research-article

An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems

Authors:

Özgür Ozan Şen,

Marco Hannemann,

Martin Schrön,

Thomas Kalbacher,

Olaf KolditzAuthors Info & Claims

Volume 103, Issue C

Pages 192 - 200

https://doi.org/10.1016/j.cag.2022.02.009

Published: 01 April 2022 Publication History

Abstract

We propose a framework for the 3D exploration of heterogeneous environmental data within a geographical region of interest. Based on Unity, the system can be built for a variety of platforms and works both on regular machines as well as in virtual reality environments, where it can be considered a basis for a virtual geographic environment. Focussing on the catchment of the Müglitz River in south-eastern Germany, a large collection of observation data acquired via a wide range of measurement devices has been integrated in a geographical reference frame for the region. Results of area-wide numerical simulations for both groundwater and soil moisture have been added to the scene and allow for the exploration of the delayed consequences of transient phenomena such as heavy rainfall events and their impact on the catchment scale. This study focusses on the concurrent visualisation and synchronised animation of multiple area wide datasets from different environmental compartments. The resulting application allows to explore the region of interest during specific hydrological events for an assessment of the interrelation of processes. As such, it offers the opportunity for knowledge transfer between researchers of different domains as well as for outreach to an interested public.

Graphical abstract

Display Omitted

Highlights

•

Visualisation of a large heterogeneous data collection in a unified geographical context

•

Concurrent visualisation of time-dependent observation and simulation data

•

Visual demonstration of multi-compartment analysis from basic input data

•

Interactive framework for outreach activities and stakeholder information

•

General framework for creating similar applications for other regions of interest.

References

[1]

Fischer E.M., Knutti R., Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes, Nature Clim Change 5 (2015) 560–564,.

[2]

Bastos A., Fu Z., Ciais P., et al., Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018, Phil Trans Biol Sci 375 (2020),.

[3]

Herring S.C., Christidis N., Hoell A., et al., Explaining extreme events of 2018 from a climate perspective, Bull Am Meteorol Soc 101 (2020) S1–S140,.

[4]

Buras A., Rammig A., Zang C.S., Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003, Biogeosciences 17 (2020) 1655–1672,.

[5]

Arnell N.W., Gosling S.N., The impacts of climate change on river flood risk at the global scale, Clim Change 134 (2016) 387–401,.

[6]

Bronstert A., Agarwal A., Boessenkool B., et al., Forensic hydro-meteorological analysis of an extreme flash flood: The 2016-05-29 event in Braunsbach, SW Germany, Sci Total Environ 630 (2018) 977–991,.

[7]

Haerdle B., Hufe S., MOSES – Zur Rechten Zeit am rechten Ort (MOSES – in the right place at the right time), UFZ Umweltperspektiven Dec 2019 (2019) 4–13,.

[8]

Weber U., Attinger S., Baschek B., et al., MOSES: A Novel observation system to monitor dynamic events across earth compartments, Bull Am Meteorol Soc (2022),. (Accepted).

[9]

Schröter K., Kunz M., Elmer F., et al., What made the June 2013 flood in Germany an exceptional event? A hydro-meteorological evaluation, Hydrol Earth Syst Sci 19 (2015) 309–327,.

[10]

Unity Technologies, Unity (version 2020.1), 2020, https://unity3d.com/.

[11]

Lin H., Chen M., Lu G., et al., Virtual geographic environments (VGEs): A new generation of geographic analysis tool, Earth Sci Rev 126 (2013) 74–84,.

[12]

Lü G., Yu Z., Zhou L., et al., Data environment construction for virtual geographic environment, Environ Earth Sci 74 (2015) 7003–7013,.

[13]

Gu S., Fang C., Wang Y., Virtual geographic environment for WATLAC hydrological model integration, in: Proc of 25th international conference on geoinformatics, IEEE Computer Society, 2017,.

[14]

Rink K., Nixdorf E., Zhou C., Hillmann M., Bilke L., A virtual geographic environment for multi-compartment water and solute dynamics in large catchments, J Hydrol 582 (2020),. art. 124507.

