The session presents the state of art information systems in oceanography (metadata, vocabularies, ISO and OGC applications, data models), interoperability (Virtual Research Infrastructures, Interoperability forms, Web services, Quality of Services, Open standards), data circulation and services (quality assurance / quality control, preservation, network services) and Education in ocean science (Education and Research, Internet tools for education).
The 2020 session should provide new ideas on the interoperability issues deriving from different sources of data.
ISO standards introduce the necessary elements in the abstract process aiming to assess ‘how’ and ‘how much’ data meets applicable regulatory requirements and aims to enhance user needs. Data management infrastructures should include an evaluation of data by assuring relevance, reliability and fitness-for-purposes / fitness-for-use, adequacy, comparability and compatibility. Presenters are strongly encouraged to demonstrate how their efforts will benefit their user communities, facilitate collaborative knowledge building, decision making and knowledge management in general, intended as a range of strategies and practices to identify, create, represent and distribute data, products and information.

NEMO (Nucleus for European Modelling of the Ocean) is a state-of-the-art modelling framework of the ocean that includes components for the ocean dynamics, the sea-ice and the biogeochemistry, so as a nesting package allowing to set up zooms and a versatile data assimilation interface (see https://www.nemo-ocean.eu/).
NEMO is used by a large community in Europe and world-wide (~200 projects, ~100 publications each year) covering a wide range of applications : oceanographic research, operational oceanography, seasonal forecast and climate projections.
NEMO is in particular used in 6 Earth System Models within CMIP6 and in Copernicus Marine Services (CMEMS) model-based products.

This session will provide a forum to properly address the new scientific advances in numerical modelling of the ocean and their implication for NEMO developments associated with:
• Ocean dynamics at large to coastal scales, up to 1km resolution ;
• Ocean biogeochemistry
• Sea-ice
• New numerical schemes associated to energy conservation constraints
• High performance computing challenges and techniques

The session will cover both research and operationnal activities contributing to new analysis, ideas and developments of ocean numerical models.
Presentations of results based on new NEMO functionalities and new NEMO model configurations are welcome.

Public information:
Registration for virtual session: https://framaforms.org/virtual-egu-os48-session-1587740583

This session covers an overview of the progress and new scientific approaches for investigating landslides using state-of-the-art techniques such as: Earth Observation (EO), close-range Remote Sensing techniques (RS) and Geophysical Surveying (GS).

A series of remarkable technological progresses are driven new scientific opportunities to better understand landslide dynamics worldwide, including integrated information about rheological properties, water content, rate of deformation and time-varying changes of these parameters through seasonal changes and/or progressive slope damage.

This session welcomes innovative contributions and lessons learned from significant case studies and/or original methods aiming to increase our capability to detect, model and predict landslide processes at different scales, from site specific to regional studies, and over multiple dimensions (e.g. 2D, 3D and 4D).

A special emphasis is expected not only on the particularities of data collection from different platforms (e.g. satellite, aerial, UAV, Ground Based...) and locations (e.g. surface- and borehole-based geophysics) but also on new solutions for digesting and interpreting datasets of high spatiotemporal resolution, landslide characterization, monitoring, modelling, as well as their integration on real-time EWS, rapid mapping and other prevention and protection initiatives. Examples of previous submissions include using one or more of the following techniques: optical and radar sensors, new satellite constellations (including the emergence of the Sentinel-1A and 1B), Remotely Piloted Aircraft Systems (RPAS) / Unmanned Aerial Vehicles (UAVs) / drones, high spatial resolution airborne LiDAR missions, terrestrial LIDAR, Structure-from-Motion (SfM) photogrammetry, time-lapse cameras, multi-temporal DInSAR, GPS surveying, Seismic Reflection, Surface Waves Analysis, Geophysical Tomography (seismic and electrical), Seismic Ambient Vibrations, Acoustic Emissions, Electro-Magnetic surveys, low-cost sensors, commercial use of small satellites, Multi-Spectral images, etc. Other pioneering applications using big data treatment techniques, data-driven approaches and/or open code initiatives for investigating mass movements using the above-described techniques will also be very welcomed.

GUEST SPEAKER: this year, we invited professor Jonathan Chambers, team leader of the geophysical tomography cluster at the British Geological Survey (BGS).

A remote sensing signal acquired by a sensor system results from electromagnetic radiation (EM) interactions from incoming or emitted EM with atmospheric constituents, vegetation structures and pigments, soil surfaces or water bodies. Vegetation, soil and water bodies are functional interfaces between terrestrial ecosystems and the atmosphere. The physical types of EM used in RS has increased during the years of remote sensing development. Originally, the main focus was on optical remote sensing. Now, thermal, microwave, polarimetric, angular and quite recently also fluorescence have been added to the EM regions under study.
This has led to the definition of an increasing number of bio-geophysical variables in RS. Products include canopy structural variables (e.g. biomass, leaf area index, fAPAR, leaf area density) as well as ecosystem mass flux exchanges dominated by carbon and water exchange. Many other variables are considered as well, like chlorophyll fluorescence, soil moisture content and evapotranspiration. New modelling approaches including models with fully coupled atmosphere, vegetation and soil matrices led to improved interpretations of the spectral and spatio-temporal variability of RS signals including those of atmospheric aerosols and water vapour.
This session solicits for papers presenting methodologies and results leading to the assimilation in biogeoscience and atmospheric models of cited RS variables as well as data measured in situ for RS validation purposes. Contributions should preferably focus on topics related to climate change, food production (and hence food security), nature preservation and hence biodiversity, epidemiology, and atmospheric chemistry and pollution (stratospheric and troposphere ozone, nitrogen oxides, VOC’s, etc). It goes without saying that we also welcome papers focusing on the assimilation of remote sensing and in situ measurements in bio-geophysical and atmospheric models, as well as the RS extraction techniques themselves.
This session aims to bring together scientists developing remote sensing techniques, products and models leading to strategies with a higher (bio-geophysical) impact on the stability and sustainability of the Earth’s ecosystems.

