[5]:
# Templates to setup project enviroment
%run ../template.ipynb
import numpy as np
import os
import pandas as pd
import scenario_tool_nodes.standard_tests as stn
sti = ScenarioToolInterface(api_url="https://stable-api.dance4water.org/api",
                 results_url="https://stable-sql.dance4water.org/resultsdb/")
sti.login("", "")
import seaborn as sns

# Below are some smaller helper functions
# Scenarios are executed asynchronous
def wait_till_scenario_done(scenario_id):
    while True:
        status = sti.check_status(scenario_id)
        if status["status"] > 6:
            print(datetime.datetime.now(), "Scenario complete")
            break
        if status["status"] < 1:
            print(datetime.datetime.now(), "Scenario failed")
            sti.show_log(scenario_id)
            break
        clear_output(wait = True)
        print(datetime.datetime.now(), status)
        time.sleep(5)
The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

Test for Urban Water Cycle Model

The urban water cycle catchment

The water cycle is assessed within a sub catchment. Sub catchments are subdevided into parcels. A catchment is a geographical unit and may not overlap.

Lot scale

Parcel

Lot’s in the UWC consume and produce water. A lot in the UWC is a generic unit that can represent a residential lot with a building, a commercial or industrial lot, public spaces including roads, parks or sport ovals.

Following parameters describe a lot in the UWC:

  • persons (number of people)

  • area (total are in m2)

  • roof area (in m2)

  • impervious area (in m2) (without the roof area)

  • garden area (in m2) (this area is assumed to be irrigated)

  • soil profile (see soil profile table)

  • demand profile (see demand table)

  • pervious area is calculated as area - roof area - impervious area - graden area

Not all parameters need to be set. Eg. for irrigated public space the number of persons can be 0.

The lot scale considers following internal streams

  • potable demand > Potable demand is defined by the demand profile and multiplied with the persons on lot

  • non potable demand

Non-potable demand is defined by the demand profile and multiplied with the persons on lot

  • black water > Black water is defined by the demand profile and multiplied with the persons on lot

  • grey water >Grey water is defined by the demand profile and multiplied with the persons on lot

Note: To ensure the mass balance potable + non potable demand = back water + grey water

Areas not covered by lits

Areas that are not covered by a parcel as part of a catchment will be lumped into a virtual parcel. Those areas usually includs transport networks. The parameters are caluclated as follows.

  • impervious area >catchment area * ( average roof cover + average concrete cover + average road cover) - \(\sum\) parcel roof area + \(\sum\) parcel impervious area

  • pervious area >catchment area - \(\sum\) parcel area - catchment area * (average water cover) - impervious area

Note that if not explicitly defined as part of a parcel green spaces and trees are considered non irrigated.

Streams

The following streams are calculated based on a runoff model. The run off model is based on the standard model used in Australia see MUSIC’s rainfall runoff model. T he values are calculated per \(m^2\) and multiplied with the corresponding area’s of the lost.

  • roof runoff >roof runoff times roof area

  • impervious runoff >impervious runoff times impervious area

  • pervious runoff >pervious runoff based times pervious area

  • evapotranspiration >the evapotranspiration is the sum of the evapotranspiration from roofs, impervious areas, pervious areas and irrigated pervious areas.

  • infiltration >the infiltration into the deep groundwater layer is based on the soil chracteristics from the pervious area. Infiltration into the deep groundwater layer is not evapotranspirated. The infitration into the pervious soil storage is internally assessed and drives the evapotranspiration and infiltration into the deep groundwater layer.

  • outdoor demand > the outdoor demand is caculated as the actual evapotranspiration of the pervious area multiplied with a crop factor see

  • rainfall > rainfall multiplied by the total area of the lot

Runoff model results

The results below show the input parameters (default values for Melbourne) for the catchment model and annual flows for 2001 in m or (m3/m2).

[2]:
# Create new project in Melbourne
project_id = sti.create_project()
region_id = sti.get_region( "melbourne")

with open(r"../../resources/boundaries/test_small.geojson", 'r') as file:
         geojson_file = json.loads(file.read())
geojson_id = sti.upload_geojson(geojson_file, project_id)

# Set project parameters
sti.update_project(project_id, {
    "name": "Water Balance Model v2 Tests",
    "active": True,
    'region_id': region_id,
    "case_study_area_id": geojson_id,
})



[3]:
sti.set_project_data_model(project_id, {"data_model_id": 1, "parameters":
                                        {"micro_climate_grid.grid_size": "20",
                                         "district.source": 1,
                                         "district.layer_name":
                                         "sa1_2011_aust",
                                         "district.epsg_from": "4283",
                                         "parcel.source": 2,
                                         "parcel.layer_name": "property_vic",
                                         "parcel.epsg_from": "4283",
                                         "building.source": 2,
                                         "building.layer_name": "building_geoscape",
                                         "building.epsg_from": "3857",
                                         "landcover_geoscape.raster_file": 3
                                        }})




[4]:
# Add assessment models
lst_model = sti.get_assessment_model("Land Surface Temperature")
water_cycle_model = sti.get_assessment_model("Water Cycle Model v2")
[5]:
# Set assessment models

sti.set_project_assessment_models(project_id, [{"assessment_model_id": lst_model, "parameters" : ""},
                                               {"assessment_model_id": water_cycle_model, "parameters" : ""}])
[6]:
project_id
[6]:
34438
[7]:
baseline_id = sti.create_scenario(project_id, None)
sti.execute_scenario(baseline_id)
[8]:
wait_till_scenario_done(baseline_id)
2020-05-25 21:53:28.607825 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:53:33.782758 Scenario complete
[6]:
# Get results
def build_query_string(table, definitions):
    query = 'ogc_fid'
    for key, t in definitions[table].items():
        query+=','
        if t == 'DOUBLEVECTOR':
            query+=f'dm_vector_to_string({key}) as {key}'
            continue
        query+= key
    return query

def get_results(scenario_id, results_tables):
    while True:
        r = sti.run_query(scenario_id, "SELECT view_name, attribute_name, data_type from dynamind_table_definitions")
        if r['status'] == 'loaded':
            break
        if r['status'] == 'error':
            print(r)
            break
    definitions = {}
    for entry in r['data']:
        if entry['view_name'] not in definitions:
            definitions[entry['view_name']] = {}
        if entry['attribute_name'] != 'DEFINITION':
            definitions[entry['view_name']][entry['attribute_name']] =  entry['data_type']

    results = {}
    for table in results_tables:
        while True:
            r = sti.run_query(scenario_id, f"SELECT {build_query_string(table, definitions)} from {table}")
            if r['status'] == 'loaded':
                results[table] = []
                for row in r['data']:
                    converted_row = {}
                    for key, val in row.items():
                        converted_row[key] = val
                        if key in definitions[table]:
                            if definitions[table][key] == 'DOUBLEVECTOR':
                                converted_row[key] = np.array([float(d) for d in val.split(" ")])

                    results[table].append(converted_row)
                break
    return results

[ ]:

[10]:
# Model paramaters and results
runoff_model_results = pd.DataFrame(get_results(baseline_id, ['wb_demand_profile'])['wb_demand_profile']).T
rr = runoff_model_results.T
runoff_model_results
[10]:
0
black_water 19.0
crop_factor 1.0
grey_water 77.0
non_potable_demand_per_person 19.0
ogc_fid 1.0
potable_demand_per_person 77.0
[10]:
# Model paramaters and results
runoff_model_results = pd.DataFrame(get_results(baseline_id, ['wb_soil'])['wb_soil']).T
rr = runoff_model_results.T
runoff_model_results
[10]:
0
actual_infiltration [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
daily_deep_seepage_rate 0
daily_drainage_rate 0.05
daily_recharge_rate 0.25
effective_evapotranspiration [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
evapotranspiration [0.00277419, 0.00277419, 0.00277419, 0.0027741...
field_capacity 0.02
groundwater_infiltration [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
impervious_evapotranspiration [0.0004, 0.0, 0.0, 0.0034, 0.0014, 0.0, 0.0, 0...
impervious_runoff [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
impervious_threshold 0.001
infiltration_capacity 0.2
infiltration_exponent 1
initial_groundwater_store 0.01
initial_soil_storage 0.3
ogc_fid 1
outdoor_demand [0.00237419, 0.00277419, 0.00277419, 0.0, 0.00...
pervious_evapotranspiration [0.0004, 0.0, 0.0, 0.0034, 0.0014, 0.0, 0.0, 0...
pervious_evapotranspiration_irrigated [0.00277419, 0.00277419, 0.00277419, 0.0034, 0...
pervious_runoff [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
pervious_storage [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
possible_infiltration [2.16, 2.16, 2.16, 0.024, 2.16, 2.16, 2.16, 2....
rainfall [0.0004, 0.0, 0.0, 0.0034, 0.0014, 0.0, 0.0, 0...
roof_evapotranspiration [0.0004, 0.0, 0.0, 0.0034, 0.0014, 0.0, 0.0, 0...
roof_runoff [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...
soil_store_capacity 0.03
transpiration_capacity 7

Mass balance Impervious surfaces

[14]:
def plot_timeseries(vec):
    fig, ax = plt.subplots(len(vec),1,figsize=(20, len(vec)*5));


    for idx, p in enumerate(vec):
        ax[idx].plot(list(range(len(rr[p][0]))), rr[p][0]);
        ax[idx].set_title(p);
[15]:
# Mass balance impervious surfaces
print(f"+ {sum(rr['rainfall'][0]):.5f} rainfall")
print(f"- {sum(rr['impervious_runoff'][0]):.5f} impervious_runoff ")
print(f"- {sum(rr['impervious_evapotranspiration'][0]):.5f} impervious_evapotranspiration")
print(f"= {sum((rr['rainfall'][0] - rr['impervious_runoff'][0] -rr['impervious_evapotranspiration'][0])):.5f}")

