黑河上游高寒山区土壤水分模拟对Noah-MP模型参数化方案的敏感性评估
网络出版日期: 2025-02-24
基金资助
国家自然科学基金项目(42101361);安徽省重点研究与开发计划项目(2022m07020003);安徽省高等学校科学研究重点项目(2023AH050143);中 国 博 士 后 科 学 基 金 面 上 项 目(2024M753092);安 徽 师 范 大 学 大 学 生 创 新 创 业 训 练 计 划 项 目
(202310370056,202310370051,202410370018)
Sensitivity Analysis of Noah-MP Model Parameterization Schemes for Soil Moisture Simulation in the High-Cold Region of the Upper Heihe River Basin
Online published: 2025-02-24
在气候变化的背景下,利用陆面过程模型准确模拟土壤水分对气象预报、农业生产和水文过程都具有重要意义。本文利用黑河上游阿柔站的气象观测资料作为 Noah-MP模型的驱动数据,开展了土壤水分模拟试验,评估了 Noah-MP模型在黑河上游高寒山区的土壤水分模拟性能。在不考虑模型参数和驱动数据不确定性的条件下,对 Noah-MP模型不同物理过程的参数化方案进行任意组合,设计了包含17280种不同组合方案的土壤水分多参数化方案集合模拟试验,选用Natural Selection敏感性分析方法分析了浅层土壤水分模拟结果对参数化方案的敏感性,并进一步量化了土壤水分多参数化方案集合模拟结果的不确定性范围。研究结果表明,Noah-MP模型可用于黑河上游高寒山区的土壤水分模拟,模型对浅层土壤水分的模拟精度较高,模拟的土壤水分变化趋势与观测资料基本一致;而深层土壤水分模拟结果精度较差,模拟的土壤水分变化趋势与观测资料偏差较大。浅层土壤水分模拟结果对冻结土壤中过冷液态水、冻结土壤渗透、雨雪分离和第一层积雪或土壤温度的时间方案4个物理过程的参数化方案敏感,其中对冻结土壤渗透物理过程的参数化方案特别敏感。上游高寒山区土壤冻融循环过程中,冻结时段内的土壤水分模拟结果对参数化方案更加敏感,使得土壤冻结物理过程参数化方案的选择是导致土壤水分多参数化方案集合模拟结果不确定性的主要因素。
关键词:
土壤水分; 集合模拟; 参数化方案敏感性分析
黄克秀, 尤元红, 卢燕宇, 郝 莹, 汪 左, 孙 京 . 黑河上游高寒山区土壤水分模拟对Noah-MP模型参数化方案的敏感性评估 [J]. 高原气象, 0 : 1 . DOI: 10. 7522/j. issn. 1000-0534. 2024. 00114
In the context of climate change,accurately simulating soil moisture using land surface process models holds significant importance for weather forecasting,agricultural production,and hydrological processes. This study utilized meteorological observation data from the Arou site in the upper reaches of the Heihe River as the driving data for the Noah-MP model to conduct soil moisture simulation experiments,aiming to assess the soil moisture simulation performance of the Noah-MP model in the alpine mountainous area of the upper reaches of the Heihe River. Without considering uncertainties in model parameters and driving data,arbitrary combinations of the parameterization schemes for different physical processes of the Noah-MP model were made. A soil moisture multi-parameterization ensemble simulation experiment encompassing 17,280 different combination schemes was designed. The Natural Selection sensitivity analysis method was employed to analyze the sensitivity of shallow soil moisture simulation results to the parameterization schemes and further quantify the uncertainty range of the simulation results of the soil moisture multi-parameterization ensemble. The results of this research indicate that the Noah-MP model can be applied to simulate soil moisture in the alpine mountainous area of the upper reaches of the Heihe river basin. The model demonstrates relatively high accuracy in simulating shallow soil moisture,and the simulated soil moisture change trends are generally consistent with the observed data. This consistency suggests that the Noah-MP model is well-suited for capturing the dynamics of shallow soil moisture in these regions. However,the simulation accuracy for deep soil moisture is relatively poor,with the simulated soil moisture change trends showing considerable deviations from the observed data. This suggests that there are still challenges in accurately modeling moisture dynamics at greater soil depths,potentially due to the complexity of subsurface hydrological processes in cold and mountainous environments. The analysis also reveals that shallow soil moisture simulation results are sensitive to the parameterization schemes of four physical processes: supercooled liquid water in frozen soil,frozen soil permeability,partitioning precipitation into rainfall and snow‐ fall,and the first-layer snow or soil temperature time scheme. Among these,the parameterization scheme of frozen soil permeability is particularly sensitive,indicating that it plays a crucial role in determining the accuracy of the simulation results. During the soil freeze-thaw cycle in the alpine mountainous area of the upper reaches of the Heihe River,the simulation results of soil moisture during the freezing period showed increased sensitivity to parameterization schemes,making the selection of the parameterization scheme for the soil freezing process the main factor contributing to the uncertainty of the simulation results of the soil moisture multi-parameterization ensemble.
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