TY - JOUR A1 - Cao, Xianyong A1 - Herzschuh, Ulrike A1 - Telford, Richard J. A1 - Ni, Jian T1 - A modern pollen-climate dataset from China and Mongolia: assessing its potential for climate reconstruction JF - Review of palaeobotany and palynology : an international journal N2 - A modern pollen dataset from China and Mongolia (18-52 degrees N, 74-132 degrees E) is investigated for its potential use in climate reconstructions. The dataset includes 2559 samples, 229 terrestrial pollen taxa and four climatic variables - mean annual precipitation (P-ann): 35-2091 mm, mean annual temperature (T-ann): -12.1-25.8 degrees C, mean temperature in the coldest month (Mt(co).): -33.8-21.7 degrees C, and mean temperature in the warmest month (Mt(wa)): 03-29.8 degrees C. Modern pollen-climate relationships are assessed using canonical correspondence analysis (CCA), Huisman-Olff-Fresco (HOF) models, the modern analogue technique (MAT), and weighted averaging partial least squares (WA-PLS). Results indicate that P-ann is the most important climatic determinant of pollen distribution and the most promising climate variable for reconstructions, as assessed by the coefficient of determination between observed and predicted environmental values (r(2)) and root mean square error of prediction (RMSEP). Mt(co) and Mt(wa) may be reconstructed too, but with caution. Samples from different depositional environments influence the performance of cross-validation differently, with samples from lake sediment-surfaces and moss polsters having the best fit with the lowest RMSEP. The better model performances of MAT are most probably caused by spatial autocorrelation. Accordingly, the WA-PLS models of this dataset are deemed most suitable for reconstructing past climate quantitatively because of their more reliable predictive power. (C) 2014 Elsevier B.V. All rights reserved. KW - Calibration KW - Huisman-Olff-Fresco models KW - MAT KW - Pollen-climate transfer function KW - Spatial autocorrelation KW - WA-PLS Y1 - 2014 U6 - https://doi.org/10.1016/j.revpalbo.2014.08.007 SN - 0034-6667 SN - 1879-0615 VL - 211 SP - 87 EP - 96 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Tian, Fang A1 - Herzschuh, Ulrike A1 - Telford, Richard J. A1 - Mischke, Steffen A1 - Van der Meeren, Thijs A1 - Krengel, Michael T1 - A modern pollen-climate calibration set from central-western Mongolia and its application to a late glacial-Holocene record JF - Journal of biogeography N2 - AimFossil pollen spectra from lake sediments in central and western Mongolia have been used to interpret past climatic variations, but hitherto no suitable modern pollen-climate calibration set has been available to infer past climate changes quantitatively. We established such a modern pollen dataset and used it to develop a transfer function model that we applied to a fossil pollen record in order to investigate: (1) whether there was a significant moisture response to the Younger Dryas event in north-western Mongolia; and (2) whether the early Holocene was characterized by dry or wet climatic conditions. LocationCentral and western Mongolia. MethodsWe analysed pollen data from surface sediments from 90 lakes. A transfer function for mean annual precipitation (P-ann) was developed with weighted averaging partial least squares regression (WA-PLS) and applied to a fossil pollen record from Lake Bayan Nuur (49.98 degrees N, 93.95 degrees E, 932m a.s.l.). Statistical approaches were used to investigate the modern pollen-climate relationships and assess model performance and reconstruction output. ResultsRedundancy analysis shows that the modern pollen spectra are characteristic of their respective vegetation types and local climate. Spatial autocorrelation and significance tests of environmental variables show that the WA-PLS model for P-ann is the most valid function for our dataset, and possesses the lowest root mean squared error of prediction. Main conclusionsPrecipitation is the most important predictor of pollen and vegetation distributions in our study area. Our quantitative climate reconstruction indicates a dry Younger Dryas, a relatively dry early Holocene, a wet mid-Holocene and a dry late Holocene. KW - Central-western Mongolia KW - Lake Bayan Nuur KW - modern pollen KW - ordination KW - palaeoclimate reconstruction KW - palaeoecology KW - transfer functions KW - WA-PLS KW - Younger Dryas Y1 - 2014 U6 - https://doi.org/10.1111/jbi.12338 SN - 0305-0270 SN - 1365-2699 VL - 41 IS - 10 SP - 1909 EP - 1922 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Tian, Fang A1 - Herzschuh, Ulrike A1 - Mischke, Steffen A1 - Schluetz, Frank T1 - What drives the recent intensified vegetation degradation in Mongolia - Climate change or human activity? JF - The