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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
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Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions

Authors: Pushpendra Raghav; Mukesh Kumar; Yanlan Liu;

Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions

Abstract

This archive includes the scripts and related input data to produce results for the paper entitled - "Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions". Following is the description of files/folders: 1. Input_Data: This folder contains all the required input data including FluxNet data, soil properties, quality controlled training-validation data, and metadata & other supporting information of the sites. 2. Model_EMP: This folder contains all the scripts for empirical model of stomatal conductance. (Note: Scripts have been written in MATLAB"). No need to change anything except the MATLAB executive path in two files "run_all_tasks_to_optimize_params.sh" and "prediction.sh". Read "ReadMe.txt" file in the folder "Model_EMP" for more instructions on running the model. 3. Model_ML: This folder contains all the scripts for pure machine learning model of stomatal conductance. It contains four sub-folders: 1. Model_Config_1 (Model with configuration-1); 2. Model_Config_2_TEA (Model with Configuration-2 & TEA-based T estimates); 3. Model_Config_2_uWUE (Model with Configuration-2 & uWUE-based T estimates); 4. Model_Config_2_Yu22 (Model with Configuration-2 & Yu22-based T estimates). Further instructions have been given in each jupyter notebooks. Briefly, in folder "Model_Config_1", the notebook "train_ML_config_1.ipynb" trains the model parameters and notebook "Predictions_ML_config_1" is used to do predictions. Similar instructions apply for other subfolders. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed. 4. Model_PH_exp: This folder contains all the scripts for plant hydraulics model with explicit representation. All the scripts are self explanatory and further instructions are provided in the scripts as needed. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed. 5. Model_PN_imp: This folder contains all the scripts for plant hydraulics model with implicit representation. Instructions given for "Model_ML" are applicable here. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed. Versions: Tensorflow 2.11.0, MATLAB_R2022a, Python 3.10.9

Scripts (updated as needed) also can be found at https://github.com/praghav444/Modeling_Stomatal_Conductance_for_ET_Partitioning.

Keywords

Plant hydraulics, Machine learning, Stomatal conductance, Flux partitioning

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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