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Journal of Arid Land
Journal of Arid Land  2020, Vol. 12 Issue (3): 462-472    DOI: 10.1007/s40333-020-0068-8
Research article     
Factors determining soil water heterogeneity on the Chinese Loess Plateau as based on an empirical mode decomposition method
GONG Yidan1, XING Xuguang2, WANG Weihua1,*()
1 Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming 650500, China
2 Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, China
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Abstract  

Soil water is a critical resource, and as such is the focus of considerable physical research. Characterization of the distribution and spatial variability of soil water content (SWC) offers important agronomic and environmental information. Estimation of non-stationary and non-linear SWC distribution at different scales is a research challenge. Based on this context, we performed a case study on the Chinese Loess Plateau, with objectives of investigating spatial variability of SWC and soil properties (i.e., soil particle composition, organic matter and bulk density), and determining multi-scale correlations between SWC and soil properties. A total of 86 in situ sampling sites were selected and 516 soil samples (0-60 cm depth with an interval of 10 cm) were collected in May and June of 2019 along the Yangling-Wugong-Qianxian transect, with a length of 25.5 km, in a typical wheat-corn rotation region of the Chinese Loess Plateau. Classical statistics and empirical mode decomposition (EMD) method were applied to evaluate characteristics of the overall and scale-specific spatial variation of SWC, and to explore scale-specific correlations between SWC and soil properties. Results showed that the spatial variability of SWC along the Yangling-Wugong-Qianxian transect was medium to weak, with a variability coefficient range of 0.06-0.18, and it was gradually decreased as scale increased. We categorized the overall SWC for each soil layer under an intrinsic mode function (IMF) number based on the scale of occurrence, and found that the component IMF1 exhibited the largest contribution rates of 36.45%-56.70%. Additionally, by using EMD method, we categorized the general variation of SWC under different numbers of IMFs according to occurrence scale, and the results showed that the calculated scales among SWC for each soil layer increased in correspondence with higher IMF numbers. Approximately 78.00% of the total variance of SWC was extracted in IMF1 and IMF2. Generally, soil texture was the dominant control on SWC, and the influence of the three types of soil properties (soil particle composition, organic matter and bulk density) was more prominent at larger scales along the sampling transect. The influential factors of soil water spatial distribution can be identified and ranked on the basis of the decomposed signal from the current approach, thereby providing critical information for other researchers and natural resource managers.

Key words： bulk density      loess plateau      soil water      soil organic matter      soil texture      spatial variability     
Received: 03 July 2019      Published: 10 May 2020
Corresponding Authors: Weihua WANG     E-mail: wangweihua1220@163.com
About author: *Corresponding author: WANG Weihua (E-mail: wangweihua1220@163.com)
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Yidan GONG
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Cite this article:

GONG Yidan, XING Xuguang, WANG Weihua. Factors determining soil water heterogeneity on the Chinese Loess Plateau as based on an empirical mode decomposition method. Journal of Arid Land, 2020, 12(3): 462-472.

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http://jal.xjegi.com/10.1007/s40333-020-0068-8     OR     http://jal.xjegi.com/Y2020/V12/I3/462

Fig. 1 Location of the study area on the Chinese Loess Plateau (a) and overview of the Yangling-Wugong-Qianxian transect (b)
Soil depth
(cm)		 SWC
Mean (%) Maximum (%) Minimum (%) Standard deviation (%) Variability coefficient
0-10 23.00 38.05 16.22 4.21 0.18
10-20 25.47 48.43 18.13 4.40 0.17
20-30 24.55 41.85 17.61 3.62 0.15
30-40 24.05 34.38 19.24 2.69 0.11
40-50 23.32 35.73 19.38 2.45 0.10
50-60 21.55 26.55 16.61 1.31 0.06
Table 1 Statistical characteristic values of soil water content (SWC)
Fig. 2 Intrinsic mode functions (IMFs) and residues of soil water content (SWC) at various soil depths. The numbers in each square bracket, in order, represent the characteristic scale value (m) and the contribution rate (%) of each IMF or residue. -, no value.
