Working with GDM models

ERAD’s AssetSystem provides a class method for users working with a GDM DistributionSystem. Distribution system components are automatically mapped to the asset models and added to the AssetSystem. A hazard simulation can then be set up using the steps listed above. We start by loading a GDM model using the gdmloader package. This package can be installed using the command

pip install gdmloader

from IPython.display import display, HTML
import plotly.graph_objects as go
import plotly.io as pio

pio.renderers.default = "plotly_mimetype"

from gdm.distribution import DistributionSystem
from gdmloader.constants import GCS_CASE_SOURCE
from gdmloader.source import SystemLoader

gdm_loader = SystemLoader()
gdm_loader.add_source(GCS_CASE_SOURCE)

distribution_system: DistributionSystem = gdm_loader.load_dataset(
    source_name=GCS_CASE_SOURCE.name,
    system_type=DistributionSystem,
    dataset_name="p1rhs7_1247",
    version="2_3_0",
)
distribution_system.name = "p1rhs7_1247"
distribution_system.info()

/opt/homebrew/Caskroom/miniconda/base/envs/erad/lib/python3.12/site-packages/kaleido/__init__.py:14: UserWarning:



Warning: You have Plotly version 6.0.1, which is not compatible with this version of Kaleido (1.0.0).

This means that static image generation (e.g. `fig.write_image()`) will not work.

Please upgrade Plotly to version 6.1.1 or greater, or downgrade Kaleido to version 0.2.1.


WARNING	Task(Task-2) google.auth.compute_engine._metadata:_metadata.py:ping()- Compute Engine Metadata server unavailable on attempt 1 of 3. Reason: timed out
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WARNING	Task(Task-2) google.auth._default:_default.py:_get_gce_credentials()- Authentication failed using Compute Engine authentication due to unavailable metadata server.
WARNING	Task(Task-2) google.auth.compute_engine._metadata:_metadata.py:get()- Compute Engine Metadata server unavailable on attempt 1 of 5. Reason: HTTPConnectionPool(host='metadata.google.internal', port=80): Max retries exceeded with url: /computeMetadata/v1/instance/service-accounts/default/?recursive=true (Caused by NameResolutionError("<urllib3.connection.HTTPConnection object at 0x11ee5e4b0>: Failed to resolve 'metadata.google.internal' ([Errno 8] nodename nor servname provided, or not known)"))
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WARNING	Task(Task-2) google.auth.compute_engine._metadata:_metadata.py:get()- Compute Engine Metadata server unavailable on attempt 3 of 5. Reason: HTTPConnectionPool(host='metadata.google.internal', port=80): Max retries exceeded with url: /computeMetadata/v1/instance/service-accounts/default/?recursive=true (Caused by NameResolutionError("<urllib3.connection.HTTPConnection object at 0x11eeec7d0>: Failed to resolve 'metadata.google.internal' ([Errno 8] nodename nor servname provided, or not known)"))
WARNING	Task(Task-2) google.auth.compute_engine._metadata:_metadata.py:get()- Compute Engine Metadata server unavailable on attempt 4 of 5. Reason: HTTPConnectionPool(host='metadata.google.internal', port=80): Max retries exceeded with url: /computeMetadata/v1/instance/service-accounts/default/?recursive=true (Caused by NameResolutionError("<urllib3.connection.HTTPConnection object at 0x11eeec950>: Failed to resolve 'metadata.google.internal' ([Errno 8] nodename nor servname provided, or not known)"))
WARNING	Task(Task-2) google.auth.compute_engine._metadata:_metadata.py:get()- Compute Engine Metadata server unavailable on attempt 5 of 5. Reason: HTTPConnectionPool(host='metadata.google.internal', port=80): Max retries exceeded with url: /computeMetadata/v1/instance/service-accounts/default/?recursive=true (Caused by NameResolutionError("<urllib3.connection.HTTPConnection object at 0x11eeec500>: Failed to resolve 'metadata.google.internal' ([Errno 8] nodename nor servname provided, or not known)"))

Next, we built the asset system from the gdm DistributionSystem using the from_gdm method.

from erad.systems import AssetSystem

asset_system = AssetSystem.from_gdm(distribution_system)
asset_system.info()

for a in asset_system.iter_all_components():
    a.pprint()
    break

Plotting an AssetSystem¶

fig = asset_system.plot(show=False)
fig.show()

Building a HazardModel¶

In this section, we built a hazard model and apply the model the asset system.

from datetime import datetime

from shapely.geometry import Polygon
from gdm.quantities import Distance

from erad.models.hazard import FloodModelArea, FloodModel
from erad.systems import HazardSystem
from erad.quantities import Speed

flood_area = FloodModelArea(
    affected_area=Polygon(
        [
            (-122.38, 38.70),
            (-122.35, 38.68),
            (-122.343, 38.69),
            (-122.37, 38.7035),
        ]
    ),
    water_velocity=Speed(0, "meter/second"),
    water_elevation=Distance(160, "meter"),
)

flood = FloodModel(
    name="flood 1",
    timestamp=datetime.now(),
    affected_areas=[flood_area],
)

user_defined_flood_event = HazardSystem(auto_add_composed_components=True)
user_defined_flood_event.add_component(flood)

Overlaying the HazardModel¶

We can overlay the hazard model on the same plot using the add_trace method. The show method can be used to render the image again.

polygon = flood.affected_areas[0].affected_area
lon, lat = polygon.exterior.xy  # returns x and y sequences

fig.add_trace(
    go.Scattermap(
        fill="toself",
        lon=lon.tolist(),
        lat=lat.tolist(),
        marker={"size": 10, "color": flood.affected_areas[0].water_velocity.magnitude},
    )
)

fig.show()

Finally, we can run the actual simulation using the HazardSimulator class from erad.runner.

from erad.runner import HazardSimulator

user_defined_flood_event.info()

hazard_scenario = HazardSimulator(asset_system=asset_system)
hazard_scenario.run(hazard_system=user_defined_flood_event)

2026-03-06 08:50:08.997 | WARNING  | erad.runner:run:52 - No HazardFragilityCurves definitions found in the passed HazardSystem, using default curve definitions
2026-03-06 08:50:08.997 | INFO     | erad.runner:run:60 - Simulating hazard at 2026-03-06 08:50:08.879438

Once the simulation is complete, we can visualize the results by plotting the survival_prob from the updated gdf dataframe.

fig = asset_system.plot(show=False)
fig.show()