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- La rete di aree protette alpine (ALPARC) (1)
- Lehrstuhl für Fernerkundung der Uni Würzburg, in Kooperation mit dem Deutschen Fernerkundungsdatenzentrum (DFD) des Deutschen Zentrums für Luft- und Raumfahrt (DLR) (1)
- Leuphana Universität Lüneburg (1)
- Salzburger Institut für Raumordnung und Wohnen (1)
- Sonderforschungsbereich Re-Figuration von Räumen (Technische Universität Berlin) (1)
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- 227159 (1)
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- LIFE20 NAT/AT/000049 (1)
Das Ziel dieser Arbeit war neue Eingangsdaten für die Landoberflächenbeschreibung des regionalen Klimamodells REMO zu finden und ins Modell zu integrieren, um die Vorhersagequalität des Modells zu verbessern. Die neuen Daten wurden so in das Modell eingebaut, dass die bisherigen Daten weiterhin als Option verfügbar sind. Dadurch kann überprüft werden, ob und in welchem Umfang sich die von jedem Klimamodell benötigten Rahmendaten auf Modellergebnisse auswirken. Im Zuge der Arbeit wurden viele unterschiedliche Daten und Methoden zur Generierung neuer Parameter miteinander verglichen, denn neben dem Ersetzen der konstanten Eingangswerte für verschiedene Oberflächenparameter und den damit verbundenen Änderungen wurden als zusätzliche Verbesserung auch Veränderungen an der Parametrisierung des Bodens speziell in Hinblick auf die Bodentemperaturen in REMO vorgenommen. Im Rahmen dieser Arbeit wurden die durch die verschiedenen Änderungen ausgelösten Auswirkungen für das CORDEX-Gebiet EUR-44 mit einer Auflösung von ca. 50km und für das in dem darin eingebetteten neu definierten Deutschlandgebiet GER-11 mit einer Auflösung von ca. 12km getestet sowie alle Änderungen anhand von verschiedenen Beobachtungsdatensätzen validiert.
Die vorgenommenen Arbeiten gliederten sich in drei Hauptteile. Der erste Teil bestand in dem vom eigentlichen Klimamodell unabhängigen Vergleich der verschiedenen Eingangsdaten auf unterschiedlichen Auflösungen und deren Performanz in allen Teilen der Erde, wobei ein besonderer Fokus auf der Qualität in den späteren Modellgebieten lag. Unter Berücksichtigung der Faktoren, wie einer globalen Verfügbarkeit der Daten, einer verbesserten räumlichen Auflösung und einer kostenlosen Nutzung der Daten sowie verschiedener Validationsergebnissen von anderen Studien, wurden in dieser Arbeit vier neue Topographiedatensätze (SRTM, ALOS, TANDEM und ASTER) und drei neue Bodendatensätze (FAOn, Soilgrid und HWSD) für die Verwendung im Präprozess von REMO aufbereitet und miteinander sowie mit den bisher in REMO verwendeten Daten verglichen. Auf Grundlage dieser Vergleichsstudien schieden bei den Topographiedaten die verwendeten Datensatz-Versionen von SRTM, ALOS und TANDEM für die in dieser Arbeit durchgeführten REMO-Läufe aus. Bei den neuen Bodendatensätzen wurde ausgenutzt, dass diese verschiedenen Bodeneigenschaften für unterschiedliche Tiefen als Karten zur Verfügung stellen. In REMO wurden bisher alle benötigten Bodenparameter abhängig von fünf verschiedenen Bodentexturklassen und einer zusätzlichen Torfklasse ausgewiesen und als konstant über die gesamte Modellbodensäule (bis ca. 10m) angenommen. Im zweiten Teil wurden auf Basis der im ersten Teil ausgewählten neuen Datensätze und den neu verfügbaren Bodenvariablen verschiedene Sensitivitätsstudien über das Beispieljahr 2000 durchgeführt. Dabei wurden verschiedene neue Parametrisierungen für die bisher aus der Textur abgeleiteten Bodenvariablen und die Parametrisierung von weiteren hydrologischen und thermalen Bodeneigenschaften verglichen. Ferner wurde aufgrund der neuen nicht über die Tiefe konstanten