IMFs/residue 10-20 cm 20-30 cm 30-40 cm 40-50 cm 50-60 cm
Decomposition of SWC at the 0-10 cm depth IMF1 0.852** 0.528* 0.128 0.105 -0.082
IMF2 0.707* 0.450* 0.055 0.124 -0.055
IMF3 -0.398 0.467 -0.033 0.111 -0.082
IMF4 0.569 0.555 0.467 0.250 -1.138
IMF5 0.463 0.025 -0.074 -0.071 0.051
Residue 0.362 -0.387 -0.290** -0.132 -0.238*
IMFs/residue 20-30 cm 30-40 cm 40-50 cm 50-60 cm
Decomposition of SWC at the 10-20 cm depth IMF1 0.787** 0.191 0.031 0.035
IMF2 -0.494 -0.199 -0.042 -0.072
IMF3 -0.698** -0.154 -0.040 -0.050
IMF4 0.268 0.184 -0.029 -0.034
IMF5 0.196 -0.155 -0.186 0.021
Residue -0.397 -0.309** -0.164 -0.273*
IMFs/residue 30-40 cm 40-50 cm 50-60 cm
Decomposition of SWC at the 20-30 cm depth IMF1 0.654** 0.517 0.117
IMF2 0.628* 0.551* 0.055
IMF3 0.627 -0.524 0.117
IMF4 0.273 0.342 -0.183
IMF5 0.137 0.331 0.138
Residue 0.322* 0.139 0.159
IMFs/residue 40-50 cm 50-60 cm
Decomposition of SWC at the 30-40 cm depth IMF1 0.517** 0.340
IMF2 0.413 0.248
IMF3 0.423 -0.233
IMF4 0.641* 0.145
Residue 0.546* 0.329
IMFs/residue 50-60 cm
Decomposition of SWC at the 40-50 cm depth IMF1 0.349*
IMF2 0.156
IMF3 -0.143
IMF4 0.024
IMF5 0.342**
Residue -0.079
Table 2 Correlation coefficients of measured SWC with intrinsic mode functions (IMFs) and residues of SWC at different soil depths
Fig. 3 Intrinsic mode functions (IMFs) and residues of mean SWC and soil properties (soil particle content, organic matter and bulk density) at the soil depth of 0-60 cm. The numbers in each square bracket, in order, represent the characteristic scale value (m) and the contribution rate (%) of each IMF or residue. -, no value.
IMFs/residue Clay Silt Sand Soil organic matter Soil bulk density
IMF1 0.520** 0.508** -0.514** 0.411* 0.159
IMF2 0.589** 0.605** -0.592** 0.501* 0.431*
IMF3 0.672** 0.669** -0.665** 0.545** 0.363
IMF4 0.714** 0.705** -0.706** -0.582** -0.689**
Residue -0.847** -0.840** 0.433** 0.463** -0.973**
Table 3 Correlation coefficients of soil properties with IMFs and residue of mean SWC at the depth of 0-60 cm
[1]   Ahmad F M, Shen R, Zaheer A B, et al.2018. Study capability of Standardized Precipitation Evapotranspiration Index to represent soil moisture variability during winter season over Pakistan for the period 1951-2010. Meteorology and Atmospheric Physics, 130: 689-699.
doi: 10.1007/s00703-017-0529-6
[2]   Arhonditsis G B, Stow C A, Steinberg L J, et al.2006. Exploring ecological patterns with structural equation modeling and Bayesian analysis. Ecological Modelling, 192(3-4): 385-409.
doi: 10.1016/j.ecolmodel.2005.07.028
[3]   Bao S D.2008. Soil Agrochemical Analysis (3rd ed.). Beijing: China Agriculture Press, 27-38. (in Chinese)
[4]   Biswas A, Si B C.2011. Revealing the controls of soil water storage at different scales in a hummocky landscape. Soil Science Society of America Journal, 75(4): 1295-1306.
doi: 10.2136/sssaj2010.0131
[5]   Cai J S, Yeh T C J, Yan E C, et al.2019. Importance of variability in initial soil moisture and rainfalls on slope stability. Journal of Hydrology, 571: 265-278.
doi: 10.1016/j.jhydrol.2019.01.046
[6]   Coppola A, Comegna A, Dragonetti G, et al.2011. Average moisture saturation effects on temporal stability of soil water spatial distribution at field scale. Soil & Tillage Research, 114(2): 155-164.
