Investigating the potential of a global precipitation forecast to inform landslide prediction

Khan, S.; Kirschbaum, D. B.; Stanley, Thomas

Investigating the potential of a global precipitation forecast to inform landslide prediction

dc.contributor.author	Khan, S.
dc.contributor.author	Kirschbaum, D. B.
dc.contributor.author	Stanley, Thomas
dc.date.accessioned	2022-10-03T20:43:33Z
dc.date.available	2022-10-03T20:43:33Z
dc.date.issued	2021-08-06
dc.description.abstract	Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.	en_US
dc.description.sponsorship	The GEOS data used in this study have been provided by Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center (GSFC). The authors would like to thank the NASA GMAO for providing the GEOS-FP data and Precipitation Processing System (PPS) for providing the IMERG data. Computing resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at NASA's GSFC. We thank Dr. Dimitrios Zekkos for sharing Twitter-based landslide inventory. This project was funded by the NASA Disasters Program under grant no. 18-DISASTER18-0022.	en_US
dc.description.uri	https://www.sciencedirect.com/science/article/pii/S2212094721000554?via%3Dihub	en_US
dc.format.extent	15 pages	en_US
dc.genre	journal articles	en_US
dc.identifier	doi:10.13016/m2iekj-7qgt
dc.identifier.citation	Khan, S., D. Kirschbaum, and T. Stanley. 2021. "Investigating the potential of a global precipitation forecast to inform landslide prediction." Weather and Climate Extremes, 33: 100364. https://doi.org/10.1016/j.wace.2021.100364	en_US
dc.identifier.uri	https://doi.org/10.1016/j.wace.2021.100364
dc.identifier.uri	http://hdl.handle.net/11603/26083
dc.language.iso	en_US	en_US
dc.publisher	Elsevier	en_US
dc.relation.isAvailableAt	The University of Maryland, Baltimore County (UMBC)
dc.relation.ispartof	UMBC GESTAR II Collection
dc.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.	en_US
dc.rights	Public Domain Mark 1.0	*
dc.rights.uri	http://creativecommons.org/publicdomain/mark/1.0/	*
dc.title	Investigating the potential of a global precipitation forecast to inform landslide prediction	en_US
dc.type	Text	en_US
dcterms.creator	https://orcid.org/0000-0003-2288-0363	en_US