@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{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{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}
}
@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}
}
@article{BernhardtSchwanghart2021,
  author    = {Bernhardt, Anne and Schwanghart, Wolfgang},
  title     = {Where and why do submarine canyons remain connected to the shore during sea-level rise?},
  series = {Geophysical research letters : GRL / American Geophysical Union},
  volume    = {48},
  journal   = {Geophysical research letters : GRL / American Geophysical Union},
  number    = {10},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2020GL092234},
  pages     = {15},
  year      = {2021},
  abstract  = {The efficiency of sediment routing from land to the ocean depends on the position of submarine canyon heads with regard to terrestrial sediment sources. We aim to identify the main controls on whether a submarine canyon head remains connected to terrestrial sediment input during Holocene sea-level rise. Globally, we identified 798 canyon heads that are currently located at the 120m-depth contour (the Last Glacial Maximum shoreline) and 183 canyon heads that are connected to the shore (within a distance of 6 km) during the present-day highstand. Regional hotspots of shore-connected canyons are the Mediterranean active margin and the Pacific coast of Central and South America. We used 34 terrestrial and marine predictor variables to predict shore-connected canyon occurrence using Bayesian regression. Our analysis shows that steep and narrow shelves facilitate canyon-head connectivity to the shore. Moreover, shore-connected canyons occur preferentially along active margins characterized by resistant bedrock and high river-water discharge.},
  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}
}
@article{HagedoornBubeckHudsonetal.2021,
  author    = {Hagedoorn, Liselotte and Bubeck, Philip and Hudson, Paul and Brander, Luke and Pham Thi Dieu, My and Lasage, Ralph},
  title     = {Preferences of vulnerable social groups for ecosystem-based adaptation to flood risk in Central Vietnam},
  series = {World development : the multi-disciplinary international journal devoted to the study and promotion of world development},
  volume    = {148},
  journal   = {World development : the multi-disciplinary international journal devoted to the study and promotion of world development},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0305-750X},
  doi       = {10.1016/j.worlddev.2021.105650},
  pages     = {24},
  year      = {2021},
  abstract  = {Developing countries are increasingly impacted by floods, especially in Asia. Traditional flood risk man-agement, using structural measures such as levees, can have negative impacts on the livelihoods of social groups that are more vulnerable. Ecosystem-based adaptation (EbA) provides a complementary approach that is potentially more inclusive of groups that are commonly described as more vulnerable, such as the poor and women. However, there is a lack of disaggregated and quantitative information on the potential of EbA to support vulnerable groups of society. This paper provides a quantitative analysis of the differ-ences in vulnerability to flooding as well as preferences for EbA benefits across income groups and gen -der. We use data collected through a survey of households in urban and rural Central Vietnam which included a discrete choice experiment on preferences for ecosystem services. A total of 1,010 households was surveyed during 2017 through a random sampling approach. Preferences are measured in monetary and non-monetary terms to avoid issues that may arise from financial constraints faced by respondents and especially the more vulnerable groups. Our results reveal that lower income households and women are overall more vulnerable than their counterparts and have stronger preferences for the majority of the EbA benefits, including flood protection, seafood abundance, tourism, and recreation suitability. These findings strongly indicate that EbA is indeed a promising tool to support groups of society that are espe-cially vulnerable to floods. These results provide crucial insights for future implementation of EbA pro-jects and for the integration of EbA with goals targeted at complying with the Sendai Framework and Sustainable Development Goals. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).},
  language  = {en}
}
@article{MacdonaldOteroButler2021,
  author    = {Macdonald, Elena and Otero, Noelia and Butler, Tim},
  title     = {A comparison of long-term trends in observations and emission inventories of NOx},
  series = {Atmospheric chemistry and physics / European Geosciences Union},
  volume    = {21},
  journal   = {Atmospheric chemistry and physics / European Geosciences Union},
  number    = {5},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1680-7316},
  doi       = {10.5194/acp-21-4007-2021},
  pages     = {4007 -- 4023},
  year      = {2021},
  abstract  = {Air pollution is a pressing issue that is associated with adverse effects on human health, ecosystems, and climate. Despite many years of effort to improve air quality, nitrogen dioxide (NO2) limit values are still regularly exceeded in Europe, particularly in cities and along streets. This study explores how concentrations of nitrogen oxides (NOx = NO + NO2) in European urban areas have changed over the last decades and how this relates to changes in emissions. To do so, the incremental approach was used, comparing urban increments (i.e. urban background minus rural concentrations) to total emissions, and roadside increments (i.e. urban roadside concentrations minus urban background concentrations) to traffic emissions. In total, nine European cities were assessed. The study revealed that potentially confounding factors like the impact of urban pollution at rural monitoring sites through atmospheric transport are generally negligible for NOx. The approach proves therefore particularly useful for this pollutant. The estimated urban increments all showed downward trends, and for the majority of the cities the trends aligned well with the total emissions. However, it was found that factors like a very densely populated surrounding or local emission sources in the rural area such as shipping traffic on inland waterways restrict the application of the approach for some cities. The roadside increments showed an overall very diverse picture in their absolute values and trends and also in their relation to traffic emissions. This variability and the discrepancies between roadside increments and emissions could be attributed to a combination of local influencing factors at the street level and different aspects introducing inaccuracies to the trends of the emis-sion inventories used, including deficient emission factors. Applying the incremental approach was evaluated as useful for long-term pan-European studies, but at the same time it was found to be restricted to certain regions and cities due to data availability issues. The results also highlight that using emission inventories for the prediction of future health impacts and compliance with limit values needs to consider the distinct variability in the concentrations not only across but also within cities.},
  language  = {en}
}
@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{MuellerZechHesse2021,
  author    = {M{\"u}ller, Sebastian and Zech, Alraune and Hesse, Falk},
  title     = {ogs5py: APython-APIfor theOpenGeoSys5 Scientific Modeling Package},
  series = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association},
  volume    = {59},
  journal   = {Groundwater : journal of the Association of Ground-Water Scientists and Engineers, a division of the National Ground Water Association},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0017-467X},
  doi       = {10.1111/gwat.13017},
  pages     = {117 -- 122},
  year      = {2021},
