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5 results for “evapotranspiration partitioning”
Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions
<p>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:</p> <p>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. </p> <p>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. </p> <p>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.</p> <p>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.</p> <p>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.<br> </p> <p>Versions: Tensorflow 2.11.0, MATLAB_R2022a, Python 3.10.9</p>
An Isotopic Approach to Partition Evapotranspiration in a Mixed Deciduous Forest at the University of Michigan Biological Station, Pellston, MI (2017)
Transpiration (T) is perhaps the largest fluxes of water from the land surface to the atmosphere and is susceptible to changes in climate, land use and vegetation structure. However, predictions of future transpiration fluxes vary widely and are poorly constrained. Stable water isotopes can help expand our understanding of land–atmosphere water fluxes but are limited by a lack of observations and a poor understanding of how the isotopic composition of transpired vapour (δT) varies. Here, we present isotopic data of water vapour, terrestrial water and plant water from a deciduous forest to understand how vegetation affects water budgets and land–atmosphere water fluxes. We measured subdiurnal variations of δ18OT from three tree species and used water isotopes to partition T from evapotranspiration (ET) to quantify the role of vegetation in the local water cycle. We find that δ18OT deviated from isotopic steady‐state during the day but find no species‐specific patterns. The ratio of T to ET varied from 53% to 61% and was generally invariant during the day, indicating that diurnal evaporation and transpiration fluxes respond to similar atmospheric and micrometeorological conditions at this site. Finally, we compared the isotope‐inferred ratio of T to ET with results from another ET partitioning approach that uses eddy covariance and sap flux data. We find broad midday agreement between these two partitioning techniques, in particular, the absence of a diurnal cycle, which should encourage future ecohydrological isotope studies. Isotope‐inferred estimates of transpiration can inform land surface models and improve our understanding of land–atmosphere water fluxes.
Dataset for the SFmodel, applied in Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Dataset used for the SFmodel, applied in the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>For units and nomenclature of the variables refer to Units_and_Nomenclature_for_SFmodel_in_Evapotranspiration_dynamics_and_partitioning_in_a_grassed_vineyard.pdf. </p>
Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches
<p>Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches".</p> <p>You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>. </p>
Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest
<p>Dataset</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table> <p> </p> <p>Codes for Analysis</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table> <p> </p> <p>ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl)</p> <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> <p>To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this:</p> <p>wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95}</p> </li> <li> <p>Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart)</p> </li> </ul> <p> </p> <p> </p>
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