Effects of Soil Hydraulic Properties on Soil Moisture Estimation

Xiaolei FU; Haishen LYU; Zhongbo YU; Xiaolei JIANG; Yongjian DING; Donghai ZHENG; Jinbai HUANG; Hongyuan FANG

doi:10.1007/s13351-023-2049-2

PDF (4708 KB)

Effects of Soil Hydraulic Properties on Soil Moisture Estimation

土壤水力属性对土壤湿度估算的影响

Xiaolei FU^{1, 2, 3,},
Haishen LYU^3,,
Zhongbo YU^{3, 4, 5,},
Xiaolei JIANG^{1, 2, ,},
Yongjian DING^6,,
Donghai ZHENG^7,,
Jinbai HUANG^1,,
Hongyuan FANG^1,

+ Author Affiliations + Find other works by these authors

1.
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009
2.
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science & Technology, Nanjing 210044
3.
State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098
4.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098
5.
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, 210098
6.
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000
7.
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101

Corresponding author: Xiaolei JIANG, xljiang715@yzu.edu.cn;

Funds:

Supported by the National Natural Science Foundation of China (52109036, 51709046, 51539003, 41761134090, 41830752, and 42071033), Belt and Road Special Foundation of the State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering of Hohai University (2021490611), Open Foundation of Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources (HYMED202203, HYMED202210), and Lanzhou Institute of Arid Meteorology (IAM202119). Data are provided by the National Tibetan Plateau Data Center of China

DOI: 10.1007/s13351-023-2049-2

Abstract

Abstract

Accurate quantification of soil moisture is essential to understand the land surface processes. Soil hydraulic properties influence water transport in soil and thus affect the estimation of soil moisture. However, some soil hydraulic properties are only observable at a few field sites. In this study, the effects of soil hydraulic properties on soil moisture estimation are investigated by using the one-dimensional (1-D) Richards equation at ELBARA, which is part of the Maqu monitoring network over the Tibetan Plateau (TP), China. Soil moisture assimilation experiments are then conducted with the unscented weighted ensemble Kalman filter (UWEnKF). The results show that the soil hydraulic properties significantly affect soil moisture simulation. Saturated soil hydraulic conductivity (K_sat) is optimized based on its observations in each soil layer with a genetic algorithm (GA, a widely used optimization method in hydrology), and the 1-D Richards equation performs well using the optimized values. If the range of K_sat for a complete soil profile is known for a particular soil texture (rather than for arbitrary layers within the horizon), optimized K_sat for each soil layer can be obtained by increasing the number of generations in GA, although this increases the computational cost of optimization. UWEnKF performs well with optimized K_sat, and improves the accuracy of soil moisture simulation more than that with calculated K_sat. Sometimes, better soil moisture estimation can be obtained by using opti-mized saturated volumetric soil moisture content K_sat. In summary, an accurate soil profile can be obtained by using soil moisture assimilation with optimized soil hydraulic properties.
摘要

土壤湿度(SM)的精确量化对理解陆面过程至关重要。然而，影响SM估算的一些土壤水力属性(SHP)的观测，仅限于有限的实验站点。为评估SHP对SM的影响，本文在青藏高原玛曲观测网的ELBARA站点开展了基于一维理查德方程的SM模拟结果对SHP观测值的敏感性研究，然后利用无迹加权集合卡尔曼滤波(UWEnKF)同化方法进行SM估算并探讨估算结果的相关敏感性。结果表明，SHP显著影响SM的模拟结果。当利用遗传算法对饱和水力传导度在每层观测范围内进行优化后，SM的模拟结果得到提高。另外，仅仅知道某个土壤类型的变化范围时，可以增加遗传迭代次数获取的优化值。基于优化后的，利用UWEnKF可以显著提高SM的估算精度。利用优化后的饱和土壤含水量也可能获得较好的SM估算值。本文揭示通过同化方法叠加参数优化可以提高SM的估算精度，为获取不同时空尺度高精度的SM奠定基础。
- 土壤湿度(SM),
- 一维理查德方程,
- 无迹加权集合卡尔曼滤波(UWEnKF),
- 土壤水力属性(SHP),
- 遗传算法

FullText(HTML)

Fig. 14. Soil moisture assimilation using optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ and optimized ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ with an assimilation interval 12 ∆t.

