Operation of Reservoirs in California (ORCA)
A simulation mode incorporating CVP and SWP Delta exports, reservoir releases, and snow-pack to streamflow forecasts. This branch simulates both historical and projected cmip5 scenarios.
Requirements:
NumPy, pandas, Matplotlib, Scipy, and scikit-learn.
Running ORCA
Running ORCA in historial mode:
- In main.py, set projection to False.
- Choose whether to calculate R2s and plot resuts (set variables to True or False. If running any data processing scripts, set process_hist_data to True. If downloading up-to-date historical data, set cdec to True. Set hist_indices to True if re-processing new data. Set hist_forcast to True if re-running forecasts.
- Run main.py and results will be in historical_runs_data folder.
Running ORCA in projection mode for individual cmip5 scenario:
- Choose cmip5 scenario to run. Its input data file can be found in the git repository orca_cmip5_inputs. Copy desired input file from this repository to orca's input_climate_files folder.
- In main.py, set variable sc to desired scenario to read. Set options for plot, process_climate_data, climate_indices, and climate_forecasts. Historical data can also be processed in same execution of script if updating forecast and gains regression coefficients is desired.
- After executing main.py for the projection will be in the individual_projection_runs folder.
Running ORCA in projection mode for multiple cmip5 scenarios:
- Copy input files for all desired cmip5 scenarios from the orca_cmip5_inputs repository to ORCA's scenario_runs folder.
- Write names of all desired cmip5 scenarios to scenario_names.txt file in data folder.
- Set climate_indices , climate_forecasts, and run_projection options in run_all_climate_projections.py. Set consolidate_outputs to format results for each scenario in the same csv files.
- After executing run_all_climate_projections.py, results will be in the scenario_runs and climate_results folders.
License
Copyright (C) 2019 CALFEWS Team. Released under the MIT license.