TY - JOUR A1 - Bürger, Gerd T1 - Intraseasonal oscillation indices from complex EOFs JF - Journal of climate N2 - 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. KW - Madden-Julian oscillation KW - Oscillations KW - Empirical orthogonal functions KW - Filtering techniques KW - Statistical techniques KW - Forecasting techniques Y1 - 2021 U6 - https://doi.org/10.1175/JCLI-D-20-0427.1 SN - 0894-8755 SN - 1520-0442 VL - 34 IS - 1 SP - 107 EP - 122 PB - American Meteorological Soc. CY - Boston ER - TY - JOUR A1 - Ayzel, Georgy A1 - Heistermann, Maik T1 - The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU BT - a case study for six basins from the CAMELS dataset JF - 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 N2 - 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. KW - Artificial neural networks KW - Calibration KW - Deep learning KW - Rainfall-runoff KW - modelling Y1 - 2021 U6 - https://doi.org/10.1016/j.cageo.2021.104708 SN - 0098-3004 SN - 1873-7803 VL - 149 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Lange, Bastian A1 - Bürkner, Hans-Joachim T1 - Geographien experimenteller Arbeitsformen BT - offene Werkstätten als Auskunftgeber über Mikro-Produktionsstandorte in Postwachstumskontexten JF - Mitteilungen der Österreichischen Geographischen Gesellschaft N2 - Die Diskussion um Postwachstumsprozesse hat die kleinen, früher unbeachtet gebliebenen Orte der Innovation entdeckt. Ungeplant und unkoordiniert entstandene Produktions- und Arbeitsformen wie zum Beispiel Fab Labs, Offene Werkstätten, Reallabore, Techshops, Repair Cafés und andere entziehen sich weitgehend den gewohnten Erklärungs- und Beschreibungskategorien der sozialwissenschaftlichen Forschung. Die Komplexität ihrer Erscheinungsformen, ihre heterogene Verursachung, ihre kontingente Weiterentwicklung und ihre hybriden Arbeitsprozesse erfordern ergebnisoffene analytische Rekonstruktionen. Das Ziel dieses Beitrags ist es, auf der Basis praxisnaher Tätigkeitsbeschreibungen jeweils Prozesse der Raumkontextualisierung und -zuschreibung zu rekonstruieren. Dies geschieht auf der Basis der leitenden Frage, inwieweit neue Arbeitsformen mit spezifischen Raumbezügen einhergehen und eine differenzierte Sicht auf unterschiedliche Prozesse der Ortsbildung erforderlich machen. Als analytischer Referenzfall werden Offene Werkstätten und die in ihnen vorherrschenden Arbeitsformen genauer betrachtet. KW - experimentelle Arbeitsformen KW - Innovationsorte KW - Hybridität KW - Alltagskultur und Ökonomie KW - Heterogenität KW - Wertschöpfung Y1 - 2021 SN - 978-3-901313-34-9 SN - 978-3-7001-8885-8 U6 - https://doi.org/10.1553/moegg162s287 SN - 0029-9138 SN - 2708-0307 VL - 162 SP - 287 EP - 312 PB - Österreichische Geographische Gesellschaft CY - Wien ER - TY - JOUR A1 - Vogel, Johannes A1 - Paton, Eva Nora A1 - Aich, Valentin T1 - Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean JF - Biogeosciences N2 - 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. Y1 - 2021 U6 - https://doi.org/10.5194/bg-18-5903-2021 SN - 1726-4189 VL - 18 SP - 5903 EP - 5927 PB - Copernicus CY - Göttingen ET - 22 ER - TY - JOUR A1 - Vogel, Johannes A1 - Rivoire, Pauline A1 - Deidda, Cristina A1 - Rahimi, Leila A1 - Sauter, Christoph A. A1 - Tschumi, Elisabeth A1 - van der Wiel, Karin A1 - Zhang, Tianyi A1 - Zscheischler, Jakob T1 - Identifying meteorological drivers of extreme impacts BT - an application to simulated crop yields JF - Earth System Dynamics N2 - 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. Y1 - 2020 U6 - https://doi.org/10.5194/esd-12-151-2021 SN - 2190-4987 SN - 2190-4979 VL - 12 SP - 151 EP - 172 ER - TY - JOUR A1 - Skålevåg, Amalie A1 - Vormoor, Klaus Josef T1 - Daily streamflow trends in Western versus Eastern Norway and their attribution to hydro-meteorological drivers JF - Hydrological processes : an international journal N2 - 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. KW - attribution KW - climate change KW - hydrological change KW - hydro-meteorological KW - driver KW - streamflow trend KW - trend analysis Y1 - 2021 U6 - https://doi.org/10.1002/hyp.14329 SN - 0885-6087 SN - 1099-1085 VL - 35 IS - 8 PB - Wiley CY - New York ER - TY - JOUR A1 - Natho, Stephanie T1 - How Flood Hazard Maps Improve the Understanding of Ecologically Active Floodplains JF - Water / Molecular Diversity Preservation International (MDPI) N2 - 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. KW - active floodplain KW - frequent flood KW - flood hazard map KW - inundation KW - land use Y1 - 2021 U6 - https://doi.org/10.3390/w13070937 SN - 2073-4441 VL - 13 IS - 7 PB - MDPI CY - Basel ER - TY - GEN A1 - Vogel, Johannes A1 - Rivoire, Pauline A1 - Deidda, Cristina A1 - Rahimi, Leila A1 - Sauter, Christoph A. A1 - Tschumi, Elisabeth A1 - van der Wiel, Karin A1 - Zhang, Tianyi A1 - Zscheischler, Jakob T1 - Identifying meteorological drivers of extreme impacts BT - an application to simulated crop yields T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1126 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-496273 SN - 1866-8372 IS - 1126 SP - 151 EP - 172 ER - TY - THES A1 - Ayzel, Georgy T1 - Advancing radar-based precipitation nowcasting T1 - Fortschritte bei der radarbasierten Niederschlagsvorhersage BT - an open benchmark and the potential of deep learning BT - ein offener Benchmark und das Potenzial von Deep Learning N2 - 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. N2 - Niederschlagsvorhersagen haben einen wichtigen Platz in unserem täglichen Leben. Und die breite Abdeckung mit Niederschlagsradaren ermöglicht es uns, den Niederschlag mit einer viel höheren räumlich-zeitlichen Auflösung vorherzusagen (Minuten in der Zeit, Hunderte von Metern im Raum). Solche radargestützten Niederschlagsvorhersagen mit sehr kurzem Vorhersagehorizont (1–3 Stunden) nennt man auch "Niederschlagsnowcasting." Sie sind in verschiedenen Anwendungsbereichen (z.B. in der Frühwarnung, der Stadtentwässerung sowie in der Landwirtschaft) zu einer wichtigen Technologie geworden. Eine erhebliche Schwierigkeit in Modellentwicklung zum Niederschlagsnowcastings ist jedoch die Verfügbarkeit offener Softwarewerkzeuge und Implementierungen, die als Benchmark für den Entwicklungsfortschritt auf diesem Gebiet dienen können. Um diese Lücke zu schließen, haben wir eine Gruppe von Modellen auf der Grundlage verschiedener Tracking- und Extrapolationsverfahren entwickelt und systematisch verglichen. Es konnte gezeigt werden, dass die Vorhersagen dieser einen Skill haben, der sich mit dem Skill operationeller Vorhersagesysteme messen kann, teils sogar überlegen sind. Diese Benchmark-Modelle sind nun in Form der quelloffenen Software-Bibliothek rainymotion allgemein verfügbar (https://github.com/hydrogo/rainymotion). Eine der vielversprechenden Perspektiven für die weitere Modellentwicklung besteht in der Untersuchung des Potenzials von "Deep Learning" – einem Teilgebiet des maschinellen Lernens, das sich auf künstliche neuronale Netze mit sog. "tiefen Architekturen" bezieht, die aus einer Vielzahl von Schichten (computational layers) bestehen können. Im Rahmen dieser Arbeit wurde daher RainNet entwickelt: ein Tiefes Neuronales Netz für radargestütztes Niederschlags-Nowcasting. RainNet wurde zunächst zur Vorhersage der Niederschlagsintensität mit einem Vorhersagehorizont von fünf Minuten trainiert. Als Datengrundlage dazu dienten mehrere Jahre qualitätskontrollierter Radarkompositprodukte des Deutschen Wetterdienstes (DWD). RainNet übertraf die verfügbaren Benchmark-Modelle für Vorhersagezeiten bis zu 60 min in Bezug auf den Mittleren Absoluten Fehler (MAE) und den Critical Success Index (CSI) für Intensitätsschwellenwerte von 0.125, 1 und 5 mm/h. Allerdings erwies sich das das Benchmark-Modell aus dem Softwarepaket rainymotion bei der Vorhersage der Überschreitung höherer Intensitätsschwellen (10 und 15 mm/h) als überlegen. Die eingeschränkte Fähigkeit von RainNet zur Vorhersage hoher Niederschlagsintensitäten ist eine unerwünschte Eigenschaft, die wir auf ein hohes Maß an räumlicher Glättung durch das Modell zurückführen. Im Kontext der Frühwarnung ist die Glättung besonders ungünstig, da