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

被引:42
|
作者
He, Xiaogang [1 ,2 ,3 ]
Hong, Yang [1 ,2 ,7 ,8 ]
Vergara, Humberto [1 ,2 ,4 ,5 ]
Zhang, Ke [4 ]
Kirstetter, Pierre-Emmanuel [1 ,2 ,5 ]
Gourley, Jonathan J. [5 ]
Zhang, Yu [3 ]
Qiao, Gang [6 ]
Liu, Chun [6 ]
机构
[1] Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK 73019 USA
[2] Univ Oklahoma, Adv Radar Res Ctr, Norman, OK 73019 USA
[3] Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA
[4] Univ Oklahoma, Cooperat Inst Mesoscale Meteorol Studies, Norman, OK 73019 USA
[5] Natl Severe Storms Lab, Norman, OK 73069 USA
[6] Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China
[7] Tsinghua Univ, Dept Hydraul Engn, Beijing, Peoples R China
[8] Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing, Peoples R China
关键词
CRESLIDE; Landslide; Satellite remote sensing; Storm-triggered; Hydrological-geotechnical; SHALLOW LANDSLIDES; SOIL DEPTH; MODEL; SUSCEPTIBILITY; PRECIPITATION; COUNTY; GEOTOP; BASIN; WATER; POWER;
D O I
10.1016/j.jhydrol.2016.10.016
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this paper, we propose a new coupled hydrological-geotechnical model called CRESLIDE (Coupled Routing and Excess Storage and SLope-Infiltration-Distributed Equilibrium), which can alleviate the chronic flaws of landslides simulation and prediction. CRESLIDE is designed to improve the original landslides model (SLIDE) through the coupling of hydrological model (CREST) and to deliver an integrated system for predicting storm-triggered landslides. This coupled system is implemented and evaluated in Macon County, North Carolina, where Hurricane Ivan triggered widespread landslides in September 2004 during the hurricane season. Model simulations from CRESLIDE show its reliability to predict landslides occurrence (location and timing). Receiver Operating Characteristic (ROC) analysis demonstrates that the coupled system (CRESLIDE) has higher specificity (94.10%) and higher sensitivity (11.36%) compared to the original SLIDE model (specificity = 93.32%, sensitivity = 10.23%) and a well-known landslide model (TRIGRS, whose sensitivity is 6.98%). This improved predictive performance demonstrates the advantage of coupling hydrological and geotechnical models with a more realistic representation of infiltration. It warrants a better depiction of the spatial and temporal dependence of hydrological and geotechnical processes in the course of the rainfall-triggered landslide event. This kind of model integration is useful for landslides prediction and early warning. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:395 / 405
页数:11
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