The Distrito Federal in Western Central Brazil is characterized by a high share of urban population and a predicted growth of population. The corresponding increasing water consumption is associated with a falling groundwater table. The existing wastewater treatment plants could reach their capacity limits due to further increasing amounts of urban wastewater in conjunction with heavy precipitation events. During these events, untreated wastewater can become a contamination source for protected resources such as soil and water. This problem can be solved by soil aquifer recharge. This is a unique technique to take the load off the treatment plants, to store the water in the aquifer, and to improve the water quality during the soil passage. The controlled infiltration of pretreated municipal wastewater in the tropical soils can ensure the regional groundwater balance and backup high quality water as a suitable drinking water resource. The objective of the present thesis was to define suitable soils and areas for a soil aquifer treatment in the Distrito Federal to support the decentralized wastewater treatment management. For the identification of suitable soils, representative samples were taken. The soil samples were examined with respect to their pedological and geochemical properties. For the characterization of the unsaturated hydraulic conditions, in situ infiltration tests and a 3D infiltration monitoring were performed. Additionally, the retention potentials for sewage ingredients were determined for each soil with unsaturated, hydrochemical and geoelectrical column tests. Thus, it was possible to calculate the retention potentials of each soil against the wastewater content of an artificial wastewater. The artificial wastewater with known composition was infiltrated through the soil columns. The retention potentials of the soils were calculated by balancing the hydrochemical data. Afterwards, for the implementation of the hydraulic data and the data from the column tests, a utility analysis was performed to merge all parameters. Finally, the field and laboratory studies were combined within a GIS-based usability analysis to blend the determined parameter with geo-data to identify suitable areas for a soil aquifer treatment. Parameters as slope, land use and land cover were included in this analysis. After the evaluation of the hydrochemical balances, predominantly the younger soils turn out as suitable for an artificial recharge with water of impaired quality because of their high retention potential for TOC. The Gleissolo featured a retention potential of almost 100 % for TOC. However, a high hydraulic conductivity in the soils is necessary to ensure an effective artificial recharge. The Gleissolo featured the lowest measured hydraulic conductivity in the field test with 1,12 x 10-8 m/s. Due to the consideration of the determined hydraulic and hydrochemical data in the utility analysis, each parameter was weighted to embrace the requirement of an effective artificial recharge. The results of the laboratory and field tests have shown that the soil group of Latossolos is suitable for a soil aquifer treatment. These soils offer the best infiltration characteristics and a good retention potential against sewage ingredients for an effective artificial groundwater recharge with pretreated wastewater. Additional to that, slope, land use/land cover, depth of soil, and a defined distance to conservation area were considered in a GIS-based usability analysis. Additionally, the categorized soils from the utility analysis were included to identify suitable regions. The outcome of the thesis is a GIS-based usability analysis map which shows suitable areas for a soil aquifer treatment ranked by their category of suitability. Additionally, the thesis provides crucial evidences for soil parameters which have positive effects on wastewater infiltration. In addition, the 3D infiltration monitoring and the measured resistivity distribution showed that the gravitatively controlled infiltration influences only the soil zone immediately beneath the irrigation. Furthermore, it was found that the proxy of the geoelectrical resistivity is not (sufficiently) conclusive for the sorption potential of conductive wastewater ingredients. / O Distrito Federal, localizado no Centro-Oeste Brasileiro, é caracterizado por um intenso processo de urbanização o qual deve aumentar ainda mais nas próximas décadas. O correspondente crescimento no consumo de água está, também, associado ao rebaixamento dos níveis das águas subterrâneas. O atual sistema de tratamento de efluentes pode atingir seu limite de operação diante do crescimento demográfico e aumento de eventos extremos de chuva. Durante esses eventos, efluentes não tratados podem tornar-se uma fonte de contaminação aos recursos hídricos e solos. Uma solução é recarga artificial de águas subterrâneas. A mesma armazena o efluente pré-tratado nas águas subterrâneas e melhora a qualidade do mesmo durante o processo de percolação no solo. A infiltração controlada em solos tropicais pode contribuir para a qualidade das águas subterrâneas e balanço hídrico, servindo assim como fonte para o abastecimento de água. Essa tese tem como objetivo a identificação de áreas e solos propícios à recarga artificial de águas subterrâneas no Distrito Federal a fim de dar suporte à gestão decentralizada de tratamento de efluentes. Para a identificação dessas áreas, amostras representativas à diferentes tipos de solos foram obtidas. As amostras foram examinadas de acordo com suas propriedades pedológicas e geoquímicas. Para a caracterização das condições hidráulicas não saturadas, realizaram-se diversos testes de infiltração in situ, bem como o monitoramento 3D. Ainda, determinou-se para cada solo o potencial de retenção de poluentes através de testes de coluna insaturados, hidroquímico e geoelétricos. Dessa forma foi possível calcular os potenciais de retenção para cada solo quando utilizados efluentes sintéticos. Com base na contabilização de todos os dados hidroquímicos, calculou-se os potenciais de retenção de poluentes para o determinado efluente de composição conhecida. Mais a frente, para a implementação de dados hidráulicos e dados dos testes de coluna, utilizou-se uma análise de utilidade considerando todos os parâmetros. Finalmente, estudos de laboratório e campo foram combinados em uma análise baseada em sistema de informação geográfica (SIG). O mesmo permite o cruzamento de um determinado parâmetro obtido com uma base de dados georeferenciados (p. ex. classes de solos) a fim de identificar as áreas mais propícias. Parâmetros tais como declividade, uso e cobertura de solo foram incluídos nessa análise. Após a avaliação dos balanços hidroquímicos, identificou-se que os solos jovens possuem um alto potencial de retenção de carbono orgânico total (COT). Os mesmos mostraram-se adequados para recarga de águas