  abstract  = {High-performance numerical codes are an indispensable tool for hydrogeologists when modeling subsurface flow and transport systems. But as they are written in compiled languages, like C/C++ or Fortran, established software packages are rarely user-friendly, limiting a wider adoption of such tools. OpenGeoSys (OGS), an open-source, finite-element solver for thermo-hydro-mechanical-chemical processes in porous and fractured media, is no exception. Graphical user interfaces may increase usability, but do so at a dramatic reduction of flexibility and are difficult or impossible to integrate into a larger workflow. Python offers an optimal trade-off between these goals by providing a highly flexible, yet comparatively user-friendly environment for software applications. Hence, we introduceogs5py, a Python-API for the OpenGeoSys 5 scientific modeling package. It provides a fully Python-based representation of an OGS project, a large array of convenience functions for users to interact with OGS and connects OGS to the scientific and computational environment of Python.},
  language  = {en}
}
@article{VaidyaSchmidtRakowskietal.2021,
  author    = {Vaidya, Shrijana and Schmidt, Marten and Rakowski, Peter and Bonk, Norbert and Verch, Gernot and Augustin, J{\"u}rgen and Sommer, Michael and Hoffmann, Mathias},
  title     = {A novel robotic chamber system allowing to accurately and precisely determining spatio-temporal CO2 flux dynamics of heterogeneous croplands},
  series = {Agricultural and forest meteorology},
  volume    = {296},
  journal   = {Agricultural and forest meteorology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0168-1923},
  doi       = {10.1016/j.agrformet.2020.108206},
  pages     = {9},
  year      = {2021},
  abstract  = {The precise and accurate assessment of carbon dioxide (CO2) exchange is crucial to identify terrestrial carbon (C) sources and sinks and for evaluating their role within the global C budget. The substantial uncertainty in disentangling the management and soil impact on measured CO2 fluxes are largely ignored especially in cropland. The reasons for this lies in the limitation of the widely used eddy covariance as well as manual and automatic chamber systems, which either account for short-term temporal variability or small-scale spatial heterogeneity, but barely both. To address this issue, we developed a novel robotic chamber system allowing for dozens of spatial measurement repetitions, thus enabling CO2 exchange measurements in a sufficient temporal and high small-scale spatial resolution. The system was tested from 08th July to 09th September 2019 at a heterogeneous field (100 m x 16 m), located within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The field is foreseen for a longer-term block trial manipulation experiment extending over three erosion induced soil types and was covered with spring barley. Measured fluxes of nighttime ecosystem respiration (R-eco) and daytime net ecosystem exchange (NEE) showed distinct temporal patterns influenced by crop phenology, weather conditions and management practices. Similarly, we found clear small-scale spatial differences in cumulated (gap-filled) R-eco, gross primary productivity (GPP) and NEE fluxes affected by the three distinct soil types. Additionally, spatial patterns induced by former management practices and characterized by differences in soil pH and nutrition status (P and K) were also revealed between plots within each of the three soil types, which allowed compensating for prior to the foreseen block trial manipulation experiment. The results underline the great potential of the novel robotic chamber system, which not only detects short-term temporal CO2 flux dynamics but also reflects the impact of small-scale spatial heterogeneity.},
  language  = {en}
}
@article{KamjunkeRodeBaborowskietal.2021,
  author    = {Kamjunke, Norbert and Rode, Michael and Baborowski, Martina and Kunz, Julia Vanessa and Zehner, Jakob and Borchardt, Dietrich and Weitere, Markus},
  title     = {High irradiation and low discharge promote the dominant role of phytoplankton in riverine nutrient dynamics},
  series = {Limnology and oceanography / American Society of Limnology and Oceanography},
  volume    = {66},
  journal   = {Limnology and oceanography / American Society of Limnology and Oceanography},
  number    = {7},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0024-3590},
  doi       = {10.1002/lno.11778},
  pages     = {2648 -- 2660},
  year      = {2021},
  abstract  = {Rivers play a relevant role in the nutrient turnover during the transport from land to ocean. Here, highly dynamic planktonic processes are more important compared to streams making it necessary to link the dynamics of nutrient turnover to control mechanisms of phytoplankton. We investigated the basic conditions leading to high phytoplankton biomass and corresponding nutrient dynamics in eutrophic, 8th order River Elbe (Germany). In a first step, we performed six Lagrangian sampling campaigns in the lower river section at different hydrological conditions. While nutrient concentrations remained high at low algal densities in autumn and at moderate discharge in summer, high algal concentrations occurred at low discharge in summer. Under these conditions, concentrations of silica and nitrate decreased and rates of nitrate assimilation were high. Soluble reactive phosphorus was depleted and particulate phosphorus increased inversely. Rising molar C:P ratios of seston indicated a phosphorus limitation of phytoplankton, so far rarely observed in eutrophic large rivers. Global radiation combined with mixing depth had a strong predictive power to explain maximum chlorophyll concentration. In a second step, we estimated nutrient turnover exemplarily for N during the campaign with the lowest discharge based on mass balances and metabolism-based process measurements. Mass balance calculations revealed a total nitrate uptake of 423 mg N m(-2)d(-1). Increasing phytoplankton density dominantly explained whole river gross primary production and related assimilatory nutrient uptake. In conclusion, riverine nutrient uptake strongly depends on the growth conditions for phytoplankton, which are favored at high irradiation and low discharge.},
  language  = {en}
}
@article{BlumeSchneiderGuentner2021,
  author    = {Blume, Theresa and Schneider, Lisa and G{\"u}ntner, Andreas},
  title     = {Comparative analysis of throughfall observations in six different forest stands},
  series = {Hydrological processes},
  volume    = {36},
  journal   = {Hydrological processes},
  number    = {3},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0885-6087},
  doi       = {10.1002/hyp.14461},
  pages     = {21},
  year      = {2021},
  abstract  = {Throughfall, that is, the fraction of rainfall that passes through the forest canopy, is strongly influenced by rainfall and forest stand characteristics which are in turn both subject to seasonal dynamics. Disentangling the complex interplay of these controls is challenging, and only possible with long-term monitoring and a large number of throughfall events measured in parallel at different forest stands. We therefore based our analysis on 346 rainfall events across six different forest stands at the long-term terrestrial environmental observatory TERENO Northeast Germany. These forest stands included pure stands of beech, pine and young pine, and mixed stands of oak-beech, pine-beech and pine-oak-beech. Throughfall was overall relatively low, with 54-68\% of incident rainfall in summer. Based on the large number of events it was possible to not only investigate mean or cumulative throughfall but also its statistical distribution. The distributions of throughfall fractions show distinct differences between the three types of forest stands (deciduous, mixed and pine). The distributions of the deciduous stands have a pronounced peak at low throughfall fractions and a secondary peak at high fractions in summer, as well as a pronounced peak at higher throughfall fractions in winter. Interestingly, the mixed stands behave like deciduous stands in summer and like pine stands in winter: their summer distributions are similar to the deciduous stands but the winter peak at high throughfall fractions is much less pronounced. The seasonal comparison further revealed that the wooden components and the leaves behaved differently in their throughfall response to incident rainfall, especially at higher rainfall intensities. These results are of interest for estimating forest water budgets and in the context of hydrological and land surface modelling where poor simulation of throughfall would adversely impact estimates of evaporative recycling and water availability for vegetation and runoff.},
