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Die Anzahl der unterernährten Menschen in der Welt steigt seit 2017 wieder an. Der Klimawandel wird den Druck auf die Landwirtschaft und die Ernährungssicherheit weiter erhöhen, insbesondere für kleinbäuerliche und von Subsistenzwirtschaft geprägte Agrarsysteme in den Tropen. Um die Widerstandsfähigkeit der Ernährungssysteme und die Ernährungssicherheit zu stärken, bedarf es eines Klimarisikomanagements und Klimaanpassung. Dies kann sowohl die Antizipation als auch die Reaktion auf die Auswirkungen der globalen Erwärmung ermöglichen. Eine zentrale Rolle spielen in dieser Hinsicht landwirtschaftliche Modelle. Sie können die Reaktionen von Pflanzen auf Veränderungen in den Klimabedingungen quantifizieren und damit Risiken identifizieren. Diese Dissertation demonstriert anhand dreier in Peru, in Tansania und in Burkina Faso durchgeführten Fallstudien, wie statistische Ertragsmodelle das Klimarisikomanagement und die Anpassung in der tropischen Landwirtschaft unterstützen können. Während die erste Studie zeigt, wie Klimaanpassungsbestrebungen unterstützt werden können, werden in Studie zwei und drei statistische Modelle genutzt, um Ertrags- und Produktionsvorhersagen zu erstellen. Die Ergebnisse können dazu beitragen, Frühwarnsysteme für Ernährungsunsicherheit zu unterstützen. In den drei Veröffentlichungen werden neue Ansätze statistischer Ertragsmodellierung auf verschiedenen räumlichen Ebenen vorgestellt. Ein besonderer Fokus liegt hierbei auf der Weiterentwicklung von bisherigen Ertragsvorhersagen, insbesondere in Bezug auf unabhängige Modellvalidierungen, eine stärkere Berücksichtigung von Wetterextremen und die Übertragbarkeit der Modelle auf andere Regionen. The number of undernourished people in the world has been increasing since 2017. Climate change will further exacerbate pressure on agriculture and food security, particularly for smallholder and subsistence-based farming systems in the tropics. Anticipating and responding to global warming through climate risk management is needed to increase the resilience of food systems and food security. Crop models play an indispensable role in this regard. They allow quantifying crop responses to changes in climatic conditions and thus identify risks. This dissertation demonstrates how statistical crop modelling can inform climate risk management and adaptation in tropical agriculture in the case studies of Peru, Tanzania and Burkina Faso. While the first study shows how statistical crop models can support climate adaptation, studies two and three provide yield and production forecasts. The results can contribute to supporting early warning systems on food insecurity. The three publications present novel approaches of statistical yield modelling at different spatial scales. A particular focus is on further developing existing yield forecasts, especially with regard to independent rigorous model validations, improved consideration of weather extremes, and the transferability of the models to other regions.

Despite their ecological importance, dry forests’ contribution to climate change adaptation is often neglected. Hence, this study was initiated to assess the socioeconomic contribution of dry forests to climate change adaptation in Tigray Region, Ethiopia. A mixed quantitative and qualitative research design was used to examine the role of dry forests in climate change adaptation. Household questionnaire survey, key informants, and a focus group discussion were used to collect data. The results indicated that 94% of all households visited a dry forest at least once a month to access the forest and forest products. While the dry forest income level varied significantly (p < 0.05), the overall dry forest income level contributed to 16.8% of the total household income. Dry forest income enabled the reduction of the area between the line of equality and the Lorenz curve by 21% in dry evergreen Afromontane Forest users, by 3.02% in Combretum–Terminalia woodland users, and by 3% in Acacia–Commiphora woodland users. Gender, occupation, wealth status, and distance from the forest to their homes are all factors that significantly affected Combretum–Terminalia woodland users’ income level. Among Acacia–Commiphora woodland users, the respondents’ age influenced the dry forest income level, whereas, among dry evergreen Afromontane Forest users, the family size of the household influenced the dry forest income level. The findings of this study could help policy makers understand the crucial role of dry forest income in the livelihood of the community and in climate change adaptation. Policymakers could reduce the pressure on dry forests by introducing policies that recognize the role of dry forest income in reducing poverty and income inequality and by establishing farmer cooperation in commercializing the non-timber forest products which support the long-term coping and adaptation strategy. Further research is needed to understand the increasing role of dry forest products in climate change adaptation over time and its contribution to the national economy at large. Ethiopian Environment and Forestry Research Institute Open Access Fund of the Leibniz Association Peer Reviewed

