Centered Parameter Study with HydroTrend

HydroTrend is a numerical model that creates synthetic river discharge and sediment load time series as a function of climate trends and basin morphology.

In this example, we'll perform a centered parameter study, evaluating how changing two HydroTrend input parameters:

starting_mean_annual_temperature and
total_annual_precipitation

affects two output parameters

median long-term suspended sediment load at the river mouth and
mean discharge at the river mouth

over a one-year run.

Before we start, make sure that you've installed Dakota, HydroTrend, and this package on your computer, using the instructions in the README file.

Start by importing the Dakota class.



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from dakotathon import Dakota

Create a Dakota instance to perform a centered parameter study with HydroTrend.



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d = Dakota(method='centered_parameter_study', plugin='hydrotrend')

Define the HydroTrend input variables to be used in the parameter study, as well as the initial point in parameter space, the step size, and the range of the variables.



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d.variables.descriptors = ['starting_mean_annual_temperature', 'total_annual_precipitation']
d.variables.initial_point = [15.0, 2.0]
d.method.steps_per_variable = [2, 5]
d.method.step_vector = [2.5, 0.2]

Define the HydroTrend outputs to be used in the parameter study, as well as the statistics to be calculated from them.



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d.responses.response_descriptors = ['Qs_median', 'Q_mean']
d.responses.response_files = ['HYDROASCII.QS', 'HYDROASCII.Q']
d.responses.response_statistics = ['median', 'mean']

HydroTrend requires a set of files to run. They're included in the data directory of this example. They can also be obtained directly from the HydroTrend GitHub repository. Set paths to these files with the following statements.



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import os

data_dir = os.path.join(os.getcwd(), 'data')
template_file = os.path.join(data_dir, 'hydrotrend.in.tmpl')
parameters_file = os.path.join(data_dir, 'parameters.yaml')
hypsometry_file = os.path.join(data_dir, 'HYDRO0.HYPS')

The template file provides the configuration file for HydroTrend, but with all parameter values replaced by variables in the form {parameter_name}. The parameters file provides descriptions, ranges, and default values for all of the parameters represented in the template file. The hypsometry file describes the change in elevation along the river's course from source to sea.

From the template and parameters files, we can create an input file that HydroTrend can run. Included in the CSDMS Dakota package is a routine that replaces the variables in the template file with default values from the parameters file. Import this routine and use it to create a HydroTrend input file.



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from dakotathon.plugins.base import write_dflt_file

default_input_file = write_dflt_file(template_file, parameters_file, run_duration=365)
print default_input_file

Next, we must replace the default values for the variables for starting_mean_annual_temperature and total_annual_precipitation with variable names for Dakota to substitute into. The CSDMS Dakota package also includes a routine to do this. Import this routine and use it to create a Dakota template file.



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from dakotathon.plugins.base import write_dtmpl_file

dakota_template_file = write_dtmpl_file(template_file, default_input_file, d.variables.descriptors)
print dakota_template_file

Associate the Dakota template file and the hypsometry file with the Dakota instance.



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d.template_file = dakota_template_file
d.auxiliary_files = hypsometry_file

Call the setup method to create files needed by Dakota, then run the experiment.



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d.setup()
d.run()

Check the output; in particular, the dakota.dat file.



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%cat dakota.dat

I've included my output for comparison.

%eval_id interface starting_mean_annual_temperature total_annual_precipitation      Qs_median         Q_mean 
       1     CSDMS             15              2          4.907    101.3142499 
       2     CSDMS             10              2          2.435    101.2442425 
       3     CSDMS           12.5              2          3.524    101.3139131 
       4     CSDMS           17.5              2          6.608    101.3142499 
       5     CSDMS             20              2           8.67    101.3142499 
       6     CSDMS             15              1          3.783    49.13904014 
       7     CSDMS             15            1.2          4.061    59.54595737 
       8     CSDMS             15            1.4          4.263    69.96313812 
       9     CSDMS             15            1.6          4.518    80.40633878 
      10     CSDMS             15            1.8          4.567    90.85803505 
      11     CSDMS             15            2.2           5.13    111.7797497 
      12     CSDMS             15            2.4          5.324    122.2484547 
      13     CSDMS             15            2.6          5.474    132.7260839 
      14     CSDMS             15            2.8          5.626     143.206995 
      15     CSDMS             15              3          5.812       153.6976