Bodeneigenschaften eine neue numerische Methode zur Berechnung der Bodentemperaturen der fünf Schichten in REMO getestet, welche wiederum andere Anpassungen erforderte. Der Test und die Auswahl der verschiedenen Datensatz- und Parametrisierungsversionen auf die Modellperformanz wurde in drei Experimentpläne unterteilt. Im ersten Plan wurden die Auswirkungen der ausgewählten Topographie- und Bodendatensätze überprüft. Der zweite Plan behandelte die Unterschiede der verschiedenen Parametrisierungsarten der Bodenvariablen hinsichtlich der verwendeten Variablen zur Berechnung der Bodeneigenschaften, der über die Tiefe variablen oder konstanten Eigenschaften und der verwendeten Berechnungsmethode der Bodentemperaturänderungen. Durch die Erkenntnisse aus diesen beiden Experimentplänen, die für beide Untersuchungsgebiete durchgeführt wurden, ergaben sich im dritten Plan weitere Parametrisierungsänderungen. Alle Änderungen dieses dritten Experimentplans wurden sukzessiv getestet, sodass der paarweise Vergleich von zwei aufeinanderfolgenden Modellläufen die Auswirkungen der Neuerung im jeweils zweiten Lauf widerspiegelt. Der letzte Teil der Arbeit bestand aus der Analyse von fünf längeren Modellläufen (2000-2018), die zur Überprüfung der Ergebnisse aus den Sensitivitätsstudien sowie zur Einschätzung der Performanz in weiteren teilweise extremen atmosphärischen Bedingungen durchgeführt wurden. Hierfür wurden die bisherige Modellversion von REMO (id01) für die beiden Untersuchungsgebiete EUR-44 und GER-11 als Referenzläufe, zwei aufgrund der Vergleichsergebnisse von Experimentplan 3 selektierte Modellversionen (id06 und id15a für GER-11) sowie die finale Version (id18a für GER-11), die alle vorgenommenen Änderungen dieser Arbeit enthält, ausgewählt.
Es stellte sich heraus, dass sowohl die neuen Topographiedaten als auch die neuen Bodendaten große Differenzen zu den bisherigen Daten in REMO haben. Zudem änderten sich die von diesen konstanten Eingangsdaten abgeleiteten Hilfsvariablen je nach verwendeter Parametrisierung sehr deutlich. Dies war besonders gut anhand der Bodenparameter zu erkennen. Sowohl die räumliche Verteilung als auch der Wertebereich der verschiedenen Modellversionen unterschieden sich stark. Eine Einschätzung der Qualität der resultierenden Parameter wurde jedoch dadurch erschwert, dass auch die verschiedenen zur Validierung herangezogenen Bodendatensätze für diese Parameter deutlich voneinander abweichen. Die finale Modellversion id18a ähnelte trotz der umfassenden Änderungen in den meisten Variablen den Ergebnissen der bisherigen REMO-Version. Je nach zeitlicher und räumlicher Aggregation sowie unterschiedlichen Regionen und Jahreszeiten wurden leichte Verbesserungen, aber auch leichte Verschlechterungen im Vergleich zu den klimatologischen Validationsdaten festgestellt. Größere Veränderungen im Vergleich zur bisherigen Modellversion konnten in den tieferen Bodenschichten aufgezeigt werden, welche allerdings aufgrund von fehlenden Validationsdaten nicht beurteilt werden konnten. Für alle 2m-Temperaturen konnte eine tendenzielle leichte Erwärmung im Vergleich zum bisherigen Modelllauf beobachtet werden, was sich einerseits negativ auf die ohnehin durchschnittlich zu hohe Minimumtemperatur, aber andererseits positiv auf die bisher zu niedrige Maximumtemperatur des Modells in den betrachteten Gebieten auswirkte. Im Niederschlagssignal und in den 10m-Windvariablen konnten keine signifikanten Änderungen nachgewiesen werden, obwohl die neue Topographie an manchen Stellen im Modellgebiet deutlich von der bisherigen abweicht. Des Weiteren variierte das Ranking der verschiedenen Modellversionen jeweils nach dem angewendeten Qualitätsindex.
