Ecosystem models are important tools for exploring the temporal and spatial patterns of ecosystem processes and their responses to climate change. However, the implications of uncertainty in model parameters are often overlooked, especially in regional ecosystem model simulations. Here, we use eddy‐covariance observations to estimate parameters in an ecosystem model, which was developed from Shuttleworth‐Wallace model, and examine the effect on estimates of evapotranspiration (ET). Using a simple ecosystem model as an example, we use Monte Carlo techniques to optimize key model parameters using eddy covariance (EC) data from 163 FLUXNET sites. The optimization process revealed a strong spatial correlation between key parameters and environmental variables, particularly leaf area index (LAI) and soil characteristics (e.g., clay fraction). The optimization of parameters related to canopy conductance and soil surface resistance greatly improved model performance, particularly when incorporating the identified spatial variation of parameters. The improved model agreed well with the measurements with an increase in the coefficient of determination (R²) from 73% to 80% in the 8‐day averaged ET estimation and a decrease in the root mean square error (RMSE) from 130.2 to 104.3 mm year⁻¹ compared with the original model. The results suggest the potential of eddy‐covariance flux observations to identify predictable spatial variations of key parameters, which can be used to better constrain ecosystem models. And in this case, a universal and efficient method for reducing the uncertainties in key parameters across different PFTs and ecosystem applications is suggested.

中文翻译：

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在参数中纳入空间变化以改善蒸散模型

生态系统模型是探索生态系统过程的时空格局及其对气候变化的响应的重要工具。但是，模型参数不确定性的影响常常被忽略，尤其是在区域生态系统模型仿真中。在这里，我们使用涡度协方差观测值来估算由Shuttleworth-Wallace模型开发的生态系统模型中的参数，并研究其对蒸散量（ET）估算的影响。以简单的生态系统模型为例，我们使用蒙特卡洛技术使用来自163个FLUXNET站点的涡动协方差（EC）数据来优化关键模型参数。优化过程揭示了关键参数和环境变量之间的空间相关性很强，尤其是叶面积指数（LAI）和土壤特性（例如粘土分数）。与冠层电导率和土壤表面电阻有关的参数的优化极大地改善了模型的性能，尤其是在纳入已确定的参数空间变化时。改进的模型与测量结果吻合得很好，确定系数增加了（与原始模型相比，在8天的平均ET估算中R ²）从73％降低到80％，并且均方根误差（RMSE）从130.2降低到104.3 mm年^-1。结果表明，涡度-协方差通量观测有潜力确定关键参数的可预测空间变化，可用于更好地约束生态系统模型。并且在这种情况下，提出了一种通用有效的方法来减少不同PFT和生态系统应用中关键参数的不确定性。