[7]   Dari J, Morbidelli R, Saltalippi C, et al.2019. Spatial-temporal variability of soil moisture: Addressing the monitoring at the catchment scale. Journal of Hydrology, 570: 436-444.
doi: 10.1016/j.jhydrol.2019.01.014
[8]   Entin J K, Robock A, Vinnikov K Y, et al.2000. Temporal and spatial scales of observed soil moisture variations in the extratropics. Journal of Geophysical Research, 105(D9): 11865-11877.
doi: 10.1029/2000JD900051
[9]   Feki M, Ravazzani G, Ceppi A, et al.2018. Influence of soil hydraulic variability on soil moisture simulations and irrigation scheduling in a maize field. Agricultural Water Management, 202: 183-194.
doi: 10.1016/j.agwat.2018.02.024
[10]   Fu X L, Shao M A, Wei X R, et al.2013. Effects of monovegetation restoration types on soil water distribution and balance on a hillslope in northern Loess Plateau of China. Journal of Hydrologic Engineering, 18(4): 413-421. (in Chinese)
doi: 10.1061/(ASCE)HE.1943-5584.0000628
[11]   He Z B, Zhao M M, Zhu X, et al.2019. Temporal stability of soil water storage in multiple soil layers in high-elevation forests. Journal of Hydrology, 569: 532-545.
doi: 10.1016/j.jhydrol.2018.12.024
[12]   Heathman G C, Cosh M H, Han E, et al.2012. Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks. Geoderma, 170: 195-205.
doi: 10.1016/j.geoderma.2011.11.004
[13]   Hu W, Si B C.2014. Revealing the relative influence of soil and topographic properties on soil water content distribution at the watershed scale in two sites. Journal of Hydrology, 516: 107-118.
doi: 10.1016/j.jhydrol.2013.10.002
[14]   Jadidoleslam N, Mantilla R, Krajewski W F, et al.2019. Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture. Journal of Hydrology, 576: 85-97.
doi: 10.1016/j.jhydrol.2019.06.026
[15]   Jia Y H, Shao M A.2014. Dynamics of deep soil moisture in response to vegetational restoration on the Loess Plateau of China. Journal of Hydrology, 519: 523-531.
doi: 10.1016/j.jhydrol.2014.07.043
[16]   Joiner J, Yoshida Y, Anderson M, et al.2018. Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. Remote Sensing of Environment, 219: 339-352.
doi: 10.1016/j.rse.2018.10.020
[17]   Kargas G, Kerkides P, Sotirakoglou K, et al.2016. Temporal variability of surface soil hydraulic properties under various tillage systems. Soil & Tillage Research, 158: 22-31.
[18]   Kerry R, Oliver M A.2007. Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likehood. Geoderma. 140(4): 383-396.
doi: 10.1016/j.geoderma.2007.04.019
[19]   Kong D, Wang L Q, Liu J L, et al.2017. Scale effect of spatial variability of cropland soil water content in black soil region. Journal of Hydraulic Engineering, 48(5): 608-612, 622. (in Chinese)
[20]   Körschens M.2002. Importance of soil organic matter (SOM) for biomass production and environment (a review). Archives of Agronomy and Soil Science, 48(2): 89-94.
doi: 10.1080/03650340214162
[21]   Lai X M, Zhu Q, Zhou Z W, et al.2017. Influences of sampling size and pattern on the uncertainty of correlation estimation between soil water content and its influencing factors. Journal of Hydrology, 555: 41-50.