Um diese Ergebnisse besser einordnen zu können, muss berücksichtigt werden, dass die neuen Daten für Modellgebiete mit 50 bzw. 12km räumlicher Auflösung und der damit verbundenen hydrostatischen Modellversion getestet wurden. Zudem sind vor allem in Fall der Topographie die bisher enthaltenen GTOPO-Daten (1km Auflösung) für die Aggregation auf diese gröbere Modellauflösung geeignet. Die bisherigen Bodendaten stoßen jedoch mit 50km Auflösung bereits an ihre Grenzen. Zusätzlich ist zu beachten, dass nicht nur die Mittelwerte dieser Daten, sondern auch deren Subgrid-Variabilität als Variablen im Modell für verschiedene Parametrisierungen verwendet werden. Daher ist es essentiell, dass die Eingangsdaten eine deutlich höhere Auflösung bereitstellen als die zur Modellierung definierte Auflösung. Für lokale Klimasimulationen mit Auflösungen im niedrigen Kilometerbereich spielen auch die Vertikalbewegungen (nicht-hydrostatische Modellversion) eine wichtige Rolle, die stark von der Topographie sowie deren horizontaler und vertikaler Änderungsrate beeinflusst werden, was die in dieser Arbeit eingebauten wesentlich höher aufgelösten Daten für die zukünftige Weiterentwicklung von REMO wertvoll machen kann.
Open Spaces in Alpine Countries: Analytical Concepts and Preservation Strategies in Spatial Planning
(2020)
Open spaces in the Alps are becoming noticeably scarcer, and the long-term consequences for humans and the environment are often overlooked. Open spaces preserve ecosystem services but are under pressure in many Alpine valleys due to demographic and economic development as well as corresponding technical and tourism infrastructure. This article conceptualizes and measures open spaces in Alpine environments. In addition to analyzing existing spatial planning instruments and the open spaces resulting from 2 of them-the Bavarian Alpenplan in Germany and the Tyrolean Ruhegebiete in Austria-we identify open spaces in Switzerland using a geographic information system. More generally, we discuss how spatial planning deals with open spaces. Results show that both the Alpenplan and the Ruhegebiete have contributed significantly to the protection of open spaces in the Bavarian and Tyrolean Alps since the 1970s. Indeed, both approaches prevented several development projects. In the Swiss Alps, open spaces cover 41.9% of the Alpine Convention area. A share of 40.3% vegetation-free open spaces shows that they are concentrated in high alpine areas. Of the open spaces identified, 64.6% are covered by protected areas. Hence, about one third of the open spaces still existing in the Swiss Alps need preservation, not only for ecological connectivity reasons but also to preserve them for generations to come. We conclude that different sectoral approaches for the conservation of open spaces for people and natural heritage in the Alps and other high mountain ranges should be better coordinated. In addition, much more intensive crossborder cooperation in spatial development and planning is needed to preserve open spaces throughout the Alpine arc.
Episodic low oxygenated conditions on the sea-floor are likely responsible for exceptional preservation of animal remains in the upper Amouslek Formation (lower Cambrian, Stage 3) on the northern slope of the western Anti-Atlas, Morocco. This stratigraphic interval has yielded trilobite, brachiopod, and hyolith fossils with preserved soft parts, including some of the oldest known trilobite guts. The "Souss fossil lagerstatte" (newly proposed designation) represents the first Cambrian fossil lagerstatte in Cambrian strata known from Africa and is one of the oldest trilobite-bearing fossil lagerstatten on Earth. Inter-regional correlation of the Souss fossil lagerstatte in West Gondwana suggests its development during an interval of high eustatic levels recorded by dark shales that occur in informal upper Cambrian Series 2 in Siberia, South China, and East Gondwana.
Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.
Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients.
Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales.
Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements.
The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs.
Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa
(2022)
Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent’s high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 % of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 % in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 %). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO – which is highly correlated with the most important predictor (PC2) – seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa.
Die imperiale Lebensweise westlicher Industrienationen, die sich durch ein permanentes Streben nach Wirtschaftswachstum ausdrückt, bringt den Planeten an die Grenzen seiner Tragfähigkeit. In den letzten Jahren wurden jedoch – bestärkt durch die Weltwirtschaftskrise 2007/08 – Alternativen zum Modell des permanenten Wachstums immer populärer, die sich anstatt auf ökonomischen Wohlstand vermehrt auf soziale und ökologische Belange des gesellschaftlichen Zusammenlebens fokussierten. Unter dem Begriff der Postwachstumsbewegung sammelten sich Ansätze, Ideen und Akteure, die gemeinsam für eine Zukunft fernab jeglicher Wachstumszwänge und innerhalb der planetaren Grenzen kämpfen.
Vor dem Hintergrund der zunehmenden sozialen und ökologischen Herausforderungen wurden nun erstmals sozial-ökologische Nischenakteure aus drei unterschiedlichen Bereichen der Postwachstumsbewegung gemeinsam in einem Forschungsvorhaben – unter besonderer Berücksichtigung gesellschaftlicher, organisatorischer und territorialer Einbettungsprozesse – untersucht. Eingebettet ist diese Untersuchung in den theoretisch-konzeptionellen Ansatz der sozial-ökologischen Transformation, deren inkrementeller Wandel mithilfe der Multi-Level-Perspektive beschrieben werden kann. Die Kombination dieses spezifischen theoretisch-konzeptionellen Ansatzes und der empirischen Erhebung ist das Alleinstellungsmerkmal der vorliegenden Untersuchung.
Es zeigte sich, dass alle untersuchten Nischenakteure eine deutlich progressive Unternehmungsphilosophie vertreten, die häufig in einer Unternehmungsorganisation mit flachen Hierarchien und konsensbasierten Entscheidungsfindungen mündet. Besonders gesellschaftliche Einbettungsprozesse bedingen den Erfolg oder Misserfolg der Nischenentwicklung. Organisatorische Einbettung kommt derweil vor allem im Aufbau weitreichender Netzwerkstrukturen zum Tragen, die die Innovationsfähigkeit und Stabilität der Nische unterstützen. Eine starke territoriale Einbettung steigert den lokal-regionalen Einfluss der Nischeninnovationen und generiert Rückhalt in der Bevölkerung.
Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.
"Die Innenstadt braucht den Handel, der Handel aber nicht die Innenstadt", lautet eine oft formulierte These bezüglich des Verhältnisses von Handel und Innenstadt – nicht erst seit der Covid-19-Pandemie. Die Krise hat die Herausforderungen des Strukturwandels im Einzelhandel erneut offengelegt und teils Entwicklungen beschleunigt. Besonders hervorzuheben sind zum einen Handlungsbedarfe im Bereich der Digitalisierung sowie die dringende Notwendigkeit einer überdachten Auseinandersetzung über das Verhältnis von Innenstadt und Einzelhandel. Neben Fragen zur zukünftigen Gestaltung des Einzelhandels und seiner Bedeutung für Innenstädte, sind auch Fragen zur Bedeutung anderer Branchen/Einrichtungen/Angebote (z.B. Gastronomie, Handwerk, Kultureinrichtungen, Kitas, Sport- und Bildungseinrichtungen, aber auch Freiräume, Grünflächen, verkehrsberuhigte Bereiche oder lokale Kurierdienste) für den Einzelhandel vermehrt aus Perspektive der geographischen Handelsforschung zu beantworten. Mit der Krise wurden Defizite und Handlungsfelder in den Blick gerückt, deren Bearbeitung schon lange ansteht. Die Chance liegt darin, diesen Aufmerksamkeitsschub konstruktiv zu nutzen und realistische fall- und standortspezifische Perspektiven für Innenstädte und ihre Akteur*innen jetzt zu verhandeln und nicht weiter auf die lange Bank zu schieben. Der vorliegende Band vereint neun handelsgeographische Beiträge von Wissenschaftler*innen und Praktiker*innen, die die Auswirkungen der Covid-19-Pandemie erörtern und damit einen wichtigen Beitrag für die notwendige Diskussion der Zukunft von Innenstädten und Handel leisten.