subterrâneas com efluente residuário pré-tratado. Como exemplo, o Gleisolo apresentou um potencial de retenção de aproximadamente 100% de COT. No entanto, é importante lembrar que, a fim de garantir uma retenção efetiva, a condutividade hidráulica do solo deve ser alta. Nesse caso, o Gleisolo apresentou baixos valores no teste de campo com uma condutividade hidráulica de 1,12 x 10-8 m/s. Ainda, os dados hidráulicos e hidroquímicos medidos foram integrados à uma análise de uso-benefício. No caso, cada parâmetro recebe um peso de modo a atender às demandas de uma recarga efetiva de águas subterrâneas. Resultados mostram que o grupo de solo classificado com Latossolo é propício para a recarga de águas subterrâneas. O mesmo oferece a melhor característica de infiltração e potencial de retenção de contaminantes para uma recarga artificial com efluentes pré-tratados. Declividade, uso e ocupação de solo, profundidade do solo e uma distância devida de áreas de conservação foram contemplados na análise de utilidade baseada em SIG. Além disso, os colos caracterizados pela análise de uso-benefício foram incluídos com o intuito de identificar as regiões mais propícias para a prática. O resultado da tese é uma representação gráfica georeferenciada das áreas propícias para a aplicação de recarga artificial de águas subterrâneas. Ainda, a tese fornece evidências cruciais de parâmetros do solo que tem efeitos positivos na infiltração de efluentes pré-tratados. O monitoramento 3D de infiltração e a distribuição de resistividade medida mostram que infiltração controlada por gravidade influencia apenas a zona do solo imediata à superfície de irrigação. Mas a frente, foi determinado que o proxy da resistividade geoelétrica não é válido para o potencial de retenção de contaminantes condutores. / Der Distrito Federal ist durch eine starke Urbanisierung geprägt. Das ohnehin bereits starke Bevölkerungswachstum wird Prognosen zufolge weiter zunehmen. Der damit verbundene Anstieg des Wasserverbrauchs wird auch in absinkenden Grundwasserständen widergespiegelt. Weiterhin ist das westliche Zentralbrasilien aufgrund seiner Lage in den wechselfeuchten Tropen durch eine starke Saisonalität der Niederschläge geprägt. Das erhöhte Abwasseraufkommen in Verbindung mit extremen Niederschlagsereignissen kann zu einer deutlichen Überbelastung der vorhandenen Kläranlagen führen. In Extremsituationen kann ungeklärtes Abwasser in die Bodenzone gelangen und somit Schutzgüter wie Boden und Wasser kontaminieren. Eine effiziente Maßnahme gegenüber der unkontrollierten Versickerung von ungeklärtem Abwasser sowie zur Vorbeugung absinkender Grundwasserspiegel ist die gezielte Infiltration von vorbehandeltem Abwasser in Verbindung mit einer Grundwasseranreicherung. Diese Maßnahme umfasst einen zusätzlichen Reinigungsschritt des vorgereinigten Abwassers und dient zur Vorbeugung gegenüber absinkenden Grundwasserspiegeln. Ziel der Arbeit war es, geeignete Böden und Flächen für eine Grundwasseranreicherung mit vorbehandeltem Abwasser im Distrito Federal auszuweisen. Zur Identifizierung geeigneter Böden wurden repräsentative Bodenproben auf ihre pedologischen und geochemischen Eigenschaften hin untersucht. Zusätzlich wurden in situ Durchlässigkeitstests und ein Versickerungsmonitoring aufgebaut, um zur Klärung der Infiltrationseigenschaften beizutragen. Für die Laboruntersuchungen wurden die Bodenproben im Hinblick auf ihre Schadstoffrückhaltepotentiale untersucht. Dazu wurden kombinierte hydrochemische und geoelektrische sowie ungesättigte Säulenversuche durchgeführt. Dadurch war es möglich, Rückschlüsse auf die Sorptionsleistung der Böden gegenüber den Abwasserinhaltsstoffen einer künstlichen Abwasserlösung schließen zu können. Die Bodensäulen wurden mit einem künstlichen Abwasser mit bekannter Schadstoffzusammensetzung überstaut. Auf der Grundlage der Bilanzierung aller hydrochemischen Daten wurden die Schadstoffrückhaltepotentiale für die Abwasserinhaltsstoffe berechnet. Anschließend wurden die experimentell ermittelten Parameter in einer Nutzwertanalyse zusammengefasst, um so geeignete Böden für eine effiziente Grundwasseranreicherung mit vorbehandeltem Abwasser zu identifizieren. Abschließend wurden die Feld- und Laboruntersuchungen mit einer GIS-basierten Nutzbarkeitsanalyse komplettiert, um die gemessenen Parameter mit weiteren vorliegenden, räumlich aufgelösten Daten verschneiden zu können. In diese Analyse wurden Parameter wie Gefälle, Landnutzung und Landbedeckung einbezogen. Bei der Auswertung der hydrochemischen Bilanzen kristallisierten sich vorwiegend die jungen Böden aufgrund ihrer hohen Schadstoffrückhaltepotentiale als geeignete Böden für eine Grundwasseranreicherung mit vorbehandeltem Abwasser heraus. Exemplarisch wies der Gleissolo ein Sorptionspotential von fast 100 % für den Parameter TOC auf. Allerdings müssen Böden für eine effektive Grundwasseranreicherung mit vorbehandeltem Abwasser zusätzlich zu den hohen Sorptionsleistungen auch hohe hydraulische Durchlässigkeiten aufweisen. Für den Gleissolo wurde im Feldversuch die niedrigste hydraulische Durchlässigkeit von 1,12 x 10-8 m/s bestimmt. Durch die Einbindung der gemessenen hydraulischen und hydrochemischen Daten in eine Nutzwertanalyse konnten diese Parameter gewichtet werden, um so den Ansprüchen einer effektiven Grundwasseranreicherung gerecht zu werden. Im Ergebnis der durchgeführten Untersuchungen hat sich gezeigt, dass sich die Bodengruppe der Latossole besonders gut für eine Grundwasseranreicherung mit vorbehandeltem Abwasser eignet. Diese Böden weisen die besten Infiltrationsbedingungen sowie gute Sorptionseigenschaften für eine effizient gestaltete Grundwasseranreicherung auf. Mit Hilfe der GIS-basierten Nutzbarkeitsanalyse geografischer Parameter wurden Flächen ausgezeichnet, die sich in Bezug auf Gefälle, Landnutzung/Landbedeckung, Bodentiefe und einer adäquaten Distanz zu Schutzgebieten für eine Grundwasseranreicherung mit vorbehandeltem Abwasser eignen. Zusätzlich wurden die mit Hilfe der Nutzwertanalyse kategorisierten Böden in die Flächenidentifizierung einbezogen. Das Resultat dieser Arbeit liegt in Form einer Karte vor, in der die Gebiete, die für eine Grundwasseranreicherung mit vorbehandeltem Abwasser geeignet sind, nach Kategorien geordnet, verzeichnet sind. Zusätzlich dazu liefert die Arbeit entscheidende Hinweise auf Bodeneigenschaften, die sich positiv auf eine Abwasserinfiltration auswirken. Darüber hinaus konnte durch das geoelektrische 3D-Monitoring und der dabei gemessenen Verteilung des spezifischen elektrischen Widerstandes gezeigt werden, dass die Infiltration gravitativ gesteuert nur die Bodenzone direkt unterhalb der Verrieselung beeinflusste. Weiterhin wurde festgestellt, dass das Proxy des geoelektrischen Widerstandes im Labormaßstab nicht aussagekräftig genug ist, um Rückschlüsse auf die Sorption leitfähigkeitsrelevanter Abwasserinhaltsstoffe geben zu können.

