Development of a coupled hydrological-geotechnical framework for rainfall-induced landslides prediction

He, Xiao-Gang; Hong, Yang; Vergara, Humberto; Zhang, Ke; Kirstetter, Pierre‐Emmanuel; Gourley, Jonathan J.; Zhang, Yu; Qiao, Gang; Liu, Chun

doi:10.1016/j.jhydrol.2016.10.016

Cited by 51 publications

(25 citation statements)

References 41 publications

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“…For instance, they can help stakeholders identify risk hotspots of natural hazards (rainfall triggered shallow landslides, flash floods, soil erosion, etc.) [ Hong et al ., ; He et al , ; Zhang et al ., ] and water‐related pollution or water‐borne disease [ Singh et al ., ]. Precipitation downscaling also acts as an underpinning to investigate subgrid‐scale parameterization [ Yano , ], uncertainty propagation [ Bastola and Misra , ], hyperresolution modeling [ Wood et al ., ], and seasonal forecast [ Sheffield et al ., ].…”

Section: Introductionmentioning

confidence: 98%

Spatial downscaling of precipitation using adaptable random forests

et al. 2016

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This paper introduces Prec‐DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine‐learning based method for statistical downscaling of precipitation. Prec‐DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge‐radar precipitation data at 0.125° from NLDAS‐2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec‐DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec‐DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine‐scale spatial structure, especially for the 1° experiments. Prec‐DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse‐scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec‐DWARF and machine‐learning based techniques in general for the statistical downscaling of precipitation.

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Section: Introductionmentioning

confidence: 98%

Spatial downscaling of precipitation using adaptable random forests

et al. 2016

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“…Rainfall-triggered shallow landslides are worldwide natural hazard (Hong et al, 2006;An et al, 2016), causing a large number of fatalities and property losses (Liao et al, 2010;He et al, 2016;Godt et al, 2009). Globally, landslide hazards cause approximately 1000 deaths and USD 4 billion losses per year (Pradhan and Youssef, 2010).…”

Section: Introductionmentioning

confidence: 99%

Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment

Wang

Zhang

Beek

et al. 2020

Environmental Modelling & Software

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Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 kmresolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all <20% and >0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which finescale topographical information is incorporated.

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“…SLIDE is a shallow rainfallinduced landslide model. Based on the rainfall infiltrating process, it simplifies the saturated-unsaturated seepage equation (Richards 1931) and integrates the contribution of the apparent cohesion to the shear strength of the soil and water level when the surface of the soil attains saturation (Liao et al 2012;He et al 2016). The detailed theory is available in the work of Liao et al (2012).…”

Section: Test Of Assimilation Results Based On Slope Mechanismmentioning

confidence: 99%

Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

Liu

Shao

2018

Geomatics, Natural Hazards and Risk

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Multi-sensor observation is very important for monitoring landslide disasters. Since various surveying techniques are currently available for detecting variational slope activities from different perspectives, studies have focused on integration of multi-source information for the analysing landslide displacements. In this study, a general multi-source data fusion scheme for landslide monitoring based on three-dimensional variation (3DVar) data assimilation was developed. The scheme was used to fuse different observations of Xishancun Landslide in Li County, Sichuan Province, China. First, the displacement observations obtained by a Global Positioning System (GPS) and Borehole Inclinometers (BIs) were assimilated for accurate evaluation of slope activities. Then, slope Stability Index (SI) was introduced to validate the assimilation results within a time interval. SI Assi values calculated using the integration model developed in the present study were compared with SI FS simulated by a physically based landslide model. The correlation coefficient between them ss 0.75, which is larger than those with SI GPS (0.45) or SI BIs (0.41) values determined by the GPS and BIs respectively. The assimilation results are thus confirmed to be more credible for slope stability simulation.

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Development of a coupled hydrological-geotechnical framework for rainfall-induced landslides prediction

Cited by 51 publications

References 41 publications

Spatial downscaling of precipitation using adaptable random forests

Spatial downscaling of precipitation using adaptable random forests

Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment

Multi-sensor observation fusion scheme based on 3D variational assimilation for landslide monitoring

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