The Arctic Ocean is undergoing a major transition from a year-round sea ice cover to ice-free summers with global consequences. Sea ice thickness is at the center of the ongoing changes because the thickness regulates key processes of the Arctic climate system and in the last six decades, the mean thickness has more than halved. With the most scientific attention on the increased melting and delayed freezing of Arctic sea ice, dynamic thickness change caused by sea ice deformation has remained less studied. Dynamic thickness change alters the sea ice thickness through colliding floes that raft or form pressure ridges or floes breaking apart resulting in leads. Because sea ice grows faster in open water and under thin ice, new ice formation is enhanced in those leads compared to the surrounding ice during the growth season. Because thinner ice is easier to break and move, the ongoing thinning of Arctic sea ice may result in more ridges and leads, which, in turn, could increase ice thickness in winter. However, our limited quantitative understanding of dynamic thickness change has hampered any robust prediction if and to which extent such increased dynamic thickening in winter could mitigate summer thinning in the warming Arctic. To address this gap, we need more robust estimates of the current magnitude as well as a better understanding and representation of the different processes in state-of-the-art sea ice models. Thus, the overarching goal of this thesis is to resolve and quantify dynamic thickness change and to link it to the corresponding sea ice deformation. I focus on the freezing period addressing the following research questions: (1) How large is the dynamic contribution to the mean sea ice thickness in different dynamic regimes? (2) How is deformation shaping the ice thickness distribution? (3) How can high-resolution microwave synthetic aperture radar (SAR) satellite data be used to estimate dynamic thickness change? I answer them in two regional case studies: a unique month-long deformation event during the closing of a polynya north of Greenland and in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The combination of available high-resolution electromagnetic (EM) induction sounding ice thickness data and high-resolution deformation data offer unique research opportunities to study the highly localized and intermittent dynamic thickness changes. My results show that dynamic thickness change plays an important role in both convergent and divergent drift regimes. Studying the polynya closing event reveals that convergence can locally double the thickness of young, thin (<1 m) ice and restore the mean thickness of 2 m of the surrounding multi-year ice within one month. In more divergent regimes like the Transpolar Drift, new ice formation in leads contributes 30% to the sea ice mass balance. There are indicators that this fraction may increase in a more seasonal Arctic sea ice cover. Besides the mean changes, I show how deformation shapes the ice thickness distribution (ITD) with a particular focus on the transfer of observational results into modeling concepts. I identify the ice that participates in ridging, show that the current ridging parameterization in state-of-the-art models is not able to reproduce the observed changes in the shape of the ITD, and suggest an updated parameterization that relates the shape of the ITD proportionally to the observed deformation. Lastly, I demonstrate that SAR-derived deformation can successfully be used to describe sea ice dynamics and to estimate the dynamic contribution to the ice thickness on regional scales. In conclusion, this dissertation substantially advances our understanding of dynamic thickness change with robust and quantitative estimates. The high-resolution EM ice thickness data with simultaneously collected high-resolution deformation data provide an excellent opportunity to deepen our process understanding and to evaluate and improve the modeling of the dynamic processes shaping the ITD. With the increasing availability of SAR data in the Arctic and the presented deformation datasets and methods, new opportunities are opening up to derive dynamic thickness change on Arctic-wide scales and to study the temporal trends in dynamic thickness change over the last decade.

