switzerland). The system was operational during a period of 9 mo, and measurements were conducted at high temporal resolution and under different soil hydrological conditions. Electrical resistivity was measured along two perpendicular profile lines in Wenner–Schlumberger configuration at 0.25‐m electrode spacing. Soil saturation was calculated by the Archie's law and the parameters were fitted with colocated soil moisture sensors. Comparison of ERT‐derived soil moisture with soil wetness from in situ sensors showed a good correlation, and infiltration properties critical for landslide early warning could be reliably reproduced. Further, analysis of spatial saturation variation revealed that ERT was capable to detect heterogeneities of soil hydrological process. Under highly saturated conditions, the reliability of the saturation estimation was affected by an increased number of faulty measurements and the spatial heterogeneity of the infiltration process.

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Section: Introductionmentioning

confidence: 99%

Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

Wicki

Hauck

2022

Vadose Zone Journal

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“…Catastrophic landslides in mountainous terrains interact with human environment and cause adverse impacts on lives and properties [1]. Aids for managing the risk due to landslides is a topic of which several decades of research has been devoted [2,3]. Mapping the spatial distribution of landslide hazard is one of the most-adopted strategies for risk management, as the landslide susceptibility maps can be used by the government for strategic planning and development [4].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Abraham

Satyam

Lokesh

et al. 2021

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Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

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“…Several studies on the temporal forecasting of landslides [46][47][48] often aim to create landslide warning systems, usually through real-time monitoring of rainfall data [49][50][51][52][53][54]. Rainfall thresholds can be fundamental elements for the implementation of landslide forecasting and monitoring.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece

Lainas

Depountis

Sabatakakis

2021

Land

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A new methodology for shallow landslide forecasting in wildfire burned areas is proposed by estimating the annual probability of rainfall threshold exceedance. For this purpose, extensive geological fieldwork was carried out in 122 landslides, which have been periodically activated in Western Greece, after the devastating wildfires that occurred in August 2007 and burned large areas in several parts of Western Greece. In addition, daily rainfall data covering more than 40 years has been collected and statistically processed to estimate the exceedance probability of the rainfall threshold above which these landslides are activated. The objectives of this study are to quantify the magnitude and duration of rainfall above which landslides in burned areas are activated, as well as to introduce a novel methodology on rainfall-induced landslide forecasting. It has been concluded that rainfall-induced landslide annual exceedance probability in the burned areas is higher when cumulative rainfall duration ranges from 6 to 9 days with local differences due to the prevailing geological conditions and landscape characteristics. The proposed methodology can be used as a basis for landslide forecasting in wildfire-affected areas, especially when triggered by rainfall, and can be further developed as a tool for preliminary landslide hazard assessment.

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Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

Cited by 50 publications

References 62 publications

Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Preliminary Forecasting of Rainfall-Induced Shallow Landslides in the Wildfire Burned Areas of Western Greece

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