Water Resources Management

, Volume 30, Issue 6, pp 1973–1986 | Cite as

Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing

Article

Abstract

Information of Soil Moisture Content (SMC) at different depths i.e. vertical Soil Moisture (SM) profile is important as it influences several hydrological processes. In the era of microwave remote sensing, spatial distribution of soil moisture information can be retrieved from satellite data for large basins. However, satellite data can provide only the surface (~0–10 cm) soil moisture information. In this study, a methodological framework is proposed to estimate the vertical SM profile knowing the information of SMC at surface layer. The approach is developed by coupling the memory component of SMC within a layer and the forcing component from soil layer lying above by an Auto-Regressive model with an exogenous input (ARX) where forcing component is the exogenous input. The study highlights the mutual reliance between SMC at different depths at a given location assuming the ground water table is much below the study domain. The methodology is demonstrated for three depths: 25, 50 and 80 cm using SMC values of 10 cm depth. Model performance is promising for all three depths. It is further observed that forcing is predominant than memory for near surface layers than deeper layers. With increase in depth, contribution of SM memory increases and forcing dissipates. Potential of the proposed methodology shows some promise to integrate satellite estimated surface soil moisture maps to prepare a fine resolution, 3-dimensional soil moisture profile for large areas, which is kept as future scope of this study.

Keywords

Soil moisture (SM) Vertical Soil Moisture Profile Memory Forcing Auto-Regressive Model with Exogenous Input (ARX) 

Notes

Acknowledgments

Authors wish to acknowledge Dr. Shivam Tripathi, IIT Kanpur for providing the daily soil moisture data from the study area. This is a part of International Soil Moisture Network. For details: https://ismn.geo.tuwien.ac.at/.

Compliance with Ethical Standards

Funding

This study is partially supported by a research project sponsored by the Ministry of Earth Science (MoES), Government of India (Sanction order# MoES/PAMC/H&C/30/2013-PC-II).

Conflict of Interest

Corresponding author Dr. Rajib Maity has received research grants from the Ministry of Earth Science (MoES), Government of India (Sanction order# MoES/PAMC/H&C/30/2013-PC-II). The other authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals

The authors declare that the research does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

The authors declare that the ‘Informed Consent’ is not applicable in the research since it does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

11269_2016_1263_MOESM1_ESM.docx (80 kb)
ESM 1(DOCX 80.1 kb)

References

  1. Argyrokastritis I, Kargas G, Kerkides P (2009) Simulation of soil moisture profiles using K(h) from coupling experimental retention curves and one-step outflow data. Water Resour Manag 23(15):3255–3266. doi:10.1007/s11269-009-9432-3 CrossRefGoogle Scholar
  2. Chen ZQ, Govindaraju RS, Kavvas ML (1994) Spatial averaging of unsaturated flow equations under infiltration conditions areally heterogeneous fields:Numerical simulations. Water Resour Res 30(2):535–548CrossRefGoogle Scholar
  3. Dorigo WA, Wagner W, Hohensinn R, Hahn S, Paulik C, Xaver A, Gruber A, Drusch M, Mecklenburg S, van Oevelen P, Robock A, Jackson T (2011) The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements. Hydrol Earth Syst Sci 15:1675–1698. doi:10.5194/hess-15-1675-2011 CrossRefGoogle Scholar
  4. Downer CW, Ogden FL (2004) Appropriate vertical discretization of Richards’ equation for two-dimensional watershed-scale modelling. Hydrol Process 18(1):1–22. doi:10.1002/hyp.1306 CrossRefGoogle Scholar
  5. Haverkamp R, Parlange JY, Cuenca R, Ross PJ, Steenhuis TS (1998) Scaling of the Richards’ equation and its application to watershed modeling. In: Sposito G (ed) In scale invariance and scale dependence in hydrology. Cambridge University Press, New York, pp 190–223CrossRefGoogle Scholar
  6. Hoeben R, Troch PA (2000) Assimilation of active microwave observation data for soil moisture profile estimation. Water Resour Res 36:2805–2819. doi:10.1029/2000WR900100 CrossRefGoogle Scholar
  7. Hsu SM, Asce M, Ni C et al. (2002) Assessment of three infiltration formulasbased on model fitting on Richards equation. J Hydrol Eng (October), 373–379Google Scholar
  8. Jishnu RB, Naik SP, Patra NR, Malik JN (2013) Ground response analysis of Kanpur soil along Indo-Gangetic Plains. Soil Dyn Earthq Eng 51:47–57CrossRefGoogle Scholar
  9. Kale R, Sahoo B (2011) Green–Ampt infiltration models for varied field conditions: a revisit. Water Resour Manag 25(14):3505–3536CrossRefGoogle Scholar
  10. Kerr YH, Wadlteufel P, Wigneron JP, Delwart S, Cabot F, Boutin J, Escorihuela MJ, Font J, Reul N, Gruhier C, Juglea SE, Drinkwater MR, Hahne A, Martı’n-Neira M, Mecklenburg S (2010) The SMOS Mission: new tool for monitoring key elements of the global water cycle. Proc IEEE 98(5):666–687CrossRefGoogle Scholar
  11. Kim S (2009) Multivariate analysis of soil moisture history for a hillslope. J Hydrol 374(3-4):318–328. doi:10.1016/j.jhydrol.2009.06.025 CrossRefGoogle Scholar
  12. Kim S, Kim H (2007) Stochastic analysis of soil moisture to understand spatial and temporal variations of soil wetness at a steep hillside. J Hydrol 341(1–2):1–11. doi:10.1016/j.jhydrol.2007.04.012 CrossRefGoogle Scholar
  13. Kim S, Sun H, Jung S (2011) Configuration of the relationship of soil moistures for vertical soil profiles on a steep hillslope using a vector time series model. J Hydrol 399(3–4):353–363. doi:10.1016/j.jhydrol.2011.01.012 CrossRefGoogle Scholar
  14. Mahmood R, Littell A, Hubbard KG, You J (2012) Observed data-based assessment of relationships among soil moisture at various depths, precipitation, and temperature. Appl Geogr 34:255–264. doi:10.1016/j.apgeog.2011.11.009 CrossRefGoogle Scholar
  15. Moran MS, Peters-Lidard CD, Watts JM, McElroy S (2004) Estimating soil moisture at the watershed scale with satellite-based radar and land surface models. Can J Remote Sens 30(5):805–826. doi:10.5589/m04-043 CrossRefGoogle Scholar
  16. Pielke RA Sr (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Am Geophys Union 39(2):151–177Google Scholar
  17. Singh VP (2010) Entropy theory for movement of moisture in soils. Water Resour Res 46:W03516. doi:10.1029/2009WR008288 Google Scholar
  18. Varado N, Braud I, Ross PJ, Haverkamp R (2006) Assessment of an efficient numerical solution of the 1D Richards’ equation on bare soil. J Hydrol 323(1–4):244–257. doi:10.1016/j.jhydrol.2005.07.052 CrossRefGoogle Scholar
  19. Zucco G, Brocca L, Moramarco T et al. (2014) Influence of land use on soil moisture spatial–temporal variability and monitoring. J Hydrol 1–7. doi: 10.1016/j.jhydrol.2014.01.043

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Water ResourcesIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Civil EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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