Articles | Volume 18, issue 8
Hydrol. Earth Syst. Sci., 18, 3279–3299, 2014
https://doi.org/10.5194/hess-18-3279-2014
Hydrol. Earth Syst. Sci., 18, 3279–3299, 2014
https://doi.org/10.5194/hess-18-3279-2014

Research article 28 Aug 2014

Research article | 28 Aug 2014

Evaluating topographic wetness indices across central New York agricultural landscapes

B. P. Buchanan1, M. Fleming1, R. L. Schneider2, B. K. Richards1, J. Archibald1, Z. Qiu3, and M. T. Walter1 B. P. Buchanan et al.
  • 1Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
  • 2Department of Natural Resources, Cornell University, Ithaca, NY, USA
  • 3Department of Chemistry and Environmental Science, New Jersey Institute of Technology, Newark, NJ, USA

Abstract. Accurately predicting soil moisture patterns in the landscape is a persistent challenge. In humid regions, topographic wetness indices (TWIs) 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 – especially in the US. We calculated TWIs using over 400 unique formulations that considered different digital elevation model (DEM) resolutions (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, with each field visited five to eight times between August and November 2012. Using a mixed effects modeling approach, we were able to identify optimal TWI formulations applicable to moderate relief agricultural settings 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 correlations.

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