bluuebunny/crossref_metadata_2025 huggingface dataset:
An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering
Authors: Moreno-Torres Benjamin , Völker Christoph , Kruschwitz Sabine
Published: March 2021

Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.

To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.

A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.
Representing chemical history for ozone time-series predictions - a method development study for deep learning models
Authors: Kleinert Felix , Leufen Lukas H. , Lupascu Aurelia , Butler Tim , Schultz Martin G.
Published: March 2021

Machine learning techniques like deep learning gained enormous momentum in recent years. This was mainly caused by the success story of the main drivers like image and speech recognition, video prediction and autonomous driving, to name just a few.
Air pollutant forecasting models are an example, where earth system scientists start picking up deep learning models to enhance the forecast quality of time series. Almost all previous air pollution forecasts with machine learning rely solely on analysing temporal features in the observed time series of the target compound(s) and additional variables describing precursor concentrations and meteorological conditions. These studies, therefore, neglect the "chemical history" of air masses, i.e. the fact that air pollutant concentrations at a given observation site are a result of emission and sink processes, mixing and chemical transformations along the transport pathways of air.
This study develops a concept of how such factors can be represented in the recently published deep learning model IntelliO3. The concept is demonstrated with numerical model data from the WRF-Chem model because the gridded model data provides an internally consistent dataset with complete spatial coverage and no missing values.
Furthermore, using model data allows for attributing changes of the forecasting performance to specific conceptual aspects. For example, we use the 8 wind sectors (N, NE, E, SE, etc.) and circles with predefined radii around our target locations to aggregate meteorological and chemical data from the intersections. Afterwards, we feed this aggregated data into a deep neural network while using the ozone concentration of the central point's next timesteps as targets. By analysing the change of forecast quality when moving from 4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r, φ, t) sectors and thinning out the underlying model data, we can deliver first estimates of expected performance gains or losses when applying our concept to station based surface observations in future studies.
Post-fire carbon dioxide emissions from a peatland undergoing restoration
Authors: Artz Rebekka , Coyle Mhairi , Gilbert Pete , Andersen Roxane , Bass Adrian
Published: March 2021

In May 2019, a major wildfire event affected >60 km2 within the 4000 km2 Flow Country in Northern Scotland, UK, a flagship blanket bog peatland that is being considered for UNESCO World Heritage Status. While the fire itself created significant damage, it also led to an extraordinary and unique opportunity to compare burned and unburned landscape scale greenhouse gas flux and surface energy dynamics using sites that, crucially, have otherwise identical biophysical characteristics (slope, aspect, peat depth) and land management histories. Since September 2019, carbon dioxide and methane flux data have been collected alongside other micrometeorological variables. Due to the COVID-19 lockdown in the UK, the team had severe difficulties in maintaining the equipment and hence, only partial and preliminary data will be reported here to showcase the findings from this project to date. The data obtained so far suggest a post-fire reduction in net CO2 emissions for a period of one year since the beginning of our monitoring campaign.
Identifying key drivers of peat-fires across Kalimantan’s ex‑Mega Rice Project area using machine learning
Authors: Horton Alexander , Virkki Vili , Lounela Anu , Miettinen Jukka , Kummu Matti
Published: March 2021

Throughout Indonesia - ecological degradation, agricultural expansion, and the digging of draining canals has compromised the integrity and functioning of large swathes of peatland forest, leaving behind a fragmented landscape of scrubland, successional forest, and newly established plantations. These landscapes are susceptible to extensive and intensive wildfires that rage out of control each year. One of the most affected regions is the ex-Mega Rice Project (EMRP) area in Central Kalimantan on the island of Borneo, where 1 million ha of peatland forest were cleared and 4000 km of canals were dug between 1996-1998, in an attempt to initiate large scale industrial rice cultivation. This led to disturbances to the underlying hydrology, the local ecology, and the ability of the local population to maintain a livelihood, who’s efforts are thwarted each year but the returning wildfires.

Directing  fire prevention and mitigation efforts requires a detailed understanding of the main drivers of fire distribution and the conditions of initiation. To this end, we have developed a fire susceptibility model using machine learning (XGBoost random forest) that characterises the relationships between key predictor variables and the distribution of historic fire locations. Using the model, we determine the relative importance of each predictor variable in controlling the initiation and spread of fires. We included land-cover classifications, a forest clearance index, vegetation indices , drought indexes, distances to infrastructure , topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model was trained to separate burnt areas from not burnt areas using point samples of predictor variables taken from both, and then tested by applying the model across the entire study area for all years. The model performance consistently scores highly in both accuracy and precision across all years (>0.75 and >0.68 respectively), though recall metrics are much lower (>0.25).