Download: Larger image PowerPoint slide

Fig. 1. ELBARA experimental site at the Maqu monitoring network in SRYR (Fu et al., 2022).

Download: Larger image PowerPoint slide

Fig. 2. Schematic diagram for numerical scheme (Lawrence et al., 2018; Fu et al., 2022).

Download: Larger image PowerPoint slide

Fig. 3. Variations of soil moisture simulations and observations in different depths. The gray shaded area (Simulation range) shows the range of an open loop with observed values of ${\mathrm{\theta }}_{\mathrm{sat}}$ varied; the purple shaded area (Simulation-cal range) shows the range of an open loop with calculated values of ${\rm{\theta }}_{\rm sat}$ varied using Eq. (5); the purple area appears to be a line, because the range of calculated ${\mathrm{\theta }}_{\mathrm{sat}}$ is small, which leads to a small difference in soil moisture simulations for different calculated ${\mathrm{\theta }}_{\mathrm{sat}}$ ; ${\rm{\theta }}_{{\rm sat}{{\text -}{{\rm{min}}}}}$ and ${\mathrm{\theta }}_{{\mathrm{sat}}{\text-}{\rm max} }$ are the soil moisture simulations for the smallest and largest observed values of ${\mathrm{\theta }}_{\mathrm{sat}}$ .

Download: Larger image PowerPoint slide

Fig. 4. RMSE between the soil moisture simulation and observation with different values of ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ . The left column shows RMSE with observed ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ varied; the right column shows RMSE with calculated ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ in Eq. (5), and RMSE seems a constant because of the small change in calculated ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ .

Download: Larger image PowerPoint slide

Fig. 5. As in Fig. 3, but for ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ .

Download: Larger image PowerPoint slide

Fig. 6. RMSE between soil moisture simulation and observation for various ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ . The left column shows RMSE for varied observed ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ ; the right column shows RMSE for varied calculated ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ in Eq. (4).

Download: Larger image PowerPoint slide

Fig. 7. Variations of soil moisture simulations and observations. The gray shaded area (simulation-cal range) shows the range of an open loop with various calculated values of ${\mathrm{\psi }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ in the left column of Eq. (7) and various calculated values of B in the right column of Eq. (6).

Download: Larger image PowerPoint slide

Fig. 8. Optimized K_sat with different ranges depending on the values for four soil interfaces (Table 1): K_sat ∈ (1.14 × 10⁻³, 8.53 × 10⁻³) mm s⁻¹ for interface 1, K_sat ∈ (7.44 × 10⁻⁴, 6.27 × 10⁻³) mm s⁻¹ for interface 2, K_sat ∈ (1.57 × 10⁻⁴, 7.33 × 10⁻⁴) mm s⁻¹ for interface 3, and K_sat ∈ (2.67 × 10⁻⁴, 2.63 × 10⁻²) mm s⁻¹ for interface 4.

Download: Larger image PowerPoint slide

Fig. 9. Soil moisture assimilation using optimized ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ (left column; a1–a4) and calculated ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ (right column; b1–b4) with an assimilation interval 12 ∆t.

Download: Larger image PowerPoint slide

Fig. 10. Soil moisture assimilation using optimized ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ (left column; a1–a4) and calculated ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ ; (right column; b1–b4) with 24 ∆t assimilation interval.

Download: Larger image PowerPoint slide

Fig. 11. Optimized ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ for the same range 1.57 × 10⁻⁴ to 2.63 × 10⁻² mm s⁻¹ at the four interfaces between two adjacent soil layers, i.e., ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ ∈ (1.57 × 10⁻⁴, 2.63 × 10⁻²) mm s⁻¹ for interfaces 1 to 4.

Download: Larger image PowerPoint slide

Fig. 12. Optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ with the range 0.37 to 0.85 m³ m⁻³ by GA for four soil layers.

Download: Larger image PowerPoint slide

Fig. 13. Soil moisture assimilation using optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ with assimilation interval 12 ∆t.

