Refine
Year of publication
- 2024 (7)
- 2023 (41)
- 2022 (174)
- 2021 (143)
- 2020 (206)
- 2019 (276)
- 2018 (314)
- 2017 (298)
- 2016 (292)
- 2015 (256)
- 2014 (228)
- 2013 (187)
- 2012 (171)
- 2011 (141)
- 2010 (90)
- 2009 (120)
- 2008 (22)
- 2007 (15)
- 2006 (78)
- 2005 (73)
- 2004 (66)
- 2003 (24)
- 2002 (19)
- 2001 (30)
- 2000 (46)
- 1999 (64)
- 1998 (75)
- 1997 (74)
- 1996 (49)
- 1995 (29)
- 1994 (33)
- 1993 (3)
- 1992 (4)
- 1991 (1)
Document Type
- Article (2794)
- Doctoral Thesis (506)
- Postprint (139)
- Other (73)
- Review (52)
- Monograph/Edited Volume (34)
- Preprint (17)
- Conference Proceeding (13)
- Habilitation Thesis (12)
- Master's Thesis (6)
Keywords
- climate change (53)
- Holocene (44)
- erosion (28)
- permafrost (27)
- Himalaya (26)
- remote sensing (26)
- Climate change (23)
- Tibetan Plateau (22)
- Andes (20)
- Earthquake source observations (20)
Institute
- Institut für Geowissenschaften (3654) (remove)
The subsurface harbors a large fraction of Earth's living biomass, forming complex microbial ecosystems. Without a profound knowledge of the ongoing biologically mediated processes and their reaction to anthropogenic changes it is difficult to assess the long-term stability and feasibility of any type of geotechnical utilization, as these influence subsurface ecosystems. Despite recent advances in many areas of subsurface microbiology, the direct quantification of turnover processes is still in its infancy, mainly due to the extremely low cell abundances. We provide an overview of the currently available techniques for the quantification of microbial turnover processes and discuss their specific strengths and limitations. Most techniques employed so far have focused on specific processes, e.g. sulfate reduction or methanogenesis. Recent studies show that processes that were previously thought to exclude each other can occur simultaneously, albeit at very low rates. Without the identification of the respective processes it is impossible to quantify total microbial activity. Even in cases where all simultaneously occurring processes can be identified, the typically very low rates prevent quantification. In many cases a simple measure of total microbial activity would be a better and more robust measure than assays for several specific processes. Enzyme or molecular assays provide a more general approach as they target key metabolic compounds. Depending on the compound targeted a broader spectrum of microbial processes can be quantified. The two most promising compounds are ATP and hydrogenase, as both are ubiquitous in microbes. Technical constraints limit the applicability of currently available ATP-assays for subsurface samples. A recently developed hydrogenase radiotracer assay has the potential to become a key tool for the quantification of subsurface microbial activity.
The behaviour of individuals, businesses, and government entities before, during, and immediately after a disaster can dramatically affect the impact and recovery time. However, existing risk-assessment methods rarely include this critical factor. In this Perspective, we show why this is a concern, and demonstrate that although initial efforts have inevitably represented human behaviour in limited terms, innovations in flood-risk assessment that integrate societal behaviour and behavioural adaptation dynamics into such quantifications may lead to more accurate characterization of risks and improved assessment of the effectiveness of risk-management strategies and investments. Such multidisciplinary approaches can inform flood-risk management policy development.
Numerical simulation of fluid-flow processes in a 3D high-resolution carbonate reservoir analogue
(2014)
A high-resolution three-dimensional (3D) outcrop model of a Jurassic carbonate ramp was used in order to perform a series of detailed and systematic flow simulations. The aim of this study was to test the impact of small- and large-scale geological features on reservoir performance and oil recovery. The digital outcrop model contains a wide range of sedimentological, diagenetic and structural features, including discontinuity surfaces, shoal bodies, mud mounds, oyster bioherms and fractures. Flow simulations are performed for numerical well testing and secondary oil recovery. Numerical well testing enables synthetic but systematic pressure responses to be generated for different geological features observed in the outcrops. This allows us to assess and rank the relative impact of specific geological features on reservoir performance. The outcome documents that, owing to the realistic representation of matrix heterogeneity, most diagenetic and structural features cannot be linked to a unique pressure signature. Instead, reservoir performance is controlled by subseismic faults and oyster bioherms acting as thief zones. Numerical simulations of secondary recovery processes reveal strong channelling of fluid flow into high-permeability layers as the primary control for oil recovery. However, appropriate reservoir-engineering solutions, such as optimizing well placement and injection fluid, can reduce channelling and increase oil recovery.
