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Session programme

ESSI

ESSI – Earth & Space Science Informatics

MAL16
Convener: Helen Glaves

ESSI1 – Community-driven challenges and solutions dealing with Informatics

ESSI1.1

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.

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Co-organized by OS4
Convener: Antonio Novellino | Co-conveners: Luca BonofiglioECSECS, Cristian MunozECSECS, Simona Simoncelli
OS4.8

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.

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Co-organized by CL5/ESSI1, co-sponsored by IMMERSE and NEMO
Convener: Claire Levy | Co-conveners: Mike Bell, Jerome Chanut, Dorotea IovinoECSECS, Julien Le Sommer
NH3.8

This session will provide an overview of the progress and new scientific approaches for investigating landslides using Earth Observation (EO), close-range Remote Sensing (RS) techniques as well as of surface- and borehole-based geophysical surveying.

We invited distinguished professor Jonathan Chambers, team leader of the geophysical tomography cluster at the British Geological Survey, as guest speaker for this session.

New scientific opportunities to investigate –and better understand- landslide dynamics worldwide is being driven by a series of remarkable technological progress during the last decade, including integrated information about geometry, rheological properties, water content, rate of deformation and time-varying changes of these parameters is ultimately controlling our capability to detect, model and predict landslide processes at different scales (from site specific to regional studies) and over multiple dimensions (2D, 3D and 4D).

This session welcomes innovative contributions and lessons learned from significant case studies and innovative methods using a myriad of techniques, including 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 Synthetic Aperture Radar differential interferometry (DInSAR), GPS surveying, Seismic Reflection, Surface Waves Analysis, Geophysical Tomography (seismic and electrical), Seismic Ambient Vibrations, Acoustic Emissions, Electro-Magnetic surveys, low-cost (/cost-efficient) sensors, commercial use of small satellites, Multi-Spectral images, etc.

A special emphasis is expected not only on the real-time collection but also on the digesting and interpretation when using high spatiotemporal resolution datasets to characterize the main components of slope stability and dynamics, especially with regard to their use on (rapid) mapping, characterizing, monitoring and modelling of landslide behaviour, as well as their integration on real-time Early Warning Systems and other prevention and protection initiatives. 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.

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Co-organized by ESSI1/GI6/GM4
Convener: Antonio Abellan | Co-conveners: Janusz Wasowski, Masahiro Chigira, Oriol Monserrat, Jan BurjanekECSECS
NH6.6 | PICO

Since last decade of the 20th century, the use of remote sensing techniques for geological hazard prevention, mapping and monitoring has grown significantly contributing to both disaster risk reduction and disaster response. The increase of available techniques from different platforms like satellites, drone based or terrestrial platforms has significantly changed the game rules providing operational tools for prevention purposes, crisis management and recovery tasks monitoring. In particular, the EO missions of the Copernicus program of the European Union has supposed a significant step forward of the EO potentialities. Supported by a series of national EO missions, the Sentinel satellites can contribute to monitor a broad range of geological hazard types such as active volcanoes, seismic zones, landslides, droughts just to cite some examples. The integration of this new EO potentialities with short range remote sensing techniques mounted in drone or terrestrial platforms allowed the development of operational downscaling procedures allowing the monitoring of geohazards at different geographical level, from regional application to very local. This has result in an actual widening of the EO products to support disaster risk management field moving them from tools for the scientific community to operational tools able to meet the needs of local and national organizations with a mandate concerning the risks of geological hazards. This session focuses on recent advances in remote sensing based operational tools for risk management and monitoring of geological hazards. Research and development works in the areas of remote sensing (Satellite, Airborne or Terrestrial) for better understanding geological hazards, geohazard prevention and territorial planning, operational tools for risk prevention and combination of in situ measurements and remote sensing data for disaster risk reduction are welcome.

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Co-organized by ESSI1
Convener: Corey Froese | Co-conveners: Davide Bertolo, Daniele Giordan, Dalia Kirschbaum, Oriol Monserrat
NH2.2

Thermal infrared sensors providing multi-spectral data at different temporal and spatial resolution are largely employed for active volcano monitoring and investigations, especially during large eruption events or in remote areas where ground-based geophysical equipment is commonly lacking. New generation instruments such as VIIRS (Visible Infrared Imaging Radiometer Suite), aboard Suomi NPP (National Polar-Orbiting Partnership) and JPSS-1 (Joint Polar Satellite System) satellite missions, MSI (Multispectral Instrument) aboard Sentinel-2 and SLSTR (Sea and Land Surface Temperature Radiometer) aboard Sentinel-3 have further extended the capacity in mapping and characterizing volcanic thermal anomalies (e.g. lava flows, fumarole fields) better supporting traditional monitoring systems. UAV (Unmanned Aircraft Vehicle) systems, equipped with thermal cameras, are opening new scenarios for investigating volcanic activity in critical conditions (e.g. when eruptions preclude volcanic surveys using manned aircraft). In situ thermal measurements (e.g. ground-based cameras) provide very high temporal information about ongoing volcanic activity, with a high accuracy and detail. This session focuses on innovative thermal infrared remote sensing techniques developed for a better understanding of all volcanic processes. We encourage the submission of contributions in any of these topics, with a particular focus on investigations of volcanic eruptions in high-risk areas.

