py-ewr 2.4.4

Environmental Water Requirement calculator

ewr tool version 2.4.4 README

Notes on recent version updates

Documentation

• Working version of the EWR User Guide has been added

EWR handling and outputs

• Adding handling for cases where there are single MDBA bigmod site IDs mapping to multiple different gauges
• New EWRs: New Qld EWRs - SF_FD and BF_FD used to look into the FD EWRs in closer detail.
• Adding state and Surface Water SDL (SWSDL) to py-ewr output tables
• Including metadata report (this is still being ironed out and tested)
• New handling capabilities for FIRM model formated files

Model metadata

• Added new FIRM ID file mapping FIRM ID to gauge number.

parameter metadata

• Missing CLLMM ewrs added to calc-config
• minor bug fix for get_objective_mapping
• Removed EWRs from the parameter sheet that are not evaluated by the tool
• Updated parameter sheet and objective mapping correcting some mismatched links between environmental objectives and EWRs
• Fix SDL resource unit mapping in the parameter sheet
• Adding lat and lon to the parameter sheet
• Added in model handling for FIRM model outputs
• Various minor variable renamings for consistency
• Renamed MaxLevelRise to MaxLevelChange
• Removed AnnualFlowSum and MinLevelRise column from parameter sheet
• New format of objective mapping includes the adding of objective mapping back into the parameter sheet and one secondary dataframe (objective_reference.csv) in parameter metadata.
• Removed ACT EWRs. These will be re-added once the ACT LTWP has been finalised
• "EnvObj" has been changed to "EcoObj" for consistency purposes

Installation

Note - requires Python 3.9 to 3.13 (inclusive)

Step 1. Upgrade pip

python -m pip install –-upgrade pip

Step 2.

pip install py-ewr

Option 1: Running the observed mode of the tool

The ewr tool will use a second program called gauge getter to first download the river data at the locations and dates selected and then run this through the ewr tool. For more information please visit the MDBA Gauge Getter github page.

from datetime import datetime

# USER INPUT REQUIRED>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

dates = {'start_date': datetime(YYYY, 7, 1), 
        'end_date': datetime(YYYY, 6, 30)}

gauges = ['Gauge1', 'Gauge2']

# END USER INPUT<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

from py_ewr.observed_handling import ObservedHandler

# Running the ewr tool:
ewr_oh = ObservedHandler(gauges=gauges, dates=dates)

# Generating tables:
# Table 1: Summarised ewr results for the entire timeseries
ewr_results = ewr_oh.get_ewr_results()

# Table 2: Summarised ewr results, aggregated to water years:
yearly_results = ewr_oh.get_yearly_results()

# Table 3: All events details regardless of duration 
all_events = ewr_oh.get_all_events()

# Table 4: Inverse of Table 3 showing the interevent periods
all_interEvents = ewr_oh.get_all_interEvents()

# Table 5: All events details that also meet the duration requirement:
all_successfulEvents = ewr_oh.get_all_successful_events()

# Table 6: Inverse of Table 5 showing the interevent periods:
all_successful_interEvents = ewr_oh.get_all_successful_interEvents()

Option 2: Running model scenarios through the ewr tool

1. Tell the tool where the model files are (can either be local or in a remote location). For each scenario list the file path of each file. Python will default to the path relative to the directory it is being run from, but if you put a full file path you can choose any file.

scenarios = {'Scenario1': ['path/to/file1', 'path/to/file2', 'path/to/file3'],
             'Scenario2': ['path/to/file1', 'path/to/file2', 'path/to/file3']}

2. Tell the tool what format the model files are in. The current model format options are:

   • 'Bigmod - MDBA' Bigmod formatted outputs

   • 'Source - NSW (res.csv)' Source res.csv formatted outputs

   • 'FIRM - MDBA' FIRM ID formatted outputs

   • 'Standard time-series' The first column header should be Date with the date values in the YYYY-MM-DD format. The next columns should have the gauge followed by _ followed by either flow or level E.g.