Our results confirm the anthropogenic dependence of extreme fire events in the region, with the distance to settlements, and distance to canals consistently weighted as some of the most important driving factors within the model structure. In combination, the vegetation indices were the strongest indicators of fire prevalence. Ours is the first analysis in the region to encompass the full range of driving factors within a single model that captures the inter-annual variation as well as the spatial distribution of peatland fires. Our results can be used to target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future wildfires.
Groundwater phosphorus concentrations: global trends and links with agricultural and oil and gas activities
Authors: Warrack Jessica , Kang Mary , von Sperber Christian
Published: March 2021

Although observations show that anthropogenic phosphorus (P) can reach groundwater supplies, there has been no comprehensive evaluation of P in groundwater at the global scale. Additionally, there have been minimal studies on distributed sources, such as agriculture, and the effects of oil and gas activities on P contamination in groundwater are poorly understood. We compile and analyze 181,653 groundwater P concentrations from 13 government agencies and 8 individual research studies in 11 different countries in order to determine the extent of P pollution at the global scale. We find that every country with data has groundwater P concentrations that pose a significant risk of eutrophication to surface waters. In Canada and the United States, we study the relationship between land use, focusing on crop/pastureland, and increased P concentrations in groundwater. In Ontario and Alberta, two Canadian provinces with different histories of oil and gas development, we find areas with a high concentration of P groundwater pollution to coincide with regions of intense oil and gas activity. Understanding the effects of anthropogenic sources on phosphorus contamination of groundwater and identifying all possible pathways through which contamination can occur will assist regulators in planning and implementing effective strategies to manage groundwater and surface water quality and sustain ecosystem health.
The kids aren't alright
Authors: Thiery Wim , Lange Stefan , Rogelj Joeri , Schleussner Carl-Friedrich , Gudmundsson Lukas , Seneviratne Sonia I. , Frieler Katja , Emanuel Kerry , Geiger Tobias , Bresch David N. , Zhao Fang , Willner Sven N. , Büchner Matthias , Volkholz Jan ,   ISIMIP modelling team
Published: March 2021

People are being affected by climate change around the globe today at around 1°C of warming above pre-industrial levels. Current policies towards climate mitigation would result in about twice as much warming over the next 80 years, roughly the lifetime of a today's newborn. Here we quantify the stronger climate change burden that will fall on younger generations by introducing a novel analysis framework that expresses impacts as a function of how they are experienced along the course of a person's life. Combining projections of population, temperature, and 15 impact models encompassing droughts, heatwaves, tropical cyclones, crop failure, floods, and wildfires, we show that, under current climate pledges, newborns in 2020 are projected to experience 2-13 times more extreme events during their life than a person born in 1960, with substantial variations across regions. Limiting warming to 1.5°C consistently reduces that burden, while still leaving younger generations with unavoidable impacts that are unmatched by the impacts experienced by older generations. Our results provide a quantified scientific basis to understand the position from which younger generations challenge the present shortfall of adequate climate action.
Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project
Authors: Tsamados Michel ,   POLAR+ Snow of Sea Ice team
Published: March 2021

Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent ‘golden era in polar altimetry’ with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent ‘operational‘ period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.

 
Clustering CDF and IDF curves of rainfall extremes
Authors: El Hachem Abbas , Bárdossy András , Seidel Jochen , Goshtsasbpour Golbarg , Haberlandt Uwe
Published: March 2021

Precipitation extremes are a space-time variant. Understanding how they vary from one location to another is an essential information for identifying spatially homogeneous and heterogeneous areas. By identifying the boundaries of these areas, a better characterization of the underlying spatial behavior is possible. Intensity-duration-frequency (IDF) curves are a mathematical function that relates the rainfall intensity with its duration and frequency of occurrence. The clustering approach is helpful for finding homogeneous regions for grouping stations (or radar cells) to estimate regional cumulative distribution functions (CDF) or regional IDF curves. This offers a new possibility to include the spatial aspect of rainfall extremes. For this purpose, CDF and IDF curves were calculated from the observed rainfall data at the rain gauges of the German weather service network.   Data from almost 5000 stations with daily resolution and 1000 stations with higher temporal resolution were used. The Kolmogorov–Smirnov (KS) test, a statistical nonparametric test was used to compare the similarity (or dissimilarity) between the distribution functions. Eventually a KS distance matrix was obtained and a multidimensional scaling analysis along a K-means clustering algorithm was performed. As a main result, similar and dissimilar regions within the stations were identified.
AI-Ready Training Datasets for Earth Observation: Enabling FAIR data principles for EO training data.
Authors: McKinstry Alastair , Boydell Oisin , Le Quan , Preet Inder , Hanafin Jennifer , Fernandez Manuel , Warde Adam , Kannan Venkatesh , Griffiths Patrick
Published: March 2021

The ESA-funded AIREO project [1] sets out to produce AI-ready training dataset specifications and best practices to support the training and development of machine learning models on Earth Observation (EO) data. While the quality and quantity of EO data has increased drastically over the past decades, availability of training data for machine learning applications is considered a major bottleneck. The goal is to move towards implementing FAIR data principles for training data in EO, enhancing especially the finability, interoperability and reusability aspects.  To achieve this goal, AIREO sets out to provide a training data specification and to develop best practices for the use of training datasets in EO. An additional goal is to make training data sets self-explanatory (“AI-ready) in order to expose challenging problems to a wider audience that does not have expert geospatial knowledge. 