  language  = {en}
}
@article{SteidlLischeidEngelkeetal.2021,
  author    = {Steidl, J{\"o}rg and Lischeid, Gunnar and Engelke, Clemens and Koch, Franka},
  title     = {The curse of the past},
  series = {Agriculture, Ecosystems \& Environment},
  volume    = {326},
  journal   = {Agriculture, Ecosystems \& Environment},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-8809},
  doi       = {10.1016/j.agee.2021.107787},
  pages     = {14},
  year      = {2021},
  abstract  = {One challenge for modern agricultural management schemes is the reduction of harmful effects on the envi-ronment, e.g. in terms of the emission of nutrients. Sampling the effluent of tile drains is a very efficient way to sample seepage water from larger areas directly underneath the main rooting zone. Time series of solute con-centration in tile drains can be linked to agricultural management data and thus indicate the efficacy of individual management measures. To that end, the weekly runoff and solute concentration were determined in long-term measurement campaigns at 25 outlets of artificial tile drains at 19 various arable fields in the German federal state of Mecklenburg-Vorpommern. The study sites were distributed within a 23,000 km(2) region and were deemed representative of intense arable land use. In addition, comprehensive meteorological and man-agement data were provided. To disentangle the different effects, monitoring data were subjected to a principal component analysis. Loadings on the prevailing principal components and spatial and temporal patterns of the component scores were considered indicative of different processes. Principal component scores were then related to meteorological and management data via random forest modelling. Hydrological conditions and weather were identified as primary driving forces for the nutrient discharge behaviour of the drain plots, as well as the nitrogen balance. In contrast, direct effects of recent agricultural management could hardly be identified. Instead, we found clear evidence of the long-term and indirect effects of agriculture on nearly all solutes. We conclude that tile drain effluent quality primarily reflected the soil-internal mobilisation or de-mobilisation of nutrients and related solutes rather than allowing inferences to be drawn about recent individual agricultural management measures. On the other hand, principal component analysis revealed a variety of indirect and long-term effects of fertilisation on solutes other than nitrogen or phosphorus that are still widely overlooked in nutrient turnover studies.},
  language  = {en}
}
@article{IturrizagaCharrierG2021,
  author    = {Iturrizaga, Lasafam and Charrier G., Reynaldo},
  title     = {Sudden glacier advances in the Cachapoal Valley, Southern Central Andes of Chile (34 degrees S)},
  series = {Journal of South American earth sciences},
  volume    = {105},
  journal   = {Journal of South American earth sciences},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0895-9811},
  doi       = {10.1016/j.jsames.2020.102787},
  pages     = {22},
  year      = {2021},
  abstract  = {Throughout the Andes Mountains of South America, a general trend of glacier shrinkage has taken place in modern times. However, a few glaciers have undergone considerable temporally advances or even surged during the mid-19th to 20th century CE. These valley glaciers are mainly located in the Central Andes of Chile and Argentina. The research presented here focuses on the changes of the Cachapoal Glacier in the Southern Central Andes of Chile. Spectacular glacier advances occurred at least three times in historical times, which lead to river blockages and successive lake outburst floods. The glacier advances were reconstructed with a multi-method approach including geomorphological mapping, Be-10 cosmogenic exposure dating of moraines, multi-temporal comparison of historical and recent photographs and paintings as well as the interpretation of aerial photographs and satellite images and the analysis of early travel reports. The article highlights the diversity of environmental conditions for the formation of glaciers in terms of the topographical and climatic setting and the resulting distinct glacier behavior along the Andes Mountains. It is argued for the Cachapoal Glacier that the glacier advances are intrinsic to the glacier type and may not be necessarily climate-dependent. This is characteristic for avalanche-fed glaciers of which the glacier dynamic is strongly controlled by the topographic setting and sudden inputs of ice and rock avalanches as well as by the specific debris transfer system and hydrological drainage pattern. At the regional level, the fluctuations of the Cachapoal Glacier are compared with glaciers of neighboring mountain ranges in the Southern Central Andes and at the global scale with those of the Karakoram Mountains in High Asia with a similar dynamic glacier behavior.},
  language  = {en}
}
@article{SchusterRybackiBonnelyeetal.2021,
  author    = {Schuster, Valerian and Rybacki, Erik and Bonnelye, Audrey and Herrmann, Johannes and Schleicher, Anja Maria and Dresen, Georg},
  title     = {Experimental deformation of opalinus clay at elevated temperature and pressure conditions},
  series = {Rock mechanics and rock engineering},
  volume    = {54},
  journal   = {Rock mechanics and rock engineering},
  publisher = {Springer},
  address   = {Wien},
  issn      = {0723-2632},
  doi       = {10.1007/s00603-021-02474-3},
  pages     = {4009 -- 4039},
  year      = {2021},
  abstract  = {The mechanical behavior of the sandy facies of Opalinus Clay (OPA) was investigated in 42 triaxial tests performed on dry samples at unconsolidated, undrained conditions at confining pressures (p(c)) of 50-100 MPa, temperatures (T) between 25 and 200 degrees C and strain rates (epsilon) (over dot ) of 1 x-10(-3)-5 x-10(-6) -s(-1). Using a Paterson-type deformation apparatus, samples oriented at 0 degrees, 45 degrees and 90 degrees to bedding were deformed up to about 15\% axial strain. Additionally, the influence of water content, drainage condition and pre-consolidation was investigated at fixed p(c)-T conditions, using dry and re-saturated samples. Deformed samples display brittle to semi-brittle deformation behavior, characterized by cataclastic flow in quartz-rich sandy layers and granular flow in phyllosilicate-rich layers. Samples loaded parallel to bedding are less compliant compared to the other loading directions. With the exception of samples deformed 45 degrees and 90 degrees to bedding at p(c) = 100 MPa, strain is localized in discrete shear zones. Compressive strength (sigma(max)) increases with increasing pc, resulting in an internal friction coefficient of approximate to 0.31 for samples deformed at 45 degrees and 90 degrees to bedding, and approximate to 0.44 for samples deformed parallel to bedding. In contrast, pre-consolidation, drainage condition, T and epsilon(over dot )do not significantly affect deformation behavior of dried samples. However, sigma(max) and Young's modulus (E) decrease substantially with increasing water saturation. Compared to the clay-rich shaly facies of OPA, sandy facies specimens display higher strength sigma(max) and Young's modulus E at similar deformation conditions. Strength and Young's modulus of samples deformed 90 degrees and 45 degrees to bedding are close to the iso-stress Reuss bound, suggesting a strong influence of weak clay-rich layers on the deformation behavior.},
  language  = {en}
}
@article{RainerSeppeyHammeretal.2021,
  author    = {Rainer, Edda M. and Seppey, Christophe Victor William and Hammer, Caroline and Svenning, Mette M. and Tveit, Alexander Tosdal},
  title     = {The influence of above-ground herbivory on the response of Arctic soil methanotrophs to increasing CH4 concentrations and temperatures},
  series = {Microorganisms : open access journal},
  volume    = {9},
  journal   = {Microorganisms : open access journal},
  number    = {10},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-2607},
