Abstract To provide scientific support for improvements in land surface modeling on the Tibetan Plateau (TP) by reducing uncertainties in the physical parameters of models, comprehensive uncertainty and sensitivity evaluations were performed for the simulation of surface soil moisture (SSM). Five observational stations were selected for the study. The conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach and the Common Land Model (CoLM) with 28 uncertain parameters were employed to evaluate the maximal uncertainty of the simulated SSM. The uncertainty analysis indicated that the parameter errors could induce large uncertainties. These uncertainties in the SSM generally fluctuated over the range from 0.33 to 0.64 m3 m−3 in terms of absolute changes and from 235% to 510% in terms of percentage changes. The uncertainty analysis addressed the necessity of decreasing the uncertainties in the parameters. When resources are limited, the most important and sensitive parameter set should first be identified in order to reduce uncertainties. To find this parameter set, a sensitivity analysis framework based on the CNOP-P approach was applied. The results showed that the most sensitive and important combinations of 4 parameters changed slightly among the study sites and consisted of soil texture-related parameters. Although the most sensitive and important parameter combinations only had 4 elements, the uncertainties that they could induce accounted for a large proportion of the uncertainties caused by all 28 uncertain parameters. Furthermore, the decreases in parameter errors, which were derived from the CNOP-Ps of the most sensitive and important parameter combinations, led to the maximal reductions in the uncertainties of the simulated SSM. The above results imply that we should prioritize reducing the uncertainty of sensitive parameters or parameter combinations in order to improve prediction and simulation abilities.

中文翻译：

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青藏高原土壤水分模拟的不确定性和敏感性评价

摘要 为降低模型物理参数的不确定性，为改进青藏高原陆面建模提供科学支持，对表层土壤水分（SSM）模拟进行了综合不确定性和敏感性评估。研究选择了五个观测站。与参数相关的条件非线性最优扰动（CNOP-P）方法和具有28个不确定参数的公共土地模型（CoLM）被用来评估模拟SSM的最大不确定性。不确定性分析表明，参数误差会导致较大的不确定性。SSM 中的这些不确定性在绝对变化方面通常在 0.33 到 0.64 m3 m-3 的范围内波动，在百分比变化方面从 235% 到 510% 波动。不确定性分析解决了降低参数不确定性的必要性。当资源有限时，应首先确定最重要和最敏感的参数集，以减少不确定性。为了找到该参数集，应用了基于 CNOP-P 方法的敏感性分析框架。结果表明，4 个参数的最敏感和最重要的组合在研究地点之间略有变化，并且由土壤质地相关参数组成。尽管最敏感和最重要的参数组合只有 4 个元素，但它们所引起的不确定性在所有 28 个不确定参数引起的不确定性中占了很大比例。此外，参数误差的减少，从最敏感和最重要的参数组合的 CNOP-P 得出的​​结果，最大限度地降低了模拟 SSM 的不确定性。上述结果意味着我们应该优先降低敏感参数或参数组合的不确定性，以提高预测和模拟能力。