ausgeprägte Merkmale von Starkniederschlägen bei längeren Vorlaufzeiten tendenziell verloren gehen. In dieser Arbeit werden daher mehrere Optionen vorgeschlagen, um dieses Problem in der zukünftigen Forschung zur Modellentwicklung anzugehen. Ein weiterer Beitrag dieser Arbeit liegt in der Quantifizierung einer spezifischen Fehlerquelle von Niederschlagsnowcasts. Der Gesamtfehler eines Nowcasts besteht aus einem Fehler in der vorhergesagten Lage eines Niederschlagsfeatures (Ortsfehler) sowie einem Fehler in der Änderung der Intensität eines Features über die Vorhersagezeit (Intensitätsfehler). Herkömmliche Verifikationsmaße waren bislang nicht in der Lage, das Ausmaß des Ortsfehlers zu isolieren. Um diese Lücke zu füllen, haben wir einen Ansatz zur direkten Quantifizierung des Ortsfehlers entwickelt. Mit Hilfe dieses Ansatzes wurde wir Benchmarking-Experiment auf Grundlage eines fünfminütigen DWD Radarkompositprodukts für das komplette Jahr 2016 umgesetzt. In diesem Experiment wurden vier Nowcasting-Modelle aus der rainymotion-Softwarebibliothek verwendet im Hinblick auf den Ortsfehler der Vorhersage verglichen. Die Ergebnisse zeigen, dass für alle konkurrierenden Modelle die Ortsfehler von Bedeutung sind: die Größenordnung dieser Fehler entspricht etwa der typischen Ausdehnung einer konvektiven Zelle oder einer mittelgroßen Stadt (5–10 km). Insgesamt zeigt diese Arbeit die Vorteile eines "Open Science"-Ansatzes für die Modellentwicklung im Bereich der Niederschlagsnowcastings. Alle vorgestellten Modelle und Modellsysteme stehen als offene, gut dokumentierte Repositorien zusammen mit entsprechenden offenen Datensätzen öffentlich zu Verfügung für, was die Transparenz und Reproduzierbarkeit des methodischen Ansatzes, aber auch die Anwendbarkeit in der Praxis erhöht. KW - Weather radar KW - nowcasting KW - optical flow KW - deep learning KW - Wetterradar KW - Deep Learning KW - Nowcasting KW - Optischer Fluss Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-504267 ER - TY - JOUR A1 - Reich, Marvin A1 - Mikolaj, Michal A1 - Blume, Theresa A1 - Güntner, Andreas T1 - Field-scale subsurface flow processes inferred from continuous gravity monitoring during a sprinkling experiment JF - Water resources research : WRR / American Geophysical Union N2 - 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. KW - Hydrogravimetry Y1 - 2021 U6 - https://doi.org/10.1029/2021WR030044 SN - 0043-1397 SN - 1944-7973 VL - 57 IS - 10 PB - Wiley CY - New York ER - TY - JOUR A1 - Wehrhan, Marc A1 - Sommer, Michael T1 - 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 JF - Remote sensing / Molecular Diversity Preservation International (MDPI) N2 - 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. KW - Unmanned Aerial System (UAS) KW - multispectral KW - Topographic Position Index KW - (TPI) KW - Multiple Linear Regression (MLR) KW - soil organic carbon (SOC) KW - agriculture KW - erosion KW - soil landscape KW - hummocky ground moraine Y1 - 2021 U6 - https://doi.org/10.3390/rs13183557 SN - 2072-4292 VL - 13 IS - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ayzel, Georgy T1 - Deep neural networks in hydrology BT - the new generation of universal and efficient models BT - новое поколение универсальных и эффективных моделей JF - Vestnik of Saint Petersburg University. Earth Sciences N2 - 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. N2 - В течение последнего десятилетия глубокое обучение - область машинного обучения, относящаяся к искусственным нейронным сетям, состоящим из множества вычислительных слоев, - изменяет ландшафт развития статистических моделей во многих областях исследований, таких как классификация изображений, машинный перевод, распознавание речи. Географические науки, а также входящая в их состав область исследования гидрологии суши, не стоят в стороне от этого движения. В последнее время применение современных технологий и методов глубокого обучения активно набирает популярность для решения широкого спектра гидрологических задач: моделирования и прогнозирования речного стока, районирования модельных параметров, оценки