This repository provides the complete data package necessary to fully reproduce the computational performance analysis of the distributed hydrological model, TETIS v9.1, as presented in the associated publication, "Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Pre-diction" The package is organized into key directories to support both model execution and data analysis: Modelos (TETIS Configuration Files): Contains 30 compressed archives (e.g., 001_Po_200m_Base.7z) corresponding to the 30 experimental basin configurations (2 catchments $\times$ 5 spatial resolutions $\times$ 3 reconditioning schemes). Each archive includes the TETIS execution files (Tetis.exe, Hantec.exe), the necessary ASCII parameter maps (dem_200b.asc, slope_200b.asc), and all simulation configuration files (Control.exe, Settings.txt). Each model contains folfer FE includes the input files to execute the model scenarios Monitor (Hardware & Execution Data): Includes the raw data logs from the hardware monitoring tool (e.g., equipo.csv, monitoreo.csv, HWINFO64.exe). These files contain the Key Performance Indicators (KPIs) measured during the experiments, such as execution time, CPU utilization, Max Turbo Frequency, and memory data, which were used to train the Random Forest predictive models. This comprehensive package ensures the highest level of reproducibility, allowing researchers to either re-run the TETIS simulations or re-validate the Machine Learning models based on the collected execution metrics. Cite the associated article when using this repository: Cortés-Torres, N., Salazar Galán, S., & Francés García, F. (2025). Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Prediction. (Submitted for publication). DOI: [Submitted for publication]