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Uncertainty analysis in near-surface soil moisture estimation on two typical land-use hillslopes

  • Soils, Sec 2 • Global Change, Environ Risk Assess, Sustainable Land Use • Research Article
  • Published:
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Abstract

Purpose

Spatial prediction of near-surface soil moisture content (NSSMC) is necessary for both hydrologic modeling and land use planning. However, uncertainties associated with the prediction are always neglected and lack of quantitative analysis. The objective of this study was to investigate the influences of different sources of uncertainty on NSSMC estimation at two typical hillslopes (i.e., tea garden and forest).

Materials and methods

In this study, stepwise multiple regression models with terrain indices and soil texture were built to spatially estimate NSSMC on two typical land use hillslopes (tea garden and forest) at different dates. The uncertainties due to limited sample sizes used for developing regression models (uncertainty of model parameter), digital elevation model resolutions of 1, 2, 3, 4, and 5 m (uncertainty of terrain indices) and spatial interpolations of soil texture by kriging or cokriging with electromagnetic induction (uncertainty of soil texture), were investigated using bootstrap, resampling, and Latin hypercube sampling techniques, respectively.

Results and discussion

The accuracies of NSSMC predictions were acceptable for both tea garden (the Nash-Sutcliffe efficiency or NSE = 0.34) and forest hillslopes (NSE = 0.57). The model parameter uncertainty was more important on tea garden hillslope than on forest hillslope. A significant negative correlation (P < 0.05) was observed between the model parameter uncertainty and the mean NSSMC of the hillslopes, indicating that the model parameter uncertainty was small when the hillslope was wet. The resolution uncertainty from digital elevation model had a minor effect on NSSMC predictions on both hillslopes. The texture uncertainty was weak on NSSMC estimations on tea garden hillslope. However, it was more important than the model parameter uncertainty on the forest hillslope.

Conclusions

Improving the regression model structure and the hillslope soil texture mapping are critical in the accurate spatial prediction of NSSMC on tea garden and forest hillslopes, respectively. This study presents techniques for analyzing three different uncertainties that can be used to identify the main sources of uncertainties in soil mapping.

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References

  • Chen H, Zhang W, Wang K, Fu W (2010) Soil moisture dynamics under different land uses on karst hillslope in northwest Guangxi, China. Environ Earth Sci 61:1105–1111

    Article  Google Scholar 

  • Chirico GB, Medina H, Romano N (2007) Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods. J Hydrol 334(3/4):405–422

    Article  Google Scholar 

  • Chirico GB, Medina H, Romano N (2010) Functional evaluation of PTF prediction uncertainty: an application at hillslope scale. Geoderma 155(3/4):193–202

    Article  CAS  Google Scholar 

  • Cichota R, Snow VO, Vogeler I (2013) Modelling nitrogen leaching from overlapping urine patches. Environ Model Softw 41:15–26

    Article  Google Scholar 

  • Coleman ML, Niemann JD (2012) An evaluation of nonlinear methods for estimating catchment-scale soil moisture patterns based on topographic attributes. J Hydroinformatics 14:800–814

    Article  Google Scholar 

  • Deng HL, Ye M, Schaap MG, Khaleel R (2009) Quantification of uncertainty in pedotransfer function-based parameter estimation for unsaturated flow modeling. Water Resour Res 45, W04409. doi:10.1029/2008WR007477

    Article  Google Scholar 

  • Deutsch CV, Journel AG (1998) GSLIB-geostatistical software library and user’s guide. Oxford Univ. Press, Oxford

    Google Scholar 

  • Famiglietti JS, Rudnicki JW, Rodell M (1998) Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas. J Hydrol 210:259–281

    Article  Google Scholar 

  • FAO/ISRIC/ISSS (1998) World reference base for soil resources. World Soil Resources Reports, FAO, Rome

    Google Scholar 

  • Florian A (1992) An efficient sampling scheme: updated Latin hypercube sampling. Probab Eng Mech 7:123–130

    Article  Google Scholar 

  • Fu B, Chen L (2000) Agricultural landscape spatial pattern analysis in the semi-arid hill area of the Loess Plateau, China. J Arid Environ 44:291–303

    Article  Google Scholar 

  • Green TR, Erskine RH (2004) Measurement, scaling, and topographic analyses of spatial crop yield and soil water content. Hydrol Process 18:1447–1465