Eine Standardformulierung der Randwertaufgabe für die Beschreibung zeitharmonischer elektromagnetischer Phänomene hat die Vektor-Helmholtzgleichung für das elektrische Feld zur Grundlage. Bei niedrigen Frequenzen führt der große Nullraum des Rotationsoperators zu einem instabilen Lösungsverhalten. Wird die Randwertaufgabe zum Beispiel mit Hilfe der Methode der Finiten Elemente in ein lineares Gleichungssystem überführt, äußert sich die Instabilität in einer schlechten Konditionszahl ihrer Koeffizientenmatrix. Eine stabilere Formulierung wird durch die explizite Berücksichtigung der Kontinuitätsgleichung erreicht. Zur numerischen Lösung der Randwertaufgaben wurde eine Finite-Elemente-Software erstellt. Sie berücksichtigt unter anderem unstrukturierte Gitter, räumlich variable, anisotrope Materialparameter sowie die Erweiterung der Maxwell-Gleichungen durch Perfectly Matched Layers. Die Software wurde anhand von Anwendungen in der marinen Geophysik erfolgreich getestet. Insbesondere demonstriert die Einbeziehung von Seebodentopographie in Form einer stetigen Oberflächentriangulierung die geometrische Flexibilität der Software. / The physics of time-harmonic electromagnetic phenomena can be mathematically described by boundary value problems. A standard approach is based on the vector Helmholtz equation in terms of the electric field. The curl operator involved has a large, non-trivial kernel which leads to an instable solution behaviour at low frequencies. If the boundary value problem is solved approximately using, e. g., the finite element method, the instability expresses itself by a badly conditioned coefficient matrix of the ensuing system of linear equations. A stable formulation is obtained by taking the continuity equation explicitly into account. In order to solve the boundary value problem numerically a finite element software package has been implemented. Its features comprise, amongst others, the treatment of unstructured meshes and piecewise polynomial, anisotropic constitutive parameters as well as the extension of Maxwell’s equations to the Perfectly Matched Layer. Successful application of the software is demonstrated with examples from marine geophysics. In particular, the incorporation of seafloor topography by a continuous surface triangulation illustrates the geometric flexibility of the software.

Geophysical methods are essential for exploration and monitoring of subsurface formations, e.g. in carbon dioxide sequestration or enhanced geothermal energy. One of the keys to their successful application is the knowledge of how the measured physical quantities are related to the desired reservoir parameters. The work presented in this thesis shows that the presence of carbon dioxide (CO2) in pore space gives rise to multiple processes all of which contribute to the electrical rock conductivity variation. Basically, three mechanisms take place: (1) CO2 partially replaces the pore water, which is equivalent to a decrease in water saturation. (2) CO2 chemically interacts with the pore water by dissolution and dissociation. These processes change both the chemical composition and the pH of the pore filling fluid. (3) The low-pH environment can give rise to mineral dissolution and/or precipitation processes and changes the properties of the grain-water interface. Investigations on the pore water phase show that the reactive nature of CO2 in all physical states significantly acts on the electrical conductivity of saline pore waters. The physico-chemical interaction appears in different manifestations depending mainly on the pore water composition (salinity, ion types) but also on both temperature and pressure. The complex behaviour includes a low- and a high-salinity regime originating from the conductivity increasing effect of CO2 dissociation, which is opposed by the conductivity decreasing effect of reduced ion activity caused by the enhanced mutual impediment of all solutes. These results are fundamental since the properties of the water phase significantly act on all conduction mechanisms in porous media. In order to predict the variation of pore water conductivity, both a semi-analytical formulation and an empirical relationship for correcting the pore water conductivity, which depends on salinity, pressure and temperature, are derived. The central part of the laboratory experiments covers the spectral complex conductivity of water-bearing sand during exposure to and flow-through by CO2 at pressures up to 30MPa and temperatures up to 80°C. It is shown that the impact of CO2 on the real part of conductivity of a clean quartz sand is dominated by the low- and high-salinity regime of the pore water. The obtained data further show that chemical interaction causes a reduction of interface conductivity, which could be related to the low pH in the acidic environment. This effect is described by a correction term, which is a constant value as a first approximation. When the impact of CO2 is taken into account, a correct reconstruction of fluid saturation from electrical measurements is possible. In addition, changes of the inner surface area, which are related to mineral dissolution or precipitation processes, can be quantified. Both the knowledge gained from the laboratory experiments and a new workflow for the description and incorporation of geological geometry models enable realistic finite element simulations. Those were conducted for three different electromagnetic methods applied in the geological scenario of a fictitious carbon dioxide sequestration site. The results show that electromagnetic methods can play an important role in monitoring CO2 sequestration. Compared to other geophysical methods, electromagnetic techniques are generally very sensitive to pore fluids. The proper configuration of sources and receivers for a suitable electromagnetic method that generates the appropriate current systems is essential. Its reactive nature causes CO2 to interact with a water-bearing porous rock in a much more complex manner than non-reactive gases. Without knowledge of the specific interactions between CO2 and rock, a determination of saturation and, consequently, a successful monitoring are possible only to a limited extend. The presented work provides fundamental laboratory investigations for the understanding of the electrical properties of rocks when the reactive gas CO2 enters the rock-water system. All laboratory results are put in the context of potential monitoring applications. The transfer from petrophysical investigations to the planning of an operational monitoring design by means of close-to-reality 3D FE simulations is accomplished.