Public information:
BG2.7
Remote Sensing applications in the Biogeosciences

Chairperson: Frank Veroustraete & Willem Verstraeten
10:45
Welcome
1
D530 | EGU2020-5174
10:50
Potential of LiDAR for species richness prediction at Mount Kilimanjaro
Alice Ziegler and the Research Group at the Kilimanjaro
2
D512 | EGU2020-288
10:57
Understanding wetland dynamics using geostatistics of multi-temporal Earth Observation datasets
Manudeo Narayan Singh and Rajiv Sinha
3
D515 | EGU2020-5421
11:04
Twelve years of SIFTER Sun-Induced Fluorescence retrievals from GOME-2 as an independent constraint on photosynthesis across continents and biomes
Maurits L. Kooreman, K. Folkert Boersma, Erik van Schaik, Anteneh G. Mengistu, Olaf N. E. Tuinder, Piet Stammes, Gerbrand Koren, and Wouter Peters
4
D516 | EGU2020-6674
11:11
Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe
Jan-Peter George Jan Pisek and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8) et al.
5
D517 | EGU2020-8263
11:18
Probing the relationship between formaldehyde column concentrations and soil moisture using mixed models and attribution analysis
Susanna Strada, Josep Penuelas, Marcos Fernández Martinez, Iolanda Filella, Ana Maria Yanez-Serrano, Andrea Pozzer, Maite Bauwens, Trissevgeni Stavrakou, and Filippo Giorgi
6
D518 | EGU2020-9071
11:25
Validation of seasonal time series of remote sensing derived LAI for hydrological modelling
Charlotte Wirion, Boud Verbeiren, and Sindy Sterckx
7
D519 | EGU2020-12000
11:32
Potassium estimation of cotton leaves based on hyperspectral reflectance
Adunias dos Santos Teixeira, Marcio Regys Rabelo Oliveira, Luis Clenio Jario Moreira, Francisca Ligia de Castro Machado, Fernando Bezerra Lopes, and Isabel Cristina da Silva Araújo
8
D528 | EGU2020-4418
11:39
Comparison of the Photochemical Reflectance Index and Solar-induced Fluorescence for Estimating Gross Primary Productivity
Qian Zhang and Jinghua Chen
9
D529 | EGU2020-4582
11:46
Weed-crop competition and the effect on spectral reflectance and physiological processes as demonstrated in maize
Inbal Ronay, Shimrit Maman, Jhonathan E. Ephrath, Hanan Eizenberg, and Dan G. Blumberg
10
D531 | EGU2020-6059
11:53
Remote sensing-aid assessment of wetlands in central Malawi
Emmanuel Ogunyomi, Byongjun Hwang, and Adrian Wood
12:00
Open discussion
12:30
End morning session

Chat time: Wednesday, 6 May 2020, 14:00–15:45
Chairperson: Willem Verstraeten Frank Veroustraete
14:00
Welcome back!
1
D534 | EGU2020-10014
14:05
On the surface apparent reflectance exploitation: Entangled Solar Induced Fluorescence emission and aerosol scattering effects at oxygen absorption regions
Neus Sabater, Pekka Kolmonen, Luis Alonso, Jorge Vicent, José Moreno, and Antti Arola
2
D536 | EGU2020-15832
14:12
Evaluating the impact of different spaceborne land cover distributions on isoprene emissions and their trends using the MEGAN model.
Beata Opacka, Jean-François Müller, Jenny Stavrakou, Maite Bauwens, and Alex B. Guenther
3
D537 | EGU2020-10633
14:19
Application of Copernicus Global Land Service vegetation parameters and ESA soil moisture data to analyze changes in vegetation with respect to the CORINE database
Hajnalka Breuer and Amanda Imola Szabó
4
D538 | EGU2020-13332
14:26
How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals
Sina C. Truckenbrodt, Friederike Klan, Erik Borg, Klaus-Dieter Missling, and Christiane C. Schmullius
5
D539 | EGU2020-18493
14:33
Learning main drivers of crop dynamics and production in Europe
Anna Mateo Sanchis, Maria Piles, Julia Amorós López, Jordi Muñoz Marí, and Gustau Camps Valls
6
D540 | EGU2020-19003
14:40
Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model
Dominic Fawcett, Jonathan Bennie, and Karen Anderson
7
D543 | EGU2020-5151
14:47
Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets
Lars Nerger, Himansu Pradhan, Christoph Völker, Svetlana Losa, and Astrid Bracher
8
D544 | EGU2020-5251
14:53
PROSPECT-PRO: a leaf radiative transfer model for estimation of leaf protein content and carbon-based constituents
Jean-Baptiste Féret, Katja Berger, Florian de Boissieu, and Zbyněk Malenovský
9
D547 | EGU2020-13447
15:00
Inverting a comprehensive crop model in parsimonious data context using Sentinel 2 images and yield map to infer soil water storage capacity.
André Chanzy and Karen Lammoglia
10
D550 | EGU2020-18798
15:07
Study on The Extraction Method and Spatial-temporal Characteristics of Irrigated Land in Zhangjiakou City
Zijuan Zhu, Lijun Zuo, Zengxiang Zhang, Xiaoli Zhao, Feifei Sun, and TianShi Pan
11
D551 | EGU2020-19953
15:14
Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO)
Amelie McKenna, Alfred Schultz, Erik Borg, Matthias Neumann, and Jan-Peter Mund
15:21
Open discussion
15:45
End afternoon session