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Co-organized by ESSI1/GMPV9
Convener: Francesco Marchese | Co-conveners: Nicola Pergola, Simon Plank, Michael Ramsey
BG2.7

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 tropospheric 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.

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Co-organized by AS5/ESSI1/HS6/NH6/OS3
Convener: Frank Veroustraete | Co-convener: Willem Verstraeten
GI2.4

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.

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Co-organized by CL2/ESSI1/NH6
Convener: Guoyong LengECSECS | Co-conveners: Jian PengECSECS, Shengzhi Huang, Zheng DuanECSECS, Shiqiang Zhang
NH9.7

Global losses due to natural hazards have shown an increasing trend over the last decades, which is expected to continue due to growing exposure in disaster-prone areas and the effects of climate change. In response, recent years have seen greater worldwide commitment to reducing disaster risk. Working towards this end requires the implementation of increasingly effective disaster risk management (DRM) strategies. These must necessarily be supported by reliable estimates of risk and loss before, during, and after a disaster. In this context, innovation plays a key role.
This session aims to provide a forum to the scientific, public and private discourse on the challenges to innovate DRM. We welcome submissions on the development and application of groundbreaking technologies, big data, and innovative modeling and visualization approaches for disaster risk assessment and DRM decision-making. This includes the quantification and mapping of natural hazard risks and their components (i.e. hazard, exposure, and vulnerability), as well as the forecasting of hazard and impacts prior to a disaster event, or as it is unfolding (in real- or near real-time). We are particularly interested in contributions covering one or more of the following thematic areas in the context of disaster risk assessment and reduction: artificial intelligence and machine learning, big data, remote sensing, social media, volunteered geographic information (VGI), mobile applications, crowdsourcing, internet of things (IoT), and blockchain. We also welcome submissions exploring how these or other innovations can support real-world DRM strategies and translate into improved DRM decisions.

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Co-organized by ESSI1/GI6
Convener: Rui FigueiredoECSECS | Co-conveners: Carmine Galasso, Mario Lloyd Virgilio Martina, Xavier Romão, Kai Schröter
G3.2

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.

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Co-organized by AS5/CL2/ESSI1/OS4
Convener: Anna KlosECSECS | Co-conveners: Carmen Boening, Henryk Dobslaw, Roelof RietbroekECSECS, Bert Wouters
SM7.1

Recent years have been witness to new developments in theoretical and computational seismology driven by advances in forward and inverse modeling and the ever-increasing power and availability of high-performance computers.

The goal of this session is to bring together seismologists, computer scientists and related disciplines to discuss novel results and future directions in this field. We welcome contributions focussing on, but not limited to:

- innovative modeling techniques and advancements in numerical solvers,
- seismic tomography and full-waveform inversion from local to global scales,
- multi-scale, multi-parameter and joint inversions of Earth structure and sources,
- statistical inverse problems and uncertainty quantification,
- homogenization and effective medium theory,
- machine learning algorithms for seismic problems,
- big data (seismic & computational) problems on emerging HPC architectures

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Co-organized by ESSI1
Convener: Christian Boehm | Co-conveners: Ebru Bozdag, Martin van Driel
GD10.2

Inverse geodynamics is a rapidly evolving field that holds the promise of unravelling the current and past dynamics of the solid Earth. By proposing mathematical methods to combine our dynamical theories with observations, it has the potential to tackle some of the big questions of Earth sciences with a fresh perspective: How have the crust, mantle and core of the Earth evolved through time, can we estimate their current dynamical state and how well can we forecast their future state?
Nowadays, a wide range of community databases are only a few clicks away from any researcher. Computational power is also more available than ever, and allows us to consider inverse methods that were not applicable a few decades ago. Forward models of the dynamics of the solid Earth and the liquid core are becoming more and more realistic, some reaching Earth-like regime. This has led to a growing interest in the geodynamics community to combine forward models and observations, in order to estimate the past and present dynamics of crust/mantle/core systems.
We share common challenges. We often deal with heavy and highly non-linear forward models, observations are generally restricted to the surface and their amount decreases drastically as we go back in time.
With this session, we would like to bring together geophysicists working on inverse methods applied to the dynamics of the crust, mantle and core. Be it either method development or reconstructing the evolution of specific systems, we would like to hear what is your strategy to decipher the structure and dynamics of the Solid Earth and liquid core, and how you couple observations with dynamical models.
Contributions on the following topics are encouraged (but not limited to): Machine Learning in geodynamics; Inverse methods estimating the past and/or present dynamics of the solid Earth and the liquid core; data assimilation for geodynamic systems: core, mantle, lithosphere dynamics, volcanic systems, earthquake cycle...