+ 0.62960 rainfall
- 0.46088 impervious_runoff
- 0.16872 impervious_evapotranspiration
= -0.00000
[17]:
plot_timeseries(['rainfall', 'impervious_runoff', 'impervious_evapotranspiration'])
../_images/notebooks_urban_metabolism_model_doc_17_0.png
[18]:
# Mass balance roof surfaces
#print(f"rainfall {rr['rainfall'][0]:.3f} - impervious_runoff {rr['impervious_runoff'][0]:.3f} - impervious_evapotranspiration {rr['impervious_evapotranspiration'][0]:.3f} = {(rr['rainfall'][0] - rr['impervious_runoff'][0] -rr['impervious_evapotranspiration'][0]):.3f}")
# currently the initial loss for roof and other impervious surfaces is the same. This might change in a later release
# @todo
roof_evapo = rr['rainfall'][0] - rr['roof_runoff'][0]
print(f"+ {sum(rr['rainfall'][0]):.3f} rainfall")
print(f"- {sum(rr['roof_runoff'][0]):.3f} roof_runoff ")
print(f"- {sum(rr['roof_evapotranspiration'][0]):.3f} roof_evapotranspiration ")
print(f"= {sum(rr['rainfall'][0] - rr['roof_runoff'][0] - rr['roof_evapotranspiration'][0]):.3f}")
+ 0.630 rainfall
- 0.474 roof_runoff
- 0.156 roof_evapotranspiration
= -0.000
[19]:
plot_timeseries(['rainfall', 'roof_runoff', 'roof_evapotranspiration'])
../_images/notebooks_urban_metabolism_model_doc_19_0.png

Mass balance Pervious surfaces

[20]:
# Mass balance pervious surfaces
print(f"+ {sum(rr['rainfall'][0]):.5f} rainfall")
print(f"- {sum(rr['pervious_runoff'][0]):.5f} pervious_runoff ")
print(f"- {sum(rr['pervious_evapotranspiration'][0]):.5f} pervious_evapotranspiration")
print(f"- {sum(rr['groundwater_infiltration'][0]):.5f} groundwater_infiltration ")
print(f"= {sum(rr['rainfall'][0] - rr['pervious_runoff'][0] -rr['pervious_evapotranspiration'][0]-rr['groundwater_infiltration'][0]):.5f}")

+ 0.62960 rainfall
- 0.02736 pervious_runoff
- 0.32352 pervious_evapotranspiration
- 0.27872 groundwater_infiltration
= -0.00000
[21]:
# rr['rainfall'][0] \
# - rr['pervious_runoff'][0] \
# - rr['actual_infiltration'][0] + rr['effective_evapotranspiration'][0]


[22]:
plot_timeseries(['rainfall', 'pervious_runoff', 'pervious_evapotranspiration', 'groundwater_infiltration'])
../_images/notebooks_urban_metabolism_model_doc_23_0.png
[23]:
# Mass balance pervious irrigated
print(f"+ {sum(rr['rainfall'][0]):.5f} rainfall")
print(f"+ {sum(rr['outdoor_demand'][0]):.5f} outdoor_demand")
print(f"- {sum(rr['pervious_runoff'][0]):.5f} pervious_runoff ")
print(f"- {sum(rr['pervious_evapotranspiration_irrigated'][0]):.5f} pervious_evapotranspiration_irrigated")
print(f"- {sum(rr['groundwater_infiltration'][0]):.5f} groundwater_infiltration ")
print(f"= {sum((rr['rainfall'][0] - rr['pervious_runoff'][0] -rr['pervious_evapotranspiration_irrigated'][0]-rr['groundwater_infiltration'][0]) + rr['outdoor_demand'][0]):.5f}")


+ 0.62960 rainfall
+ 0.44930 outdoor_demand
- 0.02736 pervious_runoff
- 0.77282 pervious_evapotranspiration_irrigated
- 0.27872 groundwater_infiltration
= 0.00000
[24]:

[25]:
plot_timeseries(['rainfall', 'outdoor_demand', 'pervious_runoff', 'pervious_evapotranspiration_irrigated', 'groundwater_infiltration'])
../_images/notebooks_urban_metabolism_model_doc_26_0.png

Water Cycle Balance

The water balance is assessed on a small urban catchment in Melbourne

e0199eb0b355498e8e69f70f4eab4332

[26]:
# Setup new residental development
def create_residental_development():
        return {
        "node_type_id": sti.get_node_id("Residential"),
        "area": geojson_id,
        "parameters":
            {}
        }
def create_storage(demand_stream, inflow_stream,demand_stream_1=0, demand_stream_2=0):
        return {
        "node_type_id": sti.get_node_id("Lot Scale Storage"),
        "area": geojson_id,
        "parameters":
            {
                "dance4water_template_id.equation" : 1,
                "dance4water_inflow_stream.equation": inflow_stream,
                "dance4water_demand_stream.equation": demand_stream,
                "dance4water_demand_stream_1.equation": demand_stream_1,
                "dance4water_demand_stream_2.equation": demand_stream_2,
                "dance4water_volume.equation": 5
                    }
        }
def wb_catchment(name, geojson_id):
        return [{
        "node_type_id": sti.get_node_id("Copy Feature"),
        "area": geojson_id,
        "parameters":
            {'dance4water_copy_feature.from_view': 'dance4water',
             'dance4water_copy_feature.to_view': 'sub_catchment',
             'dance4water_copy_feature.add_link': '0',
             'dance4water_copy_feature.copy_features': '1'}
        },
        {"node_type_id": sti.get_node_id("SQL query"),
        "area": geojson_id,
        "parameters":
            {'dance4water_sql_query.attribute': 'sub_catchment.name',
             'dance4water_sql_query.query': f'UPDATE sub_catchment set name = \'{name}\' WHERE name is null',
             'dance4water_sql_query.attribute_type': 'DOUBLE'}
        }]
def setup_and_start_project(name,
                            with_RWHT_storage = False,
                            with_grey_and_rwht_storage=False,
                            with_residual_storage=False):
    scenario_id = sti.create_scenario(project_id, baseline_id, name)



    nodes = []
#     nodes+=wb_catchment("default", geojson_id)
    nodes.append(create_residental_development())
    if with_RWHT_storage:
        nodes.append(create_storage(int(LotStream.outdoor_demand), int(LotStream.roof_runoff)))

    if with_residual_storage:
        nodes.append(create_storage(int(LotStream.outdoor_demand),
                                    int(LotStream.roof_runoff),
                                    int(LotStream.non_potable_demand)))

    if with_grey_and_rwht_storage:
        nodes.append(create_storage(int(LotStream.outdoor_demand), int(LotStream.roof_runoff)))
        nodes.append(create_storage(int(LotStream.outdoor_demand), int(LotStream.grey_water)))

    sti.set_scenario_workflow(scenario_id, nodes)
    sti.execute_scenario(scenario_id)



    return scenario_id



[29]:
scenarios = {}
scenarios["Residential"] = setup_and_start_project("Residential")
[30]:
wait_till_scenario_done(scenarios["Residential"])
2020-05-25 21:54:39.100631 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:54:44.293305 Scenario complete

Mass balance Impervious surfaces

[31]:
# Mass balance all units are in SI
def calcuate_mass_balance(df: pd.DataFrame):
    df["mass_balance"]= df["rainfall"] \
    + df["potable_demand"] \
    + df["non_potable_demand"] \
    + df["outdoor_demand"]  \
    - df["grey_water"] \
    - df["black_water"] \
    - df["evapotranspiration"] \
    - df["impervious_runoff"] \
    - df["pervious_runoff"] \
    - df["roof_runoff"] \
    - df["infiltration"]

    df["mass_balance_runoff_only"] = df["rainfall"] \
    + df["outdoor_demand"]  \
    - df["evapotranspiration"] \
    - df["impervious_runoff"] \
    - df["pervious_runoff"] \
    - df["roof_runoff"] \
    - df["infiltration"]

    return df

def inflow(df: pd.DataFrame):
    df["inflow"]= df["rainfall"] \
    + df["potable_demand"] \
    + df["non_potable_demand"] \
    + df["outdoor_demand"]

def outflow(df: pd.DataFrame):
    df["outflow"] = df["evapotranspiration"] \
    + df["grey_water"] \
    + df["black_water"] \
    + df["impervious_runoff"] \
    + df["pervious_runoff"] \
    + df["roof_runoff"] \
    + df["infiltration"]

from IPython.display import display, HTML

def print_parcel_mass_balance(df):
     display(HTML(df.filter(["area",
                   "persons",
                   "impervious_area",
                   "roof_area" ,
                   "garden_area",
                   "outdoor_imp",
                   "potable_demand",
                   "outdoor_demand",
                   "non_potable_demand",
                   "grey_water",
                   "black_water",
                   "rainfall",
                   "evapotranspiration",
                   "impervious_runoff",
                   "pervious_runoff",
                   "roof_runoff",
                   "infiltration",
                   "mass_balance_runoff_only",
                   "mass_balance",
                   "wb_lot_template_id"
                   ]).to_html()))

