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Mapping Field Surface Soil Moisture for Hydrological Modeling

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  • Mustafa Tombul

Abstract

Soil moisture is a major control variable on hydrological processes both at the storm event scale and in the long term. The aggregate effect on the mean water balance over an area can be quantified successfully using hydrological models. However, determination of soil moisture distribution for semi or fully distributed models is difficult. In some types of landscape, the distribution of soil moisture is dictated by topography, in others by soil characteristics, or by a combination of both. Distribution of area-average soil moisture according to the likely effect of local topography is presented and tested. The heterogeneity of the soil moisture is described by the Xinanjiang distribution, commonly used to describe the natural spatial heterogeneity of the landscape. This distribution is then mapped onto the terrain using topographic index to locate the wettest and driest areas. Soil moisture data from Bilecik-Kurukavak catchments are used to test the method. Cumulative density functions (CDF) for soil moisture data obtained from 68 locations in eight different dates and corresponding topographic index values are obtained. From these functions the deviations of the wet values from the mean soil moisture are observed as positive and larger compared to the dry values. The temporal stability of moisture patterns was studied in order to identify optimal sampling points for field-average soil moisture. Such points were identified by calculating their deviation over time from field average. Topographic data were analyzed to determine if these sampling points could be identified from time-invariant data. Copyright Springer Science+Business Media, Inc. 2007

Suggested Citation

  • Mustafa Tombul, 2007. "Mapping Field Surface Soil Moisture for Hydrological Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1865-1880, November.
    • Handle: RePEc:spr:waterr:v:21:y:2007:i:11:p:1865-1880
    DOI: 10.1007/s11269-006-9134-z
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    Citations

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    Cited by:

    1. Lu Zhuo & Dawei Han & Qiang Dai & Tanvir Islam & Prashant Srivastava, 2015. "Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3503-3517, August.
    2. Edyta Kruk & Wioletta Fudała, 2021. "Concept of Soil Moisture Ratio for Determining the Spatial Distribution of Soil Moisture Using Physiographic Parameters of a Basin and Artificial Neural Networks (ANNs)," Land, MDPI, vol. 10(7), pages 1-13, July.
    3. Prashant K. Srivastava, 2017. "Satellite Soil Moisture: Review of Theory and Applications in Water Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3161-3176, August.
    4. Renato Morbidelli & Corrado Corradini & Carla Saltalippi & Luca Brocca, 2012. "Initial Soil Water Content as Input to Field-Scale Infiltration and Surface Runoff Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1793-1807, May.
    5. Ahmet Emre Tekeli & Hesham Fouli, 2017. "Reducing False Flood Warnings of TRMM Rain Rates Thresholds over Riyadh City, Saudi Arabia by Utilizing AMSR-E Soil Moisture Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1243-1256, March.

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