iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Zhang, Ke; Xue, Xianwu; Hong, Yang; Gourley, Jonathan J.; Lu, Ning; Wan, Zhanming; Hong, Zhen; Wooten, Rick

Severe storm-triggered floods and landslides are two major natural hazards in the US, causing property losses of USD 6 billion and approximately 110–160 fatalities per year nationwide. Moreover, floods and landslides often occur in a cascading manner, posing significant risk and leading to losses that are significantly greater than the sum of the losses from the hazards when acting separately. It is pertinent to couple hydrological and geotechnical modeling processes to an integrated flood–landslide cascading disaster modeling system for improved disaster preparedness and hazard management. In this study, we developed the iCRESTRIGRS model, a coupled flash flood and landslide initiation modeling system, by integrating the Coupled Routing and Excess STorage (CREST) model with the physically based Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) landslide model. The iCRESTRIGRS system is evaluated in four river basins in western North Carolina that experienced a large number of floods, landslides and debris flows triggered by heavy rainfall from Hurricane Ivan during 16–18 September 2004. The modeled hourly hydrographs at four USGS gauge stations show generally good agreement with the observations during the entire storm period. In terms of landslide prediction in this case study, the coupled model has a global accuracy of 98.9 % and a true positive rate of 56.4 %. More importantly, it shows an improved predictive capability for landslides relative to the stand-alone TRIGRS model. This study highlights the important physical connection between rainfall, hydrological processes and slope stability, and provides a useful prototype model system for operational forecasting of flood and landslide.



Zhang, Ke / Xue, Xianwu / Hong, Yang / et al: iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting. 2016. Copernicus Publications.


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