Download: Larger image PowerPoint slide

Table 1 Profiles of basic soil properties at Maqu

Depth (cm)	Sand (%)			Clay (%)			Silt (%)			Bulk density (g cm⁻³)			Porosity (m³ m⁻³)			K_sat (mm s⁻¹)
Depth (cm)	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max
5	14.45	26.95	41.37	8.66	9.86	11.03	49.70	63.19	75.02	0.39	0.76	1.06	0.60	0.73	0.85
10	14.44	29.03	47.59	8.40	9.95	11.25	44.01	61.02	74.31	0.53	0.95	1.21	0.57	0.66	0.80	1.14 × 10⁻³	3.87 × 10⁻³	8.53 × 10⁻³
20	17.00	29.20	45.34	9.42	10.15	10.90	44.30	60.65	72.81	0.86	1.23	1.49	0.51	0.59	0.69	7.44 × 10⁻⁴	3.85 × 10⁻³	6.27 × 10⁻³
40	17.81	31.60	53.11	9.01	10.43	11.55	37.88	57.97	70.86	1.19	1.40	1.55	0.37	0.51	0.62	1.57 × 10⁻⁴	3.64 × 10⁻⁴	7.33 × 10⁻⁴
80	19.06	34.83	63.31	5.47	9.35	13.87	31.22	55.82	69.46	1.15	1.49	1.71	0.41	0.47	0.60	2.67 × 10⁻⁴	8.76 × 10⁻³	2.63 × 10⁻²
Note: Sand, silt, and clay are the standard particle size classes of the United States Department of Agriculture (USDA), porosity is saturated volumetric soil moisture content.

Download: Download as CSV

Table 2 The ranges of soil hydraulic parameters calculated using Eqs. (4)–(7)

Depth (cm)	${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ (m³ m⁻³)		${{K} }_{\mathrm{s}\mathrm{a}\mathrm{t} }$ (mm s⁻¹)		${\mathrm{\psi }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ (mm)		B
Depth (cm)	min	max	min	max	min	max	min	max
5	0.4885	0.4888	7.97 × 10⁻⁴↓	8.05 × 10⁻⁴↓	−755.28	−749.17	2.9238	2.9275
10	0.4884	0.4888	6.86 × 10⁻⁴↓	6.94 × 10⁻⁴↓	−755.28	−747.77	2.9234	2.9279
20	0.4884	0.4888	5.09 × 10⁻⁴↓	5.14 × 10⁻⁴↓	−754.70	−748.27	2.9250	2.9273
40	0.4883	0.4888	2.79 × 10⁻⁴↓	2.83 × 10⁻⁴↓	−754.51	−746.52	2.9243	2.9284
80	0.4882	0.4888	1.26 × 10⁻⁴↓	1.28 × 10⁻⁴↓	−754.23	−744.23	2.9187	2.9321
Note: “↓” is the saturated hydraulic conductivity at the downward interface of two adjacent layers.

Download: Download as CSV

Table 3 RMSE and SS for simulation and assimilation using GA-optimized ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ and calculated ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ with an assimilation interval 12 ∆t

Depth (cm)	Optimized ${ {K} }_{\mathrm{s}\mathrm{a}\mathrm{t} }$				Calculated (without optimization) ${ {K} }_{\mathrm{s}\mathrm{a}\mathrm{t} }$
	RMSE (m³ m⁻³)		AE (m³ m⁻³)	SS	RMSE (m³ m⁻³)		AE (m³ m⁻³)	SS
	Simulation	Assimilation	AE (m³ m⁻³)	SS	Simulation	Assimilation	AE (m³ m⁻³)	SS
5	0.0281	0.0215	0.0066	0.4120	0.0483	0.0426	0.0057	0.2244
20	0.0232	0.0148	0.0084	0.5911	0.0396	0.0320	0.0076	0.3469
50	0.0261	0.0161	0.0100	0.6178	0.0326	0.0216	0.0110	0.5615
80	0.0394	0.0260	0.0134	0.5642	0.0522	0.0423	0.0099	0.3425
Note: “AE” is the absolute error between RMSE for simulation and RMSE for assimilation; calculated K_sat is obtained using Eq. (4) according to the observed mean values of basic soil properties (Table 1).