Holocene : an interdisciplinary journal focusing on recent environmental change N2 - This study examines the course and driving forces of recent vegetation change in the Mongolian steppe. A sediment core covering the last 55years from a small closed-basin lake in central Mongolia was analyzed for its multi-proxy record at annual resolution. Pollen analysis shows that highest abundances of planted Poaceae and highest vegetation diversity occurred during 1977-1992, reflecting agricultural development in the lake area. A decrease in diversity and an increase in Artemisia abundance after 1992 indicate enhanced vegetation degradation in recent times, most probably because of overgrazing and farmland abandonment. Human impact is the main factor for the vegetation degradation within the past decades as revealed by a series of redundancy analyses, while climate change and soil erosion play subordinate roles. High Pediastrum (a green algae) influx, high atomic total organic carbon/total nitrogen (TOC/TN) ratios, abundant coarse detrital grains, and the decrease of C-13(org) and N-15 since about 1977 but particularly after 1992 indicate that abundant terrestrial organic matter and nutrients were transported into the lake and caused lake eutrophication, presumably because of intensified land use. Thus, we infer that the transition to a market economy in Mongolia since the early 1990s not only caused dramatic vegetation degradation but also affected the lake ecosystem through anthropogenic changes in the catchment area. KW - central Mongolia KW - grain size KW - human impact KW - lake eutrophication KW - pollen KW - vegetation degradation Y1 - 2014 U6 - https://doi.org/10.1177/0959683614540958 SN - 0959-6836 SN - 1477-0911 VL - 24 IS - 10 SP - 1206 EP - 1215 PB - Sage Publ. CY - London ER - TY - JOUR A1 - Frolova, Larisa A1 - Nazarova, Larisa B. A1 - Pestryakova, Luidmila Agafyevna A1 - Herzschuh, Ulrike T1 - Subfossil Cladocera from surface sediment in thermokarst lakes in northeastern Siberia, Russia, in relation to limnological and climatic variables JF - Journal of paleolimnolog N2 - Subfossil Cladocera were sampled and examined from the surface sediments of 35 thermokarst lakes along a temperature gradient crossing the tree line in the Anabar-river basin in northwestern Yakutia, northeastern Siberia. The lakes were distributed through three environmental zones: typical tundra, southern tundra and forest tundra. All lakes were situated within the continuous permafrost zone. Our investigation showed that the cladoceran communities in the lakes of the Anabar region are diverse and abundant, as reflected by taxonomic richness, and high diversity and evenness indices (H = 1.89 +/- A 0.51; I = 0.8 +/- A 0.18). CONISS cluster analysis indicated that the cladoceran communities in the three ecological zones (typical tundra, southern tundra and forest-tundra) differed in their taxonomic composition and structure. Differences in the cladoceran assemblages were related to limnological features and geographical position, vegetation type, climate and water chemistry. The constrained redundancy analysis indicated that T-July, water depth and both sulphate (SO4 (2-)) and silica (Si4+) concentrations significantly (p a parts per thousand currency sign 0.05) explained variance in the cladoceran assemblage. T-July featured the highest percentage (17.4 %) of explained variance in the distribution of subfossil Cladocera. One of the most significant changes in the structure of the cladoceran communities in the investigated transect was the replacement of closely related species along the latitudinal and vegetation gradient. The results demonstrate the potential for a regional cladoceran-based temperature model for the Arctic regions of Russia, and for and Yakutia in particular. KW - Cladocera KW - Russian Arctic KW - Temperature KW - Water depth KW - Palaeolimnology Y1 - 2014 U6 - https://doi.org/10.1007/s10933-014-9781-7 SN - 0921-2728 SN - 1573-0417 VL - 52 IS - 1-2 SP - 107 EP - 119 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Liu, Xingqi A1 - Herzschuh, Ulrike A1 - Wang, Yongbo A1 - Kuhn, Gerhard A1 - Yu, Zhitong T1 - Glacier fluctuations of Muztagh Ata and temperature changes during the late Holocene in westernmost Tibetan Plateau, based on glaciolacustrine sediment records JF - Geophysical research letters N2 - Late Holocene glacier variations in westernmost Tibetan Plateau were studied based on the analysis of grain size, magnetic susceptibility, and elements from an 8.3m long distal glaciolacustrine sediment core of Kalakuli Lake. Our results show that there are four glacier expansion episodes occurring in 4200-3700calibrated years (cal years) B.P., 2950-2300cal years B.P., 1700-1070cal years B.P., and 570-100cal years B.P. and four glacier retreat periods of 3700-2950cal years B.P., 2300-1700cal years B.P., 1070-570cal years B.P., and 50cal years B.P.