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
Viewed
Total article views: 3,152 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,121
920
111
3,152
104
121
HTML: 2,121
PDF: 920
XML: 111
Total: 3,152
BibTeX: 104
EndNote: 121
Views and downloads (calculated since 02 Aug 2021)
Cumulative views and downloads
(calculated since 02 Aug 2021)
Total article views: 2,120 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,496
541
83
2,120
88
106
HTML: 1,496
PDF: 541
XML: 83
Total: 2,120
BibTeX: 88
EndNote: 106
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads
(calculated since 23 May 2022)
Total article views: 1,032 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
625
379
28
1,032
16
15
HTML: 625
PDF: 379
XML: 28
Total: 1,032
BibTeX: 16
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: 3,152 (including HTML, PDF, and XML)
Thereof 2,970 with geography defined
and 182 with unknown origin.
Total article views: 2,120 (including HTML, PDF, and XML)
Thereof 2,018 with geography defined
and 102 with unknown origin.
Total article views: 1,032 (including HTML, PDF, and XML)
Thereof 952 with geography defined
and 80 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...