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Prof. John Clague
Simon Fraser University

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Research Keywords & Expertise

0 Climate Change
0 Geomorphology
0 Natural Hazards
0 Quaternary geology
0 Glacial geology

Honors and Awards

Member, Royal Society of Canada

Royal Society of Canada


Canadian Professional Geoscientist Award

Geoscientists Canada


Officer, Order of Canada

Government of Canada




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Short Biography

John Clague is a leading authority in environmental earth sciences and climate change. He has made major contributions in geologic mapping, engineering and environmental interpretations of surficial geological information, and understanding natural hazards and risk. He is noted for local, national, and international research collaboration with geologists, geographers, biologists, and physicists. Clague has performed innovative research on the landslide, earthquake, tsunami hazards in British Columbia; his work has been a catalyst for earthquake research carried out by government, university, and private-sector scientists in Canada.

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Research article
Published: 02 July 2021 in Earth Surface Processes and Landforms
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Predicting the spatial impact of debris flows on fans is challenging due to complex runout behaviour. Debris flow mobility is highly variable and flows can sporadically avulse the channel. For hazard and risk assessments, practitioners typically base the probability of spatial impact or avulsion on their experience and expert judgment. To support decision-making with empirical observations, we studied spatial impact distributions on 30 active debris-flow fans in southwestern British Columbia, Canada. We mapped 146 debris-flow impact areas over an average observation period of 74 years using orthorectified airphotos, satellite imagery, topographic base maps, lidar data, orthophotos, and field observations. We devised a graphical method to convert our geospatial mapping into spatial impact heat maps normalized by fan boundaries, enabling comparison of runout distributions across different fans. About 90% of the mapped debris flows reached beyond the mid-points of fans, while less than 10% avulsed more than half-way across the fan relative to the previous flow path. Most avulsions initiated at distances of 20% to 40% of the maximum fan length from the fan apex and upstream of the fan intersection point. Large volume events tend to be more mobile in the down-fan direction, but the relation between volume and cross-fan runout (e.g. avulsions) is more complex. Differences in spatial impact distributions can be explained, in part, by the degree of fan incision and whether a fan is truncated at its toe by a river or lake. There were no significant differences in spatial impact distributions based on the geology of the source area, sediment supply condition, or hydrogeomorphic process classification.

ACS Style

Sophia Zubrycky; Andrew Mitchell; Scott McDougall; Alex Strouth; John J. Clague; Brian Menounos. Exploring new methods to analyze spatial impact distributions on debris‐flow fans using data from southwestern British Columbia. Earth Surface Processes and Landforms 2021, 1 .

AMA Style

Sophia Zubrycky, Andrew Mitchell, Scott McDougall, Alex Strouth, John J. Clague, Brian Menounos. Exploring new methods to analyze spatial impact distributions on debris‐flow fans using data from southwestern British Columbia. Earth Surface Processes and Landforms. 2021; ():1.

Chicago/Turabian Style

Sophia Zubrycky; Andrew Mitchell; Scott McDougall; Alex Strouth; John J. Clague; Brian Menounos. 2021. "Exploring new methods to analyze spatial impact distributions on debris‐flow fans using data from southwestern British Columbia." Earth Surface Processes and Landforms , no. : 1.

Research article
Published: 10 June 2021 in Science
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On 7 February 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. More than 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27 × 106 cubic meters of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders greater than 20 meters in diameter and scoured the valley walls up to 220 meters above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

ACS Style

D. H. Shugar; M. Jacquemart; D. Shean; S. Bhushan; K. Upadhyay; A. Sattar; W. Schwanghart; S. McBride; M. Van Wyk de Vries; M. Mergili; A. Emmer; C. Deschamps-Berger; M. McDonnell; R. Bhambri; S. Allen; E. Berthier; J. L. Carrivick; J. J. Clague; M. Dokukin; S. A. Dunning; H. Frey; S. Gascoin; U. K. Haritashya; C. Huggel; A. Kääb; J. S. Kargel; J. L. Kavanaugh; P. Lacroix; D. Petley; S. Rupper; M. F. Azam; S. J. Cook; A. P. Dimri; M. Eriksson; D. Farinotti; J. Fiddes; K. R. Gnyawali; S. Harrison; M. Jha; M. Koppes; A. Kumar; S. Leinss; U. Majeed; S. Mal; A. Muhuri; J. Noetzli; F. Paul; I. Rashid; K. Sain; J. Steiner; F. Ugalde; C. S. Watson; M. J. Westoby. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science 2021, 373, 300 -306.

