@article{Buerger2021,
  author    = {B{\"u}rger, Gerd},
  title     = {Intraseasonal oscillation indices from complex EOFs},
  series = {Journal of climate},
  volume    = {34},
  journal   = {Journal of climate},
  number    = {1},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {0894-8755},
  doi       = {10.1175/JCLI-D-20-0427.1},
  pages     = {107 -- 122},
  year      = {2021},
  abstract  = {Indices of oscillatory behavior are conveniently obtained by projecting the fields in question into a phase space of a few (mostly just two) dimensions; empirical orthogonal functions (EOFs) or other, more dynamical, modes are typically used for the projection. If sufficiently coherent and in quadrature, the projected variables simply describe a rotating vector in the phase space, which then serves as the basis for predictions. Using the boreal summer intraseasonal oscillation (BSISO) as a test case, an alternative procedure is introduced: it augments the original fields with their Hilbert transform (HT) to form a complex series and projects it onto its (single) dominant EOF. The real and imaginary parts of the corresponding complex pattern and index are compared with those of the original (real) EOF. The new index explains slightly less variance of the physical fields than the original, but it is much more coherent, partly from its use of future information by the HT. Because the latter is in the way of real-time monitoring, the index can only be used in cases with predicted physical fields, for which it promises to be superior. By developing a causal approximation of the HT, a real-time variant of the index is obtained whose coherency is comparable to the noncausal version, but with smaller explained variance of the physical fields. In test cases the new index compares well to other indices of BSISO. The potential for using both indices as an alternative is discussed.},
  language  = {en}
}
@article{AyzelHeistermann2021,
  author    = {Ayzel, Georgy and Heistermann, Maik},
  title     = {The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU},
  series = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  volume    = {149},
  journal   = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0098-3004},
  doi       = {10.1016/j.cageo.2021.104708},
  pages     = {12},
  year      = {2021},
  abstract  = {We systematically explore the effect of calibration data length on the performance of a conceptual hydrological model, GR4H, in comparison to two Artificial Neural Network (ANN) architectures: Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU), which have just recently been introduced to the field of hydrology. We implemented a case study for six river basins across the contiguous United States, with 25 years of meteorological and discharge data. Nine years were reserved for independent validation; two years were used as a warm-up period, one year for each of the calibration and validation periods, respectively; from the remaining 14 years, we sampled increasing amounts of data for model calibration, and found pronounced differences in model performance. While GR4H required less data to converge, LSTM and GRU caught up at a remarkable rate, considering their number of parameters. Also, LSTM and GRU exhibited the higher calibration instability in comparison to GR4H. These findings confirm the potential of modern deep-learning architectures in rainfall runoff modelling, but also highlight the noticeable differences between them in regard to the effect of calibration data length.},
  language  = {en}
}
@article{LangeBuerkner2021,
  author    = {Lange, Bastian and B{\"u}rkner, Hans-Joachim},
  title     = {Geographien experimenteller Arbeitsformen},
  series = {Mitteilungen der {\"O}sterreichischen Geographischen Gesellschaft},
  volume    = {162},
  journal   = {Mitteilungen der {\"O}sterreichischen Geographischen Gesellschaft},
  publisher = {{\"O}sterreichische Geographische Gesellschaft},
  address   = {Wien},
  isbn      = {978-3-901313-34-9},
  issn      = {0029-9138},
  doi       = {10.1553/moegg162s287},
  pages     = {287 -- 312},
  year      = {2021},
  abstract  = {Die Diskussion um Postwachstumsprozesse hat die kleinen, fr{\"u}her unbeachtet gebliebenen Orte der Innovation entdeckt. Ungeplant und unkoordiniert entstandene Produktions- und Arbeitsformen wie zum Beispiel Fab Labs, Offene Werkst{\"a}tten, Reallabore, Techshops, Repair Caf{\´e}s und andere entziehen sich weitgehend den gewohnten Erkl{\"a}rungs- und Beschreibungskategorien der sozialwissenschaftlichen Forschung. Die Komplexit{\"a}t ihrer Erscheinungsformen, ihre heterogene Verursachung, ihre kontingente Weiterentwicklung und ihre hybriden Arbeitsprozesse erfordern ergebnisoffene analytische Rekonstruktionen. Das Ziel dieses Beitrags ist es, auf der Basis praxisnaher T{\"a}tigkeitsbeschreibungen jeweils Prozesse der Raumkontextualisierung und -zuschreibung zu rekonstruieren. Dies geschieht auf der Basis der leitenden Frage, inwieweit neue Arbeitsformen mit spezifischen Raumbez{\"u}gen einhergehen und eine differenzierte Sicht auf unterschiedliche Prozesse der Ortsbildung erforderlich machen. Als analytischer Referenzfall werden Offene Werkst{\"a}tten und die in ihnen vorherrschenden Arbeitsformen genauer betrachtet.},
