Using different multimodel ensemble approaches to simulate soil moisture in a forest site with six traditional pedotransfer functions

Abstract

Pedotransfer functions (PTFs) have routinely been used to estimate the soil hydraulic properties (SHPs) from easily measurable soil properties, such as particle-size distribution, organic matter content and bulk density. However, different PTFs often yielded different prediction results. In order to deal with the PTF selection problem, this study used multimodel ensemble approaches to simulate forest soil moisture based on the modelling results of different PTFs. A total of 300 days of observed soil moisture data at four depths (10-, 20-, 40- and 60-cm) were adopted to calibrate the Richards equation and obtain the SHPs by using the inverse option in HYDRUS-1D. Six published PTFs were selected to predict the SHPs, which were used to predict soil moisture temporal variations at these four different depths. Two multimodel ensemble methods, including the simple model average (SMA) and the multiple linear regression (MLR)-based superensemble, were used in this study. Under different selections of training periods (i.e. 50, 100 and 150 days), performances of these multimodel ensemble approaches were compared with those of the best single PTF model. The SMA always had worse performance than the best single model. However, the performances of the superensemble approach were better than those of the best single model, and even comparable to those of the calibrated soil water flow model. Results show that given the relatively long training period (>50 days), it is worthwhile to consider the superensemble method to simulate soil moisture contents in forestland.