Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3805-2023
https://doi.org/10.5194/nhess-23-3805-2023
Research article
 | 
14 Dec 2023
Research article |  | 14 Dec 2023

Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling

Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy

Related authors

Improving the fire weather index system for peatlands using peat-specific hydrological input data
Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024,https://doi.org/10.5194/nhess-24-445-2024, 2024
Short summary
Estimating global landslide susceptibility and its uncertainty through ensemble modeling
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022,https://doi.org/10.5194/nhess-22-3063-2022, 2022
Short summary

Related subject area

Landslides and Debris Flows Hazards
Limit analysis of earthquake-induced landslides considering two strength envelopes
Di Wu, Yuke Wang, and Xin Chen
Nat. Hazards Earth Syst. Sci., 24, 4617–4630, https://doi.org/10.5194/nhess-24-4617-2024,https://doi.org/10.5194/nhess-24-4617-2024, 2024
Short summary
The vulnerability of buildings to a large-scale debris flow and outburst flood hazard cascade that occurred on 30 August 2020 in Ganluo, southwest China
Li Wei, Kaiheng Hu, Shuang Liu, Lan Ning, Xiaopeng Zhang, Qiyuan Zhang, and Md. Abdur Rahim
Nat. Hazards Earth Syst. Sci., 24, 4179–4197, https://doi.org/10.5194/nhess-24-4179-2024,https://doi.org/10.5194/nhess-24-4179-2024, 2024
Short summary
Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci., 24, 3991–4013, https://doi.org/10.5194/nhess-24-3991-2024,https://doi.org/10.5194/nhess-24-3991-2024, 2024
Short summary
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024,https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Size scaling of large landslides from incomplete inventories
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024,https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary

Cited articles

Baum, R. L., Godt, J. W., and Savage, W. Z.: Estimating the Timing and Location of Shallow Rainfall-Induced Landslides Using a Model for Transient, Unsaturated Infiltration, J. Geophys. Res.-Earth, 115, F03013, https://doi.org/10.1029/2009JF001321, 2010. a
Bordoni, M., Vivaldi, V., Lucchelli, L., Ciabatta, L., Brocca, L., Galve, J. P., and Meisina, C.: Development of a Data-Driven Model for Spatial and Temporal Shallow Landslide Probability of Occurrence at Catchment Scale, Landslides, 18, 1209–1229, https://doi.org/10.1007/s10346-020-01592-3, 2020. a, b, c, d, e
Brocca, L., Ciabatta, L., Moramarco, T., Ponziani, F., Berni, N., and Wagner, W.: Chapter 12 – Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy, in: Satellite Soil Moisture Retrieval, edited by: Srivastava, P. K., Petropoulos, G. P., and Kerr, Y. H., Elsevier, 231–247, https://doi.org/10.1016/B978-0-12-803388-3.00012-7, 2016. a, b, c
Broeckx, J., Vanmaercke, M., Duchateau, R., and Poesen, J.: A Data-Based Landslide Susceptibility Map of Africa, Earth-Sci. Rev., 185, 102–121, https://doi.org/10.1016/j.earscirev.2018.05.002, 2018. a
Caine, N.: The Rainfall Intensity: Duration Control of Shallow Landslides and Debris Flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.2307/520449, 1980. a
Download
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Altmetrics
Final-revised paper
Preprint