@article{DrewesMoreirasKorup2018, author = {Drewes, Julia and Moreiras, Stella and Korup, Oliver}, title = {Permafrost activity and atmospheric warming in the Argentinian Andes}, series = {Geomorphology : an international journal on pure and applied geomorphology}, volume = {323}, journal = {Geomorphology : an international journal on pure and applied geomorphology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0169-555X}, doi = {10.1016/j.geomorph.2018.09.005}, pages = {13 -- 24}, year = {2018}, abstract = {Rock glaciers are permafrost or glacial landforms of debris and ice that deform under the influence of gravity. Recent estimates hold that, in the semiarid Chilean Andes for example, active rock glaciers store more water than glaciers. However, little is known about how many rock glaciers might decay because of global warming and how much this decay might contribute to water and sediment release. We investigated an inventory of >6500 rock glaciers in the Argentinian Andes, spanning the climatic gradient from the Desert Andes to cold-temperate Tierra del Fuego. We used active rock glaciers as a diagnostic of permafrost, assuming that the toes mark the 0 degrees C isotherm in climate scenarios for the twenty-first century and their impact on freezing conditions near the rock glacier toes. We find that, under future worst case warming, up to 95\% of rock glaciers in the southern Desert Andes and in the Central Andes will rest in areas above 0 degrees C and that this freezing level might move up more than twice as much (similar to 500 m) as during the entire Holocene (similar to 200 m). Many active rock glaciers are already well below the current freezing level and exemplify how local controls may confound regional prognoses. A Bayesian Multifactor Analysis of Variance further shows that only in the Central Andes are the toes of active rock glaciers credibly higher than those of inactive ones. Elsewhere in the Andes, active and inactive rock glaciers occupy indistinguishable elevation bands, regardless of aspect, the formation mechanism, or shape of rock glaciers. The state of rock glacier activity predicts differences in elevations of toes to 140 m at best so that regional inference of the distribution of discontinuous permafrost from rock-glacier toes cannot be more accurate than this in the Argentinian Andes. We conclude that the Central Andes-where rock glaciers are largest, cover the most area, and have a greater density than glaciers-is likely to experience the most widespread disturbance to the thermal regime of the twenty-first century. (C) 2018 Elsevier B.V. All rights reserved.}, language = {en} } @misc{FanScaringiKorupetal.2019, author = {Fan, Xuanmei and Scaringi, Gianvito and Korup, Oliver and West, A. Joshua and van Westen, Cees J. and Tanyas, Hakan and Hovius, Niels and Hales, Tristram C. and Jibson, Randall W. and Allstadt, Kate E. and Zhang, Limin and Evans, Stephen G. and Xu, Chong and Li, Gen and Pei, Xiangjun and Xu, Qiang and Huang, Runqiu}, title = {Earthquake-Induced Chains of Geologic Hazards}, series = {Reviews of geophysics}, volume = {57}, journal = {Reviews of geophysics}, number = {2}, publisher = {American Geophysical Union}, address = {Washington}, issn = {8755-1209}, doi = {10.1029/2018RG000626}, pages = {421 -- 503}, year = {2019}, abstract = {Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate- and large-magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake-induced landslides and their consequences: the magnitude M 7.6 Chi-Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.}, language = {en} } @misc{FischerBrettinRoessneretal.2022, author = {Fischer, Melanie and Brettin, Jana and Roessner, Sigrid and Walz, Ariane and Fort, Monique and Korup, Oliver}, title = {Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1284}, issn = {1866-8372}, doi = {10.25932/publishup-57120}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-571209}, pages = {3105 -- 3123}, year = {2022}, abstract = {Pokhara (ca. 850 m a.s.l.), Nepal's second-largest city, lies at the foot of the Higher Himalayas and has more than tripled its population in the past 3 decades. Construction materials are in high demand in rapidly expanding built-up areas, and several informal settlements cater to unregulated sand and gravel mining in the Pokhara Valley's main river, the Seti Khola. This river is fed by the Sabche glacier below Annapurna III (7555 m a.s.l.), some 35 km upstream of the city, and traverses one of the steepest topographic gradients in the Himalayas. In May 2012 a sudden flood caused >70 fatalities and intense damage along this river and rekindled concerns about flood risk management. We estimate the flow dynamics and inundation depths of flood scenarios using the hydrodynamic model HEC-RAS (Hydrologic Engineering Center's River Analysis System). We simulate the potential impacts of peak discharges from 1000 to 10 000 m3 s-1 on land cover based on high-resolution Maxar satellite imagery and OpenStreetMap data (buildings and road network). We also trace the dynamics of two informal settlements near Kaseri and Yamdi with high potential flood impact from RapidEye, PlanetScope, and Google Earth imagery of the past 2 decades. Our hydrodynamic simulations highlight several sites of potential hydraulic ponding that would largely affect these informal settlements and sites of sand and gravel mining. These built-up areas grew between 3- and 20-fold, thus likely raising local flood exposure well beyond changes in flood hazard. Besides these drastic local changes, about 1 \% of Pokhara's built-up urban area and essential rural road network is in the highest-hazard zones highlighted by our flood simulations. Our results stress the need to adapt early-warning strategies for locally differing hydrological and geomorphic conditions in this rapidly growing urban watershed.