This study examines the relationship between variations of the Southern Annular Mode (SAM) and black carbon (BC) at 550 nm aerosol optical depth (AOD) in the Western Cape province (WC). Variations of the positive (negative) phase of the SAM are found to be related to regional circulation types (CTs) in southern Africa, associated with suppressed (enhanced) westerly wind over the WC through the southward (northward) migration of Southern Hemisphere mid-latitude cyclones. The CTs related to positive (negative) SAM anomalies induce stable (unstable) atmospheric conditions over the southwestern regions of the WC, especially during the austral winter and autumn seasons. Through the control of CTs, positive (negative) SAM phases tend to contribute to the build-up (dispersion and dilution) of BC in the study region because they imply dry (wet) conditions which favor the build-up (washing out) of pollutant particles in the atmosphere. Indeed, recent years with an above-average frequency of CTs related to positive (negative) SAM anomalies are associated with a high (low) BC AOD over southwesternmost Africa.
Sacred water canals or lakes, which provided water for all kinds of purification rites and other activities, were very specific and important features of temples in ancient Egypt. In addition to the longer-known textual record, preliminary geoarchaeological surveys have recently provided evidence of a sacred canal at the Temple of Bastet at Bubastis. In order to further explore the location, shape, and course of this canal and to find evidence of the existence of a second waterway, also described by Herodotus, 34 drillings and five 2D geoelectrical measurements were carried out in 2019 and 2020 near the temple. The drillings and 2D ERT surveying revealed loamy to clayey deposits with a thickness of up to five meters, most likely deposited in a very low energy fluvial system (i.e., a canal), allowing the reconstruction of two separate sacred canals both north and south of the Temple of Bastet. In addition to the course of the canals, the width of about 30 m fits Herodotus’ description of the sacred waterways. The presence of numerous artefacts proved the anthropogenic use of the ancient canals, which were presumably connected to the Nile via a tributary or canal located west or northwest of Bubastis.
Background
Breast cancer (BC), which is most common in elderly women, requires a multidisciplinary and continuous approach to care. With demographic changes, the number of patients with chronic diseases such as BC will increase. This trend will especially hit rural areas, where the majority of the elderly live, in terms of comprehensive health care.
Methods
Accessibility to several cancer facilities in Bavaria, Germany, was analyzed with a geographic information system. Facilities were identified from the national BC guideline and from 31 participants in a proof‐of‐concept study from the Breast Cancer Care for Patients With Metastatic Disease registry. The timeframe for accessibility was defined as 30 or 60 minutes for all population points. The collection of address information was performed with different sources (eg, a physician registry). Routine data from the German Census 2011 and the population‐based Cancer Registry of Bavaria were linked at the district level.
Results
Females from urban areas (n = 2,938,991 [ie, total of females living in urban areas]) had a higher chance for predefined accessibility to the majority of analyzed facilities in comparison with females from rural areas (n = 3,385,813 [ie, total number of females living in rural areas]) with an odds ratio (OR) of 9.0 for cancer information counselling, an OR of 17.2 for a university hospital, and an OR of 7.2 for a psycho‐oncologist. For (inpatient) rehabilitation centers (OR, 0.2) and genetic counselling (OR, 0.3), women from urban areas had lower odds of accessibility within 30 or 60 minutes.
Conclusions
Disparities in accessibility between rural and urban areas exist in Bavaria. The identification of underserved areas can help to inform policymakers about disparities in comprehensive health care. Future strategies are needed to deliver high‐quality health care to all inhabitants, regardless of residence.