     Date	409025_flow	409025_level	414203_flow
     1895-07-01	8505	5.25	8500
     1895-07-02	8510	5.26	8505

   • 'ten thousand year' This has the same formatting requirements as the 'Standard time-series'. This can handle ten thousand years worth of hydrology data. The first column header should be Date with the date values in the YYYY-MM-DD format. The next columns should have the gauge followed by _ followed by either flow or level E.g.

     Date	409025_flow	409025_level	414203_flow
     105-07-01	8505	5.25	8500
     105-07-02	8510	5.26	8505

# USER INPUT REQUIRED>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

# Minimum 1 scenario and 1 related file required
scenarios = {'Scenario1': ['path/to/file1', 'path/to/file2', 'path/to/file3'],
             'Scenario2': ['path/to/file1', 'path/to/file2', 'path/to/file3']}

model_format = 'Bigmod - MDBA'

#-----  other model formats include -----#
# 'Bigmod - MDBA'
# 'Source - NSW (res.csv)'
# 'FIRM - MDBA'
# 'Standard time-series'
# 'ten thousand year'

# END USER INPUT<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

File names will be incorporated into the Scenario column of the ewr tool output tables for tracebility of ewr tool output and corresponding model file. In the code written below, scenario name will be appendeded to the file name. eg. Scenario_name1_all_results.csv, therefore it is suggested to have informative scenario names that can traceback to your model files as well.

from py_ewr.scenario_handling import ScenarioHandler
import pandas as pd

summary_results_dict = {}
yearly_results_dict = {}
all_events_dict = {}
all_interEvents_dict = {}
all_successful_Events_dict = {}
all_successful_interEvents_dict = {}

for scenario_name, scenario_list in scenarios.items():
    summary_results = pd.DataFrame()
    yearly_results = pd.DataFrame()
    all_events = pd.DataFrame()
    all_interEvents = pd.DataFrame()
    all_successful_Events = pd.DataFrame()
    all_successful_interEvents = pd.DataFrame()
    for file in scenarios[scenario_name]:

        # Running the ewr tool:
        ewr_sh = ScenarioHandler(scenario_file = file, 
                                model_format = model_format)

        # Return each table and stitch the different files of the same scenario together:
        # Table 1: Summarised ewr results for the entire timeseries
        temp_summary_results = ewr_sh.get_ewr_results()
        summary_results = pd.concat([summary_results, temp_summary_results], axis = 0)
        # Table 2: Summarised ewr results, aggregated to water years:
        temp_yearly_results = ewr_sh.get_yearly_ewr_results()
        yearly_results = pd.concat([yearly_results, temp_yearly_results], axis = 0)
        # Table 3: All events details regardless of duration 
        temp_all_events = ewr_sh.get_all_events()
        all_events = pd.concat([all_events, temp_all_events], axis = 0)
        # Table 4: Inverse of Table 3 showing the interevent periods
        temp_all_interEvents = ewr_sh.get_all_interEvents()
        all_interEvents = pd.concat([all_interEvents, temp_all_interEvents], axis = 0)
        # Table 5: All events details that also meet the duration requirement:
        temp_all_successfulEvents = ewr_sh.get_all_successful_events()
        all_successful_Events = pd.concat([all_successful_Events, temp_all_successfulEvents], axis = 0)
        # Table 6: Inverse of Table 5 showing the interevent periods:
        temp_all_successful_interEvents = ewr_sh.get_all_successful_interEvents()
        all_successful_interEvents = pd.concat([all_successful_interEvents, temp_all_successful_interEvents], axis = 0)
        

    # Optional code to output results to csv files:
    summary_results.to_csv(scenario_name + 'summary_results.csv')
    yearly_results.to_csv(scenario_name + 'yearly_results.csv')
    all_events.to_csv(scenario_name + 'all_events.csv')
    all_interEvents.to_csv(scenario_name + 'all_interevents.csv')
    all_successful_Events.to_csv(scenario_name + 'all_successful_Events.csv')
    all_successful_interEvents.to_csv(scenario_name + 'all_successful_interEvents.csv')