}, language = {en} } @article{FischerBrettinRoessneretal.2022, author = {Fischer, Melanie and Brettin, Jana and Roessner, Sigrid and Walz, Ariane and Fort, Monique and Korup, Oliver}, title = {Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal}, series = {Natural Hazards and Earth System Sciences}, volume = {22}, journal = {Natural Hazards and Earth System Sciences}, edition = {9}, publisher = {Copernicus Publications}, address = {Katlenburg-Lindau}, issn = {1684-9981}, doi = {10.5194/nhess-22-3105-2022}, pages = {3105 -- 3123}, year = {2022}, abstract = {Pokhara (ca. 850 m a.s.l.), Nepal's second-largest city, lies at the foot of the Higher Himalayas and has more than tripled its population in the past 3 decades. Construction materials are in high demand in rapidly expanding built-up areas, and several informal settlements cater to unregulated sand and gravel mining in the Pokhara Valley's main river, the Seti Khola. This river is fed by the Sabche glacier below Annapurna III (7555 m a.s.l.), some 35 km upstream of the city, and traverses one of the steepest topographic gradients in the Himalayas. In May 2012 a sudden flood caused >70 fatalities and intense damage along this river and rekindled concerns about flood risk management. We estimate the flow dynamics and inundation depths of flood scenarios using the hydrodynamic model HEC-RAS (Hydrologic Engineering Center's River Analysis System). We simulate the potential impacts of peak discharges from 1000 to 10 000 m3 s-1 on land cover based on high-resolution Maxar satellite imagery and OpenStreetMap data (buildings and road network). We also trace the dynamics of two informal settlements near Kaseri and Yamdi with high potential flood impact from RapidEye, PlanetScope, and Google Earth imagery of the past 2 decades. Our hydrodynamic simulations highlight several sites of potential hydraulic ponding that would largely affect these informal settlements and sites of sand and gravel mining. These built-up areas grew between 3- and 20-fold, thus likely raising local flood exposure well beyond changes in flood hazard. Besides these drastic local changes, about 1 \% of Pokhara's built-up urban area and essential rural road network is in the highest-hazard zones highlighted by our flood simulations. Our results stress the need to adapt early-warning strategies for locally differing hydrological and geomorphic conditions in this rapidly growing urban watershed.}, language = {en} } @article{FischerKorupVehetal.2021, author = {Fischer, Melanie and Korup, Oliver and Veh, Georg and Walz, Ariane}, title = {Controls of outbursts of moraine-dammed lakes in the greater Himalayan region}, series = {The Cryosphere}, volume = {15}, journal = {The Cryosphere}, publisher = {Copernicus Publications}, address = {G{\"o}ttingen}, issn = {1994-0416}, doi = {10.5194/tc-15-4145-2021}, pages = {19}, year = {2021}, abstract = {Glacial lakes in the Hindu Kush-Karakoram-Himalayas-Nyainqentanglha (HKKHN) region have grown rapidly in number and area in past decades, and some dozens have drained in catastrophic glacial lake outburst floods (GLOFs). Estimating regional susceptibility of glacial lakes has largely relied on qualitative assessments by experts, thus motivating a more systematic and quantitative appraisal. Before the backdrop of current climate-change projections and the potential of elevation-dependent warming, an objective and regionally consistent assessment is urgently needed. We use an inventory of 3390 moraine-dammed lakes and their documented outburst history in the past four decades to test whether elevation, lake area and its rate of change, glacier-mass balance, and monsoonality are useful inputs to a probabilistic classification model. We implement these candidate predictors in four Bayesian multi-level logistic regression models to estimate the posterior susceptibility to GLOFs. We find that mostly larger lakes have been more prone to GLOFs in the past four decades regardless of the elevation band in which they occurred. We also find that including the regional average glacier-mass balance improves the model classification. In contrast, changes in lake area and monsoonality play ambiguous roles. Our study provides first quantitative evidence that GLOF susceptibility in the HKKHN scales with lake area, though less so with its dynamics. Our probabilistic prognoses offer improvement compared to a random classification based on average GLOF frequency. Yet they also reveal some major uncertainties that have remained largely unquantified previously and that challenge the applicability of single models. Ensembles of multiple models could be a viable alternative for more accurately classifying the susceptibility of moraine-dammed lakes to GLOFs.