Local estimates of available water capacity and effect of measurement errors on the spatial estimates and their uncertainties

Abstract

The purpose of this work was to: (i) propose a methodology to infer local estimates of the available water capacity (AWC) at a plot from a few measurements in laboratory of AWC carried out on horizons of a pit on the same plot, (ii) examine the effect of measurement errors on spatial estimates of AWC and the associated uncertainties. For each horizon identified in the pit, the water content was determined at field capacity and at the permanent wilting point, and thus AWC. 47 soundings were carried out on a regular grid covering the plot. For each sounding, an AWC value was estimated by matching sounding horizons to the pit horizons. Laboratory measurements of AWC on 14 sites were used to validate the 47 local estimates. Statistics such as correlation coefficient, mean error and root mean square error are promising at 0.84, 4.0 mm and 17.5 mm, respectively. The spatialization of AWC was carried out by ordinary kriging, considering or ignoring the measurement errors on AWC. The precision of AWC estimates allowed making evident that accounting for measurement errors provided estimates that were more precise. This result, confirmed by the prediction interval coverage probability statistic, underlined that taking into account measurement errors in spatial modeling is an effective way to reduce the confidence interval of any estimate. These results suggest that it would be better to fix the nugget based on a preliminary test on the measurement error rather than to fix it based on the experimental variogram.

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References

  1. Asgarzadeh, H., Mosaddeghi, M. R., Dexter, A. R., Mahboubi, A. A., & Neyshabour, M. R. (2014). Determination of soil available water for plants: Consistency between laboratory and field measurements. Geoderma, 226–227, 8–20. https://doi.org/10.1016/j.geoderma.2014.02.020.

    Article  Google Scholar 

  2. Barker, J. B., Franz, T. E., Heeren, D. M., Neale, C. M. U., & Luck, J. D. (2017). Soil water content monitoring for irrigation management: A geostatistical analysis. Agricultural Water Management, 188, 36–49. https://doi.org/10.1016/j.agwat.2017.03.024.

    Article  Google Scholar 

  3. Bourennane, H., & King, D. (2003). Using multiple external drifts to estimate a soil variable. Geoderma, 114, 1–18. https://doi.org/10.1016/S0016-7061(02)00338-5.

    Article  Google Scholar 

  4. Bourennane, H., King, D., Couturier, A., Nicoullaud, B., Mary, B., & Richard, G. (2007). Uncertainty assessment of soil water content spatial patterns using geostatistical simulations: An empirical comparison of a simulation accounting for single attribute and a simulation accounting for secondary information. Ecological Modelling, 205, 323–335. https://doi.org/10.1016/j.ecolmodel.2007.02.034.

    Article  Google Scholar 

  5. Christensen, W. F. (2011). Filtered kriging for spatial data with heterogeneous measurement error variances. Biometrics, 67, 947–957. https://doi.org/10.1111/j.1541-0420.2011.01563.x.

    Article  PubMed  Google Scholar 

  6. Cressie, N. A. C. (1993). Statistics for spatial data. New York, USA: Wiley-Blackwell. https://doi.org/10.1002/9781119115151.

    Google Scholar 

  7. Delhomme, J. P. (1978). Kriging in the hydrosciences. Advances in Water Resources, 1, 251–266. https://doi.org/10.1016/0309-1708(78)90039-8.

    Article  Google Scholar 

  8. Duval, O., & Isambert, M. (1992). Notice explicative de la carte pédologique de Villamblain (Beauce) au 1/10 000e (Soils map of Villamblain (Beauce) at 1/10 000) (p. 38). Orléans, France: SESCPF-INRA.

    Google Scholar 

  9. Entin, J. K., Robock, A., Vinnikov, K. Y., Hollinger, S. E., Liu, S. X., & Namkhai, A. (2000). Temporal and spatial scales of observed soil moisture variations in the extratropics. Journal of Geophysical Research, 105, 11865–11877. https://doi.org/10.1029/2000JD900051.

    Article  Google Scholar 

  10. Evett, S. R. (2007). Soil water and monitoring technology Irrigation of agricultural crops, agronomy monograph no. 30 (2nd ed., pp. 25–84). Madison, WI, USA: ASA-CSSA-SSSA. https://doi.org/10.2134/agronmonogr30.2ed.c2.

    Google Scholar 

  11. Givi, J., Prasher, S. O., & Patel, R. M. (2004). Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point. Agricultural Water Management, 70, 83–96. https://doi.org/10.1016/j.agwat.2004.06.009.

    Article  Google Scholar 

  12. Goovaerts, P. (2001). Geostatistical modeling of uncertainty in soil science. Geoderma, 103, 3–26. https://doi.org/10.1016/S0016-7061(01)00067-2.