The objective of our studies is the combination of electromagnetic and direct current (DC) resistivity methods in a joint inversion approach to improve the reconstruction of a given conductivity distribution. We utilize the distinct sensitivity patterns of different methods to enhance the overall resolution power and ensure a more reliable imaging result. In order to simplify the work with more than one electromagnetic method and establish a flexible and state-of-the-art software basis, we developed new DC resistivity and electromagnetic forward modeling and inversion codes based on finite elements of second order on unstructured grids. The forward operators are verified using analytical solutions and convergence studies before we apply a regularized Gauss-Newton scheme and successfully invert synthetic data sets. Finally, we link both codes with each other in a joint inversion. In contrast to most widely used joint inversion strategies, where different data sets are combined in a single least-squares problem resulting in a large system of equations, we introduce a sequential approach that cycles through the different methods iteratively. This way, we avoid several difficulties such as the determination of the full set of regularization parameters or a weighting of the distinct data sets. The sequential approach makes use of a smoothness regularization operator which penalizes the deviation of the model parameters from a given reference model. In our sequential strategy, we use the result of the preceding individual inversion scheme as reference model for the following one. We successfully apply this approach to synthetic data sets and show that the combination of at least two methods yields a significantly improved parameter model compared to the individual inversion results. / Ziel der vorliegenden Arbeit ist die gemeinsame Inversion (\"joint inversion\") elektromagnetischer und geoelektrischer Daten zur Verbesserung des rekonstruierten Leitfähigkeitsmodells. Dabei nutzen wir die verschiedenartigen Sensitivitäten der Methoden aus, um die Auflösung zu erhöhen und ein zuverlässigeres Ergebnis zu erhalten. Um die Arbeit mit mehr als einer Methode zu vereinfachen und eine flexible Softwarebasis auf dem neuesten Stand der Forschung zu etablieren, wurden zwei Codes zur Modellierung und Inversion geoelektrischer als auch elektromagnetischer Daten neu entwickelt, die mit finiten Elementen zweiter Ordnung auf unstrukturierten Gittern arbeiten. Die Vorwärtsoperatoren werden mithilfe analytischer Lösungen und Konvergenzstudien verifiziert, bevor wir ein regularisiertes Gauß-Newton-Verfahren zur Inversion synthetischer Datensätze anwenden. Im Gegensatz zur meistgenutzten \"joint inversion\"-Strategie, bei der verschiedene Daten in einem einzigen Minimierungsproblem kombiniert werden, was in einem großen Gleichungssystem resultiert, stellen wir schließlich einen sequentiellen Ansatz vor, der zyklisch durch die einzelnen Methoden iteriert. So vermeiden wir u.a. eine komplizierte Wichtung der verschiedenen Daten und die Bestimmung aller Regularisierungsparameter in einem Schritt. Der sequentielle Ansatz wird über die Anwendung einer Glättungsregularisierung umgesetzt, bei der die Abweichung der Modellparameter zu einem gegebenen Referenzmodell bestraft wird. Wir nutzen das Ergebnis der vorangegangenen Einzelinversion als Referenzmodell für die folgende Inversion. Der Ansatz wird erfolgreich auf synthetische Datensätze angewendet und wir zeigen, dass die Kombination von mehreren Methoden eine erhebliche Verbesserung des Inversionsergebnisses im Vergleich zu den Einzelinversionen liefert.

The field of geosciences, including geophysics, plays a crucial role in addressing society's pressing concerns related to energy demand, climate change, resource preservation, and environmental protection. Reactive systems encountered in this context are characterized by intricate interactions among various phases, environmental conditions, physical and chemical processes. Achieving a comprehensive understanding of these processes and quantitatively evaluating reactive systems necessitates a holistic scientific approach. This approach encompasses efficient categorization of reactive systems, the development of appropriate experimental and computational tools, and the collection and dissemination of relevant data. In this context, this thesis contributes to geophysics and petrophysics with a focus on reactive systems. It presents and interprets laboratory datasets that address various complex aspects of rock behavior, including the presence of graphite, resulting anisotropy, and the challenging petrophysical characteristics of carbonate rocks. This compilation of research results provides a multifaceted perspective on the complex nature of rocks, including their mineralogical, physical, and chemical properties. It thus contributes to a deeper comprehension of electrical rock properties and their practical utility. Upon examining carbonate rocks and the response of graphitic schist to CO$_\mathrm{2}$ under reservoir conditions, it becomes clear that the impact of increased reactivity in a system on geophysical parameters varies depending on the specific characteristics of the rocks and systems under investigation. Consequently, geophysical methods aiming at a quantitative assessment of reactive systems must exhibit robustness and efficiency in order to be effectively applied in a site- and system-specific manner. Expanding on this foundation, computational methods have been developed to aid in the quantitative analysis of reactive processes in laboratory experiments. These methods also serve as tools for gaining insights into the origin of rock properties and the impact of microstructure variation. Furthermore, inversion techniques are introduced in conjunction with custom-designed experiments within the field of petrophysics. The resultant software tool is made publicly accessible. The research further delves into the exploration of how physical properties of rocks are influenced by their microstructure, as well as how the stochastic nature of pore space geometry can introduce variability and uncertainty in rock physics data. This investigation was carried out through microstructure modeling and finite element simulations. Leveraging these tailored computational techniques allowed for a comprehensive understanding of laboratory data, facilitating robust generalizations and contextualization for field applications and site-specific integrated interpretation. To illustrate the application in a complex natural reactive system, a field study focusing on coastal fumarolic vents in volcanic terrain was carried out and is presented. The challenges, prospects and visualization strategies for integrating simulation or inversion results from different methods are examined. Effective evaluation of complex sites requires open access to existing knowledge, including laboratory datasets. Consequently, this work documents and provides openly accessible examples of complex multi-method laboratory datasets to facilitate better understanding, re-evaluation and application in the field. Finally, the handling of multi-reactive systems in field applications is discussed. It involves the integration of three-dimensional subsurface models with petrophysical insights related to multi-reactive systems. These models are calibrated using additional complementary data from surface or borehole sources. This integrated approach enables a quantitative assessment of site-specific multi-reactive systems.