AMA Style

D. H. Shugar, M. Jacquemart, D. Shean, S. Bhushan, K. Upadhyay, A. Sattar, W. Schwanghart, S. McBride, M. Van Wyk de Vries, M. Mergili, A. Emmer, C. Deschamps-Berger, M. McDonnell, R. Bhambri, S. Allen, E. Berthier, J. L. Carrivick, J. J. Clague, M. Dokukin, S. A. Dunning, H. Frey, S. Gascoin, U. K. Haritashya, C. Huggel, A. Kääb, J. S. Kargel, J. L. Kavanaugh, P. Lacroix, D. Petley, S. Rupper, M. F. Azam, S. J. Cook, A. P. Dimri, M. Eriksson, D. Farinotti, J. Fiddes, K. R. Gnyawali, S. Harrison, M. Jha, M. Koppes, A. Kumar, S. Leinss, U. Majeed, S. Mal, A. Muhuri, J. Noetzli, F. Paul, I. Rashid, K. Sain, J. Steiner, F. Ugalde, C. S. Watson, M. J. Westoby. A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya. Science. 2021; 373 (6552):300-306.

Chicago/Turabian Style

D. H. Shugar; M. Jacquemart; D. Shean; S. Bhushan; K. Upadhyay; A. Sattar; W. Schwanghart; S. McBride; M. Van Wyk de Vries; M. Mergili; A. Emmer; C. Deschamps-Berger; M. McDonnell; R. Bhambri; S. Allen; E. Berthier; J. L. Carrivick; J. J. Clague; M. Dokukin; S. A. Dunning; H. Frey; S. Gascoin; U. K. Haritashya; C. Huggel; A. Kääb; J. S. Kargel; J. L. Kavanaugh; P. Lacroix; D. Petley; S. Rupper; M. F. Azam; S. J. Cook; A. P. Dimri; M. Eriksson; D. Farinotti; J. Fiddes; K. R. Gnyawali; S. Harrison; M. Jha; M. Koppes; A. Kumar; S. Leinss; U. Majeed; S. Mal; A. Muhuri; J. Noetzli; F. Paul; I. Rashid; K. Sain; J. Steiner; F. Ugalde; C. S. Watson; M. J. Westoby. 2021. "A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya." Science 373, no. 6552: 300-306.

Research article
Published: 07 June 2021 in Geocarto International
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This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8).

ACS Style

Elham Rafiei Sardooi; Hamid Reza Pourghasemi; Ali Azareh; Farshad Soleimani Sardoo; John J. Clague. Comparison of statistical and machine learning approaches in land subsidence modelling. Geocarto International 2021, 1 -21.

AMA Style

Elham Rafiei Sardooi, Hamid Reza Pourghasemi, Ali Azareh, Farshad Soleimani Sardoo, John J. Clague. Comparison of statistical and machine learning approaches in land subsidence modelling. Geocarto International. 2021; ():1-21.

Chicago/Turabian Style

Elham Rafiei Sardooi; Hamid Reza Pourghasemi; Ali Azareh; Farshad Soleimani Sardoo; John J. Clague. 2021. "Comparison of statistical and machine learning approaches in land subsidence modelling." Geocarto International , no. : 1-21.

Review article
Published: 28 April 2021 in Global and Planetary Change
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Earth's climate is warming and will continue to warm as the century progresses. High mountains and high latitudes are experiencing the greatest warming of all regions on Earth and also are some of the most sensitive areas to climate change, in part because ecosystems and natural processes in these areas are intimately linked to the cryosphere. Evidence is mounting that warming will further reduce permafrost and snow and ice cover in high mountains, which in turn will destabilize many slopes, alter sediment delivery to streams, and change subalpine and alpine ecosystems. This paper contributes to the continuing discussion of impacts of climate change on mountain environments by comparing and discussing processes and trends in the mountains of western Canada and the European Alps. We highlight the effects of physiography and climate on physical processes occurring in the two regions. Processes of interest include landslides and debris flows induced by glacier debuttressing, alpine permafrost thaw, changes in rainfall regime, formation and sudden drainage of glacier- and moraine-dammed lakes, ice avalanches, glacier surges, and large-scale sediment transfers due to rapid deglacierization. Our analysis points out the value of integrating observations and data from different areas of the world to better understand these processes and their impacts.

