Sustainable potential of shallow geothermal heat recycling

Supplementary Information for Shallow geothermal heat recycling as a sustainable and just heating opportunity by V. Dohmwirth (1), K. Menberg (2), P. Bayer (3), P. Blum (2), M. Mauder (4), S. A. Benz (1) in Philosophical Transactions of the Royal Society A {1} Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology {2} Institute of Applied Geosciences, Karlsruhe Institute of Technology {3} Department of Applied Geology, University of Halle-Wittenberg {4} Institute of Hydrology and Meteorology, TU Dresden} All Codes written by V.Dohmwirth file name titel description data needed GEE-1-census data https://code.earthengin e.google.com/b4b4f8d0 2d579fc7b481de98c3d3 ad2b Code to prepare census 2022 date for processing This Google Earth Engine (GEE) code processes demographic data for Dresden and creates raster images and statistics for the individual districts in this case: Census data from 2022 Germany, can be found here: Eine neue Datenbasis für Deutschland - Zensus 2022 GEE-2-status_quo https://code.earthengin e.google.com/210aabf8 e7770b1a82559fb3904e 0dfd Code to analyse the status quo scenario This GEE code performs a comprehensive geothermal analysis for the city. The main steps are: 1. Gathering geodata, water table and groundwater temperature analysis, implementing land use and urban structure, 2. Calculating heat accumulation and heat flow, Calculation of stored heat energy and heat flows from various sources, e.g. tunnels, buildings, district heating systems, calculating technical Geothermal potential, getting available space for BHE installation 3. Analysis of heating requirements based on living space and heating energy consumption 4. Data export we used: - GWT und water table from the Municipality of Dresden (not allowed to share) - Tunnels, Water protaction areas, Geothermal extraction rate in 100m depth, Elbe river outline, districts (https://opendata.dresden.de, Themenstadtplan Dresden : powered by cardo.Map) - Land cover by Corine Urban Atlas 2018 (Urban Atlas — Copernicus Land Monitoring Service) - Building footprints (Downloadbereich Hausumringe - Offene Geodaten - sachsen.de) - Air temperature data by DWD (Index of /climate_environment/CDC/grids_germany/hourly/hostr ada/air_temperature_mean/) - q_up from Davis (2013): Global map of solid Earth surface heat flow (https://agupubs.onlinelibrary.wiley.com/doi/full/10.100 2/ggge.20271) - other data and values can be found in supplemantary materials of the paper - livingspace/ha from zensus data from 2022 Germany (Eine neue Datenbasis für Deutschland - Zensus 2022) GEE-3-other_scenarios https://code.earthengin e.google.com/9fd568fa 4f95b58948946dd61a9a d616 Code to analyse future scenarios same as GEE-2-status_quo, but for „recycled“ condition - SSP air temperature projections from CMIP6 (CMIP6 climate projections) GEE-4-SusS https://code.earthengin e.google.com/8d21fc3a d66a8858041aca5a2d1 38561 Code to calculate different SusS The GEE code examines how much of the heat demand can be covered under the various sustainable heat supply scenarios. It shows the suitable areas for geothermal installations and the number of people living there. The results are exported. GEE-5preparation_for_Python https://code.earthengin e.google.com/9501b391 b0c1b31a5e8670f371d2 1223 Code to prepare all data for further Python analysis The GEE code creates a combined data table with demographic and scenario parameters for each district. Python-1-CO2_tax Code to calculate payback time and fair CO2 price The python codes calculates the payback time of geothermal systems in comparison to gas heater systems as a funtion of time with different variables that change differently depending on the scenario. Python-2SusS_and_demography Code to analyse the prepared data from GEE The python code analyzes whether various demographic factors correlate with sustainable geothermal potential. The visualizations help to identify trends and patterns in the data. The codes should be run in chronological order. Code 15 are Ccodes for Google Earth Engine and code 6 and 7 are codes for jupyter Notebook. Google Earth Engine codes are linked to the authors account.