  language  = {de}
}
@article{VogelPatonAich2021,
  author    = {Vogel, Johannes and Paton, Eva Nora and Aich, Valentin},
  title     = {Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean},
  series = {Biogeosciences},
  volume    = {18},
  journal   = {Biogeosciences},
  edition   = {22},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1726-4189},
  doi       = {10.5194/bg-18-5903-2021},
  pages     = {5903 -- 5927},
  year      = {2021},
  abstract  = {Mediterranean ecosystems are particularly vulnerable to climate change and the associated increase in climate anomalies. This study investigates extreme ecosystem responses evoked by climatic drivers in the Mediterranean Basin for the time span 1999-2019 with a specific focus on seasonal variations as the seasonal timing of climatic anomalies is considered essential for impact and vulnerability assessment. A bivariate vulnerability analysis is performed for each month of the year to quantify which combinations of the drivers temperature (obtained from ERA5-Land) and soil moisture (obtained from ESA CCI and ERA5-Land) lead to extreme reductions in ecosystem productivity using the fraction of absorbed photosynthetically active radiation (FAPAR; obtained from the Copernicus Global Land Service) as a proxy. The bivariate analysis clearly showed that, in many cases, it is not just one but a combination of both drivers that causes ecosystem vulnerability. The overall pattern shows that Mediterranean ecosystems are prone to three soil moisture regimes during the yearly cycle: they are vulnerable to hot and dry conditions from May to July, to cold and dry conditions from August to October, and to cold conditions from November to April, illustrating the shift from a soil-moisture-limited regime in summer to an energy-limited regime in winter. In late spring, a month with significant vulnerability to hot conditions only often precedes the next stage of vulnerability to both hot and dry conditions, suggesting that high temperatures lead to critically low soil moisture levels with a certain time lag. In the eastern Mediterranean, the period of vulnerability to hot and dry conditions within the year is much longer than in the western Mediterranean. Our results show that it is crucial to account for both spatial and temporal variability to adequately assess ecosystem vulnerability. The seasonal vulnerability approach presented in this study helps to provide detailed insights regarding the specific phenological stage of the year in which ecosystem vulnerability to a certain climatic condition occurs. How to cite. Vogel, J., Paton, E., and Aich, V.: Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean, Biogeosciences, 18, 5903-5927, https://doi.org/10.5194/bg-18-5903-2021, 2021.},
  language  = {en}
}
@article{VogelRivoireDeiddaetal.2021,
  author    = {Vogel, Johannes and Rivoire, Pauline and Deidda, Cristina and Rahimi, Leila and Sauter, Christoph A. and Tschumi, Elisabeth and van der Wiel, Karin and Zhang, Tianyi and Zscheischler, Jakob},
  title     = {Identifying meteorological drivers of extreme impacts},
  series = {Earth System Dynamics},
  volume    = {12},
  journal   = {Earth System Dynamics},
  issn      = {2190-4987},
  doi       = {10.5194/esd-12-151-2021},
  pages     = {151 -- 172},
  year      = {2021},
  abstract  = {Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.},
  language  = {en}
}
@article{SkalevagVormoor2021,
  author    = {Sk{\aa}lev{\aa}g, Amalie and Vormoor, Klaus Josef},
  title     = {Daily streamflow trends in Western versus Eastern Norway and their attribution to hydro-meteorological drivers},
  series = {Hydrological processes : an international journal},
  volume    = {35},
  journal   = {Hydrological processes : an international journal},
  number    = {8},
  publisher = {Wiley},
  address   = {New York},
  issn      = {0885-6087},
  doi       = {10.1002/hyp.14329},
  pages     = {17},
  year      = {2021},