    Article  Google Scholar 

  13. de Gruijter, J., Brus, D., Bierkens, M., & Knotters, M. (2006). Sampling for natural resource monitoring (p. 333). Berlin, Germany: Springer. https://doi.org/10.1007/3-540-33161-1.

    Google Scholar 

  14. Hedley, C. B., & Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture, 10, 342–355. https://doi.org/10.1007/s11119-009-9119-z.

    Article  Google Scholar 

  15. IUSS Working Group WRB. (2006). World reference base for soil resources. World soil resources report no. 103. Rome, Italy: FAO.

    Google Scholar 

  16. Meylan, P. (1986). Régionalisation de données entachées d’erreurs de mesure par krigeage: Application à la pluviométrie (Kriging used in the regionalization of data affected by measurement errors: Application to rainfall). Hydrologie Continentale, 1, 25–34.

    Google Scholar 

  17. Nicoullaud, B., Darthout, R., & Duval, O. (1995). Etude de l’enracinement du blé tendre d’hivers et du maïs dans les sols argilo-limoneux de Petite Beauce (Vertical distribution of winter wheat and maize roots in loamly clay soils of “Petite Beauce”). Etude et Gestion des Sols, 2(3), 183–200.

    Google Scholar 

  18. Padarian, J., Minasny, B., McBratney, A. B., & Dalgliesh, N. (2014). Predicting and mapping the soil available water capacity of Australian wheatbelt. Geoderma Regional, 2–3, 110–118. https://doi.org/10.1016/j.geodrs.2014.09.005.

    Article  Google Scholar 

  19. R Core Team. (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

  20. Robinet, J., von Hebel, Ch., Govers, G., van der Kruk, J., Minella, J. P. G., Schlesner, A., et al. (2018). Spatial variability of soil water content and soil electrical conductivity across scales derived from electromagnetic induction and time domain reflectometry. Geoderma, 314, 160–174. https://doi.org/10.1016/j.geoderma.2017.10.045.

    Article  Google Scholar 

  21. Román Dobarco, M., Bourennane, H., Arrouays, D., Saby, N. P. A., Cousin, I., & Martin, M. P. (2019a). Uncertainty assessment of GlobalSoilMap soil available water capacity products: A French case study. Geoderma, 344, 14–30. https://doi.org/10.1016/j.geoderma.2019.02.036.

    Article  Google Scholar 

  22. Román Dobarco, M., Cousin, I., Le Bas, Ch., & Martin, M. P. (2019b). Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty. Geoderma, 336, 81–95. https://doi.org/10.1016/j.geoderma.2018.08.022.

    CAS  Article  Google Scholar 

  23. Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., et al. (2010). Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Science Reviews, 99, 125–161. https://doi.org/10.1016/j.earscirev.2010.02.004.

    CAS  Article  Google Scholar 

  24. Shrestha, D. L., & Solomatine, D. P. (2006). Machine learning approaches for estimation of prediction interval for the model output. Neural Networks, 19, 225–235. https://doi.org/10.1016/j.neunet.2006.01.012.

    Article  PubMed  Google Scholar 

  25. Somarathna, P. D. S. N., Minasny, B., Malone, B. P., Stockmann, U., & McBratney, A. B. (2018). Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions. Science of the Total Environment, 631–632, 377–389. https://doi.org/10.1016/j.scitotenv.2018.02.302.

    CAS  Article  Google Scholar 

  26. Ströβenreuther, U., Horwath, M., & Schröder, L. (2020). How different analysis and interpolation methods affect the accuracy of ice surface elevation changes inferred from satellite altimetry. Mathematical Geosciences, 52, 499–525. https://doi.org/10.1007/s11004-019-09851-3.

    Article  Google Scholar 

  27. Ugbaje, S. U., & Reuter, H. I. (2013). Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data. Vadose Zone Journal 12(4). https://doi.org/10.2136/vzj2013.07.0140.

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Acknowledgements

Financial support was provided by the ANR (Agence Nationale de la Recherche) (ANR-14-CE01-0011-01) project RUEdesSOLS and is gratefully acknowledged.

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Correspondence to Hocine Bourennane.

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Bourennane, H., Lagacherie, P., Román Dobarco, M. et al. Local estimates of available water capacity and effect of measurement errors on the spatial estimates and their uncertainties. Precision Agric (2021). https://doi.org/10.1007/s11119-021-09794-y

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Keywords

  • Measurement error
  • Filtered kriging
  • Soil horizons matching
  • Soil water capacity
  • Mapping
  • Precision