TY - JOUR A1 - Schönfeldt, Elisabeth A1 - Winocur, Diego A1 - Pánek, Tomáš A1 - Korup, Oliver T1 - Deep learning reveals one of Earth's largest landslide terrain in Patagonia JF - Earth & planetary science letters N2 - 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. KW - landslide detection KW - convolutional neural network KW - Patagonia Y1 - 2022 U6 - https://doi.org/10.1016/j.epsl.2022.117642 SN - 0012-821X SN - 1385-013X VL - 593 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Ozturk, Ugur A1 - Pittore, Massimiliano A1 - Behling, Robert A1 - Rößner, Sigrid A1 - Andreani, Louis A1 - Korup, Oliver T1 - How robust are landslide susceptibility estimates? JF - Landslides N2 - Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change. KW - Landslide susceptibility KW - Logistic regression KW - Southern Kyrgyzstan KW - Landslide inventory KW - Remote sensing Y1 - 2020 U6 - https://doi.org/10.1007/s10346-020-01485-5 SN - 1612-510X SN - 1612-5118 VL - 18 IS - 2 SP - 681 EP - 695 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Günther, Oliver A1 - Schüle, Manja A1 - Zurell, Damaris A1 - Jeltsch, Florian A1 - Roeleke, Manuel A1 - Kampe, Heike A1 - Zimmermann, Matthias A1 - Scholz, Jana A1 - Mikulla, Stefanie A1 - Engbert, Ralf A1 - Elsner, Birgit A1 - Schlangen, David A1 - Agrofylax, Luisa A1 - Georgi, Doreen A1 - Weymar, Mathias A1 - Wagener, Thorsten A1 - Bookhagen, Bodo A1 - Eibl, Eva P. S. A1 - Korup, Oliver A1 - Oswald, Sascha A1 - Thieken, Annegret A1 - van der Beek, Peter T1 - Portal Wissen = Excellence JF - Portal Wissen: The research magazine of the University of Potsdam N2 - When something is not just good or very good, we often call it excellent. But what does that really mean? Coming from the Latin word “excellere,” it describes things, persons, or actions that are outstanding or superior and distinguish themselves from others. It cannot get any better. Excellence is the top choice for being the first or the best. Research is no exception. At the university, you will find numerous exceptional researchers, outstanding projects, and, time and again, sensational findings, publications, and results. But is the University of Potsdam also excellent? A question that will certainly create a different stir in 2023 than it did perhaps 20 years ago. Since the launch of the Excellence Initiative in 2005, universities that succeed in winning the most comprehensive funding program for research in Germany have been considered – literally – excellent. Whether in the form of graduate schools, research clusters, or – since the program was continued in 2019 under the title “Excellence Strategy” – entire universities of excellence: Anyone who wants to be among the best research universities needs the seal of excellence. The University of Potsdam is applying for funding with three cluster proposals in the recently launched new round of the “Excellence Strategy of the German Federal and State Governments.” One proposal comes from ecology and biodiversity research. The aim is to paint a comprehensive picture of ecological processes by examining the role of single individuals as well as the interactions among many species in an ecosystem to precisely determine the function of biodiversity. A second proposal has been submitted by the cognitive sciences. Here, the complex coexistence of language and cognition, development and learning, as well as motivation and behavior will be researched as a dynamic interrelation. The projects will include cooperation with the educational sciences to constantly consider linked learning and educational processes. The third proposal from the geo and environmental sciences concentrates on extreme and particularly devastating natural hazards and processes such as floods and droughts. The researchers examine these extreme events, focusing on their interaction with society, to be able to better assess the risks and damages they might involve and to initiate timely measures in the future. “All three proposals highlight the excellence of our performance,” emphasizes University President Prof. Oliver Günther, Ph.D. “The outlines impressively document our commitment, existing research excellence, and the potential of the University of Potsdam as a whole. The fact that three powerful consortia have come together in different subject areas shows that we have taken a good step forward on our way to becoming one of the top German universities.” In this issue, we are looking at what is in and behind these proposals: We talked to the researchers who wrote them. We asked them about their plans in case their proposals are successful and they bring a cluster of excellence to the university. But we also looked at the research that has led to the proposals, has long shaped