  abstract  = {Regional warming and modifications in precipitation regimes has large impacts on streamflow in Norway, where both rainfall and snowmelt are important runoff generating processes. Hydrological impacts of recent changes in climate are usually investigated by trend analyses applied on annual, seasonal, or monthly time series. None of these detect sub-seasonal changes and their underlying causes. This study investigated sub-seasonal changes in streamflow, rainfall, and snowmelt in 61 and 51 catchments respectively in Western (Vestlandet) and Eastern (ostlandet) Norway by applying the Mann-Kendall test and Theil-Sen estimator on 10-day moving averaged daily time series over a 30-year period (1983-2012). The relative contribution of rainfall versus snowmelt to daily streamflow and the changes therein have also been estimated to identify the changing relevance of these driving processes over the same period. Detected changes in 10-day moving averaged daily streamflow were finally attributed to changes in the most important hydro-meteorological drivers using multiple-regression models with increasing complexity. Earlier spring flow timing in both regions occur due to earlier snowmelt. ostlandet shows increased summer streamflow in catchments up to 1100 m a.s.l. and slightly increased winter streamflow in about 50\% of the catchments. Trend patterns in Vestlandet are less coherent. The importance of rainfall has increased in both regions. Attribution of trends reveals that changes in rainfall and snowmelt can explain some streamflow changes where they are dominant processes (e.g., spring snowmelt in ostlandet and autumn rainfall in Vestlandet). Overall, the detected streamflow changes can be best explained by adding temperature trends as an additional predictor, indicating the relevance of additional driving processes such as increased glacier melt and evapotranspiration.},
  language  = {en}
}
@article{Natho2021,
  author    = {Natho, Stephanie},
  title     = {How Flood Hazard Maps Improve the Understanding of Ecologically Active Floodplains},
  series = {Water / Molecular Diversity Preservation International (MDPI)},
  volume    = {13},
  journal   = {Water / Molecular Diversity Preservation International (MDPI)},
  number    = {7},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w13070937},
  pages     = {17},
  year      = {2021},
  abstract  = {Floodplains are threatened ecosystems and are not only ecologically meaningful but also important for humans by creating multiple benefits. Many underlying functions, like nutrient retention, carbon sequestration or water regulation, strongly depend on regular inundation. So far, these are approached on the basis of what are called 'active floodplains'. Active floodplains, defined as statistically inundated once every 100 years, represent less than 10\% of a floodplain's original size. Still, should this remaining area be considered as one homogenous surface in terms of floodplain function, or are there any alternative approaches to quantify ecologically active floodplains? With the European Flood Hazard Maps, the extent of not only medium floods (T-medium) but also frequent floods (T-frequent) needs to be modelled by all member states of the European Union. For large German rivers, both scenarios were compared to quantify the extent, as well as selected indicators for naturalness derived from inundation. It is assumed that the more naturalness there is, the more inundation and the better the functioning. Real inundation was quantified using measured discharges from relevant gauges over the past 20 years. As a result, land uses indicating strong human impacts changed significantly from T-frequent to T-medium floodplains. Furthermore, the extent, water depth and water volume stored in the T-frequent and T-medium floodplains is significantly different. Even T-frequent floodplains experienced inundation for only half of the considered gauges during the past 20 years. This study gives evidence for considering regulation functions on the basis of ecologically active floodplains, meaning in floodplains with more frequent inundation that T-medium floodplains delineate.},
  language  = {en}
}
@misc{VogelRivoireDeiddaetal.2021,
  author    = {Vogel, Johannes and Rivoire, Pauline and Deidda, Cristina and Rahimi, Leila and Sauter, Christoph A. and Tschumi, Elisabeth and van der Wiel, Karin and Zhang, Tianyi and Zscheischler, Jakob},
  title     = {Identifying meteorological drivers of extreme impacts},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1126},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-49627},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-496273},
  pages     = {151 -- 172},
  year      = {2021},
  abstract  = {Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.},
  language  = {en}
}
@phdthesis{Ayzel2021,
  author    = {Ayzel, Georgy},
  title     = {Advancing radar-based precipitation nowcasting},
  doi       = {10.25932/publishup-50426},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-504267},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xx, 68},
  year      = {2021},