The climate is a complex dynamical system involving interactions and feedbacks among different processes at multiple temporal and spatial scales. Although numerous studies have attempted to understand the climate system, nonetheless, the studies investigating the multiscale characteristics of the climate are scarce. Further, the present set of techniques are limited in their ability to unravel the multi-scale variability of the climate system. It is completely plausible that extreme events and abrupt transitions, which are of great interest to climate community, are resultant of interactions among processes operating at multi-scale. For instance, storms, weather patterns, seasonal irregularities such as El Niño, floods and droughts, and decades-long climate variations can be better understood and even predicted by quantifying their multi-scale dynamics. This makes a strong argument to unravel the interaction and patterns of climatic processes at different scales. With this background, the thesis aims at developing measures to understand and quantify multi-scale interactions within the climate system.
In the first part of the thesis, I proposed two new methods, viz, multi-scale event synchronization (MSES) and wavelet multi-scale correlation (WMC) to capture the scale-specific features present in the climatic processes. The proposed methods were tested on various synthetic and real-world time series in order to check their applicability and replicability. The results indicate that both methods (WMC and MSES) are able to capture scale-specific associations that exist between processes at different time scales in a more detailed manner as compared to the traditional single scale counterparts.
In the second part of the thesis, the proposed multi-scale similarity measures were used in constructing climate networks to investigate the evolution of spatial connections within climatic processes at multiple timescales. The proposed methods WMC and MSES, together with complex network were applied to two different datasets.
In the first application, climate networks based on WMC were constructed for the univariate global sea surface temperature (SST) data to identify and visualize the SSTs patterns that develop very similarly over time and distinguish them from those that have long-range teleconnections to other ocean regions. Further investigations of climate networks on different timescales revealed (i) various high variability and co-variability regions, and (ii) short and long-range teleconnection regions with varying spatial distance. The outcomes of the study not only re-confirmed the existing knowledge on the link between SST patterns like El Niño Southern Oscillation and the Pacific Decadal Oscillation, but also suggested new insights into the characteristics and origins of long-range teleconnections.
In the second application, I used the developed non-linear MSES similarity measure to quantify the multivariate teleconnections between extreme Indian precipitation and climatic patterns with the highest relevance for Indian sub-continent. The results confirmed significant non-linear influences that were not well captured by the traditional methods. Further, there was a substantial variation in the strength and nature of teleconnection across India, and across time scales.
Overall, the results from investigations conducted in the thesis strongly highlight the need for considering the multi-scale aspects in climatic processes, and the proposed methods provide robust framework for quantifying the multi-scale characteristics.
Im Graduiertenkolleg NatRiskChange der Universität Potsdam und anderen Forschungseinrichtungen werden beobachtete sowie zukünftig mögliche Veränderungen von Naturgefahren untersucht. Teil des strukturierten Doktorandenprogramms sind sogenannte Task-Force-Einsätze, bei denen die Promovierende zeitlich begrenzt ein aktuelles Ereignis auswerten. Im Zuge dieser Aktivität wurde die Sturzflut vom 29.05.2016 in Braunsbach (Baden-Württemberg) untersucht.
In diesem Bericht werden erste Auswertungen zur Einordnung der Niederschläge, zu den hydrologischen und geomorphologischen Prozessen im Einzugsgebiet des Orlacher Bachs sowie zu den verursachten Schäden beleuchtet.