Key elements that are addressed in the AIREO specification are granular and interoperable metadata (based on STAC), innovative Quality Assurance metrics, data provenance and processing history as well as integrated feature engineering recipes that optimize platform independence. Several initial pilot datasets are being developed following the AIREO data specifications. These pilot applications include for example  forest biomass, sea ice detection and the estimation of atmospheric parameters.An API for the easy exploitation of these datasets will be provided.to allow the Training Datasets (TDS) to work against EO catalogs (based on OGC STAC catalogs and best practises from ML community) to allow updating and updated model training over time.

 

This presentation will present the first version of the AIREO training dataset specification and will showcase some elements of the best-practices that were developed. The AIREO compliant pilot datasets will be presented which are openly accessible and community feedback is explicitly encouraged. 

[1] https://aireo.net/
Application of Remote Sensing Technologies in environmental monitoring: legal framework, limitations and potential in the European Union
Authors: Maniadaki Maria , Papathanasopoulos Athanasios , Mitrou Lilian , Maria Efpraxia-Aithra
Published: March 2021

    Remote sensing technologies, such as satellite and drone imagery, have been proven over the years- due to their constant development- to be extremely useful for environmental monitoring. They may collect and provide data pertaining to natural disasters, state of oceans, atmosphere, land, vegetation, food, public health etc, which are further essential for the effective decision making of public authorities. At the same time such data may facilitate the right for access to environmental information to the public. They also consist valuable tools for environmental law enforcement by allowing to detect for example planning breaches, illegal dumping of waste, illegal logging or illegal oil spills, on which inspections could then focus.

    The article briefly presents the legal framework regarding the application of Remote Sensing Technologies in environmental monitoring in the European Union. It also outlines certain limitations of such technologies, such as the need for data verification and the need for data procession according to privacy and personal data law requirements. Important ECtHR and CJEU case law on the issue is approached, while it is examined under what legal circumstances a wider application of Remote Sensing Technologies in environmental monitoring could be envisaged. Finally, Greek legislation on the subject is as a “case study” analyzed.

   This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014-2020” in the context of the project “Legal issues derived from the use of monitoring and earth observation technologies to ensure environmental compliance in the Hellenic legal order- HELLASNOMOSAT” (MIS  5047355).

 
A Single-Agent Urban Coastal Adaptation Model: Adaptive decision-making within the VIABLE modeling framework
Authors: Sengupta Shubhankar , Scheffran Jürgen , Kovalevsky Dmitry
Published: March 2021

We develop a single-agent-based model in Netlogo of a coastal city facing climate change, using the VIABLE framework. The coastal city is threatened by damages from sea level rise and subsequent extreme sea level events. The agent, representing an urban planner, uses capital generated by the city to mitigate these damages by investing into one of two adaptation options available to it- developing coastal defenses or relocating the vulnerable coastal territories of the city inland. As the simulation progresses, gradually rising sea levels and randomly occurring extreme sea level events incur damages, and the agent alters its investments to optimize its value, resulting in dynamic reactive behavior. We track the response of this agent to the changing system through its investment patterns.
Statistical EMIC diffusion models calculated by averaging observation specific diffusion coefficients
Authors: Ross Johnathan , Glauert Sarah , Horne Richard , Meredith Nigel , Watt Clare
Published: March 2021

Electromagnetic ion cyclotron (EMIC) waves play an important role in relativistic electron losses in the radiation belts through diffusion via resonant wave-particle interactions. We present a new statistical model of electron diffusion by EMIC waves calculated, using Van Allen Probe observations, by averaging observation specific diffusion coefficients. The resulting diffusion coefficients therefore capture a wider range of wave-particle interactions than previous average models which are calculated using average observations. These calculations, and their role in radiation belt simulations, are then compared against existing diffusion models. The new diffusion coefficients are found to significantly improve the agreement between the calculated decay of relativistic electrons and Van Allen Probes data.

