bluuebunny/crossref_metadata_2025 huggingface dataset:
What is the role of oblique whistler waves in shaping of the solar wind electron function between 0.17 and 1 AU ?
Authors: Colomban Lucas , Kretzschmar Matthieu , Krasnoselskikh Vladimir , Maksimovic Milan , Graham Daniel , Khotyainsev Yuri , Berĉiĉ Laura , Berthomier Matthieu , Froment Clara
Published: March 2022

In the solar wind, whistler waves are thought to play an important role on the evolution of the electron velocity distribution function as a function of distance. In particular, oblique whistler waves may diffuse the Strahl electrons into the halo population. Using AC magnetic and electric field measured by the SCM (search coil magnetometer) and electric antenna of Solar Orbiter and Parker Solar Probe, we search for the presence of whistler waves at heliocentric distance between 0.17 and 1 AU. Spectral matrices computation and minimum variance analysis on continuous waveforms make it possible to identify whistler wave modes and to determine their direction of propagation with respect to the ambiant magnetic field (angle and direction : sunward or anti-sunward) . A statistical study of the inclination of these waves and of their parameters is presented and allows us to make assumptions about their roles. Single events are also presented in details
IGM, a glacier evolution model accelerated by deep-learning and GPU
Authors: Jouvet Guillaume , Cordonnier Guillaume , Kim ByungsooKim , Luethi Martin , Vieli Andreas , Aschwanden Andy
Published: March 2022

We give an overview of the Instructed Glacier Model (IGM) -- a new framework to model the evolution of glaciers at any scale by coupling ice dynamics, surface mass balance, and mass conservation. The key novelty of IGM is that it models the ice flow by a Convolutional Neural Network (CNN), which is trained from physical high-order ice flow mechanical models. Doing so has major advantages in both forward and inverse modelling.

In forward modelling, the most computationally demanding model component (the ice flow) is substituted by a very cheap CNN emulator. Once trained with representative data, IGM permits to model individual mountain glaciers several orders of magnitude faster than high-order ones on CPU with fidelity levels above 90 % in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to Graphics Processing Unit (GPU) permits additional significant speed-ups, especially when modelling large-scale glacier networks and/or high spatial resolutions.

In inverse modelling, the substitution by a CNN emulator does not only speed up but facilitates dramatically the data assimilation step, i.e. the search for optimal ice thickness and ice flow parameter spatial distributions that match spatial observations at best (such as ice flow, surface topography or ice thickness profiles) while being consistent with the high-order ice flow mechanics. The reason is that inverting a CNN can take great benefit from the tools used for its training such as automatic differentiation, stochastic gradient methods, and GPU.

IGM is an open-source Python code (https://github.com/jouvetg/igm), which deals with two-dimensional gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, computationally highly-efficient, easy-to-customize, and mechanically state-of-the-art glacier forward and inverse modelling at any scale. We illustrate its potential by replicating a simulation of the great Aletsch Glacier, Switzerland, from 1880 to 2100, based on a Stokes model. The complete workflow (data assimilation and 220 years long forward modelling) at 100 m of resolution takes about 1-2 min on the GPU of a laptop and can be replicated and adapted easily using an online Colab notebook.
Emergence of the physiological effects of increasing CO2 in the land-atmosphere exchange of carbon and water
Authors: Zhan Chunhui , Orth René , Migliavacca Mirco , Zaehle Sönke , Reichstein Markus , Engel Jan , Rammig Anja , J. Winkler Alexander
Published: March 2022

Increasing atmospheric CO2 concentration influences the carbon assimilation rate of plants and stomatal conductance, and consequently affects the global cycles of carbon and water. However, the extent to which these physiological effects of increasing CO2 significantly alters the land-atmosphere exchange of carbon and water is unclear.

To address this issue, we apply a comprehensive process-based land surface model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) to study the propagation of effects of increasing atmospheric CO2 concentrations into the carbon and water cycles. We analyze century-long simulations using factorial combinations of historical forcings for representative ecosystems across different climate regimes and biomes. We develop a statistical method based on the signal-to-noise ratio to detect the emergence of the increasing CO2 effects. We find that the signal in gross primary production (GPP) emerges at relatively small CO2 increase (Δ[CO2] ~ 20 ppm) since the starting point of the time period (i.e., 1901), especially at sites where the leaf area index (LAI) is relatively high. The CO2 signal in the transpiration water flux (normalized to evaporative leaf area) emerges only at relatively high CO2 increase (Δ[CO2] >> 40 ppm), rooted in its high sensitivity to climate variability. In general, the increasing CO2 effect is stronger when plant productivity is not strongly limited by climatic constraints, stronger in forest-dominated rather than in grass-dominated ecosystems. The water cycle is less susceptible to the increasing CO2 effects, mainly due to the compensatory effects of increasing LAI and reduced transpiration at leaf level. Our results from model simulations indicate when and where we expect to detect physiological CO2 effects in in-situ flux measurements. Finally, we apply the statistical methods to quantify the increasing CO2 effects on carbon and water flux measurements across the FLUXNET network. Overall, the model-based analyses along with the observational study focused on the detection and potential quantification of iCO2 effects, are critical and provide robust assessments of how the system will continue to change as CO2 continues to rise.
The eWaterCycle platform for open and FAIR computational hydrological research
Authors: Hut Rolf , Drost Niels , van de Giesen Nick , van Werkhoven Ben , Aerts Jerom , Alidoost Fakhereh , Kalverla Peter , Verhoeven Stefan , Andela Bouwe , Camphuijsen Jaro , Dzigan Yifat , van den Oord Gijs , Pelupessy Inti , Vreede Barbara
Published: March 2022