  abstract  = {Precipitation forecasting has an important place in everyday life - during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows. While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1-3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture. There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development. The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing. One of the promising directions for model development is to challenge the potential of deep learning - a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods. The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data - both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points. To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library. RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact - an analogue to numerical diffusion - that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance. The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction. To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation. For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25\% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales - just as a result of locational errors - can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data. Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.},
  language  = {en}
}
@article{ReichMikolajBlumeetal.2021,
  author    = {Reich, Marvin and Mikolaj, Michal and Blume, Theresa and G{\"u}ntner, Andreas},
  title     = {Field-scale subsurface flow processes inferred from continuous gravity monitoring during a sprinkling experiment},
  series = {Water resources research : WRR / American Geophysical Union},
  volume    = {57},
  journal   = {Water resources research : WRR / American Geophysical Union},
  number    = {10},
  publisher = {Wiley},
  address   = {New York},
  issn      = {0043-1397},
  doi       = {10.1029/2021WR030044},
  pages     = {18},
  year      = {2021},
  abstract  = {Field-scale subsurface flow processes are difficult to observe and monitor. We investigated the value of gravity time series to identify subsurface flow processes by carrying out a sprinkling experiment in the direct vicinity of a superconducting gravimeter. We demonstrate how different water mass distributions in the subsoil affect the gravity signal and show the benefit of using the shape of the gravity response curve to identify different subsurface flow processes. For this purpose, a simple hydro-gravimetric model was set up to test different scenarios in an optimization approach, including the processes macropore flow, preferential flow, wetting front advancement (WFA), bypass flow and perched water table rise. Besides the gravity observations, electrical resistivity and soil moisture data were used for evaluation. For the study site, the process combination of preferential flow and WFA led to the best correspondence to the observations in a multi-criteria assessment. We argue that the approach of combining field-scale sprinkling experiments in combination with gravity monitoring can be transferred to other sites for process identification, and discuss related uncertainties including limitations of the simple model used here. The study stresses the value of advancing terrestrial gravimetry as an integrative and non-invasive monitoring technique for assessing hydrological states and dynamics.},
  language  = {en}
}
@article{WehrhanSommer2021,
  author    = {Wehrhan, Marc and Sommer, Michael},
  title     = {A parsimonious approach to estimate soil organic carbon applying Unmanned Aerial System (UAS) multispectral imagery and the topographic position index in a heterogeneous soil landscape},
  series = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  volume    = {13},
  journal   = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs13183557},
  pages     = {20},
  year      = {2021},
  abstract  = {Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11\% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07\%; R-2 = 0.79, RMSE = 0.06\%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.},
  language  = {en}
}
@article{Ayzel2021,
  author    = {Ayzel, Georgy},
  title     = {Deep neural networks in hydrology},
  series = {Vestnik of Saint Petersburg University. Earth Sciences},
  volume    = {66},
  journal   = {Vestnik of Saint Petersburg University. Earth Sciences},
  number    = {1},
  publisher = {Univ. Press},
  address   = {St. Petersburg},
  issn      = {2541-9668},
  doi       = {10.21638/spbu07.2021.101},
  pages     = {5 -- 18},
  year      = {2021},
  abstract  = {For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies.},
  language  = {en}
}
@misc{VogelPatonAich2021,
  author    = {Vogel, Johannes Joscha and Paton, Eva Nora and Aich, Valentin},
  title     = {Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  volume    = {18},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  edition   = {22},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-55497},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-554974},
  pages     = {5903 -- 5927},
