Orchestrating end-to-end reproducible NextGen and NextGen adjacent workflows: Part 1: Model setup and configuration
Day 1 Session 2 (0:00AM)
Presenters:
Darri Eythorsson, University of Calgary
Raymond Spiteri, University of Saskatchewan
Jordan Read, CUASHI
James Halgren, Brigham Young University
Martyn Clark, University of Calgary
In this workshop, we will go through the exercise of setting up and executing a full modeling workflow for NextGen or a NextGen-adjacent model using intelligent workflows. This will include community solutions for key components of the NextGen ecosystem (e.g., static data acquisition, domain definition and discretization, forcing data acquisition and model input pre-processing, default model instantiation, model optimization and evaluation). In the first session of the workshop, participants will select a USGS gauging station and build and optimize a model for the watershed upstream of it. At the end of the first session, participants will have a model that has achieved demonstrable improvement through sequential calibration methods. We will discuss best practices for reproducibility and provenance management throughout the workflow. We will consider key model decision points, discuss alternative model design structures, and assess their impact on model performance. We will discuss the sources of uncertainty in the model workflow and methods for characterizing and estimating them.
Learning Outcomes:
- Configure and execute a NextGen calibration workflow.
- Understand the flow of data through a general NextGen workflow
- Understand the key decision points in a NextGen model setup.
- Understand approaches for scaling hydrological calibration workflows using parallel computing resources.
Prerequisites:
- Familiarity with Python and Jupyter notebook environments
- Familiarity with NextGen principles and hydrological modeling concepts
- CIROH-2i2c account
- Git clone of Symfluence repository
Additional Details:
- Complements existing NGIAB workshops by addressing the pathway from single-basin calibration to large-sample and continental-scale inference using HPC resources
- Directly supports NOAA-OWP’s stated objectives of “model interoperability, intercomparison, testing of research hypotheses, and deploying into operations science-driven, evidence-based models”
- Demonstrates reproducible end-to-end workflow provenance tracking—a critical gap for operational adoption and community contribution
- Symfluence extends beyond single-model calibration to orchestrate workflows across NextGen and NextGen-adjacent models, enabling systematic model intercomparison within a unified framework.