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NWM Coastal: Automated Coastal Model Calibration#

Why This Tool?#

Running coastal models for calibration and forecasting involves many moving parts: downloading and preparing meteorological, ocean boundary, river discharge data, and configuring the model, running on HPC, and validating against observations. Previously, this required 20+ bash scripts with dozens of environment variables, fragile date arithmetic, and manual data management.

coastal-calibration replaces that workflow with a single Python package. One configuration file drives the entire pipeline, from data download through model execution to validation against NOAA tide gauges.

What It Does#

  • Domain definition: QGIS plugin for interactively drawing the model domain and selecting NWM river discharge points
  • Model creation (SFINCS): automated quadtree mesh generation from an AOI polygon
  • Data management: download of NWM forcing (retrospective 1979-2023 or real-time analysis), STOFS ocean boundary conditions, and TPXO tidal data with date-range validation
  • Forcing preparation: atmospheric regridding via ESMF, boundary condition processing with STOFS/TPXO fallback
  • Model execution: SFINCS (single-node OpenMP) and SCHISM (multi-node MPI) with resumable stage-based pipelines
  • Validation: automatic NOAA CO-OPS station discovery, water level comparison plots, and flood depth mapping

QGIS Plugin: model domain with watershed boundaries
QGIS plugin: defining the model domain aligned to watershed boundaries

Flood depth map, Narragansett Bay
Flood depth map from a SFINCS simulation of Narragansett Bay, RI

Supported Models#

SFINCS SCHISM
Grid Quadtree (regular) Unstructured triangular
Compute Single-node, OpenMP Multi-node, MPI
Model Creation Automated Prebuilt mesh required
Flood Depth Map Yes (downscaled to DEM) Not yet supported
Domains CONUS, Hawaii, Puerto Rico CONUS, Hawaii, Puerto Rico

Design#

  • Single configuration: one YAML file (or Python dictionary) defines the entire simulation, with validation, sensible defaults, and support for multi-run templates
  • Resumable pipeline: each stage (download, forcing, run, plot, ...) can be restarted individually, so a failed run does not need to start from scratch
  • Portable: both SFINCS and SCHISM are included as submodules and compiled from source during environment setup, with no containers required. The same workflow runs on a local workstation or an HPC cluster.
  • Unified API: the same two Python calls (CoastalCalibConfig + CoastalCalibRunner) work for both models; only the model-specific settings differ
  • Built for large models: although written in Python, the package orchestrates compiled libraries for the heavy lifting: ESMF for atmospheric regridding, GDAL/NetCDF/HDF5 for spatial I/O on large files, and lazy loading to avoid reading entire datasets into memory. Python handles the workflow logic while performance-critical operations run in compiled code.

Use Cases#

This package supports two primary use cases:

  1. Retrospective calibration: run historical simulations against NWM retrospective data (1979-2023) to tune model parameters and validate performance at NOAA gage stations
  2. Operational forecasting: integrate with the Next Generation Water Modeling Framework (NGEN) as a coastal model realization, where coastal-calibration handles the model setup, forcing, and execution while NGEN orchestrates catchment-level workflows

Quick Start#

Installation#

Prerequisites: Git and Pixi. Pixi handles all other dependencies including Python and compiling SFINCS and SCHISM from source.

git clone https://github.com/NGWPC/nwm-coastal
cd nwm-coastal
pixi install -e dev

All commands below should be run with pixi r -e dev to activate the environment.

CLI#

Generate a configuration file, adjust it, then run:

# SCHISM (default)
pixi r -e dev coastal-calibration init config.yaml --domain hawaii

# SFINCS
pixi r -e dev coastal-calibration init config.yaml --domain atlgulf --model sfincs

Validate and run:

pixi r -e dev coastal-calibration validate config.yaml
pixi r -e dev coastal-calibration run config.yaml

The pipeline supports --start-from and --stop-after for partial workflows:

pixi r -e dev coastal-calibration run config.yaml --start-from schism_boundary
pixi r -e dev coastal-calibration run config.yaml --stop-after schism_sflux

Python API#

from coastal_calibration import CoastalCalibConfig, CoastalCalibRunner

config = CoastalCalibConfig.from_yaml("config.yaml")
runner = CoastalCalibRunner(config)
result = runner.run()

if result.success:
    print(f"Completed in {result.duration_seconds:.1f}s")

Creating a SFINCS Model#

Build a new SFINCS quadtree model from an AOI polygon:

pixi r -e dev coastal-calibration create create_config.yaml

The AOI polygon and discharge points can be created interactively using the QGIS plugin.

Supported Data Sources#

Source Date Range Description
nwm_retro 1979-02-01 to 2023-01-31 NWM Retrospective 3.0
nwm_ana 2018-09-17 to present NWM Analysis
stofs 2020-12-30 to present STOFS water levels
tpxo N/A (local installation) TPXO tidal model

Domains: atlgulf, pacific, hawaii, prvi

Workflow Stages#

SCHISM (11 stages)#

download → schism_forcing_prep → schism_forcing → schism_sflux → schism_params → schism_obs → schism_boundary → schism_prep → schism_run → schism_postprocess → schism_plot

SFINCS (14 stages)#

download → sfincs_symlinks → sfincs_data_catalog → sfincs_init → sfincs_timing → sfincs_forcing → sfincs_discharge → sfincs_precip → sfincs_wind → sfincs_pressure → sfincs_write → sfincs_run → sfincs_floodmap → sfincs_plot

SFINCS Creation (9 stages)#

create_grid → create_fetch_data → create_elevation → create_mask → create_boundary → create_discharge → create_subgrid → create_obs → create_write

Roadmap#

  • SCHISM model creation: integrate the existing SCHISM mesh subsetting capability into the package, providing an automated create workflow for SCHISM similar to what SFINCS already has. This will allow users to extract a regional subdomain from a larger SCHISM mesh using the same QGIS plugin and API.
  • Improved tidal boundary conditions: replace the current 8-constituent TPXO implementation with a pure-Python module supporting all 32+ constituents and minor constituent inference, removing the dependency on an external Fortran binary
  • Multi-cycle STOFS stitching: download and stitch multiple STOFS forecast cycles to cover simulations of any duration, eliminating the current 180-hour single-cycle limit
  • Regional STOFS subsetting: spatially subset the global STOFS output to the model domain before download, reducing data transfer from ~12 GB per cycle to a few hundred MB
  • PyPI and conda-forge: publish the package on PyPI and conda-forge so users can install with pip install coastal-calibration or conda install coastal-calibration without cloning the repository

Credits#

  1. NextGen Water Modeling Framework Prototype
  2. schism-dev community
  3. Deltares community

License#

BSD-2-Clause. See LICENSE for details.