Global change biology (2022). doi:10.1111/gcb.16462 Published by Wiley-Blackwell, Oxford [u.a.]

Peer Reviewed

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.

Die Kartierung der städtische Oberflächenmaterialien ist aufgrund der komplexen räumlichen Muster eine Herausforderung. Daten von bildgebenden Spektrometern können hierbei durch die feine und kontinuierliche Abtastung des elektromagnetischen Spektrums detaillierte spektrale Merkmale von Oberflächenmaterialien erkennen, was mit multispektralen oder RGB-Bildern nicht mit der gleichen Genauigkeit erreicht werden kann. Bislang wurden in zahlreichen Studien zur Kartierung von städtischen Oberflächenmaterialien Daten von flugzeuggestützten abbildenden Spektrometern mit hoher räumlicher Auflösung verwendet, die ihr Potenzial unter Beweis stellen und gute Ergebnisse liefern. Im Vergleich zu diesen Sensoren haben weltraumgestützte abbildende Spektrometer eine regionale oder globale Abdeckung, eine hohe Wiederholbarkeit und vermeiden teure, zeit- und arbeitsaufwändige Flugkampagnen. Allerdings liegt die räumliche Auflösung der aktuellen weltraumgestützten abbildenden Spektroskopiedaten bei etwa 30 m, was zu einem Mischpixelproblem führt, welches mit herkömmlichen Kartierungsansätzen nur schwer zu bewältigen ist. Das Hauptziel dieser Studie ist die Kartierung städtischer Materialien mit bildgebenden Spektroskopiedaten in verschiedenen Maßstäben und die gleichzeitige Nutzung des Informationsgehalts dieser Daten, um die chemischen und physikalischen Eigenschaften von Oberflächenmaterialien zu erfassen sowie das Mischpixelproblem zu berücksichtigen. Konkret zielt diese Arbeit darauf ab, (1) photovoltaische Solarmodule mit Hilfe von luftgestützten bildgebenden Spektroskopiedaten auf der Grundlage ihrer spektralen Merkmale zu kartieren; (2) die Robustheit der Stichprobe von städtischen Materialgradienten zu untersuchen; (3) die Übertragbarkeit von städtischen Materialgradienten auf andere Gebiete zu analysieren. Mapping urban surface materials is challenging due to the complex spatial patterns. Data from imaging spectrometers can identify detailed spectral features of surface materials through the fine and continuous sampling of the electromagnetic spectrum, which cannot be achieved with the same accuracy using multispectral or RGB images. To date, numerous studies in urban surface material mapping have been using data from airborne imaging spectrometers with high spatial resolution, demonstrating the potential and providing good results. Compared to these sensors, spaceborne imaging spectrometers have regional or global coverage, high repeatability, and avoid expensive, time-consuming, and labor-intensive flight campaigns. However, the spatial resolution of current spaceborne imaging spectroscopy data (also known as hyperspectral data) is about 30 m, resulting in a mixed pixel problem that is challenging to handle with conventional mapping approaches. The main objective of this study is to perform urban surface material mapping with imaging spectroscopy data at different spatial scales, simultaneously explore the information content of these data to detect the chemical and physical properties of surface materials, and take the mixed-pixel problem into account. Specifically, this thesis aims to (1) map solar photovoltaic modules using airborne imaging spectroscopy data based on their spectral features; (2) investigate the sampling robustness of urban material gradients; (3) analyze the area transferability of urban material gradients.

