Predicting soil depth in a humid tropical watershed: A comparative analysis of best-fit regression and geospatial models

Abstract

As the climate changes, water scarcity is becoming a major global concern. The accurate prediction of soil depth is critical for hydrological modeling under different climate change scenarios for the effective management of humid tropical watersheds. This study determined the best-fit assumption-based (i.e., ordinary least square (OLS)) and assumption-relaxed (partial least squared, quantile, and elastic net) regression models, and compared them with geospatial models (ordinary kriging, universal kriging, regression kriging, and cokriging), for predicting soil depth in a 50 × 150 m humid tropical watershed. Soil depth, apparent electrical soil conductivity (ECa) at two depths of exploration (ECas, 0–0.5 m, and ECad, 0–1.6 m using a DUALEM-1S EC meter), slope gradient, and soil physical and chemical properties measured in-field or using soil samples (0–0.2 m), were determined at each sampling location. Multivariate regression models explained 86–89% of the variability in soil depth and had a comparative root mean square error (RMSE), Lin’s concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and prediction error rate (PER) to corresponding ECad univariate models. ECad was a significant predictor in all models except the elastic net regression model. The cokriging model was superior to all regression and geospatial models with the lowest RMSE (0.04) and PER (10 %), and highest LCCC (0.99) and RPD (6.71), and can be used to accurately predict soil depth across the watershed, thereby improving hydrological models for watershed management in this and similar vulnerable humid tropical watersheds. Most interestingly, our results suggest that OLS models are in some cases robust enough to handle violations of their assumptions, implying that data transformation may not always be required for OLS regression. Therefore, it is recommended that the traditional workflow for OLS regression include a validation check using an assumption-relaxed model, the outcome of which would determine the necessity of data transformation.