the university’s profile, and earned it national and international recognition. We present a small selection of projects, methods, and researchers to illustrate why there really is excellent research in these proposals! By the way, “excellence” is also not the end of the flagpole. After all, the adjective “excellent” even has a comparative and a superlative. With this in mind, I wish you the most excellent pleasure reading this issue! T3 - Portal Wissen: The research magazine of the University of Potsdam [Englische Ausgabe] - 02/2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-611456 SN - 2198-9974 IS - 02/2023 ER - TY - JOUR A1 - Günther, Oliver A1 - Schüle, Manja A1 - Zurell, Damaris A1 - Jeltsch, Florian A1 - Roeleke, Manuel A1 - Kampe, Heike A1 - Zimmermann, Matthias A1 - Scholz, Jana A1 - Engbert, Ralf A1 - Elsner, Birgit A1 - Schlangen, David A1 - Agrofylax, Luisa A1 - Georgi, Doreen A1 - Weymar, Mathias A1 - Wagener, Thorsten A1 - Bookhagen, Bodo A1 - Eibl, Eva P. S. A1 - Korup, Oliver A1 - Oswald, Sascha A1 - Thieken, Annegret A1 - van der Beek, Peter T1 - Portal Wissen = Exzellenz JF - Portal Wissen: Das Forschungsmagazin der Universität Potsdam N2 - Was nicht nur gut oder sehr gut ist, nennen wir gern exzellent. Aber was meint das eigentlich? Vom lateinischen „excellere“ kommend, beschreibt es Dinge, Personen oder Handlungen, die „hervor-“ oder „herausragen“ aus der Menge, sich „auszeichnen“ gegenüber anderen. Mehr geht nicht. Exzellenz ist das Mittel der Wahl, wenn es darum geht, der Erste oder Beste zu sein. Und das macht auch vor der Forschung nicht halt. Wer auf die Universität Potsdam schaut, findet zahlreiche ausgezeichnete Forschende, hervorragende Projekte und immer wieder auch aufsehenerregende Erkenntnisse, Veröffentlichungen und Ergebnisse. Aber ist die UP auch exzellent? Eine Frage, die 2023 ganz sicher andere Wellen schlägt als vielleicht vor 20 Jahren. Denn seit dem Start der Exzellenzinitiative 2005 gelten als – wörtlich – exzellent jene Hochschulen, denen es gelingt, in dem umfangreichsten Förderprogramm für Wissenschaft in Deutschland einen Zuschlag zu erhalten. Egal ob in Form von Graduiertenschulen, Forschungsclustern oder – seit Fortsetzung des Programms ab 2019 unter dem Titel „Exzellenzstrategie“ – ganzen Exzellenzuniversitäten: Wer im Kreis der Forschungsuniversitäten zu den Besten gehören will, braucht das Siegel der Exzellenz. In der gerade eingeläuteten neuen Wettbewerbsrunde der „Exzellenzstrategie des Bundes und der Länder“ bewirbt sich die Universität Potsdam mit drei Clusterskizzen um Förderung. Ein Antrag kommt aus der Ökologie- und Biodiversitätsforschung. Ziel ist es, ein komplexes Bild ökologischer Prozesse zu zeichnen – und dabei die Rolle von einzelnen Individuen ebenso zu betrachten wie das Zusammenwirken vieler Arten in einem Ökosystem, um die Funktion der Artenvielfalt genauer zu bestimmen. Eine zweite Skizze haben die Kognitionswissenschaften eingereicht. Hier soll das komplexe Nebeneinander von Sprache und Kognition, Entwicklung und Lernen sowie Motivation und Verhalten als dynamisches Miteinander erforscht werden – wobei auch mit den Erziehungswissenschaften kooperiert wird, um verknüpfte Lernund Bildungsprozesse stets mitzudenken. Der dritte Antrag aus den Geo- und Umweltwissenschaften nimmt extreme und besonders folgenschwere Naturgefahren und -prozesse wie Überschwemmungen und Dürren in den Blick. Die Forschenden untersuchen die Extremereignisse mit besonderem Fokus auf deren Wechselwirkung mit der Gesellschaft, um mit ihnen einhergehende Risiken und Schäden besser einschätzen sowie künftig rechtzeitig Maßnahmen einleiten zu können. „Alle drei Anträge zeichnen ein hervorragendes Bild unserer Leistungsfähigkeit“, betont der Präsident der Universität, Prof. Oliver Günther, Ph.D. „Die Skizzen dokumentieren eindrucksvoll unser Engagement, vorhandene Forschungsexzellenz sowie die Potenziale der Universität Potsdam insgesamt. Allein die Tatsache, dass sich drei schlagkräftige Konsortien in ganz unterschiedlichen Themenbereichen zusammengefunden haben, zeigt, dass wir auf unserem Weg in die Spitzengruppe der deutschen Universitäten einen guten Schritt vorangekommen sind.