Publications

Books

  • Bouwer, L.M., Dransch, D., Ruhnke, R., Rechid, D., Frickenhaus, S., & Greinert, J. (eds.) (in press). Integrating Data Science and Earth System Science: Challenges and Solutions. (Briefs in Earth System Science, Springer, Cham)

Peer-Reviewed Papers

2022

  • Nixdorf, E. Eggert, Morstein, P. Kalbacher, T. & Dransch, D. (2022): Tocap: a web tool for ad-hoc campaign planning in terrestrial hydrology. J. Hydroinformatics. jh2022057. doi: https://doi.org/10.2166/hydro.2022.057
  • Bouwer, L.M., Dransch, D., Ruhnke, R., Rechid, D., Frickenhaus, S., & Greinert, J. (eds.) (in press). Integrating Data Science and Earth System Science: Challenges and Solutions. (Briefs in Earth System Science, Springer, Cham)
  • Koedel, U., Schuetze, C., Fischer, F.P., Bussmann, I., Sauer, P.K., Nixdorf, E., Kalbacher, T., Wiechert, V., Rechid, D., Bouwer, L.M. & Dietrich, P. (2022): Challenges in the evaluation of observational data trustworthiness from a data producers viewpoint (FAIR+). Front. Environ. Sci. 9. https://doi.org/10.3389/fenvs.2021.772666

2021

  • Fischer, P., Dietrich, P., Achterberg, E.P., Anselm, N., Brix, H., Bussmann, I., Eickelmann, L., Flöser, G., Friedrich, M., Rust, H., Schütze, C. & Koedel, U. (2021): Effects of measuring devices and sampling strategies on the interpretation of monitoring data for long-term trend analysis. Front. Mar. Sci. 8., https://doi.org/10.3389/fmars.2021.770977
  • Marien, L., Valizadeh, M., zu Castell, W., Nam, C., Rechid, D., Schneider, A., Meisinger, C., Linseisen, J., Wolf, K., & Bouwer, L.: Machine learning models to predict myocardial infarctions from past climatic and environmental conditions, Nat. Hazards Earth Syst. Sci. Discuss. (preprint), https://doi.org/10.5194/nhess-2021-389, in review, 2022.
  • Vlasenko, A., Matthias, V. & Callies, U. (2021): Simulation of Chemical Transport Model Estimates by means of Neural Network using Meteorological Data. Atmospheric Environment, 118236. ISSN 1352-2310. https://doi.org/10.1016/j.atmosenv.2021.118236
  • González, E., Purkiani, K., Buck, F. Stäbler, F. & Greinert J. (2021): Spatiotemporal visualisation of a deep sea sediment plume dispersion experiment. Workshop on Visualisation in Environmental Sciences (EnvirVis) (2021) S. Dutta and K. Feige and K. Rink and D. Zeckzer (Editors). https://doi.org/10.2312/envirvis.20211082
  • Buck V., Stäbler F., González E.  & Greinert J. (2021): Digital Earth Viewer: a 4D visualisation platform for geoscience datasets. Workshop on Visualisation in Environmental Sciences (EnvirVis) (2021) S. Dutta and K. Feige and K. Rink and D. Zeckzer (Editors). https://doi.org/10.2312/envirvis.20211081
  • Tanhua, T., Lauvset, S.K., Lange, N. et al. A vision for FAIR ocean data products. Commun Earth Environ 2, 136 (2021). https://doi.org/10.1038/s43247-021-00209-4
  • Haroon, A., Micallef, A., Jegen, M., Schwalenberg, K., Karstens, J., Berndt, C., et al. (2021). Electrical resistivity anomalies offshore a carbonate coastline: Evidence for freshened groundwater? Geophysical Research Letters, 48. e2020GL091909. https://doi.org/10.1029/2020GL091909
  • Graf, M., Hachem, A. E., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H. & Bárdossy, A. (2021). Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, Volume 37, 100883, ISSN 2214-5818. https://doi.org/10.1016/j.ejrh.2021.100883.
  • Sieck, K., Nam, C., Bouwer, L. M., Rechid, D., & Jacob, D. (2021): Weather extremes over Europe under 1.5 and 2.0 °C global warming from HAPPI regional climate ensemble simulations, Earth Syst. Dynam., 12, 457–468. https://doi.org/10.5194/esd-12-457-2021
  • Dreier, N., Nehlsen, E., Fröhle, P., Rechid, D., Bouwer, L.M. & Pfeifer, S. (2021). Future changes in wave conditions at the German Baltic Sea coast based on a hybrid approach using an ensemble of regional climate change projections. Water, 13(2), 167. https://doi.org/10.3390/w13020167

