DOI: 10.5194/tc-14-77-2020
论文题名: Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data
作者: Jafarov E.E. ; Harp D.R. ; Coon E.T. ; Dafflon B. ; Phuong Tran A. ; Atchley A.L. ; Lin Y. ; Wilson C.J.
刊名: Cryosphere
ISSN: 19940416
出版年: 2020
卷: 14, 期: 1 起始页码: 77
结束页码: 91
语种: 英语
英文关键词: data set
; detection method
; electrical resistivity
; estimation method
; permafrost
; porosity
; soil temperature
; temperature effect
; thermal conductivity
; tundra
; Arctic
英文摘要: Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved. © Author(s) 2020. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164489
Appears in Collections: 气候变化与战略
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作者单位: Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, United States; Climate Change Science Institute and Environmental Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Climate and Ecosystem Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Department of Water Research Engineering and Technology, Water Research Institute, Hanoi, Viet Nam
Recommended Citation:
Jafarov E.E.,Harp D.R.,Coon E.T.,et al. Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data[J]. Cryosphere,2020-01-01,14(1)