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HydroGraphNet
=============

Overview
--------

``hydrographnet`` is the PyHazards entrypoint for flood forecasting on irregular meshes with graph-structured hydrologic state updates.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Flood

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Implemented

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Streamflow

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``hydrographnet`` is the PyHazards entrypoint for flood forecasting on irregular meshes with graph-structured hydrologic state updates.

In PyHazards, this model is typically paired with the ERA5-based hydrograph adapter ``load_hydrograph_data`` for end-to-end smoke validation.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Flood Benchmark </benchmarks/flood_benchmark>`

**Mapped benchmark ecosystems:** :doc:`HydroBench </benchmarks/hydrobench>`

External References
-------------------

**Paper:** `Interpretable physics-informed graph neural networks for flood forecasting <https://onlinelibrary.wiley.com/doi/10.1111/mice.13484>`_

Registry Name
-------------

Primary entrypoint: ``hydrographnet``

Supported Tasks
---------------

- Streamflow

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(
       name="hydrographnet",
       task="regression",
       node_in_dim=2,
       edge_in_dim=3,
       out_dim=1,
   )

   batch = {
       "x": torch.randn(1, 3, 6, 2),
       "adj": torch.eye(6).unsqueeze(0),
       "coords": torch.randn(6, 2),
   }
   preds = model(batch)
   print(preds.shape)

Notes
-----

- The smoke test uses a synthetic graph batch so it stays CPU-safe in CI.