Multi-model based soil moisture simulation approach under contrasting weather conditions

We developed and tested a multi-model based soil moisture simulation approach for improving parameterization scheme and its transferability under contrasting weather conditions. Three popular hydrologic models in combination with three optimization schemes were adapted to consider uncertainties in physical and optimization model structures for estimating near-surface soil moisture dynamics. In order to improve parameterization and its transferability under contrasting weather conditions, a multiple set of weights based on different land surface wetness conditions (wet, normal, and dry) were used using a Multiple Weighting Algorithm (MWA). A Differential Split Sample Testing (DSST) scheme was used to test the transferability of parameterizations in the optimization and physical model domains. Data from three experimental sites (Little Washita, LW13 in Oklahoma, Everglades in Florida and Bondville/Olney sites in Illinois) were used to verify our proposed approach. Our findings indicated that the multi-model outputs were highly influenced both by the physical and optimization model structures. Overall, our approach performed well in the test of transferability under different weather conditions. However, we confirmed that the overfitted parameters (to the in-situ measurements during the calibration period) due to the optimization model structures can undermine the transferability of multi-model approach. Also, our findings indicate that the estimated soil hydraulic properties during the dry years can be limited in representing the soil wetness conditions for the wet years.