}, language = {en} } @misc{Korup2020, author = {Korup, Oliver}, title = {Bayesian geomorphology}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1}, issn = {1866-8372}, doi = {10.25932/publishup-53989}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-539892}, pages = {24}, year = {2020}, abstract = {The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.}, language = {en} } @article{Korup2020, author = {Korup, Oliver}, title = {Bayesian geomorphology}, series = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, volume = {46}, journal = {Earth surface processes and landforms : the journal of the British Geomorphological Research Group}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {0197-9337}, doi = {10.1002/esp.4995}, pages = {151 -- 172}, year = {2020}, abstract = {The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.}, language = {en} } @article{KorupMohrManga2021, author = {Korup, Oliver and Mohr, Christian Heinrich and Manga, Michael M.}, title = {Bayesian detection of streamflow response to earthquakes}, series = {Water resources research : an AGU journal}, volume = {57}, journal = {Water resources research : an AGU journal}, number = {7}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {0043-1397}, doi = {10.1029/2020WR028874}, pages = {10}, year = {2021}, abstract = {Detecting whether and how river discharge responds to strong earthquake shaking can be time-consuming and prone to operator bias when checking hydrographs from hundreds of gauging stations. We use Bayesian piecewise regression models to show that up to a fifth of all gauging stations across Chile had their largest change in daily streamflow trend on the day of the M-w 8.8 Maule earthquake in 2010. These stations cluster distinctly in the near field though the number of detected streamflow changes varies with model complexity and length of time window considered. Credible seismic streamflow changes at several stations were the highest detectable in eight months, with an increased variance of discharge surpassing the variance of discharge following rainstorms. We conclude that Bayesian piecewise regression sheds new and unbiased insights on the duration, trend, and variance of streamflow response to strong earthquakes, and on how this response compares to that following rainstorms.}, language = {en} } @misc{KorzeniowskaKorup2017, author = {Korzeniowska, Karolina and Korup, Oliver}, title = {Object-based detection of lakes prone to seasonal ice cover on the Tibetan Plateau}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1037}, issn = {1866-8372}, doi = {10.25932/publishup-47503}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-475037}, pages = {25}, year = {2017}, abstract = {The Tibetan Plateau, the world's largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the Tibetan Plateau has changed rapidly in recent years, possibly because of atmospheric warming. Tracking these changes systematically with remote sensing data is challenging given the different spectral signatures of water, the potential for confusing lakes with glaciers, and difficulties in classifying frozen or partly frozen lakes. Object-based image analysis (OBIA) offers new opportunities for automated classification in this context, and we have explored this method for mapping lakes from LANDSAT images and Shuttle Radar Topography Mission (SRTM) elevation data. We tested our algorithm for most of the Tibetan Plateau, where lakes in tectonic depressions or blocked by glaciers and sediments have different surface colours and seasonal ice cover in images obtained in 1995 and 2015. We combined a modified normalised difference water index (MNDWI) with OBIA and local topographic slope data in order to classify lakes with an area > 10 km(2). Our method derived 323 water bodies, with a total area of 31,258 km(2), or 2.6\% of the study area (in 2015). The same number of lakes had covered only 24,892 km(2) in 1995; lake area has increased by -26\% in the past two decades. The classification had estimated producer's and user's accuracies of 0.98, with a Cohen's kappa and F-score of 0.98, and may thus be a useful approximation for quantifying regional hydrological budgets. We have shown that our method is flexible and transferable to detecting lakes in diverse physical settings on several continents with similar success rates.