Description: In January 2020, a scientific borehole planning workshop sponsored by the International Continental Scientific Drilling Program was convened at Cornell University in the northeastern United States. Cornell is planning to drill test wells to evaluate the potential to use geothermal heat from depths in the range of 2700–4500 m and rock temperatures of about 60 to 120 ∘C to heat its campus buildings. Cornell encourages the Earth sciences community to envision how these boreholes can also be used to advance high-priority subsurface research questions. Because nearly all scientific boreholes on the continents are targeted to examine iconic situations, there are large gaps in understanding of the “average” intraplate continental crust. Hence, there is uncommon and widely applicable value to boring and investigating a “boring” location. The workshop focused on designing projects to investigate the coupled thermal–chemical–hydrological–mechanical workings of continental crust. Connecting the practical and scientific goals of the boreholes are a set of currently unanswered questions that have a common root: the complex relationships among pore pressure, stress, and strain in a heterogeneous and discontinuous rock mass across conditions spanning from natural to human perturbations and short to long timescales. The need for data and subsurface characterization vital for decision-making around the prospective Cornell geothermal system provides opportunities for experimentation, measurement, and sampling that might lead to major advances in the understanding of hydrogeology, intraplate seismicity, and fluid/chemical cycling. Subsurface samples could also enable regional geological studies and geobiology research. Following the workshop, the U.S. Department of Energy awarded funds for a first exploratory borehole, whose proposed design and research plan rely extensively on the ICDP workshop recommendations.

Description: Background: Agriculture is a large and dynamic sector, essential for the supply of the population and thus in a complex area of tension. The growing population and the resulting need for optimization, greater efficiency and intensification are in direct conflict with the demand for sustainability, environmental compatibility and, above all, mitigation of climate change and its consequences. Precision agriculture can make a decisive contribution to increasing efficiency in particular. Because through the targeted and demand-oriented application of fertilizers and pesticides, but also spatially variable sowing, resources can be used better and in the best case even increase yields. Above all, if fertilizers are applied in the way that the plants need and can absorb them, in contrast to uniform application across the entire field, a surplus that can be washed into the groundwater can be avoided. A basis is therefore needed on which this variable application of resources can be determined. In practice, maps of current condition in the form of zones in the field or on-the-go measurements from sensors on the tractor are often used here. However, for comprehensive planning and holistic cultivation of crop, current and past spatial information maps, such as zone maps, are necessary. Satellite data are a data basis for such zones, as they are available in various types, current and retrospective and cover large areas spatially. Objective: This work explores possibilities to derive this zoning from satellite data and developes different approaches. The interrelations between satellite data, geoinformation data and agricultural data such as yield will be investigated and combined. The focus of the method development is the applicability in practice and the associated requirements of the farmer. Data: For method development and analysis 179 RapidEye scenes, 512 Landsat scenes, 43 Sentinel-2 scenes and 21 Planetscope scenes were used. Furthermore, the soil map „Bodenschätzung“, which not only transmits the information about the respective soil type, but also a quantification of the fertility respectively the yield potential in the form of „Bodenzahl“ and „Ackerzahl“. Digital terrain models in different spatial resolutions were used as well as in-situ measurements of nutrients, electrical conductivity and phenology. Methods and Results: In this thesis two methods and a data analysis are presented. The first method uses only optical satellite data (RapidEye) and processes these automatically into five relative yield zones, which reflect the expected relative yield averaged over several years. The method independently selects the appropriate data sets for a prescribed field, using different thresholds resulting from the reflectance values of individual bands. The zones are then separated on the basis of quantile values using an synthetic, averaged raster of the near infrared bands. The method is validated with actual yield data using the characteristics of box plots. The yield zones generated can then be used as management zones in precision farming. The second method also generates relative yield zones, suitable for use as a management zone, using RapidEye satellite data as well as soil map and relief information. This data fusion for yield zone modeling is based on belief structures and uses the Transferable Belief Model. Thus, individual expert knowledge from practical agriculture can be integrated into the fusion process. The knowledge generated in the course of method development about the relationship between remote sensing and GIS data and the actual yield on the field will be extended and consolidated in a large-scale data analysis with a time series of 13 years and 755 satellite scenes. It shows that there is a strong correlation between satellite data and yield data (up to a correlation value of r = 0.75, some values even higher). However, this correlation depends strongly on the phenological timing of - in this case - cereals and canola. In addition, the spectral and spatial resolution, as well as the growing conditions and the soil available water. Conclusion: Satellite data are very well suited for agricultural applications and for the derivation of management zones for precision crop cultivation. However, a lot of expert knowledge has to be applied in the selection of the appropriate remote sensing data as well as in the processing and methodology. The scientific and practical use of remote sensing data should be adapted to the specific problem and external conditions.

