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In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.
Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.
To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.
We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.
We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.
In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.
Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.
To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.
We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.
We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.
The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At midaltitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.
The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At midaltitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.
Understanding the rates and processes of denudation is key to unraveling the dynamic processes that shape active orogens. This includes decoding the roles of tectonic and climate-driven processes in the long-term evolution of high- mountain landscapes in regions with pronounced tectonic activity and steep climatic and surface-process gradients. Well-constrained denudation rates can be used to address a wide range of geologic problems. In steady-state landscapes, denudation rates are argued to be proportional to tectonic or isostatic uplift rates and provide valuable insight into the tectonic regimes underlying surface denudation. The use of denudation rates based on terrestrial cosmogenic nuclide (TCN) such as 10Beryllium has become a widely-used method to quantify catchment-mean denudation rates. Because such measurements are averaged over timescales of 102 to 105 years, they are not as susceptible to stochastic changes as shorter-term denudation rate estimates (e.g., from suspended sediment measurements) and are therefore considered more reliable for a comparison to long-term processes that operate on geologic timescales. However, the impact of various climatic, biotic, and surface processes on 10Be concentrations and the resultant denudation rates remains unclear and is subject to ongoing discussion. In this thesis, I explore the interaction of climate, the biosphere, topography, and geology in forcing and modulating denudation rates on catchment to orogen scales.
There are many processes in highly dynamic active orogens that may effect 10Be concentrations in modern river sands and therefore impact 10Be-derived denudation rates. The calculation of denudation rates from 10Be concentrations, however, requires a suite of simplifying assumptions that may not be valid or applicable in many orogens. I investigate how these processes affect 10Be concentrations in the Arun Valley of Eastern Nepal using 34 new 10Be measurements from the main stem Arun River and its tributaries. The Arun Valley is characterized by steep gradients in climate and topography, with elevations ranging from <100 m asl in the foreland basin to >8,000 asl in the high sectors to the north. This is coupled with a five-fold increase in mean annual rainfall across strike of the orogen. Denudation rates from tributary samples increase toward the core of the orogen, from <0.2 to >5 mm/yr from the Lesser to Higher Himalaya. Very high denudation rates (>2 mm/yr), however, are likely the result of 10Be TCN dilution by surface and climatic processes, such as large landsliding and glaciation, and thus may not be representative of long-term denudation rates. Mainstem Arun denudation rates increase downstream from ~0.2 mm/yr at the border with Tibet to 0.91 mm/yr at its outlet into the Sapt Kosi. However, the downstream 10Be concentrations may not be representative of the entire upstream catchment. Instead, I document evidence for downstream fining of grains from the Tibetan Plateau, resulting in an order-of-magnitude apparent decrease in the measured 10Be concentration.
In the Arun Valley and across the Himalaya, topography, climate, and vegetation are strongly interrelated. The observed increase in denudation rates at the transition from the Lesser to Higher Himalaya corresponds to abrupt increases in elevation, hillslope gradient, and mean annual rainfall. Thus, across strike (N-S), it is difficult to decipher the potential impacts of climate and vegetation cover on denudation rates. To further evaluate these relationships I instead took advantage of an along-strike west-to-east increase of mean annual rainfall and vegetation density in the Himalaya. An analysis of 136 published 10Be denudation rates from along strike of the revealed that median denudation rates do not vary considerably along strike of the Himalaya, ~1500 km E-W. However, the range of denudation rates generally decreases from west to east, with more variable denudation rates in the northwestern regions of the orogen than in the eastern regions. This denudation rate variability decreases as vegetation density increases (R=- 0.90), and increases proportionately to the annual seasonality of vegetation (R=0.99). Moreover, rainfall and vegetation modulate the relationship between topographic steepness and denudation rates such that in the wet, densely vegetated regions of the Himalaya, topography responds more linearly to changes in denudation rates than in dry, sparsely vegetated regions, where the response of topographic steepness to denudation rates is highly nonlinear. Understanding the relationships between denudation rates, topography, and climate is also critical for interpreting sedimentary archives. However, there is a lack of understanding of how terrestrial organic matter is transported out of orogens and into sedimentary archives. Plant wax lipid biomarkers derived from terrestrial and marine sedimentary records are commonly used as paleo- hydrologic proxy to help elucidate these problems. I address the issue of how to interpret the biomarker record by using the plant wax isotopic composition of modern suspended and riverbank organic matter to identify and quantify organic matter source regions in the Arun Valley. Topographic and geomorphic analysis, provided by the 10Be catchment-mean denudation rates, reveals that a combination of topographic steepness (as a proxy for denudation) and vegetation density is required to capture organic matter sourcing in the Arun River.
My studies highlight the importance of a rigorous and careful interpretation of denudation rates in tectonically active orogens that are furthermore characterized by strong climatic and biotic gradients. Unambiguous information about these issues is critical for correctly decoding and interpreting the possible tectonic and climatic forces that drive erosion and denudation, and the manifestation of the erosion products in sedimentary archives.