}, language = {en} } @article{ParraHormazabalMohrKorup2021, author = {Parra Hormaz{\´a}bal, Eric and Mohr, Christian Heinrich and Korup, Oliver}, title = {Predicting Patagonian landslides}, series = {Geophysical research letters : GRL / American Geophysical Union}, volume = {48}, journal = {Geophysical research letters : GRL / American Geophysical Union}, number = {23}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0094-8276}, doi = {10.1029/2021GL095224}, pages = {10}, year = {2021}, abstract = {Dense tree stands and high wind speeds characterize the temperate rainforests of southern Chilean Patagonia, where landslides frequently strip hillslopes of soils, rock, and biomass. Assuming that wind loads on trees promote slope instability, we explore the role of forest cover and wind speed in predicting landslides with a hierarchical Bayesian logistic regression. We find that higher crown openness and wind speeds credibly predict higher probabilities of detecting landslides regardless of topographic location, though much better in low-order channels and on midslope locations than on open slopes. Wind speed has less predictive power in areas that were impacted by tephra fall from recent volcanic eruptions, while the influence of forest cover in terms of crown openness remains.
Plain Language Summary Chilean Patagonia hosts some of Earth's largest swaths of temperate rainforests, where frequent landslides erode soil, rock, and vegetation. We explore the role of forest cover and wind disturbances in promoting such landslides with a model that predicts from crown openness and wind speed the probability of detecting landslide terrain. We find that both forest cover and wind speed play important, yet previously underappreciated, roles in this context, especially when grouped by landform types and previous volcanic disturbance, which may override the comparable modest control of wind on landslides. Our study is the first of its kind in one of the windiest spots on Earth and encourages a more discerning approach to landslide prediction.}, language = {en} } @article{SamprognaMohorThiekenKorup2021, author = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver}, title = {Residential flood loss estimated from Bayesian multilevel models}, series = {Natural Hazards and Earth System Sciences}, volume = {21}, journal = {Natural Hazards and Earth System Sciences}, publisher = {European Geophysical Society}, address = {Katlenburg-Lindau}, issn = {2195-9269}, doi = {10.5194/nhess-21-1599-2021}, pages = {1599 -- 1614}, year = {2021}, abstract = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.}, language = {en} } @misc{SamprognaMohorThiekenKorup2021, author = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver}, title = {Residential flood loss estimated from Bayesian multilevel models}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, issn = {1866-8372}, doi = {10.25932/publishup-51774}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517743}, pages = {1599 -- 1614}, year = {2021}, abstract = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.}, language = {en} } @article{SchoenfeldtWinocurPaneketal.2022, author = {Sch{\"o}nfeldt, Elisabeth and Winocur, Diego and P{\´a}nek, Tom{\´a}š and Korup, Oliver}, title = {Deep learning reveals one of Earth's largest landslide terrain in Patagonia}, series = {Earth \& planetary science letters}, volume = {593}, journal = {Earth \& planetary science letters}, publisher = {Elsevier}, address = {Amsterdam [u.a.]}, issn = {0012-821X}, doi = {10.1016/j.epsl.2022.117642}, pages = {13}, year = {2022}, abstract = {Hundreds of basaltic plateau margins east of the Patagonian Cordillera are undermined by numerous giant slope failures. However, the overall extent of this widespread type of plateau collapse remains unknown and incompletely captured in local maps. To detect giant slope failures consistently throughout the region, we train two convolutional neural networks (CNNs), AlexNet and U-Net, with Sentinel-2 optical data and TanDEM-X topographic data on elevation, surface roughness, and curvature. We validated the performance of these CNNs with independent testing data and found that AlexNet performed better when learned on topographic data, and UNet when learned on optical data. AlexNet predicts a total landslide area of 12,000 km2 in a study area of 450,000 km2, and thus one of Earth's largest clusters of giant landslides. These are mostly lateral spreads and rotational failures in effusive rocks, particularly eroding the margins of basaltic plateaus; some giant landslides occurred along shores of former glacial lakes, but are least prevalent in Quaternary sedimentary rocks. Given the roughly comparable topographic, climatic, and seismic conditions in our study area, we infer that basalts topping weak sedimentary rocks may have elevated potential for large-scale slope failure. Judging from the many newly detected and previously unknown landslides, we conclude that CNNs can be a valuable tool to detect large-scale slope instability at the regional scale. However, visual inspection is still necessary to validate results and correctly outline individual landslide source and deposit areas.