Articles | Volume 22, issue 7
https://doi.org/10.5194/nhess-22-2219-2022
https://doi.org/10.5194/nhess-22-2219-2022
Research article
 | 
06 Jul 2022
Research article |  | 06 Jul 2022

Spatial assessment of probable recharge areas – investigating the hydrogeological controls of an active deep-seated gravitational slope deformation

Jan Pfeiffer, Thomas Zieher, Jan Schmieder, Thom Bogaard, Martin Rutzinger, and Christoph Spötl

Related authors

Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023,https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
SIMULATING UNMANNED-AERIAL-VEHICLE BASED LASER SCANNING DATA FOR EFFICIENT MISSION PLANNING IN COMPLEX TERRAIN
M. Bremer, V. Wichmann, M. Rutzinger, T. Zieher, and J. Pfeiffer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 943–950, https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-943-2019, 2019
COMPARISON AND TIME SERIES ANALYSIS OF LANDSLIDE DISPLACEMENT MAPPED BY AIRBORNE, TERRESTRIAL AND UNMANNED AERIAL VEHICLE BASED PLATFORMS
J. Pfeiffer, T. Zieher, M. Rutzinger, M. Bremer, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 421–428, https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-421-2019, 2019
ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA
T. Zieher, M. Bremer, M. Rutzinger, J. Pfeiffer, P. Fritzmann, and V. Wichmann
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 461–467, https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-461-2019, 2019

Related subject area

Landslides and Debris Flows Hazards
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024,https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024,https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Temporal clustering of precipitation for detection of potential landslides
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024,https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary
Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China
Jianqi Zhuang, Jianbing Peng, Chenhui Du, Yi Zhu, and Jiaxu Kong
Nat. Hazards Earth Syst. Sci., 24, 2615–2631, https://doi.org/10.5194/nhess-24-2615-2024,https://doi.org/10.5194/nhess-24-2615-2024, 2024
Short summary
Optimizing Rainfall-Triggered Landslide Thresholds to Warning Daily Landslide Hazard in Three Gorges Reservoir Area
Bo Peng and Xueling Wu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-109,https://doi.org/10.5194/nhess-2024-109, 2024
Revised manuscript accepted for NHESS
Short summary

Cited articles

Binet, S., Jomard, H., Lebourg, T., Guglielmi, Y., Tric, E., Bertrand, C., and Mudry, J.: Experimental analysis of groundwater flow through a landslide slip surface using natural and artificial water chemical tracers, Hydrol. Process., 21, 3463–3472, https://doi.org/10.1002/hyp.6579, 2007. a, b
Blasch, K. W. and Bryson, J. R.: Distinguishing Sources of Ground Water Recharge by Using δ2H and δ18O, Groundwater, 45, 294–308, https://doi.org/10.1111/j.1745-6584.2006.00289.x, 2007. a, b
Bogaard, T., Guglielmi, Y., Marc, V., Emblanch, C., Bertrand, C., and Mudry, J.: Hydrogeochemistry in landslide research: a review, Bulletin de la Société Géologique de France, 178, 113–126, https://doi.org/10.2113/gssgfbull.178.2.113, 2007. a
Bogaard, T. A. and Greco, R.: Landslide hydrology: from hydrology to pore pressure, WIREs Water, 3, 439–459, https://doi.org/10.1002/wat2.1126, 2016. a
Bonzanigo, L., Eberhardt, E., and Loew, S.: Long-term investigation of a deep-seated creeping landslide in crystalline rock. Part I. Geological and hydromechanical factors controlling the Campo Vallemaggia landslide, Can. Geotech. J., 44, 1157–1180, https://doi.org/10.1139/T07-043, 2007. a
Download
Short summary
The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.
Altmetrics
Final-revised paper
Preprint