Park−People Relationships: The Socioeconomic Monitoring of National Parks in Bavaria, Germany
(2021)
Questions about park–people relationships and the understanding and handling of the conflicts that may result from the creation and management of national parks in the surrounding area are prerequisites for both successful park management and sustainable rural tourism development. This paper analyzes the roles that research may play in relation to park–people relationships in the context of the two oldest German national parks located in Bavaria. The different fields of action of national parks are used to identify the potential for conflict, using detailed case studies from the Bavarian Forest and Berchtesgaden National Parks using quantitative population surveys carried out in 2018. The overall attitude towards both national parks is overwhelmingly positive, with trust towards park administrations and the perceived economic benefits from rural tourism being the attitudes most strongly correlated to the overall level of park–people relationships. Nevertheless, some points of contention still exist, like the ecological integrity approach towards strict nature conservation and related landscape changes (e.g., deadwood cover). A comparison over time shows in both cases that the spatial proximity to the protected area negatively influences people’s attitudes towards the parks, but less so than in the past. Recommendations for national park management include communicating proactively and with greater transparency with locals and decision-makers, to identify conflicts earlier and, where possible, to eliminate them. Furthermore, developing a standardized method to monitor park–people relationships in Germany is a must and would benefit integrated approaches in research and management based on conservation social science.
Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs’ activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the ‘Lieberoser Heide’ in northeastern Germany, is home to the country’s largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.
This study investigates circulation types (CTs) in Africa, south of the equator, that are related to wet and dry conditions in the Western Cape, the statistical relationship between the selected CTs and the Southern Annular Mode (SAM), and changes in the frequency of occurrence of the CTs related to the SAM under the ssp585 scenario. Obliquely rotated principal component analysis applied to sea level pressure (SLP) was used to classify CTs in Africa, south of the equator. Three CTs were found to have a high probability of being associated with wet days in the Western Cape, and four CTs were equally found to have a high probability of being associated with dry days in the Western Cape. Generally, the dry/wet CTs feature the southward/northward track of the mid-latitude cyclone, adjacent to South Africa; anti-cyclonic/cyclonic relative vorticity, and poleward/equatorward track of westerlies, south of South Africa. One of the selected wet CTs was significantly related to variations of the SAM. Years with an above-average SAM index correlated with the below-average frequency of occurrences of the wet CT. The results suggest that through the dynamics of the CT, the SAM might control the rainfall variability of the Western Cape. Under the ssp585 scenario, the analyzed climate models indicated a possible decrease in the frequency of occurrence of the aforementioned wet CT associated with cyclonic activity in the mid-latitudes, and an increase in the frequency of the occurrence of CT associated with enhanced SLP at mid-latitudes.
Strategies in Times of Pandemic Crisis — Retailers and Regional Resilience in Würzburg, Germany
(2021)
Research on the COVID-19 crisis and its implications on regional resilience is still in its infancy. To understand resilience on its aggregate level it is important to identify (non)resilient actions of individual actors who comprise regions. As the retail sector among others represents an important factor in an urban regions recovery, we focus on the resilience of (textile) retailers within the city of Würzburg in Germany to the COVID-19 pandemic. To address the identified research gap, this paper applies the concept of resilience. Firstly, conducting expert interviews, the individual (textile) retailers’ level and their strategies in coping with the crisis is considered. Secondly, conducting a contextual analysis of the German city of Würzburg, we wish to contribute to the discussion of how the resilience of a region is influenced inter alia by actors. Our study finds three main strategies on the individual level, with retailers: (1) intending to “bounce back” to a pre-crisis state, (2) reorganising existing practices, as well as (3) closing stores and winding up business. As at the time of research, no conclusions regarding long-term impacts and resilience are possible, the results are limited. Nevertheless, detailed analysis of retailers’ strategies contributes to a better understanding of regional resilience.
Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.
This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m\(^2\)) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m\(^2\)). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).