Description: Hintergrund: Die Landwirtschaft ist ein großer und dynamischer Sektor, essentiell für die Versorgung der Bevölkerung und dadurch in einem komplexen Spannungsfeld. Die steigende Bevölkerung und der dadurch bestehende Bedarf an Optimierung, mehr Effizienz und Intensivierung steht im direkten Konflikt mit dem Anspruch nach Nachhaltigkeit, Umweltverträglichkeit aber vor allem der Eindämmung des Klimawandels und seiner Folgen. Gerade bei Fragen der Effizienzsteigerung kann der Bereich der Präzisionslandwirtschaft einen entscheidenden Beitrag leisten. Denn durch die gezielte und bedarfsorientierte Anwendung von Dünger und Pflanzenschutzmitteln, aber auch die gezielte und räumlich variable angepasste Aussaat, können Ressource besser genutzt werden und im besten Falle den Ertrag sogar steigern. Vor allem wenn Düngemittel so ausgebracht werden, wie die Pflanzen ihn benötigen und aufnehmen können, im Gegensatz zu einer uniformen Ausbringung über das ganze Feld hinweg, kann ein Überschuss, welcher in das Grundwasser ausgewaschen werden kann, vermieden werden. Es braucht also eine Grundlage, auf welcher diese variable Ausbringung von Ressourcen bestimmt wird. Hier werden in der Praxis oft Zustandskarten in Form von Zonen im Feld verwendet oder „on-the-go“-Messungen von Sensoren auf dem Traktor. Für die umfassende Planung und eine holistische Bearbeitung der Bestände sind aber aktuelle und zurückliegende, wie zusammenfassende Zustandskarten, beziehungsweise Zonenkarten nötig. Eine Datengrundlage für solche Zonen sind Satellitendaten, da sie in verschiedenster Art, aktuell und retroperspektiv vorliegen und große Flächen räumlich erfassen. Ziel: Diese Arbeit erforscht Möglichkeiten aus Satellitendaten eben diese Zonierung abzuleiten und sucht dabei verschiedene Herangehensweisen. Es sollen die Zusammenhänge zwischen Satellitendaten, Daten der Geoinformation und landwirtschaftlicher Daten wie Ertrag untersucht und miteinander kombiniert werden. Im Fokus der Methodenentwicklung steht die Anwendbarkeit in der Praxis und die somit einhergehenden Anforderungen des Landwirtes. Daten: Für die Methodenentwicklung und die Analyse wurden 179 RapidEye Szenen, 512 Landsat-Szenen, 43 Sentinel-2 Szenen und 21 Planetscope-Szenen verwendet. Weiterhin die Bodenkarte Bodenschätzung, welche nicht nur die Informationen über die jeweilige Bodenart übermittelt, aber auch eine Quantifizierung der Fruchtbarkeit beziehungsweise des Ertragspotentials in Form von „Bodenzahl“ und „Ackerzahl“. Digitale Geländemodell in unterschiedlichen räumlichen Auflösungen wurden verwendet, ebenso wie in-situ-Messungen von Nährstoffen, elektrischer Leitfähigkeit und Phänologie. Methoden und Ergebnisse: In dieser Doktorarbeit werden zwei Methoden und eine Datenanalyse vorgestellt. Die erste Methode verwendet einzig optische Satellitendaten (RapidEye) und verarbeitet diese automatisiert zu fünf relativen Ertragszonen, welche den zu erwartenden relativen Ertrag gemittelt über mehrere Jahre spiegelt. Die Methode wählt dabei eigenständig die passenden Datensätze für ein vorgeschriebenes Feld aus, unter Verwendung verschiedener Schwellwerte, die sich aus den Rückstrahlwerten einzelner Bänder ergeben. Auf Basis eines gemittelten Rasters der Bänder des nahen Infrarots werden dann auf Basis von Quartilswerten die Zonen separiert. Die Methode wird mit tatsächlichen Ertragsdaten mithilfe der Charakteristika von Boxplots validiert Die erzeugten Ertragszonen können dann als Bearbeitungszonen in der Präzisionslandwirtschaft verwendet werden. Die zweite Methode erzeugt ebenfalls relative Ertragszonen, geeignet für die Verwendung als Management Zone, verwendet neben RapidEye Satellitendaten auch die Informationen der Bodenkarte und des Reliefs. Diese Datenfusion zur Modellierung von Ertragszonen basiert auf Überzeugungsstrukturen und verwendet das Transferable Belief Model. Somit kann individuelles Expertenwissen aus der praktischen Landwirtschaft in den Fusionsprozess integrieren werden. Die Erkenntnisse, die im Laufe der Methodenentwicklung über die Zusammenhänge von Fernerkundungs- und GIS Daten und dem tatsächlichen Ertrag auf dem Feld generiert wurden, werden in einer großangelegten Datenanalyse mit einer Zeitreihe von 13 Jahren und 755 Satellitenszenen erweitert und gefestigt. Sie zeigt, dass es einen starken Zusammenhang zwischen Satellitendaten und Ertragsdaten gibt (bis zu einem Korrelationswert von r = 0.75, einzelne Werte höher). Diese Korrelation hängt aber stark ab vom phänologischen Zeitpunkt von – in diesem Falle – Getreide und Raps. Außerdem von der spektralen und räumlichen Auflösung, sowie den Wachstumsbedingungen und dem bodenverfügbaren Wasser. Fazit: Satellitendaten eignen sich sehr gut für die Anwendung in der Landwirtschaft und für die Ableitung von Bearbeitungszonen für den Präzisionspflanzenbau. Allerdings muss in der Auswahl der passenden Fernerkundungsdaten und auch der Verarbeitung und Methodik viel Expertenwissen angewandt werden. Die wissenschaftliche und praktische Verwendung von Fernerkundungsdaten sollte an die spezifische Fragestellung und die äußeren Bedingungen angepasst werden.

