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The Soil Moisture during Dry Spells Model and Its Verification

Author

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  • Małgorzata Biniak-Pieróg

    (Institute of Environmental Development and Protection, Wrocław University of Environmental and Life Sciences, Plac Grunwaldzki 24, 50-363 Wrocław, Poland)

  • Mieczysław Chalfen

    (Department of Mathematics, Wrocław University of Environmental and Life Sciences, Grunwaldzka Street 53, 50-357 Wrocław, Poland)

  • Andrzej Żyromski

    (Institute of Environmental Development and Protection, Wrocław University of Environmental and Life Sciences, Plac Grunwaldzki 24, 50-363 Wrocław, Poland)

  • Andrzej Doroszewski

    (Department of Agrometeorology and Applied Informatics, Institute of Soil Science and Plant Cultivation—State Research Institute, Czartoryskich Street 8, 24-100 Puławy, Poland)

  • Tomasz Jóźwicki

    (Department of Agrometeorology and Applied Informatics, Institute of Soil Science and Plant Cultivation—State Research Institute, Czartoryskich Street 8, 24-100 Puławy, Poland)

Abstract

The objective of this study was the development and verification of a model of soil moisture decrease during dry spells—SMDS. The analyses were based on diurnal information of the occurrence of atmospheric precipitation and diurnal values of soil moisture under a bare soil surface, covering the period of 2003–2019, from May until October. A decreasing exponential trend was used for the description of the rate of moisture decrease in six layers of the soil profile during dry spells. The least squares method was used to determine, for each dry spell and soil depth, the value of exponent α , which described the rate of soil moisture decrease. Data from the years 2003–2015 were used for the identification of parameter α of the model for each of the layers separately, while data from 2016–2019 were used for model verification. The mean relative error between moisture values measured in 2016–2019 and the calculated values was 3.8%, and accepted as sufficiently accurate. It was found that the error of model fitting decreased with soil layer depth, from 8.1% for the surface layer to 1.0% for the deepest layer, while increasing with the duration of the dry spell at the rate of 0.5%/day. The universality of the model was also confirmed by verification made with the use of the results of soil moisture measurements conducted in the years 2009–2019 at two other independent locations. However, it should be emphasized that in the case of the surface horizon of soil, for which the process of soil drying is a function of factors occurring in the atmosphere, the developed model may have limited application and the obtained results may be affected by greater errors. The adoption of calculated values of coefficient α as characteristic for the individual measurement depths allowed calculation of the predicted values of moisture as a function of the duration of a dry spell, relative to the initial moisture level adopted as 100%. The exponential form of the trend of soil moisture changes in time adopted for the analysis also allowed calculation of the duration of a hypothetical dry spell t, after which soil moisture at a given depth drops from the known initial moisture θ 0 to the predicted moisture θ. This is an important finding from the perspective of land use.

Suggested Citation

  • Małgorzata Biniak-Pieróg & Mieczysław Chalfen & Andrzej Żyromski & Andrzej Doroszewski & Tomasz Jóźwicki, 2020. "The Soil Moisture during Dry Spells Model and Its Verification," Resources, MDPI, vol. 9(7), pages 1-27, July.
  • Handle: RePEc:gam:jresou:v:9:y:2020:i:7:p:85-:d:382589
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    References listed on IDEAS

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    1. Prashant K. Srivastava & Prem C. Pandey & George P. Petropoulos & Nektarios N. Kourgialas & Varsha Pandey & Ujjwal Singh, 2019. "GIS and Remote Sensing Aided Information for Soil Moisture Estimation: A Comparative Study of Interpolation Techniques," Resources, MDPI, vol. 8(2), pages 1-17, April.
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    3. Abdelaaziz Merabti & Diogo S. Martins & Mohamed Meddi & Luis S. Pereira, 2018. "Correction to: Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2917-2918, June.
    4. Huang, Shengzhi & Huang, Qiang & Chang, Jianxia & Leng, Guoyong & Xing, Li, 2015. "The response of agricultural drought to meteorological drought and the influencing factors: A case study in the Wei River Basin, China," Agricultural Water Management, Elsevier, vol. 159(C), pages 45-54.
    5. Alessia Flammini & Corrado Corradini & Renato Morbidelli & Carla Saltalippi & Tommaso Picciafuoco & Juan Vicente Giráldez, 2018. "Experimental Analyses of the Evaporation Dynamics in Bare Soils under Natural Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1153-1166, February.
    6. Keshavarz, Mohammad Reza & Vazifedoust, Majid & Alizadeh, Amin, 2014. "Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data," Agricultural Water Management, Elsevier, vol. 132(C), pages 37-45.
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    Cited by:

    1. Brunella Bonaccorso & David J. Peres, 2022. "Analysis of Extreme Hydrometeorological Events," Resources, MDPI, vol. 11(6), pages 1-3, June.

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