Flood generation at the scale of large river basins is triggered by the interaction of the hydrological pre-conditions and the meteorological event conditions at different spatial and temporal scales. This interaction controls diverse flood generating processes and results in floods varying in magnitude and extent, duration as well as socio-economic consequences. For a process-based understanding of the underlying cause-effect relationships, systematic approaches are required. These approaches have to cover the complete causal flood chain, including the flood triggering meteorological event in combination with the hydrological (pre-)conditions in the catchment, runoff generation, flood routing, possible floodplain inundation and finally flood losses.
In this thesis, a comprehensive probabilistic process-based understanding of the causes and effects of floods is advanced. The spatial and temporal dynamics of flood events as well as the geophysical processes involved in the causal flood chain are revealed and the systematic interconnections within the flood chain are deciphered by means of the classification of their associated causes and effects. This is achieved by investigating the role of the hydrological pre-conditions and the meteorological event conditions with respect to flood occurrence, flood processes and flood characteristics as well as their interconnections at the river basin scale.
Broadening the knowledge about flood triggers, which up to now has been limited to linking large-scale meteorological conditions to flood occurrence, the influence of large-scale pre-event hydrological conditions on flood initiation is investigated. Using the Elbe River basin as an example, a classification of soil moisture, a key variable of pre-event conditions, is developed and a probabilistic link between patterns of soil moisture and flood occurrence is established. The soil moisture classification is applied to continuously simulated soil moisture data which is generated using the semi-distributed conceptual rainfall-runoff model SWIM. Applying successively a principal component analysis and a cluster analysis, days of similar soil moisture patterns are identified in the period November 1951 to October 2003.
The investigation of flood triggers is complemented by including meteorological conditions described by a common weather pattern classification that represents the main modes of atmospheric state variability. The newly developed soil moisture classification thereby provides the basis to study the combined impact of hydrological pre-conditions and large-scale meteorological event conditions on flood occurrence at the river basin scale.
A process-based understanding of flood generation and its associated probabilities is attained by classifying observed flood events into process-based flood types such as snowmelt floods or long-rain floods. Subsequently, the flood types are linked to the soil moisture and weather patterns. Further understanding of the processes is gained by modeling of the complete causal flood chain, incorporating a rainfall-runoff model, a 1D/2D hydrodynamic model and a flood loss model. A reshuffling approach based on weather patterns and the month of their occurrence is developed to generate synthetic data fields of meteorological conditions, which drive the model chain, in order to increase the flood sample size. From the large number of simulated flood events, the impact of hydro-meteorological conditions on various flood characteristics is detected through the analysis of conditional cumulative distribution functions and regression trees.
The results show the existence of catchment-scale soil moisture patterns, which comprise of large-scale seasonal wetting and drying components as well as of smaller-scale variations related to spatially heterogeneous catchment processes. Soil moisture patterns frequently occurring before the onset of floods are identified. In winter, floods are initiated by catchment-wide high soil moisture, whereas in summer the flood-initiating soil moisture patterns are diverse and the soil moisture conditions are less stable in time. The combined study of both soil moisture and weather patterns shows that the flood favoring hydro-meteorological patterns as well as their interactions vary seasonally. In the analysis period, 18 % of the weather patterns only result in a flood in the case of preceding soil saturation. The classification of 82 past events into flood types reveals seasonally varying flood processes that can be linked to hydro-meteorological patterns. For instance, the highest flood potential for long-rain floods is associated with a weather pattern that is often detected in the presence of so-called ‘Vb’ cyclones. Rain-on-snow and snowmelt floods are associated with westerly and north-westerly wind directions. The flood characteristics vary among the flood types and can be reproduced by the applied model chain. In total, 5970 events are simulated. They reproduce the observed event characteristics between September 1957 and August 2002 and provide information on flood losses. A regression tree analysis relates the flood processes of the simulated events to the hydro-meteorological (pre-)event conditions and highlights the fact that flood magnitude is primarily controlled by the meteorological event, whereas flood extent is primarily controlled by the soil moisture conditions.
Describing flood occurrence, processes and characteristics as a function of hydro-meteorological patterns, this thesis is part of a paradigm shift towards a process-based understanding of floods. The results highlight that soil moisture patterns as well as weather patterns are not only beneficial to a probabilistic conception of flood initiation but also provide information on the involved flood processes and the resulting flood characteristics.
Over the past decades, rapid and constant advances have motivated GNSS technology to approach the ability to monitor transient ground motions with mm to cm accuracy in real-time. As a result, the potential of using real-time GNSS for natural hazards prediction and early warning has been exploited intensively in recent years, e.g., landslides and volcanic eruptions monitoring. Of particular note, compared with traditional seismic instruments, GNSS does not saturate or tilt in terms of co-seismic displacement retrieving, which makes it especially valuable for earthquake and earthquake induced tsunami early warning. In this thesis, we focus on the application of real-time GNSS to fast seismic source inversion and tsunami early warning.