The eWaterCycle platform (https://www.ewatercycle.org/) is a fully Open Source and ‘FAIR by Design’ Platform where hydrologists can do computational hydrological research using their own, or other’s, models and data. Using eWaterCycle, computational hydrologist can focus on the hydrological part of their work, without the headache that often comes with the computational part.

In eWaterCycle experiments are separated from models: experiments are build and run in Jupyter notebooks and models can be accessed as objects in these notebooks. The models themselves are ‘hidden’ in (Docker) containers and accessed through an easy interface. This interface and the technology behind it that we’ve build allows computational hydrologists to work with (each others) models written in different programming languages without having to access that code. Currently PCRGlobWB 2.0, Hype, LISFlood, WFLOW and MARMoT are among the models supported by eWaterCycle.

Furthermore, pre-processing of atmospheric forcing data is handled transparently by ESMValTool, which separates the process of selecting and standardising variables from the steps needed to make forcing compatible with a specific model. If a source of forcing data has been made ready for one model in eWaterCycle it is easy to use it for any other. If a model has been used with one forcing data source, it is easy to swap it with another. Currently ERA5 and ERA-Interim are supported in eWaterCycle.

With eWaterCycle use cases such as (but not limited to!) these are now easier to implement:

Comparing two models for the same region against observation data (GRDC discharge observations are standard supported) to determine which model performs best for a given research question

Coupling two models (in different programming languages) to exchange information at every timestep, for example making a

Running a multi-model ensemble (including adding data assimilation of observations)

Previously we have announced eWaterCycle as work in progress. At the 2022 General Assembly we will demonstrate the release of v1.0 of the eWaterCycle platform, giving the computational hydrological community access to a platform that supports fully reproducible, open, and FAIR Hydrological modelling.

This work is currently under open review for publication in GMD: https://gmd.copernicus.org/preprints/gmd-2021-344/ and parts of this work have been presented at the AGU 2021 Fall Meeting.
Significance of lacustrine groundwater discharge for the rapid eutrophication of formerly oligotrophic Lake Stechlin
Authors: Lewandowski Jörg , Jäger Anna , Mehler Franziska , Goldhammer Tobias , Hupfer Michael
Published: March 2022

Lake Stechlin was an oligotrophic lake but experienced rapid eutrophication in the last 12 years. The speed of the deterioration is surprising, especially since the lake is located in a nature reserve, its catchment is almost entirely forested, there is no agriculture and there are only two small settlements with a total of less than 1000 inhabitants. Undoubtedly, groundwater is a crucial component of the water and compound balances of Stechlin, as there are no surface inflows. However, if one considers the total amount of groundwater entering the lake and the maximum compound loads possible with it, groundwater alone cannot explain the rapid increase in P concentrations in the Stechlin. Thus, internal P cycling, i.e. the mobilization of previously imported P, is an important process in the biogeochemistry of the lake. However, even if it is true that most of the P is remobilized by internal processes, it can still originate originally from groundwater. And groundwater might be the decisive trigger of the eutrophication in recent years. We provide evidence that not only P import from groundwater is the trigger, but also changes in loads of compounds that are closely coupled to the P cycle, namely calcium, manganese, iron and sulphate. While there is some evidence of elevated concentrations in the aquifer below the two settlements, the main P import into the Stechlin probably originates from the eutrophic Lake Dagow, which drains into Lake Stechlin via the aquifer.
Planning for a Large Ensemble based on the HadGEM3 climate model
Authors: Schiemann Reinhard , Hatcher Rosalyn , Lawrence Bryan , Lister Grenville , Shaffrey Len
Published: March 2022

Large ensembles of climate-scale model simulations are key tools for assessing climate risks, separating internal variability from external forcing, and interpreting the observational record. Several modelling centres have produced such ensembles over the past years. Here we present early plans for the development of a new Large Ensemble based on the HadGEM3 (Hadley Centre Global Environment Model version 3) climate model. The initial plan envisages a 40-member ensemble spanning 150 years of historical/scenario climate (1950-2100) at a resolution of N216 (about 60 km) in the atmosphere and ¼° in the ocean.

