Institut für Erd- und Umweltwissenschaften
Filtern
Erscheinungsjahr
- 2019 (25) (entfernen)
Dokumenttyp
- Wissenschaftlicher Artikel (10)
- Dissertation (9)
- Postprint (4)
- Sonstiges (1)
- Rezension (1)
Gehört zur Bibliographie
- ja (25)
Schlagworte
- AMOC (2)
- Climate (2)
- Connectivity (2)
- Forecasting Framework (2)
- Jaguaribe Basin (2)
- Nordeste (2)
- Precipitation (2)
- Reservoir Networks (2)
- Sediment Transport (2)
- Uncertainty Processor (2)
Institut
On a planetary scale human populations need to adapt to both socio-economic and environmental problems amidst rapid global change. This holds true for coupled human-environment (socio-ecological) systems in rural and urban settings alike. Two examples are drylands and urban coasts. Such socio-ecological systems have a global distribution. Therefore, advancing the knowledge base for identifying socio-ecological adaptation needs with local vulnerability assessments alone is infeasible: The systems cover vast areas, while funding, time, and human resources for local assessments are limited. They are lacking in low an middle-income countries (LICs and MICs) in particular.
But places in a specific socio-ecological system are not only unique and complex – they also exhibit similarities. A global patchwork of local rural drylands vulnerability assessments of human populations to socio-ecological and environmental problems has already been reduced to a limited number of problem structures, which typically cause vulnerability. However, the question arises whether this is also possible in urban socio-ecological systems. The question also arises whether these typologies provide added value in research beyond global change. Finally, the methodology employed for drylands needs refining and standardizing to increase its uptake in the scientific community. In this dissertation, I set out to fill these three gaps in research.
The geographical focus in my dissertation is on LICs and MICs, which generally have lower capacities to adapt, and greater adaptation needs, regarding rapid global change. Using a spatially explicit indicator-based methodology, I combine geospatial and clustering methods to identify typical configurations of key factors in case studies causing vulnerability to human populations in two specific socio-ecological systems. Then I use statistical and analytical methods to interpret and appraise both the typical configurations and the global typologies they constitute.
First, I improve the indicator-based methodology and then reanalyze typical global problem structures of socio-ecological drylands vulnerability with seven indicator datasets. The reanalysis confirms the key tenets and produces a more realistic and nuanced typology of eight spatially explicit problem structures, or vulnerability profiles: Two new profiles with typically high natural resource endowment emerge, in which overpopulation has led to medium or high soil erosion. Second, I determine whether the new drylands typology and its socio-ecological vulnerability concept advance a thematically linked scientific debate in human security studies: what drives violent conflict in drylands? The typology is a much better predictor for conflict distribution and incidence in drylands than regression models typically used in peace research. Third, I analyze global problem structures typically causing vulnerability in an urban socio-ecological system - the rapidly urbanizing coastal fringe (RUCF) – with eleven indicator datasets. The RUCF also shows a robust typology, and its seven profiles show huge asymmetries in vulnerability and adaptive capacity. The fastest population increase, lowest income, most ineffective governments, most prevalent poverty, and lowest adaptive capacity are all typically stacked in two profiles in LICs. This shows that beyond local case studies tropical cyclones and/or coastal flooding are neither stalling rapid population growth, nor urban expansion, in the RUCF. I propose entry points for scaling up successful vulnerability reduction strategies in coastal cities within the same vulnerability profile.
This dissertation shows that patchworks of local vulnerability assessments can be generalized to structure global socio-ecological vulnerabilities in both rural and urban socio-ecological systems according to typical problems. In terms of climate-related extreme events in the RUCF, conflicting problem structures and means to deal with them are threatening to widen the development gap between LICs and high-income countries unless successful vulnerability reduction measures are comprehensively scaled up. The explanatory power for human security in drylands warrants further applications of the methodology beyond global environmental change research in the future. Thus, analyzing spatially explicit global typologies of socio-ecological vulnerability is a useful complement to local assessments: The typologies provide entry points for where to consider which generic measures to reduce typical problem structures – including the countless places without local assessments. This can save limited time and financial resources for adaptation under rapid global change.
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.
The semiarid northeast of Brazil is one of the most densely populated dryland regions in the world and recurrently affected by severe droughts. Thus, reliable seasonal forecasts of streamflow and reservoir storage are of high value for water managers. Such forecasts can be generated by applying either hydrological models representing underlying processes or statistical relationships exploiting correlations among meteorological and hydrological variables. This work evaluates and compares the performances of seasonal reservoir storage forecasts derived by a process-based hydrological model and a statistical approach.
Driven by observations, both models achieve similar simulation accuracies. In a hindcast experiment, however, the accuracy of estimating regional reservoir storages was considerably lower using the process-based hydrological model, whereas the resolution and reliability of drought event predictions were similar by both approaches. Further investigations regarding the deficiencies of the process-based model revealed a significant influence of antecedent wetness conditions and a higher sensitivity of model prediction performance to rainfall forecast quality.
Within the scope of this study, the statistical model proved to be the more straightforward approach for predictions of reservoir level and drought events at regionally and monthly aggregated scales. However, for forecasts at finer scales of space and time or for the investigation of underlying processes, the costly initialisation and application of a process-based model can be worthwhile. Furthermore, the application of innovative data products, such as remote sensing data, and operational model correction methods, like data assimilation, may allow for an enhanced exploitation of the advanced capabilities of process-based hydrological models.
