Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding
Abstract
:1. Introduction
2. Study Area and Data
2.1. Underreported Landslide Events in the WEAR
2.2. Satellite-Based Rainfall
2.3. Susceptibility Models
3. Problem Statement
4. Improving the Data Distribution over the S Range of the Data Used for Threshold Calculation
4.1. Rationale
- Data preparation.
- A.1.
- AR values associated with each day of a reported landslide plus the days prior and after these dates are extracted from the AR time series of the corresponding pixels calculated according to Equation (1) and the parameterization adopted in [35], i.e., a = b = 1.2, n = 42 days, for which the index is relevant for landslide types ranging from shallow to deep-seated landslides [35,63]. Data with AR < 5 mm are discarded from the data set as unlikely to have been triggered by rainfall [35]. The size of the provisional data set Q is then q ≤ 3p, where p is the number of landslide events in the raw calibration set.
- A.2.
- The data are weighted to account for the event date uncertainty: w = 24/36 for the day a landslide was reported, w = 6/36 for the days prior and after the landslide was reported. This weighting is implemented by expanding the data set as described in [35]. The expanded set is noted R.
- Threshold calibration.
- B.1.
- The number tC of data to be selected per S class is determined as
- B.2.
- The data of R are grouped by S class. For each S class, data with the lowest AR values are selected until they amount to tC. The set of selected data points over all S classes is referred to as T and contains a number of data t ≤ (2 × TPE × r).
- B.3.
- Thresholds are then calculated through linear least-square regressions using the log-transformed AR and S data from T and the bootstrap technique as in [35] to obtain threshold relations in the form of Equation (2).
- Threshold evaluation
- Threshold quality is evaluated through the correspondence between the obtained false negative rate (FNR, actual ratio of data in R below the calculated threshold) and the nominal TPE. Differences may result from t significantly smaller than (2 × TPE × r), large outliers in T, and possibly also from bootstrap issues (see Section 5).
4.2. Increased Efficiency of the Method
5. Bootstrapping Called into Question
6. Robustness of the Modified AR-S Threshold Method
6.1. First Test: Sensitivity to New Data on Landslide Occurrence
6.2. Second Test: Robustness to Different S Data Sets
7. Relevance to Landslide Hazard and Early Warning Studies
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Threshold | with Bootstrap | without Bootstrap |
---|---|---|
5% threshold, S = 0.10 | 69.4 | 69.6 |
5% threshold, S = 0.72 | 7.0 | 6.8 |
10% threshold, S = 0.10 | 76.9 | 78.1 |
10% threshold, S = 0.72 | 9.1 | 8.9 |
Threshold | Continental S Data | Regional S Data |
---|---|---|
5% threshold, min S | 66.7 | 62.1 |
5% threshold, max S | 6.1 | 6.5 |
10% threshold, min S | 86.9 | 73.3 |
10% threshold, max S | 8.1 | 8.7 |
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Monsieurs, E.; Dewitte, O.; Depicker, A.; Demoulin, A. Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding. Water 2019, 11, 2202. https://doi.org/10.3390/w11112202
Monsieurs E, Dewitte O, Depicker A, Demoulin A. Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding. Water. 2019; 11(11):2202. https://doi.org/10.3390/w11112202
Chicago/Turabian StyleMonsieurs, Elise, Olivier Dewitte, Arthur Depicker, and Alain Demoulin. 2019. "Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding" Water 11, no. 11: 2202. https://doi.org/10.3390/w11112202