Remote sensing, numerical models, and machine learning have been widely used for investigating environmental risks under climate change. It is known that they tend to do an excellent job in mapping, simulating, and projecting the long-term changes in average conditions. However, damages associated with extreme weathers by droughts, floods, forest fires, heat-related mortality, and crop yield loss are often more devastating than those caused by gradual climate changes. How remote sensing, numerical models, and machine learning can be used for assessing the impacts of extreme weathers on the natural and human systems remains uncertain.
This session aims to summarize current progress in assessing the ability of remote sensing, numerical models, and machine learning for quantifying climate risks in multiple sectors, such as water, agriculture, and human health.
We especially welcome investigations focusing on the inter-comparison of methodologies, as well as multi-sectoral, cross-sectoral, and integrated assessments.

This session invites innovative Earth system and climate studies based on geodetic measuring techniques. Modern geodetic observing systems document a wide range of changes in the Earth’s solid and fluid layers at very diverging spatial and temporal scales related to processes as, e.g., glacial isostatic adjustment, the terrestrial water cycle, ocean dynamics and ice-mass balance. Different time spans of observations need to be cross-compared and combined to resolve a wide spectrum of climate-related signals. Geodetic observables are also often compared with geophysical models, which helps to explain observations, evaluate simulations, and finally merge measurements and numerical models via data assimilation.
We appreciate contributions utilizing geodetic data from diverse geodetic satellites including altimetry, gravimetry (CHAMP, GRACE, GOCE and GRACE-FO), navigation satellite systems (GNSS and DORIS) or remote sensing techniques that are based on both passive (i.e., optical and hyperspectral) and active (i.e., SAR) instruments. We welcome studies that cover a wide variety of applications of geodetic measurements and their combination to observe and model Earth system signals in hydrological, ocean, atmospheric, climate and cryospheric sciences. Any new approaches helping to separate and interpret the variety of geophysical signals are equally appreciated. Contributions working towards the newly established Inter-Commission Committee on "Geodesy for Climate Research" (ICCC) of the International Association of Geodesy (IAG) would be particularly interesting for this session.
With author consent, highlights from this session will be tweeted with a dedicated hashtag during the conference in order to increase the impact of the session.

Innovative forward and inverse modeling techniques, advances in numerical solvers and the ever-increasing power of high-performance compute clusters have driven recent developments in inverting seismic and other geophysical data to reveal properties of the Earth at all scales.

The interpretation of single disciplinary geophysical field data often allows for various, equally probable models that may not always sufficiently discern plausible hypotheses that are challenged. Therefore, co-validation of data from different disciplines is critical.

This session provides a forum to present, discuss and learn the state-of-the-art in computational seismology, non-linear and joint inversion, uncertainty quantification and collaborative interpretation.

Invited Speakers:
Christel Tiberi, "Joint inversion and collaborative interpretations in complex geodynamical context";
Andrew Curtis, "Variational Probabilistic Tomography";
Yann Capdeville, "Intrinsic non-uniqueness of the acoustic full waveform inverse problem"

Comprehensive evaluations of Earth Systems Science Prediction (ESSP) systems (e.g., numerical weather prediction, hydrologic prediction, climate prediction and projection, etc.) are essential to understand sources of prediction errors and to improve earth system models. However, numerous roadblocks limit the extent and depth of ESSP system performance evaluations. Observational data used for evaluation are often not representative of the physical structures that are being predicted. Satellite and other large spatial and temporal observations datasets can help provide this information, but the community lacks tools to adequately integrate these large datasets to provide meaningful physical insights on the strengths and weaknesses of predicted fields. ESSP system evaluations also require large storage volumes to handle model simulations, large spatial datasets, and verification statistics which are difficult to maintain. Standardization, infrastructure, and communication in one scientific field is already a challenge. Bridging different communities to allow knowledge transfers, is even harder. The development of innovative methods in open frameworks and platforms is needed to enable meaningful and informative model evaluations and comparisons for many large Earth science applications from weather to climate.