  doi       = {10.3390/microorganisms9102080},
  pages     = {20},
  year      = {2021},
  abstract  = {Rising temperatures in the Arctic affect soil microorganisms, herbivores, and peatland vegetation, thus directly and indirectly influencing microbial CH4 production. It is not currently known how methanotrophs in Arctic peat respond to combined changes in temperature, CH4 concentration, and vegetation. We studied methanotroph responses to temperature and CH4 concentration in peat exposed to herbivory and protected by exclosures. The methanotroph activity was assessed by CH4 oxidation rate measurements using peat soil microcosms and a pure culture of Methylobacter tundripaludum SV96, qPCR, and sequencing of pmoA transcripts. Elevated CH4 concentrations led to higher CH4 oxidation rates both in grazed and exclosed peat soils, but the strongest response was observed in grazed peat soils. Furthermore, the relative transcriptional activities of different methanotroph community members were affected by the CH4 concentrations. While transcriptional responses to low CH4 concentrations were more prevalent in grazed peat soils, responses to high CH4 concentrations were more prevalent in exclosed peat soils. We observed no significant methanotroph responses to increasing temperatures. We conclude that methanotroph communities in these peat soils respond to changes in the CH4 concentration depending on their previous exposure to grazing. This "conditioning " influences which strains will thrive and, therefore, determines the function of the methanotroph community.},
  language  = {en}
}
@article{MadrugadeBritoOttoKuhlicke2021,
  author    = {Madruga de Brito, Mariana and Otto, Danny and Kuhlicke, Christian},
  title     = {Tracking topics and frames regarding sustainability transformations during the onset of the COVID-19 crisis},
  series = {Sustainability / Multidisciplinary Digital Publishing Institute (MDPI)},
  volume    = {13},
  journal   = {Sustainability / Multidisciplinary Digital Publishing Institute (MDPI)},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2071-1050},
  doi       = {10.3390/su131911095},
  pages     = {19},
  year      = {2021},
  abstract  = {Many researchers and politicians believe that the COVID-19 crisis may have opened a "window of opportunity " to spur sustainability transformations. Still, evidence for such a dynamic is currently lacking. Here, we propose the linkage of "big data " and "thick data " methods for monitoring debates on transformation processes by following the COVID-19 discourse on ecological sustainability in Germany. We analysed variations in the topics discussed by applying text mining techniques to a corpus with 84,500 newspaper articles published during the first COVID-19 wave. This allowed us to attain a unique and previously inaccessible "bird's eye view " of how these topics evolved. To deepen our understanding of prominent frames, a qualitative content analysis was undertaken. Furthermore, we investigated public awareness by analysing online search behaviour. The findings show an underrepresentation of sustainability topics in the German news during the early stages of the crisis. Similarly, public awareness regarding climate change was found to be reduced. Nevertheless, by examining the newspaper data in detail, we found that the pandemic is often seen as a chance for sustainability transformations-but not without a set of challenges. Our mixed-methods approach enabled us to bridge knowledge gaps between qualitative and quantitative research by "thickening " and providing context to data-driven analyses. By monitoring whether or not the current crisis is seen as a chance for sustainability transformations, we provide insights for environmental policy in times of crisis.},
  language  = {en}
}
@article{LangeBuerkner2021,
  author    = {Lange, Bastian and B{\"u}rkner, Hans-Joachim},
  title     = {Ambiguous avant-gardes and their geographies},
  series = {Die Erde : journal of the Geographical Society of Berlin ; Zeitschrift der Gesellschaft f{\"u}r Erdkunde zu Berlin},
  volume    = {152},
  journal   = {Die Erde : journal of the Geographical Society of Berlin ; Zeitschrift der Gesellschaft f{\"u}r Erdkunde zu Berlin},
  number    = {4},
  publisher = {Gesellschaft f{\"u}r Erdkunde},
  address   = {Berlin},
  issn      = {0013-9998},
  doi       = {10.12854/erde-2021-566},
  pages     = {273 -- 287},
  year      = {2021},
  abstract  = {In the following article, the focus is on the transformative potentials created by so-called persistence avant-gardes and prevention innovators. The text extends Bluhdorn's guiding concept of narratives of hope (Bluhdorn 2017; Bluhdorn and Butzlaff 2019) by considering those groups that are marginalized within debates on socio-ecological transformation. With a closer look at the narratives of prevention and blockade that these actors engage, the ambiguous nature of postgrowth avant-gardes is carved out. Their discursive, argumentative, and effective inhibition of transitory policies is interpreted as a pro-active potential, rather than a mere obstacle to socio-ecological transformation. Adding a geographical perspective, the paper pleads for a more precise theoretical penetration of the ambivalent figure of avantgardes when analyzing processes of local and regional postgrowth.},
  language  = {en}
}
@article{WehrhanPuppeKaczoreketal.2021,
  author    = {Wehrhan, Marc and Puppe, Daniel and Kaczorek, Danuta and Sommer, Michael},
  title     = {Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment},
  series = {Biogeosciences : BG},
  volume    = {18},
  journal   = {Biogeosciences : BG},
  number    = {18},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1726-4170},
  doi       = {10.5194/bg-18-5163-2021},
  pages     = {5163 -- 5183},
  year      = {2021},
  abstract  = {Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epige-jos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epige-jos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Sim(-2)), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.},
  language  = {en}
}
@article{MesterWillnerFrieleretal.2021,
  author    = {Mester, Benedikt and Willner, Sven N. and Frieler, Katja and Schewe, Jacob},
  title     = {Evaluation of river flood extent simulated with multiple global hydrological models and climate forcings},
  series = {Environmental research letters : ERL / Institute of Physics},
  volume    = {16},
  journal   = {Environmental research letters : ERL / Institute of Physics},
  number    = {9},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/ac188d},
  pages     = {15},
  year      = {2021},
  abstract  = {Global flood models (GFMs) are increasingly being used to estimate global-scale societal and economic risks of river flooding. Recent validation studies have highlighted substantial differences in performance between GFMs and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent to satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events spread over four continents and various climate zones. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that when flood protection standards are accounted for, many models underestimate flood extent, pointing to deficiencies in their flood frequency distribution. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains.},
  language  = {en}
}
@article{SchneiderWalzAlbertetal.2021,
  author    = {Schneider, Philipp and Walz, Ariane and Albert, Christian and Lipp, Torsten},
  title     = {Ecosystem-based adaptation in cities},
  series = {Land use policy},
  volume    = {109},
  journal   = {Land use policy},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0264-8377},
  doi       = {10.1016/j.landusepol.2021.105722},
  pages     = {11},
  year      = {2021},