Download: Download as CSV

Table 4 RMSE and SS for simulation and assimilation using GA-optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ and ${{K}}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ with assimilation interval 12 ∆t

Depth (cm)	Optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$			Optimized ${\mathrm{\theta }}_{\mathrm{s}\mathrm{a}\mathrm{t}}$ and ${ {K} }_{\mathrm{s}\mathrm{a}\mathrm{t} }$
	RMSE (m³ m⁻³)		SS	RMSE (m³ m⁻³)		SS
	Simulation	Assimilation	SS	Simulation	Assimilation	SS
5	0.0273	0.0247	0.1839	0.0141	0.0134	0.0914
20	0.0192	0.0150	0.3865	0.0103	0.0092	0.2074
50	0.0164	0.0095	0.6652	0.0108	0.0083	0.4144
80	0.0358	0.0278	0.3961	0.0225	0.0162	0.4799

Download: Download as CSV

References (49)

References

Allen, R. G., 2000: Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J. Hydrol., 229, 27–41. doi: 10.1016/S0022-1694(99)00194-8

Arifovic, J., 1994: Genetic algorithm learning and the cobweb model. J. Econom. Dyn. Control., 18, 3–28. doi: 10.1016/0165-1889(94)90067-1

Assouline, S., and J. Selker, 2017: Introduction and evaluation of a Weibull hydraulic conductivity-pressure head relationship for unsaturated soils. Water Resour. Res., 53, 4956–4964. doi: 10.1002/2017WR020796

Bies, R. R., M. F. Muldoon, B. G. Pollock, et al., 2006: A genetic algorithm-based, hybrid machine learning approach to model selection. J. Pharmacokinet. Pharmacodyn., 33, 195–221. doi: 10.1007/s10928-006-9004-6

Brandhorst, N., D. Erdal, and I. Neuweiler, 2017: Soil moisture prediction with the ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters. Adv. Water Resour., 110, 360–370. doi: 10.1016/j.advwatres.2017.10.022

Chen, J. L., J. Wen, H. Tian, et al., 2018: A study of soil thermal and hydraulic properties and parameterizations for CLM in the SRYR. J. Geophys. Res. Atmos., 123, 8487–8499. doi: 10.1029/2017JD028034

Chirico, G. B., H. Medina, and N. Romano, 2014: Kalman filters for assimilating near-surface observations into the Richards equation–Part 1: Retrieving state profiles with linear and nonlinear numerical schemes. Hydrol. Earth Syst. Sci., 18, 2503–2520. doi: 10.5194/hess-18-2503-2014

Clapp, R. B., and G. M. Hornberger, 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601–604. doi: 10.1029/WR014i004p00601

Deng, Y. H., S. J. Wang, X. Y. Bai, et al., 2020: Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations. Hydrol. Process., 34, 836–851. doi: 10.1002/hyp.13636

Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy, 1993: Biosphere–Atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model. No. NCAR/TN-387+STR, National Center for Atmospheric Research, Boulder, doi: 10.5065/D67W6959.

Dong, J. Z., S. C. Steele-Dunne, J. Judge, et al., 2015: A particle batch smoother for soil moisture estimation using soil temperature observations. Adv. Water Resour., 83, 111–122. doi: 10.1016/j.advwatres.2015.05.017

Dong, J. Z., S. C. Steele-Dunne, T. E. Ochsner, et al., 2016: Determining soil moisture and soil properties in vegetated areas by assimilating soil temperatures. Water Resour. Res., 52, 4280–4300. doi: 10.1002/2015wr018425

Famiglietti, J. S., and E. F. Wood, 1994: Multiscale modeling of spatially variable water and energy balance processes. Water Resour. Res., 30, 3061–3078. doi: 10.1029/94WR01498

Fu, X. L., Z. B. Yu, L. F. Luo, et al., 2014: Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter. Stoch. Environ. Res. Risk Assess., 28, 681–693. doi: 10.1007/s00477-013-0781-3

Fu, X. L., L. F. Luo, M. Pan, et al., 2018: Evaluation of TOPMODEL-based land surface-atmosphere transfer scheme (TOPLATS) through a soil moisture simulation. Earth Interact., 22, 1–19. doi: 10.1175/EI-D-17-0037.1

Fu, X. L., Z. B. Yu, Y. Tang, et al., 2019: Evaluating soil moisture predictions based on ensemble Kalman filter and SiB2 model. J. Meteor. Res., 33, 190–205. doi: 10.1007/s13351-019-8138-6

Fu, X. L., Z. B. Yu, Y. J. Ding, et al., 2020: Unscented weighted ensemble Kalman filter for soil moisture assimilation. J. Hydrol., 580, 124352. doi: 10.1016/j.jhydrol.2019.124352

