• How can we design Open Science workflows that enable integration across platforms designed for diverse applications used in different domains to increase their value?
• How can we lower barriers for end users (decision makers, managers in industry, scientists, community groups) who need to create Open Science workflows, processes, and data flows across domains and sectors remains technically difficult and practically resource intensive, creating?
• How can Open Science workflows and platforms enable collaboration between stakeholders in different domains and sectors?
• How can we empower organizations to demonstrate accountability in their analytical workflows, data, and representations of information through Open Science?
• What Open Science tools do organizations need to take action effectively?
• How can Open Science and FAIR data standards practically support accountability?
• How can we make it simple to connect data and platforms together in transparent, reusable and reproducible (FAIR) workflows?
• What are the specific challenges of using geospatial, earth observation (EO), and complementary data in this context?

How to cite: Simonis, I., Voidrot, M.-F., Opitz, R., and Zaborowski, P.: Open Science Collaboration across Eath Observation Platforms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-271, https://doi.org/10.5194/egusphere-egu24-271, 2024.

Mathematical models are widely used today to study the intricate physical processes and interactions among the different components of the Earth’s system. Such models are often used synergistically with Earth Observation (EO) data allowing to derive spatiotemporal estimates of key parameters characterising land surface interactions. This synergy allows combining the horizontal coverage and spectral resolution of EO data with the vertical coverage and fine temporal continuity of those models. SimSphere is a mathematical model belonging to the Soil Vegetation Atmosphere Transfer (SVAT) models. As a software toolkit, it has been developed in Java and it is used either as a stand-alone application or synergistically with EO data. The model use is constantly expanding worldwide both as an educational and as a research tool. Herein we present recent advancements introduced to SimSphere. We have comprehensively tested and updated the model code and added new functionalities which are illustrated herein using a variety of case studies. For example, it presents herein the new functionality that allows it to be applied over complex/heterogeneous landscapes, and this new model capability is demonstrated in experimental settings in various European ecosystems. The present study contributes towards efforts ongoing nowadays by the users' community of the model and is also very timely, given the increasing interest in SimSphere particularly towards the development of EO-based operational products characterising the Earth’s water cycle.  The research presented herein has been conducted in the framework of the project LISTEN-EO (DeveLoping new awareness and Innovative toolS to support efficient waTer rEsources man-agement Exploiting geoinformatiOn technologies), funded by the Hellenic Foundation for Research and Innovation programme (ID 015898). 

Keywords: SVAT, SimSphere, Earth Observation, land surface interactions, LISTEN-EO

How to cite: Lekka, C., Petropoulos, G. P., Anagnostopoulos, V., Detsikas, S. E., Katsafados, P., and Karympalis, E.: A Software Toolkit for Advancing our Understanding of Land Surface Interactions: Recent developments to the SimSphere SVAT model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1445, https://doi.org/10.5194/egusphere-egu24-1445, 2024.

Tropical Cyclones (TCs) are synoptic-scale storm systems rapidly rotating around a center of low atmospheric pressure which primarily derive their energy from exchanges of heat and moisture between the air and sea. These cyclones are among the most impactful geophysical phenomena, inflicting substantial economic damages and numerous fatalities. Key hazards associated with TCs include intense winds, extreme rainfall, and storm surges, which frequently result in extensive coastal flooding. Because of the severe consequences of their impacts, precise monitoring of these events and effective preparation for their occurrences are crucial to ensure the safety and resilience of populations and infrastructure.

For successful monitoring and preparation, the access to relevant factors associated with TC forecasts, such as risk projections and impact variables, must be adequate and user-friendly, enabling users to rapidly locate and comprehend the information they seek. To achieve this objective, visual tools and dashboards that concentrate interdisciplinary information and data from diverse sources serve as powerful summarization methods. Summary dashboards and tools facilitate easy access to information for all users ranging from experts and policymakers to common citizens. They consist of a platform offering a comprehensive overview of the situation, supporting informed decision-making. Current open-source tools for consulting TC data have limitations. They tend to be highly specialized, offering a limited selection of maps or graphs that cover only a portion of TC-related information. They also often lack interactivity, which restricts the user experience and the search for specific information. Furthermore, these tools can be complex to use due to inadequate documentation or challenges in presenting multiple pieces of information concurrently.