ACS Style

Marta Chiarle; Marten Geertsema; Giovanni Mortara; John J. Clague. Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps. Global and Planetary Change 2021, 103499 .

AMA Style

Marta Chiarle, Marten Geertsema, Giovanni Mortara, John J. Clague. Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps. Global and Planetary Change. 2021; ():103499.

Chicago/Turabian Style

Marta Chiarle; Marten Geertsema; Giovanni Mortara; John J. Clague. 2021. "Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps." Global and Planetary Change , no. : 103499.

Encyclopedia
Published: 31 March 2021 in Reference Module in Earth Systems and Environmental Sciences
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Sea-level rise is one of the most damaging effects of anthropogenic climate change that humans face as the century progresses. Unlike most hazards, it is slow onset, but it will ultimately displace many tens of millions of people, with cascading social and economic effects. Of course, the sea surface has changed dramatically over geologic time and in this context can be considered “natural.” However, those changes have not played out on a planet inhabited by 7.8 billion people, hundreds of millions of whom live close to or at ocean shorelines. Further, today we are witnessing a rate of change in ocean level that exceeds those of most times in Earth history. This article reviews the many factors, operating on different timescales, that alter the level of the sea relative to the land. The focus in on those factors that are responsible for the current rise in sea level. I next review current scientific thought on the level of the sea regionally and globally later in this century and beyond, and discuss the hazards this poses. Finally, I discuss the geomorphic and ecological impacts of rising sea level on coastal environments.

ACS Style

John J. Clague. Sea-Level Change: Emergent Hazard in a Warming World. Reference Module in Earth Systems and Environmental Sciences 2021, 1 .

AMA Style

John J. Clague. Sea-Level Change: Emergent Hazard in a Warming World. Reference Module in Earth Systems and Environmental Sciences. 2021; ():1.

Chicago/Turabian Style

John J. Clague. 2021. "Sea-Level Change: Emergent Hazard in a Warming World." Reference Module in Earth Systems and Environmental Sciences , no. : 1.

Preprint content
Published: 04 March 2021
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On 28 November 2020, about 18 Mm3 of quartz diorite detached from a steep rock face at the head of Elliot Creek in the southern Coast Mountains of British Columbia. The rock mass fragmented as it descended 1000 m and flowed across a debris-covered glacier. The rock avalanche was recorded on local and distant seismometers, with long-period amplitudes equivalent to a M 4.9 earthquake. Local seismic stations detected several earthquakes of magnitude <2.4 over the minutes and hours preceding the slide, though no causative relationship is yet suggested. More than half of the rock debris entered a 0.6 km2 lake, where it generated a huge displacement wave that overtopped the moraine at the far end of the lake. Water that left the lake was channelized along Elliot Creek, deeply scouring the valley fill over a distance of 10 km before depositing debris on a 2 km2 fan in the Southgate River valley. Debris temporarily dammed the river, and turbid water continued down the Southgate River to Bute Inlet, where it produced a 70 km turbidity current and altered turbidity and water chemistry in the inlet for weeks. The landslide followed a century of rapid glacier retreat and thinning that exposed a growing lake basin. The outburst flood extended the damage of the landslide far beyond the limit of the landslide, destroying forest and impacting salmon spawning and rearing habitat. We expect more cascading impacts from landslides in the glacierized mountains of British Columbia as glaciers continue to retreat, exposing water bodies below steep slopes while simultaneously removing buttressing support.

ACS Style

Marten Geertsema; Brian Menounos; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Pierre Friele; Andrew Schaeffer; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes. A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia  . 2021, 1 .

AMA Style

Marten Geertsema, Brian Menounos, Dan Shugar, Tom Millard, Brent Ward, Göran Ekstrom, John Clague, Patrick Lynett, Pierre Friele, Andrew Schaeffer, Jennifer Jackson, Bretwood Higman, Chunli Dai, Camille Brillon, Derek Heathfield, Gemma Bullard, Ian Giesbrecht, Katie Hughes. A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia  . . 2021; ():1.

Chicago/Turabian Style

Marten Geertsema; Brian Menounos; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Pierre Friele; Andrew Schaeffer; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes. 2021. "A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia  ." , no. : 1.

