Hierarchical Multimodel Ensemble Estimates of Soil Water Retention with Global Coverage

TL;DR

This paper develops and evaluates a hierarchical ensemble of pedotransfer functions to improve global estimates of soil water retention, providing more accurate and uncertain maps for land-atmosphere modeling.

Contribution

It introduces a multi-model ensemble approach that combines 13 PTFs, enhancing accuracy over individual functions for global soil water retention estimation.

Findings

Ensemble models outperform individual PTFs in accuracy.

Global maps reveal regional differences in soil water estimates.

Full ensemble provides the most reliable predictions.

Abstract

A correct quantification of mass and energy exchange processes among land surface and atmosphere requires an accurate description of unsaturated soil hydraulic properties. Soil pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters. Here, 13 PTFs were grouped according to input data requirements and evaluated against a well-documented soil database with global coverage. Weighted ensembles (calibrated by four groups and the full 13-member set of PTFs) were shown to have improved performance over individual PTFs in terms of root mean square error and other model selection criteria. Global maps of soil water retention data from the ensemble models as well as their uncertainty were provided. These maps demonstrate that five PTF ensembles tend to have different estimates, especially in middle and high latitudes in the Northern Hemisphere. Our full 13-member ensemble model provides more accurate estimates than PTFs that are currently being used in earth system models.