Description: Natural hazards and climate-related disasters disregard political borders, where additional barriers can complicate mitigation, response and recovery efforts within and between the sectors of Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR). The ESPREssO Project (Enhancing Synergies for Disaster Prevention in the European Union) aims to improve management of transboundary disasters by encouraging closer synergies between the CCA and DRR communities. Using targeted stakeholder interviews, questionnaires, Think Tank discussions and purpose-built serious games, ESPREssO draws on both CCA and DRR stakeholder experiences and informed perspectives in order to identify current gaps. Set within a fictitious border zone, ESPREssO’s RAMSETE II serious game challenges CCA and DRR stakeholders in making coordinated decisions before, during and after a simulated disaster, in protection of population and critical infrastructure. Results highlight the essential role of local governance mechanisms as the sharp end of the policy wedge, with current examples of proactivity that require to be championed and supported at national level in order to thrive. These good practice examples reflect the fact that transboundary settings, despite their challenges, act as fertile ground for mutual growth, offering opportunities for CCA and DRR communities to find innovative ways to cooperate and unite in developing synergies and strengthening their mutual efforts towards resilience. Stakeholders emphasise a need to invest more resources in informal cooperation and call on policy makers to recognise that each border zone raises its own unique set of complex challenges that requires flexibility and special consideration by transboundary authorities in management of disasters.

