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本文引用的文献

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2
Modeling Nitrogen and Carbon dynamics in wetland soils and water using a mechanistic wetland model.使用机理湿地模型模拟湿地土壤和水体中的氮碳动态。
J Hydrol Eng. 2017;22(1):1-18. doi: 10.1061/(ASCE)HE.1943-5584.0001441.
3
Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model.用于不确定性估计和风险管理的贝叶斯蒙特卡罗和最大似然方法:在湖泊氧气恢复模型中的应用
Water Res. 2017 Jan 1;108:301-311. doi: 10.1016/j.watres.2016.11.012. Epub 2016 Nov 3.
4
Investigation of hydrogeologic processes in a dipping layer structure: 1. The flow barrier effect.倾斜层状结构中水文地质过程的研究:1. 流动屏障效应。
J Contam Hydrol. 2004 Apr;69(3-4):157-72. doi: 10.1016/j.jconhyd.2003.08.005.

土壤水分动态的两层数值模型:模型评估与贝叶斯不确定性估计

Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.

作者信息

He Junhao, Hantush Mohamed M, Kalin Latif, Isik Sabahattin

机构信息

State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 712100, China.

U.S. EPA the Center for Environmental Solutions and Emergency Response, 26 West Martin Luther King Dr., Cincinnati, OH, 45268, USA.

出版信息

J Hydrol (Amst). 2022 Oct;613(A):1-15. doi: 10.1016/j.jhydrol.2022.128327.

DOI:10.1016/j.jhydrol.2022.128327
PMID:37324646
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10266505/
Abstract

A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below ( <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.

摘要

最近开发了一种基于理查兹方程(RE)积分形式的两层模型,用于模拟根层和包气带中地下水位相对较浅且动态变化时的土壤水分运动。该模型模拟的是厚度平均体积含水量和基质吸力,而非点值,并以HYDRUS为基准对三种土壤质地进行了数值验证。然而,两层模型的优缺点及其在分层土壤和实际田间条件下的性能尚未得到检验。本研究通过两个数值验证实验进一步检验了两层模型,最重要的是,在实际的、高度可变的水文气候条件下,在场地层面测试了其性能。此外,使用贝叶斯框架估计了模型参数,并对不确定性和误差来源进行了量化。首先,在均匀土壤剖面且土层厚度不同的情况下,对231种土壤质地的两层模型进行了评估。其次,对顶层和底层土壤水力传导率不同的分层条件下的两层模型进行了评估。通过将土壤水分和通量估计值与HYDRUS模型的估计值进行比较来评估该模型。最后,给出了一个使用土壤气候分析网络(SCAN)站点数据的模型应用案例研究。采用贝叶斯蒙特卡洛(BMC)方法进行模型校准,并量化实际水文气候和土壤条件下的不确定性来源。对于均匀土壤剖面,两层模型在估计体积含水量和通量方面通常具有优异的性能,而模型性能随着土层厚度增加和土壤质地变粗而略有下降。进一步提出了关于土层厚度和土壤质地的模型配置,这些配置能够产生准确的土壤水分和通量估计值。对于两层渗透率不同的情况,模型模拟的土壤含水量和通量与HYDRUS计算的结果吻合良好,表明两层模型能够准确处理层界面周围的水流动力学。在田间应用中,考虑到高度可变的水文气候条件,两层模型与BMC方法相结合,与观测到的根区和下方包气带的平均土壤水分显示出良好的一致性(校准期间<0.021,验证期间<0.023)。与其他来源相比,参数不确定性对总模型不确定性的贡献太小。数值试验和场地层面的应用表明,两层模型能够可靠地模拟厚度平均土壤水分,并估计各种土壤和水文气候条件下包气带的通量。结果还表明,BMC方法可能是一个用于包气带水力参数识别和模型不确定性估计的强大框架。

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