Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data Source and Pre-Processing
3. Methods
3.1. Preparation of Shallow Landslide Susceptibility Map
3.2. Determination of Shallow Landslide Initiations
3.3. Shallow Landslide Runout Distance Assessment Methodology
3.4. Selection of Model Parameters
4. Results
4.1. Shallow Landslide Runout Distance Assessment
4.2. Shallow Landslide Runout Distance Assessment Considering RCP Scenarios
5. Discussion
Limitations of this Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Research | Landslide Initiations | Runout Method | Flow Direction Algorithm |
---|---|---|---|---|
Horton et al. [42], Jiang et al. [51], Xu et al. [54], Park et al. [61], Sharma et al. [63] | Debris flow runout susceptibility map | Initiations detected by Flow-R | SFLM | Modified Holmgren |
Pastorello et al. [46], McCoy [47], Paudel [48], Putra et al. [53] | Debris flow runout susceptibility map | User-defined initiations considering flow accumulation and slope [46], landslide susceptibility map [48], and Sentinel images [53] | SFLM | Modified Holmgren |
Ali et al. [37], Bera et al. [62] | Debris flow runout susceptibility map | User-defined initiations based on remote sensing, slope angle distribution [37], and GPS and multi-temporal satellite images [61] | SFLM | Holmgren |
Giano et al. [2] | Debris flow runout susceptibility map | Initiations detected by Flow-R | Perla | Modified Holmgren |
Polat and Erik [60] | Debris flow runout susceptibility map | User-defined initiations considering landslide susceptibility map | Perla | Modified Holmgren |
Charbel and El Hage Hassan [50] | Mudflow runout susceptibility map | Initiations detected by Flow-R | SFLM | Holmgren |
Do et al. [49] | Landslide runout susceptibility map | Initiations detected by Flow-R | SFLM | Modified Holmgren |
Liu et al. [52] | Landslide runout susceptibility map | User-defined initiations based on previous studies and D-InSAR technology | SFLM | Modified Holmgren |
Sub-Basin | N | Statistics | Area (m2) | Failure Depth (m) | Travel Angle (°) | Observed Runout Distance (m) |
---|---|---|---|---|---|---|
Egerci | 111 | Min. | 21 | 0.2 | 1 | 7 |
Max. | 4116 | 3.2 | 49 | 122 | ||
Mean | 536 | 1.0 | 23 | 53 | ||
Median | 344 | 0.9 | 24 | 47 | ||
Std. Deviation | 572 | 0.5 | 12 | 29 | ||
Beycuma | 15 | Min. | 23 | 0.2 | 4 | 7 |
Max. | 1843 | 2.1 | 28 | 83 | ||
Mean | 376 | 0.9 | 13 | 36 | ||
Median | 258 | 0.8 | 13 | 31 | ||
Std. Deviation | 438 | 0.5 | 8 | 20 | ||
Ihsanoglu | 136 | Min. | 7 | 0.1 | 1 | 5 |
Max. | 1710 | 2.1 | 32 | 122 | ||
Mean | 140 | 0.5 | 11 | 25 | ||
Median | 65 | 0.4 | 10 | 17 | ||
Std. Deviation | 226 | 0.3 | 7 | 21 |
Sub-Basin | Statistics | A | SG (°) | SA (°) | PLC | PRC | CI | CD | TWI | SLF | CNBL (m) | CND (m) | VD (m) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Egerci | Min. | 41 | 0 | 0 | −0.01 | −0.01 | −92 | 0 | 2 | 0 | 41 | −49 | −3 |
Max. | 1574 | 60 | 6.28 | 0.01 | 0.012 | 96 | 20 | 21 | 38 | 1382 | 449 | 549 | |
Mean | 444 | 15 | 3.25 | 0 | 0 | 0 | 0 | 7 | 5 | 381 | 64 | 236 | |
Median | 425 | 14 | 3.04 | 0 | 0 | 0 | 0 | 6 | 4 | 369 | 49 | 229 | |
Std. Deviation | 259 | 8 | 1.91 | 0.002 | 0.002 | 8 | 1 | 2 | 3 | 233 | 60 | 133 | |
Beycuma | Min. | 13 | 0 | 0 | −0.01 | −0.01 | −100 | 0 | 3 | 0 | 13 | −27 | 0 |
Max. | 658 | 52 | 6.28 | 0.008 | 0.009 | 100 | 19 | 20 | 24 | 444 | 304 | 277 | |
Mean | 170 | 11 | 3.27 | 0 | 0 | 0 | 0 | 7 | 3 | 129 | 41 | 140 | |
Median | 158 | 9 | 3.33 | 0 | 0 | 0 | 0 | 7 | 2 | 118 | 32 | 146 | |
Std. Deviation | 91 | 6 | 1.91 | 0.001 | 0.001 | 9 | 1 | 2 | 2 | 69 | 38 | 61 | |
Ihsanoglu | Min. | 4 | 0 | 0 | −0.01 | −0.02 | −98 | 0 | 2 | 0 | 6 | −39 | 0 |
Max. | 633 | 70 | 6.28 | 0.017 | 0.021 | 92 | 32 | 20 | 30 | 284 | 468 | 339 | |
Mean | 121 | 11 | 3.35 | 0 | 0 | 0 | 0 | 7 | 3 | 79 | 42 | 117 | |
Median | 104 | 10 | 3.43 | 0 | 0 | 0 | 0 | 6 | 2 | 69 | 32 | 117 | |
Std. Deviation | 71 | 7 | 1.83 | 0.001 | 0.001 | 10 | 1 | 2 | 2 | 43 | 42 | 51 |
Sub-Basin | Failure | Flow Direction Algorithm | x | dh (m) | Travel Angle (°) | Velocity (m/s) |
---|---|---|---|---|---|---|
Egerci | Debris flow | Modified Holmgren | 4 | 1 | 24 | 15 |
Shallow landslides | Modified Holmgren | 25 | 1 | 24 | 15 | |
Beycuma | Debris flow | Modified Holmgren | 4 | 1 | 13 | 15 |
Shallow landslides | Modified Holmgren | 25 | 1 | 13 | 15 | |
Ihsanoglu | Debris flow | Modified Holmgren | 4 | 1 | 10 | 15 |
Shallow landslides | Modified Holmgren | 25 | 1 | 10 | 15 |
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Komu, M.P.; Nefeslioglu, H.A.; Gokceoglu, C. Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye). ISPRS Int. J. Geo-Inf. 2024, 13, 84. https://doi.org/10.3390/ijgi13030084
Komu MP, Nefeslioglu HA, Gokceoglu C. Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye). ISPRS International Journal of Geo-Information. 2024; 13(3):84. https://doi.org/10.3390/ijgi13030084
Chicago/Turabian StyleKomu, Muge Pinar, Hakan Ahmet Nefeslioglu, and Candan Gokceoglu. 2024. "Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)" ISPRS International Journal of Geo-Information 13, no. 3: 84. https://doi.org/10.3390/ijgi13030084