Fu, X. L., X. L. Jiang, Z. B. Yu, et al., 2022: Understanding the key factors that influence soil moisture estimation using the unscented weighted ensemble Kalman filter. Agric. Forest Meteor., 313, 108,745. doi: 10.1016/j.agrformet.2021.108745

Gevaert, A. I., L. J. Renzullo, A. I. J. M. van Dijk, et al., 2018: Joint assimilation of soil moisture retrieved from multiple passive microwave frequencies increases robustness of soil moisture state estimation. Hydrol. Earth Syst. Sci., 22, 4605–4619. doi: 10.5194/hess-22-4605-2018

Han, X. J., and X. Li, 2008: An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation. Remote Sens. Environ., 112, 1434–1449. doi: 10.1016/j.rse.2007.07.008

Han, X. J., H. J. H. Franssen, C. Montzka, et al., 2014: Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations. Water Resour. Res., 50, 6081–6105. doi: 10.1002/2013WR014586

Huang, C. L., W. J. Chen, Y. Li, et al., 2016: Assimilating multi-source data into land surface model to simultaneously improve estimations of soil moisture, soil temperature, and surface turbulent fluxes in irrigated fields. Agric. Forest Meteor., 230–231, 142–156, doi: 10.1016/j.agrformet.2016.03.013.

Jia, D. Y., J. Wen, X. Wang, et al., 2019: Soil hydraulic conductivity and its influence on soil moisture simulations in the source region of the Yellow River—take Maqu as an example. Sci. Cold Arid Reg., 11, 360–370. doi: 10.3724/SP.J.1226.2019.00360

Ju, F., R. An, and Y. X. Sun, 2019: Immune evolution particle filter for soil moisture data assimilation. Water, 11, 211. doi: 10.3390/w11020211

Keefer, T. O., M. S. Moran, and G. B. Paige, 2008: Long-term meteorological and soil hydrology database, walnut gulch experimental watershed, Arizona, United States. Water Resour. Res., 44, W05S07. doi: 10.1029/2006WR005702

Kolassa, J., R. H. Reichle, Q. Liu, et al., 2018: Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sens. Environ., 204, 43–59. doi: 10.1016/j.rse.2017.10.045

Lawrence, D., R. Fisher, C. Koven, et al., 2018: Technical Description of version 5.0 of the Community Land Model (CLM). National Center for Atmospheric Research, Boulder. Available online at https://escomp.github.io/ctsm-docs/versions/release-clm5.0/html/users_guide/index.html. Accessed on 29 January 2023.

Lyu, H. S., Z. B. Yu, Y. H. Zhu, et al., 2011: Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation. Adv. Water Resour., 34, 395–406. doi: 10.1016/j.advwatres.2010.12.005

Murphy, A. H., 1996: General decompositions of MSE-based skill scores: Measures of some basic aspects of forecast quality. Mon. Wea. Rev., 124, 2353–2369. doi: 10.1175/1520-0493(1996)124<2353:gdombs>2.0.co;2

Naz, B. S., S. Kollet, H. J. H. Franssen, et al., 2020: A 3 km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Sci. Data, 7, 111. doi: 10.1038/s41597-020-0450-6

Oleson, K. W., Y. J. Dai, G. Bonan, et al., 2004: Technical Description of the Community Land Model (CLM). No. NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder. Available online at http://dx.doi.org/10.5065/D6N877R0. Accessed on 29 January 2023.

Reichle, R. H., G. J. M. De Lannoy, Q. Liu, et al., 2017: Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements. J. Hydrometeor., 18, 2621–2645. doi: 10.1175/JHM-D-17-0063.1

Richards, L. A., 1931: Capillary conduction of liquids through porous mediums. Physics, 1, 318–333. doi: 10.1063/1.1745010

Sellers, P. J., D. A. Randall, G. J. Collatz, et al., 1996: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1: Model formulation. J. Climate, 9, 676–705. doi: 10.1175/1520-0442(1996)009<0676:arlspf>2.0.co;2

Seo, E., M.-I. Lee, and R. H. Reichle, 2021: Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman filter. Remote Sens. Environ., 253, 112222. doi: 10.1016/j.rse.2020.112222

Su, Z., J. Wen, Y. Zeng, et al., 2020: Multiyear in-situ L-band microwave radiometry of land surface processes on the Tibetan Plateau. Sci. Data, 7, 317. doi: 10.1038/s41597-020-00657-1

