Hydroclimatic vulnerability of peat carbon in the central Congo Basin: codes for age-depth models, geospatial data processing and analysis

Version 1.0
Garcin, Yannick; Schefuß, Enno; Dargie, Greta C.; Hawthorne, Donna; Lawson, Ian T.; Sebag, David; Biddulph, George E.; Crezee, Bart; Bocko, Yannick E.; Ifo, Suspense A.; Mampouya Wenina, Y. Emmanuel; Mbemba, Mackline; Ewango, Corneille E.N.; Emba, Ovide; Bola, Pierre; Kanyama Tabu, Joseph; Tyrrell, Genevieve; Young, Dylan M.; Gassier, Ghislain; Girkin, Nicholas T.; Vane, Christopher H.; Adatte, Thierry; Baird, Andy J.; Boom, Arnoud; Gulliver, Pauline; Morris, Paul J.; Page, Susan E.; Sjögersten, Sofie; Lewis, Simon L., 2022, "Hydroclimatic vulnerability of peat carbon in the central Congo Basin: codes for age-depth models, geospatial data processing and analysis", https://doi.org/10.23708/FO2HGM, DataSuds, V1, UNF:6:ewI8zj0Z/m4OMWGoSn0k3Q== [fileUNF]
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Description
This dataset includes two packages: 'Age_depth_models' and 'Tropical_Peats' for the creation of the age-depth models and the processing and analysis of the geospatial data, which are presented in the article "Hydroclimatic vulnerability of peat carbon in the central Congo Basin".

The package 'Age_depth_models' includes the data and R codes to run the age/depth models of cores CEN-17.4, LOK5-5 and BDM1-7.

The package 'Tropical_Peats' (JupyterHub notebooks https://jupyter.org) includes all the codes for the processing and analysis of the climate spaces (precipitation amount and seasonality index), tropical peatland distribution and precipitation reconstruction. In summary, the data processing starts with the upload of the CHELSA data Version 1.2 (climatologies) publicly available at https://chelsa-climate.org/downloads/ and of the PEATMAP dataset (current geographical extents of peat-bearing regions) publicly available at https://archive.researchdata.leeds.ac.uk/251/. Then the data are reprojected (if necessary) and downsampled, the tropical areas are extracted, matched, analysed and ultimately plotted.

Note that the sedimentary data presented in this study are available at https://doi.pangaea.de/10.1594/PANGAEA.938019. (2022-03-15)
Subject Earth and Environmental Sciences
Keyword Central Congo, Peat, Carbon, Hydrology, Isotopes, Holocene
Related Publication Garcin, Y., Schefuß, E., Dargie, G., Hawthorne, D., Lawson, I.T., Sebag, D., Biddulph, G.E., Crezee, B., Bocko, Y.E., Ifo, S.A., Mampouya Wenina, Y.E., Mbemba, M., Ewango, C.E.N., Emba, O., Bola, P., Kanyama Tabu, J., Tyrrell, G., Young, D., Gassier, G., Girkin, N.T., Vane, C.H., Adatte, T., Baird, A.J., Boom, A., Gulliver, P., Morris, P.J., Page, S.E., Sjögersten, S., Lewis, S.L., Hydroclimatic vulnerability of peat carbon in the central Congo Basin, Nature, 2022.
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1_Data_Import_Precipitation.ipynb
Tropical_peats/Jupyter/Jupyter Notebook - 8.6 KB
Published Oct 21, 2022
18 Downloads
MD5: 48a731c69580534c9dc3435e2b0331f4
This notebook will download raster data (monthly precipitation) in high-resolution (~1x1 km), clip the tropical region and downsample raster at low-resolution (~10x10 km).
Code
1_Data_Import_Precipitation.py
Tropical_peats/Python/Python Source Code - 5.3 KB
Published Oct 21, 2022
15 Downloads
MD5: 7b4cb0723bfc35ab6251601c993a94e4
This code will download raster data (monthly precipitation) in high-resolution (~1x1 km), clip the tropical region and downsample raster at low-resolution (~10x10 km).
Code
2_Africa_Data_Import_Peat.ipynb
Tropical_peats/Jupyter/Peat_Notebook/Jupyter Notebook - 11.5 KB
Published Oct 21, 2022
17 Downloads
MD5: 40073efc9b96795aa894a027776558f3
This notebook will download and process (reproject, rasterize, synchronize in HR or LR and encoding) Africa peat shapefiles
Code
2_Africa_Data_Import_Peat.py
Tropical_peats/Python/Peat_Notebook/Python Source Code - 6.4 KB
Published Oct 21, 2022
15 Downloads
MD5: 750b408358fe0d10469db8eb7a728a43
This code will download and process (reproject, rasterize, synchronize in HR or LR and encoding) Africa peat shapefiles
Code
2_America_Data_Import_Peat.ipynb
Tropical_peats/Jupyter/Peat_Notebook/Jupyter Notebook - 13.1 KB
Published Oct 21, 2022
16 Downloads
MD5: 4acb1d26c60c15e8ad472188b965c80c
This notebook will download and process (reproject, union South and North America, rasterize, synchronize in HR or LR and encoding) America peat shapefiles
Code
2_America_Data_Import_Peat.py
Tropical_peats/Python/Peat_Notebook/Python Source Code - 7.6 KB
Published Oct 21, 2022
15 Downloads
MD5: ee5dc376ed5f2be49e4125105bde2bea
This code will download and process (reproject, union South and North America, rasterize, synchronize in HR or LR and encoding) America peat shapefiles
Code
2_Asia_Oceania_Data_Import_Peat.ipynb
Tropical_peats/Jupyter/Peat_Notebook/Jupyter Notebook - 12.6 KB
Published Oct 21, 2022
16 Downloads
MD5: 45dd12aa67af30d36636be39e3436737
This notebook will download and process (reproject, union Southeast Asia and Oceania, rasterize, synchronize in HR or LR and encoding) Asia and Oceania peat shapefiles
Code
2_Asia_Oceania_Data_Import_Peat.py
Tropical_peats/Python/Peat_Notebook/Python Source Code - 7.3 KB
Published Oct 21, 2022
15 Downloads
MD5: 7aadf793ebc05409196952a7e7477680
This code will download and process (reproject, union Southeast Asia and Oceania, rasterize, synchronize in HR or LR and encoding) Asia and Oceania peat shapefiles
Code
2_Data_Import_Peat.ipynb
Tropical_peats/Jupyter/Jupyter Notebook - 204.2 KB
Published Oct 21, 2022
16 Downloads
MD5: 49714f9c6d1bba4ef193a86103fd83c4
This notebook will download and process (reproject, union) peat shapefiles
Code
2_Data_Import_Peat.py
Tropical_peats/Python/Python Source Code - 6.5 KB
Published Oct 21, 2022
15 Downloads
MD5: 3b50a90ec99dc1f651bfabd5ce1faecc
This code will download and process (reproject, union) peat shapefiles
Code
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