Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2783-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2783-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Apr 2024
Model description paper |
| 15 Apr 2024
| 15 Apr 2024
REHEATFUNQ (REgional HEAT-Flow Uncertainty and aNomaly Quantification) 2.0.1: a model for regional aggregate heat flow distributions and anomaly quantification
Malte Jörn Ziebarth
CORRESPONDING AUTHOR
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Institute of Geosciences, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Sebastian von Specht
Institute of Mathematics, University of Potsdam, Karl-Liebknecht-Str. 24–25, 14476 Potsdam, Germany
Related authors
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Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310, https://doi.org/10.5194/essd-16-295-2024, https://doi.org/10.5194/essd-16-295-2024, 2024
Short summary
Short summary
We present experimental data collected in 2015 at Äspö Hard Rock Laboratory. We created six cracks in a rock mass by injecting water into a borehole. The cracks were monitored using special sensors to study how the water affected the rock. The goal of the experiment was to figure out how to create a system for generating heat from the rock that is better than what has been done before. The data collected from this experiment are important for future research into generating energy from rocks.
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Ziebarth, M. J.: REHEATFUNQ: A Python package for the inference of regional aggregate heat flow distributions and heat flow anomalies, v. 1.4.0, GFZ Data Services [code], https://doi.org/10.5880/GFZ.2.6.2023.002, 2023. a, b, c, d
Ziebarth, M. J.: REHEATFUNQ: REgional HEAT-Flow Uncertainty and aNomaly Quantification, Python Package Version 2.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.10614892, 2024. a, b
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Short summary
Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This heat flow is not spatially uniform, and its exact pattern is hard to predict since it depends on crustal and mantle properties, both varying across scales. Our new model REHEATFUNQ addresses this difficulty by treating the fluctuations of heat flow within a region statistically. REHEATFUNQ estimates the regional distribution of heat flow and quantifies known structural signals therein.
Thermal energy from Earth’s active interior constantly dissipates through Earth’s surface. This...