 
Ozone chemistry during high ozone event at semi-urban region, Shadnagar, in Southern India
Authors: K. Sagar Vijay , Asuri Lakshmi kanchana , P. Kanawade Vijay , K. Nayak Rabindra
Published: March 2021

The high level of surface ozone (O3) concentration is produced from the various complex chemical reaction of oxides of nitrogen (NOx) and volatile organic compounds (VOCs) under varied meteorological conditions. It has severe effects on human health, vegetation, and as well on infrastructure. The guidelines value for surface ozone level was set 50 ppb for an 8-hours daily average by Indian National Ambient Air Quality Standard (INAAQS, 2009) and World Health Organization (WHO, 2005) for India and worldwide respectively. Identifying the primary source of high ozone events based on observation is challenging. The relationship of the surface measured O3 with carbon monoxide (CO) and water vapor content are useful to identify the possible source of origin for the increased O3 in the stratosphere, regional or local influence.

The continuous observation of O3, NOx, CO at 1 minute temporal resolution along with the meteorological parameter (1-hour temporal resolution) were taken during August 2014 to April 2017. All parameters were averaged to 8-hourly for further analysis. The high ozone events were identified based on exceeding the surface ozone concentration limit as discussed above (50 ppb). The relationship of the surface measured O3 and CO (∆O3/∆CO) and water vapor were used to explain the source of high ozone such as stratospheric origin and anthropogenic activity. The HYSPLIT’s backward air mass trajectories of the height of 1000 meters for 120 hours were calculated for the site to understand the dispersion of the pollutants. During the high ozone event, the average concentration of O3, NOx, and CO was found to be 55.46 ppb, 5.19 ppb, and 0.180 ppb respectively which were lower than the normal conditions. The positive correlation of O3 with CO (∆O3/∆CO) and low water vapor mixing ratio (10.0 g/kg) indicate regional or local influence on observed high ozone events.

The high ozone events were explained based on the distribution of the ozone precursors such as NOx, CO, and meteorological parameters such as relative humidity solar radiation, wind speed, and wind direction at local. The local high ozone concentration was supported by local chemistry such as the low concentration of CO and NOx. The relationship between O3 and CO was used to explain the source of high ozone events.
Biogeochemical mechanisms influencing the bioavailability of P and Fe from vivianite
Authors: Metz Rouven , Kumar Naresh , Schenkeveld Walter , Kraemer Stephan
Published: March 2021

The vital element phosphorus (P) invokes two extremes in the environment; (i) scarcity, as a non-renewable resource and as a poorly bioavailable limiting nutrient for plants, and (ii) excess, as cause of eutrophication in surface waters. To tackle both these problems, the inter-relationship between the P and the iron (Fe) cycle is widely discussed with a special interest in the ferrous iron-phosphate mineral vivianite (Fe(II)3(PO4)2*8H2O). Vivianite forms naturally in sub-/anoxic environments with high Fe(II) and PO4 concentrations, and is a sink to the dissolved P concentration. On the other hand, vivianite has been proposed as a P source through application as a slow-release Fe-P fertilizer prepared from recycled P. However, vivianite is a metastable mineral under oxic conditions; it readily oxidizes, notably changing color from white to dark blue/purple. This transformation changes the properties of the mineral (surface), and thus its suitability as a fertilizer.

We investigated the oxidation and dissolution of vivianite under different environmental conditions with the aim of developing a mechanistic and kinetic model that relates the oxidation process with dissolution rates.  Moreover, the effect of secondary mineral precipitation on the ‘net’ availability of P and Fe for soil organisms was also studied. Quantifying dissolution rates and secondary mineral formation under environmentally relevant conditions provides the fundamental knowledge needed to assess the suitability of vivianite as Fe and P fertilizer. This information is also paramount to the idea of a circular economy concept: starting with the reduction of P loads of (waste) waters and using the byproduct vivianite as P source for fertilization.
From landslide characterization to nature reserve management: The Scialimata Grande landslide Geosite (Central Appennine, Italy)
Authors: Tronti Gianluca , Bollati Irene Maria , Pelfini Manuela , Belisario Filippo , Fredi Paola
Published: March 2021

The Italian territory is characterized by frequent hydrogeological instability phenomena, among which landslides, responsible for a high number of damages, are able to leave impressive marks on the landscape and to rework the physical features of a territory. Nevertheless, landslide bodies may represent key sites for thematic itineraries, educational projects, and valorization especially in protected areas. Their management can consequently become an important issue in natural reserves’ management.