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Co-organized by EMRP1/ESSI1/NP1/SM4/TS10
Convener: Marie BocherECSECS | Co-conveners: Georg ReuberECSECS, Sabrina SanchezECSECS, Ylona van DintherECSECS
ESSI1.12 | PICO

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.

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Co-organized by AS5/CL5
Convener: Christopher KadowECSECS | Co-conveners: Paul Kucera, Jerome Servonnat
HS3.7

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

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Co-organized by ESSI1/GI6/NH1/SSS10
Convener: Emmanouil Varouchakis | Co-conveners: Gerard Heuvelink, Dionissios Hristopulos, R. Murray Lark, Alessandra MenafoglioECSECS
HS1.2.7

Proper characterization of uncertainty remains a major challenge, and is inherent to many aspects of modelling such as structural development, hypothesis testing and parameter estimation, and the adequate characterization of forcing data and initial and boundary conditions. To address this challenge, methods for a) uncertainty analysis (UA) that seek to quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models), have proved to be very helpful.
This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs). This includes all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science. Topics of interest include (but are not limited to):

(1) Novel methods for effective characterization of sensitivity and uncertainty
(2) Single- versus multi-criteria SA/UA
(3) Novel methods for spatial and temporal evaluation/analysis of models
(4) The role of data information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with not data etc.)
(5) Novel approaches and benchmarking efforts for parameter estimation
(6) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)

Contributions addressing any or all aspects of sensitivity/uncertainty, including those related to model structural development, hypothesis testing, parameter estimation, forcing data, and initial and boundary conditions are invited.

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Co-organized by BG3/ESSI1/NP5
Convener: Juliane Mai | Co-conveners: Hoshin Gupta, Amin Haghnegahdar, Cristina PrietoECSECS, Saman Razavi
ESSI1.15

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

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Convener: Jens Greinert | Co-conveners: Peter Dietrich, Andreas Petzold, Roland Ruhnke, Viktoria WichertECSECS
GI5.3

The new scenario related to the global urbanization process and its impact on environmental sustainability and resilience to natural disasters, especially the ones related to the Climate Change, strongly call holistic multidisciplinary and multi-sectorial approaches for the management of urban areas and Cultural heritages.
These approach aim at providing solutions based on the integration of technologies, methodologies and best practices (remote and local monitoring, simulating and forecasting, characterizing, maintaining, restoring, etc.), with the purpose to increase the resilience of the assets, also thanks to the exploitation of dedicated ICT architectures and innovative eco-solutions and also by accounting the social and economic value of the investigated areas, especially in CH frame.
In this context, attention is also focused on the high-resolution geophysical imaging is assuming a great relevance to manage the underground and to adopt new strategies for the mitigation of geological risks.
This session represents a good forum to present, technologies best practices and share different experiences in the field of the urban areas and CH management and protection, against the multi-risk scenarios and for the different situations at European and worldwide level. Finally, great attention will be devoted to the success cases, with a specific focus on recent international projects on smart cities and Cultural heritage in Europe and other countries.

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Co-organized by CL4/ESSI1/NH6
Convener: Giuseppina Padeletti | Co-conveners: Ilaria Catapano, Vincenzo Lapenna, Filippos Vallianatos
ITS1.14/SSS9.1

Precision Agriculture makes towards achieving SDG2, especially considering the SDG2 focus on “small-scale" food producers. It is based on different types data collected in field by soil and weather sensors, proximal and remote sensing (e.g. UAV), producing large datasets which can help in the achievement of better farm management (sustainable agriculture). However, many times that farmers fail to act on information provided or never adopt technologies or practices with production benefits.
In this context, DSS (Decision Support Systems) can help the farmers to manage their field information and make the right choice in nutrients, irrigation and plant disease management through the integration of approaches that combine physical, chemical, biological and space–time techniques through the use of various types of knowledge, including stakeholder expertise and knowledge derived from scientific measurements and model simulations. Moreover, they are able to enabling both simple, rapid and cheap procedures and complex, cumbersome and expensive data-intensive procedures, according to the types of study and the spatial and temporal scale on which a solution is sought.
The session should be of interest to different scientific communities (e.g. soil science, remote sensing, plant science, etc…) and stakeholders (farmer, consortiums, decision maker, etc…).

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Co-organized by BG2/ESSI1/GI6
Convener: Antonello Bonfante | Co-convener: Anna Brook
SC1.22

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.

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Co-organized by ESSI1/OS5
Convener: Hayley Evers-King | Co-conveners: Lauren Biermann, Oliver Clements, Christine Traeger-Chatterjee
SC1.26

Bayesian approach to probability theory and statistics has various applications in hydrological sciences, particularly to solve inversion problems and to characterize model uncertainty. From calibrating a hydrological model to quantifying catchment transit time distribution, Bayesian approaches incorporate prior system knowledge that helps us to improve our understanding of the natural system. Using a number of practical case studies, this short course aims at providing a state-of-the-science overview of the usage of Bayesian statistics in different facets of hydrological modeling.

