GLORIF1

Introduction

We create 30 arcmin resolution global streamflow data from the global hydrological model PCR-GLOBWB 2 (Sutanudjaja et al., 2018) using Random Forests Regression (Breiman, 2001), for the years 1979-2019.

This framework is an update of the method by Magni et al., (2023) to a broader scale.

Input data

Input data and outputs of the 30 arcmin run are available on Zenodo (Link)
River discharge data was downloaded from the Global Runoff Data Centre (GRDC).
2286 stations with variable availability of observations were selected (min. area = 10 000 km²)
River discharge data for validation was downloaded from The Global Streamflow Indices and Metadata Archive ([GSIM[https://doi.org/10.1594/PANGAEA.887470)
1986 stations with variable availability of observations were selected (min. area = 10 000 km²)
The selected stations can be found stationLatLon.csv (merged daily and monthly).

Python module

For fast installation / update of necessary modules it is recommended to use the mamba package manager.
Current dependencies: numpy, pandas, alive_progress, netCDF4, xarray, multiprocessing.

The python module is used to extract raw data into homogeneous .csv files.

• Extraction of GRDC discharge, either daily or monthly (from .txt)
• Extraction of GSIM discharge, monthly (from .mon)
• Filtering out the GRDC stations from GSIM data.
• Exraction of the station area and pcr_area info into one file.
• Extraction of PCR-GLOBWB upstream averaged parameters (from data/allpoints_catchAttr.csv into stationLatLon.csv)
• Extraction of PCR-GLOBWB upstream averaged meteo input and state variables (from .netCDF).

R module

The R module follows the post-processing phases described in manuscript. Dependencies can be installed using fun_0_install_dependencies.R. These are loaded at the beginning of each script using fun_0_load_library.R.

0_preprocess_grdc

0. Upscales daily discharge to monthly, merges daily and monthly if a station has both, keeps upscaled daily if available at a timestep.
1. Merges stations from stationLatLon_daily.csv and stationLatLon_monthly.csv into stationLatLon.csv
2. Calculates % missing data for the modelled years (here 1979-2019).

0_preprocess_grdc

1. Turns observation discharge into flow depth for training

0_preprocess_gsim

gsim_without_grdc -> Filters out the GRDC stations from GSIM data. gsim_area_filtering -> Filters the areas above 10 000 km²

1. Prepares all the GSIM discharge data from the stations that has at least 12 month of available data within the time range 1979-2019.
2. Calculates % missing data for the modelled years (here 1979-2019).

0_preprocess_predictors

0. Parameters: generates timeseries of static catchment attributes (.csv)
1. qMeteoStatevars: merges timeseries of meteo input and state variables (.csv)
2. Merge all predictors : merges Parameters and qMeteoStatevars (.csv)

1_correlation_analysis

bigTable : binds all stations predictor tables allpredictors

2_randomForest

1. Tune -> Uses training table from 0 to tune Random Forest hyperparameters.
2. Train / Testing -> Trains the RF algorithm by using the training table created in the preprocess using values from tuning process.
3. Reanalysis -> Creates new streamflow predictions for every pixel of the globe.
4. Validation -> Calculates variable importance and KGE (before and after post-processing).

3_visualization

Used to visualize all modelling phases:

• Tuning Random Forest parameters.
• Plot of variable importance with square-rooted mean decrease in impurity values of the top twenty variables.
• KGE: boxplots of PCR-GLOBWB and GLORIF1.
• KGE: global maps (PCR-GLOBWB vs. GLORIF1).
• KGE: cumulative distribution plots averaged for each configuration and location.
• Scattering plot of PCR-GLOBWB and GLORIF1 streamflow simulations.