  abstract  = {Extreme weather events like heavy rainfall and heat waves will likely increase in intensity and frequency due to climate change. As the impacts of these extremes are particularly prominent in urban agglomerations, cities face an urgent need to develop adaptation strategies. Ecosystem-based Adaptation (EbA) provides helpful strategies that harness ecological processes in addition to technical interventions. EbA has been addressed in informal adaptation planning. Formal municipality planning, namely landscape planning, is supposed to include traditionally some EbA measures, although adaptation has not been their explicit focus. Our research aims to investigate how landscape plans incorporate climate-related extremes and EbA as well as to discuss the potential to enhance EbA uptake in formal planning. We conducted a document analysis of informal planning documents from 85 German cities and the analysis of formal landscape plans of 61 of these cities. The results suggest that city size does affect the extent of informal planning instruments and the comprehensiveness of formal landscape plans. Climate-related extremes and EbA measures have traditionally been part of landscape planning. Almost all landscape plans address heat stress, while climate change and heavy rain have been addressed less often, though more frequently since 2008. Greening of walls and roofs, on-site infiltration and water retention reveal significant potential for better integration in landscape plans. Landscape planning offers an entry point for effective climate adaptation through EbA in cities. Informal and formal planning instruments should be closely combined for robust, spatially explicit, legally binding implementation of EbA measures in the future.},
  language  = {en}
}
@article{SoergelKrieglerBodirskyetal.2021,
  author    = {Soergel, Bjoern and Kriegler, Elmar and Bodirsky, Benjamin Leon and Bauer, Nico and Leimbach, Marian and Popp, Alexander},
  title     = {Combining ambitious climate policies with efforts to eradicate poverty},
  series = {Nature Communications},
  volume    = {12},
  journal   = {Nature Communications},
  publisher = {Nature Publishing Group},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-021-22315-9},
  pages     = {12},
  year      = {2021},
  abstract  = {Climate change threatens to undermine efforts to eradicate extreme poverty. However, climate policies could impose a financial burden on the global poor through increased energy and food prices. Here, we project poverty rates until 2050 and assess how they are influenced by mitigation policies consistent with the 1.5 degrees C target. A continuation of historical trends will leave 350 million people globally in extreme poverty by 2030. Without progressive redistribution, climate policies would push an additional 50 million people into poverty. However, redistributing the national carbon pricing revenues domestically as an equal-per-capita climate dividend compensates this policy side effect, even leading to a small net reduction of the global poverty headcount (-6 million). An additional international climate finance scheme enables a substantial poverty reduction globally and also in Sub-Saharan Africa. Combining national redistribution with international climate finance thus provides an important entry point to climate policy in developing countries. Ambitious climate policies can negatively impact the global poor by affecting income, food and energy prices. Here, the authors quantify this effect, and show that it can be compensated by national redistribution of the carbon pricing revenues in combination with international climate finance.},
  language  = {en}
}
@article{SchallerScherwietesGerberetal.2021,
  author    = {Schaller, J{\"o}rg and Scherwietes, Eric and Gerber, Lukas and Vaidya, Shrijana and Kaczorek, Danuta and Pausch, Johanna and Barkusky, Dietmar and Sommer, Michael and Hoffmann, Mathias},
  title     = {Silica fertilization improved wheat performance and increased phosphorus concentrations during drought at the field scale},
  series = {Scientific reports},
  volume    = {11},
  journal   = {Scientific reports},
  number    = {1},
  publisher = {Macmillan Publishers Limited, part of Springer Nature},
  address   = {[London]},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-021-00464-7},
  pages     = {12},
  year      = {2021},
  abstract  = {Drought and the availability of mineable phosphorus minerals used for fertilization are two of the important issues agriculture is facing in the future. High phosphorus availability in soils is necessary to maintain high agricultural yields. Drought is one of the major threats for terrestrial ecosystem performance and crop production in future. Among the measures proposed to cope with the upcoming challenges of intensifying drought stress and to decrease the need for phosphorus fertilizer application is the fertilization with silica (Si). Here we tested the importance of soil Si fertilization on wheat phosphorus concentration as well as wheat performance during drought at the field scale. Our data clearly showed a higher soil moisture for the Si fertilized plots. This higher soil moisture contributes to a better plant performance in terms of higher photosynthetic activity and later senescence as well as faster stomata responses ensuring higher productivity during drought periods. The plant phosphorus concentration was also higher in Si fertilized compared to control plots. Overall, Si fertilization or management of the soil Si pools seem to be a promising tool to maintain crop production under predicted longer and more serve droughts in the future and reduces phosphorus fertilizer requirements.},
  language  = {en}
}
@article{SamprognaMohorThiekenKorup2021,
  author    = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver},
  title     = {Residential flood loss estimated from Bayesian multilevel models},
  series = {Natural Hazards and Earth System Sciences},
  volume    = {21},
  journal   = {Natural Hazards and Earth System Sciences},
  publisher = {European Geophysical Society},
  address   = {Katlenburg-Lindau},
  issn      = {2195-9269},
  doi       = {10.5194/nhess-21-1599-2021},
  pages     = {1599 -- 1614},
  year      = {2021},
  abstract  = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.},
  language  = {en}
}
@article{OttoGoepfertThieken2021,
  author    = {Otto, Antje and G{\"o}pfert, Christian and Thieken, Annegret},
  title     = {Are cities prepared for climate change?},
  series = {Mitigation and adaptation strategies for global change : an international journal devoted to scientific, engineering, socio-economic and policy responses to environmental change},
  volume    = {26},
  journal   = {Mitigation and adaptation strategies for global change : an international journal devoted to scientific, engineering, socio-economic and policy responses to environmental change},
  number    = {8},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1381-2386},
  doi       = {10.1007/s11027-021-09971-4},
  pages     = {25},
  year      = {2021},
  abstract  = {Cities can be severely affected by climate change. Hence, many of them have started to develop climate adaptation strategies or implement measures to help prepare for the challenges it will present. This study aims to provide an overview of climate adaptation in 104 German cities. While existing studies on adaptation tracking rely heavily on self-reported data or the mere existence of adaptation plans, we applied the broader concept of adaptation readiness, considering five factors and a total of twelve different indicators, when making our assessments. We clustered the cities depending on the contribution of these factors to the overall adaptation readiness index and grouped them according to their total score and cluster affiliations. This resulted in us identifying four groups of cities. First, a pioneering group comprises twelve (mainly big) cities with more than 500,000 inhabitants, which showed high scores for all five factors of adaptation readiness. Second, a set of 36 active cities, which follow different strategies on how to deal with climate adaptation. Third, a group of 28 cities showed considerably less activity toward climate adaptation, while a fourth set of 28 mostly small cities (with between 50,000 and 99,999 inhabitants) scored the lowest. We consider this final group to be pursuing a 'wait-and-see' approach. Since the city size correlates with the adaptation readiness index, we recommend policymakers introduce funding schemes that focus on supporting small cities, to help them prepare for the impact of a changing climate.},
  language  = {en}
}