The purpose of this Open Science 2.0 session is to bring experts together to discuss innovative methods for integrating, managing, evaluating, and disseminating information about the quality of ESSP fields in meaningful way. Presentations of these innovative methods applied to Earth science applications is encouraged. The session should generate some interest in communities and research projects building and maintaining these systems (e.g. ESMVal, Copernicus, Climaf, Freva, Birdhouse, MDTF, UV-CDAT, CMEC - PCMDI Metrics Package, Doppyo, MET-TOOLS, CDO, NCO, etc.). The session allows room for the exchange of ideas. An outcome of this session is to connect the scientists, develop a list of tools and techniques that could be developed and provided to the community in the future.

Geostatistics is commonly applied in the Water, Earth and Environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Extensions to the space-time domain are also a topic of current interest. Due to technological advances and abundance of new data sources from remote and proximal sensing and a multitude of environmental sensor networks, big data analysis and data fusion techniques have become a major topic of research. Furthermore, methodological advances, such as hierarchical Bayesian modeling and machine learning, have enriched the modelling approaches typically used in geostatistics.

Earth-science data have spatial and temporal features that contain important information about the underlying processes. The development and application of innovative space-time geostatistical methods helps to better understand and quantify the relationship between the magnitude and the probability of occurrence of these events.

This session aims to provide a platform for geostatisticians, soil scientists, hydrologists, earth and environmental scientists to present and discuss innovative geostatistical methods to study and solve major problems in the Water, Earth and Environmental sciences. In addition to methodological innovations, we also encourage contributions on real-world applications of state-of-the-art geostatistical methods.

Given the broad scope of this session, the topics of interest include the following non-exclusive list of subjects:
1. Advanced parametric and non-parametric spatial estimation and prediction techniques
2. Big spatial data: analysis and visualization
3. Optimisation of spatial sampling frameworks and space-time monitoring designs
4. Algorithms and applications on Earth Observation Systems
5. Data Fusion, mining and information analysis
6. Integration of geostatistics with optimization and machine learning approaches
7. Application of covariance functions and copulas in the identification of spatio-temporal relationships
8. Geostatistical characterization of uncertainties and error propagation
9. Bayesian geostatistical analysis and hierarchical modelling
10. Functional data analysis approaches to geostatistics
11. Geostatistical analysis of spatial compositional data
12. Multiple point geostatistics
13. Upscaling and downscaling techniques
14. Ontological framework for characterizing environmental processes

Earth Sciences depend on detailed multi-variate measurements and investigations to understand the physical, geological, chemical, biogeochemical and biological processes of the Earth. Making accurate prognoses and providing solutions for current questions related to climate change, water, energy and food security are important requests towards the Earth Science community worldwide. In addition to these society-driven questions, Earth Sciences are still strongly driven by the eagerness of individuals to understand processes, interrelations and tele-connections within and between small sub-systems and the Earth System as a whole. Understand and predict temporal and spatial changes in the above mentioned Micro- to Earth spanning scales is the key to understand Earth ecosystems; we need to utilize high resolution data across all scales in an integrative/holistic approach. Using Big Data, which are often distributed and particularly very in-homogenous, has become standard practice in Earth Sciences and digitalization in conjunction with Data Science promises new discoveries.
The understanding of the Earth System as a whole and its sub-systems depends on our ability to integrate data from different disciplines, between earth compartments, and across interfaces. The need to advance Data Science capabilities and to enable earth scientists to follow best possible workflows, apply methods, and use computerized tools properly and in an accessible way has been identified worldwide as an important next step for advancing scientific understanding. This is particularly necessary to access knowledge contained in already acquired data, but which due to the limitations of data integration and joint exploration possibilities currently remains invisible. This session aims to bring together researchers from Data and Earth Sciences working on, but not limited to,
• SMART monitoring designs by dealing with advancing monitoring strategies to e.g. detect observational gaps and refine sensor layouts to allow better and statistically robust extrapolation
• Data management and stewardship solutions compliant with FAIR principles, including the development and application of real-time capable data management and processing chains
• Data exploration frameworks providing qualified data from different sources and tailoring available computational and visual methods to explore and analyse multi-parameter data generated through monitoring efforts/ model simulations

Satellite data provides information on the marine environment that can be used for many applications – from water quality and early warning systems, to climate change studies and marine spatial planning. The most modern generation of satellites offer improvements in spatial and temporal resolution as well as a constantly evolving suite of products.

Data from the European Union Copernicus programme is open and free for everyone to use however they wish - whether from academic, governance, or commercial backgrounds. The programme has an operational focus, with satellite constellations offering continuity of service for the foreseeable future. There is also a growing availability of open source tools that can be used to work with this data.

This short course is an opportunity to learn about the data available from the Copernicus Sentinel-3 satellite and downstream services, and then, with support from marine Earth Observation experts, to develop your own workflows. The sessions will be interactive, using the WeKEO DIAS hosted processing, Sentinel Applications Platform (SNAP) software, and Python programming. No experience is necessary as various exercises will be provided for a wide range of skill levels and applications, however participants should bring their own laptops and be prepared to install open source software in advance.

Public information:
This course will still be held, post-EGU week, on the the 19th May 10:00 CEST - 12:00 CEST (8:00 - 10:00 UTC) . More information is available at https://tinyurl.com/ya5fhkaj

Earth systems science is fundamentally cross-disciplinary, and increasingly this requires sharing and exchange of geoscientific information across discipline boundaries. This information can be both rich and complex, and content is not always readily interpretable by either humans or machines. Difficulties arise through differing exchange formats, lack of common semantics, divergent access mechanisms, etc.

