Code: The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems

This is the code archive for the publication "The effects of model complexity on model output uncertainty in co-evolved coupled natural–human systems" in Earth's Future. Abstract: Studies have recently focused on using coupled natural–human systems (CNHS) to inform policymaking. However, model uncertainty can increase with model complexity and affect the variance of the model outcomes. Therefore, this study explores an uncertainty analysis of coupled hydrological and human decision models to better evaluate CNHS modeling properties. Five coupled models are proposed with different model complexities for human behavior settings (i.e., model structure and the number of calibrated parameters): one static, two adaptive, and two learning adaptive. Learning adaptive models (the most complex) have both a learning component (capturing long-term trends) and an adaptive component (capturing short-term variations), while adaptive models omit the learning component. The static model is the simplest, without learning or adaptive components. Applying the law of total variance, the model output uncertainty is decomposed into three sources: (1) climate change scenario uncertainty, (2) climate internal variability, and (3) different model configurations with parameter sets or model structures that are equally capable of producing similar outcomes. Our exploratory analysis demonstrated that model uncertainty would likely increase with model complexity given uncertain input data (e.g., climate forcing) and different model configurations; the inclusion of a learning mechanism in the human system can potentially offset the impact of the natural system on uncertainty through coupling natural and human systems. We also discuss other uncertainty sources, such as assumptions about model structure due to incomplete knowledge and metrics for calibration target selection for future studies.本代码存档包对应发表于《Earth's Future》的论文《耦合自然-人类系统协同演化中模型复杂度对模型输出不确定性的影响》。摘要：近年来，学界聚焦于利用耦合自然-人类系统（coupled natural–human systems, CNHS）为政策制定提供科学支撑。然而，模型不确定性往往随模型复杂度提升而增大，并会对模型输出的方差产生影响。因此，本研究针对水文与人类决策耦合模型开展不确定性分析研究，以期更全面地评估耦合自然-人类系统的建模特性。本研究针对人类行为设定（即模型结构与校准参数数量）构建了5种不同复杂度的耦合模型：1个静态模型、2个自适应模型与2个学习型自适应模型。其中学习型自适应模型复杂度最高，同时集成了用于捕捉长期趋势的学习组件与用于捕捉短期波动的自适应组件；普通自适应模型未设置学习组件，而静态模型最为简易，既无学习组件也无自适应组件。本研究应用总方差定律，将模型输出不确定性拆解为三大来源：（1）气候变化情景不确定性；（2）气候内部变率；（3）采用可生成相似结果的同等能力参数集或模型结构所带来的不同模型配置差异。探索性分析结果显示，在输入数据（如气候强迫）存在不确定性且模型配置存在差异的场景下，模型不确定性大概率会随模型复杂度提升而增大；而在人类系统中引入学习机制，可通过耦合自然与人类系统，在一定程度上抵消自然系统对不确定性的影响。本研究同时讨论了其他不确定性来源，例如因认知不足产生的模型结构假设，以及未来研究中校准目标选择的评估指标等议题。