Advertisement

Journal of Arid Land

, Volume 11, Issue 2, pp 228–240 | Cite as

Spatial distribution of water-active soil layer along the south-north transect in the Loess Plateau of China

  • Chunlei Zhao
  • Ming’an Shao
  • Xiaoxu Jia
  • Laiming Huang
  • Yuanjun ZhuEmail author
Article
  • 18 Downloads

Abstract

Soil water is an important composition of water recycle in the soil-plant-atmosphere continuum. However, intense water exchange between soil-plant and soil-atmosphere interfaces only occurs in a certain layer of the soil profile. For deep insight into water active layer (WAL, defined as the soil layer with a coefficient of variation in soil water content >10% in a given time domain) in the Loess Plateau of China, we measured soil water content (SWC) in the 0.0–5.0 m soil profile from 86 sampling sites along an approximately 860-km long south-north transect during the period 2013–2016. Moreover, a dataset contained four climatic factors (mean annual precipitation, mean annual evaporation, annual mean temperature and mean annual dryness index) and five local factors (altitude, slope gradient, land use, clay content and soil organic carbon) of each sampling site was obtained. In this study, three WAL indices (WALT (the thickness of WAL), WAL-CV (the mean coefficient of variation in SWC within WAL) and WALSWC (the mean SWC within WAL)) were used to evaluate the characteristics of WAL. The results showed that with increasing latitude, WAL-T and WAL-CV increased firstly and then decreased. WAL-SWC showed an opposite distribution pattern along the south-north transect compared with WAL-T and WAL-CV. Average WAL-T of the transect was 2.0 m, suggesting intense soil water exchange in the 0.0–2.0 m soil layer in the study area. Soil water exchange was deeper and more intense in the middle region than in the southern and northern regions, with the values of WAL-CV and WAL-T being 27.3% and 4.3 m in the middle region, respectively. Both climatic (10.1%) and local (4.9%) factors influenced the indices of WAL, with climatic factors having a more dominant effect. Compared with multiple linear regressions, pedotransfer functions (PTFs) from artificial neural network can better estimate the WAL indices. PTFs developed by artificial neural network respectively explained 86%, 81% and 64% of the total variations in WAL-T, WAL-SWC and WAL-CV. Knowledge of WAL is crucial for understanding the regional water budget and evaluating the stable soil water reserve, regional water characteristics and eco-hydrological processes in the Loess Plateau of China.

Keywords

water active layer soil water content redundancy analysis pedotransfer function artificial neural network Loess Plateau 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This study was supported by the National Natural Science Foundation of China (41530854, 41571130081), the National Key Project for Research and Development (2016YFC0501605) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (2017076). We thank the anonymous reviewers and editors for their valuable comments and suggestions for improving the manuscript.