We kindly invite early career researchers (MSc students, PhD candidates, post-doctoral researchers) to attend this short course designed to address the fundamentals of Bayesian statistics and its particular applications in hydrology.

This will be the sixth year that the Hydroinformatics for Hydrology short course takes place during the EGU conference. Information to this and former short course topics can be found online on the homepage of the cooperating Young Hydrologic Society (http://younghs.com).

Please note that pre-registration is not necessary. The course will be open to a limited number of participants selected on a first come-first served basis.

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Co-organized by ESSI1/HS11
Convener: Harsh BeriaECSECS | Co-conveners: Nilay DoguluECSECS, Sina KhatamiECSECS, Maurizio MazzoleniECSECS, Hannes Müller-ThomyECSECS

ESSI2 – Infrastructures across the Earth and Space Sciences

ESSI2.1

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

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Convener: Paolo Diviacco | Co-conveners: Kristine Asch, Paolo Mazzetti, Alaitz Zabala
ESSI2.2

The technologies to access metadata and repository catalogues were developed alongside with the emergence of the internet. XML is fairly verbose and its mark-up adds a lot of bulk to the data payload, which is manageable with catalogues containing thousands to millions of entries, but becomes a significant burden once catalogues scale to billions of entries.
Indexing the Internet at large led to the development of lightweight encodings based on JavaScript Object Notation for Linked Data (JSON-LD). Leveraging web architecture patterns around structured data for the web gives access to the semantic web and ways to encode the context around data. This makes building a multi-domain network far easier. In addition, the use of web architecture allows third parties access use and provide offerings based on the open, well-known architecture.
This session will discuss how web architectures can be used to make metadata and repository catalogues available on a gigascale.

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Convener: Jens Klump | Co-conveners: Anusuriya DevarajuECSECS, Adam Leadbetter, Adam Shepherd
ITS4.3/AS5.2

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.

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Co-organized by BG2/CL5/ESSI2/NP4
Convener: Julien Brajard | Co-conveners: Peter Düben, Redouane LguensatECSECS, Francine SchevenhovenECSECS, Maike SonnewaldECSECS
ITS4.7/SM1.4

The past few years have seen an increase in the application of machine learning methods for geophysical data analysis. This is due to the increased adoption and visibility of freely available and easy-to-use machine learning toolkits, faster computation, reduced cost of data storage, and the very large sets of continuous geophysical and laboratory experimental data. The combination of these factors means that now is the time to consider machine learning as one of the key tools in both improving routine data processing and better understanding the underlying solid-earth processes.

In this session, we welcome machine-learning focused presentations covering topics such as seismic waveform processing, earthquake cataloging, earthquake classification, earthquake cycle behavior from numerical and laboratory experiments, computer vision approaches to tectonic and volcanic monitoring, and geodynamic modelling. We also welcome abstracts from related geophysical fields that use similar data, such as from near surface processes and geophysical hazards (e.g. rockslides, avalanches, etc.).

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Co-organized by ESSI2/GD10/GM2/GMPV1/NP4/TS10
Convener: Jonathan BedfordECSECS | Co-conveners: Fabio CorbiECSECS, Leonard SeydouxECSECS
ITS4.1/NP4.2

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

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Co-organized by AS5/CL5/ESSI2/G6/GD10/HS3/SM1
Convener: Mikhail Kanevski | Co-conveners: Peter Baumann, Sandro Fiore, Kwo-sen Kuo, Nicolas Younan
ITS4.6/NH6.7 | PICO

Smart monitoring and observing system for natural hazards, including satellites, global networks, unmanned vehicles (e.g., UAV), and other linked devices, have become increasingly abundant. We keep restlessly observing and monitoring different natural hazards processes happen on the earth (e.g., landslide, debris flows, earthquake, flood, storm, tsunami, and many others). This diversity of systems and methods gives natural hazards scientists unprecedented amounts of data before, during, and after events. In parallel, new data science and machine learning techniques are constantly being developed that allow us to mine these large datasets. Such data and methods not only bring a better understanding of the processes that govern the natural hazards processes, and allow monitoring of natural hazards, but also results in a better understanding of how hazard impacts can compound and cause cascading consequences. Hence, data science and machine learning methods are dramatically changing natural hazard science. We invite abstracts from all aspects of natural-hazards research applying data science and machine learning to understand natural hazard events and hazards over both different time and spatial scale.

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Co-organized by ESSI2/GI2/GM2/HS12/NP4/SM1
Convener: Hui TangECSECS | Co-conveners: Jean Braun, Kejie ChenECSECS, Stephanie OlenECSECS, Jens Turowski
ITS4.4/CL5.13

The Earth and its climate form a complex system. In the last few years, research in machine learning has created new techniques for the analysis of high-dimensional non-linear systems. Many of these new techniques could improve our ability to understand and predict the Earth.