Recent developments in distributed, service-oriented, information systems using web-based (W3C, ISO, OGC) standards are leading to advances in data interoperability. At the same time, work is underway to understand how meaning may be represented using ontologies and other semantic mechanisms, and how this can be shared with other scientists.

This session aims to explore developments in interoperable data sharing, and the representation of semantic meaning to enable interpretation of geoscientific information. Topics may include, but are not limited to:
- standards-based information modelling
- interoperable data sharing
- use of metadata
- knowledge representation
- use of semantics in an interoperability context
- application of semantics to discovery and analysis
- metadata and collaboration

Please Note: abstracts chosen for presentation during the ESSI 2.1 session will be considered for publication in a Special Issue of (IJGI) International Journal of Geo-Information: https://www.mdpi.com/journal/ijgi, titled "On Denotation and Connotation in Web Semantics, Collaboration and Metadata” More in formation at this link: https://www.mdpi.com/journal/ijgi/special_issues/denotation_connotation

There are many ways in which machine learning promises to provide insight into the Earth System, and this area of research is developing at a breathtaking pace. If unsupervised, supervised as well as reinforcement learning can hold this promise remains an open question, particularly for predictions. Machine learning could help extract information from numerous Earth System data, such as satellite observations, as well as improve model fidelity through novel parameterisations or speed-ups. This session invites submissions spanning modelling and observational approaches towards providing an overview of the state-of-the-art of the application of these novel methods.

This session aims to bring together researchers working with big data sets generated from monitoring networks, extensive observational campaigns and detailed modeling efforts across various fields of geosciences. Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards semi or fully automated inference of relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:
• Dimensionality and complexity of big data sets
• Data mining in Earth sciences
• Machine learning, deep learning and Artificial Intelligence applications in geosciences
• Visualization and visual analytics of big and high-dimensional data
• Informatics and data science
• Emerging big data paradigms, such as datacubes

Smart monitoring and observation systems for natural hazards, including satellites, seismometers, global networks, unmanned vehicles (e.g., UAV), and other linked devices, have become increasingly abundant. With these data, we observe the restless nature of our Earth and work towards improving our understanding of natural hazard processes such as landslides, debris flows, earthquakes, floods, storms, and tsunamis. The abundance of diverse measurements that we have now accumulated presents an opportunity for earth scientists to employ statistically driven approaches that speed up data processing, improve model forecasts, and give insights into the underlying physical processes. Such big-data approaches are supported by the wider scientific, computational, and statistical research communities who are constantly developing data science and machine learning techniques and software. Hence, data science and machine learning methods are rapidly impacting the fields of natural hazards and seismology. In this session, we will see research from natural hazards and seismology for processes over a broad range of time and spatial scales.

Dr. Pui Anantrasirichai of the University of Bristol, UK will give the invited presentation:
Application of Deep Learning to Detect Ground Deformation in InSAR Data

Machine learning (ML) is now widely used across the Earth Sciences and especially its subfield deep learning (DL) has recently enjoyed increased attention in the context of Hydrology. The goal of this session is to highlight the continued integration of ML, and DL in particular, into traditional and emerging Hydrology-related workflows. Abstracts are solicited related to novel theory development, novel methodology, or practical applications of ML and DL in Hydrology. This might include, but is not limited to, the following:

(1) Identifying novel ways for DL in hydrological modelling.
(2) Testing and examining the usability of DL based approaches in hydrology.
(3) Improving understanding of the (internal) states/representations of DL models.
(4) Integrating DL with traditional hydrological models.
(5) Creating an improved understanding of the conditions for which DL provides reliable simulations. Including quantifying uncertainty in DL models.
(6) Clustering and/or classifying hydrologic systems, events and regimes.
(7) Using DL for detecting, quantifying or cope with nonstationarity in hydrological systems and modeling.
(8) Deriving scaling relationships or process-related insights directly from DL.
(8) Using DL to model or anticipate human behavior or human impacts on hydrological systems.
(10) DL based hazard analysis, detection/mitigation, event detection, etc.
(11) Natural Language Processing to analyze, interpret, or condense hydrologically-relevant peer-reviewed literature or social media data or to assess trends within the discipline.

Earth, space, and environmental scientists are pushing the boundaries of human understanding of our complex world. They seek and use larger and more varied data in their research with a growing need for data integration and synthesis across and among scientific domains. Tools, services, and data skills are critical resources to the research ecosystem in order to enable the harmonization and integration of data with different temporal and spatial ranges. This session explores the challenges, successes, and best practices the data community has for using data from multiple sources and scientific domains with unfamiliar formats, vocabularies, quality, and uncertainty, or in providing support and services for accessing these data. We seek submissions from the community of data producers, enablers, researchers, and users on methods for identifying and communicating best practices, challenges in this diverse data environment, and for building critical data skills related to data integration and data management.

