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In the arctic and high mountains it is common to measure vertical changes of ice sheets and glaciers via digital elevation model (DEM) differencing. This requires the signal of change to outweigh the noise associated with the datasets. Excluding large landslides, on the ice-free earth the land-level change is smaller in vertical magnitude and thus requires more accurate DEMs for differencing and identification of change. Previously, this has required meter to submeter data at small spatial scales. Following careful corrections, we are able to measure land-level changes in gravel-bed channels and steep hillslopes in the south-central Andes using the SRTM-C (collected in 2000) and the TanDEM-X (collected from 2010 to 2015) near-global 12–30m DEMs. Long-standing errors in the SRTM-C are corrected using the TanDEM-X as a control surface and applying cosine-fit co-registration to remove ∼ 1∕10 pixel (∼ 3m) shifts, fast Fourier transform (FFT) and filtering to remove SRTM-C short- and long-wavelength stripes, and blocked shifting to remove remaining complex biases. The datasets are then differenced and outlier pixels are identified as a potential signal for the case of gravel-bed channels and hillslopes. We are able to identify signals of incision and aggradation (with magnitudes down to ∼ 3m in the best case) in two > 100km river reaches, with increased geomorphic activity downstream of knickpoints. Anthropogenic gravel excavation and piling is prominently measured, with magnitudes exceeding ±5m (up to > 10m for large piles). These values correspond to conservative average rates of 0.2 to > 0.5myr−1 for vertical changes in gravel-bed rivers. For hillslopes, since we require stricter cutoffs for noise, we are only able to identify one major landslide in the study area with a deposit volume of 16±0.15×106m3. Additional signals of change can be garnered from TanDEM-X auxiliary layers; however, these are more difficult to quantify. The methods presented can be extended to any region of the world with SRTM-C and TanDEM-X coverage where vertical land-level changes are of interest, with the caveat that remaining vertical uncertainties in primarily the SRTM-C limit detection in steep and complex topography.
The prediction of the ground shaking that can occur at a site of interest due to an earthquake is crucial in any seismic hazard analysis. Usually, empirically derived ground-motion prediction equations (GMPEs) are employed within a logic-tree framework to account for this step. This is, however, challenging if the area under consideration has only low seismicity and lacks enough recordings to develop a region-specific GMPE. It is then usual practice to adapt GMPEs from data-rich regions (host area) to the area with insufficient ground-motion recordings (target area). Host GMPEs must be adjusted in such a way that they will capture the specific ground-motion characteristics of the target area. In order to do so, seismological parameters of the target region have to be provided as, for example, the site-specific attenuation factor kappa0. This is again an intricate task if data amount is too sparse to derive these parameters.
In this thesis, I explore methods that can facilitate the selection of non-endemic GMPEs in a logic-tree analysis or their adjustment to a data-poor region. I follow two different strategies towards this goal.
The first approach addresses the setup of a ground-motion logic tree if no indigenous GMPE is available. In particular, I propose a method to derive an optimized backbone model that captures the median ground-motion characteristics in the region of interest. This is done by aggregating several foreign GMPEs as weighted components of a mixture model in which the weights are inferred from observed data. The approach is applied to Northern Chile, a region for which no indigenous GMPE existed at the time of the study. Mixture models are derived for interface and intraslab type events using eight subduction zone GMPEs originating from different parts of the world. The derived mixtures provide satisfying results in terms of average residuals and average sample log-likelihoods. They outperform all individual non-endemic GMPEs and are comparable to a regression model that was specifically derived for that area.
The second approach is concerned with the derivation of the site-specific attenuation factor kappa0. kappa0 is one of the key parameters in host-to-target adjustments of GMPEs but is hard to derive if data amount is sparse. I explore methods to estimate kappa0 from ambient seismic noise. Seismic noise is, in contrast to earthquake recordings, continuously available. The rapidly emerging field of seismic interferometry gives the possibility to infer velocity and attenuation information from the cross-correlation or deconvolution of long noise recordings. The extraction of attenuation parameters from diffuse wavefields is, however, not straightforward especially not for frequencies above 1 Hz and at shallow depth. In this thesis, I show the results of two studies. In the first one, data of a small-scale array experiment in Greece are used to derive Love wave quality factors in
the frequency range 1-4 Hz. In a second study, frequency dependent quality factors of S-waves (5-15 Hz) are estimated by deconvolving noise recorded in a borehole and at a co-located surface station in West Bohemia/Vogtland. These two studies can be seen as preliminary steps towards the estimation of kappa0 from seismic noise.
We explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites as a reference to quantify the ground radar (GR) reflectivity bias. To this end, the 3-D volume-matching algorithm proposed by Schwaller and Morris (2011) is implemented and applied to 5 years (2012–2016) of observations. We further extend the procedure by a framework to take into account the data quality of each ground radar bin. Through these methods, we are able to assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a quality-weighted average of reflectivity differences in any sample of matching GR–SR volumes. We exemplify the idea of quality-weighted averaging by using the beam blockage fraction as the basis of a quality index. As a result, we can increase the consistency of SR and GR observations, and thus the precision of calibration bias estimates. The remaining scatter between GR and SR reflectivity as well as the variability of bias estimates between overpass events indicate, however, that other error sources are not yet fully addressed. Still, our study provides a framework to introduce any other quality variables that are considered relevant in a specific context. The code that implements our analysis is based on the wradlib open-source software library, and is, together with the data, publicly available to monitor radar calibration or to scrutinize long series of archived radar data back to December 1997, when TRMM became operational.
We explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites as a reference to quantify the ground radar (GR) reflectivity bias. To this end, the 3-D volume-matching algorithm proposed by Schwaller and Morris (2011) is implemented and applied to 5 years (2012–2016) of observations. We further extend the procedure by a framework to take into account the data quality of each ground radar bin. Through these methods, we are able to assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a quality-weighted average of reflectivity differences in any sample of matching GR–SR volumes. We exemplify the idea of quality-weighted averaging by using the beam blockage fraction as the basis of a quality index. As a result, we can increase the consistency of SR and GR observations, and thus the precision of calibration bias estimates. The remaining scatter between GR and SR reflectivity as well as the variability of bias estimates between overpass events indicate, however, that other error sources are not yet fully addressed. Still, our study provides a framework to introduce any other quality variables that are considered relevant in a specific context. The code that implements our analysis is based on the wradlib open-source software library, and is, together with the data, publicly available to monitor radar calibration or to scrutinize long series of archived radar data back to December 1997, when TRMM became operational.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
Natural hazards such as floods, earthquakes, landslides, and multi-hazard events heavily affect human societies and call for better management strategies. Due to the severity of such events, it is of utmost importance to understand whether and how they change in re-sponse to evolving hydro-climatological, geo-physical and socio-economic conditions. These conditions jointly determine the magnitude, frequency, and impact of disasters, and are changing in response to climate change and human behavior. Therefore methods are need-ed for hazard and risk quantification accounting for the transient nature of hazards and risks in response to changing natural and anthropogenic altered systems. The purpose of this conference is to bring together researchers from natural sciences (e.g. hydrology, meteorology, geomorphology, hydraulic engineering, environmental science, seismology, geography), risk research, nonlinear systems dynamics, and applied mathematics to discuss new insights and developments about data science, changing systems, multi-hazard events and the linkage between hazard and vulnerabilities under unstable environmental conditions. Knowledge transfer, communication and networking will be key issues of the conference. The conference is organized by means of invited talks given by outstanding experts, oral presentations, poster sessions and discussions.
Weltweit verursachen Städte etwa 70 % der Treibhausgasemissionen und sind daher wichtige Akteure im Klimaschutz bzw. eine wichtige Zielgruppe von Klimapolitiken. Gleichzeitig sind Städte besonders stark von möglichen Auswirkungen des Klimawandels betroffen: Insbesondere extreme Wetterereignisse wie Hitzewellen oder Starkregenereignisse mit Überflutungen verursachen in Städten hohe Sachschäden und wirken sich negativ auf die Gesundheit der städtischen Bevölkerung aus. Daher verfolgt das Projekt ExTrass das Ziel, die städtische Resilienz gegenüber extremen Wetterereignissen in enger Zusammenarbeit mit Stadtverwaltungen, Strukturen des Bevölkerungsschutzes und der Zivilgesellschaft zu stärken. Im Fokus stehen dabei (kreisfreie) Groß- und Mittelstädte mit 50.000 bis 500.000 Einwohnern, insbesondere die Fallstudienstädte Potsdam, Remscheid und Würzburg.
Der vorliegende Bericht beinhaltet die Ergebnisse der 14-monatigen Definitionsphase von ExTrass, in der vor allem die Abstimmung eines Arbeitsprogramms im Mittelpunkt stand, das in einem nachfolgenden dreijährigen Forschungsprojekt (F+E-Phase) gemeinsam von Wissenschaft und Praxispartnern umgesetzt werden soll. Begleitend wurde eine Bestandsaufnahme von Klimaanpassungs- und Klimaschutzstrategien/-plänen in 99 deutschen Groß- und Mittelstädten vorgenommen. Zudem wurden für Potsdam und Würzburg Pfadanalysen für die Klimapolitik durchgeführt. Darin wird insbesondere die Bedeutung von Schlüsselakteuren deutlich. Weiterhin wurden im Rahmen von Stakeholder-Workshops Anpassungsherausforderungen und aktuelle Handlungsbedarfe in den Fallstudienstädten identifiziert und Lösungsansätze erarbeitet, die in der F+E-Phase entwickelt und getestet werden sollen. Neben Maßnahmen auf gesamtstädtischer Ebene und auf Stadtteilebene wurden Maßnahmen angestrebt, die die Risikowahrnehmung, Vorsorge und Selbsthilfefähigkeit von Unternehmen und Bevölkerung stärken können. Daher wurde der Stand der Risikokommunikation in Deutschland für das Projekt aufgearbeitet und eine erste Evaluation von Risikokommunikationswerkzeugen durchgeführt. Der Bericht endet mit einer Kurzfassung des Arbeitsprogramms 2018-2021.