Tangdamrongsub, N., S. C. Han, I. Y. Yeo, et al., 2020: Multivariate data assimilation of GRACE, SMOS, SMAP measurements for improved regional soil moisture and groundwater storage estimates. Adv. Water Resour., 135, 103477. doi: 10.1016/j.advwatres.2019.103477

Vereecken, H., J. A. Huisman, H. Bogena, et al., 2008: On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resour. Res., 44, W00D06. doi: 10.1029/2008WR006829

Weerts, A. H., and G. Y. H. El Serafy, 2006: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resour. Res., 42, W09403. doi: 10.1029/2005WR004093

Xu, S. Q., Z. B. Yu, K. Zhang, et al., 2018: Simulating canopy conductance of the Haloxylon ammodendron shrubland in an arid inland river basin of northwest China. Agric. Forest Meteor., 249, 22–34. doi: 10.1016/j.agrformet.2017.11.015

Yan, H. X., C. M. DeChant, and H. Moradkhani, 2015: Improving soil moisture profile prediction with the particle filter-Markov Chain Monte Carlo method. IEEE Trans. Geosci. Remote Sens., 53, 6134–6147. doi: 10.1109/TGRS.2015.2432067

Yang, S. H., R. Li, T. H. Wu, et al., 2021: Evaluation of soil thermal conductivity schemes incorporated into CLM5.0 in permafrost regions on the Tibetan Plateau. Geoderma, 401, 115330. doi: 10.1016/j.geoderma.2021.115330

Yu, Z. B., T. N. Carlson, E. J. Barron, et al., 2001: On evaluating the spatial–temporal variation of soil moisture in the Susquehanna River Basin. Water Resour. Res., 37, 1313–1326. doi: 10.1029/2000WR900369

Yu, Z. B., D. Liu, H. S. Lyu, et al., 2012: A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. J. Hydrol., 475, 53–64. doi: 10.1016/j.jhydrol.2012.08.034

Yu, Z. B., X. L. Fu, L. F. Luo, et al., 2014: One-dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODIS LST with the ensemble particle filter. Water Resour. Res., 50, 6950–6965. doi: 10.1002/2012WR013473

Zhao, H., Y. J. Zeng, S. N. Lv, et al., 2018: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau. Earth Syst. Sci. Data, 10, 1031–1061. doi: 10.5194/essd-10-1031-2018

Zhao, L., Z.-L. Yang, and T. J. Hoar, 2016: Global soil moisture estimation by assimilating AMSR-E brightness temperatures in a coupled CLM4-RTM-DART system. J. Hydrometeor., 17, 2431–2454. doi: 10.1175/JHM-D-15-0218.1

Zheng, D. H., X. Wang, R. van der Velde, et al., 2017: L-band microwave emission of soil freeze-thaw process in the Third Pole environment. IEEE Trans. Geosci. Remote Sens., 55, 5324–5338. doi: 10.1109/tgrs.2017.2705248

Zohaib, M., H. Kim, and M. Choi, 2017: Evaluating the patterns of spatiotemporal trends of root zone soil moisture in major climate regions in East Asia. J. Geophys. Res. Atmos., 122, 7705–7722. doi: 10.1002/2016JD026379

Relative Articles

Supplements (1)

Supplements
Other Related Supplements
- PDF format
  Xiaolei JIANG and Xiaolei FU Download(247KB)

Cited By

Cited by

Periodical cited type(3)

1.	Lukas F. Meldau, Bailiang Li, Cheryl McKenna Neuman, et al. Constant stress layer characteristics in simulated stratified air flows: Implications for aeolian transport. Aeolian Research, 2023, 63-65: 100888. DOI:10.1016/j.aeolia.2023.100888
2.	Ping Yue, Qiang Zhang, Runyuan Wang, et al. Turbulence intensity and turbulent kinetic energy parameters over a heterogeneous terrain of Loess Plateau. Advances in Atmospheric Sciences, 2015, 32(9): 1291. DOI:10.1007/s00376-015-4258-9
3.	Qiang Zhang, Tong Yao, Ping Yue. Development and test of a multifactorial parameterization scheme of land surface aerodynamic roughness length for flat land surfaces with short vegetation. Science China Earth Sciences, 2015. DOI:10.1007/s11430-015-5137-z

Other cited types(0)

Search

Citation

Fu, X. L., H. S. Lyu, Z. B. Yu, et al., 2023: Effects of soil hydraulic properties on soil moisture estimation. J. Meteor. Res., 37(1), 58–74, doi: 10.1007/s13351-023-2049-2.