Wälder spielen global eine entscheidende Rolle bei der Regulierung des Weltklimas, da sie aktiv Kohlenstoff speichern und binden. Trotz der Bemühungen durch internationale Programme nehmen die Waldschäden weiter zu. Entwaldung und Walddegradierung sind zwei unterschiedliche Prozesse, die sich auf die globalen Wälder auswirken. Entwaldung ist eine klar definierte Umwandlung oder Abholzung der Waldflächen, während Degradierung subtiler, vorübergehend und variabel sein kann und daher schwer zu detektieren ist. Walddegradierung wird im Allgemeinen als eine funktionale Verringerung der Fähigkeit von Wäldern Ökosystemleistungen zu erbringen identifiziert. Sie wird nicht als Veränderung der Landbedeckung oder Entwaldung klassifiziert. Daraus folgt keine deutliche Verringerung der Waldfläche, sondern eher eine Abnahme der Qualität und des Zustands. Diese Veränderung kann, wie die Entwaldung dennoch mit einer signifikanten Verringerung der oberirdischen Biomasse und damit miterheblichen Treibhausgasemissionen verbunden sein. Die Schätzungen der Kohlenstoffemissionen aus Waldstörungen liegen zwischen 12 und 20 % aller weltweit emittierten Emissionen. Durch eine fehlende einheitliche Definition oder Methode zur Quantifizierung der Degradation, der Vielzahl an Einflussfaktoren und der Unsicherheit bei der Schätzung der Biomasse variieren die Werte stark. Die von der Walddegradierung betroffene Fläche könnte in der Tat viel größer sein als die der Entwaldung, die ohnehin jedes Jahr auf eine Fläche von etwa der Größe Islands geschätzt wird. Die REDD+-Mechanismen zur Finanzierung von Emissionsreduktionen zur Minderung des Klimawandels erfordern robuste, transparente und skalierbare Methoden zur Quantifizierung der Walddegradierung, zusammen mit der Erfassung der damit verbundenen Treibern. Da die Degradierung oft der Entwaldung vorausgeht, kann ein schnelles Monitoring mit einer Beurteilung der Waldschäden und ihren Treibern ein wichtiges Frühwarnsystem sein. Nur so können Maßnahmen frühzeitig ergriffen werden, die die Wälder schützen und sowohl der Natur und der Biodiversität als auch dem Lebensunterhalt, der Gesundheit und dem Wohlbefinden von Millionen von Menschen auf der ganzen Welt zugute kommen. In dieser Arbeit werden Methoden für konsistente, reproduzierbare, skalierbare und satellitengestützte Indikatoren zur Identifizierung und Quantifizierung verschiedener Arten von Walddegradation um zukünftige Risiko- und Politikszenarien zu unterstützen. Global forests play a crucial role in regulating global climate by actively storing and sequestering carbon. Despite efforts to mitigate climate through international efforts, human-caused forest disturbance and forest-related greenhouse gas emissions continue to rise. Deforestation and forest degradation are two different processes affecting global forests. Deforestation is a clearly defined conversion or removal of forest cover, while degradation can be more subtle, temporary, variable, and therefore difficult to detect. Forest degradation is generally identified as a functional reduction in the capacity of forests to provide ecosystem services, that does not qualify as a change in land cover or forest clearing. That means no clear reduction of the forest area, but rather a decrease in quality and condition. This change, like deforestation can still be associated with significant reductions in above-ground biomass and therefore considerable greenhouse gas emissions. Estimates of carbon emissions from forest degradation and disturbance range anywhere from 12-20% of all emissions emitted globally with values varying widely because of a lack of uniform definition or method for quantifying degradation, the broad number of influencing factors, and uncertainty in biomass estimates. The area affected by forest degradation could in fact be much larger than that of deforestation, which is already estimated to be an area about the size of Iceland every year. The REDD+ mechanisms of financing emissions reductions to mitigate climate change require robust, transparent and scalable methods for quantifying degradation, along with a quantification of associated direct drivers. Furthermore, as degradation often precedes deforestation, timely monitoring and assessment of forest degradation and changes in drivers can provide crucial early warning to engage interventions to keep forests intact, benefitting nature and biodiversity as well as the livelihoods, health and well-being of millions of people around the world. This research proposes methods for consistent, repeatable and scalable satellite-derived indicators for identifying and quantifying different types of forest degradation and its causes to inform future risk and policy scenarios.