  year      = {2021},
  abstract  = {Mediterranean ecosystems are particularly vulnerable to climate change and the associated increase in climate anomalies. This study investigates extreme ecosystem responses evoked by climatic drivers in the Mediterranean Basin for the time span 1999-2019 with a specific focus on seasonal variations as the seasonal timing of climatic anomalies is considered essential for impact and vulnerability assessment. A bivariate vulnerability analysis is performed for each month of the year to quantify which combinations of the drivers temperature (obtained from ERA5-Land) and soil moisture (obtained from ESA CCI and ERA5-Land) lead to extreme reductions in ecosystem productivity using the fraction of absorbed photosynthetically active radiation (FAPAR; obtained from the Copernicus Global Land Service) as a proxy. The bivariate analysis clearly showed that, in many cases, it is not just one but a combination of both drivers that causes ecosystem vulnerability. The overall pattern shows that Mediterranean ecosystems are prone to three soil moisture regimes during the yearly cycle: they are vulnerable to hot and dry conditions from May to July, to cold and dry conditions from August to October, and to cold conditions from November to April, illustrating the shift from a soil-moisture-limited regime in summer to an energy-limited regime in winter. In late spring, a month with significant vulnerability to hot conditions only often precedes the next stage of vulnerability to both hot and dry conditions, suggesting that high temperatures lead to critically low soil moisture levels with a certain time lag. In the eastern Mediterranean, the period of vulnerability to hot and dry conditions within the year is much longer than in the western Mediterranean. Our results show that it is crucial to account for both spatial and temporal variability to adequately assess ecosystem vulnerability. The seasonal vulnerability approach presented in this study helps to provide detailed insights regarding the specific phenological stage of the year in which ecosystem vulnerability to a certain climatic condition occurs. How to cite. Vogel, J., Paton, E., and Aich, V.: Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean, Biogeosciences, 18, 5903-5927, https://doi.org/10.5194/bg-18-5903-2021, 2021.},
  language  = {en}
}
@phdthesis{Mahata2021,
  author    = {Mahata, Khadak Singh},
  title     = {Spatiotemporal variations of key air pollutants and greenhouse gases in the Himalayan foothills},
  doi       = {10.25932/publishup-51991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-519910},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 144},
  year      = {2021},
  abstract  = {South Asia is a rapidly developing, densely populated and highly polluted region that is facing the impacts of increasing air pollution and climate change, and yet it remains one of the least studied regions of the world scientifically. In recognition of this situation, this thesis focuses on studying (i) the spatial and temporal variation of key greenhouse gases (CO2 and CH4) and air pollutants (CO and O3) and (ii) the vertical distribution of air pollutants (PM, BC) in the foothills of the Himalaya. Five sites were selected in the Kathmandu Valley, the capital region of Nepal, along with two sites outside of the valley in the Makawanpur and Kaski districts, and conducted measurements during the period of 2013-2014 and 2016. These measurements are analyzed in this thesis. The CO measurements at multiple sites in the Kathmandu Valley showed a clear diurnal cycle: morning and evening levels were high, with an afternoon dip. There are slight differences in the diurnal cycles of CO2 and CH4, with the CO2 and CH4 mixing ratios increasing after the afternoon dip, until the morning peak the next day. The mixing layer height (MLH) of the nocturnal stable layer is relatively constant (~ 200 m) during the night, after which it transitions to a convective mixing layer during the day and the MLH increases up to 1200 m in the afternoon. Pollutants are thus largely trapped in the valley from the evening until sunrise the following day, and the concentration of pollutants increases due to emissions during the night. During afternoon, the pollutants are diluted due to the circulation by the valley winds after the break-up of the mixing layer. The major emission sources of GHGs and air pollutants in the valley are transport sector, residential cooking, brick kilns, trash burning, and agro-residue burning. Brick industries are influential in the winter and pre-monsoon season. The contribution of regional forest fires and agro-residue burning are seen during the pre-monsoon season. In addition, relatively higher CO values were also observed at the valley outskirts (Bhimdhunga and Naikhandi), which indicates the