In this session, we aim to connect researchers from machine learning (ML) and computational geoscience to identify opportunities that advance the state-of-the-art in Earth and climate modeling. We invite participants to discuss (1) cutting-edge machine learning advances that are relevant to Earth and climate science problems, such as advances in the modeling and simulation of non-linear systems with generative adversarial networks; new tools for interpretable ML; or methods for placing physical constraints on ML models; (2) creative new applications of reinforcement learning techniques to Earth and climate science problems; and (3) geoscience problems that reveal needs for new research in machine learning, e.g. extreme event problems involving skewed or poorly-labeled datasets.

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Co-organized by CR2/ESSI2/HS12/NP4/OS4
Convener: Kelly KochanskiECSECS | Co-convener: Karthik MukkavilliECSECS
ESSI2.8

While there are successful applications of Deep Learning (DL) in Earth science, the wider adoption of DL has been limited. The challenge for wider adoption of DL in Earth science is no longer the lack of usable algorithms, tools, or compute resources, but rather the dearth of sufficient labeled training data. Access to labeled training data for supervised learning is required to entice DL practitioners to tackle Earth science problems. Creating labeled data that scales to support DL is still a bottleneck and new strategies to increase training size need to be explored. This session seeks submissions from DL practitioners and data curators using different approaches to create labeled training data. This session will enable the practitioners to share successful approaches to scale the process of generating labeled data for their Earth science applications. This session also seeks submissions focusing on best practices for labeling and structuring the labeled data based that may be useful for other practitioners.

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Convener: Manil Maskey | Co-convener: Rahul Ramachandran
HS3.4

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.

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Co-organized by ESSI2/NP4
Convener: Frederik KratzertECSECS | Co-conveners: Claire BrennerECSECS, Hoshin Gupta, Daniel KlotzECSECS, Grey Nearing
ESSI2.10

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.

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Co-sponsored by AGU
Convener: Shelley Stall | Co-convener: Nancy Ritchey
ESSI2.11

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.

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Convener: Horst Schwichtenberg | Co-convener: Wim Som de Cerff
ESSI2.12

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.

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Convener: Eun Young LeeECSECS | Co-convener: Steve Schäfer
GI2.1

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.

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Co-organized by ESSI2
Convener: Sid-Ali Ouadfeul | Co-conveners: Leila Aliouane, Ahmed Khalil
ESSI2.14

Communication patterns such as publish-subscribe provide new opportunities to actively deliver data and information to consumers as soon as it is available in near-real time. Opposed to this, traditional pull-based patterns follow a request-response approach which means that a consumer needs to request data and subsequently receives data that is available at the time of the request with a possible delay.

In several applications, push-based communication offers a significant added value. This is illustrated by the following two examples:

1.) Applications such as risk monitoring and alerting depend on the timely availability of the latest (observation) data (e.g. water level measurements, meteorological conditions, etc.). Push-based communication flows ensure that new information is immediately received by the relevant parties as soon as it has been published. Besides the minimized delay in data delivery, another advantage is the reduced load on the server infrastructure as consumers do not need to actively check for new data.

2.) An increasing number of geo-science applications consists of processes that create new information products from multiple inputs such as Earth Observation (e.g. Copernicus) and in-situ measurement networks (e.g. Sensor Webs). Often, the generation of such products shall be triggered by specific events (e.g. critical measurements of a sensor) or by the availability of new/updated input data (e.g. if a new satellite scene is available that fulfills certain quality requirements).

Within this session, we aim at collecting use cases of push-based data delivery flows, implementations, lessons learned, experiences with emerging technologies that enable publish-subscribe patterns as well as application examples. Based on this, we intent to derive recommendations on future directions for allowing more efficient dissemination of near-real-time data and push-based information flows in research data infrastructures.


Topics include:
- Technologies for enabling push/publish-subscribe communication patterns
- Triggering of geo-processing chains
- Monitoring and alerting applications
- Interoperability standards
- Discovery and metadata of (near) real-time data sources
- Sensor Web technologies
- Distributed event detection

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Convener: Simon Jirka | Co-convener: Matthes Rieke
NP4.1

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.

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Co-organized by CL5/EMRP2/ESSI2/HS3
Convener: Reik Donner | Co-conveners: Tommaso AlbertiECSECS, Andrea Toreti
ESSI2.16

Addressing global challenges demands analytics over a variety of heterogeneous data sets across the globe. An increasing number of data collectors and providers make their data available online. Volunteered geographic information and citizen science projects (e.g. https://luftdaten.info, https://openSenseMap.org) further increase the volume and variety of the set of available data. However, these data sets are provided in different formats or accessed via different APIs or only support certain dialects of wider standards. The automatic meaningful integration of these data sets is often hindered due to semantic and structural differences between data and poor meta(data) quality. Also the size of the current data sets calls for solutions to combine data sets without moving the data from one to the other silo or to a processing platform where applicable.