Preprint content
Published: 04 March 2021
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On 28 November 2020, some 18 Mm3 of quartz diorite detached from a steep rock face at the head of Elliot Creek in the southern Coast Mountains of British Columbia. The rock mass fragmented as it descended 1000 m and flowed across a debris-covered glacier. The rock avalanche was recorded on local and distant seismometers, with long-period amplitudes equivalent to a M 4.9 earthquake. Local seismic stations detected several earthquakes of magnitude <2.4 over the minutes and hours preceding the slide, though no causative relationship is yet suggested. Pre-slide optical and radar remote sensing data indicated some slope deformation leading up to failure. More than half of the rock debris entered a 0.6 km lake, where it generated a 115 m displacement wave that overtopped the moraine at the far end of the lake. We estimate that some 13.5 Mm3 of water left the lake from the combined impact of the landslide as well as erosion of the dam. The water that left the lake was channelized along Elliot Creek, scouring the valley more than 40 m in some places over a distance of 10 km before depositing debris on a 2 km2 fan in the Southgate River valley. Debris temporarily dammed the river, and turbid water continued down the Southgate River to Bute Inlet, where it produced a 70 km turbidity current and altered turbidity and water chemistry in the inlet for weeks. The landslide followed a century of rapid glacier retreat and thinning that exposed a growing lake basin. The outburst flood extended the damage of the landslide far beyond the limit of the landslide, destroying forest and impacting salmon spawning and rearing habitat. We expect more cascading impacts from landslides in the glacierized mountains of British Columbia as glaciers continue to retreat, exposing water bodies below steep slopes while simultaneously removing buttressing support.

ACS Style

Marten Geertsema; Brian Menounous; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Jonathan Carrivick; Pierre Friele; Andrew Schaeffer; Davide Donatti; Doug Stead; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes; Mylène Jacquemart. Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia . 2021, 1 .

AMA Style

Marten Geertsema, Brian Menounous, Dan Shugar, Tom Millard, Brent Ward, Göran Ekstrom, John Clague, Patrick Lynett, Jonathan Carrivick, Pierre Friele, Andrew Schaeffer, Davide Donatti, Doug Stead, Jennifer Jackson, Bretwood Higman, Chunli Dai, Camille Brillon, Derek Heathfield, Gemma Bullard, Ian Giesbrecht, Katie Hughes, Mylène Jacquemart. Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia . . 2021; ():1.

Chicago/Turabian Style

Marten Geertsema; Brian Menounous; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Jonathan Carrivick; Pierre Friele; Andrew Schaeffer; Davide Donatti; Doug Stead; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes; Mylène Jacquemart. 2021. "Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia ." , no. : 1.

Preprint content
Published: 03 March 2021
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Frequency-magnitude relations derived from historic and prehistoric datasets underpin many natural hazard risk assessments. For example, probabilistic estimates of seismic risk rely on instrumented records of past earthquakes, in some cases supplemented by prehistoric seismicity inferred from proxy geologic evidence. Yet, there are several problems in these datasets that compromise the reliability of derived frequency-magnitude relations. In this presentation, I briefly discuss these problems. First, historic records of past events are temporally biased. Using seismicity as an example, earthquake catalogues are complete only for the past several decades, the period during which seismic networks have been sufficiently extensive to capture all events. During the first half of the twentieth century, small and even moderate earthquakes went unrecorded, and farther back in time, the occurrence of even large earthquakes is limited to eyewitness accounts. Prior to the last century, there is only limited knowledge of rare, but large events with low average return periods. Yet, low social and political tolerance for risk requires knowledge of events with return periods of hundreds to thousands of years. Temporal biases of this type result in huge uncertainties about the future occurrence of events with large return periods. A second limitation, which applies particularly to prehistoric events, is the large uncertainty in the times and magnitudes of events inferred using geologic proxy data. The example I use in this talk is the large debris-flow prone Cheekye River fan in southwestern British Columbia. Relatively small debris flows have happened on the fan in the historic period, and there is geologic evidence for several much larger prehistoric events during the Holocene. A new residential subdivision has been proposed for the apex of the fan, requiring that geologists estimate the sizes of debris flows with return periods up to 10,000 years. The Cheekye fan has been better studied than any other fan in western Canada, yet there are very large uncertainties in the sizes and times of events that are more than 100 years old. Event times are imprecise because radiocarbon ages carry inherent uncertainties of several decades to centuries. Furthermore, the geologic record of past events is incomplete. The frequency-magnitude curve for debris flows on Cheekye fan is ‘better than nothing’, but the very low societal tolerance for risk in Canada means that decisions about development on the fan likely will be based on worst-case scenarios of long return-period events that are poorly grounded in science. A third limitation that I highlight in my presentation pertains to weather-related hazards (floods, severe storms, and many landslides). An assumption made when using frequency-magnitude relations to evaluate hazard and risk is that the past can be applied to the near-future. This assumption is invalid for weather-related hazards, because climate is changing. Climate non-stationarity implies, for example, that historic hydrometric data, upon which flood frequency analyses were based in the past century may be of limited use in planning for future extreme floods.