[15]

Ellis S.R., What are virtual environments?, IEEE Comput Graph Appl 14 (1994) 17–22,.

Digital Library

[16]

Batty M., Virtual geography, Futures 29 (1997) 337–352,.

[17]

Batty M., Virtual reality in geographic information systems, in: Wilson J.P., Fotheringham A.S. (Eds.), The handbook of geographic information science, Blackwell Publishing, Oxford, UK, 2008, pp. 317–334.

[18]

Yin L., Integrating 3D visualization and GIS in planning education, J Geogr High Educ 34 (3) (2010) 419–438.

[19]

Paris S., Mekni M., Moulin B., Informed virtual geographic environments: An accurate topological approach, in: Proc of int conf on advanced geographic information systems & web services, IEEE, 2009,.

Digital Library

[20]

Lin H, Zhu J, Xu B, Lin W, Hu Y. A virtual geographic environment for a simulation of air pollution dispersion in the pearl River Delta (PRD) region. In: Lee J, Zlatanova S, editors. 3D geo-information sciences. Springer Berlin Heidelberg; ISBN: 978-3-540-87395-2, 2009, p. 3–13. https://doi.org/10.1007/978-3-540-87395-2_1.

[21]

Rink K., Chen C., Bilke L., Liao Z., Rinke K., Frassl M., et al., Virtual geographic environments for water pollution control, Int J Dig Earth 11 (4) (2018) 397–407,.

[22]

Ding Y., Zhu Q., Lin H., An integrated virtual geographic environmental simulation framework: a case study of flood disaster simulation, Geo Spatial Inform Sci 17 (2014) 190–200,.

[23]

Helbig C., Bilke L., Bauer H.-S., et al., MEVA - An interactive visualization application for validation of multifaceted meteorological data with multiple 3D devices, PLoS ONE 10 (4) (2015),.

[24]

Staatsbetrieb Geobasisinformation und Vermessung Sachsen, Digitales Geländemodell 2, 2015, https://www.geodaten.sachsen.de/digitale-hoehenmodelle-3994.html Metadaten-ID:a3dba5b2-0118-4d76-ab78-ba656a1b489e.

[25]

United States Geological Survey, Shuttle Radar Topography Mission 1 Arc-Second Global, 2018,.

[26]

Bundesamt für Kartographie und Geodäsie, GeoBasis-DE, 2021, (downloaded via Google Earth).

[27]

Wetterdienst Deutscher, RADOLAN (Radar-Online-Aneichung), 2019, https://www.dwd.de/DE/leistungen/radolan/radolan.html.

[28]

Schwarze R., Hauffe C., Baldy A., et al., Klimawandel und Wasserhaushalt in Sachsen, in: Schriftenreihe des LfULG, (ISSN ) vol. 32/2014, Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie, 2014.

[29]

Desilets D., Overview of the cosmic ray technique including measurement principles and calculation, in: Cosmic ray neutron sensing: use, calibration, and validation for soil moisture estimation, International Atomic Energy Agency, ISBN 978-92-0-101017-9, 2017, IAEA-TECDOC-1809.

[30]

Baatz R., Bogena H.R., Hendricks Franssen H.-J., et al., An empirical vegetation correction for soil water content quantification using cosmic-ray probes, Water Resour Res 51 (2015) 2030–2046,.

[31]

Schrön M., Rosolem R., Köhli, et al., Cosmic-ray neutron rover surveys of field soil moisture and the influence of roads, Water Resour Res 54 (2018) 6441–6459,.

[32]

Cho Y., Sudduth K.A., Chung S.-O., Soil physical property estimation from soil strength and apparent electrical conductivity sensor data, Biosyst Eng 152 (2016) 68–78,.

[33]

Martini E., Werban U., Zacharias S., et al., Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: Validation with data from a wireless soil moisture monitoring network, Hydrol Earth Syst Sci 21 (2017) 495–513,.

[34]

Rentschler T., Werban U., Ahner M., et al., 3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning, Vadose Zone J 19 (e20062) (2020),.

