Sensitivity Analysis

Sensitivity analysis helps quantify how variation in input parameters affects model outputs. This tutorial demonstrates how to perform sensitivity analysis on SWAT+ model parameters.
The parameter sampling is handled by the SALib Python package using Sobol sampling from a defined parameter space.

• Configuration Settings

  import pySWATPlus
  
  # Initialize the project's TxtInOut folder
  txtinout_reader = pySWATPlus.TxtinoutReader(
      path=r"C:\Users\Username\project\Scenarios\Default\TxtInOut"
  )
  
  # Copy required files to an empty custom directory
  target_dir = r"C:\Users\Username\custom_folder" 
  target_dir = txtinout_reader.copy_required_files(
      target_dir=target_dir
  )
  
  # Initialize TxtinoutReader with the custom directory
  target_reader = pySWATPlus.TxtinoutReader(
      path=target_dir
  )
  
  # Disable CSV file generation to save time
  target_reader.disable_csv_print()
  
  # Disable daily time series in print.prt (saves time and space)
  target_reader.enable_object_in_print_prt(
      obj=None,
      daily=False,
      monthly=True,
      yearly=True,
      avann=True
  )
  
  # Run a trial simulation to verify expected time series outputs
  target_reader.run_swat(
      begin_date='01-Jan-2010',
      end_date='31-Dec-2012',
      warmup=1,
      print_prt_control={
          'channel_sd': {}
      }  # enable daily time series for 'channel_sd'
  

---

• Sobol-Based Interface

  This high-level interface builds on the above configuration to run sensitivity simulations using Sobol sampling. It includes:

  • Automatic generation of Sobol samples for the parameter space
  • Parallel computation to speed up simulations
  • Output extraction with filtering options (by date, column values, etc.)
  • Structured export of results for downstream analysis

  The results can be used to compute performance metrics, compare with observed data, and calculate Sobol indices.

  # Sensitivity parameter space
  parameters = [
      {
          'name': 'esco',
          'change_type': 'absval',
          'lower_bound': 0,
          'upper_bound': 1
      },
      {
          'name': 'perco',
          'change_type': 'absval',
          'lower_bound': 0,
          'upper_bound': 1
      }
  ]
  
  # Target data extraction from sensitivity simulation
  simulation_data = {
      'channel_sdmorph_yr.txt': {
          'has_units': True,
          'ref_day': 15,
          'ref_month': 6,
          'apply_filter': {'gis_id': [561]},
          'usecols': ['gis_id', 'flo_out']
      },
      'channel_sd_mon.txt': {
          'has_units': True,
          'begin_date': '01-Jun-2011',
          'ref_day': 15,
          'apply_filter': {'name': ['cha561'], 'yr': [2012]},
          'usecols': ['gis_id', 'flo_out']
      }
  }
  
  # Sensitivity simulation
  if __name__ == '__main__':
      output = pySWATPlus.SensitivityAnalyzer().simulation_by_sobol_sample(
          parameters=parameters,
          sample_number=1,
          simulation_folder=r"C:\Users\Username\simulation_folder",
          txtinout_folder=target_dir,
          simulation_data=simulation_data,
          clean_setup=True
      )
      print(output)