располагаемых водных ресурсов, идентификации факторов, влияющих на современные изменения водного режима. Такой рост популярности глубоких нейронных сетей продиктован прежде всего их высокой универсальностью и эффективностью. Представленные качества в совокупности с быстрорастущим количеством накопленной информации о состоянии окружающей среды, а также ростом доступности вычислительных средств и ресурсов, позволяют говорить о глубоких нейронных сетях как о новом поколении математических моделей, призванных если не заменить существующие решения, то значительно обогатить область моделирования геофизических процессов. В данной работе представлен краткий обзор текущего состояния области разработки и применения глубоких нейронных сетей в гидрологии. Также в работе предложен качественный долгосрочный прогноз развития технологии глубокого обучения для решения задач гидрологического моделирования на основе использования «кривой ажиотажа Гартнера», в общих чертах описывающей жизненный цикл современных технологий. T2 - Глубокие нейронные сети в гидрологии KW - deep neural networks KW - deep learning KW - machine learning KW - hydrology KW - modeling KW - глубокие нейронные сети KW - глубокое обучение KW - машинное обучение KW - гидрология KW - моделирование Y1 - 2021 U6 - https://doi.org/10.21638/spbu07.2021.101 SN - 2541-9668 SN - 2587-585X VL - 66 IS - 1 SP - 5 EP - 18 PB - Univ. Press CY - St. Petersburg ER - TY - GEN A1 - Vogel, Johannes Joscha A1 - Paton, Eva Nora A1 - Aich, Valentin T1 - Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1252 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-554974 SN - 1866-8372 VL - 18 SP - 5903 EP - 5927 PB - Universitätsverlag Potsdam CY - Potsdam ET - 22 ER - TY - THES A1 - Mahata, Khadak Singh T1 - Spatiotemporal variations of key air pollutants and greenhouse gases in the Himalayan foothills T1 - Raumzeitliche Variationen der wichtigsten Luftschadstoffe und Treibhausgase in den Ausläufern des Himalaya N2 - 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. N2 - Südasien ist eine sich schnell entwickelnde, dicht besiedelte und stark umweltbelastete Region, die mit den Auswirkungen der zunehmenden Luftverschmutzung und des Klimawandels konfrontiert ist, und dennoch bleibt sie wissenschaftlich gesehen eine der am wenigsten untersuchten Regionen der Welt. In Anerkennung dieser Situation liegt der Schwerpunkt dieser Arbeit auf der Untersuchung (i) der räumlichen und zeitlichen Variation der wichtigsten Treibhausgase (CO2 und CH4) und Luftschadstoffe (CO und O3) und (ii) der vertikalen Verteilung der Luftverschmutzung (PM, BC) in den Vorgebirgen des Himalayas. Fünf Standorte wurden im Kathmandu-Tal, der Hauptstadtregion Nepals, sowie zwei Standorte außerhalb des Tals in den Distrikten Makawanpur und Kaski ausgewählt und im Zeitraum 2013-2014 und 2016 wurden Messungen durchgeführt. Diese Messungen werden in dieser Arbeit analysiert. Die CO-Messungen an mehreren Standorten im Kathmandu-Tal zeigten einen klaren Tagesablauf: Die Werte am Morgen und am Abend waren hoch, mit einem Rückgang am Nachmittag. Es gibt leichte Unterschiede in den Tageszyklen von CO2 und CH4, wobei die Mischungsverhältnisse von CO2 und CH4 nach dem Nachmittagsdip bis zu den höchsten Werten am nächsten Morgen zunehmen. Die Höhe der nächtlichen stabilen planetaren Grenzschicht ist relativ konstant (~ 200 m), danach geht sie tagsüber in eine konvektive Mischschicht über und die MLH ("Mixing layer height") steigt am Nachmittag auf bis zu 1400 m an. So werden Schadstoffe vom Abend bis zum Sonnenaufgang des folgenden Tages weitgehend im Tal gefangen, und die Schadstoffkonzentration steigt durch nächtliche Emissionen an. Während des Nachmittags werden die Schadstoffe aufgrund der Zirkulation durch die Talwinde nach dem Aufbrechen der Mischschicht verdünnt. Die Hauptemissionsquellen für GHGs und Luftschadstoffe im Tal sind der Verkehrssektor, das Kochen in privaten Haushalten, Ziegeleien, die Müllverbrennung und die Verbrennung von landwirtschaftlichen Reststoffen. Die Ziegelindustrie ist in der Winter- und