[33]:
# Download results
scenario_results = get_results(scenarios["Residential"], ['parcel', 'wb_soil'])
lot_residential = pd.DataFrame(scenario_results['parcel'])
lot_residential = calcuate_mass_balance(lot_residential)
print_parcel_mass_balance(lot_residential)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 628.974360 2.0 None 377.384616 150.953846 100.635898 56.364 67.823171 13.908 56.364 13.908 396.002257 192.525340 46.380798 4.129480 178.715501 42.074310 -1.563194e-13 -1.563194e-13 1
1 639.210270 4.0 None 383.526162 153.410465 102.273643 112.728 68.926924 27.816 112.728 27.816 402.446786 195.658491 47.135598 4.196683 181.623912 42.759026 5.684342e-13 5.684342e-13 1
2 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 69.492412 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 -2.131628e-13 -9.947598e-14 1
3 644.201990 3.0 None 386.521194 154.608478 103.072318 84.546 69.465188 20.862 84.546 20.862 405.589573 197.186427 47.503689 4.229456 183.042249 43.092939 -1.207923e-13 -6.394885e-14 1
4 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 69.492412 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 -4.618528e-13 -4.050094e-13 1
5 644.033676 2.0 None 386.420206 154.568082 103.045388 56.364 69.447038 13.908 56.364 13.908 405.483603 197.134908 47.491277 4.228351 182.994425 43.081680 -2.415845e-13 -1.278977e-13 1
6 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 69.492412 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 2.842171e-13 2.842171e-13 1
7 643.865363 4.0 None 386.319218 154.527687 103.018458 112.728 69.428889 27.816 112.728 27.816 405.377633 197.083388 47.478866 4.227246 182.946601 43.070421 6.394885e-14 6.394885e-14 1
8 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 69.492412 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 5.684342e-14 1.705303e-13 1
9 643.697050 1.0 None 386.218230 154.487292 102.991528 28.182 69.410739 6.954 28.182 6.954 405.271662 197.031868 47.466454 4.226141 182.898777 43.059162 -7.815970e-14 -7.815970e-14 1
10 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 69.492412 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 1.421085e-13 1.421085e-13 1
11 643.528736 3.0 None 386.117242 154.446897 102.964598 84.546 69.392590 20.862 84.546 20.862 405.165692 196.980348 47.454043 4.225036 182.850952 43.047903 -1.207923e-13 -1.207923e-13 1
12 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 69.492412 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 5.684342e-13 6.821210e-13 1
13 643.360423 2.0 None 386.016254 154.406502 102.937668 56.364 69.374440 13.908 56.364 13.908 405.059722 196.928829 47.441631 4.223931 182.803128 43.036644 -2.913225e-13 -2.913225e-13 1
14 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 69.492412 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 3.552714e-14 3.552714e-14 1
15 643.192110 2.0 None 385.915266 154.366106 102.910738 56.364 69.356291 13.908 56.364 13.908 404.953752 196.877309 47.429220 4.222825 182.755304 43.025385 6.394885e-14 6.394885e-14 1
16 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 69.492412 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 3.339551e-13 2.771117e-13 1
17 643.023796 3.0 None 385.814278 154.325711 102.883807 84.546 69.338141 20.862 84.546 20.862 404.847782 196.825789 47.416808 4.221720 182.707480 43.014126 6.252776e-13 6.252776e-13 1
18 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 69.492412 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 -5.115908e-13 -5.115908e-13 1
19 642.855483 4.0 None 385.713290 154.285316 102.856877 112.728 69.319992 27.816 112.728 27.816 404.741812 196.774270 47.404397 4.220615 182.659655 43.002867 -1.705303e-13 -1.705303e-13 1
20 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 69.492412 13.908 56.364 13.908 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 -3.126388e-13 -2.557954e-13 1
21 642.687170 4.0 None 385.612302 154.244921 102.829947 112.728 69.301843 27.816 112.728 27.816 404.635842 196.722750 47.391985 4.219510 182.611831 42.991608 1.634248e-13 1.634248e-13 1
22 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 69.492412 13.908 56.364 13.908 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 -8.526513e-14 -2.842171e-14 1
23 642.518856 1.0 None 385.511314 154.204526 102.803017 28.182 69.283693 6.954 28.182 6.954 404.529872 196.671230 47.379574 4.218405 182.564007 42.980349 -3.197442e-13 -3.197442e-13 1
24 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 69.492412 13.908 56.364 13.908 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 3.197442e-13 3.197442e-13 1
25 642.350543 4.0 None 385.410326 154.164130 102.776087 112.728 69.265544 27.816 112.728 27.816 404.423902 196.619710 47.367162 4.217300 182.516183 42.969090 9.947598e-14 9.947598e-14 1
26 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 69.492412 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 183.113985 43.109828 5.684342e-14 0.000000e+00 1
27 642.182230 1.0 None 385.309338 154.123735 102.749157 28.182 69.247394 6.954 28.182 6.954 404.317932 196.568191 47.354751 4.216195 182.468359 42.957831 -6.536993e-13 -6.536993e-13 1
28 629.627695 4.0 None 377.776617 151.110647 100.740431 112.728 67.893621 27.816 112.728 27.816 396.413597 192.725322 46.428975 4.133769 178.901139 42.118013 3.694822e-13 3.694822e-13 1
29 597.765026 1.0 None 358.659016 143.463606 95.642404 28.182 64.457826 6.954 28.182 6.954 376.352860 182.972347 44.079410 3.924578 169.847744 39.986607 -8.526513e-14 -2.842171e-14 1
30 4920.163308 NaN None NaN NaN 2043.436272 0.000 0.000000 0.000 0.000 0.000 3097.734818 1275.455995 941.773332 78.695489 0.000000 801.810002 -1.818989e-12 -1.818989e-12 1
[34]:
# Download results
scenario_results = get_results(baseline_id, ['parcel', 'wb_soil'])
lot_residential = pd.DataFrame(scenario_results['parcel'])
lot_residential = calcuate_mass_balance(lot_residential)
print_parcel_mass_balance(lot_residential)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 442.291056 None None 167.515769 None None 0.0 0.0 0.0 0.0 0.0 278.466449 115.034208 0.0 7.516728 79.329319 76.586193 9.947598e-14 9.947598e-14 1
1 224.502687 None None NaN None None 0.0 0.0 0.0 0.0 0.0 141.346891 72.631353 0.0 6.141476 0.000000 62.574063 7.105427e-14 7.105427e-14 1
2 441.455388 None None 159.516779 None None 0.0 0.0 0.0 0.0 0.0 277.940312 116.103558 0.0 7.712687 75.541291 78.582776 4.263256e-14 4.263256e-14 1
3 472.476300 None None 207.092681 None None 0.0 0.0 0.0 0.0 0.0 297.471079 118.171257 0.0 7.259811 98.071492 73.968520 -9.947598e-14 -9.947598e-14 1
4 229.969140 None None 134.562405 None None 0.0 0.0 0.0 0.0 0.0 144.788570 51.862765 0.0 2.609938 63.723815 26.592052 -3.552714e-14 -3.552714e-14 1
5 452.063127 None None 230.621051 None None 0.0 0.0 0.0 0.0 0.0 284.618945 107.626530 0.0 6.057750 109.213664 61.721001 -1.705303e-13 -1.705303e-13 1
6 457.705755 None None 187.208012 None None 0.0 0.0 0.0 0.0 0.0 288.171543 116.723045 0.0 7.399712 88.654843 75.393944 5.684342e-14 5.684342e-14 1
7 482.809249 None None 166.472338 None None 0.0 0.0 0.0 0.0 0.0 303.976703 128.317456 0.0 8.653684 78.835189 88.170374 1.421085e-14 1.421085e-14 1
8 441.958057 None None 189.436842 None None 0.0 0.0 0.0 0.0 0.0 278.256793 111.255039 0.0 6.907948 89.710334 70.383472 2.984279e-13 2.984279e-13 1
9 445.505812 None None 144.138375 None None 0.0 0.0 0.0 0.0 0.0 280.490459 119.989597 0.0 8.244181 68.258643 83.998037 8.526513e-14 8.526513e-14 1
10 382.776842 None None 145.744326 None None 0.0 0.0 0.0 0.0 0.0 240.996300 99.426481 0.0 6.484240 69.019163 66.066415 -7.105427e-14 -7.105427e-14 1
11 452.442852 None None 361.484372 None None 0.0 0.0 0.0 0.0 0.0 284.858019 85.831817 0.0 2.488252 171.185730 25.352221 1.207923e-13 1.207923e-13 1
12 222.966199 None None 278.984428 None None 0.0 0.0 0.0 0.0 0.0 140.379519 25.408733 0.0 -1.532430 132.116784 -15.613569 1.172396e-13 1.172396e-13 1
13 445.498908 None None 188.906277 None None 0.0 0.0 0.0 0.0 0.0 280.486113 112.489440 0.0 7.019325 89.459078 71.518269 8.526513e-14 8.526513e-14 1
14 225.551023 None None 212.172490 None None 0.0 0.0 0.0 0.0 0.0 142.006924 37.434934 0.0 0.365982 100.477103 3.728905 5.417888e-14 5.417888e-14 1
15 437.377090 None None 208.049934 None None 0.0 0.0 0.0 0.0 0.0 275.372616 106.655597 0.0 6.273453 98.524812 63.918754 6.394885e-14 6.394885e-14 1
16 724.515066 None None 325.786031 None None 0.0 0.0 0.0 0.0 0.0 456.154685 179.831829 0.0 10.907596 154.280305 111.134955 1.421085e-13 1.421085e-13 1
17 497.251203 None None 182.697084 None None 0.0 0.0 0.0 0.0 0.0 313.069358 130.272341 0.0 8.604914 86.518633 87.673469 -2.557954e-13 -2.557954e-13 1
18 450.056993 None None 346.204658 None None 0.0 0.0 0.0 0.0 0.0 283.355883 87.619055 0.0 2.840975 163.949818 28.946035 3.552714e-15 3.552714e-15 1
19 437.247884 None None 207.009283 None None 0.0 0.0 0.0 0.0 0.0 275.291268 106.788089 0.0 6.298387 98.031998 64.172794 1.421085e-13 1.421085e-13 1
20 478.855767 None None 169.127638 None None 0.0 0.0 0.0 0.0 0.0 301.487591 126.593700 0.0 8.472895 80.092641 86.328355 -2.273737e-13 -2.273737e-13 1
21 435.432567 None None 268.633170 None None 0.0 0.0 0.0 0.0 0.0 274.148344 95.879757 0.0 4.562949 127.214808 46.490829 1.207923e-13 1.207923e-13 1
22 445.803138 None None 203.006181 None None 0.0 0.0 0.0 0.0 0.0 280.677655 110.226351 0.0 6.641932 96.136276 67.673097 -1.563194e-13 -1.563194e-13 1
23 454.924256 None None 205.008282 None None 0.0 0.0 0.0 0.0 0.0 286.420312 112.841904 0.0 6.836679 97.084397 69.657331 5.684342e-14 5.684342e-14 1
24 464.169786 None None 140.536237 None None 0.0 0.0 0.0 0.0 0.0 292.241297 126.631089 0.0 8.853291 66.552803 90.204115 1.421085e-13 1.421085e-13 1
25 477.695923 None None 304.590795 None None 0.0 0.0 0.0 0.0 0.0 300.757353 103.530503 0.0 4.735448 144.243020 48.248381 9.947598e-14 9.947598e-14 1
26 464.519505 None None 184.597204 None None 0.0 0.0 0.0 0.0 0.0 292.461481 119.364707 0.0 7.657530 87.418460 78.020785 2.557954e-13 2.557954e-13 1
27 491.121048 None None 351.120162 None None 0.0 0.0 0.0 0.0 0.0 309.209812 100.080873 0.0 3.829852 166.277620 39.021468 7.815970e-14 7.815970e-14 1
28 460.921047 None None 240.476642 None None 0.0 0.0 0.0 0.0 0.0 290.195891 108.841596 0.0 6.030458 113.880910 61.442927 0.000000e+00 0.000000e+00 1
29 340.486856 None None 221.780167 None None 0.0 0.0 0.0 0.0 0.0 214.370525 73.009964 0.0 3.247330 105.026946 33.086285 -7.105427e-15 -7.105427e-15 1
30 446.773795 None None 242.479600 None None 0.0 0.0 0.0 0.0 0.0 281.288781 103.929198 0.0 5.588654 114.829438 56.941492 -3.907985e-13 -3.907985e-13 1
31 444.564346 None None 136.611036 None None 0.0 0.0 0.0 0.0 0.0 279.897712 120.945725 0.0 8.424343 64.693972 85.833672 2.842171e-14 2.842171e-14 1
32 454.703067 None None 181.610980 None None 0.0 0.0 0.0 0.0 0.0 286.281051 116.689027 0.0 7.470683 86.004294 76.117047 -8.526513e-14 -8.526513e-14 1
33 719.259607 None None 238.721034 None None 0.0 0.0 0.0 0.0 0.0 452.845849 192.713605 0.0 13.145570 113.049519 133.937155 4.263256e-13 4.263256e-13 1
34 37.077187 None None NaN None None 0.0 0.0 0.0 0.0 0.0 23.343797 11.995252 0.0 1.014280 0.000000 10.334265 0.000000e+00 0.000000e+00 1
35 483.390176 None None 213.210084 None None 0.0 0.0 0.0 0.0 0.0 304.342455 120.677556 0.0 7.391023 100.968469 75.305407 2.273737e-13 2.273737e-13 1
36 444.032897 None None 289.821623 None None 0.0 0.0 0.0 0.0 0.0 279.563112 95.113410 0.0 4.218590 137.248882 42.982229 1.207923e-13 1.207923e-13 1
37 479.787211 None None 169.512469 None None 0.0 0.0 0.0 0.0 0.0 302.074028 126.830589 0.0 8.487848 80.274883 86.480708 2.700062e-13 2.700062e-13 1
38 7742.298968 None None NaN None None 0.0 0.0 0.0 0.0 0.0 4874.551430 2504.796960 0.0 211.797641 0.000000 2157.956830 -2.728484e-12 -2.728484e-12 1
[35]:
# Display scenario
# import IPython
# from IPython.display import IFrame

