Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Zheng Jiang,Gendong Li,Guangyong Li,Quanzhong Huang

doi:10.3390/w11071520

Abstract

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

Highlights

The soil moisture status of farmland is crucial for optimizing irrigation water management, which promotes efficient water use and alleviates soil secondary salinity problems
Onsite soil moisture and solution electrical conductivity between the results of the assimilation and measurement in the study of Wu and Margulis than in the (EC) data from sensors were used for data assimilation in their study
The ensemble Kalman filter (EnKF) has been proven to be an effective tool for data assimilation in diverse areas

Summary

Introduction

The soil moisture status of farmland is crucial for optimizing irrigation water management, which promotes efficient water use and alleviates soil secondary salinity problems. Soil water and salinity are commonly estimated or predicted using the Richards equation and convection–dispersion equation-based models, respectively. The estimation and prediction of soil moisture and salinity are imprecise because of inherent process uncertainties, such as the model parameters [1], initial and boundary conditions [2], and source/sink. The accuracy of modeling and prediction can be improved by optimizing the parameters through a calibration process [4] or by directly updating the state variables involved in the modeling process [5]. Both types of improvements require exploiting observations associated with the model state variables.

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Conclusion