doi: 10.1016/j.jhydrol.2017.10.010
[22]   Lian Y Z, Cao L H, Liu H M, et al.2019. Spatial distribution of soil water content in farmland and adjacent grassland in adjacent grassland ecotone of agriculture-animal husbandry in Tibet. Southwest China Journal of Agricultural Sciences, 32(5): 1092-1097. (in Chinese)
[23]   Liang H B, Xue Y Y, Shi J W, et al.2018. Soil moisture dynamics under Caragana korshinskii shrubs of different ages in Wuzhai County on the Loess Plateau, China. Earth and Environmental Science Transactions of the Royal Society of Edinburgh, 109(3-4): 387-396.
doi: 10.1017/S1755691018000622
[24]   Liang J P, Xing X G, Gao Y.2019. A modified physical-based water-retention model for continuous soil moisture estimation during infiltration: experiments on saline and non-saline soils. Archives of Agronomy and Soil Science, doi: 10.1080/03650340.2019.1669785.
[25]   Liu B X, Shao M A.2016. Response of soil water dynamics to precipitation years under different vegetation types on the northern Loess Plateau, China. Journal of Arid Land, 8(1): 47-59.
doi: 10.1007/s40333-015-0088-y
[26]   Neris J, Jiménez C, Fuentes J, et al.2012. Vegetation and land-use effects on soil properties and water infiltration of Andisols in Tenerife (Canary Islands, Spain). Catena, 98: 55-62.
doi: 10.1016/j.catena.2012.06.006
[27]   Nosetto M D, Jobbágy E G, Tóth T, et al.2007. The effects of tree establishment on water and salt dynamics in naturally salt-affected grasslands. Oecologia, 152: 695-705.
doi: 10.1007/s00442-007-0694-2
[28]   Pangaluru K, Velicogna I, A G, et al.2019. Soil moisture variability in India: relationship of land surface-atmosphere fields using maximum covariance analysis. Remote Sensing, 11(3): 335.
doi: 10.3390/rs11030335
[29]   Peterson A M, Helgason W H, Ireson A M, et al.2019. How spatial patterns of soil moisture dynamics can explain field-scale soil moisture variability: observations from a sodic landscape. Water Resource Research, 55(5): 4410-4426.
doi: 10.1029/2018WR023329
[30]   Schneider K, Huisman, J A, Breuer L, et al.2008. Temporal stability of soil moisture in various semi-arid steppe ecosystems and its application in remote sensing. Journal of Hydrology, 359(1-2): 16-29.
doi: 10.1016/j.jhydrol.2008.06.016
[31]   She D L, Liu D D, Peng S Z, et al.2013. Multiscale influences of soil properties on soil water content distribution in a watershed on the Chinese Loess Plateau. Soil Science, 178(10): 530-539.
doi: 10.1097/SS.0000000000000021
[32]   She D L, Fei Y H, Chen Q, et al.2016. Spatial scaling of soil salinity indices along a temporal coastal reclamation area transect in China using wavelet analysis. Archives of Agronomy and Soil Science, 62(12): 1625-1639.
doi: 10.1080/03650340.2016.1155698
[33]   Si B C.2008. Spatial scaling analyses of soil physical properties: A review of spectral and wavelet methods. Vadose Zone Journal, 7(2): 547-562.
doi: 10.2136/vzj2007.0040
[34]   Siegfried J, Longchamps L, Khosla R.2019. Multispectral satellite imagery to quantify in-field soil moisture variability. Journal of Soil and Water Conservation, 74(1): 33-40.
doi: 10.2489/jswc.74.1.33
[35]   Simbahan G C, Dobermann A.2006. Sampling optimization based on secondary information and its utilization in soil carbon mapping. Geoderma, 133(3-4): 345-362.
doi: 10.1016/j.geoderma.2005.07.020
[36]   Vereecken H, Kamai T, Harter T, et al.2007. Explaining soil moisture variability as a function of mean soil moisture: A stochastic unsaturated flow perspective. Geophysical Research Letters, 34(22): 315-324.
