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: 2,806 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,894
813
99
2,806
85
99
HTML: 1,894
PDF: 813
XML: 99
Total: 2,806
BibTeX: 85
EndNote: 99
Views and downloads (calculated since 02 Aug 2021)
Cumulative views and downloads
(calculated since 02 Aug 2021)
Total article views: 1,836 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,304
460
72
1,836
73
85
HTML: 1,304
PDF: 460
XML: 72
Total: 1,836
BibTeX: 73
EndNote: 85
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads
(calculated since 23 May 2022)
Total article views: 970 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
590
353
27
970
12
14
HTML: 590
PDF: 353
XML: 27
Total: 970
BibTeX: 12
EndNote: 14
Views and downloads (calculated since 02 Aug 2021)
Cumulative views and downloads
(calculated since 02 Aug 2021)
Viewed (geographical distribution)
Total article views: 2,806 (including HTML, PDF, and XML)
Thereof 2,631 with geography defined
and 175 with unknown origin.
Total article views: 1,836 (including HTML, PDF, and XML)
Thereof 1,742 with geography defined
and 94 with unknown origin.
Total article views: 970 (including HTML, PDF, and XML)
Thereof 889 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...