Yongwei Fu
China Agricultural University
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ABSTRACT:
The uploaded dataset comprises two folders:
1. “Model Prediction” Folder: This folder contains the complete dataset utilized in this study, along with the trained neural network. It enables users to reproduce the results presented in Figure 8 (training and testing outcomes) and facilitates the application of the model to additional soil datasets.
2. “Global Map of Model Parameters” Folder: This folder demonstrates an example application of the trained model, where the authors used SSCBD data from a global hydraulic properties dataset to generate and visualize the global distribution of the MLD model parameters.
ABSTRACT:
This dataset accompanies the paper "Excluding Quartz Content from the Estimation of Saturated Soil Thermal Conductivity: Combined use of Differential Effective Medium Theory and Geometric Mean Method," published in Agricultural & Forestry Meteorology. It contains the Python code that implements the DEM-GMM methodology developed by Fu et al. (2023). The code allows for the computation of saturated thermal conductivity in soils based on variables such as sand content, porosity, and temperature. The dataset offers flexibility in data input, permitting manual entry or data importation from an Excel spreadsheet. Prior to executing the Python code, it is highly recommended to read the accompanying document titled "ReadMeFirst" for essential guidelines. For comprehensive theoretical background and definitions used within the code, we refer users to our aforementioned publication.
Fu, Y., Jones, S., Horton, R., Heitman, J., 2023. Excluding quartz content from the estimation of saturated soil thermal conductivity: combined use of differential effective medium theory and geometric mean method. Agricultural and Forest Meterology (accepted).
ABSTRACT:
The manuscript entitled "An unsaturated hydraulic conductivity model based on the capillary bundle model, the Brooks-Corey model, and the Waxman-Smits model" has been submitted to WRR. The dataset used in this study comprises a calibration set and two validation sets, which together include 260 different soil samples. Hydraulic conductivity, water retention curve, soil texture and bulk density are included.
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Created: April 19, 2023, 12:33 p.m.
Authors: Fu, Yongwei · Tusheng Ren · Robert Horton · Joshua Heitman
ABSTRACT:
The manuscript entitled "An unsaturated hydraulic conductivity model based on the capillary bundle model, the Brooks-Corey model, and the Waxman-Smits model" has been submitted to WRR. The dataset used in this study comprises a calibration set and two validation sets, which together include 260 different soil samples. Hydraulic conductivity, water retention curve, soil texture and bulk density are included.
Created: June 17, 2023, 6:34 a.m.
Authors: Fu, Yongwei
ABSTRACT:
This dataset accompanies the paper "Excluding Quartz Content from the Estimation of Saturated Soil Thermal Conductivity: Combined use of Differential Effective Medium Theory and Geometric Mean Method," published in Agricultural & Forestry Meteorology. It contains the Python code that implements the DEM-GMM methodology developed by Fu et al. (2023). The code allows for the computation of saturated thermal conductivity in soils based on variables such as sand content, porosity, and temperature. The dataset offers flexibility in data input, permitting manual entry or data importation from an Excel spreadsheet. Prior to executing the Python code, it is highly recommended to read the accompanying document titled "ReadMeFirst" for essential guidelines. For comprehensive theoretical background and definitions used within the code, we refer users to our aforementioned publication.
Fu, Y., Jones, S., Horton, R., Heitman, J., 2023. Excluding quartz content from the estimation of saturated soil thermal conductivity: combined use of differential effective medium theory and geometric mean method. Agricultural and Forest Meterology (accepted).
Created: Oct. 25, 2024, 1:56 a.m.
Authors: Fu, Yongwei
ABSTRACT:
The uploaded dataset comprises two folders:
1. “Model Prediction” Folder: This folder contains the complete dataset utilized in this study, along with the trained neural network. It enables users to reproduce the results presented in Figure 8 (training and testing outcomes) and facilitates the application of the model to additional soil datasets.
2. “Global Map of Model Parameters” Folder: This folder demonstrates an example application of the trained model, where the authors used SSCBD data from a global hydraulic properties dataset to generate and visualize the global distribution of the MLD model parameters.