The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate prediction. To overcome this difficulty RARIMAE method were evolved. Applications RARIMAE in the literature shows that amalgamating different methods can be an efficient and effective way to improve the forecasts of time series under consideration. With these motivations, attempt have been made to develop a multiple linear regression model (MLR) and a RARIMAE models for forecasting seasonal rainfall in east Africa under the following objectives:
1. To develop MLR model for seasonal rainfall prediction in East Africa.
2. To develop a RARIMAE model for seasonal rainfall prediction in East Africa.
3. Comparison of model's efficiency under consideration
In order to achieve the above objectives, the monthly precipitation data covering the period from 1949 to 2000 was obtained from Climate Research Unit (CRU). Next to that, the first differenced climate indices were used as predictors.
In the first part of this study, the analyses of the rainfall fluctuation in whole Central- East Africa region which span over a longitude of 15 degrees East to 55 degrees East and a latitude of 15 degrees South to 15 degrees North was done by the help of maps. For models’ comparison, the R-squared values for the MLR model are subtracted from the R-squared values of RARIMAE model. The results show positive values which indicates that R-squared is improved by RARIMAE model. On the other side, the root mean square errors (RMSE) values of the RARIMAE model are subtracted from the RMSE values of the MLR model and the results show negative value which indicates that RMSE is reduced by RARIMAE model for training and testing datasets.
For the second part of this study, the area which is considered covers a longitude of 31.5 degrees East to 41 degrees East and a latitude of 3.5 degrees South to 0.5 degrees South. This region covers Central-East of the Democratic Republic of Congo (DRC), north of Burundi, south of Uganda, Rwanda, north of Tanzania and south of Kenya. Considering a model constructed based on the average rainfall time series in this region, the long rainfall season counts the nine months lead of the first principal component of Indian sea level pressure (SLP_PC19) and the nine months lead of Dipole Mode Index (DMI_LR9) as selected predictors for both statistical and predictive model. On the other side, the short rainfall season counts the three months lead of the first principal component of Indian sea surface temperature (SST_PC13) and the three months lead of Southern Oscillation Index (SOI_SR3) as predictors for predictive model. For short rainfall season statistical model SAOD current time series (SAOD_SR0) was added on the two predictors in predictive model. By applying a MLR model it is shown that the forecast can explain 27.4% of the total variation and has a RMSE of 74.2mm/season for long rainfall season while for the RARIMAE the forecast explains 53.6% of the total variation and has a RMSE of 59.4mm/season. By applying a MLR model it is shown that the forecast can explain 22.8% of the total variation and has a RMSE of 106.1 mm/season for short rainfall season predictive model while for the RARIMAE the forecast explains 55.1% of the total variation and has a RMSE of 81.1 mm/season.
From such comparison, a significant rise in R-squared, a decrease of RMSE values were observed in RARIMAE models for both short rainfall and long rainfall season averaged time series. In terms of reliability, RARIMAE outperformed its MLR counterparts with better efficiency and accuracy. Therefore, whenever the data suffer from autocorrelation, we can go for MLR with ARIMA error, the ARIMA error part is more to correct the autocorrelation thereby improving the variance and productiveness of the model.
The natural cyclical development of palsas makes it difficult to use visible signs of decay as reference points for environmental change. Thus, to determine the actual development stage of a palsa, investigations of the internal structure are crucial. Our study presents 2‐D and 3‐D electrical resistivity imaging (ERI) and 2‐D ground‐penetrating radar (GPR) results, measurements of surface and subsurface temperatures, and of the soil matric potential from Orravatnsrústir Palsa Site in Central Iceland. By a joint interpretation of the results, we deduce the internal structure (i.e., thickness of thaw zone and permafrost, ice/water content) of five palsas of different size and shape. The results differentiate between initial and mature development stages and show that palsas of different development stages can exist in close proximity. While internal characteristics indicate undisturbed development of four palsas, one palsa shows indications of environmental change. Our study shows the value of the multimethod geophysical approach and introduces measurements of the soil matric potential as a promising method to assess the current state of the subsurface.