# url = f'https://staging.wsc-scenario.org.au/project/{project_id}'
# IFrame(src=url, width=1000, height=800)

Storages

Storages at lot scale can be connect to any of the internal streams listed above as inflow or demand. (demand stream \(\neq\) inflow stream). The storage overflow will become the new reduced inflow stream and the provided water stream will become the new reduced demand stream. Eg in case of a rainwater harvesting tank the roof runoff will be reduced by the storage as well as the outdoor demand.

2739e2955da3415c8051b6efa03cc5cd

Those modified streams can be used as input for a new storage. The first storage can simulate for example a rainwater tank providing non potable water for toilet flushing linked with a grey water tank that if the rainwater tank is empty will provide additional water.

b2b0c2f21bba4e48988f4e8d9236e3f2

Singe RWHT

For this test we use a 5m3 storage to provide outdoor demand from roof runoff

[36]:
scenarios["Residential with RWHT"] = setup_and_start_project("Residential with RWHT", with_RWHT_storage=True)
[37]:
wait_till_scenario_done(scenarios["Residential with RWHT"])
2020-05-25 21:55:39.860797 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:55:45.050787 Scenario complete
[38]:
# Download results
scenario_results = get_results(scenarios["Residential with RWHT"], ['parcel', 'wb_soil', 'wb_lot_storages'])
lot_residential_with_rwht = pd.DataFrame(scenario_results['parcel'])
lot_residential_with_rwht = calcuate_mass_balance(lot_residential_with_rwht)
print_parcel_mass_balance(lot_residential_with_rwht)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 628.974360 4.0 None 377.384616 150.953846 100.635898 112.728 24.868304 27.816 112.728 27.816 396.002257 192.525340 46.380798 4.129480 133.869310 42.074310 1.891325e+00 1.891325e+00 1
1 639.210270 2.0 None 383.526162 153.410465 102.273643 56.364 25.517120 13.908 56.364 13.908 402.446786 195.658491 47.135598 4.196683 136.373374 42.759026 1.840734e+00 1.840734e+00 1
2 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
3 644.201990 2.0 None 386.521194 154.608478 103.072318 56.364 25.833526 13.908 56.364 13.908 405.589573 197.186427 47.503689 4.229456 137.594525 43.092939 1.816063e+00 1.816063e+00 1
4 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
5 644.033676 3.0 None 386.420206 154.568082 103.045388 84.546 25.822858 20.862 84.546 20.862 405.483603 197.134908 47.491277 4.228351 137.553350 43.081680 1.816895e+00 1.816895e+00 1
6 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
7 643.865363 2.0 None 386.319218 154.527687 103.018458 56.364 25.812189 13.908 56.364 13.908 405.377633 197.083388 47.478866 4.227246 137.512174 43.070421 1.817727e+00 1.817727e+00 1
8 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
9 643.697050 2.0 None 386.218230 154.487292 102.991528 56.364 25.801520 13.908 56.364 13.908 405.271662 197.031868 47.466454 4.226141 137.470999 43.059162 1.818558e+00 1.818558e+00 1
10 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 25.849529 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
11 643.528736 4.0 None 386.117242 154.446897 102.964598 112.728 25.790851 27.816 112.728 27.816 405.165692 196.980348 47.454043 4.225036 137.429823 43.047903 1.819390e+00 1.819390e+00 1
12 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 25.849529 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
13 643.360423 1.0 None 386.016254 154.406502 102.937668 28.182 25.780183 6.954 28.182 6.954 405.059722 196.928829 47.441631 4.223931 137.388648 43.036644 1.820222e+00 1.820222e+00 1
14 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 25.849529 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
15 643.192110 3.0 None 385.915266 154.366106 102.910738 84.546 25.769514 20.862 84.546 20.862 404.953752 196.877309 47.429220 4.222825 137.347473 43.025385 1.821054e+00 1.821054e+00 1
16 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
17 643.023796 2.0 None 385.814278 154.325711 102.883807 56.364 25.758845 13.908 56.364 13.908 404.847782 196.825789 47.416808 4.221720 137.306297 43.014126 1.821886e+00 1.821886e+00 1
18 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 25.849529 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
19 642.855483 4.0 None 385.713290 154.285316 102.856877 112.728 25.748176 27.816 112.728 27.816 404.741812 196.774270 47.404397 4.220615 137.265122 43.002867 1.822718e+00 1.822718e+00 1
20 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 25.849529 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
21 642.687170 4.0 None 385.612302 154.244921 102.829947 112.728 25.737508 27.816 112.728 27.816 404.635842 196.722750 47.391985 4.219510 137.223947 42.991608 1.823550e+00 1.823550e+00 1
22 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 25.849529 20.862 84.546 20.862 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
23 642.518856 3.0 None 385.511314 154.204526 102.803017 84.546 25.726839 20.862 84.546 20.862 404.529872 196.671230 47.379574 4.218405 137.182771 42.980349 1.824382e+00 1.824382e+00 1
24 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 25.849529 27.816 112.728 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
25 642.350543 2.0 None 385.410326 154.164130 102.776087 56.364 25.716170 13.908 56.364 13.908 404.423902 196.619710 47.367162 4.217300 137.141596 42.969090 1.825214e+00 1.825214e+00 1
26 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 25.849529 6.954 28.182 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 1.814815e+00 1.814815e+00 1
27 642.182230 1.0 None 385.309338 154.123735 102.749157 28.182 25.705501 6.954 28.182 6.954 404.317932 196.568191 47.354751 4.216195 137.100420 42.957831 1.826045e+00 1.826045e+00 1
28 629.627695 4.0 None 377.776617 151.110647 100.740431 112.728 24.909717 27.816 112.728 27.816 396.413597 192.725322 46.428975 4.133769 134.029139 42.118013 1.888096e+00 1.888096e+00 1
29 597.765026 4.0 None 358.659016 143.463606 95.642404 112.728 23.037035 27.816 112.728 27.816 376.352860 182.972347 44.079410 3.924578 126.381379 39.986607 2.045575e+00 2.045575e+00 1
30 4920.163308 NaN None NaN NaN 2043.436272 0.000 0.000000 0.000 0.000 0.000 3097.734818 1275.455995 941.773332 78.695489 0.000000 801.810002 -1.818989e-12 -1.818989e-12 1
[39]:
def plot_timeseries_df(df, vec):
    fig, ax = plt.subplots(len(vec),1,figsize=(20, len(vec)*5));
    for idx, p in enumerate(vec):
        ax[idx].plot(list(range(len(df[p]))), df[p]);
        ax[idx].set_title(p);