2020

  • Tarasova, L., Basso, S., Wendi, D., Viglione, A., Kumar, R., & Merz, R. (2020): A process‐based framework to characterize and classify runoff events: The event typology of Germany. Water Resources Research, 56, e2019WR026951. https://doi.org/10.1029/2019WR026951
  • Graf, M., Chwala, C., Polz, J., & Kunstmann, H. (2020): Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data. Hydrology and Earth System Sciences, 24, 2931–2950. https://doi.org/10.5194/hess-24-2931-2020
  • Polz, J., Chwala, C., Graf, M., & Kunstmann, H. (2020): Rain event detection in commercial microwave link attenuation data using convolutional neural networks. Atmospheric Measurement Techniques, 13, 3835–3853. https://doi.org/10.5194/amt-13-3835-2020
  • Haroon, A, Swidinsky, A., Hölz, S., Jegen, M. & Tezkan, B. (2020): Step-on versus step-off signals in time-domain controlled source electromagnetic methods using a grounded electric dipole. Geophysical Prospecting, 0016-8025. https://doi.org/10.1111/1365-2478.13016
  • Micallef, A., Person, M., Berndt, C., Bertoni, C., Cohen, D., Dugan, B., Evans, R., Haroon, A., Hensen, C., Jegen, M., Key, K., Kooi, H., Liebetrau, V., Lofi, J., Mailloux, B.J., Martin‐Nagle, R., Michael, H.A., Mueller, T., Schmidt, M., Schwalenberg, K., Trembath‐Reichert, E., Weymer, B., Zhang, Y. & Thomas, A.T. (2020): Offshore freshened groundwater in continental margins, Reviews of Geophysics. https://doi.org/10.1029/2020RG000706
  • Graf, M., Chwala, C., Polz, J. & Kunstmann, H. (2020): Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020

2019



Other Publications

2021

  • Nam, C., Tiedje, B., Pfeifer, S., Rechid, D., & Eggert, D.: Climate Change Explorer: Extracting localized data for developing Climate Services, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12336, [https://doi.org/10.5194/egusphere-egu21-12336, 2021](https://doi.org/10.5194/egusphere-egu21-12336, 2021)
  • Marien, L., Valizadeh, M., zu Castell, W., Schneider, A., Wolf, K., Rechid, D., & Bouwer, L. (2021): Using Machine Learning to investigate Heat Waves and Myocardial Infarctions in Augsburg, Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9677, [https://doi.org/10.5194/egusphere-egu21-9677, 2021.](https://doi.org/10.5194/egusphere-egu21-9677, 2021.)
  • Schröter, K., Steinhausen, M., Brill, F., Lüdtke, S., Eggert, D., Merz, B., & Kreibich, H. (2021): Multi-source flood mapping for rapid impact assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6483, https://meetingorganizer.copernicus.org/EGU21/EGU21-6483.html.
  • Buck, V., Stäbler, F., Gonzalez, E., & Greinert, J.(2021): 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.
  • Nixdorf, E., Eggert, D., Morstein, P., Kalbacher, T., & Dransch, D. (2021): A web-based visual-analytics tool for ad-hoc campaign planning in terrestrial hydrology, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3951, https://doi.org/10.5194/egusphere-egu21-3951.
  • Sommer, P. S., Wichert, V., Eggert, D., Dinter, T., Getzlaff, K., Lehmann, A., Werner, C., Silva, B., Schmidt, L., & Schäfer, A. (2021): A new distributed data analysis framework for better scientific collaborations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1614, https://doi.org/10.5194/egusphere-egu21-1614.
  • Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., & Bárdossy, A. (2021): Combined rainfall estimates from personal weather station and commercial microwave link data in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12445, https://doi.org/10.5194/egusphere-egu21-12445.
  • Chwala, C., Graf, M., Polz, J., Rothermel, S., Glawion, L., Winterrath, T., Smiatek, G., & Kunstmann, H. (2021): Recent improvements of CML rainfall estimation and CML-Radar combination in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15207, https://doi.org/10.5194/egusphere-egu21-15207.
  • Polz, J., Schmidt, L., Glawion, L., Graf, M., Werner, C., Chwala, C., Mollenhauer, H., Rebmann, C., Kunstmann, H. & Bumberger, J. (2021): Supervised and unsupervised machine-learning for automated quality control of environmental sensor data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14485, https://doi.org/10.5194/egusphere-egu21-14485.
  • Eisele, M., Graf, M., El Hachem, A., Seidel, J., Chwala, C., Kunstmann, H. & Bárdossy, A. (2021): Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales – Geostatistical interpolation framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12415, https://doi.org/10.5194/egusphere-egu21-12415.
  • Scharun, C., Ruhnke, R., Weimer, M. & Braesicke, P. (2021): 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

