ESSI2.6

The term SMART Monitoring was also defined by the project Digital Earth (DE) , a central joint project of eight Helmholtz centers in Earth and Environment. SMART Monitoring in the sense of DE means that measured environmental parameters and values need to be specific/scalable, measurable/modular, accepted/adaptive, relevant/robust, and trackable/transferable (SMART) for sustainable use as data and improved real data acquisition. SMART Monitoring can be defined as a reliable monitoring approach with machine-learning, and artificial intelligence (A.I.) supported procedures for an “as automated as possible” data flow from individual sensors to databases. SMART Monitoring Tools must include various standardized data flows within the entire data lifecycle, e.g., specific sensor solutions, novel approaches for sampling designs, and defined standardized metadata descriptions. One of the SMART Monitoring workflows' essential components is enhancing metadata with comprehensive information on data quality. On the other hand, SMART Monitoring must be highly modular and adaptive to apply to different monitoring approaches and disciplines in the sciences.

In SMART monitoring, data quality is crucial, not only with respect to data FAIRness. It is essential to ensure data reliability and representativeness. Hence, comprehensively documented data quality is essential and required to enable meaningful data selection for specific data blending, integration, and joint interpretation. Data integration from different sources represents a prerequisite for parameterization and validation of predictive tools or models. This data integration demonstrates the importance of implementing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) for sustainable data management (Wilkinson et al. 2016). So far, the principle of FAIRdata does not include a detailed description of data quality and does not cover content-related quality aspects. Even though data may be FAIR in terms of availability, it is not necessarily “good" in accuracy and precision. Unfortunately, there is still considerable confusion in science about the definition of good or trustworthy data.

An assessment of data quality and data origin is essential to preclude the possibility of inaccurate, incomplete, or even unsatisfactory data analysis applying, e.g., machine learning methods, and avoid poorly derived, misleading or incorrect conclusions. The terms trustworthiness and representativeness summarise all aspects related to these issues. The central pillars of trustworthiness/representativeness are validity, provenience/provenance, and reliability, which are fundamental features in assessing any data collection or processing step for transparent research. For all kinds of secondary data usage and analysis, a detailed description and assessment of reliability and validity involve an appraisal of applied data collection methods.

The presentation will give exemplary examples to show the importance of data trustworthiness and representativeness evaluation and description, allowing scientists to find appropriate tools and methods for FAIR data handling and more accurate data interpretation.

How to cite: Koedel, U., Dietrich, P., Fischer, P., and Schuetze, C.: Ensuring data trustworthiness within SMART Monitoring of environmental processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8361, https://doi.org/10.5194/egusphere-egu21-8361, 2021.

Numerous applications, including generating future predictions via numerical modelling, establishing appropriate policy instruments, and effectively tracking progress against them, require the multitude of complex processes and interactions operating in rapidly changing mountainous environmental systems to be well monitored and understood. At present, however, not only are environmental available data pertaining to mountains often severely limited, but interdisciplinary consensus regarding which variables should be considered absolute observation priorities remains lacking. In this context,  the concept of so-called Essential Mountain Climate Variables (EMCVs) is introduced as a potential means to identify critical observation priorities and thereby ameliorate the situation. Following a brief overview of the most critical aspects of ongoing and expected future climate-driven change in various key mountain system components (i.e. the atmosphere, cryosphere, biosphere and hydrosphere), a preliminary list of corresponding potential EMCVs – ranked according to perceived importance – is proposed. Interestingly, several of these variables do not currently feature amongst the globally relevant Essential Climate Variables (ECVs) curated by GCOS, suggesting this mountain-specific approach is indeed well justified. Thereafter, both established and emerging possibilities to measure, generate, and apply EMCVs are summarised. Finally, future activities that must be undertaken if the concept is eventually to be formalized and widely applied are recommended.