    Article  Google Scholar 

  • Hupet F, Vanclooster M (2002) Interseasonal dynamics of soil moisture variability within a small agricultural maize cropped field. J Hydrol 261:86–101

    Article  Google Scholar 

  • Janssen PHM, Heuberger PSC, Sanders R (1992) UNCSAM 1.1: a software package for sensitivity and uncertainty analysis. Report No. 959101004. National Institute of Public Health and Environmental Protection, Bilthoven

    Google Scholar 

  • Jin Y, Li HP, Li JL (2007) The impact of non-point pollutant load of land-use changes in Taihu Basin. J Agro-Environ Sci 26:1214–1218 (In Chinese)

    CAS  Google Scholar 

  • Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust, Speech, Signal Process 29:1153–1160

    Article  Google Scholar 

  • Klier C (2012) Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments. Environ Sci Technol 46:2790–2798

    Article  CAS  Google Scholar 

  • Knotters M, Brus DJ, OudeVoshaar JHO (1995) A comparison of kriging, cokriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma 67:227–246

    Article  Google Scholar 

  • Lark RM (1999) Soil-landform relationships at within-field scales: an investigation using continuous classification. Geoderma 92:141–165

    Article  Google Scholar 

  • Lavado Contador JF, Maneta M, Schnabel S (2006) Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks. Environ Monit Assess 121:213–232

    Article  CAS  Google Scholar 

  • Liao K, Xu S, Wu J, Zhu Q (2014) Uncertainty analysis for large-scale prediction of the van Genuchten soil water retention parameters with pedotransfer functions. Soil Res 52:431–442

    Article  Google Scholar 

  • Lin YP, Chu HJ, Wang CL, Yu HH, Wang YC (2009) Remote sensing data with the conditional Latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances. Sensors 9:148–174

    Article  Google Scholar 

  • McKenzie NJ, Ryan PJ (1999) Spatial prediction of soil properties using environmental correlation. Geoderma 89:67–94

    Article  Google Scholar 

  • Minasny B, McBratney AB, Bristow KL (1999) Comparison of different approaches to the development of pedotransfer functions for water retention curves. Geoderma 93:225–253

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part 1. A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nyberg L (1996) Spatial variability of soil water content in the covered catchment at Gardsjon, Sweden. Hydrol Process 10:89–103

    Article  Google Scholar 

  • Pachepsky Y, Radcliffe DE, Selim HM (2003) Scaling methods in soil physics. CRC Press, Boca Raton

    Google Scholar 

  • Pachepsky YA, Guber AK, van Genuchten MT, Nicholson TJ, Cady RE, Simunek J, Schaap MG (2006) Model abstraction techniques for soil-water flow and transport. Rep. NUREG/CR-6884, Nucl. Regul. Comm., Washington, D.C

  • Qiu Y, Fu B, Wang J, Chen L (2001) Spatial variability of soil moisture content and its relation to environmental indices in a semi-arid gully catchment of the Loess Plateau, China. J Arid Environ 49:723–750

    Article  Google Scholar 

  • Qiu Y, Fu B, Wang J, Chen L, Meng Q, Zhang Y (2010) Spatial prediction of soil moisture content using multiple-linear regressions in a gully catchment of the Loess Plateau, China. J Arid Environ 74:208–220

    Article  Google Scholar 

  • Reedy RC, Scanlon BR (2003) Soil water content monitoring using electromagnetic induction. J Geotech Geoenviron 129:1028–1039

    Article  Google Scholar 

  • Saey T, Simpson D, Vermeersch H, Cockx L, Van Meirvenne M (2009) Comparing the EM38DD and DUALEM-21S sensors for depth-to-clay mapping. Soil Sci Soc Am J 73:7–12

    Article  CAS  Google Scholar 

  • Schaap MG, Leij FJ, van Genuchten MT (1998) Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci Soc Am J 62:847–855

    Article  CAS  Google Scholar 

  • Sivapalan M (2003) IAHS decade on predictions in ungauged basins(PUB). Hydrol Sci J 48(6):857–880

