This repository contains observed and climate projection data used in a specific case study in a landslide-prone area. This area is part of the Partenio Mountains in southern Italy.
There are 10 rain gauges in the area recording rainfall every hour. Additionally, several climate projections assess meteorological forcing through regional climate modeling until 2070, within the EURO-CORDEX initiative.
The model data (narrowed down to the 10 observation points) was bias-corrected using the Quantile Matching for Extremes (QME) method.
Currently, there are two observed and modeled variables of particular interest: rainfall and temperature. I intend to expand the dataset to include modeled evaporation, defined as:
"Mass of surface and sub-surface liquid water that evaporates from land. The data includes conversion to the vapor phase from both the liquid and solid phases (i.e., includes sublimation) and represents the mean over the aggregation period."
The observed data was downloaded from the Italian Civil Protection website for the Campania region, while the model data was obtained from two sources:
Downscaled climate projections for Italy from the CMCC (link). The Copernicus Climate Data Store (link), which contains data from the CORDEX initiative. Currently, the data is structured as follows. However, the database is still under development, so changes are expected.
. ├── Data │ ├── 00_Observed_Stations.csv │ ├── Model │ │ ├── Experiments_to_download.csv │ │ ├── Model_Rainfall │ │ │ ├── CORDEX_CDS │ │ │ │ ├── Historical │ │ │ │ │ └── download_Historical.py │ │ │ │ ├── RCP45 │ │ │ │ ├── RCP85 │ │ │ │ │ ├── 00_GCM1_RCM1_PreviousTest │ │ │ │ │ │ ├── download_CDS_85_GCM1_RCM1.py │ │ │ │ │ │ └── interpolated_precip_RCP85_GCM1_RCM1.csv │ │ │ │ │ ├── 00_GCM2_RCM1_PreviousTest │ │ │ │ │ │ ├── V2_download_CDS_85_GCM2_RCM1.py │ │ │ │ │ │ ├── download_CDS_85_GCM2_RCM1.py │ │ │ │ │ │ ├── get_model_names.py │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv │ │ │ │ │ ├── G1_Batch.py │ │ │ │ │ ├── G1_R1 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM1_RCM1.py │ │ │ │ │ │ ├── error_log.log │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv │ │ │ │ │ ├── G1_R2 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM1_RCM2.py │ │ │ │ │ │ └── get_values_by_id.py │ │ │ │ │ ├── G1_R3 │ │ │ │ │ │ ├── V2_download_CDS_85.py │ │ │ │ │ │ ├── error_log.log │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv │ │ │ │ │ ├── G2_Batch.py │ │ │ │ │ ├── G2_R1 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM2_RCM1.py │ │ │ │ │ │ ├── error_log.log │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv │ │ │ │ │ ├── G2_R2 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM2_RCM2.py │ │ │ │ │ │ ├── error_log.log │ │ │ │ │ │ └── get_values_by_id.py │ │ │ │ │ ├── G3_Batch.py │ │ │ │ │ ├── G3_R1 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM3_RCM1.py │ │ │ │ │ │ ├── error_log.log │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv │ │ │ │ │ ├── G4_Batch.py │ │ │ │ │ ├── G4_R1 │ │ │ │ │ │ ├── V2_download_CDS_85_GCM4_RCM1.py │ │ │ │ │ │ ├── get_values_by_id.py │ │ │ │ │ │ └── interpolated_precip_RCP85.csv
See the full structure to find your data of interest in the structure.txt file. Every subfolder contains a README.md file describing its internal content.
Description
Data/: Contains observational and modeled climate forcing data.
Model/: Stores model experiment metadata and results.
Model_Rainfall/: Includes rainfall data from various climate scenarios (CORDEX, RCP45, RCP85).
Python Scripts:
download_Evaluation.py, download_Historical.py: Scripts for downloading model rainfall data.
get_values_by_id.py: Processes extracted data.
G*_Batch.py: Batch processing scripts for different model groups.
error_log.log: Logs errors during data retrieval.
Usage
Download Data: Run the relevant V2_download_CDS_85_*.py script.
Process Data: Use get_values_by_id.py to extract and analyze results.
Monitor Errors: Check error_log.log files for debugging.
Requirements
Python 3.x
Required packages: netCDF4, numpy, pandas
License
The code generated within this project is licensed under the MIT License. The data produced within this project is licensed under Creative Commons 4.0 (CC BY 4.0)