Evaluating topographic wetness indices across central New York agricultural landscapes
Author:
Buchanan B. P., Fleming M., Schneider R. L., Richards B. K.ORCID, Archibald J., Qiu Z., Walter M. T.
Abstract
Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWI) are widely used to approximate relative soil moisture patterns. However, there are many ways to calculate TWIs and very few field studies have evaluated the different approaches in the US. We calculated TWIs using over 400 unique formulations that considered different: Digital Elevation Model (DEM) resolution (cell size), vertical precision of DEM, flow direction and slope algorithms, smoothing via low-pass filtering, and the inclusion of relevant soil properties. We correlated each TWI with observed patterns of soil moisture at five agricultural fields in central NY, USA; each field was visited 5–8 times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations that may provide guidance for practitioners and future studies. Overall, TWIs were moderately well correlated with observed soil moisture patterns; in the best case the relationship between TWI and soil moisture had an average R2 and Spearman correlation value of 0.61 and 0.78, respectively. In all cases, fine-scale (3 m) LiDAR-derived DEMs worked better than USGS 10 m DEMs and, in general, including soil properties improved the correlations.
Publisher
Copernicus GmbH
Reference86 articles.
1. Agnew, L. J., Lyon, S., Gérard-Marchant, P., Collins, V. B., Lembo, A. J., Steenhuis, T. S., and Walter, M. T.: Identifying hydrologically sensitive areas: bridging the gap between science and application, J. Environ. Manage., 78, 63–76, 2006. 2. Akaike, H.: Information theory and an extension of the maximum likelihood principle, in: Second International Symposium on Information Theory, vol. 1, edited by: Petrov, B. N. and Csaki, F., Akademiai Kiado, Budapest, 267–281, 1973. 3. Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974. 4. Barling, R. D., Moore, I. D., and Grayson, R. B.: A quasi-dynamic wetness index for characterising the spatial distribution of zones of surface saturation and soil water content, Water Resour. Res., 30, 1029–1044, 1994. 5. Bates, D., Maechler, M., and Bolker, B.: lme4: linear mixed-effects models using S4 classes, edited by: Bates, D., Maechler, M., and Bolker, B., Comprehensive R Archive Network, available at: http://cran.r-project.org/package=lme4 (last access: May 2013), 2011.
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