    Article  Google Scholar 

  • Song X, Zhou G, Jiang H, Yu S, Fu J, Li W, Wang W, Ma Z, Peng C (2011) Carbon sequestration by Chinese bamboo forests and their ecological benefits: assessment of potential, problems, and future challenges. Environ Rev 19:418–428

    Article  CAS  Google Scholar 

  • Sun D, Bloemendal J, Rea DK, Vandenberghe J, Jiang FC, An ZS, Su RX (2002) Grain-size distribution function of polymodal sediments in hydraulic aeolian environments, and numerical partitioning of the sedimentary components. Sediment Geol 152:263–277

    Article  Google Scholar 

  • Tromp-van Meerveld HJ, McDonnell JJ (2009) Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale. J Hydrol 368:56–67

    Article  Google Scholar 

  • van Arkel ZJ (2012) Using topographic and soils data to understand and predict field scale soil moisture patterns. Master thesis, Iowa State University

  • Vereecken H, Maes J, Feyen J, Darius P (1989) Estimating the soil moisture retention characteristics from texture, bulk density, and carbon content. Soil Sci 148:389–403

    Article  Google Scholar 

  • Webster R (1985) Quantitative spatial analysis of soil in the field. Adv Soil Sci 3:1–70

    Article  Google Scholar 

  • Western AW, Grayson RB, Blöschl G, Willgoose GR, McMahon TA (1999) Observed spatial organization of soil moisture and its relation to terrain indices. Water Resour Res 35:797–810

    Article  Google Scholar 

  • Wotling G, Bouvier C, Danloux J, Fritsch JM (2000) Regionalization of extreme precipitation distribution using the principal components of the topographical environment. J Hydrol 233:86–101

    Article  Google Scholar 

  • Wu S, Li J, Huang GH (2007) Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation. Environ Model Softw 22:1250–1260

    Article  Google Scholar 

  • Wu S, Li J, Huang GH (2008) A study on DEM-derived primary topographic attributes for hydrologic applications: sensitivity to elevation data resolution. Appl Geogr 28:210–223

    Article  Google Scholar 

  • Wu C, Wu J, Luo Y, Zhang L, DeGloria SD (2009) Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Sci Soc Am J 73:1202–1208

    Article  CAS  Google Scholar 

  • Xu Y, Gao JF, Gao YN (2011) Landscape ecological risk assessment in the Taihu region based on land use change. J Lake Sci 23:642–648

    Article  Google Scholar 

  • Ye M, Khaleel R, Schaap MG, Zhu J (2007) Simulation of field injection experiments in heterogeneous unsaturated media using cokriging and artificial neural network. Water Resour Res 43, W07413. doi:10.1029/2006WR005030

    Article  Google Scholar 

  • Zhang Y, Pinder G (2003) Latin hypercube lattice sample selection strategy for correlated random hydraulic conductivity fields. Water Resour Res 39: doi: 10.1029/2002WR001822

  • Zhu Q, Lin HS (2010) Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere 20:594–606

    Article  Google Scholar 

  • Zhu Q, Lin HS, Doolittle JA (2010) Repeated electromagnetic induction surveys for determining subsurface hydrologic dynamics in an agricultural landscape. Soil Sci Soc Am J 74:1750–1762

    Article  CAS  Google Scholar 

  • Zong LG, Zhou J, Luo M, Lu LJ, Fei XD (2006) Analysis on current status of soil environmental quality of tea gardens in Jiangsu Province. Chin. Chin J Eco-Agric 14:61–64 (in Chinese)

    Google Scholar 

Download references

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (41301234, 41571080, and 41271109) and the Natural Science Foundation of Jiangsu Province (BK20151613 and BK20151061).

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Authors and Affiliations

  1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China

    Kaihua Liao, Xiaoming Lai, Yujiao Liu & Qing Zhu

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  1. Kaihua Liao
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  2. Xiaoming Lai
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  3. Yujiao Liu
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  4. Qing Zhu
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Correspondence to Qing Zhu.

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Responsible editor: Fanghua Hao

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Liao, K., Lai, X., Liu, Y. et al. Uncertainty analysis in near-surface soil moisture estimation on two typical land-use hillslopes. J Soils Sediments 16, 2059–2071 (2016). https://doi.org/10.1007/s11368-016-1405-6

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  • DOI: https://doi.org/10.1007/s11368-016-1405-6

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