Vormonsunzeit von großer Bedeutung für die Emissionen von Ruß. Der Beitrag der regionalen Waldbrände und der Verbrennung von landwirtschaftlichen Reststoffen ist besonders wichtig in der Vormonsunzeit. Darüber hinaus wurden auch am Talrand (Bhimdhunga und Naikhandi) relativ hohe CO-Werte beobachtet, was auf den Beitrag der regionalen Emissionsquellen hinweist. Dies wurde auch durch das Vorhandensein höherer Konzentrationen von O3 während der Vormonsunzeit unterstützt. Die Mischungsverhältnisse von CO2 (419,3 ±6,0 ppmv) und CH4 (2.192 ±0,066 ppmv) im Tal waren viel höher als an bekannten Hintergrundstandorten, darunter das Observatorium Mauna Loa (CO2: 396,8 ± 2,0 ppmv, CH4:1.831 ± 0,110 ppmv) und Waligaun (CO2: 397,7 ± 3,6 ppmv, CH4: 1,879 ± 0,009 ppmv), China, sowie an einem städtischen Standort Shadnagar (CH4: 1,92 ± 0,07 ppmv) in Indien. Der tägliche 8-stündige maximale O3-Durchschnitt im Kathmandu-Tal übersteigt den WHO-Empfehlungswert an mehr als 80% der Tage während der Vormonsunzeit, was ein erhebliches Risiko für die menschliche Gesundheit und die Ökosysteme in der Region darstellt. Darüber hinaus wurde bei den Messungen der vertikalen Verteilung der Feinstaubpartikel, die mit einem Ultraleichtflugzeug durchgeführt wurden und die ersten ihrer Art in der Region sind, eine höherliegende verschmutzte Schicht, ca. 3000 m über dem mittleren Meeresspiegel über dem Pokhara-Tal, festgestellt. Die Schicht könnte mit dem großräumigen regionalen Transport von Schadstoffen in Verbindung gebracht werden. Diese Beiträge zum Verständnis der Verteilung der wichtigsten Luftschadstoffe und ihrer Hauptquellen werden hilfreiche Informationen für die Entwicklung von Mitigationsplänen und -strategien liefern, die dazu beitragen, die Risiken für die Millionen von Menschen, die in der Region leben, zu verringern. KW - Air pollution KW - Greenhouse gases KW - Himalayan foothills KW - Luftverschmutzung KW - Treibhausgase KW - Ausläufer des Himalaya Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-519910 ER - TY - GEN A1 - Natho, Stephanie T1 - How Flood Hazard Maps Improve the Understanding of Ecologically Active Floodplains T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1147 KW - active floodplain KW - frequent flood KW - flood hazard map KW - inundation KW - land use Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-517613 SN - 1866-8372 IS - 1147 ER - TY - JOUR A1 - Paton, Eva A1 - Vogel, Johannes Joscha A1 - Kluge, Björn A1 - Nehls, Thomas T1 - Ausmaß, Trend und Extrema von Dürren im urbanen Raum T1 - Extent, trend and extremes of droughts in urban areas JF - Hydrologie und Wasserbewirtschaftung N2 - Summers are currently perceived to be getting longer, hotter and more extreme - and this impression is reinforced in urban areas by the occurrence of heat island effects in densely built-up areas. To assess the real extent of increasing drought occurrences in German cities, a DWD data set of 31 urban climate stations for the period 1950 to 2019 was analysed using the standardised precipitation index (SPI) with regard to meteorological drought lengths, drought extrema, heat waves and compound events in the form of simultaneously occurring heat waves and drought months. The analysis shows a large degree of heterogeneity within Germany: a severe drought occurred in most cities in 2018, while the year 2018 was among the three years with the longest droughts (since 1950) for only one third of the cities. Some southern and central German cities show a statistically significant increase in drought months per decade since 1950, other cities, mostly in the north and northwest, only show an increase in the past two decades or even no trend at all. The compound analysis of simultaneously occurring heat and drought months shows a strong increase at most stations in the last two decades, whereby the two components are responsible with a very different proportion regionally for the increase in compound events. N2 - In der derzeitigen Wahrnehmung werden die Sommer dürrer, heißer und extremer – dieser Eindruck verstärkt sich im urbanen Raum durch das