7 October 2019

A successful landslide forecast from Heifangtai, Gansu

Posted by Dave Petley

A successful landslide forecast from Heifangtei, Gansu

Back in 2017 I wrote about the hazards from loess landslides at Heifangtai in Gansu province of China.  The focus of the piece was the failure of steep slopes in loess deposits, which can mobilise into deadly flowslides.

On Twitter, Zhenhong Li from Newcastle University yesterday tweeted about a further landslide at Heifangtai on 5th October 2019.  This slide was about 20,000 m³, travelling over a distance of about 100 metres.  But what is particularly interesting about this event is that it was successfully forecast based upon the monitoring of movement.

Sohu News has an article about the event (in Mandarin), which indicates that the landslide occurred at 4:24 am local time.  The monitoring data, collected by a team from Chang’an University and Chengdu University of Technology headed by Professor Zhang Qin, showed the hyperbolic increase in displacement rate with time that is characteristic of brittle failures, allowing a yellow alert to be issued 30 days before failure, and a red alert seven hours before the collapse.  Zenghong Li tweeted this image of the displacement rate against time:-

Heifangtai landslide

Data from the 5th October 2019 loess landslide at the Heifangtai terrace in Gansu, China. Graph tweeted by Professor Zhenghong Li of Newcastle University, data collected by Professor Zhang Qin of Chang’an University.


The Haifangtai landslide appears to show classic three phase creep behaviour, with an initial period of rapid movement (often termed primary creep), a long period of near constant movement (secondary creep), but note that in reality the movement pattern is changing during this phase), followed by a rapid acceleration to failure (tertiary creep).  It is this style of behaviour that allows forecasting of the collapse event in some cases.

Interestingly the final collapse was captured on two videos from cameras mounted near to the headscarp.  These can be seen in tweets from Professor Li here and here.