This initiative is part of the recently started UK NERC multi-centre project CANARI (Climate change in the Arctic-North Atlantic Region and Impacts on the UK). CANARI aims to advance understanding of the impacts on the UK arising from climate variability and change in the Arctic-North Atlantic region, with a focus on extreme weather and the potential for rapid, disruptive change. While we aim for the new Large Ensemble to become a resource for a wide range of applications, it will support addressing the CANARI science questions in particular. These questions are concerned with, for example, the (i) projected Arctic change and potential lower-latitude influences through atmospheric or oceanic pathways, (ii) the projected change in the large-scale (North Atlantic) ocean/atmosphere circulation, its drivers, and interaction with weather systems, and (iii) projected impacts on the UK arising from extreme weather (windstorms and flooding, blocking, heatwaves and droughts).

This poster invites discussion with the community on all aspects of the design of the new Large Ensemble, and particularly seeks input regarding

the choice/number of experiments to follow (from CMIP6 Scenario MIP),

the initialisation strategy, and the degree to which slow (10 years and longer) variability, particularly in the ocean, should be sampled, and

the desired output.
Changes in likelihood and intensity of spatially co-occurring hot, dry and wet extremes
Authors: Biess Bianca , Gudmundsson Lukas , Seneviratne Sonia I.
Published: March 2022

The recent 2021 spring-to-summer season was characterized by co-occurrent hot, dry and extremely wet extremes around the globe, raising questions regarding changing likelihoods of such extreme years in a changing climate. To address this question, we assess the likelihood of spatially compounding hot, dry and wet extremes under historic and present climate as well as under different future warming levels. The occurrence-probability of spatially compounding events and area affected in future climates under scenarios at 1.5°C, 2°C and higher levels of global warming is determined using Earth System model simulations from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). As climate change impacts are particularly severe when spatially compounding events occur in multiple regions with high exposure of people or crops, this study focuses on densely inhabited regions and important agricultural areas. 
Partitioning of evapotranspiration based on flux variance similarity theory for an urban forest land
Authors: Li Han , Huang Jinhui Jeanne , Chen Han , Zhou Ziqi , Wei Yizhao
Published: March 2022

Partitioning of evapotranspiration(ET) into its components (ET; the sum of vegetation transpiration [T] and soil evaporation [E])from urban forest land is important for guiding precise irrigation decisions in urban areas and assessing the impact of urbanization on the urban hydrological cycle. So far, the variability of T/ET in natural ecosystems has been extensively discussed, few studies have examined under urban. In this study, high frequency (10 Hz) time series eddy covariance observations collected from January 2020 to December 2021 in an urban forest land located in Tianjin, China. We observed changes in water vapor and carbon dioxide fluxes and the flux variance similarity (FVS) theory based on five water use efficiency(WUE) algorithms was applied to partition ET into E and T. We also combined with oxygen and hydrogen isotopes to verifies the partition results. The results indicated that the partitioning was partially consistent with the isotope-based approach. The growing season average T/ET ranges from 0.68 to 0.96, which can be described well as a function of leaf area index (LAI). Finally, we further discussed the characteristics, uncertainties and applicability of five WUE algorithms in urban forest land.
Multi-scale Modelling of Urban Water Demand under Urban Development and Societal Uncertainties: The Case Study of Milan, Italy.
Authors: Hao Wenjin , Cominola Andrea , Castelletti Andrea
Published: March 2022

Urban water demands vary across multiple spatio-temporal scales, driven by population growth, climate change, and urbanization. Demand-side management emerged as an important complementary measure to supply-side interventions to address urban water scarcity, foster water conservation, and inform water governance. Moreover, rapid development and deployment of Advanced Metering Infrastructure (AMI) and so-called digitalization in the water sector unfold new opportunities to uncover water demand patterns and model water demands at increasingly high spatial and temporal scales. However, challenges to modelling water demands arise from the uncertainties of water demands under abrupt environmental and societal change. The current Covid-19 Pandemic with the Stay-at-Home order is an example of such sources of uncertainty because it rapidly and unexpectedly changed people’s working patterns and lifestyles. Understanding and modelling water demands across spatial and temporal scales considering an uncertain world is, thus, key to designing robust demand management strategies.

