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Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations

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Abstract

In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the factor of safety, Fs) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated Fs values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantle’s properties (hydraulic, geotechnical, and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference “truth” Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and “null” periods using multivariate frequency distribution-based metrics such as the false discovery and false omission rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicates their inability to accurately characterize hillslope stability.

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Acknowledgements

We acknowledge David Giordano (NC OneMap Database Administrator) for providing us the 20-ft DEM for the entire state of North Carolina on August 4, 2015, Marcel Schaap (University of Arizona) for providing the ROSETTA Python code, and Thomas Stanley for help with linking the SSURGO tables in Microsoft Access. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding

This work was partially funded by the following NASA grants: NNH11ZDA001N-NIP (PI: Dr. Dalia Kirschbaum), NNH15ZDA001N-PMM (PI: Dr. Dalia Kirschbaum), and NNX12AO19G (PI: Prof. Ning Lu).

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Authors and Affiliations

  1. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20742, USA

    Soni Yatheendradas

  2. Hydrological Sciences Laboratory, NASA GSFC, Greenbelt, MD, 20771, USA

    Soni Yatheendradas & Dalia Kirschbaum

  3. Department of Geological Sciences, The University of Alabama, Tuscaloosa, AL, 35487, USA

    Grey Nearing

  4. Department of Civil and Environmental Engineering, Henry Samueli School of Engineering, University of California, Irvine, CA, 92697, USA

    Jasper A. Vrugt

  5. Department of Earth System Science, University of California, Irvine, CA, 92697, USA

    Jasper A. Vrugt

  6. Geologic Hazards Science Center, U.S. Geological Survey, Golden, CO, 80401, USA

    Rex L. Baum & Jonathan W. Godt

  7. Asheville Regional Office, North Carolina Geological Survey, Swannanoa, NC, 28778, USA

    Rick Wooten

  8. Department of Civil & Environmental Engineering, Golden, CO, 80401, USA

    Ning Lu

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  1. Soni Yatheendradas
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Yatheendradas, S., Kirschbaum, D., Nearing, G. et al. Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations. Comput Geosci 23, 495–522 (2019). https://doi.org/10.1007/s10596-018-9804-y

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  • DOI: https://doi.org/10.1007/s10596-018-9804-y

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