Storage behaviour of a single storage

Note the error in the mass balance is equal to the remaining water in the RWHT

[40]:
# Plot lot storage 1
lot_storages = pd.DataFrame(scenario_results['wb_lot_storages'])
plot_timeseries_df(lot_storages.loc[0], ['storage_behaviour', 'provided_volume'])
../_images/notebooks_urban_metabolism_model_doc_45_0.png
[41]:
# Plot lot storage 1
ls = pd.DataFrame(lot_storages.sum()).T
plot_timeseries_df(ls.loc[0], ['storage_behaviour', 'provided_volume'])
../_images/notebooks_urban_metabolism_model_doc_46_0.png
[42]:
# sti.run_query(scenarios["Residential with RWHT"],
#               "SELECT CAST(strftime('%s', '2001-01-01 00:00:00') as INTEGER) as t_start, 60*60*24 as t_step, dm_vector_to_string(dm_sum_vectors(storage_behaviour)) as t from wb_lot_storages")

Cascading RWHT

For this test we use a 5m3 storage to provide non-potable water from roof runoff and grey water

[43]:
scenarios["Residential with RWHT and Grey Water"] = setup_and_start_project("Residential with RWHT and Grey Water", with_grey_and_rwht_storage=True)
[44]:
wait_till_scenario_done(scenarios["Residential with RWHT and Grey Water"])
2020-05-25 21:56:41.783774 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:56:46.965306 Scenario complete
[45]:
# Download results
scenario_results = get_results(scenarios["Residential with RWHT and Grey Water"], ['parcel', 'wb_soil', 'wb_lot_storages'])
lot_residential_with_grey = pd.DataFrame(scenario_results['parcel'])
lot_residential_with_grey = calcuate_mass_balance(lot_residential_with_grey)
print_parcel_mass_balance(lot_residential_with_grey)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 628.974360 3.0 None 377.384616 150.953846 100.635898 84.546 2.836747 20.862 58.839183 20.862 396.002257 192.525340 46.380798 4.129480 133.869310 42.074310 -2.014023e+01 5.566584e+00 1
1 639.210270 3.0 None 383.526162 153.410465 102.273643 84.546 2.965616 20.862 58.453574 20.862 402.446786 195.658491 47.135598 4.196683 136.373374 42.759026 -2.071077e+01 5.381656e+00 1
2 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 12.825096 6.954 14.387566 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.120962e+01 2.584815e+00 1
3 644.201990 3.0 None 386.521194 154.608478 103.072318 84.546 3.028461 20.862 58.265525 20.862 405.589573 197.186427 47.503689 4.229456 137.594525 43.092939 -2.098900e+01 5.291473e+00 1
4 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 12.825096 6.954 14.387566 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.120962e+01 2.584815e+00 1
5 644.033676 4.0 None 386.420206 154.568082 103.045388 112.728 1.332342 27.816 83.237485 27.816 405.483603 197.134908 47.491277 4.228351 137.553350 43.081680 -2.267362e+01 6.816895e+00 1
6 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 12.825096 6.954 14.387566 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.120962e+01 2.584815e+00 1
7 643.865363 4.0 None 386.319218 154.527687 103.018458 112.728 1.330223 27.816 83.246035 27.816 405.377633 197.083388 47.478866 4.227246 137.512174 43.070421 -2.266424e+01 6.817727e+00 1
8 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 12.825096 6.954 14.387566 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.120962e+01 2.584815e+00 1
9 643.697050 3.0 None 386.218230 154.487292 102.991528 84.546 3.022104 20.862 58.284547 20.862 405.271662 197.031868 47.466454 4.226141 137.470999 43.059162 -2.096086e+01 5.300595e+00 1
10 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 6.012734 13.908 34.987204 13.908 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.802198e+01 3.354815e+00 1
11 643.528736 4.0 None 386.117242 154.446897 102.964598 112.728 1.325985 27.816 83.263134 27.816 405.165692 196.980348 47.454043 4.225036 137.429823 43.047903 -2.264548e+01 6.819390e+00 1
12 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 6.012734 13.908 34.987204 13.908 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.802198e+01 3.354815e+00 1
13 643.360423 4.0 None 386.016254 154.406502 102.937668 112.728 1.323866 27.816 83.271684 27.816 405.059722 196.928829 47.441631 4.223931 137.388648 43.036644 -2.263609e+01 6.820222e+00 1
14 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 1.337640 27.816 83.216111 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.269707e+01 6.814815e+00 1
15 643.192110 1.0 None 385.915266 154.366106 102.910738 28.182 12.754906 6.954 14.397393 6.954 404.953752 196.877309 47.429220 4.222825 137.347473 43.025385 -1.119355e+01 2.591054e+00 1
16 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 3.031640 20.862 58.256014 20.862 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.100307e+01 5.286912e+00 1
17 643.023796 1.0 None 385.814278 154.325711 102.883807 28.182 12.745548 6.954 14.398703 6.954 404.847782 196.825789 47.416808 4.221720 137.306297 43.014126 -1.119141e+01 2.591886e+00 1
18 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 12.825096 6.954 14.387566 6.954 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -1.120962e+01 2.584815e+00 1
19 642.855483 1.0 None 385.713290 154.285316 102.856877 28.182 12.736189 6.954 14.400013 6.954 404.741812 196.774270 47.404397 4.220615 137.265122 43.002867 -1.118927e+01 2.592718e+00 1
20 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 3.031640 20.862 58.256014 20.862 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.100307e+01 5.286912e+00 1
21 642.687170 3.0 None 385.612302 154.244921 102.829947 84.546 3.009390 20.862 58.322592 20.862 404.635842 196.722750 47.391985 4.219510 137.223947 42.991608 -2.090457e+01 5.318840e+00 1
22 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 1.337640 27.816 83.216111 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.269707e+01 6.814815e+00 1
23 642.518856 4.0 None 385.511314 154.204526 102.803017 112.728 1.313271 27.816 83.314432 27.816 404.529872 196.671230 47.379574 4.218405 137.182771 42.980349 -2.258919e+01 6.824382e+00 1
24 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 1.337640 27.816 83.216111 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.269707e+01 6.814815e+00 1
25 642.350543 3.0 None 385.410326 154.164130 102.776087 84.546 3.005152 20.862 58.335273 20.862 404.423902 196.619710 47.367162 4.217300 137.141596 42.969090 -2.088580e+01 5.324922e+00 1
26 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 1.337640 27.816 83.216111 27.816 405.748528 197.263707 47.522306 4.231113 137.656288 43.109828 -2.269707e+01 6.814815e+00 1
27 642.182230 4.0 None 385.309338 154.123735 102.749157 112.728 1.309033 27.816 83.331531 27.816 404.317932 196.568191 47.354751 4.216195 137.100420 42.957831 -2.257042e+01 6.826045e+00 1
28 629.627695 3.0 None 377.776617 151.110647 100.740431 84.546 2.844972 20.862 58.814570 20.862 396.413597 192.725322 46.428975 4.133769 134.029139 42.118013 -2.017665e+01 5.554780e+00 1
29 597.765026 4.0 None 358.659016 143.463606 95.642404 112.728 0.749823 27.816 85.440788 27.816 376.352860 182.972347 44.079410 3.924578 126.381379 39.986607 -2.024164e+01 7.045575e+00 1
30 4920.163308 NaN None NaN NaN 2043.436272 0.000 0.000000 0.000 0.000000 0.000 3097.734818 1275.455995 941.773332 78.695489 0.000000 801.810002 -1.818989e-12 -1.818989e-12 1

Storage behaviour RWHT

[46]:
# Plot lot storage 1
lot_storages = pd.DataFrame(scenario_results['wb_lot_storages'])
plot_timeseries_df(lot_storages.loc[0], ['storage_behaviour', 'provided_volume'])
../_images/notebooks_urban_metabolism_model_doc_53_0.png
[ ]:

