Watershed Modelling Exercise 2

A repository for a conceptual hydrological model. (Exercise 2 Watershed Modelling, ETH Zurich)

Instructions set up Environemnt

1. Download the zip file wat_bal_python.zip and extract it.

2. Install python 3.7.6 (Required)Its Free!

   • To check that python is installed on your desktop:
     • For Mac open terminal and type $ python3 (Mac comes with preinstalled python 2.7.6 but you still need to install python 3.7.6)
     • For Windows 10, open Windows Power Shell and then type Python

3. Install Visual Studio Code (Recommended)Its Free!

   • Visual Studio Code is an editor and will comes with autocomplete and syntax highlighting features
   • Any other editor should work as well but from here on the setup will be in Visual Studio Code
   • Download the version depending on your OS.

4. Setting up the project. Installing packages.

   • Open Terminal/Windows Power Shell
   • Install pipenv by running $ pip3 install pipenv in Terminal/Windows Power Shell
   • Open Visual Studio Code
   • A welcome screen will appear, in the left side bar select the Extension (icon with 4 squares)
   • Search Python. Select Python (Linting, Debugging,..)
   • Once it is installed open Explorer on the top left bar. (shift+ctrl+e)
   • Select Open Folder, Browse to the directory water_bal_python.
   • The folder should contain Pipfile and Pipfile.lock
   • Go to Terminal at the top and select new terminal
   • Terminal will open at the bottom, Run the command $ pipenv install
   • This step may take some time and the internet connection is required
   • Restart the VSCode. Now on the lef side of blue colored bottom bar you can see the python version adn in bracket name of the folder along with pipenv. ('wat_bal_python:pipenv')

Instruction to run the scripts

• There are 5 scripts namely model.py, plots.py, utils.py, run_opt.py, run_param_plot.py, run_sim.py and two folders name data and plots
• All the required data is in the data folder
• All the plots will be created in the plots folder
• Two more files will be added namely parameter.h5 and gof.csv
• Parameter.h5 will have the list of parameters for different runs
• gof.csv will contain the goodness of fit values for observation and simulation
• Run the scripts in order
  • run_opt.py will optimize the model and save the value of parameters in Parameters.h5
    • Number of runs nruns needs to be selected here. Roughly it takes 70-120 runs per iteration depending on the computer.
  • run_param_plot.py will generate the plots of the parameters
  • Seeing the histogram of parameters chose the value of the parameter and add the values to the script run_sim.py
  • Run run_sim.py to save the plots and gof values
  • Any change in the structure of the model needs to be made in model.py
  • For adding parameters to calibrate some changes also needs to be made in run_opt.py and run_sim.py