In this work, we investigate urban water demand changes at multiple spatio-temporal scales in Milan (Italy). We combine different state-of-art data-driven models (i.e., Ruptures breakpoint detection framework, LightGBM, Hierarchical clustering, and Recurrent Neural Networks) to extract water demand characteristics from heterogeneous data sources, including historical time series of water consumption recorded with AMI, drinking water volumes pumped in the water distribution network, and socio-demographic characteristics of different urban districts. At the city scale, we found that a significant declining trend in water consumption occurred in 2017-2020, especially during the Pandemic and the first lockdown measures. At the sub-city scale, we explored the relationships between water demand and different socio-demographic, economic, and urban form features with data from 2004 to 2020. Finally, we analyzed AMI data collected at the water account level in 2019-2021 to assess the effect of Pandemic on demand pattern change and cross-correlate it with spatial heterogeneity of neighborhood features. While the investigation of historical demand pattern change gives insights to design long-term demand management strategies, accurate prediction of future demand can help improve short-term operational efficiency for water utilities. In this regard, in the last phase of this work, we compare state-of-art predictive models to explore how accurately machine learning/deep learning models can predict water demand at city and sub-city scales. Preliminary prediction results show that advanced models like Long Short Term Memory networks (LSTM) with wavelet transform technique can attain model accuracies (R2) of 0.80 to 0.95 for 1-day ahead prediction. 
Antarctic Ice Sheet  simulations using Yelmo ice sheet model and a series of IPSL CM5A2 climate simulations between 17 Ma and 14 Ma
Authors: Segalla Diane , Blasco Navarro Javier , Ramstein Gilles , Fluteau Frédéric , Robinson Alexander James , Alvarez-Solas Jorge , Montoya Redondo Marisa Luisa , Colleoni Florence
Published: March 2022

The mid-Miocene Climatic Optimum (MMCO, ~17-15 Ma) and the mid-Miocene Climatic Transition (MCT, ~15-13.5 Ma),  represents a period of high policy relevance because of the high atmospheric pCO2 concentrations. Exploring this period offers the opportunity to investigate the Antarctic Ice Sheet (AIS) response to CO2 forcings that are close to those projected in the medium to worse case emission scenarios. A set of equilibrium simulations with the 3D ice sheet model Yelmo allows us to study the envelope of the AIS volume and extent during the MMCO (17 Ma) and MCT (14 Ma). These simulations are forced off-line with equilibrium climatic conditions  obtained with the Atmosphere-Ocean General Circulation Model (AOGCM) IPSL CM5A2.  Two values of the reconstructed atmospheric pCO2, i.e. 420 ppm and 700 ppm, are prescribed, for an orbital configuration corresponding to minimum and maximum insolation values at 75°S each (9 climate simulations in total). Thanks to these different configurations we simulated the AIS dynamics. Results show that at 17 Ma, warmer conditions produce an AIS that is drastically reduced with respect to today’s configuration. At 14 Ma, cooler climatic conditions allow the AIS to expand again. This is in agreement with the geological records of the AIS dynamics that reveal a substantial expansion of the ice sheet at the end of the MCT. Since Antarctica is the only ice sheet at this time, our set of climate and ice-sheet simulations capture the envelope of ice volume and extent of the AIS. Moreover, such studies contribute to a better understanding of the 𝛿18O records and of the evolution of deep ocean temperature versus ice volume and global mean sea level change.
3D geomechanical modelling of induced seismicity: simulated finite-source to moment tensor inversion
Authors: Ruan Jingming , Masfara La Ode Marzujriban , Ghose Ranajit , Mulder Wim
Published: March 2022

Geomechanical modelling is widely used to simulate the triggering of induced earthquakes in a gas-producing region, such as in the Groningen gas field. Dynamic simulation can provide information on the process of dynamic rupture during earthquake nucleation and on the generated seismic wavefield. Through geomechanical modelling, one can investigate the effects of the model parameters, e.g., depletion pattern and friction parameters. In the modelling, the dynamic rupture at a finite fault is simulated both in space and time. The resulting seismic wavefield from such a finite source should be more realistic than that from a point source. Previous studies on the inversion of induced-earthquake data in the Groningen area usually assumed a point source. In the present research, we implement the full moment tensor inversion of the synthetic waveforms caused by the dynamic rupture of a geomechanically simulated finite fault. We then link this moment tensor to the moment tensor obtained from the inversion of field-seismic data for an earlier earthquake, using the same inversion approach in both cases. The inverted moment tensor from the field seismic observation serves as a constraint to our geomechanical simulation. This enables us to perform a more realistic simulation of an induced earthquake.
100 years of river flow timing characteristics and extreme flow analysis
Authors: Jalali Shahrood Abolfazl , Torabi Haghighi Ali
Published: March 2022