Standardisation and evolving best practices are in place to overcome these issues. However, these are regularly adapted on a case to case basis and many systems following the same purpose exists for different or even the same domain. This session aims at synchronizing efforts and learn from success stories and experiences made across domains towards an easier integration of spatio-temporal data sources for spatial data analytics. Besides the technical integration, also the meaningful integration for different spatial and temporal support or measurement scales is an important part of this process.

This session will contribute to the FAIR data principle in especially increasing the Interoperability and Reusability of data sets. This session calls for abstracts on infrastructure, software, statistical models and applications that address cross-domain data integration supporting spatial analytics.

Fields of interest (but not limited to):
- Cloud Based Processing
- Semantic Mappings
- Distributed Computing
- Cloud Storage
- History, Versioning and Synchronisation of Data Sets
- Meaningful Automated Analytics
- Data fusion: official Statistics with Geoinformation
- Sensor Web

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Convener: Benedikt GrälerECSECS | Co-conveners: Anusuriya DevarajuECSECS, Matthes Rieke
ITS4.9/ESSI2.17

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).

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Co-organized by GM2/HS12/NH8/NP4/SSS12
Convener: Federico AmatoECSECS | Co-conveners: Fabian GuignardECSECS, Luigi LombardoECSECS, Marj Tonini
GI2.3

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.

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Co-organized by ESSI2/GM2/SSS10
Convener: Caterina GozziECSECS | Co-conveners: Marco Cavalli, Sebastiano Trevisani
ESSI2.19 | PICO

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. 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 exchange strategies and security concepts are required to interconnecting distributed data infrastructures.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.

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Convener: Dorit KerschkeECSECS | Co-conveners: Ralf Kunkel, Johannes Peterseil
GM2.1 | PICO

This session aims to bridge the existing gap between the process-focused fields and the technical domain where terrain analysis is developed.
The rapid growth of technology and the availability of topographic information from various platforms and sensors has led to a vast data swamp with unprecedented spatio-temporal range, density, and resolution. Deriving meaningful products from such a large pool of data sets new challenges.

We aim to foster inter-disciplinarity in digital terrain analysis from ANY discipline which touches on geomorphometry, including but not exclusive to geomorphology (tectonic/volcanic/climatic/glacial), planetary science, archaeology, geo-biology, natural hazards, computer science, remote sensing, statistics and image analysis.
We invite submissions related to the application of geomorphometric methods at multiple scales (from local to global). We welcome works related to innovative geomorphometric variables as well as their physical, mathematical and geographical meanings. Submissions related to new techniques in high-resolution terrain or global-scale data production and analysis, as well as studies focused on the associated error and uncertainty, are also welcome.
We actively encourage contributors to present works “in development”, as well as novel ways to apply established techniques.
Geomorphometry facilitates cross-disciplinary research with improved investigations across space and time, blurring the line between traditional approaches and computer modelling. Early-career scientists are in a position to be game-changers, ushering in a new era of digital terrain analysis: we strongly encourage them to contribute and help drive innovation in our community, presenting their work to this session.

The currently available multi-resolution topographic datasets require that scientists continue to develop new tools and analysis approaches. While geomorphometry attracts many researchers, the potential lack of communication across disciplines results in efforts to be mainly focused on problems within individual fields of application. We want 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 knowledge inherent in our digital landscape.

Just remember, the driver for new ideas and applications often comes from another speciality, discipline or subject: Your solution may already be out there waiting for you!

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Co-organized by ESSI2/NH3/PS4
Convener: Giulia Sofia | Co-conveners: Susan Conway, Stuart GrieveECSECS, John K. HillierECSECS, Michael Smith
SC1.8

The research of the Earth and other planetary bodies implies the analysis and visualization of large data. For understanding the dynamics, preconditions, and trends related to the surface, subsurface and the atmosphere of the planets from these data, techniques like statistics, signal processing, or image processing, and mapping are a prerequisite to establish models. One trending technology to combine process understanding and data handling is Deep Learning, which introduces state of the art methodologies to geoscience research applications.
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. MATLAB allows to build models and to simulate processes of past, present and future environmental events in this wide range of disciplines.

# Outline

The MATLAB based Deep Learning platform provides algorithms and tools for creating and training deep neural networks.

This short course will focus on modern, data driven analytical methods in the field of Deep Learning with MATLAB:

- Perform classification and regression on many datatypes, like images, signals, text, and numerical values.
- Import models from TensorFlow Keras, Caffe, and using the Open Neural Network Exchange (ONNX) format.
- Deploy MATLAB based Deep Learning algorithms on GPUs.
- Foster the learning experience of the attendees with hands-on exercises

The hands-on part of the session aims to provide attendees with a working knowledge on Deep Learning on GPU instances, scientific applications, and techniques for scaling.

We will provide MATLAB instances for all participants on Amazon Web Services and different MATLAB-based examples, as well as datasets, to allow attendees to experiment hands-on with training, inference, and scaling of deep neural network machine learning models.

# Target Audience

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 60, in order to guarantee direct supervision for the hands-on part of the session.