@article{KocNathoThieken2021,
  author    = {Ko{\c{c}}, Gamze and Natho, Stephanie and Thieken, Annegret},
  title     = {Estimating direct economic impacts of severe flood events in Turkey (2015-2020)},
  series = {International journal of disaster risk reduction : IJDRR},
  volume    = {58},
  journal   = {International journal of disaster risk reduction : IJDRR},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-4209},
  doi       = {10.1016/j.ijdrr.2021.102222},
  pages     = {16},
  year      = {2021},
  abstract  = {Over the past decades, floods have caused significant financial losses in Turkey, amounting to US\$ 800 million between 1960 and 2014. With the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), it is aimed to reduce the direct economic loss from disasters in relation to the global gross domestic product (GDP) by 2030. Accordingly, a methodology based on experiences from developing countries was proposed by the United Nations Office for Disaster Risk Reduction (UNDRR) to estimate direct economic losses on the macro-scale. Since Turkey also signed the SFDRR, we aimed to adapt, validate and apply the loss estimation model proposed by the UNDRR in Turkey for the first time. To do so, the well-documented flood event in Mersin of 2016 was used to calibrate the damage ratios for the agricultural, commercial and residential sectors, as well as educational facilities. Case studies between 2015 and 2020 with documented losses were further used to validate the model. Finally, model applications provided initial loss estimates for floods occurred recently in Turkey. Despite the limited event documentation for each sector, the calibrated model yielded good results when compared to documented losses. Thus, by implementing the UNDRR method, this study provides an approach to estimate the direct economic losses in Turkey on the macro-scale, which can be used to fill gaps in event databases, support the coordination of financial aid after flood events and facilitate monitoring of the progress toward and achievement of Global Target C of the Sendai Framework for Disaster Risk Reduction 2015-2030.},
  language  = {en}
}
@article{GoetzKohrsParraHormazabaletal.2021,
  author    = {Goetz, Jason and Kohrs, Robin and Parra Hormaz{\´a}bal, Eric and Bustos Morales, Manuel and Araneda Riquelme, Mar{\´i}a Bel{\´e}n and Henr{\´i}quez Ruiz, Cristian and Brenning, Alexander},
  title     = {Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling},
  series = {Natural hazards and earth system sciences : NHESS / European Geophysical Society},
  volume    = {21},
  journal   = {Natural hazards and earth system sciences : NHESS / European Geophysical Society},
  number    = {8},
  publisher = {European Geophysical Society},
  address   = {Katlenburg-Lindau},
  issn      = {1561-8633},
  doi       = {10.5194/nhess-21-2543-2021},
  pages     = {2543 -- 2562},
  year      = {2021},
  abstract  = {Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random-walk and Perla et al.'s (PCM) two-parameter friction model components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial crossvalidation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. >= 80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.},
  language  = {en}
}
@phdthesis{Koc2021,
  author    = {Ko{\c{c}}, Gamze},
  title     = {A comprehensive analysis of severe flood events in Turkey},
  doi       = {10.25932/publishup-51785},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517853},
  school      = {Universit{\"a}t Potsdam},
  pages     = {209},
  year      = {2021},
  abstract  = {Over the past decades, natural hazards, many of which are aggravated by climate change and reveal an increasing trend in frequency and intensity, have caused significant human and economic losses and pose a considerable obstacle to sustainable development. Hence, dedicated action toward disaster risk reduction is needed to understand the underlying drivers and create efficient risk mitigation plans. Such action is requested by the Sendai Framework for Disaster Risk Reduction 2015-2030 (SFDRR), a global agreement launched in 2015 that establishes stating priorities for action, e.g. an improved understanding of disaster risk. Turkey is one of the SFDRR contracting countries and has been severely affected by many natural hazards, in particular earthquakes and floods. However, disproportionately little is known about flood hazards and risks in Turkey. Therefore, this thesis aims to carry out a comprehensive analysis of flood hazards for the first time in Turkey from triggering drivers to impacts. It is intended to contribute to a better understanding of flood risks, improvements of flood risk mitigation and the facilitated monitoring of progress and achievements while implementing the SFDRR. In order to investigate the occurrence and severity of flooding in comparison to other natural hazards in Turkey and provide an overview of the temporal and spatial distribution of flood losses, the Turkey Disaster Database (TABB) was examined for the years 1960-2014. The TABB database was reviewed through comparison with the Emergency Events Database (EM-DAT), the Dartmouth Flood Observatory database, the scientific literature and news archives. In addition, data on the most severe flood events between 1960 and 2014 were retrieved. These served as a basis for analyzing triggering mechanisms (i.e. atmospheric circulation and precipitation amounts) and aggravating pathways (i.e. topographic features, catchment size, land use types and soil properties). For this, a new approach was developed and the events were classified using hierarchical cluster analyses to identify the main influencing factor per event and provide additional information about the dominant flood pathways for severe floods. The main idea of the study was to start with the event impacts based on a bottom-up approach and identify the causes that created damaging events, instead of applying a model chain with long-term series as input and searching for potentially impacting events as model outcomes. However, within the frequency analysis of the flood-triggering circulation pattern types, it was discovered that events in terms of heavy precipitation were not included in the list of most severe floods, i.e. their impacts were not recorded in national and international loss databases but were mentioned in news archives and reported by the Turkish State Meteorological Service. This finding challenges bottom-up modelling approaches and underlines the urgent need for consistent event and loss documentation. Therefore, as a next step, the aim was to enhance the flood loss documentation by calibrating, validating and applying the United Nations Office for Disaster Risk Reduction (UNDRR) loss estimation method for the recent severe flood events (2015-2020). This provided, a consistent flood loss estimation model for Turkey, allowing governments to estimate losses as quickly as possible after events, e.g. to better coordinate financial aid. This thesis reveals that, after earthquakes, floods have the second most destructive effects in Turkey in terms of human and economic impacts, with over 800 fatalities and US\$ 885.7 million in economic losses between 1960 and 2020, and that more attention should be paid on the national scale. The clustering results of the dominant flood-producing mechanisms (e.g. circulation pattern types, extreme rainfall, sudden snowmelt) present crucial information regarding the source and pathway identification, which can be used as base information for hazard identification in the preliminary risk assessment process. The implementation of the UNDRR loss estimation model shows that the model with country-specific parameters, calibrated damage ratios and sufficient event documentation (i.e. physically damaged units) can be recommended in order to provide first estimates of the magnitude of direct economic losses, even shortly after events have occurred, since it performed well when estimates were compared to documented losses. The presented results can contribute to improving the national disaster loss database in Turkey and thus enable a better monitoring of the national progress and achievements with regard to the targets stated by the SFDRR. In addition, the outcomes can be used to better characterize and classify flood events. Information on the main underlying factors and aggravating flood pathways further supports the selection of suitable risk reduction policies. All input variables used in this thesis were obtained from publicly available data. The results are openly accessible and can be used for further research. As an overall conclusion, it can be stated that consistent loss data collection and better event documentation should gain more attention for a reliable monitoring of the implementation of the SFDRR. Better event documentation should be established according to a globally accepted standard for disaster classification and loss estimation in Turkey. Ultimately, this enables stakeholders to create better risk mitigation actions based on clear hazard definitions, flood event classification and consistent loss estimations.},