Extensive deforestations, particularly in the (sub)tropics, have led to intense soil degradation and erosion with concomitant reduction in soil fertility. Reforestations or plantations on those degraded sites may provide effective measures to mitigate further soil degradation and erosion, and can lead to improved soil quality. However, a change in land use from, e.g., grassland to forest may have a crucial impact on water balance. This may affect water availability even under humid tropical climate conditions where water is normally not a limiting factor. In this context, it should also be considered that according to climate change projections rainfall may decrease in some of these regions. To mitigate climate change related problems (e.g. increases in erosion and drought), reforestations are often carried out. Unfortunately, those measures are seldom completely successful, because the environmental conditions and the plant specific requirements are not appropriately taken into account. This is often due to data-scarcity and limited financial resources in tropical regions. For this reason, innovative approaches are required that are able to measure environmental conditions quasi-continuously in a cost-effective manner. Simultaneously, reforestation measures should be accompanied by monitoring in order to evaluate reforestation success and to mitigate, or at least to reduce, potential problems associated with reforestation (e.g. water scarcity). To avoid reforestation failure and negative implications on ecosystem services, it is crucial to get insights into the water balance of the actual ecosystem, and potential changes resulting from reforestation. The identification and prediction of water balance changes as a result of reforestation under climate change requires the consideration of the complex feedback system of processes in the soil-vegetation-atmosphere continuum. Models that account for those feedback system are Soil-Vegetation-Atmosphere-Transfer (SVAT) models. For the before-mentioned reasons, this study targeted two main objectives: (i) to develop and test a method combination for site evaluation under data scarcity (i.e. study requirements) (Part I) and (ii) to investigate the consequences of prediction uncertainty of the SVAT model input parameters, which were derived using geophysical methods, on SVAT modeling (Part II). A water balance modeling approach was set at the center of the site evaluation approach. This study used the one-dimensional CoupModel, which is a SVAT model. CoupModel requires detailed spatial soil information for (i) model parameterization, (ii) upscaling of model results and accounting for local to regional-scale soil heterogeneity, and (iii) monitoring of changes in soil properties and plant characteristics over time. Since traditional approaches to soil and vegetation sampling and monitoring are time consuming and expensive (and therefore often limited to point information), geophysical methods were used to overcome this spatial limitation. For this reason, vis-NIR spectroscopy (visible to near-infrared wavelength range) was applied for the measurement of soil properties (physical and chemical), and remote sensing to derive vegetation characteristics (i.e. leaf area index (LAI)). Since the estimated soil properties (mainly texture) could be used to parameterize a SVAT model, this study investigated the whole processing chain and related prediction uncertainty of soil texture and LAI, and their impact on CoupModel water balance prediction uncertainty. A greenhouse experiment with bamboo plants was carried out to determine plant-physiological characteristics needed for CoupModel parameterization. Geoelectrics was used to investigate soil layering, with the intent of determining site-representative soil profiles for model parameterization. Soil structure was investigated using image analysis techniques that allow the quantitative assessment and comparability of structural features. In order to meet the requirements of the selected study approach, the developed methodology was applied and tested for a site in NE-Brazil (which has low data availability) with a bamboo plantation as the test site and a secondary forest as the reference (reference site). Nevertheless, the objective of the thesis was not the concrete modeling of the case study site, but rather the evaluation of the suitability of the selected methods to evaluate sites for reforestations and to monitor their influence on the water balance as well as soil properties. The results (Part III) highlight that one needs to be aware of the measurement uncertainty related to SVAT model input parameters, so for instance the uncertainty of model input parameters such as soil texture and leaf area index influences meaningfully the simulated model water balance output. Furthermore, this work indicates that vis-NIR spectroscopy is a fast and cost-efficient method for soil measurement, mapping, and monitoring of soil physical (texture) and chemical (N, TOC, TIC, TC) properties, where the quality of soil prediction depends on the instrument (e.g. sensor resolution), the sample properties (i.e. chemistry), and the site characteristics (i.e. climate). Additionally, also the sensitivity of the CoupModel with respect to texture prediction uncertainty with respect to surface runoff, transpiration, evaporation, evapotranspiration, and soil water content depends on site conditions (i.e. climate and soil type). For this reason, it is recommended that SVAT model sensitivity analysis be carried out prior to field spectroscopic measurements to account for site specific climate and soil conditions. Nevertheless, mapping of the soil properties estimated via spectroscopy using kriging resulted in poor interpolation (i.e. weak variograms) results as a consequence of a summation of uncertainty arising from the method of field measurement to mapping (i.e. spectroscopic soil prediction, kriging error) and site-specific ‘small-scale’ heterogeneity. The selected soil evaluation method (vis-NIR spectroscopy, structure comparison using image analysis, traditional laboratory analysis) showed that there are significant differences between the bamboo soil and the adjacent secondary forest soil established on the same soil type (Vertisol). Reflecting on the major study results, it can be stated that the selected method combination is a way forward to a more detailed and efficient way to evaluate the suitability of a specific site for reforestation. The results of this study provide insights into where and when during soil and vegetation measurements a high measurement accuracy is required to minimize uncertainties in SVAT modeling.