Die Region war Zentrum extremer Regenfälle in der Größenordnung von 100 mm innerhalb von 2 Stunden. Das 6 km² kleine Einzugsgebiet hat eine sehr schnelle Reaktionszeit, zumal bei vorgesättigtem Boden. Im steilen Bachtal haben mehrere kleinere und größere Hangrutschungen über 8000 m³ Geröll, Schutt und Schwemmholz in das Gewässer eingetragen und möglicherweise kurzzeitige Aufstauungen und Durchbrüche verursacht. Neben den großen Wassermengen mit einer Abflussspitze in einer Größenordnung von 100 m³/s hat gerade die Geschiebefracht zu großen Schäden an den Gebäuden entlang des Bachlaufs in Braunsbach geführt.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
Hydrologic regionalization deals with the investigation of homogeneity in watersheds and provides a classification of watersheds for regional analysis. The classification thus obtained can be used as a basis for mapping data from gauged to ungauged sites and can improve extreme event prediction. This paper proposes a wavelet power spectrum (WPS) coupled with the self-organizing map method for clustering hydrologic catchments. The application of this technique is implemented for gauged catchments. As a test case study, monthly streamflow records observed at 117 selected catchments throughout the western United States from 1951 through 2002. Further, based on WPS of each station, catchments are classified into homogeneous clusters, which provides a representative WPS pattern for the streamflow stations in each cluster.
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Quantifying the roles of single stations within homogeneous regions using complex network analysis
(2018)
Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
In the last decade, the number and dimensions of catastrophic flooding events in the Niger River Basin (NRB) have markedly increased. Despite the devastating impact of the floods on the population and the mainly agriculturally based economy of the riverine nations, awareness of the hazards in policy and science is still low. The urgency of this topic and the existing research deficits are the motivation for the present dissertation.
The thesis is an initial detailed assessment of the increasing flood risk in the NRB. The research strategy is based on four questions regarding (1) features of the change in flood risk, (2) reasons for the change in the flood regime, (3) expected changes of the flood regime given climate and land use changes, and (4) recommendations from previous analysis for reducing the flood risk in the NRB.
The question examining the features of change in the flood regime is answered by means of statistical analysis. Trend, correlation, changepoint, and variance analyses show that, in addition to the factors exposure and vulnerability, the hazard itself has also increased significantly in the NRB, in accordance with the decadal climate pattern of West Africa. The northern arid and semi-arid parts of the NRB are those most affected by the changes.
As potential reasons for the increase in flood magnitudes, climate and land use changes are attributed by means of a hypothesis-testing framework. Two different approaches, based on either data analysis or simulation, lead to similar results, showing that the influence of climatic changes is generally larger compared to that of land use changes. Only in the dry areas of the NRB is the influence of land use changes comparable to that of climatic alterations.
Future changes of the flood regime are evaluated using modelling results. First ensembles of statistically and dynamically downscaled climate models based on different emission scenarios are analyzed. The models agree with a distinct increase in temperature. The precipitation signal, however, is not coherent. The climate scenarios are used to drive an eco-hydrological model. The influence of climatic changes on the flood regime is uncertain due to the unclear precipitation signal. Still, in general, higher flood peaks are expected. In a next step, effects of land use changes are integrated into the model. Different scenarios show that regreening might help to reduce flood peaks. In contrast, an expansion of agriculture might enhance the flood peaks in the NRB. Similarly to the analysis of observed changes in the flood regime, the impacts of climate- and land use changes for the future scenarios are also most severe in the dry areas of the NRB.
In order to answer the final research question, the results of the above analysis are integrated into a range of recommendations for science and policy on how to reduce flood risk in the NRB. The main recommendations include a stronger consideration of the enormous natural climate variability in the NRB and a focus on so called “no-regret” adaptation strategies which account for high uncertainty, as well as a stronger consideration of regional differences. Regarding the prevention and mitigation of catastrophic flooding, the most vulnerable and sensitive areas in the basin, the arid and semi-arid Sahelian and Sudano-Sahelian regions, should be prioritized. Eventually, an active, science-based and science-guided flood policy is recommended. The enormous population growth in the NRB in connection with the expected deterioration of environmental and climatic conditions is likely to enhance the region´s vulnerability to flooding. A smart and sustainable flood policy can help mitigate these negative impacts of flooding on the development of riverine societies in West Africa.