-present. The four glacier expansion episodes are generally in agreement with the glacier activities indicted by the moraines at Muztagh Ata and Kongur Shan, as well as with the late Holocene ice-rafting events in the North Atlantic. Over the last 2000years, our reconstructed glacier variations are in temporal agreement with reconstructed temperature from China and the Northern Hemisphere, indicating that glacier variations at centennial time scales are very sensitive to temperature in western Tibetan Plateau. KW - glaciolacustrine sediment KW - westernmost Tibetan Plateau KW - glacier variation KW - Kalakuli Lake KW - late Holocene KW - temperature Y1 - 2014 U6 - https://doi.org/10.1002/2014GL060444 SN - 0094-8276 SN - 1944-8007 VL - 41 IS - 17 SP - 6265 EP - 6273 PB - American Geophysical Union CY - Washington ER - TY - THES A1 - Liebs, Göran T1 - Ground penetration radar wave velocities and their uncertainties T1 - Georadar-Wellengeschwindigkeiten mit Fehlerrechnung BT - global inversion of GPR traveltimes to assess uncertainties in CMP velocity models N2 - We develop three new approaches for ground penetration wave velocity calcultaions. The first is based on linear moveout spectra to find the optimum ground wave velocity including uncertainties from multi-offset data gathers. We used synthetic data to illustrate the principles of the method and to investigate uncertainties in ground wave velocity estimates. To demonstrate the applicability of the approach to real data, we analyzed GPR data sets recorded at field sites in Canada over an annual cycle from Steelman & Endres [2010]. The results obtained by this efficient and largely automated procedure agree well with the manual achieved results of Steelman & Endres [2010], derived by a more laborious largely manual analysis strategy. Then we develop a second methodology to global invert reflection traveltimes with a particle swarm optimization approach more precise then conventional spectral NMO-based velocity analysis (e.g., Greaves et al. [1996]). For global optimization, we use particle swarm optimization (PSO; Kennedy & Eberhart [1995]) in the combination with a fast eikonal solver as forward solver (Sethian [1996]; Fomel [1997a]; Sethian & Popovici [1999]). This methodology allows us to generate reliability CMP derived models of subsurface velocities and water content including uncertainties. We test this method with synthetic data to study the behavior of the PSO algorithm. Afterward, We use this method to analyze our field data from a well constrained test site in Horstwalde, Germany. The achieved velocity models from field data showed good agreement to borehole logging and direct-push data (Schmelzbach et al. [2011]) at the same site position. For the third method we implement a global optimization approach also based on PSO to invert direct-arrival traveltimes of VRP data to obtain high resolution 1D velocity models including quantitative estimates of uncertainty. Our intensive tests with several traveltime data sets helped to understand the behavior of PSO algorithm for inversion. Integration of the velocity model to VRP reflection imaging and attenuation model improved the potential of VRP surveying. Using field data, we examine this novel analysis strategy for the development of petrophysical models and the linking between GPR borehole and other logging data to surface GPR reflection data. N2 - Unterschiedliche Verfahren zur Ermittlung von Georadar-Wellengeschwindigkeiten wurden entwickelt und erfolgreich angewendet. Für die Verfahren wurden statistische Methoden und Schwarmintelligenz-Algorithmen benutzt. Es wurde gezeigt, dass die neuen Verfahren schneller, präziser und besser reproduzierbare Ergebnisse für Georadar-Wellengeschwindigkeit erzielen als herkömmliche Verfahren. Mit verbesserten Werten der Georadar-Wellengeschwindigkeit lassen sich die verzerrten dreidimensionalen Abbilder der obersten zehn Meter des Untergrundes, welche sich mit Georadar-Daten erzeugen lassen, korrigieren. In diesen korrigierten Abbildern sind dann realistische Tiefen von Schichten oder Objekten im Untergrund besser messbar. Außerdem verbessern präzisere Wellengeschwindigkeiten die Bestimmung von Bodenparametern, wie Wassergehalt oder Tonanteil. Die präsentierten Verfahren erlauben eine quantitative Angabe von Fehlern der bestimmten Wellengeschwindigkeit und der daraus folgenden Tiefen und Bodenparametern im Untergrund. Die Vorteile dieser neu entwickelten Verfahren zur Charakterisierung des Untergrundes der oberen Meter wurde an Feldbeispielen demonstriert. KW - ground-penetration