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Umfangreiche Abholzungen, besonders in den (Sub-)Tropen, habe zu intensiver Bodendegradierung und Erosion mit einhergehendem Verlust der Bodenfruchtbarkeit geführt. Eine wirksame Maßnahme zur Vermeidung fortschreitender Bodendegradierung und Erosion sind Aufforstungen auf diesen Flächen, die bisweilen zu einer verbesserten Bodenqualität führen können. Eine Umwandlung von Grünland zu Wald kann jedoch einen entscheidenden Einfluss auf den Wasserhaushalt haben. Selbst unter humid-tropischen Klimabedingungen, wo Wasser in der Regel kein begrenzender Faktor ist, können sich Aufforstungen negativ auf die Wasserverfügbarkeit auswirken. In diesem Zusammenhang muss auch berücksichtigt werden, dass Klimamodelle eine Abnahme der Niederschläge in einigen dieser Regionen prognostizieren. Um die Probleme, die mit dem Klimawandel in Verbindung stehen zu mildern (z.B. Zunahme von Erosion und Dürreperioden), wurden und werden bereits umfangreiche Aufforstungsmaßnahmen durchgeführt. Viele dieser Maßnahmen waren nicht immer umfassend erfolgreich, weil die Umgebungsbedingungen sowie die pflanzenspezifischen Anforderungen nicht angemessen berücksichtigt wurden. Dies liegt häufig an der schlechten Datengrundlage sowie an den in vielen Entwicklungs- und Schwellenländern begrenzter verfügbarer finanzieller Mittel. Aus diesem Grund werden innovative Ansätze benötigt, die in der Lage sind quasi-kontinuierlich und kostengünstig die Standortbedingungen zu erfassen und zu bewerten. Gleichzeitig sollte eine Überwachung der Wiederaufforstungsmaßnahme erfolgen, um deren Erfolg zu bewerten und potentielle negative Effekte (z.B. Wasserknappheit) zu erkennen und diesen entgegenzuwirken bzw. reduzieren zu können. Um zu vermeiden, dass Wiederaufforstungen fehlschlagen oder negative Auswirkungen auf die Ökosystemdienstleistungen haben, ist es entscheidend, Kenntnisse vom tatsächlichen Wasserhaushalt des Ökosystems zu erhalten und Änderungen des Wasserhaushalts durch Wiederaufforstungen vorhersagen zu können. Die Ermittlung und Vorhersage von Wasserhaushaltsänderungen infolge einer Aufforstung unter Berücksichtigung des Klimawandels erfordert die Berücksichtigung komplex-verzahnter Rückkopplungsprozesse im Boden-Vegetations-Atmosphären Kontinuum. Hydrologische Modelle, die explizit den Einfluss der Vegetation auf den Wasserhaushalt untersuchen sind Soil-Vegetation-Atmosphere-Transfer (SVAT) Modelle. Die vorliegende Studie verfolgte zwei Hauptziele: (i) die Entwicklung und Erprobung einer Methodenkombination zur Standortbewertung unter Datenknappheit (d.h. Grundanforderung des Ansatzes) (Teil I) und (ii) die Untersuchung des Einflusses der mit geophysikalischen Methoden vorhergesagten SVAT-Modeleingangsparameter (d.h. Vorhersageunsicherheiten) auf die Modellierung (Teil II). Eine Wasserhaushaltsmodellierung wurde in den Mittelpunkt der Methodenkombination gesetzt. In dieser Studie wurde das 1D SVAT Model CoupModel verwendet. CoupModel benötigen detaillierte räumliche Bodeninformationen (i) zur Modellparametrisierung, (ii) zum Hochskalierung von Modellergebnissen unter Berücksichtigung lokaler und regionaler Bodenheterogenität, und (iii) zur Beobachtung (Monitoring) der zeitlichen Veränderungen des Bodens und der Vegetation. Traditionelle Ansätze zur Messung von Boden- und Vegetationseigenschaften und deren Monitoring sind jedoch zeitaufwendig, teuer und beschränken sich daher oft auf Punktinformationen. Ein vielversprechender Ansatz zur Überwindung der räumlichen Einschränkung sind die Nutzung geophysikalischer Methoden. Aus diesem Grund wurden vis-NIR Spektroskopie (sichtbarer bis nah-infraroter Wellenlängenbereich) zur quasi-kontinuierlichen Messung von physikalischer und chemischer Bodeneigenschaften und Satelliten-basierte Fernerkundung zur Ableitung von Vegetationscharakteristika (d.h. Blattflächenindex (BFI)) eingesetzt. Da die mit geophysikalisch hergeleiteten Bodenparameter (hier Bodenart) und Pflanzenparameter zur Parametrisierung eines SVAT Models verwendet werden können, wurde die gesamte Prozessierungskette und die damit verbundenen Unsicherheiten und deren potentiellen Auswirkungen auf die Wasserhaushaltsmodellierung mit CoupModel untersucht. Ein Gewächshausexperiment mit Bambuspflanzen wurde durchgeführt, um die zur CoupModel Parametrisierung notwendigen pflanzenphysio- logischen Parameter zu bestimmen. Geoelektrik wurde eingesetzt, um die Bodenschichtung der Untersuchungsfläche zu untersuchen und ein repräsentatives Bodenprofil zur Modellierung zu definieren. Die Bodenstruktur wurde unter Verwendung einer Bildanalysetechnik ausgewertet, die die qualitativen Bewertung und Vergleichbarkeit struktureller Merkmale ermöglicht. Um den Anforderungen des gewählten Standortbewertungsansatzes gerecht zu werden, wurde die Methodik auf einem Standort mit einer Bambusplantage und einem Sekundärregenwald (als Referenzfläche) in NO-Brasilien (d.h. geringe Datenverfügbarkeit) entwickelt und getestet. Das Ziel dieser Arbeit war jedoch nicht die Modellierung dieses konkreten Standortes, sondern die Bewertung der Eignung des gewählten Methodenansatzes zur Standortbewertung für Aufforstungen und deren zeitliche Beobachtung, als auch die Bewertung des Einfluss von Aufforstungen auf den Wasserhaushalt und die Bodenqualität. Die Ergebnisse (Teil III) verdeutlichen, dass es notwendig ist, sich den potentiellen Einfluss der Messunsicherheiten der SVAT Modelleingangsparameter auf die Modellierung bewusst zu sein. Beispielsweise zeigte sich, dass die Vorhersageunsicherheiten der Bodentextur und des BFI einen bedeutenden Einfluss auf die Wasserhaushaltsmodellierung mit CoupModel hatte. Die Arbeit zeigt weiterhin, dass vis-NIR Spektroskopie zur schnellen und kostengünstigen Messung, Kartierung und Überwachung boden-physikalischer (Bodenart) und -chemischer (N, TOC, TIC, TC) Eigenschaften geeignet ist. Die Qualität der Bodenvorhersage hängt vom Instrument (z.B. Sensorauflösung), den Probeneigenschaften (z.B. chemische Zusammensetzung) und den Standortmerkmalen (z.B. Klima) ab. Die Sensitivitätsanalyse mit CoupModel zeigte, dass der Einfluss der spektralen Bodenartvorhersageunsicherheiten auf den mit CoupModel simulierten Oberflächenabfluss, Evaporation, Transpiration und Evapotranspiration ebenfalls von den Standortbedingungen (z.B. Klima, Bodentyp) abhängt. Aus diesem Grund wird empfohlen eine SVAT Model Sensitivitätsanalyse vor der spektroskopischen Feldmessung von Bodenparametern durchzuführen, um die Standort-spezifischen Boden- und Klimabedingungen angemessen zu berücksichtigen. Die Anfertigung einer Bodenkarte unter Verwendung von Kriging führte zu schlechten Interpolationsergebnissen in Folge der Aufsummierung von Mess- und Schätzunsicherheiten (d.h. bei spektroskopischer Feldmessung, Kriging-Fehler) und der kleinskaligen Bodenheterogenität. Anhand des gewählten Bodenbewertungsansatzes (vis-NIR Spektroskopie, Strukturvergleich mit Bildanalysetechnik, traditionelle Laboranalysen) konnte gezeigt werden, dass es bei gleichem Bodentyp (Vertisol) signifikante Unterschiede zwischen den Böden unter Bambus und Sekundärwald gibt. Anhand der wichtigsten Ergebnisse kann festgehalten werden, dass die gewählte Methodenkombination zur detailreicheren und effizienteren Standortuntersuchung und -bewertung für Aufforstungen beitragen kann. Die Ergebnisse dieser Studie geben einen Einblick darauf, wo und wann bei Boden- und Vegetationsmessungen eine besonders hohe Messgenauigkeit erforderlich ist, um Unsicherheiten bei der SVAT Modellierung zu minimieren.