This study aims to compare impacts of climate change on streamflow in four large representative African river basins: the Niger, the Upper Blue Nile, the Oubangui and the Limpopo. We set up the eco-hydrological model SWIM (Soil and Water Integrated Model) for all four basins individually. The validation of the models for four basins shows results from adequate to very good, depending on the quality and availability of input and calibration data.
For the climate impact assessment, we drive the model with outputs of five bias corrected Earth system models of Coupled Model Intercomparison Project Phase 5 (CMIP5) for the representative concentration pathways (RCPs) 2.6 and 8.5. This climate input is put into the context of climate trends of the whole African continent and compared to a CMIP5 ensemble of 19 models in order to test their representativeness. Subsequently, we compare the trends in mean discharges, seasonality and hydrological extremes in the 21st century. The uncertainty of results for all basins is high. Still, climate change impact is clearly visible for mean discharges but also for extremes in high and low flows. The uncertainty of the projections is the lowest in the Upper Blue Nile, where an increase in streamflow is most likely. In the Niger and the Limpopo basins, the magnitude of trends in both directions is high and has a wide range of uncertainty. In the Oubangui, impacts are the least significant. Our results confirm partly the findings of previous continental impact analyses for Africa. However, contradictory to these studies we find a tendency for increased streamflows in three of the four basins (not for the Oubangui). Guided by these results, we argue for attention to the possible risks of increasing high flows in the face of the dominant water scarcity in Africa. In conclusion, the study shows that impact intercomparisons have added value to the adaptation discussion and may be used for setting up adaptation plans in the context of a holistic approach.
Climate or Land Use?
(2015)
This study intends to contribute to the ongoing discussion on whether land use and land cover changes (LULC) or climate trends have the major influence on the observed increase of flood magnitudes in the Sahel. A simulation-based approach is used for attributing the observed trends to the postulated drivers. For this purpose, the ecohydrological model SWIM (Soil and Water Integrated Model) with a new, dynamic LULC module was set up for the Sahelian part of the Niger River until Niamey, including the main tributaries Sirba and Goroul. The model was driven with observed, reanalyzed climate and LULC data for the years 1950-2009. In order to quantify the shares of influence, one simulation was carried out with constant land cover as of 1950, and one including LULC. As quantitative measure, the gradients of the simulated trends were compared to the observed trend. The modeling studies showed that for the Sirba River only the simulation which included LULC was able to reproduce the observed trend. The simulation without LULC showed a positive trend for flood magnitudes, but underestimated the trend significantly. For the Goroul River and the local flood of the Niger River at Niamey, the simulations were only partly able to reproduce the observed trend. In conclusion, the new LULC module enabled some first quantitative insights into the relative influence of LULC and climatic changes. For the Sirba catchment, the results imply that LULC and climatic changes contribute in roughly equal shares to the observed increase in flooding. For the other parts of the subcatchment, the results are less clear but show, that climatic changes and LULC are drivers for the flood increase; however their shares cannot be quantified. Based on these modeling results, we argue for a two-pillar adaptation strategy to reduce current and future flood risk: Flood mitigation for reducing LULC-induced flood increase, and flood adaptation for a general reduction of flood vulnerability.
Climate or land use?