ACS Style

John Clague. Limitations in the Use of Past Datasets for Future Hazard Analysis . 2021, 1 .

AMA Style

John Clague. Limitations in the Use of Past Datasets for Future Hazard Analysis . . 2021; ():1.

Chicago/Turabian Style

John Clague. 2021. "Limitations in the Use of Past Datasets for Future Hazard Analysis ." , no. : 1.

Journal article
Published: 05 November 2020 in Geomorphology
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Near the Pleistocene Termination, a glacier-dammed lake in central British Columbia suddenly drained to the south along the Fraser River valley. Floodwater travelled 330 km down the valley to Hope, British Columbia, and from there to the west into the Salish Sea near Vancouver. The flood was caused by the failure of an ice dam formed by the terminus of glaciers flowing from the central Coast Mountains across the British Columbia Interior Plateau. The ice dam impounded several hundred cubic kilometres of water to a maximum elevation of about 810 m asl (above sea level); at its maximum, the lake at the ice dam was over 250 m deep. Geomorphic and sedimentary evidence for the flood includes streamlined boulder-strewn bars, gravel dune fields, and terraces sloping up Fraser and lowermost Thompson valleys, opposite the present direction of current river flow. The gravel bars and flood terraces are underlain by sheets of massive to poorly sorted gravel containing large boulders and rip-up clasts of silt and till. Shortly after the flood, a landslide near the northern margin of the former glacier dam impounded water to an elevation of about 550 m asl. This lake emptied due to overflow and incision of the landslide dam. The outburst flood from glacial Lake Fraser and the subsequent draining of the landslide-dammed lake deeply incised the older sediment fill in Fraser Valley and transported much of this sediment into the proto-Salish Sea west of Vancouver, British Columbia and Bellingham, Washington. TCN ages on flood-transported boulders at three localities along the flood path agree with radiocarbon ages on inferred flood layers in ODP cores collected from Saanich Inlet, a fiord on southern Vancouver Island, 80 km south-southwest of Vancouver.

ACS Style

John J. Clague; Nicholas J. Roberts; Brendan Miller; Brian Menounos; Brent Goehring. A huge flood in the Fraser River valley, British Columbia, near the Pleistocene Termination. Geomorphology 2020, 374, 107473 .

AMA Style

John J. Clague, Nicholas J. Roberts, Brendan Miller, Brian Menounos, Brent Goehring. A huge flood in the Fraser River valley, British Columbia, near the Pleistocene Termination. Geomorphology. 2020; 374 ():107473.

Chicago/Turabian Style

John J. Clague; Nicholas J. Roberts; Brendan Miller; Brian Menounos; Brent Goehring. 2020. "A huge flood in the Fraser River valley, British Columbia, near the Pleistocene Termination." Geomorphology 374, no. : 107473.

Journal article
Published: 08 July 2020 in International Journal of Environmental Research and Public Health
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We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

ACS Style

Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; John J. Clague; Abolfazl Jaafari; Wei Chen; Hoang Nguyen. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health 2020, 17, 4933 .

AMA Style

Viet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, John J. Clague, Abolfazl Jaafari, Wei Chen, Hoang Nguyen. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health. 2020; 17 (14):4933.

Chicago/Turabian Style

Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; John J. Clague; Abolfazl Jaafari; Wei Chen; Hoang Nguyen. 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment." International Journal of Environmental Research and Public Health 17, no. 14: 4933.

Journal article
Published: 16 April 2020 in International Journal of Environmental Research and Public Health
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Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

ACS Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Sushant K. Singh; Nadhir Al-Ansari; John J. Clague; Abolfazl Jaafari; Wei Chen; Shaghayegh Miraki; Jie Dou; Chinh Luu; Krzysztof Górski; Binh Thai Pham; Huu Duy Nguyen; Baharin Bin Ahmad. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. International Journal of Environmental Research and Public Health 2020, 17, 2749 .