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Co-organized by AS6/ESSI2/NP9
Convener: Dmytro Martynenko | Co-conveners: Maike Brigitte NeulandECSECS, Steve Schäfer
SC1.9

ESMValTool is a community diagnostic and performance metrics tool for the evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Now at version 2, ESMValTool has been specifically developed to target the increased data volume and complexity of CMIP Phase 6 (CMIP6).

In ESMValTool, common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. ESMValTool comes with a large amount of well-established analyses, such as those in Chapter 9 of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) (Flato et al., 2013). The expectation is that in this way a routine and systematic evaluation of model results can be made more efficient, thereby enabling scientists to focus on developing more innovative methods of analysis rather than constantly having to “reinvent the wheel”.

During this Short Course we will walk you through how to get ESMValTool up and running, run included diagnostics, and how to customize the diagnostics and metrics performed. A number of core developers of ESMValTool will be present to answer any and all questions you may have.

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Co-organized by CL6/ESSI2
Convener: Bouwe Andela | Co-conveners: Lee de Mora, Niels Drost, Valeriu Predoi, Klaus Zimmermann
SC1.7

Machine learning (ML) is a well-established approach to complex data analysis and modelling in different scientific fields and in many practical applications. Nowadays, ML algorithms are widely used as efficient tools in GI Sciences, remote sensing, environmental monitoring and space-time forecasting. The short course gives an overview of ML algorithms application in exploration, visualization and modelling of high dimensional and multivariate geoscientific data. The main topics of the course, between others, deal with an introduction of a generic methodology of learning from environmental data, patterns detection and predictability, feature engineering and feature selection, basic algorithms of unsupervised, supervised and active learning. An important attention is paid to the techniques and tools helping in understanding and interpretability of data and the results. Real case studies consider environmental pollution, natural hazards and renewable energy resources assessments.

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Co-organized by ESSI2/NH11/NP9
Convener: Mikhail Kanevski | Co-convener: Vasily Demyanov

ESSI3 – Open Science 2.0 Informatics for Earth and Space Sciences

ESSI3.1 | PICO

This session will look at the role of Free and Open Source Software (FOSS) in the geosciences with a special emphasis on the interoperability among established and developing FOSS-tools within geoinformatics. The session will be a forum for the latest advances in FOSS-empowered research, for successful applications of existing FOSS tools for geoscientific tasks, as well as for new developments in geoscience related to FOSS.

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

Providing open access to source code also permits reuse of data, reproducibility of science, and creates scientific transparency. Open science is only possible when access to data is open, and data is analysed using open source software. This requires taking responsibility for software development, and adopting stewardship practices for managing, processing and disseminating scientific data products and related services. We will also discuss the review, publication and citation of scientific free and open source software as part of the general record of science and as part of the track record of the scientists who create or apply FOSS tools in their research.

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Convener: Peter Löwe | Co-conveners: Bernadette Fritzsch, Jens Klump, Edzer Pebesma
ESSI3.2

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.

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Co-sponsored by AGU
Convener: Florian Haslinger | Co-conveners: Ari Asmi, Helen Glaves, Shelley Stall, Lesley Wyborn
ESSI3.3

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 Earth observation data volumes continue to grow, researchers will need new platforms to both enable analysis at scale and to support sharing of data and software.

Recognizing that different communities have different needs and that there is no singular platform solution for supporting Earth science research, this session seeks submissions from the community of practice that highlight best practices and lessons learned in developing collaborative platforms. Submissions could include presentations on cloud-based architecture solutions, metadata and other user interfaces to support sharing, analytics at scale solutions such as analytics optimized data stores and other relevant topics.

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Convener: Kaylin Bugbee | Co-convener: Rahul Ramachandran
ESSI3.4

Physical samples play a central role in the earth, environmental, and planetary sciences. Studies of these samples will inform future science, but also critical decisions, laws, and policies of societal relevance. Most of these samples are national assets due to the sources of funding for their curation, and governments are recognizing the requirements to make these samples discoverable and available for ongoing research and reproducibility. Valuable samples are also collected and studied by the academic community, but essential infrastructure and policies for preservation and access of these samples are lagging behind to fulfill requirements for FAIR (Findable, Accessible, Interoperable, Reusable) samples. This session invites contributions regarding national and organizational policies that: increase sample visibility, use and tracking; promote recommended practices in sample management; and measure and demonstrate the value of physical samples to science and society.

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Co-sponsored by AGU
Convener: Kerstin Lehnert | Co-conveners: Lindsay Powers, Lesley Wyborn
ESSI3.5

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.

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Co-sponsored by AGU
Convener: Daniela FranzECSECS | Co-conveners: Ari Asmi, Helen Glaves, Lesley Wyborn
ESSI3.6

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.