Auftreten von Hitzeinseleffekten in dicht bebauten Gebieten. Um das wirkliche Ausmaß der Dürre bewerten zu können, wurden Zeitreihendaten von 31 urbanen Klimastationen (DWD) für den Zeitraum 1950 bis 2019 mittels des standardisierten Niederschlagsindex (SPI) bezüglich Dürrelängen, Dürreextrema, Hitzewellen und gleichzeitig auftretenden Hitze- und Dürremonaten ausgewertet. Die Analyse zeigt eine große Heterogenität innerhalb von Deutschland: In den meisten Städten trat 2018 eine lange Dürre von einer durchschnittlichen Dauer von 6 Monaten auf, gleichzeitig gehörte das Jahr 2018 nur bei einem Drittel der Städte zu den drei Jahren mit den längsten Dürren seit 1950. Bei den meisten betrachteten Stationen traten die längsten Dürren in den Jahren 1953, 1971 und 1976 auf. Bei einigen südlichen und mitteldeutschen Städten kann man eine statistisch signifikante Zunahme der Anzahl der Dürremonate pro Dekade seit 1950 verzeichnen. Andere Städte, eher im Norden und Nordwesten gelegen, zeigen nur in den letzten zwei Dekaden eine Zunahme oder gar keinen Trend. Die Compoundanalyse von gleichzeitig auftretenden Hitze- und Dürremonaten zeigt bei den meisten Stationen eine starke Zunahme innerhalb der letzten zwei Dekaden, wobei die beiden Komponenten regional mit einem sehr unterschiedlichen Anteil zur Zunahme der Compoundereignisse beitragen. KW - meteorological droughts KW - heat waves KW - compound events KW - standardised KW - precipitation index (SPI) KW - urban hydrology KW - meteorologische Dürren KW - Hitzewellen KW - Compoundereignisse KW - standardisierter Niederschlagsindex (SPI KW - urbane Hydrologie Y1 - 2021 U6 - https://doi.org/10.5675/HyWa_2021.1_1 SN - 1439-1783 SN - 2749-859X VL - 65 IS - 1 SP - 5 EP - 16 PB - Bundesanstalt für Gewässerkunde CY - Koblenz ER - TY - JOUR A1 - Susman, Roni A1 - Gütte, Annelie Maja A1 - Weith, Thomas T1 - Drivers of land use conflicts in infrastructural mega projects in coastal areas BT - a case study of Patimban Seaport, Indonesia JF - Land : open access journal N2 - 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. KW - infrastructural mega projects KW - land use conflicts KW - land tenure KW - land use KW - planning KW - Patimban Seaport Y1 - 2021 U6 - https://doi.org/10.3390/land10060615 SN - 2073-445X VL - 10 IS - 6 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bernhardt, Anne A1 - Schwanghart, Wolfgang T1 - Where and why do submarine canyons remain connected to the shore during sea-level rise? BT - Insights from global topographic analysis and Bayesian regression JF - Geophysical research letters : GRL / American Geophysical Union N2 - 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. KW - Bayesian statistics KW - headward erosion KW - seascape KW - shoreline KW - submarine KW - canyon KW - turbidity current Y1 - 2021 U6 - https://doi.org/10.1029/2020GL092234 SN - 0094-8276 SN - 1944-8007 VL - 48 IS - 10 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Hagedoorn, Liselotte A1 - Bubeck, Philip A1 - Hudson, Paul A1 - Brander, Luke A1 - Pham Thi Dieu, My A1 - Lasage, Ralph T1 - Preferences of vulnerable social groups for ecosystem-based adaptation to flood risk in Central Vietnam JF - World development : the multi-disciplinary international journal devoted to the study and promotion of world development N2 - 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/). KW - Ecosystem-based Adaptation (EbA) KW - Vulnerability KW - Gender equality KW - Poverty alleviation KW - Discrete choice experiment KW - Payment vehicle Y1 - 2021 U6 - https://doi.org/10.1016/j.worlddev.2021.105650 SN - 0305-750X VL - 148 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Macdonald, Elena A1 - Otero, Noelia A1 - Butler, Tim T1 - A comparison of long-term trends in observations and emission inventories of NOx JF - Atmospheric chemistry and physics / European Geosciences Union N2 - 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. Y1 - 2021 U6 - https://doi.org/10.5194/acp-21-4007-2021 SN - 1680-7316 SN - 1680-7324 VL - 21 IS - 5 SP - 4007 EP - 4023 PB - Copernicus CY - Göttingen ER -