AMA Style

Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Sushant K. Singh, Nadhir Al-Ansari, John J. Clague, Abolfazl Jaafari, Wei Chen, Shaghayegh Miraki, Jie Dou, Chinh Luu, Krzysztof Górski, Binh Thai Pham, Huu Duy Nguyen, Baharin Bin Ahmad. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. International Journal of Environmental Research and Public Health. 2020; 17 (8):2749.

Chicago/Turabian Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Sushant K. Singh; Nadhir Al-Ansari; John J. Clague; Abolfazl Jaafari; Wei Chen; Shaghayegh Miraki; Jie Dou; Chinh Luu; Krzysztof Górski; Binh Thai Pham; Huu Duy Nguyen; Baharin Bin Ahmad. 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms." International Journal of Environmental Research and Public Health 17, no. 8: 2749.

Journal article
Published: 09 April 2020 in Forests
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We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

ACS Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Wei Chen; John J Clague; Marten Geertsema; Abolfazl Jaafari; Mohammadtaghi Avand; Shaghayegh Miraki; Davood Talebpour Asl; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran. Forests 2020, 11, 421 .

AMA Style

Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Wei Chen, John J Clague, Marten Geertsema, Abolfazl Jaafari, Mohammadtaghi Avand, Shaghayegh Miraki, Davood Talebpour Asl, Binh Thai Pham, Baharin Bin Ahmad, Saro Lee. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran. Forests. 2020; 11 (4):421.

Chicago/Turabian Style

Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Wei Chen; John J Clague; Marten Geertsema; Abolfazl Jaafari; Mohammadtaghi Avand; Shaghayegh Miraki; Davood Talebpour Asl; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. 2020. "Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran." Forests 11, no. 4: 421.

Journal article
Published: 13 January 2020 in Remote Sensing
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Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

ACS Style

Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing 2020, 12, 266 .

AMA Style

Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad, Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing. 2020; 12 (2):266.

Chicago/Turabian Style

Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier." Remote Sensing 12, no. 2: 266.

Journal article
Published: 19 November 2019 in Quaternary Science Reviews
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At least seven late Pliocene tills cap plateaus (mesetas) south of Lago Viedma, just east of the Andes in Argentine Patagonia. Chronologic constraints on the tills are provided by 40Ar/39Ar ages and magnetic polarities on associated basalt flows and sediments. The tills were deposited by piedmont glaciers that reached at least 80 km east of the crest of the Andes and flowed on a low-relief surface sloping gently downward in that direction. The oldest of the tills is about 3.6 Ma old. Glacial deposits dating to the Pliocene-Pleistocene transition are present at least 40 km beyond the east limit of the Pliocene tills at Lago Viedma, and tills of similar age occur at Condor Cliff in the Río Santa Cruz valley to the southeast. A sequence of at least seven Early Pleistocene (2.1–1.1 Ma) tills is present between basalt flows in the Cerro del Fraile meseta south of Lago Argentino. The glaciers that deposited these Early Pleistocene tills reached far beyond the Marine Isotope Stage 2 limit in the Río Santa Cruz valley. Based on positions, extents, and ages of the un-deformed, basalt-capped mesetas flanking Lago Viedma, we conclude that the topography in this area was profoundly changed during the Pleistocene – the low to moderate relief Pliocene surface was deeply incised by glaciers that became increasingly confined to, and flowed within, troughs. The valley floors today are up to 1350 m below the late Pliocene surface.

ACS Style

John J. Clague; Rene W. Barendregt; Brian Menounos; Nicholas J. Roberts; Jorge Rabassa; Oscar Martinez; Bettina Ercolano; Hugo Corbella; Sidney R. Hemming. Pliocene and Early Pleistocene glaciation and landscape evolution on the Patagonian Steppe, Santa Cruz province, Argentina. Quaternary Science Reviews 2019, 227, 105992 .

AMA Style

John J. Clague, Rene W. Barendregt, Brian Menounos, Nicholas J. Roberts, Jorge Rabassa, Oscar Martinez, Bettina Ercolano, Hugo Corbella, Sidney R. Hemming. Pliocene and Early Pleistocene glaciation and landscape evolution on the Patagonian Steppe, Santa Cruz province, Argentina. Quaternary Science Reviews. 2019; 227 ():105992.