This session aims to highlight Earth Science research concerned with state of the art computational and data infrastructures such as HPC (Supercomputer, Cluster, accelerator-based systems GPGU, FPGA), Clouds and accelerator-based systems (GPGPU, FPGA).
We will focus on data intensive workflows (scientific workflows) between Infrastructures e.g. European data and compute infrastructures down to complex analysis workflows on an HPC system e.g. in situ coupling frameworks.

The session presents an opportunity for everyone to present and learn from results achieved, success stories and experience gathered during the process of study, adaptation and exploitation of these systems.
Further contributions are welcome that showcase middleware and tools developed to support Earth Science applications on HPC systems and Cloud infrastructures, e.g. to increase effectivity, robustness or ease of use.

Topics of interest include:
- Data intensive Earth Science applications and how they have been adapted to different HPC infrastructures
- Data mining software stacks in use for large environmental data
- HPC simulation and High Performance Data Analytics e.g. code coupling, in-situ workflows
- Experience with Earth Science applications in Cloud environments e.g. solutions on Amazon EC2, Microsoft Azure, and Earth Science simulation codes in private and European Cloud infrastructures (Open Science Cloud)
- Tools and services for Earth Science data management, workflow execution, web services and portals to ease access to compute resources.
- Tools and middleware for Earth Science applications on Grid, Cloud and on High Performance Computing infrastructures.

This session provides a multi-disciplinary overview of Geoscience research and applied case studies involving MATLAB, and it further discusses technical resources and new capabilities available to researchers and educators. MATLAB is a multi-paradigm numerical computing environment and programming language developed by MathWorks, which is supported by a large community of skilled toolbox developers and active users. It allows matrix manipulations, data plotting, algorithms implementation, creation of user interfaces, and interfacing with programs written in other programming languages. These characteristics of MATLAB functions and tools have attracted various projects in geoscientific fields of academia and industry, and particularly in data analysis, 2D/3D visualization and program development. Many scientific articles, including MATLAB-based applications, have been published in international journals. This session encourages studies introducing/applying MATLAB-based programs and applications. Contributions from all related fields of Earth Science are welcome.
In this session, we would like to provide an overview over the MATLAB ecosystem for geoscientists and engineers and to discuss recent technological developments. Useful techniques will be introduced to manage large distributed files and leverage cluster solutions for geoscientific computations. We will focus on the visualization of results for scientific publication and present state of the art capabilities to visualize geo-referenced data.

The aim of this session is to present the latest research and case studies related to various data analysis and improvement methods and modeling techniques, and demonstrate their applications from the various fields of earth sciences like: hydrology, geology and paleogeomorphology, to geophysics, seismology, environmental and climate change.

This interdisciplinary session welcomes contributions on novel conceptual approaches and methods for the analysis of observational as well as model time series from all geoscientific disciplines.

Methods to be discussed include, but are not limited to:
- linear and nonlinear methods of time series analysis
- time-frequency methods
- predictive approaches
- statistical inference for nonlinear time series
- nonlinear statistical decomposition and related techniques for multivariate and spatio-temporal data
- nonlinear correlation analysis and synchronisation
- surrogate data techniques
- filtering approaches and nonlinear methods of noise reduction
- artificial intelligence and machine learning based analysis and prediction for univariate and multivariate time series

Contributions on methodological developments and applications to problems across all geoscientific disciplines are equally encouraged.

Most of the processes studied by geoscientists are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modelling and forecasting complex spatio-temporal systems through the use of stochastics non-linear models.
This session aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal geostatistics or data mining on topics that include, but are not limited to:
- advances in spatio-temporal modeling using geostatistics and machine learning;
- uncertainty quantification and representation;
- innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
The main applications will be closely related to the research in environmental sciences and quantitative geography. A non-complete list of possible applications includes:
- natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; soil, water, and air pollution);
- interaction between geosphere and anthroposphere (e.g. land degradation; urban sprawl);
- socio-economic sciences, characterized by the spatial and temporal dimension of the data (e.g. census data; transport; commuter traffic).

The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.

Together with the rapid development of sensor technologies and the implementation of environmental observation networks (e.g. MOSES, TERENO, Digital Earth, eLTER, CUAHSI, ICOS, ENOHA,…) a large number of data infrastructures are being created to manage and provide access to observation data. However, significant advances in earth system understanding can only be achieved through better and easier integration of data from distributed infrastructures. In particular, the development of methods for the automatic real-time processing and integration of observation data in models is required in many applications. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor metadata quality. Improvement in this field strongly depends on the capabilities of dealing with fast growing multi-parameter data and on effort employing data science methods, adapting new algorithms and developing digital workflows tailored to specific scientific needs. Automated quality assessment/control algorithms, data discovery and exploration tools, standardized interfaces and vocabularies as well as data and processing exchange strategies and security concepts are required to interconnecting distributed data infrastructures. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important aspect. This session focuses on the specific requirements, techniques and solutions to process, provide and couple observation data from (distributed) infrastructures and to make observation data available for modelling and other scientific needs.