River ice has a significant impact on nearly 60% of rivers in the northern hemisphere where ice jams due to river ice are responsible for some severe and recurrent floods in the northern rivers. The ice formation (i.e., freeze-up event) occurs during the wintertime when the temperature drops to freezing point and when the flow gradually reaches the lowest value for a while, and it keeps the river frozen with low flow occurring close to the period of the maximum ice cover before the spring melt is initiated.  In this research, we focus on detecting the periods with low flow (i.e., freezing period). Daily discharge time series were used to derive the annual freezing periods as well as extreme discharge values over a century in Tana and Tornio Rivers in the Finnish borders of Sweden and Norway.  Therefore, the timing characteristics such as duration, probable shifts through time, and overall flow extremes including the average low and high flow in a period of 90 days in each water year were quantified. The study showed that both low and high flows in two rivers had a significant negative trend in their occurrence date by a confidence level of 95%. In addition, it was observed that the seasonal 90-day low and high flow periods happened earlier in recent years. On the other hand, Tana River showed a negative trend in its annual minimum flow over the century which is an opposite event in comparison with Tornio River. The duration of low flow in the Tana River has been significantly increased by the confidence level of 95% from a range of 50-70 days to a range of 100-140 days. In Tornio River, the duration has been significantly decreased by the confidence level of 95%. In the first ten years, the duration is about 120 days on average, while the duration in the last ten years is about 50 days.
Spreading of the Pechora river plume in the southeastern part of the Barents Sea in 2017-2021
Authors: Rogozhin Vladimir , Osadchiev Alexander
Published: March 2022

The Barents Sea is a shelf marginal sea of the Arctic Ocean. The river runoff in the Barents Sea is small (260 km3 per year) and does not have a significant effect on hydrophysical processes with the exception of the southeastern part of the sea, where the large Pechora River flows with an annual runoff of 130–160 km3 (Gordeev et al., 1996) . Most of the annual Pechora runoff flows into this shallow water area, also called the Pechora Sea, during the summer flood in June-July. Because of this, the hydrological regime of the Pechora Sea in the warm season seems to largely depend on the processes of propagation and mixing of the Pechora plume.

In this work, the study of the structure and variability of the Pechora River plume in 2017-2021 was carried out based on the data of detailed field measurements, wind reanalysis, as well as satellite images for the first time. Seasonal increases in the Pechora plume area (up to 35,500 km²) were recorded in the Pechora Sea in 2018 and 2020. At the same time, the salinity in the plume increased from 9-10 psu near the Pechora Bay to 18-20 psu at the outer boundary of the plume, the plume thickness was 12-15 m. The Pechora plume flowed into the Kara Sea through the Kara Gates Strait it was recorded for the first time. Analysis of the literature data on salinity in the studied region showed that in similar periods in 2017 and 2019, the Pechora plume was noticeably less pronounced, i.e., it had a much smaller area. Apparently, such variability is caused by the influence of external influences (wind, river runoff, tides), which requires further study.
On the structure and sensitivity of North Atlantic thermohaline circulation
Authors: Skrefsrud Johanne , Eldevik Tor , Årthun Marius , Asbjørnsen Helene
Published: March 2022

Changes in the Atlantic Meridional Overturning Circulation (AMOC) are often assumed to lead to equivalent changes in poleward ocean heat transport. Such an assumption leaves only a small role for the ocean gyres in transporting heat poleward. Here, the structure and sensitivity of the North Atlantic thermohaline circulation are investigated with a focus on the comparative role of the horizontal and the vertical circulation components. We use the ECCOv4-r4 ocean state estimate for the period 1992-2017 to evaluate the gyre and overturning contribution in terms of northward volume transport, poleward heat transport, and freshwater transport. The total poleward heat transport increases from the equatorial region northward with a maximum of about 1 PW around 15N, followed by a gradual decrease northward disrupted by another maximum of about 0.5 PW at 50-60N. An important contribution from both the gyre and overturning components is seen at subtropical latitudes, though the components are notably not independent of each other. From about 50N, the gyre component is found to be the dominant contributor to poleward heat transport and equatorward freshwater transport. The results indicate that the gyre circulation in the North Atlantic cannot be ignored in the discussion of mechanisms behind poleward ocean heat transport. 
A new free software to reconstruct stress trajectories: the Atmo-stress service 
Authors: Bressan Sofia , Gounari Olympia , Ntouskos Valsamis , Corti Noemi , Bonali Fabio Luca , Karantzalos Konstantinos , Tibaldi Alessandro
Published: March 2022

The reconstruction of present-day stress and palaeostress trajectories is of paramount importance to study the tectonic regime and its evolution, in a specific area. Its comprehension is crucial also for seismic and volcanic hazard assessment, especially focusing on the shallow crust.

In the framework of the NEANIAS project (https://www.neanias.eu/), EU H2020 RIA, it has been developed the so called ATMO-Stress service (https://docs.neanias.eu/projects/a2-1-service/en/latest/), an open-source cloud service, currently hosted on the GARR Kubernetes platform, which allows to calculate stress trajectories in plain view, based on the concepts from Lee and Angelier (1994). It is designed to run on modern computers for both academics and non-academics purposes, spanning from research activity to oil and gas industries, natural hazard prevention and management.