References

  1. Biswas A, Si B C. 2011. Application of continuous wavelet transform in examining soil spatial variation: A review. Mathematical Geosciences, 43(3): 379–396.CrossRefGoogle Scholar
  2. Brocca L, Melone F, Moramarco T, et al. 2010. Spatial-temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46(2): W02516.CrossRefGoogle Scholar
  3. Chen H, Shao M A, Li Y Y. 2008. Soil desiccation in the Loess Plateau of China. Geoderma, 143(1–2): 91–100.CrossRefGoogle Scholar
  4. Choi M, Jacobs J M. 2007. Soil moisture variability of root zone profiles within SMEX02 remote sensing footprints. Advances in Water Resources, 30(4): 883–896.CrossRefGoogle Scholar
  5. Crave A, Gascuel-Odoux C. 1997. The influence of topography on time and space distribution of soil surface water content. Hydrological Processes, 11(2): 203–210.CrossRefGoogle Scholar
  6. Gao L, Shao M A, Peng X H, et al. 2015. Spatio-temporal variability and temporal stability of water contents distributed within soil profiles at a hillslope scale. CATENA, 132: 29–36.CrossRefGoogle Scholar
  7. Gasch C K, Brown D J, Campbell C S, et al. 2017. A field-sensor network data set for monitoring and modeling the spatial and temporal variation of soil water content in a dryland agricultural field. Water Resources Research, 53(12): 10878–10887.CrossRefGoogle Scholar
  8. He X B, Li Z B, Hao M D, et al. 2003. Down-scale analysis for water scarcity in response to soil-water conservation on Loess Plateau of China. Agriculture, Ecosystems & Environment, 94(3): 355–361.CrossRefGoogle Scholar
  9. Heathman G C, Larose M, Cosh M H, et al. 2009. Surface and profile soil moisture spatio-temporal analysis during an excessive rainfall period in the Southern Great Plains, USA. CATENA, 78(2): 159–169.CrossRefGoogle Scholar
  10. Hu W, Shao M, Han F P, et al. 2010. Watershed scale temporal stability of soil water content. Geoderma, 158(3–4): 181–198.CrossRefGoogle Scholar
  11. Hu W, Tallon L K, Si B C. 2012. Evaluation of time stability indices for soil water storage upscaling. Journal of Hydrology, 475: 229–241.CrossRefGoogle Scholar
  12. Hupet F, Vanclooster M. 2002. Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field. Journal of Hydrology, 261(1–4): 86–101.CrossRefGoogle Scholar
  13. Jia X X, Shao M A, Wei X R, et al. 2013. Hillslope scale temporal stability of soil water storage in diverse soil layers. Journal of Hydrology, 498: 254–264.CrossRefGoogle Scholar
  14. Jia X X, Shao M A, Zhang C, et al. 2015. Regional temporal persistence of dried soil layer along south–north transect of the Loess Plateau, China. Journal of Hydrology, 528: 152–160.CrossRefGoogle Scholar
  15. Jia X X, Shao M A, Zhu Y J, et al. 2017. Soil moisture decline due to afforestation across the Loess Plateau, China. Journal of Hydrology, 546: 113–122.CrossRefGoogle Scholar
  16. Jia X X, Shao M A, Yu D X, et al. 2019. Spatial variations in soil-water carrying capacity of three typical revegetation species on the Loess Plateau, China. Agriculture, Ecosystems & Environment, 273: 25–35.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Li Y S. 1983. The properties of water cycle in soil and their effect on water cycle for land in the Loess Plateau. Acta Ecologica Sinica, 3: 91–101. (in Chinese)Google Scholar
  19. Minasny B, Mcbratney A B. 2002. Uncertainty analysis for pedotransfer functions. European Journal of Soil Science, 53(3): 417–429.CrossRefGoogle Scholar
  20. Mohanty B P, Famiglietti J S, Skaggs T H. 2000. Evolution of soil moisture spatial structure in a mixed vegetation pixel during the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Water Resources Research, 36(12): 3675–3686.CrossRefGoogle Scholar
  21. Motaghian H R, Mohammadi J. 2011. Spatial estimation of saturated hydraulic conductivity from terrain attributes using regression, kriging, and artificial neural networks. Pedosphere, 21(2): 170–177.CrossRefGoogle Scholar
  22. Nyberg L. 1996. Spatial variability of soil water content in the covered catchment at Gårdsjön, Sweden. Hydrological Processes, 10(1): 89–103.CrossRefGoogle Scholar
  23. Patil N G, Chaturvedi A. 2012. Estimation of bulk density of waterlogged soils from basic properties. Archives of Agronomy and Soil Science, 58(5): 499–509.CrossRefGoogle Scholar
  24. Starr G C. 2005. Assessing temporal stability and spatial variability of soil water patterns with implications for precision water management. Agricultural Water Management, 72(3): 223–243.CrossRefGoogle Scholar
  25. Wang J, Ge Y, Heuvelink G B M, et al. 2015. Upscaling in situ soil moisture observations to pixel averages with spatio-temporal geostatistics. Remote Sensing, 7(9): 11372–11388.CrossRefGoogle Scholar
  26. Wang S, Fu B J, Piao S L, et al. 2016. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nature Geoscience, 9: 38–41.CrossRefGoogle Scholar
  27. Wang X C, Li J, Tahir M N, et al. 2012. Validation of the EPIC model and its utilization to research the sustainable recovery of soil desiccation after alfalfa (Medicago sativa L.) by grain crop rotation system in the semi-humid region of the Loess Plateau. Agriculture, Ecosystems & Environment, 161: 152–160.CrossRefGoogle Scholar
  28. Wang Y Q, Shao M A, Zhu Y J, et al. 2011. Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agricultural and Forest Meteorology, 151(4): 437–448.CrossRefGoogle Scholar
  29. Wang Y Q, Shao M A, Liu Z P. 2012. Pedotransfer functions for predicting soil hydraulic properties of the Chinese Loess Plateau. Soil Science, 177(7): 424–432.CrossRefGoogle Scholar
  30. Wang Y Q, Shao M A, Liu Z P, et al. 2014. Prediction of bulk density of soils in the Loess Plateau region of China. Surveys in Geophysics, 35(2): 395–413.CrossRefGoogle Scholar
  31. Xiong W, Li J, Chen Y Y, et al. 2016. Determinants of community structure of zooplankton in heavily polluted river ecosystems. Scientific Reports, 6: 22043.CrossRefGoogle Scholar
  32. Xiong Y W, Wallach R, Furman A. 2011. Modeling multidimensional flow in wettable and water-repellent soils using artificial neural networks. Journal of Hydrology, 410(1–2): 92–104.CrossRefGoogle Scholar
  33. Yang Q Y, Zhang B P, Zheng D. 1988. On the boundary of the Loess Plateau. Journal of Natural Resources, 3(1): 9–15. (in Chinese)Google Scholar
  34. Zhao C L, Shao M A, Jia X X, et al. 2016. Particle size distribution of soils (0–500 cm) in the Loess Plateau, China. Geoderma Regional, 7(3): 251–258.CrossRefGoogle Scholar
  35. Zhao C L, Jia X X, Zhu Y J, et al. 2017a. Long-term temporal variations of soil water content under different vegetation types in the Loess Plateau, China. CATENA, 158: 55–62.CrossRefGoogle Scholar
  36. Zhao C L, Shao M A, Jia X X, et al. 2017b. Estimation of spatial variability of soil water storage along the south–north transect on China’s Loess Plateau using the state-space approach. Journal of Soils and Sediments, 17(4): 1009–1020.CrossRefGoogle Scholar

Copyright information

© Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chunlei Zhao
    • 1
    • 2
  • Ming’an Shao
    • 1
    • 2
    • 3
  • Xiaoxu Jia
    • 1
    • 2
  • Laiming Huang
    • 1
    • 2
  • Yuanjun Zhu
    • 3
    • 4
    Email author
  1. 1.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess PlateauNorthwest A&F UniversityYanglingChina
  4. 4.College of Natural Resources and EnvironmentNorthwest A&F UniversityYanglingChina

Personalised recommendations