Infiltration Capacity of Three Types of Soil in the Sahuayo River Basin

Abstract

Infiltration is the process by which surface water enters soils, is part of the regulatory ecosystem services and of the utmost importance for environmental and agricultural management.  Its spatial and temporal variability is particular for each soil type and intended usage. This research´s objective was to determine which of the land types and land use provides the highest ecosystemic service based on their water infiltration and its spatial prediction. This research evaluates the Sahuayo River basin, in the State of Michoacán, Mexico, on agricultural and non-agricultural land use Luvisol, Vertisol, and Leptosol soil types. 60 soil samples were collected at stratified random points and their organic matter content, texture, and apparent density were assessed via laboratory methods. An analysis of variance with multiple means comparisons was performed on the infiltration variable results. The SCORPAN model was used to predict the infiltration´s spatial distribution using three machine learning algorithms (with default and hyperparameter tuning), and 11 variables. These variables correspond to topographic attributes, climate, soil properties, and remote sensing data. The model`s predictive power was evaluated using its mean square root error (RMSE) and mean absolute error (MAE) values. The non-agricultural Leptosol is the combination providing the highest infiltration capacity (fp=2210.6 mm·h-1), while the agricultural Vertisol the lowest (fp= 275.8 mm·h-1). Both combinations significantly differ from each other. The highest precision model was recorded with the artificial neural networks algorithm, which reported the lowest MAE and RMSE values, for both variable selection methods, in their default configurations and with hyperparameter adjustment.

Keywords: Soil properties, spatial variation, digital soil mapping, parameterization