How to cite: Thornton, J., Palazzi, E., Pepin, N., Cristofanelli, P., Essery, R., Kotlarski, S., Giuliani, G., Guigoz, Y., Kulonen, A., Li, X., Pritchard, D., Fowler, H., Randin, C., Shahgedanova, M., Steinbacher, M., Zebisch, M., and Adler, C.: Towards a definition of Essential Mountain Climate Variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7747, https://doi.org/10.5194/egusphere-egu21-7747, 2021.

The study of the earth’s systems depends on a large amount of observations from homogeneous sources, which are usually scattered around time and space and are tightly intercorrelated to each other. The understanding of said systems depends on the ability to access diverse data types and contextualize them in a global setting suitable for their exploration. While the collection of environmental data has seen an enormous increase over the last couple of decades, the development of software solutions necessary to integrate observations across disciplines seems to be lagging behind. To deal with this issue, we developed the Digital Earth Viewer: a new program to access, combine, and display geospatial data from multiple sources over time.

Choosing a new approach, the software displays space in true 3D and treats time and time ranges as true dimensions. This allows users to navigate observations across spatio-temporal scales and combine data sources with each other as well as with meta-properties such as quality flags. In this way, the Digital Earth Viewer supports the generation of insight from data and the identification of observational gaps across compartments.

Developed as a hybrid application, it may be used both in-situ as a local installation to explore and contextualize new data, as well as in a hosted context to present curated data to a wider audience.

In this work, we present this software to the community, show its strengths and weaknesses, give insight into the development process and talk about extending and adapting the software to custom usecases.

How to cite: Buck, V., Stäbler, F., Gonzalez, E., and Greinert, J.: The Digital Earth Viewer: A new visualization approach for geospatial time series data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15623, https://doi.org/10.5194/egusphere-egu21-15623, 2021.

How to cite: Darroch, L., Thomas, G., Margaret, Y., Christopher, C., Emma, S., Elizabeth, B., Justin, B., Robert, J., Andrew, H., and Jennifer, B.: The use of ERDDAP in a self-monitoring and nowcast hazard alerting coastal flood system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14621, https://doi.org/10.5194/egusphere-egu21-14621, 2021.

How to cite: Boog, J. and Kalbacher, T.: Point to Space: Data-driven Soil Moisture Spatial Mapping using Machine-Learning for Small Catchments in Western Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16308, https://doi.org/10.5194/egusphere-egu21-16308, 2021.

Methane (CH₄) is the second most important greenhouse gas after CO₂ affecting global warming. Various sources (e.g. fossil fuel production, agriculture and waste, biomass burning and natural wetlands) and sinks (the reaction with the OH-radical as the main sink contributes to tropospheric ozone production) determine the methane budget. Due to its long lifetime in the atmosphere methane can be transported over long distances.

Disused and active offshore platforms can emit methane, the amount being difficult to quantify. In addition, explorations of the sea floor in the North Sea showed a release of methane near the boreholes of both, oil and gas producing platforms. The basis of this study is the established emission data base EDGAR (Emission Database for Global Atmospheric Research), an inventory that includes methane emission fluxes in the North Sea region. While methane emission fluxes in the EDGAR inventory and platform locations are matching for most of the oil platforms almost all of the gas platform sources are missing in the database. We develop a method for estimating the missing emission sources based on the EDGAR inventory and the known locations of gas platforms as additional point sources will be inserted in the model.

In this study the global model ICON-ART (ICOsahedral Nonhydrostatic model - Aerosols and Reactive Trace gases) is used. ART is an online-coupled model extension for ICON that includes chemical gases and aerosols. One aim of the model is the simulation of interactions between the trace substances and the state of the atmosphere by coupling the spatiotemporal evolution of tracers with atmospheric processes. ICON-ART sensitivity simulations are performed with inserted and adjusted sources to access their influence on the methane and OH-radical distribution on regional (North Sea) and global scales.

How to cite: Scharun, C., Ruhnke, R., Weimer, M., and Braesicke, P.: Modeling methane from the North Sea region with ICON-ART, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9290, https://doi.org/10.5194/egusphere-egu21-9290, 2021.