Public information:
16:15–16:25: Introduction
16:25–16:29: MOSAiC goes O2A - Arctic Expedition Data Flow from Observations to Archives
16:29–16:33: Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube
16:33–16:37: Implementing FAIR principles for dissemination of data from the French OZCAR Critical Observatory network: the Theia/OZCAR information system
16:37–16:47: Discussion
16:47–16:51: Solutions for providing web-accessible, semi-standardised ecosystem research site information
16:51–16:55: Put your models in the web - less painful
16:55–16:59: Improving future optical Earth Observation products using transfer learning
16:59–17:03: Design and Development of Interoperable Cloud Sensor Services to Support Citizen Science Projects
17:03–17:13: Discussion
17:13–17:17: Providing a user-friendly outlier analysis service implemented as open REST API
17:17–17:21: Graph-based river network analysis for rapid discovery and analysis of linked hydrological data
17:21–17:25: SIMILE: An integrated monitoring system to understand, protect and manage sub-alpine lakes and their ecosystem
17:25–17:35: Discussion

Geomorphometry and geomorphological mapping are important tools used for understanding landscape processes and dynamics on Earth and other planetary bodies. Recent rapid growth of technology and advances in data collection methods has made available vast quantities of geospatial data for such morphometric analysis and mapping, with the geospatial data offering unprecedented spatio-temporal range, density, and resolution. This explosion in the availability of geospatial data opens up considerable possibilities for morphometric analysis and mapping (e.g. for recognising new landforms and processes), but it also presents new challenges in terms of data processing and analysis.

This inter-disciplinary session on geomorphometry and landform mapping aims to bridge the gap between process-focused research fields and the technical domain where geospatial products and analytical methods are developed. The increasing availability of a wide range of geospatial datasets requires the continued development of new tools and analytical approaches as well as landform/landscape classifications. However, a potential lack of communication across disciplines results in efforts to be mainly focused on problems within individual fields. We aim to foster collaboration and the sharing of ideas across subject-boundaries, between technique developers and users, enabling us as a community to fully exploit the wealth of geospatial data that is now available.

We welcome perspectives on geomorphometry and landform mapping from ANY discipline (e.g. geomorphology, planetary science, natural hazard assessment, computer science, remote sensing). This session aims to showcase both technical and applied studies, and we welcome contributions that present (a) new techniques for collecting or deriving geospatial data products, (b) novel tools for analysing geospatial data and extracting innovative geomorphometric variables, (c) mapping and/or morphometric analysis of specific landforms as well as whole landscapes, and (d) mapping and/or morphometric analysis of newly available geospatial datasets. Contributions that demonstrate multi-method or inter-disciplinary approaches are particularly encouraged. We also actively encourage contributors to present tools/methods that are “in development”.

This short course will focus on modern, data driven analytical methods in the field of Deep Learning with MATLAB. Deep Learning represents powerful artificial intelligence tools used to solve complex modeling problems in earth and ocean sciences, planetary and atmospheric sciences, and related math and geoscience fields. The MATLAB based Deep Learning platform provides algorithms and tools for creating and training deep neural networks. These networks are used to simulate processes of past, present and future environmental events in this wide range of disciplines.

Participants will be able to adopt concepts of Deep Learning for their areas of research such as dynamics, preconditions, and trends related to the surface, subsurface and the atmosphere of the planets. The content level will be 80% beginner, 10% intermediate, and 10% advanced. Scientists from all disciplines are invited to participate in this course. Any previous experience with Deep Learning and distributed computing will be beneficial but not necessary for participation.

The maximum number of participants is 65, in order to guarantee direct supervision for the hands-on part of the session.

Public information:
The seminar will take place on Wed, 13 May, 10:30-12:00 CEST. Register at:

https://www.mathworks.com/company/events/seminars/deep-learning-for-geosciences-with-matlab-made-easy-3090396.html

This is the replacement event for the physical EGU session and is held in addition to our EGU Sharing Geosciences Online session. The number of participants for this WebEx is not limited.

Earth science research has become increasingly collaborative. Researchers work together on data, software and algorithms to answer interesting research questions. Teams also share these data and software with other collaborators to refine and improve these products. As data volumes continue to grow, researchers will need new platforms to both enable analysis at scale and to support the sharing of data and software.

Software is critical to the success of science. Creating and using Free and Open Source Software (FOSS) fosters contributions from the scientific community, creates a peer-reviewed and consensus-oriented environment, and promotes the sustainability of science infrastructures.

This session will look at the role of Free and Open Source Software (FOSS), cloud-based architecture solutions, metadata and other user interfaces to support information sharing, scientific collaboration, scientific reproducibility and analytics at scale solutions.

Digital data, software and samples are key inputs that underpin research and ultimately scholarly publications, and there are increasing expectations from policy makers and funders that they will be Open and FAIR (Findable, Accessible, Interoperable, Reusable). Open, accessible, high-quality data, software and samples are critical to ensure the integrity of published research and to facilitate reuse of these inputs in future scientific efforts. In Europe, adherence to the INSPIRE directive becomes gradually more enforced by national legislations, affecting also the data lifecycle in Earth and Environmental Sciences.

These issues and challenges get addressed at increasing pace today, with journals changing their policies towards openness of data and software connected to publications, and national, European and global initiatives and institutions developing more and more services around Open and FAIR data, covering curation, distribution and processing. Yet, researchers, as producers as well as users of data, products, and software, continue to struggle with the requirements and conditions they encounter in this evolving environment.