contribution of regional emission sources. This was also supported by the presence of higher concentrations of O3 during the pre-monsoon season. The mixing ratios of CO2 (419.3 ±6.0 ppm) and CH4 (2.192 ±0.066 ppm) in the valley were much higher than at background sites, including the Mauna Loa observatory (CO2: 396.8 ± 2.0 ppm, CH4:1.831 ± 0.110 ppm) and Waligaun (CO2: 397.7 ± 3.6 ppm, CH4: 1.879 ± 0.009 ppm), China, as well as at an urban site Shadnagar (CH4: 1.92 ± 0.07 ppm) in India. The daily 8 hour maximum O3 average in the Kathmandu Valley exceeds the WHO recommended value during more than 80\% of the days during the pre-monsoon period, which represents a significant risk for human health and ecosystems in the region. Moreover, in the measurements of the vertical distribution of particulate matter, which were made using an ultralight aircraft, and are the first of their kind in the region, an elevated polluted layer at around ca. 3000 m asl. was detected over the Pokhara Valley. The layer could be associated with the large-scale regional transport of pollution. These contributions towards understanding the distributions of key air pollutants and their main sources will provide helpful information for developing management plans and policies to help reduce the risks for the millions of people living in the region.},
  language  = {en}
}
@misc{Natho2021,
  author    = {Natho, Stephanie},
  title     = {How Flood Hazard Maps Improve the Understanding of Ecologically Active Floodplains},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1147},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51761},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517613},
  pages     = {19},
  year      = {2021},
  abstract  = {Floodplains are threatened ecosystems and are not only ecologically meaningful but also important for humans by creating multiple benefits. Many underlying functions, like nutrient retention, carbon sequestration or water regulation, strongly depend on regular inundation. So far, these are approached on the basis of what are called 'active floodplains'. Active floodplains, defined as statistically inundated once every 100 years, represent less than 10\% of a floodplain's original size. Still, should this remaining area be considered as one homogenous surface in terms of floodplain function, or are there any alternative approaches to quantify ecologically active floodplains? With the European Flood Hazard Maps, the extent of not only medium floods (T-medium) but also frequent floods (T-frequent) needs to be modelled by all member states of the European Union. For large German rivers, both scenarios were compared to quantify the extent, as well as selected indicators for naturalness derived from inundation. It is assumed that the more naturalness there is, the more inundation and the better the functioning. Real inundation was quantified using measured discharges from relevant gauges over the past 20 years. As a result, land uses indicating strong human impacts changed significantly from T-frequent to T-medium floodplains. Furthermore, the extent, water depth and water volume stored in the T-frequent and T-medium floodplains is significantly different. Even T-frequent floodplains experienced inundation for only half of the considered gauges during the past 20 years. This study gives evidence for considering regulation functions on the basis of ecologically active floodplains, meaning in floodplains with more frequent inundation that T-medium floodplains delineate.},
  language  = {en}
}
@phdthesis{Muench2021,
  author    = {M{\"u}nch, Steffen},
  title     = {The relevance of the aeolian transport path for the spread of antibiotic-resistant bacteria on arable fields},
  doi       = {10.25932/publishup-53608},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-536089},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XVI, 140},
  year      = {2021},
  abstract  = {The spread of antibiotic-resistant bacteria poses a globally increasing threat to public health care. The excessive use of antibiotics in animal husbandry can develop resistances in the stables. Transmission through direct contact with animals and contamination of food has already been proven. The excrements of the animals combined with a binding material enable a further potential path of spread into the environment, if they are used as organic manure in agricultural landscapes. As most of the airborne bacteria are attached to particulate matter, the focus of the work will be the atmospheric dispersal via the dust fraction. Field measurements on arable lands in Brandenburg, Germany and wind erosion studies in a wind tunnel were conducted to investigate the risk of a potential atmospheric dust-associated spread of antibiotic-resistant bacteria from poultry manure fertilized agricultural soils. The focus was