Chicago/Turabian Style

John J. Clague; Rene W. Barendregt; Brian Menounos; Nicholas J. Roberts; Jorge Rabassa; Oscar Martinez; Bettina Ercolano; Hugo Corbella; Sidney R. Hemming. 2019. "Pliocene and Early Pleistocene glaciation and landscape evolution on the Patagonian Steppe, Santa Cruz province, Argentina." Quaternary Science Reviews 227, no. : 105992.

Journal article
Published: 28 August 2019 in Forests
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We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.

ACS Style

Dieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Marten Geertsema; Ebrahim Omidvar; John J. Clague; Binh Thai Pham; Jie Dou; Dawood Talebpour Asl; Baharin Bin Ahmad; Saro Lee. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed. Forests 2019, 10, 743 .

AMA Style

Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, Marten Geertsema, Ebrahim Omidvar, John J. Clague, Binh Thai Pham, Jie Dou, Dawood Talebpour Asl, Baharin Bin Ahmad, Saro Lee. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed. Forests. 2019; 10 (9):743.

Chicago/Turabian Style

Dieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Marten Geertsema; Ebrahim Omidvar; John J. Clague; Binh Thai Pham; Jie Dou; Dawood Talebpour Asl; Baharin Bin Ahmad; Saro Lee. 2019. "New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed." Forests 10, no. 9: 743.

Journal article
Published: 17 April 2019 in Remote Sensing
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We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.

ACS Style

Dieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing 2019, 11, 931 .

AMA Style

Dieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, Ataollah Shirzadi, Marten Geertsema, John J. Clague, Khabat Khosravi, Biswajeet Pradhan, Binh Thai Pham, Kamran Chapi, Zahra Barati, Baharin Bin Ahmad, Hosein Rahmani, Gyula Gróf, Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing. 2019; 11 (8):931.

Chicago/Turabian Style

Dieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. 2019. "Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm." Remote Sensing 11, no. 8: 931.

Journal article
Published: 29 March 2019 in Natural Hazards and Earth System Sciences
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We characterize and compare creep preceding and following the complex 2011 Pampahasi landslide (∼40 Mm3±50 %) in the city of La Paz, Bolivia, using spaceborne radar interferometry (InSAR) that combines displacement records from both distributed and point scatterers. The failure remobilized deposits of an ancient complex landslide in weakly cemented, predominantly fine-grained sediments and affected ∼1.5 km2 of suburban development. During the 30 months preceding failure, about half of the toe area was creeping at 3–8 cm a−1 and localized parts of the scarp area showed displacements of up to 14 cm a−1. Changes in deformation in the 10 months following the landslide demonstrate an increase in slope activity and indicate that stress redistribution resulting from the discrete failure decreased stability of parts of the slope. During that period, most of the landslide toe and areas near the head scarp accelerated, respectively, to 4–14 and 14 cm a−1. The extent of deformation increased to cover most, or probably all, of the 2011 landslide as well as adjacent parts of the slope and plateau above. The InSAR-measured displacement patterns, supplemented by field observations and optical satellite images, reveal complex slope activity; kinematically complex, steady-state creep along pre-existing sliding surfaces accelerated in response to heavy rainfall, after which slightly faster and expanded steady creeping was re-established. This case study demonstrates that high-quality ground-surface motion fields derived using spaceborne InSAR can help to characterize creep mechanisms, quantify spatial and temporal patterns of slope activity, and identify isolated small-scale instabilities; such details are especially useful where knowledge of landslide extent and activity is limited. Characterizing slope activity before, during, and after the 2011 Pampahasi landslide is particularly important for understanding landslide hazard in La Paz, half of which is underlain by similar large paleolandslides.

ACS Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia, revealed by advanced InSAR. Natural Hazards and Earth System Sciences 2019, 19, 679 -696.

AMA Style

Nicholas J. Roberts, Bernhard T. Rabus, John J. Clague, Reginald L. Hermanns, Marco-Antonio Guzmán, Estela Minaya. Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia, revealed by advanced InSAR. Natural Hazards and Earth System Sciences. 2019; 19 (3):679-696.

Chicago/Turabian Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. 2019. "Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia, revealed by advanced InSAR." Natural Hazards and Earth System Sciences 19, no. 3: 679-696.