  language  = {en}
}
@misc{SamprognaMohorThiekenKorup2021,
  author    = {Samprogna Mohor, Guilherme and Thieken, Annegret and Korup, Oliver},
  title     = {Residential flood loss estimated from Bayesian multilevel models},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51774},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517743},
  pages     = {1599 -- 1614},
  year      = {2021},
  abstract  = {Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.},
  language  = {en}
}
@article{HeidenreichBuchnerWalzetal.2021,
  author    = {Heidenreich, Anna and Buchner, Martin and Walz, Ariane and Thieken, Annegret},
  title     = {How to deal with heat stress at an open-air event?},
  series = {Weather, climate \& society / American Meteorological Society},
  volume    = {13},
  journal   = {Weather, climate \& society / American Meteorological Society},
  number    = {4},
  publisher = {American Meteorological Soc.},
  address   = {Boston},
  issn      = {1948-8327},
  doi       = {10.1175/WCAS-D-21-0027.1},
  pages     = {989 -- 1002},
  year      = {2021},
  abstract  = {Heat waves are increasingly common in many countries across the globe, and also in Germany, where this study is set. Heat poses severe health risks, especially for vulnerable groups such as the elderly and children. This case study explores visitors' behavior and perceptions during six weekends in the summer of 2018 at a 6-month open-air horticultural show. Data from a face-to-face survey (n = 306) and behavioral observations ( n = 2750) were examined by using correlation analyses, ANOVA, and multiple regression analyses. Differences in weather perception, risk awareness, adaptive behavior, and activity level were observed between rainy days (maximum daily temperature, 25 degrees C), warmsummer days (25 degrees-30 degrees C), and hot days (>30 degrees C). Respondents reported a high level of heat risk awareness, butmost (90\%) were unaware of actual heat warnings. During hot days, more adaptive measures were reported and observed. Older respondents reported taking the highest number of adaptive measures. We observed the highest level of adaptation in children, but they also showed the highest activity level. From our results we discuss how to facilitate individual adaptation to heat stress at open-air events by taking the heterogeneity of visitors into account. To mitigate negative health outcomes for citizens in the future, we argue for tailored risk communication aimed at vulnerable groups. <br /> SIGNIFICANCE STATEMENT: People around the world are facing higher average temperatures. While higher temperatures make open-air events a popular leisure time activity in summer, heat waves are a threat to health and life. Since there is not much research on how visitors of such events perceive different weather conditions-especially hot temperatures-we explored this in our case study in southern Germany at an open-air horticultural show in the summer of 2018. We discovered deficits both in people's awareness of current heat risk and the heat adaptation they carry out themselves. Future research should further investigate risk perception and adaptation behavior of private individuals, whereas event organizers and authorities need to continually focus on risk communication and facilitate individual adaptation of their visitors.},
  language  = {en}
}
@article{KrummenauerCostaPrahletal.2021,
  author    = {Krummenauer, Linda and Costa, Lu{\´i}s F{\´i}l{\´i}pe Carvalho da and Prahl, Boris F. and Kropp, J{\"u}rgen},
  title     = {Future heat adaptation and exposure among urban populations and why a prospering economy alone won't save us},
  series = {Scientific reports},
  volume    = {11},
  journal   = {Scientific reports},
  number    = {1},
  publisher = {Macmillan Publishers Limited, part of Springer Nature},
  address   = {Berlin},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-021-99757-0},
  pages     = {14},
  year      = {2021},
  abstract  = {When inferring on the magnitude of future heat-related mortality due to climate change, human adaptation to heat should be accounted for. We model long-term changes in minimum mortality temperatures (MMT), a well-established metric denoting the lowest risk of heat-related mortality, as a function of climate change and socio-economic progress across 3820 cities. Depending on the combination of climate trajectories and socio-economic pathways evaluated, by 2100 the risk to human health is expected to decline in 60\% to 80\% of the cities against contemporary conditions. This is caused by an average global increase in MMTs driven by long-term human acclimatisation to future climatic conditions and economic development of countries. While our adaptation model suggests that negative effects on health from global warming can broadly be kept in check, the trade-offs are highly contingent to the scenario path and location-specific. For high-forcing climate scenarios (e.g. RCP8.5) the maintenance of uninterrupted high economic growth by 2100 is a hard requirement to increase MMTs and level-off the negative health effects from additional scenario-driven heat exposure. Choosing a 2 degrees C-compatible climate trajectory alleviates the dependence on fast growth, leaving room for a sustainable economy, and leads to higher reductions of mortality risk.},
  language  = {en}
}
@article{LiRybskiKropp2021,
  author    = {Li, Yunfei and Rybski, Diego and Kropp, J{\"u}rgen},
  title     = {Singularity cities},
  series = {Environment and planning. B, Urban analytics and city science},
  volume    = {48},
  journal   = {Environment and planning. B, Urban analytics and city science},
  number    = {1},
  publisher = {Sage Publ.},
  address   = {London},
  issn      = {2399-8083},
  doi       = {10.1177/2399808319843534},
  pages     = {43 -- 59},
  year      = {2021},
  abstract  = {We propose an upgraded gravitational model which provides population counts beyond the binary (urban/non-urban) city simulations. Numerically studying the model output, we find that the radial population density gradients follow power-laws where the exponent is related to the preset gravity exponent gamma. Similarly, the urban fraction decays exponentially, again determined by gamma. The population density gradient can be related to radial fractality and it turns out that the typical exponents imply that cities are basically zero-dimensional. Increasing the gravity exponent leads to extreme compactness and the loss of radial symmetry. We study the shape of the major central cluster by means of another three fractal dimensions and find that overall its fractality is dominated by the size and the influence of gamma is minor. The fundamental allometry, between population and area of the major central cluster, is related to the gravity exponent but restricted to the case of higher densities in large cities. We argue that cities are shaped by power-law proximity. We complement the numerical analysis by economics arguments employing travel costs as well as housing rent determined by supply and demand. Our work contributes to the understanding of gravitational effects, radial gradients, and urban morphology. The model allows to generate and investigate city structures under laboratory conditions.},
  language  = {en}
}
@article{OttoKernHauptetal.2021,