(2017)
This study intends to contribute to the ongoing discussion on whether land use and land cover changes (LULC) or climate trends have the major influence on the observed increase of flood magnitudes in the Sahel. A simulation-based approach is used for attributing the observed trends to the postulated drivers. For this purpose, the ecohydrological model SWIM (Soil and Water Integrated Model) with a new, dynamic LULC module was set up for the Sahelian part of the Niger River until Niamey, including the main tributaries Sirba and Goroul. The model was driven with observed, reanalyzed climate and LULC data for the years 1950–2009. In order to quantify the shares of influence, one simulation was carried out with constant land cover as of 1950, and one including LULC. As quantitative measure, the gradients of the simulated trends were compared to the observed trend. The modeling studies showed that for the Sirba River only the simulation which included LULC was able to reproduce the observed trend. The simulation without LULC showed a positive trend for flood magnitudes, but underestimated the trend significantly. For the Goroul River and the local flood of the Niger River at Niamey, the simulations were only partly able to reproduce the observed trend. In conclusion, the new LULC module enabled some first quantitative insights into the relative influence of LULC and climatic changes. For the Sirba catchment, the results imply that LULC and climatic changes contribute in roughly equal shares to the observed increase in flooding. For the other parts of the subcatchment, the results are less clear but show, that climatic changes and LULC are drivers for the flood increase; however their shares cannot be quantified. Based on these modeling results, we argue for a two-pillar adaptation strategy to reduce current and future flood risk: Flood mitigation for reducing LULC-induced flood increase, and flood adaptation for a general reduction of flood vulnerability.
Sediment-discharge hysteresis loops are frequently analyzed to facilitate the understanding of sediment transport processes. Hysteresis patterns, however, are often complex and their interpretation can be complicated. Particularly, quantifying hysteresis patterns remains a problematic issue. Moreover, it is currently unknown how much data is required for analyzing sediment-discharge hysteresis loops in a given area. These open questions and challenges motivated us to develop a new method for quantifying suspended-sediment hysteresis. Subsequently, we applied the new hysteresis index to three suspended-sediment and discharge datasets from a small tropical rainforest catchment. The datasets comprised a different number of events and sampling sites. Our analyses show three main findings: (1) datasets restricted to only few events, which is typical for rapid assessment surveys, were always sufficient to identify the dominating hysteresis pattern in our research area. Furthermore, some of these small datasets contained multiple-peak events that allowed identifying intra-event exhaustion effects and hence, limitations in sediment supply. (2) Datasets comprising complete hydrological years were particularly useful for analyzing seasonal dynamics of hysteresis. These analyses revealed an exhaustion of hysteresis on the inter-event scale which also points to a limited sediment supply. (3) Datasets comprising measurements from two consecutive gauges installed at the catchment outlet and on a slope within that catchment allowed analyzing the change of hysteresis patterns along the flowpath. On the slope, multiple-peak events showed a stronger intra-event exhaustion of hysteresis than at the catchment outlet. Furthermore, exhaustion of hysteresis on the inter-event scale was not evident on the slope but occurred at the catchment outlet. Our results indicate that even small sediment datasets can provide valuable insights into sediment transport processes of small catchments. Furthermore, our results may serve as a first guideline on what to expect from an analysis of hysteresis patterns for datasets of varying quality and quantity. (c) 2014 Elsevier B.V. All rights reserved.