The service is freely accessible at https://atmo-stress.neanias.eu/ and is designed to calculate the stress trajectories for a specific area, considering as input the same type of stress (e.g. σHmax or σHmin). Data input can be from different sources (e.g. field data, focal mechanism solutions, in-situ geotechnical measures). They must be listed in a homogeneous ASCII text file or Excel file format, including the geographic coordinates, azimuth of the stress and the angular error. The service is capable of processing data from local to regional scale. Following the principles from Lee and Angelier (1994), the trajectory calculation can be done using different parameters and settings. The outputs can be seen directly on the website and can be downloaded with file formats ready to be imported and analyzed in GIS environment and Google Earth.
Calibration and repositioning of an optical fibre cable from acoustic noise obtained by DAS technology
Authors: Papotto Lucas , DeCacqueray Benoit , Rivet Diane
Published: March 2022

DAS (Distributed Acoustic Sensing) turns fibre optic cables used for telecommunications into multi-sensor antenna arrays. This technology makes it possible to detect an acoustic signal from a natural source such as cetacean or micro-earthquakes, or a man-made source by measuring the deformation of the cable. At sea, the coupling between the optical fibre and the ground on which it rests, as well as the calibration of the cable, is a critical point when the configuration of the cable is unknown. Is the fibre buried or suspended? What is the depth of the sensor being studied? What impact do these parameters have on the signal? The answers to these questions have an impact on the quality of the results obtained, the location of sources - seismic or acoustic - and the characterisation of the amplitude of signals are examples of this. Here, a first approach to study this calibration is proposed. Acoustic noise generated by passing ships in the vicinity of a 42km long optical fibre off Toulon, south-east France, is used to obtain signals for which the position and the signal of the source are known. Then, the synthetic and theoretical signal representing the ship's passage is modelled (3D model, AIS Long/Lat coordinates and depth, propagation speed in water c₀ = 1530m/s). This simulation allows us to compare the real and synthetic signals, in order to make assumptions about the actual cable configuration. We compare the signals through beamforming, f-k diagram and time-frequency diagram in particular. The grid search approach allowed us to determine the new position or orientation of a portion of the antenna. This new position is then evaluated from the signals of different vessels.
The potential of automated snow avalanche detection from SAR images for the Austrian Alpine region using a learning-based approach
Authors: Kapper Kathrin Lisa , Muckenhuber Stefan , Goelles Thomas , Trügler Andreas , Kuric Muhamed , Abermann Jakob , Grahn Jakob , Malnes Eirik , Schöner Wolfgang
Published: March 2022

Each year, snow avalanches cause many casualties and tremendous damage to infrastructure. Prevention and mitigation mechanisms for avalanches are established for specific regions only. However, the full extent of the overall avalanche activity is usually barely known as avalanches occur in remote areas making in-situ observations scarce. To overcome these challenges, an automated avalanche detection approach using the Copernicus Sentinel-1 synthetic aperture radar (SAR) data has recently been introduced for some test regions in Norway. This automated detection approach from SAR images is faster and gives more comprehensive results than field-based detection provided by avalanche experts. The Sentinel-1 programme has provided - and continues to provide - free of charge, weather-independent, and high-resolution satellite Earth observations since its start in 2014. Recent advances in avalanche detection use deep learning algorithms to improve the detection rates. Consequently, the performance potential and the availability of reliable training data make learning-based approaches an appealing option for avalanche detection.  

         In the framework of the exploratory project SnowAV_AT, we intend to build the basis for a state-of-the-art automated avalanche detection system for the Austrian Alps, including a "best practice" data processing pipeline and a learning-based approach applied to Sentinel-1 SAR images. As a first step towards this goal, we have compiled several labelled training datasets of previously detected avalanches that can be used for learning. Concretely, these datasets contain 19000 avalanches that occurred during a large event in Switzerland in January 2018, around 6000 avalanches that occurred in Switzerland in January 2019, and around 800 avalanches that occurred in Greenland in April 2016. The avalanche detection performance of our learning-based approach will be quantitatively evaluated against held-out test sets. Furthermore, we will provide qualitative evaluations using SAR images of the Austrian Alps to gauge how well our approach generalizes to unseen data that is potentially differently distributed than the training data. In addition, selected ground truth data from Switzerland, Greenland and Austria will allow us to validate the accuracy of the detection approach. As a particular novelty of our work, we will try to leverage high-resolution weather data and combine it with SAR images to improve the detection performance. Moreover, we will assess the possibilities of learning-based approaches in the context of the arguably more challenging avalanche forecasting problem.
Bias teleconnections: atmospheric variability associated with biases in remote regions
Authors: Zhao Yuan-Bing , Lunkeit Frank , Žagar Nedjeljka
Published: March 2022

Atmospheric spatial and temporal variability are closely related with the former being relatively well assessed compared to the latter. New opportunities for understanding the spatio-temporal variability spectrum are offered by coupled high-resolution climate models. However, the models still suffer from significant systematic errors (biases) calling for an approach that assesses circulation variability in relation to biases. Furthermore, biases in simulated variability are often of remote origin; for example, biases in the Atlantic sea-surface temperature in boreal winter may be responsible for changes in simulated variability over Asia.

