Refine
Year of publication
- 2022 (188) (remove)
Document Type
- Article (154)
- Doctoral Thesis (19)
- Postprint (10)
- Monograph/Edited Volume (2)
- Part of a Book (1)
- Other (1)
- Review (1)
Language
- English (188) (remove)
Is part of the Bibliography
- yes (188)
Keywords
- permafrost (7)
- climate change (6)
- Andes (3)
- diffusion (3)
- machine learning (3)
- ocean color remote sensing (3)
- pollen (3)
- radiation belts (3)
- Arctic ocean (2)
- Argentina (2)
Institute
- Institut für Geowissenschaften (188) (remove)
Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar - SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015-2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30 circle) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.
On 7 January 2020, an M-w 6.4 earthquake occurred in the northeastern Caribbean, a few kilometers offshore of the island of Puerto Rico. It was the mainshock of a complex seismic sequence, characterized by a large number of energetic earthquakes illuminating an east-west elongated area along the southwestern coast of Puerto Rico. Deformation fields constrained by Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data indicate that the coseismic movements affected only the western part of the island. To assess the mainshock's source fault parameters, we combined the geodetically derived coseismic deformation with teleseismic waveforms using Bayesian inference. The results indicate a roughly east-west oriented fault, dipping northward and accommodating similar to 1.4 m of transtensional motion. Besides, the determined location and orientation parameters suggest an offshore continuation of the recently mapped North Boqueron Bay-Punta Montalva fault in southwest Puerto Rico. This highlights the existence of unmapped faults with moderate-to-large earthquake potential within the Puerto Rico region.
Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.
Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.
The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen-evergreen transition zone in Central Yakutia and the tundra-taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, ). The dataset includes structure-from-motion (SfM) point clouds and red-green-blue (RGB) and red-green-near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, ). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch ( and ) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, ). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. Larix gmeliniiLarix cajanderiDataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, ). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. <br /> The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra-taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.
The Altiplano-Puna plateau, in Central Andes, is the second-largest continental plateau on Earth, extending between 22 degrees and 27 degrees S at an average altitude of 4400 m. The Puna plateau has been formed in consequence of the subduction of the oceanic Nazca Plate beneath the continental South American plate, which has an average crustal thickness of 50 km at this location. A large seismicity cluster, the Jujuy cluster, is observed at depth of 150-250 km beneath the central region of the Puna plateau. The cluster is seismically very active, with hundreds of earthquakes reported and a peak magnitude MW 6.6 on 25th August 2006. The cluster is situated in one of three band of intermediate-depth focus seismicity, which extend parallel to the trench roughly North to South. It has been hypothesized that the Jujuy cluster could be a seismic nest, a compact seismogenic region characterized by a high stationary activity relative to its surroundings. In this study, we collected more than 40 years of data from different catalogs and proof that the cluster meets the three conditions of a seismic nest. Compared to other known intermediate depth nests at Hindu Kush (Afganisthan) or Bucaramanga (Colombia), the Jujuy nest presents an outstanding seismicity rate, with more than 100 M4+ earthquakes per year. We additionally performed a detailed analysis of the rupture process of some of the largest earthquakes in the nest, by means of moment tensor inversion and directivity analysis. We focused on the time period 2017-2018, where the seismic monitoring was the most extended. Our results show that earthquakes in the nest take place within the eastward subducting oceanic plate, but rupture along sub-horizontal planes dipping westward. We suggest that seismicity at Jujuy nest is controlled by dehydration processes, which are also responsible for the generation of fluids ascending to the crust beneath the Puna volcanic region. We use the rupture plane and nest geometry to provide a constraint to maximal expected magnitude, which we estimate as MW -6.7.
The main Marmara fault (MMF) extends for 150 km through the Sea of Marmara and forms the only portion of the North Anatolian fault zone that has not ruptured in a large event (Mw >7) for the last 250 yr. Accordingly, this portion is potentially a major source contributing to the seismic hazard of the Istanbul region. On 26 September 2019, a sequence of moderate-sized events started along the MMF only 20 km south of Istanbul and were widely felt by the population. The largest three events, 26 September Mw 5.8 (10:59 UTC), 26 September 2019 Mw 4.1 (11:26 UTC), and 20 January 2020 Mw 4.7 were recorded by numerous strong-motion seismic stations and the resulting ground motions were compared to the predicted means resulting from a set of the most recent ground-motion prediction equations (GMPEs). The estimated residuals were used to investigate the spatial variation of ground motion across the Marmara region. Our results show a strong azimuthal trend in ground-motion residuals, which might indicate systematically repeating directivity effects toward the eastern Marmara region.
Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains the example data as well as recipes that include all the Python commands featured in the book.
Due to the major role of greenhouse gas emissions in global climate change, the development of non-fossil energy technologies is essential. Deep geothermal energy represents such an alternative, which offers promising properties such as a high base load capability and a large untapped potential. The present work addresses barite precipitation within geothermal systems and the associated reduction in rock permeability, which is a major obstacle to maintaining high efficiency. In this context, hydro-geochemical models are essential to quantify and predict the effects of precipitation on the efficiency of a system.
The objective of the present work is to quantify the induced injectivity loss using numerical and analytical reactive transport simulations. For the calculations, the fractured-porous reservoirs of the German geothermal regions North German Basin (NGB) and Upper Rhine Graben (URG) are considered.
Similar depth-dependent precipitation potentials could be determined for both investigated regions (2.8-20.2 g/m3 fluid). However, the reservoir simulations indicate that the injectivity loss due to barite deposition in the NGB is significant (1.8%-6.4% per year) and the longevity of the system is affected as a result; this is especially true for deeper reservoirs (3000 m). In contrast, simulations of URG sites indicate a minor role of barite (< 0.1%-1.2% injectivity loss per year). The key differences between the investigated regions are reservoir thicknesses and the presence of fractures in the rock, as well as the ionic strength of the fluids. The URG generally has fractured-porous reservoirs with much higher thicknesses, resulting in a greater distribution of precipitates in the subsurface. Furthermore, ionic strengths are higher in the NGB, which accelerates barite precipitation, causing it to occur more concentrated around the wellbore. The more concentrated the precipitates occur around the wellbore, the higher the injectivity loss.
In this work, a workflow was developed within which numerical and analytical models can be used to estimate and quantify the risk of barite precipitation within the reservoir of geothermal systems. A key element is a newly developed analytical scaling score that provides a reliable estimate of induced injectivity loss. The key advantage of the presented approach compared to fully coupled reservoir simulations is its simplicity, which makes it more accessible to plant operators and decision makers. Thus, in particular, the scaling score can find wide application within geothermal energy, e.g., in the search for potential plant sites and the estimation of long-term efficiency.