  author    = {Otto, Antje and Kern, Kristine and Haupt, Wolfgang and Eckersley, Peter and Thieken, Annegret},
  title     = {Ranking local climate policy},
  series = {Climatic change : an interdisciplinary, international journal devoted to the description, causes and implications of climatic change},
  volume    = {167},
  journal   = {Climatic change : an interdisciplinary, international journal devoted to the description, causes and implications of climatic change},
  number    = {1-2},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0165-0009},
  doi       = {10.1007/s10584-021-03142-9},
  pages     = {23},
  year      = {2021},
  abstract  = {Climate mitigation and climate adaptation are crucial tasks for urban areas and can involve synergies as well as trade-offs. However, few studies have examined how mitigation and adaptation efforts relate to each other in a large number of differently sized cities, and therefore we know little about whether forerunners in mitigation are also leading in adaptation or if cities tend to focus on just one policy field. This article develops an internationally applicable approach to rank cities on climate policy that incorporates multiple indicators related to (1) local commitments on mitigation and adaptation, (2) urban mitigation and adaptation plans and (3) climate adaptation and mitigation ambitions. We apply this method to rank 104 differently sized German cities and identify six clusters: climate policy leaders, climate adaptation leaders, climate mitigation leaders, climate policy followers, climate policy latecomers and climate policy laggards. The article seeks explanations for particular cities' positions and shows that coping with climate change in a balanced way on a high level depends on structural factors, in particular city size, the pathways of local climate policies since the 1990s and funding programmes for both climate mitigation and adaptation.},
  language  = {en}
}
@article{SchneidemesserSibiyaCaseiroetal.2021,
  author    = {Schneidemesser, Erika von and Sibiya, Bheki and Caseiro, Alexandre and Butler, Tim and Lawrence, Mark and Leitao, Joana and Lupa{\c{s}}cu, Aura and Salvador, Pedro},
  title     = {Learning from the COVID-19 lockdown in Berlin},
  series = {Atmospheric environment: X},
  volume    = {12},
  journal   = {Atmospheric environment: X},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2590-1621},
  doi       = {10.1016/j.aeaoa.2021.100122},
  pages     = {13},
  year      = {2021},
  abstract  = {Urban air pollution is a substantial threat to human health. Traffic emissions remain a large contributor to air pollution in urban areas. The mobility restrictions put in place in response to the COVID-19 pandemic provided a large-scale real-world experiment that allows for the evaluation of changes in traffic emissions and the corresponding changes in air quality. Here we use observational data, as well as modelling, to analyse changes in nitrogen dioxide, ozone, and particulate matter resulting from the COVID-19 restrictions at the height of the lockdown period in Spring of 2020. Accounting for the influence of meteorology on air quality, we found that reduction of ca. 30-50 \% in traffic counts, dominated by changes in passenger cars, corresponded to reductions in median observed nitrogen dioxide concentrations of ca. 40 \% (traffic and urban background locations) and a ca. 22 \% increase in ozone (urban background locations) during weekdays. Lesser reductions in nitrogen dioxide concentrations were observed at urban background stations at weekends, and no change in ozone was observed. The modelled reductions in median nitrogen dioxide at urban background locations were smaller than the observed reductions and the change was not significant. The model results showed no significant change in ozone on weekdays or weekends. The lack of a simulated weekday/weekend effect is consistent with previous work suggesting that NOx emissions from traffic could be significantly underestimated in European cities by models. These results indicate the potential for improvements in air quality due to policies for reducing traffic, along with the scale of reductions that would be needed to result in meaningful changes in air quality if a transition to sustainable mobility is to be seriously considered. They also confirm once more the highly relevant role of traffic for air quality in urban areas.},
  language  = {en}
}
@article{SchallerPuppeKaczoreketal.2021,
  author    = {Schaller, J{\"o}rg and Puppe, Daniel and Kaczorek, Danuta and Ellerbrock, Ruth and Sommer, Michael},
  title     = {Silicon cycling in soils revisited},
  series = {Plants : open access journal},
  volume    = {10},
  journal   = {Plants : open access journal},
  number    = {2},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2223-7747},
  doi       = {10.3390/plants10020295},
  pages     = {33},
  year      = {2021},
  abstract  = {Silicon (Si) speciation and availability in soils is highly important for ecosystem functioning, because Si is a beneficial element for plant growth. Si chemistry is highly complex compared to other elements in soils, because Si reaction rates are relatively slow and dependent on Si species. Consequently, we review the occurrence of different Si species in soil solution and their changes by polymerization, depolymerization, and condensation in relation to important soil processes. We show that an argumentation based on thermodynamic endmembers of Si dependent processes, as currently done, is often difficult, because some reactions such as mineral crystallization require months to years (sometimes even centuries or millennia). Furthermore, we give an overview of Si reactions in soil solution and the predominance of certain solid compounds, which is a neglected but important parameter controlling the availability, reactivity, and function of Si in soils. We further discuss the drivers of soil Si cycling and how humans interfere with these processes. The soil Si cycle is of major importance for ecosystem functioning; therefore, a deeper understanding of drivers of Si cycling (e.g., predominant speciation), human disturbances and the implication for important soil properties (water storage, nutrient availability, and micro aggregate stability) is of fundamental relevance.},
  language  = {en}
}
@article{CanoCrespoTraxlThonicke2021,
  author    = {Cano Crespo, Ana and Traxl, Dominik and Thonicke, Kirsten},
  title     = {Spatio-temporal patterns of extreme fires in Amazonian forests},
  series = {European physical journal special topics},
  volume    = {230},
  journal   = {European physical journal special topics},
  number    = {14-15},
  publisher = {Springer},
  address   = {Berlin ; Heidelberg},
  issn      = {1951-6355},
  doi       = {10.1140/epjs/s11734-021-00164-3},
  pages     = {3033 -- 3044},
  year      = {2021},
  abstract  = {Fires are a fundamental part of the Earth System. In the last decades, they have been altering ecosystem structure, biogeochemical cycles and atmospheric composition with unprecedented rapidity. In this study, we implement a complex networks-based methodology to track individual fires over space and time. We focus on extreme fires-the 5\% most intense fires-in the tropical forests of the Brazilian Legal Amazon over the period 2002-2019. We analyse the interannual variability in the number and spatial patterns of extreme forest fires in years with diverse climatic conditions and anthropogenic pressure to examine potential synergies between climate and anthropogenic drivers. We observe that major droughts, that increase forest flammability, co-occur with high extreme fire years but also that it is fundamental to consider anthropogenic activities to understand the distribution of extreme fires. Deforestation fires, fires escaping from managed lands, and other types of forest degradation and fragmentation provide the ignition sources for fires to ignite in the forests. We find that all extreme forest fires identified are located within a 0.5-km distance from forest edges, and up to 56\% of them are within a 1-km distance from roads (which increases to 73\% within 5 km), showing a strong correlation that defines spatial patterns of extreme fires.},
  language  = {en}
}