@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}, 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} } @article{HeistermannBogenaFranckeetal.2022, author = {Heistermann, Maik and Bogena, Heye and Francke, Till and G{\"u}ntner, Andreas and Jakobi, Jannis and Rasche, Daniel and Schr{\"o}n, Martin and D{\"o}pper, Veronika and Fersch, Benjamin and Groh, Jannis and Patil, Amol and P{\"u}tz, Thomas and Reich, Marvin and Zacharias, Steffen and Zengerle, Carmen and Oswald, Sascha}, title = {Soil moisture observation in a forested headwater catchment: combining a dense cosmic-ray neutron sensor network with roving and hydrogravimetry at the TERENO site W{\"u}stebach}, series = {Earth system science data : ESSD}, volume = {14}, journal = {Earth system science data : ESSD}, number = {5}, publisher = {Copernicus}, address = {Katlenburg-Lindau}, issn = {1866-3516}, doi = {10.5194/essd-14-2501-2022}, pages = {2501 -- 2519}, year = {2022}, abstract = {Cosmic-ray neutron sensing (CRNS) has become an effective method to measure soil moisture at a horizontal scale of hundreds of metres and a depth of decimetres. Recent studies proposed operating CRNS in a network with overlapping footprints in order to cover root-zone water dynamics at the small catchment scale and, at the same time, to represent spatial heterogeneity. In a joint field campaign from September to November 2020 (JFC-2020), five German research institutions deployed 15 CRNS sensors in the 0.4 km2 W{\"u}stebach catchment (Eifel mountains, Germany). The catchment is dominantly forested (but includes a substantial fraction of open vegetation) and features a topographically distinct catchment boundary. In addition to the dense CRNS coverage, the campaign featured a unique combination of additional instruments and techniques: hydro-gravimetry (to detect water storage dynamics also below the root zone); ground-based and, for the first time, airborne CRNS roving; an extensive wireless soil sensor network, supplemented by manual measurements; and six weighable lysimeters. Together with comprehensive data from the long-term local research infrastructure, the published data set (available at https://doi.org/10.23728/b2share.756ca0485800474e9dc7f5949c63b872; Heistermann et al., 2022) will be a valuable asset in various research contexts: to advance the retrieval of landscape water storage from CRNS, wireless soil sensor networks, or hydrogravimetry; to identify scale-specific combinations of sensors and methods to represent soil moisture variability; to improve the understanding and simulation of land-atmosphere exchange as well as hydrological and hydrogeological processes at the hillslope and the catchment scale; and to support the retrieval of soil water content from airborne and spaceborne remote sensing platforms.}, language = {en} } @article{SchulteMeucciStoofLeichsenringetal.2022, author = {Schulte, Luise and Meucci, Stefano and Stoof-Leichsenring, Kathleen R. and Heitkam, Tony and Schmidt, Nicola and von Hippel, Barbara and Andreev, Andrei A. and Diekmann, Bernhard and Biskaborn, Boris and Wagner, Bernd and Melles, Martin and Pestryakova, Lyudmila A. and Alsos, Inger G. and Clarke, Charlotte and Krutovsky, Konstantin and Herzschuh, Ulrike}, title = {Larix species range dynamics in Siberia since the Last Glacial captured from sedimentary ancient DNA}, series = {Communications biology}, volume = {5}, journal = {Communications biology}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2399-3642}, doi = {10.1038/s42003-022-03455-0}, pages = {11}, year = {2022}, abstract = {Climate change is expected to cause major shifts in boreal forests which are in vast areas of Siberia dominated by two species of the deciduous needle tree larch (Larix). The species differ markedly in their ecosystem functions, thus shifts in their respective ranges are of global relevance. However, drivers of species distribution are not well understood, in part because paleoecological data at species level are lacking. This study tracks Larix species distribution in time and space using target enrichment on sedimentary ancient DNA extracts from eight lakes across Siberia. We discovered that Larix sibirica, presently dominating in western Siberia, likely migrated to its northern distribution area only in the Holocene at around 10,000 years before present (ka BP), and had a much wider eastern distribution around 33 ka BP. Samples dated to the Last Glacial Maximum (around 21 ka BP), consistently show genotypes of L. gmelinii. Our results suggest climate as a strong determinant of species distribution in Larix and provide temporal and spatial data for species projection in a changing climate. Using ancient sedimentary DNA from up to 50 kya, dramatic distributional shifts are documented in two dominant boreal larch species, likely guided by environmental changes suggesting climate as a strong determinant of species distribution.}, language = {en} } @article{SeleemAyzelBronstertetal.2023, author = {Seleem, Omar and Ayzel, Georgy and Bronstert, Axel and Heistermann, Maik}, title = {Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany}, series = {Natural Hazards and Earth System Sciences}, volume = {23}, journal = {Natural Hazards and Earth System Sciences}, number = {2}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1684-9981}, doi = {10.5194/nhess-23-809-2023}, pages = {809 -- 822}, year = {2023}, abstract = {Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models.}, language = {en} } @article{BuergerHeistermann2023, author = {B{\"u}rger, Gerd and Heistermann, Maik}, title = {Shallow and deep learning of extreme rainfall events from convective atmospheres}, series = {Natural hazards and earth system sciences : NHESS}, volume = {23}, journal = {Natural hazards and earth system sciences : NHESS}, number = {9}, publisher = {European Geophysical Society}, address = {Katlenburg-Lindau}, issn = {1684-9981}, doi = {10.5194/nhess-23-3065-2023}, pages = {3065 -- 3077}, year = {2023}, abstract = {Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify daily ERA5 fields of convective indices according to CatRaRE, using an array of 13 statistical methods, consisting of 4 conventional ("shallow") and 9 more recent deep machine learning (DL) algorithms; the classifiers are then applied to corresponding fields of simulated present and future atmospheres from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The inherent uncertainty of the DL results from the stochastic nature of their optimization is addressed by employing an ensemble approach using 20 runs for each network. The shallow random forest method performs best with an equitable threat score (ETS) around 0.52, followed by the DL networks ALL-CNN and ResNet with an ETS near 0.48. Their success can be understood as a result of conceptual simplicity and parametric parsimony, which obviously best fits the relatively simple classification task. It is found that, on summer days, CatRaRE convective atmospheres over Germany occur with a probability of about 0.5. This probability is projected to increase, regardless of method, both in ERA5-reanalyzed and CORDEX-simulated atmospheres: for the historical period we find a centennial increase of about 0.2 and for the future period one of slightly below 0.1.}, language = {en} } @article{HeistermannFranckeScheiffeleetal.2023, author = {Heistermann, Maik and Francke, Till and Scheiffele, Lena and Petrova, Katya Dimitrova and Budach, Christian and Schr{\"o}n, Martin and Trost, Benjamin and Rasche, Daniel and G{\"u}ntner, Andreas and Doepper, Veronika and F{\"o}rster, Michael and K{\"o}hli, Markus and Angermann, Lisa and Antonoglou, Nikolaos and Zude, Manuela and Oswald, Sascha}, title = {Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany}, series = {Earth system science data : ESSD}, volume = {15}, journal = {Earth system science data : ESSD}, number = {7}, publisher = {Copernics Publications}, address = {Katlenburg-Lindau}, issn = {1866-3508}, doi = {10.5194/essd-15-3243-2023}, pages = {3243 -- 3262}, year = {2023}, abstract = {Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany - the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral im-agery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for vari-ous disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).}, language = {en} }