In this work, we introduce a novel free open-source dashboard designed to surpass the limitations of existing tools, displaying a comprehensive set of information regarding TC hazards. TropiDash presents several strengths that enhance user experience and accessibility. Developed in the widely recognized Jupyter Notebook programming environment, it is easily accessible either through the installation guide on its GitHub repository or by initiating its Binder environment. The dashboard features a user-friendly interface utilizing Python widgets and the Voilà protocol. It aggregates data from various sources spanning multiple domains: from cyclone properties, such as track forecasts and strike probability maps, to atmospheric variable fields (wind speed and direction, temperature, precipitation), to risk and vulnerability information, such as cyclone risk, coastal flood risk, population density. All this is made available while providing the user with a wide range of interactivity, from choosing the cyclone to selecting the variables of their interest to roam over the interactive maps.

The first version of TropiDash was realized in the context of Code for Earth 2023,  a program for the development of open-source software organized by the European Centre for Medium-Range Weather Forecasts. Here we present an improved and optimized version. 

How to cite: Paredes-Fortuny, L., Dainelli, F., and Colombo, P.: TropiDash: a comprehensive open-source dashboard for Tropical Cyclone data visualization and analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5658, https://doi.org/10.5194/egusphere-egu24-5658, 2024.

How to cite: Schilperoort, B., Donnelly, C., Liu, Y., and Koren, G.: Xarray-regrid: regridding with ease, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8146, https://doi.org/10.5194/egusphere-egu24-8146, 2024.

• A clear and compact syntax for specification of processing pipelines
• ...but also syntactical backward compatibility and interoperability, through additional support for PROJ's older and more verbose syntax
• Extensibility through straightforward, tight integration betwwen system supplied and user written operators
• ..but also support for loose integration with arbitrary ancillary software, through support of plain text operator definitions and grid files
• ...and ad-hoc abstractions through support for run-time defined user macros
• Seamless interoperability with arbitrarily complex application program data structures, i.e. integrating with the user program, rather than forcing the use of library provided data structures, and
• Support of roundtrip consistency checks

How to cite: Knudsen, T.: Generic geodesy in the browser? Recent developments in Rust Geodesy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12024, https://doi.org/10.5194/egusphere-egu24-12024, 2024.

For many areas across the globe physically-based hydrological models have a fundamental role helping devise a comprehensive and robust plan for future climate change adaption and preparedness informing water management and flood initiatives. Now that the advances in satellite and sensor technology coupled with the development of cloud computing have enable the advancement of hydrology as a data-intensive science, there is a considerable impetus and interest in future research and approaches in the use of these emerging technologies to develop new insights that contribute to fundamental aspects of the hydrological sciences. Whilst increasing volumes of Earth Observation (EO) data couple with advances in cloud computing have enable the enhancement of hydrological modelling, one of the remaining challenges is ensuring a seamless data pipeline to the final hydrological prediction. As a result, this poses a significant set of questions in the use of EO data for hydrology. The current research is situated at the junction of three areas: hydrological physical modelling, satellite EO data and the implementation of the Earth Observation Data Cube (EODC) paradigma. This presentation will outline the development and use of a open source modelling workflow integrating analysis ready data (ARD) through the implementation of the Open Data Cube (ODC) data exploitation architecture with a physically-based, spatially-distributed hydrological model (SHETRAN), as glimpse into the relevance of EO data cube solutions in lowering the technology and EO data barriers. Thus, enabling users to harnes existent open source EO datasets and software at minimum cost and effort with the objective to enable a more open and reproducible hydrological science.

How to cite: Patino Velasquez, L. F., Lewis, Dr. E., Mills, P. J., and Birkinshaw, Dr. S.: Quantifying water security using hyperresolution hydrological modelling on top of an Open Data Cube (ODC), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12786, https://doi.org/10.5194/egusphere-egu24-12786, 2024.