Journal article
Published: 01 December 2018 in Canadian Journal of Earth Sciences
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In the past, researchers have disagreed over the maximum extent of the Cordilleran Ice Sheet in the Peace River valley during the Late Wisconsinan. Some workers argued that Cordilleran ice reached beyond the Rocky Mountains and briefly coalesced with the Laurentide Ice Sheet on the westernmost Interior Plains. In contrast, others asserted that Cordilleran ice did not reach beyond the eastern front of the Rocky Mountains. Stratigraphic interpretation of three sections within a Middle Wisconsinan paleovalley and re-examination of a previously published regional stratigraphic framework show that western-sourced ice (likely the Cordilleran Ice Sheet) extended east of the mountain front during the Late Wisconsinan, prior to the incursion of the Laurentide Ice Sheet into the area. This conclusion has implications for Cordilleran Ice Sheet reconstruction and modelling, and provides insight into the interactions between the Cordilleran and Laurentide ice sheets during the last glaciation.

ACS Style

Gregory M.D. Hartman; John J. Clague; Rene W. Barendregt; Alberto V. Reyes. Late Wisconsinan Cordilleran and Laurentide glaciation of the Peace River Valley east of the Rocky Mountains, British Columbia. Canadian Journal of Earth Sciences 2018, 55, 1324 -1338.

AMA Style

Gregory M.D. Hartman, John J. Clague, Rene W. Barendregt, Alberto V. Reyes. Late Wisconsinan Cordilleran and Laurentide glaciation of the Peace River Valley east of the Rocky Mountains, British Columbia. Canadian Journal of Earth Sciences. 2018; 55 (12):1324-1338.

Chicago/Turabian Style

Gregory M.D. Hartman; John J. Clague; Rene W. Barendregt; Alberto V. Reyes. 2018. "Late Wisconsinan Cordilleran and Laurentide glaciation of the Peace River Valley east of the Rocky Mountains, British Columbia." Canadian Journal of Earth Sciences 55, no. 12: 1324-1338.

Preprint content
Published: 13 August 2018
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ACS Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. Supplementary material to "Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR". 2018, 1 .

AMA Style

Nicholas J. Roberts, Bernhard T. Rabus, John J. Clague, Reginald L. Hermanns, Marco-Antonio Guzmán, Estela Minaya. Supplementary material to "Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR". . 2018; ():1.

Chicago/Turabian Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. 2018. "Supplementary material to "Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR"." , no. : 1.

Preprint content
Published: 13 August 2018
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We characterize and compare creep preceding and following the 2011 Pampahasi landslide (∼ 40 Mm3 ± 50 %) in the city of La Paz, Bolivia, using spaceborne RADAR interferometry (InSAR) that combines displacement records from both distributed and point scatterers. The failure remobilised deposits of an ancient landslide in weakly cemented, predominantly fine-grained sediments and affected ∼ 1.5 km2 of suburban development. During the 30 months preceding failure, about half of the toe area was creeping at 3–8 cm/a and localized parts of the scarp area showed displacements of up to 14 cm/a. Changes in deformation in the 10 months following the landslide are contrary to the common assumption that stress released during a discrete failure increases stability. During that period, most of the landslide toe and areas near the headscarp accelerated, respectively, to 4–14 and 14 cm/a. The extent of deformation increased to cover most, or probably all, of the 2011 landslide as well as adjacent parts of the slope and plateau above. The InSAR-measured displacement patterns – supplemented by field observations and by optical satellite images – indicate that kinematically complex, steady-state creep along pre-existing sliding surfaces temporarily accelerated in response to heavy rainfall, after which the slope quickly achieved a slightly faster and expanded steadily creeping state. This case study demonstrates that high-quality ground-surface motion fields derived using spaceborne InSAR can help to characterize creep mechanisms, quantify spatial and temporal patterns of slope activity, and identify isolated small-scale instabilities. Characterizing slope instability before, during, and after the 2011 Pampahasi landslide is particularly important for understanding landslide hazard in La Paz, half of which is underlain by similar, large paleolandslides.

ACS Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR. 2018, 2018, 1 -32.

AMA Style

Nicholas J. Roberts, Bernhard T. Rabus, John J. Clague, Reginald L. Hermanns, Marco-Antonio Guzmán, Estela Minaya. Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR. . 2018; 2018 ():1-32.

Chicago/Turabian Style

Nicholas J. Roberts; Bernhard T. Rabus; John J. Clague; Reginald L. Hermanns; Marco-Antonio Guzmán; Estela Minaya. 2018. "Changes in ground deformation prior to and following a large urban landslide in La Paz, Bolivia revealed by advanced InSAR." 2018, no. : 1-32.