to (i) characterize the conditions for aerosolization and (ii) qualify and quantify dust emissions during agricultural operations and wind erosion. PM10 (PM, particulate matter with an aerodynamic diameter smaller than 10 µm) emission factors and bacterial fluxes for poultry manure application and incorporation have not been previously reported before. The contribution to dust emissions depends on the water content of the manure, which is affected by the manure pretreatment (fresh, composted, stored, dried), as well as by the intensity of manure spreading from the manure spreader. During poultry manure application, PM10 emission ranged between 0.05 kg ha-1 and 8.37 kg ha-1. For comparison, the subsequent land preparation contributes to 0.35 - 1.15 kg ha-1 of PM10 emissions. Manure particles were still part of dust emissions but they were accounted to be less than 1\% of total PM10 emissions due to the dilution of poultry manure in the soil after manure incorporation. Bacterial emissions of fecal origin were more relevant during manure application than during the subsequent manure incorporation, although PM10 emissions of manure incorporation were larger than PM10 emissions of manure application for the non-dried manure variants. Wind erosion leads to preferred detachment of manure particles from sandy soils, when poultry manure has been recently incorporated. Sorting effects were determined between the low-density organic particles of manure origin and the soil particles of mineral origin close above the threshold of 7 m s-1. In dependence to the wind speed, potential erosion rates between 101 and 854 kg ha-1 were identified, if 6 t ha-1 of poultry manure were applied. Microbial investigation showed that manure bacteria got detached more easily from the soil surface during wind erosion, due to their attachment on manure particles. Although antibiotic-resistant bacteria (ESBL-producing E. coli) were still found in the poultry barns, no further contamination could be detected with them in the manure, fertilized soils or in the dust generated by manure application, land preparation or wind erosion. Parallel studies of this project showed that storage of poultry manure for a few days (36 - 72 h) is sufficient to inactivate ESBL-producing E. coli. Further antibiotic-resistant bacteria, i.e. MRSA and VRE, were only found sporadically in the stables and not at all in the dust. Therefore, based on the results of this work, the risk of a potential infection by dust-associated antibiotic-resistant bacteria can be considered as low.},
  language  = {en}
}
@article{PatonVogelKlugeetal.2021,
  author    = {Paton, Eva and Vogel, Johannes Joscha and Kluge, Bj{\"o}rn and Nehls, Thomas},
  title     = {Ausmaß, Trend und Extrema von D{\"u}rren im urbanen Raum},
  series = {Hydrologie und Wasserbewirtschaftung},
  volume    = {65},
  journal   = {Hydrologie und Wasserbewirtschaftung},
  number    = {1},
  publisher = {Bundesanstalt f{\"u}r Gew{\"a}sserkunde},
  address   = {Koblenz},
  issn      = {1439-1783},
  doi       = {10.5675/HyWa_2021.1_1},
  pages     = {5 -- 16},
  year      = {2021},
  abstract  = {Summers are currently perceived to be getting longer, hotter and more extreme - and this impression is reinforced in urban areas by the occurrence of heat island effects in densely built-up areas. To assess the real extent of increasing drought occurrences in German cities, a DWD data set of 31 urban climate stations for the period 1950 to 2019 was analysed using the standardised precipitation index (SPI) with regard to meteorological drought lengths, drought extrema, heat waves and compound events in the form of simultaneously occurring heat waves and drought months. The analysis shows a large degree of heterogeneity within Germany: a severe drought occurred in most cities in 2018, while the year 2018 was among the three years with the longest droughts (since 1950) for only one third of the cities. Some southern and central German cities show a statistically significant increase in drought months per decade since 1950, other cities, mostly in the north and northwest, only show an increase in the past two decades or even no trend at all. The compound analysis of simultaneously occurring heat and drought months shows a strong increase at most stations in the last two decades, whereby the two components are responsible with a very different proportion regionally for the increase in compound events.},
  language  = {de}
}
@article{SusmanGuetteWeith2021,
  author    = {Susman, Roni and G{\"u}tte, Annelie Maja and Weith, Thomas},
  title     = {Drivers of land use conflicts in infrastructural mega projects in coastal areas},
  series = {Land : open access journal},
  volume    = {10},
  journal   = {Land : open access journal},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-445X},