[37]   Wang Y G, Deng C Y, Liu Y, et al.2018. Identifying change in spatial accumulation of soil salinity in an inland river watershed, China. Science of the Total Environment, 621: 177-185.
doi: 10.1016/j.scitotenv.2017.11.222
[38]   Wang Y Q, Shao M A, Liu Z P, et al.2012. Regional spatial pattern of deep soil water content and its influencing factors. Hydrological Sciences Journal, 57(2): 265-281.
doi: 10.1080/02626667.2011.644243
[39]   Wang Y Q, Shao M A, Liu Z P, et al.2013. Vertical distribution and influencing factors of soil water content within 21-m profile on the Chinese Loess Plateau. Geoderma, 193-194: 300-310.
doi: 10.1016/j.geoderma.2012.10.011
[40]   Xing X G, Kang D G, Ma X Y, et al.2017. Differences in loam water retention and shrinkage behavior: Effects of various types and concentrations of salt ions. Soil & Tillage Research, 167: 61-72.
[41]   Xing X G, Li Y B, Ma X Y.2018. Water retention curve correction using changes in bulk density during data collection. Engineering Geology, 233: 231-237.
doi: 10.1016/j.enggeo.2017.12.018
[42]   Xing X G, Du W, Ma X Y.2019. Field-scale distribution and heterogeneity of soil salinity in the mulched-drip-irrigation cotton field. Archives of Agronomy and Soil Science, 65(9): 1248-1261.
doi: 10.1080/03650340.2018.1562273
[43]   Xu G C, Zhang T G, Li Z B, et al.2017. Temporal and spatial characteristics of soil water content in diverse soil layers on land terraces of the Loess Plateau, China. Catena, 158: 20-29.
doi: 10.1016/j.catena.2017.06.015
[44]   Yao Y F, He C X, Zeng D J, et al.2016. Spatial patterns of surface soil moisture and hydro-physical properties in Lijiang River watershed. Advances in Water Science, 27(5): 696-704. (in Chinese)
[45]   Yu X, Zhou W J, Chen Y P, et al.2020. Spatial variation of soil properties and carbon under different land use types on the Chinese Loess Plateau. Science of the Total Environment, 703: 134946, doi: 10.1016/j.scitotenv.2019.134946.
doi: 10.1016/j.scitotenv.2019.134946
[46]   Zeleke T B, Si B C.2006. Characterizing scale-dependent spatial relationships between soil properties using multifractal techniques. Geoderma, 134(3-4): 440-452.
doi: 10.1016/j.geoderma.2006.03.013
[47]   Zhang K, Wang Q Q, Chao L J, et al.2019. Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China. Journal of Hydrology, 574: 903-914.
doi: 10.1016/j.jhydrol.2019.04.087
[48]   Zhao C L, Shao M A, Jia X X, et al.2019. Spatial distribution of water-active soil layer along the south-north transect in the Loess Plateau of China. Journal of Arid Land, 11(2): 228-240.
doi: 10.1007/s40333-019-0051-4
[49]   Zhao W J, Cui Z, Li N.2016. Scale dependency of spatial variability of soil moisture in gravel-mulched field. Environmental Earth Science, 75: 1455, doi: 10.1007/s12665-016-6263-4.
doi: 10.1007/s12665-016-6263-4
[50]   Zhao W J, Cui Z, Zhang J Y, et al.2017. Temporal stability and variability of soil-water content in a gravel-mulched field in northwestern China. Journal of Hydrology, 552: 249-257.
doi: 10.1016/j.jhydrol.2017.06.031
[51]   Zou X Y, Zhang Z D, Wu M Y, et al.2019. Spatial variability of particle size distribution at slope scale in Bashang region of Hebei Province. Science of Soil and Water Conservation, 17(5): 44-53. (in Chinese)
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