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Optimization of ecosystem model parameters using spatio-temporal soil moisture information

机译：利用时空土壤水分信息优化生态系统模型参数

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摘要

Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0-10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an and and semi-arid area of northwest China, and the performance of the model for estimating daily 0-10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r(2)) between model-predicted 0-10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation.
机译：基于过程的陆地生态系统模型中的参数通常与通向大气的水通量非线性相关，并且它们在时间和空间上也会发生变化。因此，为了估算土壤湿度，基于过程的陆地生态系统模型不可避免地需要指定时空变化的模型参数。这项研究提出了一个两阶段的数据同化方案（TSDA），以在时空上优化与土壤水分密切相关的生态系统模型的一些关键参数。在第一阶段，使用简化的生态系统模型，即“北方生态系统生产力模拟器”（BEPS），来获得对每日土壤湿度的事先估计。从遥感获得0-10厘米表层土壤水分的空间分布后，通过空间优化模型参数，使用Ensemble Kalman滤波器来最小化遥感模型结果之间的差异。在第二阶段，使用优化的参数重新初始化BEPS，以提供每日土壤湿度的更新模型预测。 TSDA已应用于中国西北部和半干旱地区，并通过野外测量验证了参数优化后估算每日0-10 cm土壤水分的模型的性能。结果表明，本研究开发的TSDA在时间和空间模型参数优化方面均既强大又有效。执行优化后，模型预测的0-10厘米土壤湿度与田间测量值之间的相关性（r（2））从0.66增加到0.75。研究表明，生态系统模型参数的时空优化不仅可以改善土壤日水分模型预测，而且有助于理解生态系统模型中一些关键参数的时空变化以及控制变化的生态机制。

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