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Convener: Kirsten Elger | Co-conveners: Helen Glaves, Florian Haslinger
ESSI3.7 | PICO

Data are at the core of all research in the Geosciences. We live in an age where technologies enable the acquisition of ever-increasing amounts of digital data; government and funder directives and policies demand for Open Science and Open Data; and journals require authors to not only publish data behind research results to ensure transparency and reproducibility, but also that they are stored in a trustworthy domain repository. This environment means new strategies, principles, and practices are needed to curate, manage, and preserve data such that they are available and accessible for future generations. Nevertheless, whilst Research Data Management (RDM) is becoming more and more important, it is often underrated and considered ‘less of a priority’.

This PICO session is timely, and will showcase Early Career Researchers and Scientists (ECRs) from all different disciplines in the Geosciences who are dedicated data stewards, developing and promoting best practice in data management and data sharing. By highlighting their expertise and exceptional contributions to RDM, we hope to inspire other young researchers to take responsibility for their data and to make the best use of existing services.

The session will be led by the World Data System of the International Science Council, which has the mission to promote long-term stewardship of, and universal and equitable access to, quality-assured scientific data and data services, products, and information across all scientific disciplines.

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Convener: Rorie Edmunds | Co-conveners: Alice FremandECSECS, Sandy Harrison, Aude Chambodut, Isabelle Gärtner-Roer

ESSI4 – Visualization for scientific discovery and communication

ITS4.8/ESSI4.1

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

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Co-organized by GD10/GI2/PS6/ST4
Convener: Emily Law | Co-conveners: Simon Baillarin, Thomas Huang
ITS4.2/ESSI4.2 | PICO

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.

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Co-organized by EOS7/CL5/GD10/GM2
Convener: Niklas Röber | Co-conveners: Michael Böttinger, Joseph Daron, Susanne Lorenz
ESSI4.3

Recent technologies such as remote sensing and laser scanning provide a wealth of novel 3D datasets across multiple scales for environmental scientists: from the arrangement of animal burrows and root networks within the soil, to the shape of tree crowns, all the way to landscape-scale features such as the meandering of river channels and planetary surfaces. Compared to traditional monitoring techniques in the field, these technologies capture topological and spatial distribution information in 3D and over time, providing unprecedented insight into ecosystem functioning. The challenge lies in the processing of these large, complex 3D-4D structural datasets, and their interpretation into novel indicators of ecosystem function that can inform ecosystem health or vulnerability and guide future management plans.

This cross-disciplinary session seeks contribution from various environmental disciplines (biogeomorphology, geomorphology, ocean sciences, planetary science) that use geosciences instrumentation to extract complex 3D-4D datasets, gain novel insight into ecosystem functioning, and improve scientific communication to stakeholders.

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Convener: Clementine ChirolECSECS | Co-conveners: Yang Jiang, Peter LawrenceECSECS, Chen Wang
PS6.3 | PICO

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.

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Co-organized by ESSI4/GI3
Convener: Andrea Nass | Co-conveners: Angelo Pio Rossi, Alessandro Frigeri, Stephan van Gasselt, Valentina GalluzziECSECS
ITS1.9/ESSI4.5

Rural and urban localities are under continued pressure to ensure vibrant, liveable and sustainable environments for their inhabitants. Citizen stewards are forging ahead with innovative small-scale initiatives to provide grass roots solutions for improving environmental and cultural resilience within these landscapes. Enterprises encompass everything from home gardening for providing habitat for native wildlife, to street art for improving visual urban aesthetics, to income diversification strategies for smallholder farmers. These initiatives are often undertaken with limited access to locally relevant environmental information to help guide decisions. In turn, government agencies face challenges with understanding the scale, scope and impact of such bottom-up initiatives in the absence of effective tools for collecting data. Recently, much exciting research has emerged through co-development initiatives between researchers and public contributors to improve communal accessibility to valuable and useable geographical data. Easy-to-use mobile applications have evolved which can provide environmental information to citizen participants to help them map, plan and monitor their enterprises. Such technological enterprises can also provide data to researchers and stakeholders on how the diversity of these spaces links with broader outcomes for human and ecological wellbeing. In this interdisciplinary session we invite research which showcases the value-add of public participation mobile [often geospatial in nature] applications for supporting improved biodiversity and/or cultural inclusivity. Case studies which demonstrate a transitioning towards improved functionality and viability of landscapes under the multitude of socio-ecological threats are welcomed. Likewise, we welcome research which contributes to our broader scientific understanding of sustainable practice within landscapes through using participatory mapping processes. This could also include critical perspectives on the limitations, challenges, ethical considerations and digital divides of using participatory approaches or techniques.

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Co-organized by EOS4/GM12/SSS8
Convener: Natasha Pauli | Co-conveners: Eloise Biggs, Julia FöllmerECSECS, Billy Tusker HaworthECSECS

ESSI5 – General contributions

ESSI5.1

Note that this session will not be added to the final programme. It is included to allow those authors that are unable to identify a suitable session for their contribution to submit an abstract for consideration as part of the overall ESSI programme. The ESSI Programme Chair will transfer all abstracts to other suitable sessions for consideration.

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Convener: Helen Glaves | Co-convener: Jens Klump