rsofun v5.1: a model-data integration framework for simulating ecosystem processes

doi:10.5194/gmd-18-9855-2025

2025-12-10

PRE

Benjamin D. Stocker *

DOI：10.5194/gmd-18-9855-2025

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摘要

En 中文

机制性植被模型用于估计陆地碳通量和气候对生态系统的影响，跨越多样的生物和非生物条件。用数据系统地指导这些模型对于提高其预测准确性和估计不确定性至关重要。在此，我们演示并评估了Simulating Optimal FUNctioning (rsofun) R软件包，该软件包为生态系统光合作用和光合性状的适应性提供了计算效率高的P模型站点尺度模拟实现，并补充了贝叶斯模型-数据集成以及模型参数和预测不确定性的估计功能。我们同时从观测的生态系统总初级生产力（GPP）时间序列以及全球分布的叶片碳-13同位素判别（Delta 13C）和羧化最大生化速率与电子传递速率之比（Vcmax/Jmax）数据中估计模型参数。多目标校准对所有变量同时产生无偏预测，并产生校准和样本外测试数据的预测-观测残差相似分布，表明该模型在不同环境中稳健泛化。我们发现，逐步的模型集成和校准方法效果最佳，且与水分胁迫效应表征相关的参数间相关性导致了参数估计的不稳健。这很可能表明模型结构不确定性的主要来源与土壤和空气中干旱条件下光合作用响应的表征有关。

Keyword:

CARBON-ISOTOPE DISCRIMINATION

PHOTOSYNTHETIC CAPACITY

CO2 ASSIMILATION

EXCHANGE

TRAITS

OPTIMIZATION

PRODUCTIVITY

ACCLIMATION

UNCERTAINTY

CONSISTENT

期刊

Geoscientific Model Development

IF:

4.9

论文数:

4.0K

被引数:

2.4W

机构

University of Bern

学者数:

4.0W

论文数: 3.1W

被引数: 4.8W

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