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Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization

Author

Listed:
  • Hans Edwin Winzeler

    (USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA)

  • Phillip R. Owens

    (USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA)

  • Quentin D. Read

    (USDA ARS, Southeast Area, 3127 Ligon St., Raleigh, NC 27607, USA)

  • Zamir Libohova

    (USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA)

  • Amanda Ashworth

    (USDA-ARS, Poultry Production and Product Safety Research Unit, 1260 W. Maple St., Fayetteville, AR 72701, USA)

  • Tom Sauer

    (USDA ARS, National Laboratory for Agriculture and the Environment, 1015 N. University Boulevard, Ames, IA 50011, USA)

Abstract

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Suggested Citation

  • Hans Edwin Winzeler & Phillip R. Owens & Quentin D. Read & Zamir Libohova & Amanda Ashworth & Tom Sauer, 2022. "Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization," Land, MDPI, vol. 11(11), pages 1-23, November.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:2018-:d:970052
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    Citations

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    Cited by:

    1. Hans Edwin Winzeler & Phillip R. Owens & Tulsi Kharel & Amanda Ashworth & Zamir Libohova, 2023. "Identification and Delineation of Broad-Base Agricultural Terraces in Flat Landscapes in Northeastern Oklahoma, USA," Land, MDPI, vol. 12(2), pages 1-12, February.
    2. Mohib Ullah & Bingzhe Tang & Wenchao Huangfu & Dongdong Yang & Yingdong Wei & Haijun Qiu, 2024. "Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region," Land, MDPI, vol. 13(7), pages 1-22, July.
    3. Zahra Eslami & Khodayar Abdollahi & Ataollah Ebrahimi‬, 2023. "On the Role of Hydrological Losses in Estimating Event Runoff Coefficients Using the NRCS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4233-4252, September.

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