Partial melting is a first order process for the chemical differentiation of the crust (Vielzeuf et al., 1990). Redistribution of chemical elements during melt generation crucially influences the composition of the lower and upper crust and provides a mechanism to concentrate and transport chemical elements that may also be of economic interest. Understanding of the diverse processes and their controlling factors is therefore not only of scientific interest but also of high economic importance to cover the demand for rare metals.
The redistribution of major and trace elements during partial melting represents a central step for the understanding how granite-bound mineralization develops (Hedenquist and Lowenstern, 1994). The partial melt generation and mobilization of ore elements (e.g. Sn, W, Nb, Ta) into the melt depends on the composition of the sedimentary source and melting conditions. Distinct source rocks have different compositions reflecting their deposition and alteration histories. This specific chemical “memory” results in different mineral assemblages and melting reactions for different protolith compositions during prograde metamorphism (Brown and Fyfe, 1970; Thompson, 1982; Vielzeuf and Holloway, 1988). These factors do not only exert an important influence on the distribution of chemical elements during melt generation, they also influence the volume of melt that is produced, extraction of the melt from its source, and its ascent through the crust (Le Breton and Thompson, 1988). On a larger scale, protolith distribution and chemical alteration (weathering), prograde metamorphism with partial melting, melt extraction, and granite emplacement are ultimately depending on a (plate-)tectonic control (Romer and Kroner, 2016). Comprehension of the individual stages and their interaction is crucial in understanding how granite-related mineralization forms, thereby allowing estimation of the mineralization potential of certain areas. Partial melting also influences the isotope systematics of melt and restite. Radiogenic and stable isotopes of magmatic rocks are commonly used to trace back the source of intrusions or to quantify mixing of magmas from different sources with distinct isotopic signatures (DePaolo and Wasserburg, 1979; Lesher, 1990; Chappell, 1996). These applications are based on the fundamental requirement that the isotopic signature in the melt reflects that of the bulk source from which it is derived. Different minerals in a protolith may have isotopic compositions of radiogenic isotopes that deviate from their whole rock signature (Ayres and Harris, 1997; Knesel and Davidson, 2002). In particular, old minerals with a distinct parent-to-daughter (P/D) ratio are expected to have a specific radiogenic isotope signature. As the partial melting reaction only involves selective phases in a protolith, the isotopic signature of the melt reflects that of the minerals involved in the melting reaction and, therefore, should be different from the bulk source signature. Similar considerations hold true for stable isotopes.
The trace gases CO2 and CH4 pertain to the most relevant greenhouse gases and are important exchange fluxes of the global carbon (C) cycle. Their atmospheric quantity increased significantly as a result of the intensification of anthropogenic activities, such as especially land-use and land-use change, since the mid of the 18th century. To mitigate global climate change and ensure food security, land-use systems need to be developed, which favor reduced trace gas emissions and a sustainable soil carbon management. This requires the accurate and precise quantification of the influence of land-use and land-use change on CO2 and CH4 emissions. A common method to determine the trace gas dynamics and C sink or source function of a particular ecosystem is the closed chamber method. This method is often used assuming that accuracy and precision are high enough to determine differences in C gas emissions for e.g., treatment comparisons or different ecosystem components.
However, the broad range of different chamber designs, related operational procedures and data-processing strategies which are described in the scientific literature contribute to the overall uncertainty of closed chamber-based emission estimates. Hence, the outcomes of meta-analyses are limited, since these methodical differences hamper the comparability between studies. Thus, a standardization of closed chamber data acquisition and processing is much-needed.
Within this thesis, a set of case studies were performed to: (I) develop standardized routines for an unbiased data acquisition and processing, with the aim of providing traceable, reproducible and comparable closed chamber based C emission estimates; (II) validate those routines by comparing C emissions derived using closed chambers with independent C emission estimates; and (III) reveal processes driving the spatio-temporal dynamics of C emissions by developing (data processing based) flux separation approaches.
The case studies showed: (I) the importance to test chamber designs under field conditions for an appropriate sealing integrity and to ensure an unbiased flux measurement. Compared to the sealing integrity, the use of a pressure vent and fan was of minor importance, affecting mainly measurement precision; (II) that the developed standardized data processing routines proved to be a powerful and flexible tool to estimate C gas emissions and that this tool can be successfully applied on a broad range of flux data sets from very different ecosystem; (III) that automatic chamber measurements display temporal dynamics of CO2 and CH4 fluxes very well and most importantly, that they accurately detect small-scale spatial differences in the development of soil C when validated against repeated soil inventories; and (IV) that a simple algorithm to separate CH4 fluxes into ebullition and diffusion improves the identification of environmental drivers, which allows for an accurate gap-filling of measured CH4 fluxes.
Overall, the proposed standardized data acquisition and processing routines strongly improved the detection accuracy and precision of source/sink patterns of gaseous C emissions. Hence, future studies, which consider the recommended improvements, will deliver valuable new data and insights to broaden our understanding of spatio-temporal C gas dynamics, their particular environmental drivers and underlying processes.