We present a novel framework for the multivariate, multi-scale variability evaluation in relation to remote biases. Centennial simulations are carried out using a general circulation model PLASIM and a perfect-model framework. Biases in simulated circulation originate from regional errors in the surface forcing by prescribed sea surface temperature (SST). A reference simulation is forced with the monthly SST from ERA-20C reanalyses from January 1900 to December 2010. Sensitivity simulations are forced with the same SST with addition of regional perturbations that mimic the errors in the surface forcing of the atmosphere and lead to systematic errors in the simulated mean state and temporal variance. The erroneous SST is respectively located in tropical basins of Indian ocean, Western Pacific, Central Pacific, Eastern Pacific, and Atlantic, and in extra-tropical areas of North Pacific and North Atlantic.

The bias is the time-averaged difference between the reference and sensitivity simulations. Using the normal-mode function decomposition, the amplitude and phase of the bias can be related to deficiencies in spatial and temporal variance of the two main dynamical regimes: quasi-geostrophic regime and unbalanced circulation. The results show that biases are mainly established in the zonal-mean state and at planetary scales of balanced flow. In boreal winter, the biases at scales with zonal wavenumber k>0 are typically manifested in the barotropic Rossby wave train across the Northern Hemisphere. The structure of tropical biases is that of unbalanced flow, projecting predominantly on the Kelvin wave and the vertical baroclinic structure. The effects of biases on spatio-temporal variability are further investigated in spectral space.
Evaluating the effects of fire severity and post-fire management decisions on the carbon balance of a Swedish forest
Authors: Kelly Julia , Doerr Stefan H. , Ekroos Johan , Ibáñez Theresa S. , Santín Cristina , Soares Margarida , Kljun Natascha
Published: March 2022

Boreal forest fires are increasing in frequency and intensity due to climate change. Yet there is little knowledge on the impacts of fire severity and post-fire management decisions on the regeneration and carbon balance of production forests in Eurasia. To investigate these issues, we established 6 sites in a Swedish Pinus sylvestris forest that burned in 2018. Specifically, we evaluated the effects of (i) fire severity (low severity ground fire vs high severity stand-replacing canopy fire), (ii) post-fire wood management (salvage-logged vs unlogged) and (iii) post-fire vegetation management (natural regeneration, seeding or planting nursery seedlings of P. sylvestris). At each site, we measured soil respiration (CO2 release to the atmosphere) and methane fluxes (soil CH4 uptake) using the manual chamber approach, soil microclimate and vegetation cover for the first 3 years after the fire (2019-2021). Two of the sites also have eddy covariance flux measurements, which provided an insight into the ecosystem-scale carbon balance.

 

Fire severity had a strong impact on forest soils, with high fire severity sites having lower soil respiration, warmer soils and less vegetation regrowth compared to a low fire severity site. Surprisingly, soil respiration was similar at a low fire severity site and unburnt site, despite the almost complete loss of the soil organic layer during the ground fire. There were no clear effects of fire or post-fire management on the soil methane fluxes. Salvage-logging of a high fire severity site had no additional effects on soil respiration compared to leaving the dead trees standing. Salvage-logging of a low fire severity site led to a decline in soil respiration, but turned the ecosystem into a net source of CO2 due to the removal of the living trees. In terms of P. sylvestris regeneration, our results showed that the seedling density following natural regeneration was similar to or higher than the seedling density in sites which had been manually seeded or replanted with nursery seedlings.

 

Our results suggest that post-fire management interventions may not facilitate faster vegetation regrowth and the recovery of carbon uptake by forests compared to natural regeneration in the immediate post-fire years. Furthermore, despite the start of new vegetation growth and declines in soil CO2 release, the high fire severity and/or salvage-logged sites remain net CO2 sources 3 years after the fire, which must be considered in estimations of the net effect of fires on Sweden’s forest carbon balance.
Actual evapotranspiration of abandoned grassland on a slope in the Western Italian Alps: Impact of shrub encroachment
Authors: Gisolo Davide , Bevilacqua Ivan , van Ramshorst Justus , Knohl Alexander , Siebicke Lukas , Gentile Alessio , Previati Maurizio , Canone Davide , Ferraris Stefano
Published: March 2022

Land cover changes affect the local hydrological cycle, including actual evapotranspiration (ETa).  Encroachment by shrubs on abandoned grasslands is an increasing phenomenon in the Alps, a region already suffering climate change effects. In addition, shrub encroachment is thought to occur faster on steep slopes. Unfortunately, steep mountain slopes are rarely studied because of complex morphologies, despite a need for data to better understand these changing ecosystems.

Four growing seasons (two wet – 2014 and 2015 and two dry – 2016 and 2017) of eddy covariance, meteorological, hydrological, and soil data were collected at an abandoned grassland on a slope encroached by shrubs in the Italian Western Alps. The objectives were to: 1) study the ETa differences between two land cover types, grassland and shrubland, based on Hydrus 1D model simulations. 2) Compare the simulated ETa from the two land covers (ETaSim grass and ETaSim shrub) with the observed eddy covariance-derived evapotranspiration (ETaObs).