Citation:

Fu, X. L., H. S. Lyu, Z. B. Yu, et al., 2023: Effects of soil hydraulic properties on soil moisture estimation. J. Meteor. Res., 37(1), 58–74, doi: 10.1007/s13351-023-2049-2.

Export: BibTex EndNote

Article Metrics

Article views: 489
PDF downloads: 35 Cited by: 3

Manuscript History

Received: 14 April 2022

Revised: 19 August 2022

Accepted: 22 September 2022

Available online: 26 September 2022

Final form: 12 October 2022

Typeset Proofs: 31 December 2022

Issue in Progress: 03 January 2023

Published online: 19 February 2023

	Aoqi GU, Ye WANG. 2024: Optimizing Clear Air Turbulence Forecasts Using the K-Nearest Neighbor Algorithm. Journal of Meteorological Research, 38(6): 1064-1077. DOI: 10.1007/s13351-024-4060-7
	Zebin LU, Jianjun XU, Zhiqiang CHEN, Jinyi YANG, Jeremy Cheuk-Hin LEUNG, Daosheng XU, Banglin ZHANG. 2024: Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA). Journal of Meteorological Research, 38(1): 10-26. DOI: 10.1007/s13351-024-3105-2
	Xin HANG, Yachun LI, Xinyi LI, Meng XU, Liangxiao SUN. 2022: Estimation of Chlorophyll-a Concentration in Lake Taihu from Gaofen-1 Wide-Field-of-View Data through a Machine Learning Trained Algorithm. Journal of Meteorological Research, 36(1): 208-226. DOI: 10.1007/s13351-022-1146-y
	Xiaolei FU, Zhongbo YU, Ying TANG, Yongjian DING, Haishen LYU, Baoqing ZHANG, Xiaolei JIANG, Qin JU. 2019: Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model. Journal of Meteorological Research, 33(2): 190-205. DOI: 10.1007/s13351-019-8138-6
	Yongzhu LIU, Lin ZHANG, Zhihua LIAN. 2018: Conjugate Gradient Algorithm in the Four-Dimensional Variational Data Assimilation System in GRAPES. Journal of Meteorological Research, 32(6): 974-984. DOI: 10.1007/s13351-018-8053-2
	Jinhu WANG, Junxiang GE, Qilin ZHANG, Pan FAN, Ming WEI, Xiangchao LI. 2017: Study of Aircraft Icing Warning Algorithm Based on Millimeter Wave Radar. Journal of Meteorological Research, 31(6): 1034-1044. DOI: 10.1007/s13351-017-6796-9
	Yuanzhao CHEN, Hongping LAN, Xunlai CHEN, Wenhai ZHANG. 2017: A Nowcasting Technique Based on Application of the Particle Filter Blending Algorithm. Journal of Meteorological Research, 31(5): 931-945. DOI: 10.1007/s13351-017-6557-9
	Min MIN, Chunqiang WU, Chuan LI, Hui LIU, Na XU, Xiao WU, Lin CHEN, Fu WANG, Fenglin SUN, Danyu QIN, Xi WANG, Bo LI, Zhaojun ZHENG, Guangzhen CAO, Lixin DONG. 2017: Developing the Science Product Algorithm Testbed for Chinese Next-Generation Geostationary Meteorological Satellites: Fengyun-4 Series. Journal of Meteorological Research, 31(4): 708-719. DOI: 10.1007/s13351-017-6161-z
	YAO Cai, JIN Long, ZHAO Huasheng. 2009: Ensemble Prediction of Monsoon Index with a Genetic Neural Network Model. Journal of Meteorological Research, 23(6): 701-712.
	Guo Guang, Yah Shaojin, Jin Long. 1998: TIMESERIES PREDICTION MODEL CONSISTING OF ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM^*. Journal of Meteorological Research, 12(2): 247-256.

Effects of Soil Hydraulic Properties on Soil Moisture Estimation

土壤水力属性对土壤湿度估算的影响

Abstract