  doi       = {10.3390/land10060615},
  pages     = {24},
  year      = {2021},
  abstract  = {Coastal areas are particularly sensitive because they are complex, and related land use conflicts are more intense than those in noncoastal areas. In addition to representing a unique encounter of natural and socioeconomic factors, coastal areas have become paradigms of progressive urbanisation and economic development. Our study of the infrastructural mega project of Patimban Seaport in Indonesia explores the factors driving land use changes and the subsequent land use conflicts emerging from large-scale land transformation in the course of seaport development and mega project governance. We utilised interviews and questionnaires to investigate institutional aspects and conflict drivers. Specifically, we retrace and investigate the mechanisms guiding how mega project governance, land use planning, and actual land use interact. Therefore, we observe and analyse where land use conflicts emerge and the roles that a lack of stakeholder interest involvement and tenure-responsive planning take in this process. Our findings reflect how mismanagement and inadequate planning processes lead to market failure, land abandonment and dereliction and how they overburden local communities with the costs of mega projects. Enforcing a stronger coherence between land use planning, participation and land tenure within the land governance process in coastal land use development at all levels and raising the capacity of stakeholders to interfere with governance and planning processes will reduce conflicts and lead to sustainable coastal development in Indonesia.},
  language  = {en}
}
@phdthesis{Wienecke2021,
  author    = {Wienecke, Maik},
  title     = {Wohin mit der sozialistischen Pers{\"o}nlichkeit?},
  series = {Potsdamer Geographische Praxis},
  journal   = {Potsdamer Geographische Praxis},
  number    = {16},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-518-7},
  issn      = {2194-1599},
  doi       = {10.25932/publishup-51591},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-515911},
  school      = {Universit{\"a}t Potsdam},
  pages     = {254},
  year      = {2021},
  abstract  = {Das Schulfach Geographie war in der DDR eines der F{\"a}cher, das sehr stark mit politischen Themen im Sinne des Marxismus-Leninismus best{\"u}ckt war. Ein anderer Aspekt sind die sozialistischen Erziehungsziele, die in der Schulbildung der DDR hoch im Kurs standen. Im Fokus stand diesbez{\"u}glich die Erziehung der Kinder zu sozialistischen Pers{\"o}nlichkeiten. Die Arbeit versucht einen klaren Blick auf diesen Umstand zu werfen, um zu erfahren, was da von den Lehrkr{\"a}ften gefordert wurde und wie es in der Schule umzusetzen war. Durch den Fall der Mauer war nat{\"u}rlich auch eine Umstrukturierung des Bildungssystems im Osten unausweichlich. Hier will die Arbeit Einblicke geben, wie die Geographielehrkr{\"a}fte diese Transformation mitgetragen und umgesetzt haben. Welche Wesensz{\"u}ge aus der Sozialisierung in der DDR haben sich bei der Gestaltung des Unterrichtes und dessen Ausrichtung auf die neuen Erziehungsziele erhalten? Hierzu wurden Geographielehrkr{\"a}fte befragt, die sowohl in der DDR als auch im geeinten Deutschland unterrichtet haben. Die Fragen bezogen sich in erster Linie auf die Art und Weise des Unterrichtens vor, w{\"a}hrend und nach der Wende und der daraus entstandenen Systemtransformation. Die Befragungen kommen zu dem Ergebnis, dass sich der Geographieunterricht in der DDR thematisch von dem in der BRD nicht sonderlich unterschied. Von daher bedurfte es keiner umfangreichen inhaltlichen Ver{\"a}nderung des Geographieunterrichts. Schon zu DDR-Zeiten wurden durch die Lehrkr{\"a}fte offenbar eigenm{\"a}chtig ideologiefreie physisch-geographische Themen oft ausgedehnt, um die Ideologie des Faches zu reduzieren. So fiel den meisten eine Anpassung ihres Unterrichts an das westdeutsche System relativ leicht. Die humanistisch gepr{\"a}gte Werteerziehung des DDR-Bildungssystems wurde unter Ausklammerung des sozialistischen Aspektes ebenso fortgef{\"u}hrt, da es auch hier viele Parallelen zum westdeutschen System gegeben hat. Deutlich wird eine Charakterisierung des Faches als Naturwissenschaft von Seiten der ostdeutschen Lehrkr{\"a}fte, obwohl das Fach an den Schulen den Gesellschaftswissenschaften zugeordnet wird und auch in der DDR eine starke wirtschaftsgeographische Ausrichtung hatte. Von der Verantwortung sozialistische Pers{\"o}nlichkeiten zu erziehen, wurden die Lehrkr{\"a}fte mit dem Ende der DDR entbunden und die in dieser Arbeit aufgef{\"u}hrten Interviewausz{\"u}ge lassen keinen Zweifel daran, dass es dem Großteil der Befragten darum nicht leidtat, sie sich aber bis heute an der Werteorientierung aus DDR-Zeiten orientieren.},
  language  = {de}
}
@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}
}