Tutorial 2: Running pyWaPOR for test case

3. Run PyWaPOR test case in Jupyter Notebook

In this step, you will create new code cells that run each step in pywapor. For the test case we selected the Wad Helal area, which you should be familiar with from the MOOC. In this exercise, the code to run this test case is written. You can copy the code of each step, paste into a new code cell and run.
For each numbered item below, create a new code cell in JupyterLab (shortcut: B), copy the code to the cell and run it (shortcut: SHIFT + ENTER).

1. Import pywapor package



import pywapor


This code will import pywapor package to the current python kernel.


2. Set up project for the test case
   
   

   project_folder = r"Test_case" #Path to folder
   bb = [33.1479429498060583, 14.2657100971198449, 33.2874918465625242, 14.3487734799492763] # [xmin, ymin, xmax, ymax] #Wad_Helal
   period = ["2023-03-01", "2023-03-02"] 
   # Set up a project.
   project = pywapor.Project(project_folder, bb, period)
   

   
   This code creates a new project folder for the test case, named "Test_case", defines the bounding box (minimum longitude, minimum latitude, maximum longitude, maximum latitude) of the test case (i.e., Wad Helal), defines the time period that model will run. For this test case, we will run the model for only 2 days as an example (i.e., the first two days of March 2023).
   
   
3. Configure input dataset



summary = {
            '_ENHANCE_': {"bt": ["pywapor.enhancers.dms.thermal_sharpener.sharpen"],},
            '_EXAMPLE_': 'SENTINEL2.S2MSI2A_R20m',
            '_WHITTAKER_': {'SENTINEL2.S2MSI2A_R20m':{'method':'linear'},								
                            'VIIRSL1.VNP02IMG':{'method':'linear'},								
				},
            'elevation': {'COPERNICUS.GLO30'},
            'meteorological': {'GEOS5.inst3_2d_asm_Nx'},
            'optical': {'SENTINEL2.S2MSI2A_R20m'},
            'precipitation': {'CHIRPS.P05'},
            'soil moisture': {'FILE:{folder}{sep}se_root_out*.nc'},
            'solar radiation': {'MERRA2.M2T1NXRAD.5.12.4'},
            'statics': {'STATICS.WaPOR3'},
            'thermal': {'VIIRSL1.VNP02IMG'},
            'chunks':{'time':1,'x':100,'y':100}
            }
project.load_configuration(summary = summary)


This code will configure the input datasets for this test project. All the information about sources of input datasets and methods to preprocess input data is defined by the variable 'summary'. There are also options to run pywapor with the default set of inputs used for WaPOR data products. In the class, you will learn more about different default and customized pywapor configurations and what each line in this code means. 


4. Set up account credentials to download data



project.set_passwords()


This code will prompts you to put in the user accounts and passwords that are required to download the configured input datasets. Use the user account and password that you created in the previous step:  Create user accounts. For this test case, you will need to enter the username and password of NASA Earthdata account, and the email address and password of Copernicus account. Other accounts are used for when you want to run pywapor with other datasets.


5. Download data



datasets = project.download_data()


This code will start the process of downloading input data for pywapor. When python kernel is running the cell, make sure that you have internet connection. The amount of time for downloading data depends on the period and area and internet connection (and indication is that for the Wad Helal area it takes less than 30min to download 2 days of data).
Later, when you run pywapor for your own case study, the downloading step might take longer due to server or other interruptions not all data may have been downloaded. Check the log of the download section, if it indicates that if failed to download certain variables, re-run the step again (it will append data to the existing data).


6. Run rootzone soil moisture model



se_root_in = project.run_pre_se_root()
se_root = project.run_se_root()


This code will run the se_root module to calculate root zone soil moisture, which the an input to calculate evapotranspiration.


7. Run etlook model



et_look_in = project.run_pre_et_look()
et_look = project.run_et_look()


This code will run the et_look module to calculate evaporation, transpiration, interception, and net primary production.

When running each cell, observe the outputs printed out. 
At the end of this exercise, you should be able to create a Jupyter notebook like this example notebook , to run this notebook, and to get the output file of pywapor et_look_out.nc. Next, we will investigate this output file and discuss in the class.