Holocene climate variability is generally characterized by low frequency changes than compared to the last glaciations including the Lateglacial. However, there is vast evidence for decadal to centennial scale oscillations and millennial scale climate trends, which are within and beyond a human lifetime perception, respectively. Within the Baltic realm, a transitional zone between oceanic and continental climate influence, the impact of Holocene and Lateglacial climate and environmental change is currently partly understood. This is mainly attributed to the scarcity of well-dated and high-resolution sediment records and to the lacking continuity of already investigated archives.
The aim of this doctoral thesis is to reconstruct Holocene and Late Glacial climate variability on local to (over)regional scales based on varved (annually laminated) sediments from Lake Czechowskie down to annual resolution. This project was carried out within the Virtual Institute for Integrated Climate and Landscape Evolution Analyses (ICLEA) and funded by the Helmholtz Association and the Helmholtz Climate Initiative REKLIM (Regional Climate Change). ICLEA intended to gain a better understanding of climate variability and landscape evolution processes in the Northern Central European lowlands since the last deglaciation. REKLIM Topic 8 “Abrupt climate change derived from proxy data” aims at identifying spatiotemporal patterns of climate variability between e.g. higher and lower latitudes. The main aim of this thesis was (i) to establish a robust chronology based on a multiple dating approach for Lake Czechowskie covering the Late Glacial and Holocene and for the Trzechowskie palaeolake for the Lateglacial, respectively, (ii) to reconstruct past climatic and environmental conditions on centennial to multi-millennial time scales and (iii) to distinguish between local to regional different sediments responses to climate change.
Addressing the first aim, the Lake Czechowskie chronology has been established by a multiple dating approach comprising information from varve counting, tephrochronology, AMS 14C dating of terrestrial plant remains, biostratigraphy and 137Cs activity concentration measurements. Those independent age constraints covering the Lateglacial and the entire Holocene and have been further implemented in a Bayesian age model by using OxCal v.4.2. Thus, even within non-varved sediment intervals, robust chronological information has been used for absolute age determination. The identification of five cryptotephras, of which three are used as unambiguous isochrones, is furthermore a significant improvement of the Czechowskie chronology and currently unique for the Holocene within Poland. The first findings of coexisting early Holocene Hässeldalen and Askja-S cryptotephras within a varved sequence even allowed differential dating between both volcanic ashes and stimulated the discussion of revising the absolute ages of the Askja-S tephra.
The Trzechowskie palaeolake chronology has been established by a multiple dating approach comprising varve counting, tephrochronology, AMS 14C dating of terrestrial plant remains and biostratigraphy, covers the Lateglacial period (Allerød and Younger Dryas) and has been implemented in OxCal v.4.2. Those age constraints allowed regional correlation to other high-resolution climate archives and identifying leads and lags of proxy responses at the onset of the Younger Dryas.
The second aim has been accomplished by detailed micro-facies and geochemical analyses of the Czechowskie sediments for the entire Holocene. Thus, especially micro-facies changes had been linked to enhanced productivity at Lake Czechowskie. Most prominent changes have been recorded at 7.3, 6.5, 4.3 and 2.8 varve kyrs BP and are linked to a stepwise increasing influence of Atlantic air masses. Especially, the mid-Holocene change, which had been widely reported from palaeohydrological records in low latitudes, has been identified and linked to large scale reorganization of atmospheric circulation patterns. Thus, especially long-term changes of climatic and environmental boundary conditions are widely recorded by the Czechowskie sediments. The pronounced response to (multi)millennial scale changes is further corroborated by the lack of clear sediment responses to early Holocene centennial scale climate oscillations (e.g. the Preboreal Oscillation).
However, decadal scale changes at Lake Czechowskie during the most recent period (last 140 years) have been investigated in a lake comparison study. To fulfill the third aim of the doctoral thesis, three lakes in close vicinity to each other have been investigated in order to better distinguish how local, site-specific parameters, may superimpose regional climate driven changes. All lakes haven been unambiguously linked by the Askja AD1875 cryptotephra and independent varve chronologies. As a result, climate warming has only been recorded by sedimentation changes at the smallest and best sheltered lake (Głęboczek), whereas the largest lake (Czechowskie) and the shallowest lake (Jelonek) showed attenuated and less clear sediment responses, respectively. The different responses have been linked to morphological lake characteristics (lake size and depth, catchment area). This study highlights the potential of high-resolution lake comparison for robust proxy based climate reconstructions.
In summary, the doctoral thesis presents a high-resolution sediment record with an underlying age model, which is prerequisite for unprecedented age control down to annual resolution. Sediment proxy based climate reconstructions demonstrate the importance of the Czechowskie sediments for better understanding climate variability in the southern Baltic realm. Case studies showed the clear response on millennial time scale, while decadal scale fluctuations are either less well expressed or superimposed by local, site-specific parameters. The identification of volcanic ash layers is not only used for unambiguous isochrones, those are key tie lines for local to supra regional archive synchronization and establish the Lake Czechowskie as a key climate archive.