2021
DOI: 10.3808/jei.202100451
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Improving Soil Salinity Simulation by Assimilating Electromagnetic Induction Data into HYDRUS Model Using Ensemble Kalman Filter

Abstract: Assimilation of proximally and remotely sensed information on soil salinization-related attributes into a hydrological model is essential to improve the forecast performance of the profiled soil salinity dynamics for developing appropriate soil amendment practices. Although the family of ensemble Kalman filters (EnKF) is widely used in data assimilation, their applicability and reliability for soil salinization estimation requires further experimental validation. Here, we evaluated the assimilation performance… Show more

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Cited by 3 publications
(3 citation statements)
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“…As a result, individual climate models used to study climate change may neglect and underestimate climate change uncertainties. Ensemble approaches based on multiple models can help improve uncertainty estimation (Lu et al., 2021; Wu et al., 2020; Yao et al., 2021). A model quality metric can be chosen and converted into a weight for each model.…”
Section: Methodsmentioning
confidence: 99%
“…As a result, individual climate models used to study climate change may neglect and underestimate climate change uncertainties. Ensemble approaches based on multiple models can help improve uncertainty estimation (Lu et al., 2021; Wu et al., 2020; Yao et al., 2021). A model quality metric can be chosen and converted into a weight for each model.…”
Section: Methodsmentioning
confidence: 99%
“…Turbulence was simulated using a magnetic stirrer, with the ice formation being simulated under different stirring speeds (0, 250, 500, 750, 1000, and 1250 rpm). Various modeling approaches have been used for exploring environmental issues. A computational fluid dynamics (CFD) simulation was implemented to calculate the energy dissipation rates (ε) in beakers at different rotation speeds of the magnetic stirrer (representing varying levels of turbulence intensity in the natural environment). Ansys CFX (Ansys, US) was employed to conduct the simulation, with further details of the simulation being provided in the Supporting InformationThe beakers and magnetic stirrers were placed in an EPL-2H Platinous Series Temperature Humidity Chamber (ESPEC, Japan) at −10 °C for 3 h to simulate the ice formation process.…”
Section: Methodsmentioning
confidence: 99%
“…A set of models that can accurately simulate the movement of salt and water in saturated and unsaturated porous media based on different solute transport equations, such as MODFLOW, MODPATH, SWAP, and HYDRUS, has been widely applied in different types of research [66].Using data assimilation methods, with soil water/salt transport models as model operators and large-scale observation data as driving data, observational data are incorporated into the model using assimilation algorithms. For example, Yao et al [67] utilized ensemble Kalman filter algorithms to assimilate electromagnetic induction data into the HYDRUS-1D model, improving the spatiotemporal dynamic estimation accuracy of soil salinity. Ding Jianli et al used ensemble Kalman filter methods to assimilate MODIS and Landsat TM data information into HYDRUS-1D.…”
Section: Soil Salinity Inversion Based On Data Assimilationmentioning
confidence: 99%