An inclusive, integrated approach to Open and FAIR is required, with consistent policies, standards and guidelines covering the whole research data lifecycle, addressing also basic legal frameworks e.g. for intellectual property and licensing. At the same time, the research community needs to further develop a common understanding of best practices and appropriate scientific conduct adequate for this new era, and could still better share tools and techniques.

This session solicits papers from researchers, repositories, publishers, funders, policy makers and anyone having a story to share on and further evolution of an integrated, Open and FAIR research ecosystem.

Our global societies are facing many complex and interlinked challenges such as climate change, sea-level rise, water and food security, uncontrolled spread of infectious diseases or finding tools for sustainable development of our dwindling mineral and petroleum resources. Environmental and Earth system sciences have a significant role to play in these challenges but will require the integration of scientific data, software and tools from multiple, globally distributed resources to unlock their potential to contribute. The preconditions for interdisciplinary research are set by existing national- and continental-scale research infrastructures and e-infrastructures (e.g., EOSC, ENVRI, EPOS, EarthCube, IRIS, UNAVCO, AuScope, etc.). We now need to foster their convergence and develop innovative and FAIR data and software, as well as integrated services to enhance the efficiency and productivity of researchers as we scale up to more complex challenges upcoming. Thereby, some problems will require new solutions such as next-generation computing at exascale.
This session solicits papers from different fields of expertise in the Environmental and Earth system domain (research and e-infrastructures, repositories and data hubs, interdisciplinary data users, global initiatives etc.), who are working to support tackling the existing and upcoming challenges. We also invite papers from those who are working towards the next generation infrastructures who can point up the practical challenges, perspectives, and potential solutions related to creating an open and collaborative ecosystem of research and e-Infrastructures that will support the next phase of Environmental and Earth system science research at exascale.

Public information:
(solicited presenter: Alice-Agnes Gabriel, gabriel@geophysik.uni-muenchen.de)

In recent years, the number of Earth and environmental research data repositories has increased markedly, and so has their range of maturities and capabilities to integrate into the ecosystem of modern scientific communication. Efforts such as the FAIR Data Principles, the CoreTrustSeal Certification for the trustworthiness of research data repositories, and the Enabling FAIR Data Commitment Statement have raised expectations we have towards the capabilities of research data repositories. How do we know which ones meet these benchmarks and future expectations? What are the challenges and appropriate strategies?

This session seeks submissions from any research data repository for Earth and environmental science data. It aims to showcase the range of practices in research data repositories, data publication and the integration of data, software and samples into the scholarly publication process. The session invites repositories to discuss challenges they are facing in meeting these community best practices and expectations for maturity.

Data science, analytics and visualization technologies and methods emerge as significant capabilities for extracting insight from the ever growing volume and complexity of scientific data. The rapid advancement of these capabilities no doubt helps address a number of challenges and present new opportunities in improving Earth and Space science data usability. This session will highlight and discuss the novelty and strength of these emerging fields and technologies of these components, and their trends. We invite papers and presentations to examine and share the experience of:
- What benefits they offer to Earth and Space Science
- What science research challenges they address
- How they help transform science data into information and knowledge
- In what ways they can advance scientific research
- What lessons were learned in the development and infusion of these methods and technologies

All areas in the Earth sciences face the same problem of dealing with larger and more complex data sets that need to be analyzed, visualized and understood. Depending on the application domain and the specific scientific questions to be solved, different visualization strategies and techniques have to be applied. Yet, how we communicate those complex data sets, and the effect that visualization strategies and choices have on different (expert and non-expert) audiences as well as decision-makers remains an under-researched area of interest. For this "PICO only" session, we not only invite submissions that demonstrate how to create effective and efficient visualizations for complex and large earth science data sets but also those that discuss possibilities and challenges we face in the communication and tailoring of such complex data to different users/ audiences. Submissions are encouraged from all geoscientific areas that either show best practices or state of the art in earth science data visualization or demonstrate efficient techniques that allow an intuitive interaction with large data sets. In addition, we would like to encourage studies that integrate thematic and methodological insights from fields such as for example risk communication more effectively into the visualization of complex data. Presentations will be given as PICO (Presenting Interactive COntent) on large interactive touch screens. This session is supported by ESiWACE2. ESiWACE2 has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 823988.

Cartography and mapping are at this time the only means to conduct basic geoscientific studies (on planetary surfaces). The field of Planetary Cartography and Mapping has been stepping out of its niche existence in the last 15 years due to the availability of an unprecedented amount of new data from various planetary exploration missions from different countries and the advent of internet technology that allows to manage, process, distribute, analyze, and collaborate efficiently. Geospatial information system technology plays a pivotal role in this process and essentially all planetary surface science research in this field benefits from this technology and frequent new developments.
With the availability of data and connection, however, comes the challenge of organizing and structuring available data and research, such as maps and newly derived and refined (base) data that is about to enter its new research life cycle.
This session welcomes presentations covering planetary data and its development into cartographic products and maps. This covers aspects of data archival, dissemination, structuring, analyzing, filtering, visualizing, collaboration, and map compilation but is not limited to these topics.
It should also be emphasized that the exchange of knowledge and experiences from the Earth Sciences would be highly beneficial for the Planetary Data Sciences.