Extreme Precipitation in the Himalayan Landslide Hotspot

Thumbnail Image

Files

Stanley2020_Chapter_ExtremePrecipitationInTheHimal.pdf (1.23 MB)

Links to Files

https://link.springer.com/chapter/10.1007/978-3-030-35798-6_31

Permanent Link

https://doi.org/10.1007/978-3-030-35798-6_31
 http://hdl.handle.net/11603/26094

Collections

UMBC GESTAR II

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-2288-0363

Date

2020-04-15

Type of Work

book chapters
Text

Department

Program

Citation of Original Publication

Stanley, T., Kirschbaum, D.B., Pascale, S., Kapnick, S. (2020). Extreme Precipitation in the Himalayan Landslide Hotspot. In: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., Turk, F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-35798-6_31

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Abstract

Extreme precipitation from the South-Asian monsoon season combines with significant topographic relief within the Himalayan region to cause landslides that result in hundreds to thousands of fatalities each year. While there are few consistent and publicly available in-situ estimates of rainfall across this region, satellite products and global climate models provide insight into the extreme precipitation patterns that may impact the frequency of landsliding. In this work, we analyzed several extreme precipitation indices using data from a global climate model and the satellite-based Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis product to represent extreme precipitation over High Mountain Asia. We then compared the temporal distribution of extreme precipitation to a global database of landslides to better understand the spatiotemporal distribution of potential landslide triggering factors. We found that these indices successfully model the seasonality of landslide activity across the region, but other aspects of spatiotemporal variability require additional information and analysis before they can be applied more broadly.