The Tibetan Plateau is the largest elevated landmass in the world and profoundly influences atmospheric circulation patterns such as the Asian monsoon system. Therefore this area has been increasingly in focus of palaeoenvironmental studies. This thesis evaluates the applicability of organic biomarkers for palaeolimnological purposes on the Tibetan Plateau with a focus on aquatic macrophyte-derived biomarkers. Submerged aquatic macrophytes have to be considered to significantly influence the sediment organic matter due to their high abundance in many Tibetan lakes. They can show highly 13C-enriched biomass because of their carbon metabolism and it is therefore crucial for the interpretation of δ13C values in sediment cores to understand to which extent aquatic macrophytes contribute to the isotopic signal of the sediments in Tibetan lakes and in which way variations can be explained in a palaeolimnological context. Additionally, the high abundance of macrophytes makes them interesting as potential recorders of lake water δD. Hydrogen isotope analysis of biomarkers is a rapidly evolving field to reconstruct past hydrological conditions and therefore of special relevance on the Tibetan Plateau due to the direct linkage between variations of monsoon intensity and changes in regional precipitation / evaporation balances. A set of surface sediment and aquatic macrophyte samples from the central and eastern Tibetan Plateau was analysed for composition as well as carbon and hydrogen isotopes of n-alkanes. It was shown how variable δ13C values of bulk organic matter and leaf lipids can be in submerged macrophytes even of a single species and how strongly these parameters are affected by them in corresponding sediments. The estimated contribution of the macrophytes by means of a binary isotopic model was calculated to be up to 60% (mean: 40%) to total organic carbon and up to 100% (mean: 66%) to mid-chain n-alkanes. Hydrogen isotopes of n-alkanes turned out to record δD of meteoric water of the summer precipitation. The apparent enrichment factor between water and n-alkanes was in range of previously reported ones (≈-130‰) at the most humid sites, but smaller (average: -86‰) at sites with a negative moisture budget. This indicates an influence of evaporation and evapotranspiration on δD of source water for aquatic and terrestrial plants. The offset between δD of mid- and long-chain n-alkanes was close to zero in most of the samples, suggesting that lake water as well as soil and leaf water are affected to a similar extent by those effects. To apply biomarkers in a palaeolimnological context, the aliphatic biomarker fraction of a sediment core from Lake Koucha (34.0° N; 97.2° E; eastern Tibetan Plateau) was analysed for concentrations, δ13C and δD values of compounds. Before ca. 8 cal ka BP, the lake was dominated by aquatic macrophyte-derived mid-chain n-alkanes, while after 6 cal ka BP high concentrations of a C20 highly branched isoprenoid compound indicate a predominance of phytoplankton. Those two principally different states of the lake were linked by a transition period with high abundances of microbial biomarkers. δ13C values were relatively constant for long-chain n-alkanes, while mid-chain n-alkanes showed variations between -23.5 to -12.6‰. Highest values were observed for the assumed period of maximum macrophyte growth during the late glacial and for the phytoplankton maximum during the middle and late Holocene. Therefore, the enriched values were interpreted to be caused by carbon limitation which in turn was induced by high macrophyte and primary productivity, respectively. Hydrogen isotope signatures of mid-chain n-alkanes have been shown to be able to track a previously deduced episode of reduced moisture availability between ca. 10 and 7 cal ka BP, indicated by a 20‰ shift towards higher δD values. Indications for cooler episodes at 6.0, 3.1 and 1.8 cal ka BP were gained from drops of biomarker concentrations, especially microbial-derived hopanoids, and from coincidental shifts towards lower δ13C values. Those episodes correspond well with cool events reported from other locations on the Tibetan Plateau as well as in the Northern Hemisphere. To conclude, the study of recent sediments and plants improved the understanding of factors affecting the composition and isotopic signatures of aliphatic biomarkers in sediments. Concentrations and isotopic signatures of the biomarkers in Lake Koucha could be interpreted in a palaeolimnological context and contribute to the knowledge about the history of the lake. Aquatic macrophyte-derived mid-chain n-alkanes were especially useful, due to their high abundance in many Tibetan Lakes and their ability to record major changes of lake productivity and palaeo-hydrological conditions. Therefore, they have the potential to contribute to a fuller understanding of past climate variability in this key region for atmospheric circulation systems.
Samples of 474 forest stands in Germany were analysed for concentrations of polycyclic aromatic hydrocarbons (PAHs) in three sampling depths. Enhanced concentrations were mainly found at spots relatively close to densely industrialized and urbanized regions and at some topographically elevated areas. Average enrichment factors between mineral soil and humic layer depend on humus type i.e. decrease from mull via moder to more Based on their compound-patterns, the observed samples could be assigned to three main clusters. For some parts of our study area a uniform assignment of samples to clusters over larger regions could be identified. For instance, samples taken at vicinity to brown-coal strip-mining districts are characterized by high relative abundances of low-molecular-weight PAHs. These results suggest that PAHs are more likely originated from local and regional emitters rather than from long-range transport and that specific source-regions can be identified based on PAH fingerprints. (C) 2015 Elsevier Ltd. All rights reserved.