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Upscaling Soil Hydraulic Parameters in the Picacho Mountain Region Using Bayesian Neural Networks

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 55(2): 463-473. (doi: 10.13031/2013.41396) @2012
Authors:   R. B. Jana, B. P. Mohanty, Z. Sheng
Keywords:   Bayesian neural network, Multiscale pedotransfer functions, Rio Grande basin, Soil moisture

A multiscale Bayesian neural network (BNN) based algorithm was applied to obtain soil hydraulic parameters at multiple scales in the Rio Grande basin (near Picacho Mountain, approximately 11 km northwest of Las Cruces, New Mexico). Point-scale measurements were upscaled to 30 m and 1 km resolutions. These scaled parameters were used in a physically based hydrologic model as inputs to obtain soil moisture states across the study area. The test sites were chosen to provide variety in terrain, land use characteristics, vegetation, soil types, and soil distribution patterns. In order to validate the effectiveness of the upscaled soil water retention parameters, and thus the soil hydraulic parameters, hydrologic simulations were conducted using the HYDRUS-3D hydrologic simulation software. Outputs from the hydrologic simulations using the scaled parameters were compared with those using data from SSURGO and STATSGO soil maps. The BNN-based upscaling algorithm for soil retention parameters from point-scale measurements to 30 m and 1 km, resolutions performed reasonably well (Pearson's R > 0.6) at both scales. High correlations (>0.6) between the simulated soil moisture values based on the upscaled and the soil map-derived soil hydraulic parameters show that the methodology is applicable to semi-arid regions to obtain effective soil hydraulic parameter values at coarse scales from fine-scale measurements of soil texture, structure, and retention data.

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