radar KW - wave velocities KW - global inversion KW - particle swarm optimisation KW - soil water content KW - subsurface KW - geophyics KW - Georadar KW - Wellengeschwindigkeit KW - globale Inversion KW - Partikel Swarm Optimierung KW - Bodenwassergehalt KW - Untergrund KW - Geophysik Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-436807 ER - TY - JOUR A1 - Rumpf, Michael A1 - Tronicke, Jens T1 - Predicting 2D geotechnical parameter fields in near-surface sedimentary environments JF - Journal of applied geophysics N2 - For a detailed characterization of near-surface environments, geophysical techniques are increasingly used to support more conventional point-based techniques such as borehole and direct-push logging. Because the underlying parameter relations are often complex, site-specific, or even poorly understood, a remaining challenging task is to link the geophysical parameter models to the actual geotechnical target parameters measured only at selected points. We propose a workflow based on nonparametric regression to establish functional relationships between jointly inverted geophysical parameters and selected geotechnical parameters as measured, for example, by different borehole and direct-push tools. To illustrate our workflow, we present field data collected to characterize a near-surface sedimentary environment Our field data base includes crosshole ground penetrating radar (GPR), seismic P-, and S-wave data sets collected between 25 m deep boreholes penetrating sand- and gravel dominated sediments. Furthermore, different typical borehole and direct-push logs are available. We perform a global joint inversion of traveltimes extracted from the crosshole geophysical data using a recently proposed approach based on particle swarm optimization. Our inversion strategy allows for generating consistent models of GPR, P-wave, and S-wave velocities including an appraisal of uncertainties. We analyze the observed complex relationships between geophysical velocities and target parameter logs using the alternating conditional expectation (ACE) algorithm. This nonparametric statistical tool allows us to perform multivariate regression analysis without assuming a specific functional relation between the variables. We are able to explain selected target parameters such as characteristic grain size values or natural gamma activity by our inverted geophysical data and to extrapolate these parameters to the inter-borehole plane covered by our crosshole experiments. We conclude that the ACE algorithm is a powerful tool to analyze a multivariate petrophysical data base and to develop an understanding of how a multi-parameter geophysical model can be linked and translated to selected geotechnical parameters. KW - Crosshole tomography KW - Global inversion KW - Nonparametric statistics KW - Geotechnical parameters Y1 - 2014 U6 - https://doi.org/10.1016/j.jappgeo.2013.12.002 SN - 0926-9851 SN - 1879-1859 VL - 101 SP - 95 EP - 107 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Hamann, Göran A1 - Tronicke, Jens T1 - Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models JF - Near surface geophysics N2 - Velocity models are essential to process two-and three-dimensional ground-penetrating radar (GPR) data. Furthermore, velocity information aids the interpretation of such data sets because velocity variations reflect important material properties such as water content. In many GPR applications, common midpoint (CMP) surveys are routinely collected to determine one-dimensional velocity models at selected locations. To analyse CMP data gathers, spectral velocity analyses relying on the normal-moveout (NMO) model are commonly employed. Using Dix's formula, the derived NMO velocities can be further converted to interval velocities which are needed for processing and interpretation. Because of the inherent assumptions and limitations of such approaches, we investigate and propose an alternative procedure based on the global inversion of reflection travel-times. We use a finite-difference solver of the Eikonal equation to accurately solve the forward problem in combination with particle swarm optimization (PSO) to find one-dimensional GPR velocity models explaining our data. Because PSO is a robust and efficient global optimization tool, our inversion approach includes generating an ensemble of representative solutions that allows us to analyse uncertainties in the model space. Using synthetic data examples, we test and evaluate our inversion approach to analyse CMP data collected across typical near-surface environments. Application to a field data set recorded at a well-constrained test site including a comparison to independent borehole and direct-push data, further illustrates