    # Save the final tables to the dictionaries:   
    summary_results_dict[scenario_name] = summary_results
    yearly_results_dict[scenario_name] = yearly_results
    all_events_dict[scenario_name] = all_events
    all_interEvents_dict[scenario_name] = all_interEvents
    all_successful_Events_dict[scenario_name] = all_successful_Events
    all_successful_interEvents_dict[scenario_name] = all_successful_interEvents

Optional arguments for ScenarioHandler

        ewr_sh = ScenarioHandler(scenario_file = file, 
                                model_format = model_format,
                                parameter_sheet = parameter_sheet,
                                calc_config_path = calc_config_path)

You may add a custom parameter sheet and or calc_config_file to your EWR tool run using the parameter_sheet and calc_config_path arguments. These arguments take a string file path pointing to files. Please check this ewr_calc_config.json file found in parameter metadata to see if any EWRs in your custom parameter sheet are not represented in the calc_config_file. For an EWR to be calculated, it must be found in both calc_config.json and the parameter sheet.

Purpose

This tool has two purposes:

1. Operational: Tracking ewr success at gauges of interest in real time - option 1 above.
2. Planning: Comparing ewr success between scenarios (i.e. model runs) - option 2 above.

Support For issues relating to the script, a tutorial, or feedback please contact Martin Job at [e-mail], Sirous Safari Pour at [e-mail], Elisha Freedman at [e-mail], Rory Hackett at [e-mail] or Joel Bailey at [e-mail]

Disclaimer Every effort has been taken to ensure the ewr database represents the original EWRs from state Long Term Water Plans (LTWPs), Environmental Water Management Plans (EWMPs), Flows studies, and those derived through expert elicitation processes as best as possible. The code within this tool has been developed to interpret and analyse these EWRs in an accurate way. However, there may still be unresolved bugs in the ewr parameter sheet and/or ewr tool. Please report any bugs to the issues tab under the GitHub project so we can investigate further.

Notes on development of the dataset of EWRs The MDBA has worked with Basin state representatives to ensure scientific integrity of EWRs has been maintained when translating from raw EWRs in the Basin state LTWPs, EWMPs, Flows studies, and those derived through expert elicitation processes to the machine readable format found in the parameter sheet within this tool.

Environmental Water Requirements (EWRs) in the tool are subject to change when the relevant documents including Long Term Water Plans (LTWPs), Environmental Water Management Plans (EWMPs), Flows studies, and those derived through expert elicitation processes are updated or move from draft to final versions. LTWPs that are currently in draft form include the upper Murrumbidgee section of the NSW Murrumbidgee LTWP, and the SA LTWP.

Compatibility

• All Queensland catchments
• All New South Wales catchments
• All South Australian catchments
• Where possible all EWRs from Environmental Water Management Plans (EWMPs) and Flows studies in Victoria

Input data

• Gauge data from the relevant Basin state websites and the Bureau of Meteorology website
• Scenario data input by the user
• Model metadata for location association between gauge ID's and model nodes

optional

• parameter sheet
• ewr_calc_config.json

Objective mapping The objective mapping is located in the EWR tool package. This is intended to be used to link EWRs to the detailed objectives, theme level targets and specific goals. The objective reference file is located in the py_ewr/parameter_metadata folder:

parameter_sheet.csv (EcoObj column) objective_reference.csv

Contains the individual ecological objectives listed in the 'EcoObj' column of the parameter sheet and their ecological targets (Target) and plain english description of objectives (Objective) for each planning unit, long term water plan (LTWPShortName), surface water sustainable diversion limit region (SWSDLName), and Basin State (State) the function get_obj_mapping() is available to automatically merge the information from objective_reference.csv with the parameter sheet to link these objectives with their specific ewr_codes.