Our study focuses on the territory of the "Monte Rufeno Nature Reserve" (Central Apennines, Italy), where iconic landslides are present. Here, the "Scialimata Grande di Torre Alfina" landslide, is listed in the regional Geosite database. After creating a landslide susceptibility map for the reserve territory - based on drainage density and relief energy - the analysis was focused on the Scialimata complex landslide, to define properly its scientific and educational value. The geomorphological characterization of the landslide - carried out both on the field (geomorphological survey and GPS monitoring through wooden pins) that through remote sensing techniques (UAV drone, photointerpretation) - helped the assessment of the landslide dynamism. Moreover, dendrogeomorphological and ecological characterizations were performed to understand the relations between the geomorphic dynamics and the vegetation response (i.e., ecological support role of the Geosite). The area affected by Scialimata landslide falls into the highest landslide susceptibility class. At the local scale, its recent dynamics - as resulted from the GPS field monitoring in the 2015-2018 time interval - shows a maximum downvalley displacement of 23 meters. The landslide dynamics could be driven by a rain pattern with alternated dry and extremely wet periods; moreover, leakages from the water pipeline in proximity of the landslide crowning area, may have decreased the clays cohesion. The possible influence of the landslide, on the Paglia River dynamics, downvalley, was also hypothesized. Disturbances on plants ascribable to the landsliding, as tilted trunks and exposed roots, show distribution and characters explainable not only by the complex rotational dynamism of the landslide, but also by the hypothesized piping effect in the crowning zone. The landslide is highly representative of the geomorphic dynamics affecting the natural Reserve (i.e., scientific value) and it could be classified as an active Geosite, featured by a high ecological support role (not only towards vegetation) that increases the scientific value of the site too. Since the site was also featured by a touristic trail, that underwent disruption due to landslide movement, these results could enable adequate management strategies considering educational value and safety issues.
High-Northern-latitudes permafrost extend in MPI-ESM simulations of SSP126 and SSP585 
Authors: Georgievski Goran , De Vrese Philipp , Hagemann Stefan , Brovkin Victor
Published: March 2021

The representation of permafrost related processes in Earth System Models (ESM) remains a challenge. A recent collaboration between two related projects (Kohlenstoff im Permafrost (Carbon in Permafrost) – KoPf, and Study Of the Development of Extreme Events over Permafrost areas – SODEEP) yielded a new vertical structure of the soil column in JSBACH, the land component of the Max Planck Institute (MPI) for Meteorology ESM (MPI-ESM). This feature resulted in a better representation of the vertical soil moisture dynamics and the energy transfer due to soil freezing and thawing, which is particularly relevant for the high northern latitudes.

Although, air temperatures are simulated reasonably well with the MPI-ESM, care must be taken not to introduce a bias when implementing new processes in the model or changing existing parametrizations. Here we investigate the permafrost extent in two Shared Socioeconomic Pathways (SSP) simulations (SSP126 and SSP585) with the MPI-ESM using prescribed ocean surface boundary conditions. Our results show a consistency between terrestrial and atmospheric dynamics, when comparing the permafrost extent determined on basis of simulated active layer thickness (soil variable) and Day Degree Thaw Index (DDTI; atmospheric variable). The latter is calculated as the annual sum of positive average daily 2m air temperatures and its square root can be used as an indicator of annual maximum thaw depth.

The SSP126 simulation shows that both DDTI and thaw depth stabilize within the range of the present-day interannual variability, while SSP585 indicates a substantial deepening of the active layer – resulting in a complete disappearance of near-surface permafrost in large parts of the high northern latitudes - and DDTI in SSP585 simulation increases in excess of 2000°C. These values at present characterize northern mid-latitudes i.e. landscapes not underlined by permafrost. A preliminary analysis indicates that the decline of the permafrost extent in SSP585 occurs mostly during the second half of 21st century. Furthermore, the SSP585 simulation also shows an increase in the number of extreme events relevant for permafrost degradation. The investigated extreme climate patterns (as defined in the frame of the SODEEP project) include abrupt warming (defined as occurrence of annual mean temperature above 5-year running mean) and increase in seasonal precipitation anomalies, as well as changes in specific snow characteristics.
Clary sage cultivation and mycorrhizal inoculation influence rhizosphere fungal community structure over time in a trace-element polluted site
Authors: Raveau Robin , Fontaine Joël , Hijri Mohamed , Lounès Hadj-Sahraoui Anissa
Published: March 2021

Fungal community in the soil plays a central role in natural systems and agroecosystems, therefore it attracted much research interests. However, the fungal microbiota of aromatic plants, such as Salvia sclarea L., especially in trace-element (TE) polluted conditions and within the framework of phytomanagement approaches, remains unexplored. The presence of high concentrations of TE in the soil is likely to negatively affect not only microbial diversity and community structures, but also plant establishment and growth. The objective of this study is to investigate the soil fungal and arbuscular mycorrhizal fungi (AMF) community structure and their changes over time in TE-polluted soils in the vicinity of a former lead smelter and under the cultivation of clary sage. We used Illumina MiSeq amplicon sequencing to evaluate the effects of in situ clary sage cultivation during two successive years, combined or not with an exogenous AMF inoculation, on the rhizospheric soil and root fungal communities. We obtained 1239 and 569 fungal amplicon sequence variants (ASV) respectively in the rhizospheric soil and roots of S. sclarea in TE-polluted conditions. Remarkably, 69 AMF species were detected in our experimental site, belonging to 12 AMF genera. Besides, the inoculation treatment significantly shaped the fungal communities in soil, and increased the number of AMF ASVs in clary sage roots. In addition, successive years of clary sage cultivation also significantly shaped both fungal and AMF communities in the soil and root biotopes. Our data provide new insights on fungal and AMF communities in the rhizospheric soil and roots of clary sage grown in TE-polluted agricultural soil.