[47]:
# Fit porpuse residual demand
# Multilpe sources

Storage behaviour grey water tank

[48]:
# Plot lot storage 2
plot_timeseries_df(lot_storages.loc[1], ['storage_behaviour', 'provided_volume'])
../_images/notebooks_urban_metabolism_model_doc_57_0.png
[49]:
sti.run_query(scenarios["Residential with RWHT and Grey Water"],
              "SELECT dm_vector_sum(wb_lot_storages.provided_volume) as volume, CASE inflow_stream_id WHEN 1 THEN 'potable demand'WHEN 2 THEN 'non potable demand'WHEN 3 THEN 'outdoor demand'WHEN 4 THEN 'black water'WHEN 5 THEN 'grey water'WHEN 6 THEN 'roof runoff'WHEN 7 THEN 'impervious runoff'WHEN 8 THEN 'pervious runoff' WHEN 9 THEN 'evapotranspiration' WHEN 10 THEN 'infiltration' WHEN 11 THEN 'rainfall' ELSE 'Other' END  as inflow from wb_lot_storages LEFT JOIN wb_storages on storage_id = wb_storages.ogc_fid group by storage_id")

[49]:
{'cached': False, 'data': 'Run query', 'status': 'submitted'}

Residual Supply from Storages

[50]:
scenarios["Residential with Prioritised Demand"] = setup_and_start_project("Residential with Prioritised Demand", with_residual_storage=True)
[51]:
wait_till_scenario_done(scenarios["Residential with Prioritised Demand"])
2020-05-25 21:57:42.958608 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:57:48.141734 Scenario complete
[52]:
# Download results
scenario_results = get_results(scenarios["Residential with Prioritised Demand"], ['parcel', 'wb_soil', 'wb_lot_storages'])
lot_residential_with_res = pd.DataFrame(scenario_results['parcel'])
lot_residential_with_res = calcuate_mass_balance(lot_residential_with_res)
print_parcel_mass_balance(lot_residential_with_res)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 628.974360 4.0 None 377.384616 150.953846 100.635898 112.728 28.125470 6.408272 112.728 27.816 396.002257 192.525340 46.380798 4.129480 116.326747 42.074310 2.269105e+01 1.283325e+00 1
1 639.210270 2.0 None 383.526162 153.410465 102.273643 56.364 26.999120 2.926000 56.364 13.908 402.446786 195.658491 47.135598 4.196683 127.177374 42.759026 1.251873e+01 1.536734e+00 1
2 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 27.331529 2.926000 56.364 13.908 405.748528 197.263707 47.522306 4.231113 128.460288 43.109828 1.249282e+01 1.510815e+00 1
3 644.201990 1.0 None 386.521194 154.608478 103.072318 28.182 26.593526 1.444000 28.182 6.954 405.589573 197.186427 47.503689 4.229456 132.996525 43.092939 7.174063e+00 1.664063e+00 1
4 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 29.124408 6.536000 112.728 27.816 405.748528 197.263707 47.522306 4.231113 120.259166 43.109828 2.248682e+01 1.206815e+00 1
5 644.033676 2.0 None 386.420206 154.568082 103.045388 56.364 27.304858 2.926000 56.364 13.908 405.483603 197.134908 47.491277 4.228351 128.357350 43.081680 1.249489e+01 1.512895e+00 1
6 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 29.124408 6.536000 112.728 27.816 405.748528 197.263707 47.522306 4.231113 120.259166 43.109828 2.248682e+01 1.206815e+00 1
7 643.865363 2.0 None 386.319218 154.527687 103.018458 56.364 27.294189 2.926000 56.364 13.908 405.377633 197.083388 47.478866 4.227246 128.316174 43.070421 1.249573e+01 1.513727e+00 1
8 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 28.149145 4.560000 84.546 20.862 405.748528 197.263707 47.522306 4.231113 124.109904 43.109828 1.766082e+01 1.358815e+00 1
9 643.697050 1.0 None 386.218230 154.487292 102.991528 28.182 26.561520 1.444000 28.182 6.954 405.271662 197.031868 47.466454 4.226141 132.872999 43.059162 7.176558e+00 1.666558e+00 1
10 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 27.331529 2.926000 56.364 13.908 405.748528 197.263707 47.522306 4.231113 128.460288 43.109828 1.249282e+01 1.510815e+00 1
11 643.528736 3.0 None 386.117242 154.446897 102.964598 84.546 28.084894 4.560000 84.546 20.862 405.165692 196.980348 47.454043 4.225036 123.877866 43.047903 1.766539e+01 1.363390e+00 1
12 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 28.149145 4.560000 84.546 20.862 405.748528 197.263707 47.522306 4.231113 124.109904 43.109828 1.766082e+01 1.358815e+00 1
13 643.360423 1.0 None 386.016254 154.406502 102.937668 28.182 26.540183 1.444000 28.182 6.954 405.059722 196.928829 47.441631 4.223931 132.790648 43.036644 7.178222e+00 1.668222e+00 1
14 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 28.149145 4.560000 84.546 20.862 405.748528 197.263707 47.522306 4.231113 124.109904 43.109828 1.766082e+01 1.358815e+00 1
15 643.192110 4.0 None 385.915266 154.366106 102.910738 112.728 29.032532 6.536000 112.728 27.816 404.953752 196.877309 47.429220 4.222825 119.938491 43.025385 2.249305e+01 1.213054e+00 1
16 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 27.331529 2.926000 56.364 13.908 405.748528 197.263707 47.522306 4.231113 128.460288 43.109828 1.249282e+01 1.510815e+00 1
17 643.023796 2.0 None 385.814278 154.325711 102.883807 56.364 27.240845 2.926000 56.364 13.908 404.847782 196.825789 47.416808 4.221720 128.110297 43.014126 1.249989e+01 1.517886e+00 1
18 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 26.609529 1.444000 28.182 6.954 405.748528 197.263707 47.522306 4.231113 133.058288 43.109828 7.172815e+00 1.662815e+00 1
19 642.855483 3.0 None 385.713290 154.285316 102.856877 84.546 28.038166 4.560000 84.546 20.862 404.741812 196.774270 47.404397 4.220615 123.709112 43.002867 1.766872e+01 1.366718e+00 1
20 644.454460 1.0 None 386.672676 154.669070 103.112714 28.182 26.609529 1.444000 28.182 6.954 405.748528 197.263707 47.522306 4.231113 133.058288 43.109828 7.172815e+00 1.662815e+00 1
21 642.687170 2.0 None 385.612302 154.244921 102.829947 56.364 27.219508 2.926000 56.364 13.908 404.635842 196.722750 47.391985 4.219510 128.027947 42.991608 1.250155e+01 1.519550e+00 1
22 644.454460 2.0 None 386.672676 154.669070 103.112714 56.364 27.331529 2.926000 56.364 13.908 405.748528 197.263707 47.522306 4.231113 128.460288 43.109828 1.249282e+01 1.510815e+00 1
23 642.518856 2.0 None 385.511314 154.204526 102.803017 56.364 27.208839 2.926000 56.364 13.908 404.529872 196.671230 47.379574 4.218405 127.986771 42.980349 1.250238e+01 1.520382e+00 1
24 644.454460 4.0 None 386.672676 154.669070 103.112714 112.728 29.124408 6.536000 112.728 27.816 405.748528 197.263707 47.522306 4.231113 120.259166 43.109828 2.248682e+01 1.206815e+00 1
25 642.350543 4.0 None 385.410326 154.164130 102.776087 112.728 28.971281 6.536000 112.728 27.816 404.423902 196.619710 47.367162 4.217300 119.724707 42.969090 2.249721e+01 1.217214e+00 1
26 644.454460 3.0 None 386.672676 154.669070 103.112714 84.546 28.149145 4.560000 84.546 20.862 405.748528 197.263707 47.522306 4.231113 124.109904 43.109828 1.766082e+01 1.358815e+00 1
27 642.182230 3.0 None 385.309338 154.123735 102.749157 84.546 27.991438 4.560000 84.546 20.862 404.317932 196.568191 47.354751 4.216195 123.540358 42.957831 1.767205e+01 1.370045e+00 1
28 629.627695 2.0 None 377.776617 151.110647 100.740431 56.364 26.429717 2.888000 56.364 13.908 396.413597 192.725322 46.428975 4.133769 124.833139 42.118013 1.260410e+01 1.584096e+00 1
29 597.765026 4.0 None 358.659016 143.463606 95.642404 112.728 26.237304 6.247946 112.728 27.816 376.352860 182.972347 44.079410 3.924578 108.621593 39.986607 2.300563e+01 1.437575e+00 1
30 4920.163308 NaN None NaN NaN 2043.436272 0.000 0.000000 0.000000 0.000 0.000 3097.734818 1275.455995 941.773332 78.695489 0.000000 801.810002 -1.818989e-12 -1.818989e-12 1

Storage behaviour RWHT

[53]:
# Plot lot storage 1
lot_storages = pd.DataFrame(scenario_results['wb_lot_storages'])
plot_timeseries_df(lot_storages.loc[0], ['storage_behaviour', 'provided_volume'])
../_images/notebooks_urban_metabolism_model_doc_64_0.png
[54]:
# print_parcel_mass_balance(lot_residential_with_res)

Comparison

The plot below shows a comparison of most important streams for the above scenarios. Note that the number of persons per residential unit is stochastically generated. Therefore demands can slightly vary accross scenarios.

