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水产生态系统服务模型的不确定性分析和参数优化:中国山的案例研究

Bei Wang¹, Gao-Feng Zhu², Jun-Tao Zhong³

¹College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China; College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.

The Science of the total environment

|February 9, 2025

概括

在基良山脉的InVEST水产模型中,不确定性分析和参数优化确定了敏感的气候参数. 优化这些参数提高了水产估计的准确性和环境管理模型的稳定性.

科学领域:

环境科学环境科学
水文学的水文学
生态系统服务生态系统服务

背景情况:

准确的生态系统服务评估对于环境管理至关重要.
估计和减少参数不确定性的差距可能会损害评估可靠性,并导致错误的决策.

研究的目的:

在山脉进行InVEST水产模型的不确定性分析和参数优化.
识别敏感参数并量化它们的不确定性,以提高生态系统服务评估的模型准确性.

主要方法:

全球敏感性分析以确定敏感参数.
蒙特卡洛方法用于量化参数不确定性.
马尔科夫链蒙特卡洛 (MCMC) 方法用于使用历史流水数据进行参数优化.
在额外的子盆地中验证优化参数.

主要成果:

气候参数 (年度降水,参考蒸发) 比受气候和人类活动影响的参数更为敏感.
来自敏感参数的不确定性对整体模型不确定性作出了重大贡献.
优化参数精确估计了水产,中国气象强迫数据集/Penman-Monteith组合实现了0.71.7的纳什-萨特克利夫效率.
经过优化的参数证明了其在不同子盆地中的稳定性和可转移性.

结论:

严格的不确定性分析和参数优化对于提高生态系统服务评估可靠性至关重要.
敏感参数,特别是与气候相关的参数,是提高模型准确性的关键目标.
建议在不同地区进行进一步的验证,以确认发现的更广泛的适用性.

关键词:

生态系统服务生态系统服务扩展的里埃振幅灵敏度测试扩展的里埃振幅灵敏度测试在InVEST水产性模型中,水产性模型.马尔科夫连锁蒙特卡罗的蒙特卡罗是一个连锁城市.蒙特卡洛模拟的蒙特卡洛模拟

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