}, language = {en} } @article{VehKorupWalz2019, author = {Veh, Georg and Korup, Oliver and Walz, Ariane}, title = {Hazard from Himalayan glacier lake outburst floods}, series = {Proceedings of the National Academy of Sciences of the United States of America : PNAS}, volume = {117}, journal = {Proceedings of the National Academy of Sciences of the United States of America : PNAS}, number = {2}, publisher = {National Academy of Sciences}, address = {Washington}, issn = {0027-8424}, doi = {10.1073/pnas.1914898117}, pages = {907 -- 912}, year = {2019}, abstract = {Sustained glacier melt in the Himalayas has gradually spawned more than 5,000 glacier lakes that are dammed by potentially unstable moraines. When such dams break, glacier lake outburst floods (GLOFs) can cause catastrophic societal and geomorphic impacts. We present a robust probabilistic estimate of average GLOFs return periods in the Himalayan region, drawing on 5.4 billion simulations. We find that the 100-y outburst flood has an average volume of 33.5(+3.7)/(-3.7) x 10(6) m(3) (posterior mean and 95\% highest density interval [HDI]) with a peak discharge of 15,600(+2.000)/(-1,800) m(3).S-1. Our estimated GLOF hazard is tied to the rate of historic lake outbursts and the number of present lakes, which both are highest in the Eastern Himalayas. There, the estimated 100-y GLOF discharge (similar to 14,500 m(3).s(-1)) is more than 3 times that of the adjacent Nyainqentanglha Mountains, and at least an order of magnitude higher than in the Hindu Kush, Karakoram, and Western Himalayas. The GLOF hazard may increase in these regions that currently have large glaciers, but few lakes, if future projected ice loss generates more unstable moraine-dammed lakes than we recognize today. Flood peaks from GLOFs mostly attenuate within Himalayan headwaters, but can rival monsoon-fed discharges in major rivers hundreds to thousands of kilometers downstream. Projections of future hazard from meteorological floods need to account for the extreme runoffs during lake outbursts, given the increasing trends in population, infrastructure, and hydropower projects in Himalayan headwaters.}, language = {en} } @article{VehLuetzowKharlamovaetal.2022, author = {Veh, Georg and L{\"u}tzow, Natalie and Kharlamova, Varvara and Petrakov, Dmitry and Hugonnet, Romain and Korup, Oliver}, title = {Trends, breaks, and biases in the frequency of reported glacier lake outburst floods}, series = {Earth's future}, volume = {10}, journal = {Earth's future}, number = {3}, publisher = {American Geophysical Union}, address = {Washington}, issn = {2328-4277}, doi = {10.1029/2021EF002426}, pages = {14}, year = {2022}, abstract = {Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.}, language = {en} } @article{VehLuetzowKharlamovaetal.2022, author = {Veh, Georg and L{\"u}tzow, Natalie and Kharlamova, Varvara and Petrakov, Dmitry and Hugonnet, Romain and Korup, Oliver}, title = {Trends, Breaks, and Biases in the Frequency of Reported Glacier Lake Outburst Floods}, series = {Earth's Future}, volume = {10}, journal = {Earth's Future}, edition = {3}, publisher = {Wiley-Blackwell}, address = {Hoboken, New Jersey, United States}, issn = {2328-4277}, doi = {10.1029/2021EF002426}, pages = {1 -- 14}, year = {2022}, abstract = {Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.}, language = {en} } @misc{VehLuetzowKharlamovaetal.2022, author = {Veh, Georg and L{\"u}tzow, Natalie and Kharlamova, Varvara and Petrakov, Dmitry and Hugonnet, Romain and Korup, Oliver}, title = {Trends, Breaks, and Biases in the Frequency of Reported Glacier Lake Outburst Floods}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {1866-8372}, doi = {10.25932/publishup-56100}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-561005}, pages = {1 -- 14}, year = {2022}, abstract = {Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.}, language = {en} } @article{vonSpechtOeztuerkVehetal.2019, author = {von Specht, Sebastian and {\"O}zt{\"u}rk, Ugur and Veh, Georg and Cotton, Fabrice and Korup, Oliver}, title = {Effects of finite source rupture on landslide triggering}, series = {Solid earth}, volume = {10}, journal = {Solid earth}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1869-9510}, doi = {10.5194/se-10-463-2019}, pages = {463 -- 486}, year = {2019}, abstract = {The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wave field surrounding the fault. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (M-w 7.1) in central Kyushu (Japan). Although the distribution of some 1500 earthquake-triggered landslides as a function of rupture distance is consistent with the observed Arias intensity, the landslides were more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, the median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors sufficiently explains the landslide distribution or orientation (aspect), although the landslide head scarps have an elevated hillslope inclination and MAF. We propose a new physics-based ground-motion model (GMM) that accounts for the seismic rupture effects, and we demonstrate that the low-frequency seismic radiation pattern is consistent with the overall landslide distribution. Its spatial pattern is influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies < 2 Hz. This azimuth dependence implies that comparable landslide concentrations can occur at different distances from the rupture. This quantitative link between the prevalent landslide aspect and the low-frequency seismic radiation pattern can improve coseismic landslide hazard assessment.}, language = {en} }