PALEO-RECON is an automated tool for paleoclimate reconstruction of variables such as streamflow or precipitation, based on observed data and self-calibrated Palmer Drought Severity Index (scPDSI) cells. This platform enables fast, accurate, and reproducible reconstructions through the following steps:
- scPDSI Cell Selection: Selects relevant scPDSI cells within a specified radius around an observation point.
- Cell Mapping: Visualizes the selected scPDSI cells on a map and, if enabled, highlights the major basin in which the observation point is located.
- Stepwise Linear Regression (SLR): Conducts paleoclimate reconstruction using SLR, optimizing predictor variables and calculating performance metrics such as R², DW and VIF.
- Bias Correction: Applies Quantile Mapping (RQUANT) for bias correction, aligning reconstructed values more closely with observed data.
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Python and R: Install Python (version 3.10.12 or higher) and R (version 4.3.1 or higher) on your system.
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Anaconda Environment: Creating an Anaconda environment for this project is recommended for easier dependency management.
conda create -n paleo-recon python=3.10.12 conda activate paleo-recon
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Python and R Libraries: Required libraries are listed in the
requirements.txtfile (to be generated), which you can install with the following command:pip install -r requirements.txt
Additionally, install the
qmappackage in R for bias correction:install.packages("qmap") -
Datasets and References:
- OWDA (Old World Drought Atlas) for Europe: Cook et al., 2015
- LBDA (Living Blended Drought Atlas) for North America: Cook et al., 2010
- QMAP for Bias Correction: Robeson et al., 2020
- Major River Basins of the World (GeoJSON): Global Runoff Data Centre, 2020
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Start the Application: Run the following command in your terminal to start the application:
python app.py
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Access in Browser: Open your web browser and go to
http://127.0.0.1:5000to access the application.
The PALEO-RECON interface allows you to specify parameters for the reconstruction. Here’s the required format for each field:
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Coordinate Format: Choose between decimal or DMS (degrees, minutes, seconds) format for entering observation point coordinates.
- Decimal: Enter latitude and longitude in decimal format.
- DMS: Enter degrees, minutes, and seconds, selecting the appropriate direction (N, S, E, W).
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Auto-Detect Basin: Enable this option to highlight the major basin if the observation point falls within one of the world's largest river basins.
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Search Radius (km): Define the radius, in kilometers, around the observation point to select relevant scPDSI cells.
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Window Size for SLR: Select the size of the reconstruction window for the stepwise linear regression.
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Observed Data (CSV):
- The file should be in CSV format with two columns: "Year" and observed data.
- Years must be in integer format in the first column, and observed values should be in decimal format in the second.
- The file can use commas (
,) or semicolons (;) as delimiters.
Once all fields are filled, press the Reconstruct button. The application will:
- Select and map the scPDSI cells within the search radius.
- Execute the paleoclimate reconstruction using SLR.
- Display the map and generate download links for:
- Selected coordinates.
- Data from selected cells.
- SLR results.
If you use PALEO-RECON in your research, please cite it in either of these two ways:
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To cite PALEO-RECON as introduced in:
Ramírez Molina, A.A., Tootle, G., Formetta, G., Piechota, T., Gong, J., "Extraordinary 21st Century Drought in the Po River Basin (Italy)," Submitted to Hydrology, 2024. Once the paper is published, please update the citation accordingly.
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To cite the PALEO-RECON software directly:
Ramírez Molina, A. A. (2024). PALEO-RECON: An Automated Tool for Paleoclimate Reconstructions [Software] (v.1.0.1). Zenodo. DOI: 10.5281/zenodo.14089242
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.