Firstly, we present a new approach to get precise co-seismic displacements using cost effective single-frequency receivers. As is well known, with regard to high precision positioning, the main obstacle for single-frequency GPS receiver is ionospheric delay. Considering that over a few minutes, the change of ionospheric delay is almost linear, we constructed a linear model for each satellite to predict ionospheric delay. The effectiveness of this method has been validated by an out-door experiment and 2011 Tohoku event, which confirms feasibility of using dense GPS networks for geo-hazard early warning at an affordable cost.
Secondly, we extended temporal point positioning from GPS-only to GPS/GLONASS and assessed the potential benefits of multi-GNSS for co-seismic displacement determination. Out-door experiments reveal that when observations are conducted in an adversary environment, adding a couple of GLONASS satellites could provide more reliable results. The case study of 2015 Illapel Mw 8.3 earthquake shows that the biases between co-seismic displacements derived from GPS-only and GPS/GLONASS vary from station to station, and could be up to 2 cm in horizontal direction and almost 3 cm in vertical direction. Furthermore, slips inverted from GPS/GLONASS co-seismic displacements using a layered crust structure on a curved plane are shallower and larger for the Illapel event.
Thirdly, we tested different inversion tools and discussed the uncertainties of using real-time GNSS for tsunami early warning. To be exact, centroid moment tensor inversion, uniform slip inversion using a single Okada fault and distributed slip inversion in layered crust on a curved plane were conducted using co-seismic displacements recorded during 2014 Pisagua earthquake. While the inversion results give similar magnitude and the rupture center, there are significant differences in depth, strike, dip and rake angles, which lead to different tsunami propagation scenarios. Even though, resulting tsunami forecasting along the Chilean coast is close to each other for all three models.
Finally, based on the fact that the positioning performance of BDS is now equivalent to GPS in Asia-Pacific area and Manila subduction zone has been identified as a zone of potential tsunami hazard, we suggested a conceptual BDS/GPS network for tsunami early warning in South China Sea. Numerical simulations with two earthquakes (Mw 8.0 and Mw 7.5) and induced tsunamis demonstrate the viability of this network. In addition, the advantage of BDS/GPS over a single GNSS system by source inversion grows with decreasing earthquake magnitudes.
Effect of mass wasting on soil organic carbon storage and coastal erosion in permafrost environments
(2015)
Accelerated permafrost thaw under the warming Arctic climate can have a significant impact on Arctic landscapes. Areas underlain by permafrost store high amounts of soil organic carbon (SOC). Permafrost disturbances may contribute to increased release of carbon dioxide and methane to the atmosphere. Coastal erosion, amplified through a decrease in Arctic sea-ice extent, may also mobilise SOC from permafrost. Large expanses of permafrost affected land are characterised by intense mass-wasting processes such as solifluction, active-layer detachments and retrogressive thaw slumping. Our aim is to assess the influence of mass wasting on SOC storage and coastal erosion.
We studied SOC storage on Herschel Island by analysing active-layer and permafrost samples, and compared non-disturbed sites to those characterised by mass wasting. Mass-wasting sites showed decreased SOC storage and material compaction, whereas sites characterised by material accumulation showed increased storage. The SOC storage on Herschel Island is also significantly correlated to catenary position and other slope characteristics. We estimated SOC storage on Herschel Island to be 34.8 kg C m-2. This is comparable to similar environments in northwest Canada and Alaska.
Coastal erosion was analysed using high resolution digital elevation models (DEMs). Two LIDAR scanning of the Yukon Coast were done in 2012 and 2013. Two DEMs with 1 m horizontal resolution were generated and used to analyse elevation changes along the coast. The results indicate considerable spatial variability in short-term coastline erosion and progradation. The high variability was related to the presence of mass-wasting processes. Erosion and deposition extremes were recorded where the retrogressive thaw slump (RTS) activity was most pronounced. Released sediment can be transported by longshore drift and affects not only the coastal processes in situ but also along adjacent coasts.
We also calculated volumetric coastal erosion for Herschel Island by comparing a stereo-photogrammetrically derived DEM from 2004 with LIDAR DEMs. We compared this volumetric erosion to planimetric erosion, which was based on coastlines digitised from satellite imagery. We found a complex relationship between planimetric and volumetric coastal erosion, which we attribute to frequent occurrence of mass-wasting processes along the coasts. Our results suggest that volumetric erosion corresponds better with environmental forcing and is more suitable for the estimation of organic carbon fluxes than planimetric erosion.
Mass wasting can decrease SOC storage by several mechanisms. Increased aeration following disturbance may increase microbial activity, which accelerates organic matter decomposition. New hydrological conditions that follow the mass wasting event can cause leaching of freshly exposed material. Organic rich material can also be directly removed into the sea or into a lake. On the other hand the accumulation of mobilised material can result in increased SOC storage. Mass-wasting related accumulations of mobilised material can significantly impact coastal erosion in situ or along the adjacent coast by longshore drift. Therefore, the coastline movement observations cannot completely resolve the actual sediment loss due to these temporary accumulations. The predicted increase of mass-wasting activity in the course of Arctic warming may increase SOC mobilisation and coastal erosion induced carbon fluxes.