[55]:
# Append and generate data
ress = [lot_residential, lot_residential_with_rwht, lot_residential_with_grey]
f, axes = plt.subplots(3, 2, figsize=(20, 7));

for r in ress:
    inflow(r)
    outflow(r)
lot_residential["type"] = "lot_residential"
lot_residential_with_rwht["type"] = "lot_residential_with_rwht"
lot_residential_with_grey["type"] = "lot_residential_with_grey"
lot_residential_with_res["type"] = "lot_residential_with_res"

ress_df = lot_residential.append(lot_residential_with_rwht).append(lot_residential_with_grey).append(lot_residential_with_res)

def plot_results():
    display_ress = ["inflow", "outflow", "outdoor_demand", "roof_runoff", "grey_water", "provided_volume"]
    for idx, p in enumerate(display_ress):
        sns.catplot(x="type", y=p, data=ress_df,
                        height=6, kind="bar", palette="muted",  ax=axes[ int(idx / 2), (idx) % 2]);
    for i in range(len(display_ress)):
        plt.close(i+2)
plot_results()

/Users/christianurich/Documents/scenario-tool-nodes/.venv/lib/python3.7/site-packages/pandas/core/frame.py:7116: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  sort=sort,
../_images/notebooks_urban_metabolism_model_doc_67_1.png

Lot Stream Aggregation

Based on the template definition streams are aggregated at lot scale to be connected to sub-catchments. Following streams are used in the water cycle model.

  • potable_demand

  • non_potable_demand

  • outdoor_demand

  • sewerage

  • grey_water

  • stormwater_runoff

  • evapotranspiration

  • infiltration

  • rainfall

Default streams in the model

[56]:
# Default stream flow definitions
wb_lot_streams = get_results(baseline_id, ['wb_lot_streams'])
for row in wb_lot_streams['wb_lot_streams']:
    print(f"{LotStream(row['lot_stream_id'])} -> {Streams(row['outflow_stream_id'])}")
LotStream.rainfall -> Streams.rainfall
LotStream.potable_demand -> Streams.potable_demand
LotStream.non_potable_demand -> Streams.potable_demand
LotStream.outdoor_demand -> Streams.potable_demand
LotStream.black_water -> Streams.sewerage
LotStream.grey_water -> Streams.sewerage
LotStream.roof_runoff -> Streams.stormwater_runoff
LotStream.impervious_runoff -> Streams.stormwater_runoff
LotStream.evapotranspiration -> Streams.evapotranspiration
LotStream.infiltration -> Streams.infiltration

Sub Catchments

This scenario sets up a small residential development next to a public green space. It simulates how water can be exported form the residential development to reduce the outdoor demand.

cf71107ce3754cf0b7f9ea246717d57e

[57]:
def catchment_streams(catchment_id, stream_id):
    catchments = {
        "node_type_id": sti.get_node_id("Sub Catchment"),
        "area": geojson_id_right,
        "parameters":
            {
                "dance4water_stream.equation" : stream_id,
                "dance4water_catchment_id.equation": catchment_id
            }
    }
    return catchments
[58]:
# Setup Subcatchment without storage

with open(r"../../resources/boundaries/test_small_left.geojson", 'r') as file:
         geojson_file = json.loads(file.read())
geojson_id_left = sti.upload_geojson(geojson_file, project_id, "left")

with open(r"../../resources/boundaries/test_small_right.geojson", 'r') as file:
         geojson_file = json.loads(file.read())
geojson_id_right = sti.upload_geojson(geojson_file, project_id, "right")


nodes = [{
    "node_type_id": sti.get_node_id("Residential"),
    "area": geojson_id_left,
    "parameters":
        {
        }
}, {
    "node_type_id": sti.get_node_id("Clear area"),
    "area": geojson_id_right,
    "parameters":
        {
        }
}, {
    "node_type_id": sti.get_node_id("Copy Feature"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_copy_feature.to_view": "parcel_tmp"
        }
}, {
    "node_type_id": sti.get_node_id("SQL query"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_sql_query.attribute": "parcel_tmp.area",
            "dance4water_sql_query.query": f"UPDATE parcel_tmp SET area = ST_AREA(GEOMETRY)",
            "dance4water_sql_query.attribute_type": "DOUBLE"
        }
},
    {
        "node_type_id": sti.get_node_id("SQL query"),
        "area": geojson_id_right,
        "parameters":
            {
                "dance4water_sql_query.attribute": "parcel_tmp.garden_area",
                "dance4water_sql_query.query": f"UPDATE parcel_tmp SET garden_area = ST_AREA(GEOMETRY)",
                "dance4water_sql_query.attribute_type": "DOUBLE"
            }
    }, {
    "node_type_id": sti.get_node_id("Copy Feature"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_copy_feature.from_view": "parcel_tmp",
            "dance4water_copy_feature.to_view": "parcel"
        }
},{
    "node_type_id": sti.get_node_id("Lot Template"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_outdoor_demand.equation": 3
        }
}
]
nodes.append(catchment_streams(2,3))

# nodes += wb_catchment("default", geojson_id_left)
nodes += wb_catchment("Outdoor area", geojson_id_right)

scenario_1 = sti.create_scenario(project_id, baseline_id, "Split Catchment")


sti.set_scenario_workflow(scenario_1, nodes)
sti.execute_scenario(scenario_1)


[59]:
wait_till_scenario_done(scenario_1)
2020-05-25 21:59:08.712641 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 21:59:14.270974 Scenario complete
[60]:
scenario_results = get_results(scenario_1, ['parcel', 'wb_soil', 'wb_lot_storages', 'wb_sub_catchment', 'wb_storages', 'wb_sub_storages'])
scenario_1_pd = pd.DataFrame(scenario_results['parcel'])
scenario_1_pd = calcuate_mass_balance(scenario_1_pd)
print_parcel_mass_balance(scenario_1_pd)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 474.421724 2.0 None 284.653034 113.861214 75.907476 56.364 51.157548 13.908 56.364 13.908 298.695917 145.217690 34.984030 3.114777 134.801228 31.735740 4.014566e-13 4.014566e-13 1
1 582.576208 4.0 None 349.545725 139.818290 93.212193 112.728 62.819995 27.816 112.728 27.816 366.789980 178.323139 42.959381 3.824857 165.532024 38.970574 9.237056e-14 3.552714e-14 1
2 636.777013 3.0 None 382.066208 152.826483 101.884322 84.546 68.664542 20.862 84.546 20.862 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -5.044853e-13 -4.476419e-13 1
3 636.529886 1.0 None 381.917932 152.767173 101.844782 28.182 68.637894 6.954 28.182 6.954 400.759216 194.838042 46.937945 4.179085 180.862313 42.579725 -2.700062e-13 -2.700062e-13 1
4 636.777013 2.0 None 382.066208 152.826483 101.884322 56.364 68.664542 13.908 56.364 13.908 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -8.739676e-13 -8.739676e-13 1
5 636.365135 1.0 None 381.819081 152.727632 101.818422 28.182 68.620129 6.954 28.182 6.954 400.655489 194.787612 46.925796 4.178004 180.815501 42.568705 2.273737e-13 2.273737e-13 1
6 636.777013 4.0 None 382.066208 152.826483 101.884322 112.728 68.664542 27.816 112.728 27.816 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -7.105427e-14 -7.105427e-14 1
7 636.200384 3.0 None 381.720231 152.688092 101.792061 84.546 68.602364 20.862 84.546 20.862 400.551762 194.737183 46.913648 4.176922 180.768689 42.557684 4.121148e-13 4.121148e-13 1
8 636.777013 2.0 None 382.066208 152.826483 101.884322 56.364 68.664542 13.908 56.364 13.908 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 3.907985e-13 3.907985e-13 1
9 636.035633 3.0 None 381.621380 152.648552 101.765701 84.546 68.584598 20.862 84.546 20.862 400.448035 194.686754 46.901499 4.175840 180.721877 42.546663 -8.668621e-13 -8.668621e-13 1
10 636.777013 1.0 None 382.066208 152.826483 101.884322 28.182 68.664542 6.954 28.182 6.954 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -3.765876e-13 -3.765876e-13 1
11 635.870882 4.0 None 381.522529 152.609012 101.739341 112.728 68.566833 27.816 112.728 27.816 400.344308 194.636325 46.889350 4.174759 180.675065 42.535642 -2.131628e-13 -2.131628e-13 1
12 621.988433 4.0 None 373.193060 149.277224 99.518149 112.728 67.069869 27.816 112.728 27.816 391.603917 190.386989 45.865653 4.083614 176.730534 41.606996 -6.323830e-13 -6.892265e-13 1
13 591.446768 4.0 None 354.868061 141.947224 94.631483 112.728 63.776519 27.816 112.728 27.816 372.374885 181.038365 43.613500 3.883096 168.052487 39.563957 0.000000e+00 -5.684342e-14 1
14 12491.101688 NaN None NaN 12491.101688 NaN 0.000 5612.219568 0.000 0.000 0.000 7864.397623 9653.354334 0.000000 341.705465 0.000000 3481.557391 1.409717e-11 1.409717e-11 2
15 3005.815971 NaN None NaN NaN 9742.245511 0.000 0.000000 0.000 0.000 0.000 1892.461735 -535.638743 4489.979525 -184.281167 0.000000 -1877.597881 4.547474e-13 4.547474e-13 1
16 NaN NaN None NaN NaN NaN 0.000 0.000000 0.000 0.000 0.000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 1
[61]:
pd.DataFrame(scenario_results['wb_sub_catchment'])
[61]:
annual_flow catchment_id daily_flow ogc_fid stream
0 4532.652920 1 [17.1369, 18.7949, 18.7949, 7.296, 12.992, 18.... 1 1
1 1335.168000 1 [3.648, 3.648, 3.648, 3.648, 3.648, 3.648, 3.6... 2 4
2 7922.079010 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 3 6
3 11760.936118 1 [44.2296, 40.4022, 40.4022, 82.0496, 53.7983, ... 4 7
4 2181.606479 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 5 8
5 15193.656904 1 [9.6529, 0.0, 0.0, 82.0496, 33.7851, 0.0, 0.0,... 6 9
6 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 7 2
7 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 8 3
8 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 9 5
9 5612.219568 2 [29.6563, 34.6527, 34.6527, 0.0, 17.1652, 34.6... 10 3
[62]:
scenario_results['wb_sub_storages']
[62]:
[]
[63]:
# Setup Subcatchment with storage

