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ARS Home » Southeast Area » Booneville, Arkansas » Dale Bumpers Small Farms Research Center » Research » Publications at this Location » Publication #394842
Research Project: Sustainable Small Farm and Organic Grass and Forage Production Systems for Livestock and Agroforestry

Location: Dale Bumpers Small Farms Research Center

Title: Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization

Author
item Winzeler, Hans
item Owens, Phillip
item Read, Quentin
item Libohova, Zamir
item Ashworth, Amanda
item Sauer, Thomas

Submitted to: Land
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/8/2022
Publication Date: 11/11/2022
Citation: Winzeler, H.E., Owens, P.R., Read, Q.D., Libohova, Z., Ashworth, A.J., Sauer, T.J. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land. https://doi.org/10.3390/land11112018.
DOI: https://doi.org/10.3390/land11112018

Interpretive Summary: Soil moisture is a crucial resource for plant and crop growth. Our study examines models of soil moisture that help us determine the areas of crop fields or landscapes that may be susceptible to differences in moisture that may influence the growth and productivity of crops and plants. We assess different moisture models to see which ones are more accurate at predicting measured moisture. This research should improve management techniques relating to the use of crop fields with variable moisture characteristics.

Technical Abstract: Topographic wetness index (TWI) is often used as a proxy for long-term average soil moisture, but it is not well understood how well this surrogate variable predicts soil moisture across varying timescales and methods of calculation. To assess the effectiveness of different calculation methods of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 39 locations in an agroforestry catena. We calculated TWI 546 ways involving different flow algorithms and digital elevation model (DEM) preparations. We found that DEM filtration and resampling improved the effectiveness of the TWI at predicting spatial patterns of field moisture. Seasonal and random fluctuations of moisture influenced the strength of correlation between TWI and VWC. Map generalization was the most important step in the process of creating TWI output that best matched field moisture patterns. Most algorithms performed poorly on DEMs that were not generalized and improved substantially once DEM generalization was performed. Resampling DEMs to 7 m pixel size, filtering with a Gaussian filter, or simple circular averaging filtration were all successful at improving TWI performance. The SAGA wetness index was the only algorithm that performed with some success on ungeneralized DEMs. Pearson correlation coefficients between TWI and grand mean VWC for the entire measurement period ranged from 0.18 to 0.64 for the algorithms on generalized DEMs and 0.15 to 0.59 for the algorithms on DEMs that were not generalized .