@article{WalchSinghSoreideetal.2022,
  author    = {Walch, Daniela M. R. and Singh, Rakesh K. and Soreide, Janne E. and Lantuit, Hugues and Poste, Amanda},
  title     = {Spatio-temporal variability of suspended particulate matter in a high-arctic estuary (Adventfjorden, Svalbard) using sentinel-2 time-series},
  series = {Remote sensing},
  volume    = {14},
  journal   = {Remote sensing},
  number    = {13},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs14133123},
  pages     = {22},
  year      = {2022},
  abstract  = {Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and temporal variation of the freshwater-driven darkening of fjords remain unresolved. The present study illustrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden estuary, Svalbard, using in-situ field campaigns and ocean colour remote sensing (OCRS) via high-resolution Sentinel-2 imagery. To compute SPM concentration (C-SPMsat), a semi-analytical algorithm was regionally calibrated using local in-situ data, which improved the accuracy of satellite-derived SPM concentration by similar to 20\% (MRD). Analysis of SPM concentration for two consecutive years (2019, 2020) revealed strong seasonality of SPM in Adventfjorden. Highest estimated SPM concentrations and river plume extent (\% of fjord with C-SPMsat > 30 mg L-1) occurred during June, July, and August. Concurrently, we observed a strong relationship between river plume extent and average air temperature over the 24 h prior to the observation (R-2 = 0.69). Considering predicted changes to environmental conditions in the Arctic region, this study highlights the importance of the rapidly changing environmental parameters and the significance of remote sensing in analysing fluxes in light attenuating particles, especially in the coastal Arctic Ocean.},
  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},
  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{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{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}
}