Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale. Issue 10 (8th October 2014)

• Record Type: Journal Article
• Authors: von Ruette, J.; Lehmann, P.; Or, D.
• Is Part Of: Water resources research. Volume 50:Issue 10(2014:Oct.)
• Page Start: 7780
• Page End: 7799
• Publication Date: 2014-10-08
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2013WR015122
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall‐induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based "Catchment‐scale Hydromechanical Landslide Triggering" (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60, 000 m<sup>3</sup> for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small‐scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.</p> </abstract>