[35]

Cook S.E., Corner R.J., Groves P.R., Grealish G.J., Use of airborne gamma radiometric data for soil mapping, Aust J Soil Res 34 (1996) 183–194.

[36]

Dierke C., Werban U., Relationships between gamma-ray data and soil properties at an agricultural test site, Geoderma 199 (2013) 90–98,.

[37]

Kolditz O., Bauer S., Bilke L., et al., OpenGeoSys: An open source initiative for numerical simulation of thermo-hydro-mechanical/chemical (THM/C) processes in porous media, Environ Earth Sci 67 (2) (2012) 589–599,.

[38]

Samaniego L., Kumar R., Attinger S., Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour Res 46 (5) (2010),.

[39]

Kumar R., Samaniego L., Attinger S., Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour Res 49 (1) (2013),.

[40]

Sächsisches Staatsministerium für Energie, Klimaschutz, Umwelt und Landwirtschaft, Bodenkarte 1:50.000 (BK50), 2020, https://www.boden.sachsen.de/digitale-bodenkarte-1-50-000-19474.html Metadaten-ID:b826fa66-e20d-40b2-868b-234cbe5333dc.

[41]

Rink K., Bilke L., Kolditz O., Visualisation strategies for environmental modelling data, Environ Earth Sci 72 (10) (2014) 3857–3868,.

[42]

Ahrens J., Geveci B., Law C., ParaView: AN end-user tool for large data visualization, in: Hansen C.D., Johnson C.R. (Eds.), The visualization handbook, Elsevier, 2005.

[43]

Schroeder W., Martin K., Lorensen B., Visualization toolkit: an object-oriented approach to 3d graphics (4th edition), Kitware, Inc., 2006.

[44]

Bhatia S., Cozzi P., Knyazev A., Parisi T., glTF 2.0 specification, Khronos Group, 2017, URL https://www.khronos.org/gltf/.

[45]

Kern M., Hewson T., Schäfler, et al., Interactive 3D visual analysis of atmospheric fronts, IEEE Trans Visual Comput Graph 25 (2018) 1080–1090,.

Digital Library

[46]

Koutek M., van der Neut I., Web-based 3D meteo visualization: 3D rendering farms from a new perspective, in: Proc of workshop on visualisation in environmental sciences, The Eurographics Association, ISBN 978-3-03868-063-5, 2018,.

[47]

QGIS Development Team, QGIS Geographic information system, Open Source Geospatial Foundation, 2009, http://qgis.osgeo.org.

Cited By

Jorge J(2022)Editorial NoteComputers and Graphics10.1016/j.cag.2022.03.006103:C(A1-A2)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.cag.2022.03.006

Index Terms

An environmental exploration system for visual scenario analysis of regional hydro-meteorological systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Visualizing metadata for environmental datasets
DCMI '10: Proceedings of the 2010 International Conference on Dublin Core and Metadata Applications

Data growth in the environmental sciences has resulted in multidimensional datasets that are heterogeneous and extensive. Scientific academic research includes scalar, sensor, or vector data, which may be publically available. The datasets generated ...
A three-dimensional hydro-geochemical model to assess lake acidification risk

Biogeochemical and hydrological fluxes from riparian zones to lake environments can be significant, particularly for shallow systems experiencing large variations in water level, yet they are not considered in water quality models. To address this ...
Visual data mining of genomic databases by immersive graph-based exploration
GRAPHITE '05: Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia

Biologists are leading current research on genome characterization (sequencing, alignment, transcription), providing a huge quantity of raw data about many genome organisms. Extracting knowledge from this raw data is an important process for biologists, ...

Comments

Information & Contributors

Information

Published In

cover image Computers and Graphics

Computers and Graphics Volume 103, Issue C

Apr 2022

234 pages

ISSN:0097-8493

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jorge J(2022)Editorial NoteComputers and Graphics10.1016/j.cag.2022.03.006103:C(A1-A2)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.cag.2022.03.006

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents