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ARS Home » Southeast Area » Booneville, Arkansas » Dale Bumpers Small Farms Research Center » Research » Publications at this Location » Publication #399289
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 - Edwin
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. 11(11):2018. 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 used as a proxy for soil moisture, but it is not well understood how well it performs across varying timescales and methods of calculation. To assess the effec-tiveness 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. 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 im-proved 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.