with open(r"../../resources/boundaries/test_small_left.geojson", 'r') as file:
         geojson_file = json.loads(file.read())
geojson_id_left = sti.upload_geojson(geojson_file, project_id, "left")

with open(r"../../resources/boundaries/test_small_right.geojson", 'r') as file:
         geojson_file = json.loads(file.read())
geojson_id_right = sti.upload_geojson(geojson_file, project_id, "right")


nodes = [{
    "node_type_id": sti.get_node_id("Residential"),
    "area": geojson_id_left,
    "parameters":
        {
        }
}, {
    "node_type_id": sti.get_node_id("Clear area"),
    "area": geojson_id_right,
    "parameters":
        {
        }
}, {
    "node_type_id": sti.get_node_id("Copy Feature"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_copy_feature.to_view": "parcel_tmp"
        }
}, {
    "node_type_id": sti.get_node_id("SQL query"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_sql_query.attribute": "parcel_tmp.area",
            "dance4water_sql_query.query": f"UPDATE parcel_tmp SET area = ST_AREA(GEOMETRY)",
            "dance4water_sql_query.attribute_type": "DOUBLE"
        }
},
    {
        "node_type_id": sti.get_node_id("SQL query"),
        "area": geojson_id_right,
        "parameters":
            {
                "dance4water_sql_query.attribute": "parcel_tmp.garden_area",
                "dance4water_sql_query.query": f"UPDATE parcel_tmp SET garden_area = ST_AREA(GEOMETRY)",
                "dance4water_sql_query.attribute_type": "DOUBLE"
            }
    }, {
    "node_type_id": sti.get_node_id("Copy Feature"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_copy_feature.from_view": "parcel_tmp",
            "dance4water_copy_feature.to_view": "parcel"
        }
},{
    "node_type_id": sti.get_node_id("Lot Template"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_outdoor_demand.equation": 3
        }
},{
    "node_type_id": sti.get_node_id("Sub Catchment Storage"),
    "area": geojson_id_right,
    "parameters":
        {
            "dance4water_inflow_stream.equation": 3,
            "dance4water_demand_stream.equation": 7,
            "dance4water_storage_volume.equation": 50,
        }
}
]
nodes.append(catchment_streams(2,3))

scenario_2 = sti.create_scenario(project_id, baseline_id, "test sub")

# nodes += wb_catchment("default", geojson_id_left)
nodes += wb_catchment("Outdoor area", geojson_id_right)

sti.set_scenario_workflow(scenario_2, nodes)
sti.execute_scenario(scenario_2)


[ ]:

[64]:
wait_till_scenario_done(scenario_2)
2020-05-25 22:00:53.919002 {'status': 6, 'status_text': 'PA_RUNNING'}
2020-05-25 22:00:59.100340 Scenario complete
[65]:
scenario_results_2 = get_results(scenario_2, ['parcel', 'wb_soil', 'wb_lot_storages', 'wb_sub_catchment', 'wb_storages', 'wb_sub_storages'])
scenario_2_pd = pd.DataFrame(scenario_results['parcel'])
scenario_2_pd = calcuate_mass_balance(scenario_2_pd)
print_parcel_mass_balance(scenario_2_pd)
area persons impervious_area roof_area garden_area outdoor_imp potable_demand outdoor_demand non_potable_demand grey_water black_water rainfall evapotranspiration impervious_runoff pervious_runoff roof_runoff infiltration mass_balance_runoff_only mass_balance wb_lot_template_id
0 474.421724 2.0 None 284.653034 113.861214 75.907476 56.364 51.157548 13.908 56.364 13.908 298.695917 145.217690 34.984030 3.114777 134.801228 31.735740 4.014566e-13 4.014566e-13 1
1 582.576208 4.0 None 349.545725 139.818290 93.212193 112.728 62.819995 27.816 112.728 27.816 366.789980 178.323139 42.959381 3.824857 165.532024 38.970574 9.237056e-14 3.552714e-14 1
2 636.777013 3.0 None 382.066208 152.826483 101.884322 84.546 68.664542 20.862 84.546 20.862 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -5.044853e-13 -4.476419e-13 1
3 636.529886 1.0 None 381.917932 152.767173 101.844782 28.182 68.637894 6.954 28.182 6.954 400.759216 194.838042 46.937945 4.179085 180.862313 42.579725 -2.700062e-13 -2.700062e-13 1
4 636.777013 2.0 None 382.066208 152.826483 101.884322 56.364 68.664542 13.908 56.364 13.908 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -8.739676e-13 -8.739676e-13 1
5 636.365135 1.0 None 381.819081 152.727632 101.818422 28.182 68.620129 6.954 28.182 6.954 400.655489 194.787612 46.925796 4.178004 180.815501 42.568705 2.273737e-13 2.273737e-13 1
6 636.777013 4.0 None 382.066208 152.826483 101.884322 112.728 68.664542 27.816 112.728 27.816 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -7.105427e-14 -7.105427e-14 1
7 636.200384 3.0 None 381.720231 152.688092 101.792061 84.546 68.602364 20.862 84.546 20.862 400.551762 194.737183 46.913648 4.176922 180.768689 42.557684 4.121148e-13 4.121148e-13 1
8 636.777013 2.0 None 382.066208 152.826483 101.884322 56.364 68.664542 13.908 56.364 13.908 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 3.907985e-13 3.907985e-13 1
9 636.035633 3.0 None 381.621380 152.648552 101.765701 84.546 68.584598 20.862 84.546 20.862 400.448035 194.686754 46.901499 4.175840 180.721877 42.546663 -8.668621e-13 -8.668621e-13 1
10 636.777013 1.0 None 382.066208 152.826483 101.884322 28.182 68.664542 6.954 28.182 6.954 400.914807 194.913686 46.956168 4.180708 180.932531 42.596257 -3.765876e-13 -3.765876e-13 1
11 635.870882 4.0 None 381.522529 152.609012 101.739341 112.728 68.566833 27.816 112.728 27.816 400.344308 194.636325 46.889350 4.174759 180.675065 42.535642 -2.131628e-13 -2.131628e-13 1
12 621.988433 4.0 None 373.193060 149.277224 99.518149 112.728 67.069869 27.816 112.728 27.816 391.603917 190.386989 45.865653 4.083614 176.730534 41.606996 -6.323830e-13 -6.892265e-13 1
13 591.446768 4.0 None 354.868061 141.947224 94.631483 112.728 63.776519 27.816 112.728 27.816 372.374885 181.038365 43.613500 3.883096 168.052487 39.563957 0.000000e+00 -5.684342e-14 1
14 12491.101688 NaN None NaN 12491.101688 NaN 0.000 5612.219568 0.000 0.000 0.000 7864.397623 9653.354334 0.000000 341.705465 0.000000 3481.557391 1.409717e-11 1.409717e-11 2
15 3005.815971 NaN None NaN NaN 9742.245511 0.000 0.000000 0.000 0.000 0.000 1892.461735 -535.638743 4489.979525 -184.281167 0.000000 -1877.597881 4.547474e-13 4.547474e-13 1
16 NaN NaN None NaN NaN NaN 0.000 0.000000 0.000 0.000 0.000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000e+00 0.000000e+00 1
[66]:
pd.DataFrame(scenario_results_2['wb_sub_catchment'])
[66]:
annual_flow catchment_id daily_flow ogc_fid stream
0 4251.564920 1 [16.3689, 18.0269, 18.0269, 6.528, 12.224, 18.... 1 1
1 1194.624000 1 [3.264, 3.264, 3.264, 3.264, 3.264, 3.264, 3.2... 2 4
2 7872.079010 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 3 6
3 11760.936118 1 [44.2296, 40.4022, 40.4022, 82.0496, 53.7983, ... 4 7
4 2181.606479 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 5 8
5 15193.656904 1 [9.6529, 0.0, 0.0, 82.0496, 33.7851, 0.0, 0.0,... 6 9
6 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 7 2
7 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 8 3
8 0.000000 1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 9 5
9 5612.219568 2 [29.6563, 34.6527, 34.6527, 0.0, 17.1652, 34.6... 10 3
[67]:
scenario_results_2['wb_sub_storages']

[67]:
[{'demand_stream_id': 7,
  'dry': 22,
  'inflow_stream_id': 3,
  'ogc_fid': 1,
  'provided_volume': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
         0., 0., 0., 0., 0., 0., 0., 0., 0.]),
  'spills': 58,
  'storage_behaviour': array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
          0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50., 50.,
         50., 50.]),
  'volume': 50}]