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April 14, 20260 citationsOpen Access

Sensitivity and Uncertainty Analysis of China's Terrestrial Carbon-Water Cycle Using a Dynamic Global Vegetation Model

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FFFulai FengShaanxi Normal UniversityJYjianwu yanShaanxi Normal UniversityWLWei LiangShaanxi Normal University

Key Points

  • The research aims to analyze the sensitivity and uncertainty of China's carbon-water cycle using a dynamic model.
  • Utilized a modified LPJ-GUESS model for analysis.
  • Created Python scripts for parameter sampling and sensitivity index calculations.
  • Executed sensitivity analysis using methods like Morris, eFAST, and Sobol'.
  • Developed post-processing scripts for data visualization.
  • Identified key physiological parameters affecting the carbon-water cycle.
  • Calculated sensitivity indices to reveal influential factors.
  • Provided a framework for future research and ecological modeling.

Abstract

This repository contains the complete analytical pipeline, modified source code, and post-processed datasets for the manuscript: "Sensitivity and Uncertainty Analysis of China's Terrestrial Carbon-Water Cycle Using a Dynamic Global Vegetation Model" (currently under review in Biogeosciences). The dataset includes: Modified LPJ-GUESS (v4.1.1) C++ source code exposing hard-coded physiological parameters. Python scripts for parameter sampling, HPC parallel execution, and sensitivity index calculation (Morris, eFAST, Sobol'). Post-processing and visualization scripts to reproduce the figures in the manuscript. Calculated sensitivity indices and output matrices. For detailed instructions, please refer to the README.md files within the repository.

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Cite This Study

Feng et al. (2026) studied this question.

synapsesocial.com/papers/69ddd99ae195c95cdefd6e0ahttps://doi.org/10.5281/zenodo.19533307
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