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185 / Extensos desmatamentos que estão sendo feitos especialmente nos trópicos e sub-trópicos resultam em uma intensa degradação do solo e num aumento da erosão gerando assim uma redução na sua fertilidade. Reflorestamentos ou plantações nestas áreas degradadas podem ser medidas eficazes para atenuar esses problemas e levar a uma melhoria da qualidade do mesmo. No entanto, uma mudança no uso da terra, por exemplo de pastagem para floresta pode ter um impacto crucial no balanço hídrico e isso pode afetar a disponibilidade de água, mesmo sob condições de clima tropical úmido, onde a água normalmente não é um fator limitante. Devemos levar também em consideração que de acordo com projeções de mudanças climáticas, as precipitações em algumas dessas regiões também diminuirão agravando assim, ainda mais o quadro apresentado. Para mitigar esses problemas relacionados com as alterações climáticas, reflorestamentos são frequentemente realizados mas raramente são bem-sucedidos, pois condições ambientais como os requisitos específicos de cada espécie de planta, não são devidamente levados em consideração. Isso é muitas vezes devido, não só pela falta de dados, como também por recursos financeiros limitados, que são problemas comuns em regiões tropicais. Por esses motivos, são necessárias abordagens inovadoras que devam ser capazes de medir as condições ambientais quase continuamente e de maneira rentável. Simultaneamente com o reflorestamento, deve ser feita uma monitoração a fim de avaliar o sucesso da atividade e para prevenir, ou pelo menos, reduzir os problemas potenciais associados com o mesmo (por exemplo, a escassez de água). Para se evitar falhas e reduzir implicações negativas sobre os ecossistemas, é crucial obter percepções sobre o real balanço hídrico e as mudanças que seriam geradas por esse reflorestamento. Por este motivo, esta tese teve como objetivo desenvolver e testar uma combinação de métodos para avaliação de áreas adequadas para reflorestamento. Com esse intuito, foi colocada no centro da abordagem de avaliação a modelagem do balanço hídrico local, que permite a identificação e estimação de possíveis alterações causadas pelo reflorestamento sob mudança climática considerando o sistema complexo de realimentação e a interação de processos do continuum solo-vegetação-atmosfera. Esses modelos hidrológicos que investigam explicitamente a influência da vegetação no equilíbrio da água são conhecidos como modelos Solo-Vegetação-Atmosfera (SVAT). Esta pesquisa focou em dois objetivos principais: (i) desenvolvimento e teste de uma combinação de métodos para avaliação de áreas que sofrem com a escassez de dados (pré-requisito do estudo) (Parte I), e (ii) a investigação das consequências da incerteza nos parâmetros de entrada do modelo SVAT, provenientes de dados geofísicos, para modelagem hídrica (Parte II). A fim de satisfazer esses objetivos, o estudo foi feito no nordeste brasileiro,por representar uma área de grande escassez de dados, utilizando como base uma plantação de bambu e uma área de floresta secundária. Uma modelagem do balanço hídrico foi disposta no centro da metodologia para a avaliação de áreas. Este estudo utilizou o CoupModel que é um modelo SVAT unidimensional e que requer informações espaciais detalhadas do solo para (i) a parametrização do modelo, (ii) aumento da escala dos resultados da modelagem, considerando a heterogeneidade do solo de escala local para regional e (iii) o monitoramento de mudanças nas propriedades do solo e características da vegetação ao longo do tempo. Entretanto, as abordagens tradicionais para amostragem de solo e de vegetação e o monitoramento são demorados e caros e portanto muitas vezes limitadas a informações pontuais. Por esta razão, métodos geofísicos como a espectroscopia visível e infravermelho próximo (vis-NIR) e sensoriamento remoto foram utilizados respectivamente para a medição de propriedades físicas e químicas do solo e para derivar as características da vegetação baseado no índice da área foliar (IAF). Como as propriedades estimadas de solo (principalmente a textura) poderiam ser usadas para parametrizar um modelo SVAT, este estudo investigou toda a cadeia de processamento e as incertezas de previsão relacionadas à textura de solo e ao IAF. Além disso explorou o impacto destas incertezas criadas sobre a previsão do balanço hídrico simulado por CoupModel. O método geoelétrico foi aplicado para investigar a estratificação do solo visando a determinação de um perfil representante. Já a sua estrutura foi explorada usando uma técnica de análise de imagens que permitiu a avaliação quantitativa e a comparabilidade dos aspectos estruturais. Um experimento realizado em uma estufa com plantas de bambu (Bambusa vulgaris) foi criado a fim de determinar as caraterísticas fisiológicas desta espécie que posteriormente seriam utilizadas como parâmetros para o CoupModel. Os resultados do estudo (Parte III) destacam que é preciso estar consciente das incertezas relacionadas à medição de parâmetros de entrada do modelo SVAT. A incerteza presente em alguns parâmetros de entrada como por exemplo, textura de solo e o IAF influencia significantemente a modelagem do balanço hídrico. Mesmo assim, esta pesquisa indica que vis-NIR espectroscopia é um método rápido e economicamente viável para medir, mapear e monitorar as propriedades físicas (textura) e químicas (N, TOC, TIC, TC) do solo. A precisão da previsão dessas propriedades depende do tipo de instrumento (por exemplo da resolução do sensor), da propriedade da amostra (a composição química por exemplo) e das características das condições climáticas da área. Os resultados apontam também que a sensitividade do CoupModel à incerteza da previsão da textura de solo em respeito ao escoamento superficial, transpiração, evaporação, evapotranspiração e ao conteúdo de água no solo depende das condições gerais da área (por exemplo condições climáticas e tipo de solo). Por isso, é recomendado realizar uma análise de sensitividade do modelo SVAT prior a medição espectral do solo no campo, para poder considerar adequadamente as condições especificas do área em relação ao clima e ao solo. Além disso, o mapeamento de propriedades de solo previstas pela espectroscopia usando o kriging, resultou em interpolações de baixa qualidade (variogramas fracos) como consequência da acumulação de incertezas