Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Department of Irrigation and Drainage, School of Agricultural Engineering, College of Agriculture, Animal Sciences and Veterinary Medicine, University of Rwanda, P.O. Box 210, Musanze, Rwanda
Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
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Total article views: 2,881 (including HTML, PDF, and XML)
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1,933
848
100
2,881
88
105
HTML: 1,933
PDF: 848
XML: 100
Total: 2,881
BibTeX: 88
EndNote: 105
Views and downloads (calculated since 02 Aug 2021)
Cumulative views and downloads
(calculated since 02 Aug 2021)
Total article views: 1,898 (including HTML, PDF, and XML)
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Total
BibTeX
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1,340
485
73
1,898
75
90
HTML: 1,340
PDF: 485
XML: 73
Total: 1,898
BibTeX: 75
EndNote: 90
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads
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Total article views: 983 (including HTML, PDF, and XML)
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593
363
27
983
13
15
HTML: 593
PDF: 363
XML: 27
Total: 983
BibTeX: 13
EndNote: 15
Views and downloads (calculated since 02 Aug 2021)
Cumulative views and downloads
(calculated since 02 Aug 2021)
Viewed (geographical distribution)
Total article views: 2,881 (including HTML, PDF, and XML)
Thereof 2,703 with geography defined
and 178 with unknown origin.
Total article views: 1,898 (including HTML, PDF, and XML)
Thereof 1,801 with geography defined
and 97 with unknown origin.
Total article views: 983 (including HTML, PDF, and XML)
Thereof 902 with geography defined
and 81 with unknown origin.
This research tested the value of regional groundwater level information to improve landslide predictions with empirical models based on the concept of threshold levels. In contrast to precipitation-based thresholds, the results indicated that relying on threshold models exclusively defined using hydrological variables such as groundwater levels can lead to improved landslide predictions due to their implicit consideration of long-term antecedent conditions until the day of landslide occurrence.
This research tested the value of regional groundwater level information to improve landslide...