Description: Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

Description: The Bakken Shale and underlying Three Forks Formation is an important oil and gas reservoir in the United States. The hydrocarbon resources in this region are accessible using unconventional oil and gas extraction methods, including horizontal drilling and hydraulic fracturing. However, the geochemistry and microbiology of this region are not well understood, although they are known to have major implications for productivity and water management. In this study, we analyzed the produced water from 14 unconventional wells in the Bakken Shale using geochemical measurements, quantitative PCR (qPCR), and 16S rRNA gene sequencing with the overall goal of understanding the complex dynamics present in hydraulically fractured wells. Bakken Shale produced waters from this study exhibit high measurements of total dissolved solids (TDS). These conditions inhibit microbial growth, such that all samples had low microbial loads except for one sample (well 11), which had lower TDS concentrations and higher 16S rRNA gene copies. Our produced water samples had elevated chloride concentrations typical of other Bakken waters. However, they also contained a sulfate concentration trend that suggested higher occurrence of sulfate reduction, especially in wells 11 and 18. The unique geochemistry and microbial loads recorded for wells 11 and 18 suggest that the heterogeneous nature of the producing formation can provide environmental niches with conditions conducive for microbial growth. This was supported by strong correlations between the produced water microbial community and the associated geochemical parameters including sodium, chloride, and sulfate concentrations. The produced water microbial community was dominated by 19 bacterial families, all of which have previously been associated with hydrocarbon-reservoirs. These families include Halanaerobiaceae, Pseudomonadaceae, and Desulfohalobiaceae which are often associated with thiosulfate reduction, biofilm production, and sulfate reduction, respectively. Notably, well 11 was dominated by sulfate reducers. Our findings expand the current understanding of microbial life in the Bakken region and provide new insights into how the unique produced water conditions shape microbial communities. Finally, our analysis suggests that produced water chemistry is tightly linked with microbiota in the Bakken Shale and shows that additional research efforts that incorporate coupled microbial and geochemical datasets are necessary to understand this ecosystem.

Description: Sections PDFPDF Tools Share Abstract Recent policy changes highlight the need for citizens to take adaptive actions to reduce flood‐related impacts. Here, we argue that these changes represent a wider behavioral turn in flood risk management (FRM). The behavioral turn is based on three fundamental assumptions: first, that the motivations of citizens to take adaptive actions can be well understood so that these motivations can be targeted in the practice of FRM; second, that private adaptive measures and actions are effective in reducing flood risk; and third, that individuals have the capacities to implement such measures. We assess the extent to which the assumptions can be supported by empirical evidence. We do this by engaging with three intellectual catchments. We turn to research by psychologists and other behavioral scientists which focus on the sociopsychological factors which influence individual motivations (Assumption 1). We engage with economists, engineers, and quantitative risk analysts who explore the extent to which individuals can reduce flood related impacts by quantifying the effectiveness and efficiency of household‐level adaptive measures (Assumption 2). We converse with human geographers and sociologists who explore the types of capacities households require to adapt to and cope with threatening events (Assumption 3). We believe that an investigation of the behavioral turn is important because if the outlined assumptions do not hold, there is a risk of creating and strengthening inequalities in FRM. Therefore, we outline the current intellectual and empirical knowledge as well as future research needs. Generally, we argue that more collaboration across intellectual catchments is needed, that future research should be more theoretically grounded and become methodologically more rigorous and at the same time focus more explicitly on the normative underpinnings of the behavioral turn.

Description: A Bayesian Belief Network, validated using past observational data, is applied to conceptualize the ecological response of Lake Maninjau, a tropical lake ecosystem in Indonesia, to tilapia cage farms operating on the lake and to quantify its impacts to assist decision making. The model captures ecosystem services trade-offs between cage farming and native fish loss. It is used to appraise options for lake management related to the minimization of the impacts of the cage farms. The constructed model overcomes difficulties with limited data availability to illustrate the complex physical and biogeochemical interactions contributing to triggering mass fish kills due to upwelling and the loss in the production of native fish related to the operation of cage farming. The model highlights existing information gaps in the research related to the management of the farms in the study area, which is applicable to other tropical lakes in general. Model results suggest that internal phosphorous loading (IPL) should be recognized as one of the primary targets of the deep eutrophic tropical lake restoration efforts. Theoretical and practical contributions of the model and model expansions are discussed. Short- and longer-term actions to contribute to a more sustainable management are recommended and include epilimnion aeration and sediment capping.

Description: Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe.

Description: Persistent unique identifiers (PID) are a critical element in digital research data infrastructure to unambiguously identify, locate, and cite digital representations of a growing range of entities – publications, data, instruments, organizations, funding awards, field programs, and others. The IGSN was developed as the International Geo Sample Number to provide a persistent, globally unique, web resolvable identifier for physical samples. IGSN is both a governance and technical system for assigning globally unique persistent identifiers to physical samples. Even though initially developed for samples in the geosciences, the application of IGSN can be and has already been expanded to other domains that rely on physical samples and collections. This paper describes the current architecture and technical implementation of IGSN, how IGSN relates to other sample identifiers, and how its technical systems are supported by an international governance structure.