Full Reproduction Package: Inputs, Configurations, and Monitoring Data for TETIS Computational Scalability Study

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]本仓库提供了完整的数据包，可完全复现关联论文《基于机器学习预测的TETIS生态水文模型可扩展性与计算性能》中所展示的分布式水文模型TETIS v9.1的计算性能分析结果。 该数据包按核心目录组织，以支持模型运行与数据分析： Modelos（TETIS配置文件）： 包含30个压缩归档文件（例如001_Po_200m_Base.7z），对应30组实验流域配置（2个集水区 × 5种空间分辨率 × 3种重构方案）。 每个归档文件均包含TETIS运行文件（Tetis.exe、Hantec.exe）、所需的ASCII格式参数栅格（dem_200b.asc、slope_200b.asc），以及所有模拟配置文件（Control.exe、Settings.txt）。 每个模型的FE文件夹均包含用于执行模型场景的输入文件。 Monitor（硬件与运行数据）： 包含硬件监控工具生成的原始数据日志（例如equipo.csv、monitoreo.csv、HWINFO64.exe）。 这些文件包含实验期间测得的关键性能指标（Key Performance Indicators, KPIs），如运行时长、CPU使用率、最大睿频频率及内存数据，这些指标被用于训练随机森林预测模型。 这套完整的数据包可实现最高等级的可复现性，支持研究人员重新运行TETIS模拟，或基于收集到的运行指标重新验证机器学习模型。 使用本仓库时请引用关联论文： Cortés-Torres, N., Salazar Galán, S., & Francés García, F. (2025). 基于机器学习预测的TETIS生态水文模型可扩展性与计算性能。（已投稿待发表） DOI：[已投稿待发表]