“ In diesem Heft schauen wir, was sich in und hinter diesen Anträgen verbirgt: Wir haben mit den Wissenschaftlerinnen und Wissenschaftlern gesprochen, die sie geschrieben haben, und sie gefragt, was sie sich vornehmen, sollten sie den Zuschlag erhalten und ein Cluster an die Universität holen. Wir haben aber auch auf die Forschung geschaut, die zu den Anträgen geführt hat und die schon länger das Profil der Universität prägt und ihr national wie international Anerkennung eingebracht hat. Wir stellen eine kleine Auswahl an Projekten, Methoden und Forschenden vor, um zu zeigen, warum in diesen Anträgen tatsächlich exzellente Forschung steckt! Übrigens: Auch „Exzellenz“ ist nicht das Ende der Fahnenstange. Immerhin lässt sich das Adjektiv exzellent sogar steigern. In diesem Sinne wünschen wir exzellentestes Vergnügen beim Lesen! T3 - Portal Wissen: Das Forschungsmagazin der Universität Potsdam [Deutsche Ausgabe] - 02/2023 Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-611440 SN - 2194-4245 IS - 02/2023 ER - TY - JOUR A1 - Korup, Oliver A1 - Mohr, Christian Heinrich A1 - Manga, Michael M. T1 - Bayesian detection of streamflow response to earthquakes JF - Water resources research : an AGU journal N2 - 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. KW - Bayesian analysis KW - Chile KW - discharge KW - earthquake KW - streamflow changes Y1 - 2021 U6 - https://doi.org/10.1029/2020WR028874 SN - 0043-1397 SN - 1944-7973 VL - 57 IS - 7 PB - Wiley CY - Hoboken, NJ ER - TY - JOUR A1 - Schönfeldt, Elisabeth A1 - Pánek, Tomáš A1 - Winocur, Diego A1 - Silhan, Karel A1 - Korup, Oliver T1 - Postglacial Patagonian mass movement BT - from rotational slides and spreads to earthflows JF - Geomorphology : an international journal on pure and applied geomorphology N2 - Many of the volcanic plateau margins of the eastern, formerly glaciated, foreland of the Patagonian Andes are undermined by giant landslides (>= 10(8) m(3)). One cluster of such landslides extends along the margin of the Meseta del Lago Buenos Aires (MLBA) plateau that is formed mainly by Neogene-Quaternary basalts. The dry climate is at odds with numerous >2-km long earthflows nested within older and larger compound landslides. We present a hydrological analysis, a detailed geomorphic map, interpretations of exposed landslide interiors, and radiocarbon dating of the El Mirador landslide, which is one of the largest and morphologically most representative landslide. We find that the presence of lakes on top of the plateau, causing low infiltration rates, correlates negatively with the abundance of earthflows on compound landslides along the plateau margins. Field outcrops show that the pattern of compound landslides and earthflows is likely controlled by groundwater seepage at the contact between the basalts and underlying soft Miocene molasse. Numerous peat bogs store water and sediment and are more abundant in earthflow-affected areas than in their contributing catchment areas.
Radiocarbon dates indicate that these earthflows displaced metre-thick layers of peat in the late Holocene (<2.5 ka). We conclude that earthflows of the MLBA plateau might be promising proxies of past hydroclimatic conditions in the Patagonian foreland, if strong earthquakes or gradual crustal stress changes due to glacioisostatic rebound can be ruled out. KW - landslide KW - lateral spread KW - earthflow KW - Patagonia Y1 - 2020 U6 - https://doi.org/10.1016/j.geomorph.2020.107316 SN - 0169-555X SN - 1872-695X VL - 367 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Veh, Georg A1 - Korup, Oliver A1 - Walz, Ariane T1 - Hazard from Himalayan glacier lake outburst floods JF - Proceedings of the National Academy of Sciences of the United States of America : PNAS N2 - 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. KW - atmospheric warming KW - meltwater lakes KW - GLOF KW - extreme-value statistics KW - Bayesian modeling Y1 - 2019 U6 - https://doi.org/10.1073/pnas.1914898117 SN - 0027-8424 VL - 117 IS - 2 SP - 907 EP - 912 PB - National Academy of Sciences CY - Washington ER - TY - JOUR A1 - Parra Hormazábal, Eric A1 - Mohr, Christian Heinrich A1 - Korup, Oliver T1 - Predicting Patagonian landslides BT - roles of forest cover and wind speed JF - Geophysical research letters : GRL / American Geophysical Union N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1029/2021GL095224 SN - 0094-8276 SN - 1944-8007 VL - 48 IS - 23 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Fischer, Melanie A1 - Brettin, Jana A1 - Roessner, Sigrid A1 - Walz, Ariane A1 - Fort, Monique A1 - Korup, Oliver T1 - Rare flood scenarios for a rapidly growing high-mountain city: Pokhara, Nepal JF - Natural Hazards and Earth System Sciences N2 - 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. Y1 - 2022 U6 - https://doi.org/10.5194/nhess-22-3105-2022 SN - 1684-9981 VL - 22 SP - 3105 EP - 3123 PB - Copernicus Publications CY - Katlenburg-Lindau ET - 9 ER - TY - JOUR A1 - Korup, Oliver T1 - Bayesian geomorphology JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - 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. KW - Bayes' rule KW - probability KW - uncertainty KW - prediction Y1 - 2020 U6 - https://doi.org/10.1002/esp.4995 SN - 0197-9337 SN - 1096-9837 VL - 46 IS - 1 SP - 151 EP - 172 PB - Wiley CY - Hoboken ER -