surgidas desde a medição no campo até o seu mapeamento (ou seja, previsão do solo via espectroscopia, erro do kriging) e heterogeneidade especifica de uma pequena escala. Osmétodos selecionados para avaliação das áreas (vis-NIR espectroscopia, comparação da estrutura de solo por meio de análise de imagens, análise de laboratório tradicionais) revelou a existência de diferenças significativas entre o solo sob bambu e o sob floresta secundária, apesar de ambas terem sido estabelecidas no mesmo tipo de solo (vertissolo). Refletindo sobre os principais resultados do estudo, pode-se afirmar que a combinação dos métodos escolhidos e aplicados representam uma forma mais detalhada e eficaz de avaliar se uma determinada área é adequada para ser reflorestada. Os resultados apresentados fornecem percepções sobre onde e quando, durante a medição do solo e da vegetação, é necessário se ter uma precisão mais alta a fim de minimizar incertezas potenciais na modelagem com o modelo SVAT.:I. Development of method combination for site evaluation for reforestations in data-scarce regions .... 23 2. Motivation, objectives and study approach .... 24 2.1. Introduction and study motivation .... 24 2.1.1. Research objectives and hypotheses ..... 27 2.1.2. Study approach ..... 28 3. Site selection and characterization procedure .... 32 3.1. On large scale – landscape segmentation .... 32 3.2. On local scale - case study site selection and characterization .... 34 3.2.1. Available data and characterization of identified case study site .... 34 3.2.2. Spatial distribution of soil properties - soil structure, bulk density and porosity .... 37 4. Eco-hydrological modeling - deriving plant-physiological model parameters .... 50 4.1. Introduction .... 50 4.2. Motivation and objectives ..... 52 4.3. Methods ... 53 4.3.1. Design of greenhouse experiment .... 53 4.3.2. Derivation of climate time-series .... 56 4.3.3. Plant variables and response to water availability .... 59 4.4. Results and discussion .... 62 4.4.1. Soil sample analysis .... 62 4.4.2. Measured time-series .... 63 4.4.3. Plant response to drought stress ..... 67 4.4.4. Water balance approach and estimated time-series of plant transpiration .... 71 4.4.5. Derived SVAT model plant input parameter .... 73 5. Near-surface geophysics .... 75 5.1. Vis-NIR spectroscopy of soils .... 76 5.1.1. Methods and materials .... 77 5.1.2. Results and discussion .... 79 5.2. Geoelectrics ..... 88 5.2.1. Methods and materials .... 89 5.2.2. Results and discussion .... 94 6. Remote sensing of vegetation .... 102 6.1. Introduction .... 102 6.2. Methods and materials .... 103 6.2.1. RapidEye images and ATCOR description .... 103 6.2.2. Satellite image preparation and atmospheric correction .... 104 6.2.3. LAI field measurement and computation of vegetation indices .... 105 6.2.4. Establishment of empirical LAI retrieval model .... 106 6.3. Results and discussion .... 108 6.3.1. Vegetation index ranking .... 108 II. Uncertainty analysis of model input parameters from geophysical data .... 110 7. Deriving soil properties - vis-NIR spectroscopy technique .... 111 7.1. Motivation .... 111 7.2. Materials and methods .... 113 7.2.1. Study sites .... 113 7.2.2. Samples used for uncertainty analysis .... 114 7.2.3. Vis-NIR spectral measurement, chemometric spectral data transformation and spectroscopic modeling .... 116 7.2.4. Assessment statistics .... 118 7.2.5. Inter-instrument calibration model transferability for soil monitoring .... 119 7.2.6. Analysis of SVAT model sensitivity to soil texture .... 121 7.3. Results and discussion .... 124 7.3.1. Effect of pre-processing transformation methods on prediction accuracy .... 124 7.3.2. Effect of spectral resampling .... 125 7.3.3. Accuracy of soil property prediction .... 127 7.3.4. Spectrometer comparison .... 133 7.3.5. Inter-instrument transferability .... 134 7.3.6. Precision of spectroscopic predictions in the context of SVAT modeling ....139 7.4. Conclusion .... 146 8. Deriving vegetation properties - remote sensing techniques .... 149 8.1. Motivation .... 149 8.2. Materials and methods .... 150 8.2.1. Study site .... 150 8.2.2. RapidEye images .... 150 8.2.3. Satellite image preparation .... 152 8.2.4. Atmospheric correction with parameter variation .... 152 8.2.5. Investigation of two successive images .... 154 8.2.6. LAI field measurement and computation of vegetation indices .... 155 8.2.7. Establishment of empirical LAI retrieval model .... 155 8.2.8. Sensitivity of SVAT model to LAI uncertainty .... 157 8.3. Results and discussion .... 157 8.3.1. Influence of atmospheric correction on RapidEye bands .... 158 8.3.2. Uncertainty of LAI field measurements and empirical relationship .... 161 8.3.3. Influence of ATCOR parameterization on LAI estimation .... 161 8.3.4. LAI variability within one image .... 167 8.3.5. LAI differences within the overlapping area of successive images recorded on the same date .... 171 8.3.6. Evaluation of LAI uncertainty in context of SVAT modeling ... 174 8.4. Conclusion .... 176 III. Synthesis .... 178 9. Summary of results and conclusions .... 179 10. Perspectives .... 185

Die vorliegende Arbeit befasst sich mit der Fragestellung, inwiefern sich gegebene Verfahren zur Approximation von rationalen Krylow-Räumen zur Berechnung von Matrixfunktionen eignen. Als Modellproblem wird dazu eine 2.5D-Formulierung eines Problems aus der Gleichstrom-Geoelektrik mit finiten Elementen formuliert und dann mittels Matrixfunktionen auf rationalen Krylow-Unterräumen gelöst. Ein weiterer Teil beschäftigt sich mit dem Vergleich zweier Verfahren zur Transformation bestehender rationaler Krylow-Räume. Bei beiden Varianten werden die zugrunde liegenden Pole getauscht ohne dass ein explizites Invertieren von Matrizen notwendig ist. In dieser Arbeit werden die über mehrere Publikationen verteilten Grundlagen einheitlich zusammengetragen und fehlende Zusammenhänge ergänzt. Beide Verfahren eignen sich prinzipiell um rationale Krylow-Räume zu approximieren. Dies wird anhand mehrerer Beispiele gezeigt. Anhand des Modellproblems werden Beschränkungen der Methoden verdeutlicht.

Im Rahmen des Projektes ROHSA 3 wurden auf Basis vorhandener und neu verfügbar gemachter Daten Prognosen für Zinn, Wolfram sowie Fluss- und Schwerspat in einem 740 m² großen Gebiet im Mittelerzgebirge angefertigt. Die Karten zeigen höffige Gebieten, wobei für Zinn und Wolfram erstmals auch Mengen-Prognosen erstellt wurden. Geophysikalische, geochemische Daten sowie Lagerstättenindikatoren (z. B. Tektonik, Erz kontrollierende Lithologien) wurden durch die Software advangeo@ aufbereitet und mittels ihrer künstlich neuronalen Netze (KNN) verarbeitet. Durch höhere Datendichte, Einbeziehung dreidimensionaler geologischer Daten und Aufstellung quantitativer Modelle wurde ein deutlicher Erkenntnisfortschritt erzielt. Redaktionsschluss: 31.07.2020