2020

  • Scharun, C., Ruhnke, R., Schröter, J., Weimer, M., & Braesicke, P. (2020): Modeling methane from the North Sea region with ICON-ART, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-5028. https://meetingorganizer.copernicus.org/EGU2020/EGU2020-5028.html?pdf
  • Schäfer, A., Anselm, N., Eilers, J., Frickenhaus, S., Gerchow, P., Glöckner, F.O., Haas, A., Herrarte, I., Koppe, Macario, A., Schäfer-Neth, C., Silva, B., & Fischer, P. (2020): Implementing FAIR in a Collaborative Data Management Framework, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-19631, doi: https://doi.org/10.5194/egusphere-egu2020-19631
  • Fischer, P., Friedrich, M., Brand, M., Ködel, U., Dietrich, P., Brix, H., Kerschke, D. & Bussmann, I. (2020): The challenge of sensor selection, long term-sensor operation and data evaluation in inter- -institutional long term monitoring projects - lessons learned in the MOSES project, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-21816, doi:https://doi.org/10.5194/egusphere-egu2020-21816
  • Eggert, D. & Dransch, D. (2020): An integrative framework for data-driven investigation of environmental Systems, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-9251, https://doi.org/10.5194/egusphere-egu2020-9251
  • Hassler, S.K., Dietrich, P., Kiese, R., Mauder, M., Meyer, J., Rebmann, C., & Zehe, E. (2020): BRIDGET: a toolbox for the integration and scaling of diverse in-situ evapotranspiration measurements, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-17412, https://doi.org/10.5194/egusphere-egu2020-17412
  • Vlasenko, A., Matthias, V. & Callies, U. (2020): Estimation of NO2 and SO2 concentration changes in Europe from meteorological data with Neural Networks, EGU General Assembly, Vienna, 4 May 2020 - 8 May 2020, EGU 2020-1635, doi: https://doi.org/10.5194/egusphere-egu2020-1635
  • Henkel, D., González Ávalos, E., Kampmeier, M., Michaelis, P. & Greinert, J. (2020): Machine learning as supporting method for UXO mapping and detection. EGU2020-22594, [https://doi.org/10.5194/egusphere-egu2020-22594, EGU General Assembly 2020](https://doi.org/10.5194/egusphere-egu2020-22594, EGU General Assembly 2020)

2019

  • Schäfer-Neth, C., Haas, A., Fischer, P., Koppe, R., Gerchow, P. & Frickenhaus, S. (2019): Elbe river flood and draught scenarios - the MOSES and Digital Earth initiatives, 2nd International REKLIM conference, Berlin, 23 September 2019 - 25 September 2019. hdl:10013/epic.e3cfdcf0-86ee-4dd2-af1d-196cc4c06c41
  • Silva, B., Koppe, R., Haas, A., Schäfer-Neth, C., Fischer, P., Immoor, S., Gerchow, P., Fritzsch, B. & Frickenhaus, S. (2019): Automatic data quality control for understanding extreme climate event, 2nd International REKLIM Conference, Berlin, 23 September 2019 - 26 September 2019. hdl:10013/epic.c724a715-1330-4bca-9052-e3b01b24ed17
  • Petzold, A., et al. (2019): ENVRI-FAIR - Interoperable environmental FAIR data and services for society, innovation and research, 15th Proceedings IEEE International Conference on eScience, 1-4, doi: https://www.researchgate.net/publication/338958957...



Software Publications

Δ Flood Event Explorer

2022

  • Eggert, D.; Rabe, D.; Dransch, D.; Lüdtke, S.; Nam, C.; Nixdorf, E.; Wichert, V.; Abraham, N.; Schröter, K. & Merz, B. (2022): The Digital Earth Flood Event Explorer: A showcase for data analysis and exploration with scientific workflows. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.001
  • Eggert, D. & Nam, C. (2022): The Climate Change Workflow of the Flood Event Explorer: Analysis of climate-driven changes in flood-generating climate variables. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.002
  • Eggert, D. & Lüdtke, S. (2022): The Flood Similarity Workflow of the Flood Event Explorer: Identification, assessment and comparison of hydro-meteorological controls of flood events. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.003
  • Eggert, D. & Nixdorf, E. (2022): The Smart Monitoring Workflow (Tocap) of the Flood Event Explorer: Determining the most suitable time and location for event-driven, ad-hoc monitoring. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.004
  • Eggert, D. & Schröter, K. (2022): The Socio-Economic Flood Impacts Workflow of the Flood Event Explorer: Identification of relevant controls and useful indicators for the assessment of flood impacts. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.005
  • Rabe, D.; Eggert, D.; Wichert, V. & Abraham, N. (2022): The River Plume Workflow of the Flood Event Explorer: Detection and impact assessment of a river plume. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2022.006

Δ Backend Modules and Libraries

2022

  • Eggert, D. & Nam, C. (2022). Digital Earth Climate Change Backend Module: Exposing statistical analysis and plotting functions to determine absolute and relative changes in climate variables. Zenodo. https://doi.org/10.5281/zenodo.5833258
  • Nam, C., Eggert, D., & Pfeiffer, S. (2022). de-climate-change-analysis: statistical analysis and plotting of climate model data. Zenodo. https://doi.org/10.5281/zenodo.5833043
  • Lüdtke, S.; Eggert, D.; Wendi, D. & Schröter, K. (2022): floosdimilarity - a python module to compute the similarity between multiple flood events. GFZ Data Services. https://doi.org/10.5880/GFZ.4.4.2022.001
  • Nixdorf, E. & Eggert, D. (2022). Digital Earth Smart Monitoring Backend Module (Tocap): Exposing functions to acquire and process geo-data. Zenodo. https://doi.org/10.5281/zenodo.5824566

2021


Δ DASF

2021