Keywords: Trace Elements-polluted soils, fungal microbiota, Salvia sclarea, arbuscular mycorrhizal fungi
Investigation on the role of E-F region coupling processes on the generation of nighttime MSTIDs: Case studies over northern Germany
Authors: Sivakandan Mani , Chau Jorge L , Martinis Carlos , Otsuka Yuichi , Mielich Jens , Conte Fede
Published: March 2021

Northwest to southeast phase fronts with southwestward moving features are commonly observed in the nighttime midlatitude ionosphere during the solstice months at low solar activity. These features are identified as nighttime MSTIDs (medium scale traveling ionospheric disturbances). Initially, they were considered to be a manifestation of neutral atmospheric gravity waves. Later on, investigations showed that the nighttime MSTIDs are electrified in nature and mostly confined to the mid and low latitude ionosphere. Although the overall characteristics of the nighttime MSTIDs are mostly well understood, the causative mechanisms are not well known. Perkins instability mechanism was believed to be the cause of nighttime MSTIDs, however, the growth rate of the instability is too small to explain the perturbations observed. Recently, model simulations and observational studies suggest that coupling between sporadic-E layers and other type of E-region instabilities, and the F region may be relevant to explain the generation of the MSTIDs.

In the present study simultaneous observation from OI 630 nm all-sky airglow imager, GPS-TEC, ionosonde and Meteor radars, are used to investigate the role of E and F region coupling on the generation of MSTIDs .Nighttime MSTIDs observed on three nights (14 March 2020, 23 March 2020 and 28 May 2020) in the OI 630 nm airglow images over Kuehlungsborn (54°07'N; 11°46'E, 53.79N  mag latitude), Germany, are presented. Simultaneous detrended GPS-TEC measurements also shows presence of MSTIDs on these nights. In addition, simultaneous ionosonde observations over Juliusruh (54°37.7'N 13°22.5'E) show spread-F in the ionograms as well as sporadic-E layer occurrence.  Furthermore, we also investigate the MLT region wind variations during these nights. The role of Es-layers and the interplay between the winds and Es-layers role on the generation of the MSTIDs will be discussed in detail in this presentation.

 
The Application of Neural Operator in subsurface process simulation 
Authors: Liang Zhouji , Degen Denise , Wellmann Florian
Published: March 2021

Numerical simulations of subsurface processes are essential to the success of many geoengineering projects. These simulations often contain significant uncertainties due to imperfect knowledge of material properties and their spatial distribution, boundary conditions, and initial conditions. However, efficient implementations for the quantification of uncertainties for such simulations are big challenges in Computational Geoscience, mainly due to the curse of dimensionality. Process simulations often involve solving high-dimensional Partial Differential Equations (PDE) by using discretization methods such as Finite Difference (FD) or Finite Elements (FE) methods. Although such methods often give good approximations, they are computationally intensive and expensive and therefore infeasible in the applications such as MCMC where thousands of evaluations of the forward simulation are required. Previous work by Degen et.al. (2020) has addressed this problem by using a model order reduction method, the so-called reduced basis (RB) method. However, the method has limitations when considering complex (i.e., hyperbolic and non-linear) PDEs. In this work, we aim to employ the recently developed Fourier Neural Operator (FNO) (Li, 2020) as a tool to implement efficient approximation of PDEs in the application of Geothermal reservoir simulation. FNO involves a Fast Fourier transform to directly learn the mapping from the input function to the output function. FNO has the advantage of being independent of the resolution and complexity of the governing PDE. Our preliminary results show that FNO can provide good approximation results in solving four-dimensional PDEs and thus can be used as a tool for further probability studies of the parameters of interest.
Covid-19-related air composition changes over China based on TROPOMI and IASI observations, in situ data and model simulations 
Authors: Stavrakou Trissevgeni , Müller Jean-François , Bauwens Maite , Doumbia Thierno , Elguindi Nellie , Darras Sabine , Claire Granier Claire Granier , Yiming Liu Yiming Liu , Shi Xiaoqin , Bouarar Idir , Brasseur Guy , Wang Tao , Eskes Henk , De Smedt Isabelle , Clarisse Lieven , Coheur Pierre François , Franco Bruno
Published: March 2021

