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Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation.
Junhao He1, Mohamed M Hantush2, Latif Kalin3
1State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, 712100, China.
Journal of Hydrology
|June 16, 2023
Summary
This study validates a two-layer soil water model for simulating moisture and fluxes in the vadose zone. The model performs reliably across various soil types and hydroclimate conditions, proving effective for parameter identification and uncertainty estimation.
Area of Science:
- Environmental Science
- Hydrology
- Soil Science
Background:
- A two-layer model simulating soil water movement in the vadose zone was developed, using Richards' equation (RE).
- This model estimates thickness-averaged soil moisture and suction, verified against HYDRUS for uniform soil textures.
- Its performance in stratified soils and real-world conditions required further investigation.
Purpose of the Study:
- To rigorously test the two-layer model's performance in stratified soils and under variable field hydroclimate conditions.
- To estimate model parameters and quantify uncertainty using a Bayesian framework.
- To assess the model's reliability for simulating soil moisture and fluxes in the vadose zone.
Main Methods:
- Numerical verification experiments were conducted on 231 soil textures and stratified soil profiles.
- Model performance was evaluated by comparing soil moisture and flux estimates with HYDRUS simulations.
- A case study utilized data from a Soil Climate Analysis Network (SCAN) site, employing the Bayesian Monte Carlo (BMC) method for calibration and uncertainty quantification.
Main Results:
- The two-layer model demonstrated excellent performance for homogeneous soil profiles, with slight declines in coarser soils or thicker layers.
- Accurate simulation of soil moisture and fluxes was achieved in stratified soils with contrasting hydraulic conductivities.
- Field application showed good agreement with observed soil moisture (RMSE <0.023), with parametric uncertainty being minimal.
Conclusions:
- The two-layer model reliably simulates thickness-averaged soil moisture and estimates vadose zone fluxes under diverse conditions.
- The Bayesian Monte Carlo (BMC) method provides a robust framework for parameter identification and uncertainty estimation in vadose zone modeling.
- The model is suitable for applications requiring simulation of soil water movement in the root zone and vadose zone with dynamic water tables.
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