The simulated ETa from shrubland showed a better agreement with the observed ETa (R2=0.4 to 0.5, slope=0.8 to 1.3). The simulated ETa from shrubland (ETaSim shrub) was higher compared to the simulated ETa from grassland (ETaSim grass) with the observations (ETaObs) in between, confirming that ETaObs represents a mixture of shrubland and grassland contributions. The relative contribution was different for each year due to meteorological conditions. On average across all years, a 51:49% contribution from respectively grassland and shrubland resulted in a good approximation of ETaObs, in particular in 2015 and 2016 growing seasons, characterised by long dry spells. In those growing seasons, the differences between cumulative ETa from simulations and observations were below 10 mm. In the other two growing seasons, more frequent rainfalls and the absence of long dry spell caused cumulative ETa underestimation (-25 mm) in 2014 and overestimation (66 mm) in 2017. Differences between shrubland and grassland were enhanced during dry spells, leading to a cumulative ETaSim shrub more than 100 mm higher than the cumulative ETaSim grass. In the longest dry spells of the growing seasons, ETaObs was closer to ETaSim shrub, confirming the role of deeper roots of shrubs.

The results indicate that the shrub-covered area, expected to increase, plays already a key role in the local hydrological cycle, particularly with changes in water availability.
Citizen science for better water quality management in the Brantas catchment, Indonesia? Preliminary results
Authors: Pramana Reza , Houser Schuyler , Rini Daru , Ertsen Maurits
Published: March 2022

Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km2) is deteriorating due to various reasons, including rapid economic development, insufficient domestic water treatment and waste management, and industrial pollution. Various water quality parameters are at least measured on monthly basis by agencies involved in water resource development and management. However, measurements consistently demonstrate exceedance of the local water quality standards. Recent claims presented by the local Environmental Protection Agency indicate that the water quality is much more affected by the domestic sources compared to the others. In an attempt to examine this, we proposed a citizen science campaign by involving people from seven communities living close to the river, a network organisation that works on water quality monitoring, three government agencies, and students from a local university. Beginning in 2022, we kicked off our campaign by measuring with test strips for nitrate, nitrite, and phosphate on weekly basis at twelve different locations from upstream to downstream of the catchment. In the effort to provide education on water stewardship and empower citizens to participate in water quality management, preliminary results – the test strips, strategies, and challenges - will be shown.
A multiscale model for coupled chemical reaction and deformation of porous rocks
Authors: Yarushina Viktoriya , Podladchikov Yury
Published: March 2022

Coupled hydro-mechano-chemical (HMC) modeling is a topic of active ongoing research in various branches of Earth sciences and subsurface engineering. In engineering applications, HMC modeling is used to assess the feasibility of permanent CO2 storage in mafic and ultramafic rocks. The deformation and stresses building during the reaction is believed to induce fracturing, increase permeability and thus promote extensive reactions between CO2 and host rock. CCS in depleted reservoirs faces challenges related to possible CO2 leakage through old plugged and abandoned wells. When CO2 reaches the well, old cement compositions react with cement, compromising well integrity due to chemical degradation. In geology, coupled reactions and deformation are involved in melt extraction and migration, influencing the dynamics of volcanic systems and the evolution of subduction zones.

A large focus of previous studies was whether or not it is possible to achieve 100% of the reaction. Common reactive transport models predict that the reaction product will clog the pores, which will stop the fluid flow and thus further reactions. However, recent developments suggest that reaction progress depends on the assumed reaction kinetics and the constitutive models used in coupled models. Models that account for solid volume change as in mineral replacement reactions have a much higher potential for preserving porosity than the common dissolution-precipitation model, thus predicting the complete reaction. It is often assumed that reaction processes are transport-dominated, i.e., that all dissolved material is carried away by pore fluid. Then it precipitates on the available pore space leading to clogging and permeability reduction. However, recent observations suggest that while some reactions might be associated with dissolution and precipitation at the nano-scale, aqueous species transport is limited, and reaction products do not precipitate in the pores but rather stay attached to the primary mineral. Thus, the overall effect is the same as in mineral replacement reactions.

Using a combination of effective media theory and irreversible thermodynamics approaches, we propose a new model for reaction-driven mineral expansion, which preserves porosity and limits unrealistically high build-up of the force of crystallization by allowing inelastic failure processes at the pore scale. To fully account for the coupling between reaction, deformation, and fluid flow, we derive macroscopic poroviscoelastic stress-strain constitute laws that account for chemical alteration and viscoelastic deformation of porous rocks. These constitutive equations are further used with macroscopic conservation laws to illustrate the mutual impact of reactive transport and mechanical deformation on simple 1D examples of wellbore stability and fluid transport.