The worldwide spread of Covid-19 pandemic caused a dramatic cutback of human activities and triggered a large-scale atmospheric composition experiment. This unfortunate situation provides the opportunity to investigate the response of atmospheric composition to the shutdown measures. Our focus will be on China, where the pandemic emerged in January 2020, and thence strict lockdowns were enforced. Substantial, large-scale decreases in pollutants levels over China and subsequent recovery were revealed by spaceborne observations from TROPOMI instrument on board Sentinel-5 Precursor, as well as by in situ measurements. Most published work on this topic relied on observed changes in column abundances of nitrogen dioxide (NO2), a predominantly anthropogenic compound and an important precursor for ozone production and secondary aerosol formation. Our work adds to this picture by studing the evolution of two other satellite-derived compounds, formaldehyde (HCHO) and peroxyacylnitrate (PAN), observed by TROPOMI and IASI, respectively. HCHO is an intermediate product in the chemical processing of volatile organic compounds (VOCs) of anthropogenic and natural origin. PAN is formed in the oxidation of anthropogenic and biogenic VOCs, and constitute the principal tropospheric NOx reservoir, enabling the transport and release of NOx away from the sources. Chemistry-transport simulations of PAN are challenging due to large uncertainties in formation mechanisms and precursor emissions. We will evaluate and analyze the observed variability of NO2, HCHO, and PAN columns using model simulations with the MAGRITTE v1.1 regional CTM run at 0.5ox0.5o resolution over China for 2019 and 2020. The model uses updated anthropogenic emissions from the CONFORM dataset, which takes into account the reductions during the shutdowns based on traffic and other economic activity data. 
Antarctic-like temperature variations in the Tropical Andes recorded by glaciers and lake levels during the last deglaciation
Authors: Martin Léo , Blard Pierre-Henri , Lavé Jérôme , Jomelli Vincent , Lupker Maarten , Charreau Julien , Condom Thomas
Published: March 2021

The climatic reorganizations that occurred in the Southern and Northern hemispheres during the last deglaciation are thought to have affected the continental tropical regions. However, the respective impact of North and Southern climatic changes in the Tropics are still poorly understood. In the Norhtern Tropical Andes, moraines records indicate that the Antarctic Cold Reversal (ACR, 14.3-12.9 ka BP) stage was more represented than the Younger Dryas (12.9-11.7 ka BP) (Jomelli et al., 2014). However, further South, in the Altiplano basin (Bolivia), two cold periods of the North Hemisphere (Heinrich Stadial 1a (16.5-14.5 ka) and Younger Dryas) are synchronous with (i) major advances or stillstands of paleo-glaciers and with (ii) the highstands of the giant palaeo-lakes Tauca and Coipasa (Martin et al., 2018). Therefore, additional geochronological records of paleoglaciers fluctuations are necessary to address the respective impacts of North and South Hemisphere on the glacial dynamics in the region.

We present new Cosmic Ray Exposure (CRE) ages from glacial landforms of the Bolivian Andes that extend pre-existing datasets for four different sites spreading from 16 to 21°S. We reconstruct the Equilibrium Line Altitudes (ELA) associated with each moraine with the AAR method and use them in an inverse algorithm that combines both the palaeo-glaciers and palaeo-lake budgets to derive temperature and precipitation reconstructions. Our temperature reconstruction (ΔT vs. Present) shows a consistent trend through the four glacial sites with a progressive warming from ΔT= -5°C (17 ka BP) to –2.5°C (15-14.5 ka BP, at the end of the Tauca highstand). This is followed by a return to colder conditions, around -4°C, during the ACR (15.5-12.9 ka BP). The Coipasa highstand is coeval with another warming trend followed by ΔT stabilization at the onset of the Holocene (circa 10 ka BP), around -3°C. Precipitation is mainly characterized by increases during the lake highstands, modulated by the distance from the glacial sites to the center of the paleolakes that are moisture sources (recycling processes).

These new results highlight the decorrelation of the glacier dynamics to the temperature signal in regions that are characterized by high precipitation variability. They also provide a theoretical frame to explain how both regional and global forcings can imprint the paleo-glacial records. Our results strongly suggest that during the last deglaciation (20 – 10 ka BP), in the Tropical Andes, atmospheric temperatures follow the Antarctic variability, while precipitation is driven by the changes occurring in the Northern Hemisphere.

References

Jomelli et al., Nature, 2014; Martin et al., Sc. Advances, 2018
Multi-millennial response of the Greenland Ice Sheet to anthropogenic warming 
Authors: Petrini Michele , Vizcaino Miren , Sellevold Raymond , Muntjewerf Laura , Georgiou Sotiria , Scherrenberg Meike D.W. , Lipscomb William , Leguy Gunter
Published: March 2021

Previous coupled climate-ice sheet modeling studies indicate that the warming threshold leading to multi-millennial, large-scale deglaciation of the Greenland Ice Sheet (GrIS) is in the range of 1.6-3.0 K above the pre-industrial climate. These studies either used an intermediate complexity RCM (Robinson et al. 2012) or a low resolution GCM (Gregory et al., 2020) coupled to a zero-order ISM. Here, we investigate the warming threshold and long-term response time of the GrIS using the higher-order Community Ice Sheet Model version 2 (CISM2, Lipscomb et al. 2019), forced with surface mass balance (SMB) calculated with the Community Earth System Model version 2 (CESM2, Danabasoglu et al. 2020). We use different forcing climatologies from a coupled CESM2/CISM2 simulation under high greenhouse gas forcing (Muntjewerf et al. 2020), where each climatology corresponds to a different global warming level in the range of 1-8.5 K above the pre-industrial climate. The SMB, which is calculated in CESM2 using an advanced energy balance scheme at multiple elevation classes (Muntjewerf et al. 2020), is downscaled during runtime to CISM2, thus allowing to account for the surface elevation feedback. In all the simulations the forcing is cycled until the ice sheet is fully deglaciated or has reached a new equilibrium. In a first set of simulations, we find that for a warming level higher than 5.2 K above pre-industrial the ice sheet will disappear, with the timing ranging between 2000 (+8.5 K) and 6000 years (+5.2 K). At a warming level of 2.8 K above pre-industrial, the ice loss does not exceed 2 m SLE, and most of the retreat occurs in the first 10,000 years in the south-west and central-west basins. In contrast, with a higher warming level of 3.6 K above pre-industrial as much as 7 m SLE of ice are loss in 20,000 years, with primary contributions from the western, northern and north-eastern basins. We will conclude by showing preliminary results from a second set of simulations focusing on the 2.8-3.6 K warming above pre-industrial interval.
ESA's Earth Orientation Parameter product
Authors: Bruni Sara , Schoenemann Erik , Mayer Volker , Otten Michiel , Springer Tim , Dilssner Florian , Enderle Werner , Zandbergen René
Published: March 2021

The availability of highly accurate Earth Orientation Parameters (EOPs) in near real time is of major importance for any type of positioning and navigation applications on Earth, Sea, Air and also in Space. This is equally true for all ESA missions and the EU space programs Galileo, EGNOS and Copernicus.

To ensure operational capability, ESA’s Navigation Support Office developed independent EOP products and services.

The EOPs are estimated based on a rigorous combination of the ESA’s contributions to the International Association of Geodesy (IAG) that are used as an input for the generation of the International Earth Rotation Service (IERS) products. For the ESA/ESOC EOP products, the individual parameters are combined on normal equation level and propagated with the contribution of model-based predicted Effective Angular Momentum (EAM) functions.

The ESA/ESOC’s EOP product generation is currently running in pre-operational mode.

This presentation will provide a high-level overview of the methodology and the status of ESA’s EOP products and services. In this context, the accuracy achieved in the test operations and the roadmap for the publication of ESA’s EOP products and services will be outlined.
Mortality risk assessment for earthquake-landslide-debris flow disaster chain under different scenarios: A case study in Wenchuan, China
Authors: Han Xiang , Yin Yunhe , Wu Shaohong
Published: March 2021

Earthquake-geological disaster chain is one of the common forms of multi-disasters. Primary disaster and secondary disaster are cascaded, which often leads to the expansion of disaster losses. Since the ms8.0 earthquake in 2008, Wenchuan has continued to have landslides and debris flow disasters, which leads to the possibility of forming an earthquake-landslide-debris flow disaster chain, and the risk of population mortality. This study analyzes the key links in the formation of the earthquake-landslide-debris flow disaster chain in Wenchuan. Then according to the disaster chain assessment method, considering the impact of key factors in the disaster cascade effect, a factor model for the disaster chain is established. And mortality risks of the regional disaster chain under earthquake and heavy rainfall scenarios are quantified. The mortality risks of the earthquake-landslide-debris flow disaster chain  are 2.82 people/km2, 2.90 people/km2, 2.92 people/km2, and 2.95 people/km2 with the precipitation probability of 20%, 5%, 2% and 1% . The risk for earthquake accounts for 50.98%~51.54%, the landslide accounts for 33.90%~34.28%, and the debris flow accounts for 14.19~15.12% in Wenchuan. At the township level, the total mortality risks of Yinxing, Yingxiu, and Gengda are at a relatively high level in this region. These results could provide a basis for further investigating and quantifying the risk reduction measurements of earthquake-landslide-debris flow disaster chain based on which effective disaster prevention and control measures can be undertaken.