When developing large-scale numerical earth system models traditionally knowledge of a broad range of programming technologies is required to support hardware from laptops up to supercomputers. A knowledge that scientists specialized in a particular geosciences domain mostly do not have and often do not want to acquire. Their emphasis is on describing and implementing the processes rather than for instance dealing with parallelization of model equations. Moreover, when model characteristics or domain extents change their chosen parallelisation technique may already be obsolete or require significant refactoring to adapt to the new situation. We develop the open-source LUE modelling framework, a software environment allowing domain scientists – who may not be familiar with the development of high-performance applications – to develop numerical simulation models that seamlessly scale when adding additional hardware resources. LUE comprises of a data model for the storage of field-based and agent-based data, and provides a broad range of map algebra operations as building blocks for model construction. Each spatial operation is implemented in C++ using HPX, a library and runtime environment providing asynchronous execution of interdependent tasks on both shared-memory and distributed computing systems. LUE provides a Python module and therefore a single high-level API whether models are run on laptops or HPC systems. In our presentation we demonstrate two capabilities of LUE. First, using the built-in operations we implemented a spatially distributed hydrological model including surface water routing. The model runs for the African continent at 100 metres spatial and hourly temporal resolution. Secondly, to demonstrate the extensibility we utilise LUE’s focal operation framework to implement an individual kernel calculating greenness visibility exposure. Our PICO presentation will also include future extensions of the framework in particular for agent-based modelling and integration of machine learning model components.

How to cite: Karssenberg, D., Schmitz, O., and de Jong, K.: The LUE open source software for building numerical simulation models on HPC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15972, https://doi.org/10.5194/egusphere-egu24-15972, 2024.

In the rapidly advancing domain of environmental research, the deployment of a comprehensive, state-of-the-art Research Data Management (RDM) framework is increasingly pivotal.  Such a framework is key to ensure FAIR data, laying the groundwork for transparent and reproducible earth system sciences.

Today, datasets associated with research articles are commonly published via prominent data repositories like Pangaea or Zenodo. Conversely, data used in actual day-to-day research and inter-institutional projects tends to be shared through basic cloud storage solutions or, even worse, via email. This practice, however, often conflicts with the FAIR principles, as much of this data ends up in private, restricted systems and local storage, limiting its broader accessibility and use.

In response to this challenge, our research project Cat4KIT aims to establish a cross-institutional catalog and Research Data Management framework. The Cat4KIT framework is, hence, an important building block towards the FAIRification of environmental data. It not only streamlines the process of ensuring availability and accessibility of large-scale environmental datasets but also significantly enhances their value for interdisciplinary research and informed decision-making in environmental policy.

The Cat4KIT system comprises four essential elements: data service provision, meta(data) harvesting, catalogue service, and user-friendly data presentation. The data service provision module is tailored to facilitate access to data within typical storage systems by using well-defined and standardized community interfaces via tools like the Thredds data server, Intake Catalogues, and the OGC SensorThings API. By this, we ensure seamless data retrieval and management for typical use-casers in environmental sciences.

(Meta)data harvesting via our so-called DS2STAC-package entails collecting metadata from various data services, followed by creating STAC-metadata and integrating it into our STAC-API-based catalog service.

This catalog service module synergizes diverse datasets into a cohesive, searchable spatial catalog, enhancing data discoverability and utility via our Cat4KIT UI.

Finally, our framework's data portal is tailored to elevate data accessibility and comprehensibility for a wide audience, including researchers, enabling them to efficiently search, filter, and navigate through data from decentralized research data infrastructures.

One notable characteristic of Cat4KIT is its dependence on open-source solutions and strict adherence to community standards. This guarantees not just the framework's ability to function well with current data systems but also its simple adaption and expansion to meet future needs. Our presentation demonstrates the technical structure of Cat4KIT, examining the development and integration of each module to adhere to the FAIR principles. Additionally, it showcases examples to illustrate the practical use of the framework in real-life situations, emphasizing its efficacy in enhancing data management practices within KIT and its potential relevance in other research organizations.

How to cite: Hadizadeh, M., Lorenz, C., Barthlott, S., Fösig, R., Loewe, K., Rebmann, C., Ertl, B., Ulrich, R., and Bach, F.: FAIR Environmental Data through a STAC-Driven Inter-Institutional Data Catalog Infrastructure – Status quo of the Cat4KIT-project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19155, https://doi.org/10.5194/egusphere-egu24-19155, 2024.

