Data and Code Supplement to "Machine learning metamodelling for global sensitivity analysis"

Description This data and code supplement provides the material required to reproduce the numerical experiments and figures presented in the manuscript “Machine learning metamodelling for global sensitivity analysis”. The repository contains Monte Carlo simulations, Sobol’ GSA results, machine learning (ML) metamodel outputs, and the scripts used to compute sensitivity measures and generate all visualizations. The data were produced using three conceptual hydrologic models: Hydrologiska Byråns Vattenbalansavdelning (HBV), HyMod, and Variable Infiltration Capacity (VIC) version 5. For each model–catchment combination, parameter samples were generated using Sobol’ quasi-random sequences and were used to compute the Kling–Gupta Efficiency (KGE), which serves as the model output of interest throughout the study. The repository includes the KGE outputs of these Monte Carlo experiments for each model, which form the basis for both the variance-based global sensitivity analysis and the training of the ML metamodels. The repository includes computed Sobol first-order and total-effect indices (S_i and T_i , respectively), as well as feature importance measures derived from random forest (RF), neural network (NN), and linear model (LM) surrogates. In particular, Permutation Variable Importance (PVI_i) and SHapley Additive exPlanations (SHAP_i) are provided. For the LM metamodel, the standardized regression coefficients β_i and β_i^2 are also available. All analyses were implemented in Python. The scripts contained in the repository reproduce the computation of sensitivity and feature importance measures and the generation of the figures presented in the manuscript. The workflow is fully reproducible from the supplied data and does not require additional hydrologic model simulations. This supplement is intended to facilitate transparency, reproducibility, and reuse of the proposed framework. Researchers may use the material to benchmark alternative surrogate models, evaluate additional sensitivity metrics, or extend the analysis to different modelling contexts.数据集补充说明 本数据与代码补充材料提供了复现论文《用于全局敏感性分析的机器学习元建模》中数值实验与图表所需的全部素材。该仓库包含蒙特卡洛（Monte Carlo）模拟、Sobol全局敏感性分析（Global Sensitivity Analysis, GSA）结果、机器学习（Machine Learning, ML）元模型输出，以及用于计算敏感性指标并生成所有可视化图表的脚本。 本次实验使用了三款概念性水文模型：HBV模型（Hydrologiska Byråns Vattenbalansavdelning）、HyMod模型以及可变下渗能力模型（Variable Infiltration Capacity, VIC）第5版。针对每一组模型-集水区域组合，均采用Sobol拟随机序列生成参数样本，并以此计算Kling-Gupta效率系数（Kling–Gupta Efficiency, KGE）——该系数为本研究中核心关注的模型输出。本仓库收录了各模型蒙特卡洛实验的KGE输出结果，该结果既是基于方差的全局敏感性分析的基础数据，同时也是机器学习元模型训练的核心数据集。 仓库中还包含已计算得到的Sobol一阶与总阶敏感性指标（分别记为S_i与T_i），以及源自随机森林（Random Forest, RF）、神经网络（Neural Network, NN）与线性模型（Linear Model, LM）代理模型的特征重要性指标。具体包括置换变量重要性（Permutation Variable Importance, PVI_i）与SHapley可加解释性指标（SHapley Additive exPlanations, SHAP_i）。针对线性模型元模型，还提供了标准化回归系数β_i与β_i²。所有分析均基于Python语言实现。仓库内的脚本可复现论文中敏感性指标与特征重要性的计算过程，以及所有图表的生成流程。本工作流可通过提供的数据集完全复现，无需额外开展水文模型模拟。 本补充材料旨在提升所提出框架的透明度、可复现性与复用性。研究人员可利用该材料对替代代理模型进行基准测试、评估额外的敏感性指标，或将本分析框架拓展至其他建模场景中。