@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{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}
}
@misc{OzturkPittoreBehlingetal.2020,
  author    = {Ozturk, Ugur and Pittore, Massimiliano and Behling, Robert and R{\"o}ßner, Sigrid and Andreani, Louis and Korup, Oliver},
  title     = {How robust are landslide susceptibility estimates?},
  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    = {2},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-54198},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-541980},
  pages     = {17},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{OzturkPittoreBehlingetal.2021,
  author    = {Ozturk, Ugur and Pittore, Massimiliano and Behling, Robert and R{\"o}ßner, Sigrid and Andreani, Louis and Korup, Oliver},
  title     = {How robust are landslide susceptibility estimates?},
  series = {Landslides},
  volume    = {18},
  journal   = {Landslides},
  number    = {2},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1612-510X},
  doi       = {10.1007/s10346-020-01485-5},
  pages     = {681 -- 695},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{GuentherSchueleZurelletal.2023,
  author    = {G{\"u}nther, Oliver and Sch{\"u}le, Manja and Zurell, Damaris and Jeltsch, Florian and Roeleke, Manuel and Kampe, Heike and Zimmermann, Matthias and Scholz, Jana and Mikulla, Stefanie and Engbert, Ralf and Elsner, Birgit and Schlangen, David and Agrofylax, Luisa and Georgi, Doreen and Weymar, Mathias and Wagener, Thorsten and Bookhagen, Bodo and Eibl, Eva P. S. and Korup, Oliver and Oswald, Sascha Eric and Thieken, Annegret and van der Beek, Peter},
  title     = {Portal Wissen = Excellence},
  series = {Portal Wissen: The research magazine of the University of Potsdam},
  journal   = {Portal Wissen: The research magazine of the University of Potsdam},
  number    = {02/2023},
  issn      = {2198-9974},
  doi       = {10.25932/publishup-61145},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-611456},
  pages     = {58},
  year      = {2023},
  abstract  = {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 G{\"u}nther, 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!},
  language  = {en}
}
@article{GuentherSchueleZurelletal.2023,
  author    = {G{\"u}nther, Oliver and Sch{\"u}le, Manja and Zurell, Damaris and Jeltsch, Florian and Roeleke, Manuel and Kampe, Heike and Zimmermann, Matthias and Scholz, Jana and Engbert, Ralf and Elsner, Birgit and Schlangen, David and Agrofylax, Luisa and Georgi, Doreen and Weymar, Mathias and Wagener, Thorsten and Bookhagen, Bodo and Eibl, Eva P. S. and Korup, Oliver and Oswald, Sascha Eric and Thieken, Annegret and van der Beek, Peter},
  title     = {Portal Wissen = Exzellenz},
  series = {Portal Wissen: Das Forschungsmagazin der Universit{\"a}t Potsdam},
  journal   = {Portal Wissen: Das Forschungsmagazin der Universit{\"a}t Potsdam},
  number    = {02/2023},
  issn      = {2194-4245},
  doi       = {10.25932/publishup-61144},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-611440},
  pages     = {98},
  year      = {2023},
  abstract  = {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" gegen{\"u}ber 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 Universit{\"a}t Potsdam schaut, findet zahlreiche ausgezeichnete Forschende, hervorragende Projekte und immer wieder auch aufsehenerregende Erkenntnisse, Ver{\"o}ffentlichungen und Ergebnisse. Aber ist die UP auch exzellent? Eine Frage, die 2023 ganz sicher andere Wellen schl{\"a}gt als vielleicht vor 20 Jahren. Denn seit dem Start der Exzellenzinitiative 2005 gelten als - w{\"o}rtlich - exzellent jene Hochschulen, denen es gelingt, in dem umfangreichsten F{\"o}rderprogramm f{\"u}r 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 Exzellenzuniversit{\"a}ten: Wer im Kreis der Forschungsuniversit{\"a}ten zu den Besten geh{\"o}ren will, braucht das Siegel der Exzellenz. In der gerade eingel{\"a}uteten neuen Wettbewerbsrunde der „Exzellenzstrategie des Bundes und der L{\"a}nder" bewirbt sich die Universit{\"a}t Potsdam mit drei Clusterskizzen um F{\"o}rderung. Ein Antrag kommt aus der {\"O}kologie- und Biodiversit{\"a}tsforschung. Ziel ist es, ein komplexes Bild {\"o}kologischer Prozesse zu zeichnen - und dabei die Rolle von einzelnen Individuen ebenso zu betrachten wie das Zusammenwirken vieler Arten in einem {\"O}kosystem, 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 verkn{\"u}pfte Lernund Bildungsprozesse stets mitzudenken. Der dritte Antrag aus den Geo- und Umweltwissenschaften nimmt extreme und besonders folgenschwere Naturgefahren und -prozesse wie {\"U}berschwemmungen und D{\"u}rren in den Blick. Die Forschenden untersuchen die Extremereignisse mit besonderem Fokus auf deren Wechselwirkung mit der Gesellschaft, um mit ihnen einhergehende Risiken und Sch{\"a}den besser einsch{\"a}tzen sowie k{\"u}nftig rechtzeitig Maßnahmen einleiten zu k{\"o}nnen. „Alle drei Antr{\"a}ge zeichnen ein hervorragendes Bild unserer Leistungsf{\"a}higkeit", betont der Pr{\"a}sident der Universit{\"a}t, Prof. Oliver G{\"u}nther, Ph.D. „Die Skizzen dokumentieren eindrucksvoll unser Engagement, vorhandene Forschungsexzellenz sowie die Potenziale der Universit{\"a}t Potsdam insgesamt. Allein die Tatsache, dass sich drei schlagkr{\"a}ftige Konsortien in ganz unterschiedlichen Themenbereichen zusammengefunden haben, zeigt, dass wir auf unserem Weg in die Spitzengruppe der deutschen Universit{\"a}ten einen guten Schritt vorangekommen sind." In diesem Heft schauen wir, was sich in und hinter diesen Antr{\"a}gen 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 Universit{\"a}t holen. Wir haben aber auch auf die Forschung geschaut, die zu den Antr{\"a}gen gef{\"u}hrt hat und die schon l{\"a}nger das Profil der Universit{\"a}t pr{\"a}gt 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 Antr{\"a}gen tats{\"a}chlich exzellente Forschung steckt! {\"U}brigens: Auch „Exzellenz" ist nicht das Ende der Fahnenstange. Immerhin l{\"a}sst sich das Adjektiv exzellent sogar steigern. In diesem Sinne w{\"u}nschen wir exzellentestes Vergn{\"u}gen beim Lesen!},
  language  = {de}
}
@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}
}
@article{SchoenfeldtPanekWinocuretal.2020,
  author    = {Sch{\"o}nfeldt, Elisabeth and P{\´a}nek, Tom{\´a}š and Winocur, Diego and Silhan, Karel and Korup, Oliver},
  title     = {Postglacial Patagonian mass movement},
  series = {Geomorphology : an international journal on pure and applied geomorphology},
  volume    = {367},
  journal   = {Geomorphology : an international journal on pure and applied geomorphology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0169-555X},
  doi       = {10.1016/j.geomorph.2020.107316},
  pages     = {16},
  year      = {2020},
  abstract  = {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. <br /> 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.},
  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{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. <br /> 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}
}