The Norwegian Water Resources and Energy Directorate (NVE) is tasked with management of water- and energy resources in Norway, as well as reducing the risk of damages associated with landslides and flooding. Copernicus satellite data can provide valuable insight for those tasks.

The vast amount of Copernicus data however requires scalable and robust solutions for processing. Standardized and modular workflows help safeguarding maintainability and efficiency of service delivery. In order to implement operational Copernicus services at NVE, the Open Source OSGeo Community project actinia was introduced together with the Open Source Apache Airflow software as a platform for delivering operational Copernicus services at national scale.

actinia (https://actinia-org.github.io/) is a REST API for scalable, distributed, and high performance processing of time series of satellite images, as well as geographical raster and vector data. It is a modular system that uses mainly GRASS GIS for computational tasks.

Apache Airflow (https://airflow.apache.org/) is an orchestration solution that allows to programmatically author, schedule and monitor workflows.

In the presentation, we will illustrate how Apache Airflow and actinia work together and present selected examples of current and future applications operationalized on the platform. Those applications cover currently:

• Avalanches
• Flooding
• snow cover
• lake ice

More services related to NVE`s area of responsibility are being investigated, like landslides, slush flows, glacier lake outburst floods, and specific land cover changes...

Finally, we discuss challenges and opportunities of using Open Source Software tools and collaborative science approaches at NVE in national, operational services.

How to cite: Blumentrath, S., Are Antonsen, Y., Widforss, A., Fossli Gjersø, N., Verpe Engeset, R., and Havstad Winsvold, S.: Implementing National Copernicus services for hydrology and natural hazard monitoring at NVE using Open Source tools Apache Airflow and actinia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19421, https://doi.org/10.5194/egusphere-egu24-19421, 2024.

How to cite: Belayneh, T.: Democratizing BIM Data Access in Digital Twins Through OGC I3S 3D Streaming Standard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20842, https://doi.org/10.5194/egusphere-egu24-20842, 2024.

Modern Earth sciences produce a continuous increasing amount of data. These data consist of the measurements/observations and descriptive information (metadata) and include semantic classifications (semantics). Depending on the geoscientific parameter, metadata are stored in a variety of different databases, standards and semantics, which is obstructive for interoperability in terms of limited data access and exchange, searchability and comparability. Examples of common data types with very different structure and metadata needs are maps, geochemical data derived from field samples, or time series data measured with a sensor at a point, such as precipitation or soil moisture.

So far, there is a large gap between the capabilities of databases to capture metadata and their practical use. ALAMEDA is designed as modular structured metadata management platform for curation, compilation, administration, visualization, storage and sharing of meta information of lab-, field- and modelling datasets. As a pilot application for stable isotope and soil moisture data ALAMEDA will enable to search, access and compare meta information across organization-, system- and domain boundaries.

ALAMEDA covers 5 major categories: observation & measurements, sample & data history, sensor & devices, methods & processing, environmental characteristics (spatio & temporal). These categories are hierarchically structured, interlinkable and filled with specific metadata attributes (e.g. name, data, location, methods for sample preparation, measuring and data processing, etc.). For the pilot, all meta information will be provided by existing and wellestablished data management tools (e.g. mDIS, SMS, LI2, etc.).

In ALAMEDA, all information is brought together and will be available via web interfaces. Furthermore, the project focuses on features such as metadata curation with intuitive graphical user interfaces, the adoption of well-established standards, the use of domain-controlled vocabularies and the provision of interfaces for a standards-based dissemination of aggregated information. Finally, ALAMEDA should be integrated into the DataHub (Hub-Terra).

Currently the project is in the final phase and we want to present the developed concepts and software and lessions learned.

How to cite: Mühlbauer, F., Hammitzsch, M., Hanisch, M., Pruß, G., Häner, R., and Rach, O.: ALAMEDA – A scalable multi-domain metadata management platform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20137, https://doi.org/10.5194/egusphere-egu24-20137, 2024.