Datasets and codes for integrating multi-source high-resolution satellite data to redistribute SWE in semi-distributed hydrological modeling using a random forest approach

This repository contains the datasets, source code, and outputs used in the study: "Integrating multi-source high-resolution satellite data to redistribute SWE in semi-distributed hydrological modeling using a random forest approach" pre-printed in ESS OPEN ARCHIVE (https://doi.org/10.22541/essoar.175855515.58229601/v2). The repository is organized according to the main methodological steps adopted in the study and includes all data and scripts required to reproduce the workflow leading to the redistribution of Snow Water Equivalent (SWE) estimated by the GEOframe model in an Alpine catchment. A workflow diagram illustrating the complete methodological framework is also provided in PNG format. All R and Java scripts included in this repository are extensively commented, organized into logical sections, and designed to be easily readable and reproducible. Repository structure 1. Hydrological_modelling.zip This folder contains the datasets and scripts used for the setup and initialization of the GEOframe hydrological model, including: Input of geomorphological and hydro-meteorological data required for model inizialitation; Outputs of GEOframe Embedded Reservoir Model (ERM) and Let Us CAlibration (LUCA); R scripts used for data preparation, post-processing, and analysis; Java scripts used for model processing and simulations. The GEOframe modelling framework is available at: https://github.com/geoframecomponents. 2. Remote_sensing.zip This folder contains the datasets and R scripts used for the remote sensing analysis, including satellite-derived Snow Covered Area (SCA) maps and procedures for deriving Snow Cover Duration (SCD) maps from the SCA products. 3. SWE_products.zip This folder contains the reference SWE products used for calibration and validation, including: SWE_FSM2oshd datasets; SWE_maps datasets; R_scripts used for data processing, calibration, and validation analyses. 4. SWE_redistribution.zip This folder contains all the information related to how the Random Forest regression model has been trained and applied (RF_model) and R scripts used for model training, prediction, and result analysis. Model outputs maps are also added. Additional files Workflow diagram (SWE_redistribution_scheme.png): graphical representation of the complete methodological workflow adopted in the study. Please note that when the data are used, both the dataset and the paper should be cited.