the potential of the proposed approach, which includes a straightforward and understandable appraisal of non-uniqueness and uncertainty issues, respectively. We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models. Y1 - 2014 U6 - https://doi.org/10.3997/1873-0604.2014005 SN - 1569-4445 SN - 1873-0604 VL - 12 IS - 4 SP - 505 EP - 514 PB - European Association of Geoscientists & Engineers CY - Houten ER - TY - JOUR A1 - Tronicke, Jens A1 - Hamann, Göran T1 - Vertical radar profiling: Combined analysis of traveltimes, amplitudes, and reflections JF - Geophysics N2 - Vertical radar profiling (VRP) is a single-borehole geophysical technique, in which the receiver antenna is located within a borehole and the transmitter antenna is placed at one or various offsets from the borehole. Today, VRP surveying is primarily used to derive 1D velocity models by inverting the arrival times of direct waves. Using field data collected at a well-constrained test site in Germany, we evaluated a VRP workflow relying on the analysis of direct-arrival traveltimes and amplitudes as well as on imaging reflection events. To invert our VRP traveltime data, we used a global inversion strategy resulting in an ensemble of acceptable velocity models, and thus, it allowed us to appraise uncertainty issues in the estimated velocities as well as in porosity models derived via petrophysical translations. In addition to traveltime inversion, the analysis of direct-wave amplitudes and reflection events provided further valuable information regarding subsurface properties and architecture. The used VRP amplitude preprocessing and inversion procedures were adapted from raybased crosshole ground-penetrating radar (GPR) attenuation tomography and resulted in an attenuation model, which can be used to estimate variations in electrical resistivity. Our VRP reflection imaging approach relied on corridor stacking, which is a well-established processing sequence in vertical seismic profiling. The resulting reflection image outlines bounding layers and can be directly compared to surface-based GPR reflection profiling. Our results of the combined analysis of VRP, traveltimes, amplitudes, and reflections were consistent with independent core and borehole logs as well as GPR reflection profiles, which enabled us to derive a detailed hydro-stratigraphic model as needed, for example, to understand and model groundwater flow and transport. Y1 - 2014 U6 - https://doi.org/10.1190/GEO2013-0428.1 SN - 0016-8033 SN - 1942-2156 VL - 79 IS - 4 SP - H23 EP - H35 PB - Society of Exploration Geophysicists CY - Tulsa ER - TY - JOUR A1 - Paasche, Hendrik A1 - Tronicke, Jens T1 - Nonlinear joint inversion of tomographic data using swarm intelligence JF - Geophysics N2 - Geophysical techniques offer the potential to tomographically image physical parameter variations in the ground in two or three dimensions. Due to the limited number and accuracy of the recorded data, geophysical model generation by inversion suffers ambiguity. Linking the model generation process of disparate data by jointly inverting two or more data sets allows for improved model reconstruction. Fully nonlinear inversion using optimization techniques searching the solution space of the inverse problem globally enables quantitative assessment of the ambiguity inherent to the model reconstruction. We used two different multiobjective particle swarm optimization approaches to jointly invert synthetic crosshole tomographic data sets comprising radar and P-wave traveltimes, respectively. Beginning with a nonlinear joint inversion founded on the principle of Pareto optimality and game theoretic concepts, we obtained a set of Pareto-optimal solutions comprising commonly structured radar and P-wave velocity models for low computational costs. However, the efficiency of the approach goes along with some risk of achieving a final model ensemble not adequately illustrating the ambiguity inherent to the model reconstruction process. Taking advantage of the results of the first approach, we inverted the database using a different nonlinear joint-inversion approach reducing the multiobjective optimization problem to a single-objective one. Computational costs were significantly higher, but the final models were obtained mutually independently allowing for objective appraisal of model parameter determination. Despite the high computational effort, the approach was found to be an efficient nonlinear joint-inversion formulation compared to what could be extracted from individual nonlinear inversions of both data sets. Y1 - 2014 U6 - https://doi.org/10.1190/GEO2013-0423.1 SN - 0016-8033 SN - 1942-2156 VL - 79 IS - 4 SP - R133 EP - R149 PB - Society of Exploration Geophysicists CY - Tulsa ER -