An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model

The National Water Model (NWM), a configuration of the Weather Research and Forecasting Hydrological model, operates as the United States’ hydrological model. The NWM predicts streamflow at more than 2.7 million river reaches; and is a subject of growing attention in the hydrological modeling commun...

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Bibliographic Details
Published in:Journal of the American Water Resources Association 2022-02, Vol.58 (1), p.75-85
Main Authors: Raney, Austin, Maghami, Iman, Feng, Yenchia, Mandli, Kyle, Cohen, Sagy, Goodall, Jonathan
Format: Article
Language:English
Subjects:
Automation
Case studies
Docker
Fluid dynamics
hydroinformatics
Hydrologic models
Hydrology
Libraries
Mathematical models
Modelling
National Water Model
parameter ensembles
Parameters
Physical simulation
Python
Simulation
Stream discharge
Stream flow
Weather forecasting
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Summary:The National Water Model (NWM), a configuration of the Weather Research and Forecasting Hydrological model, operates as the United States’ hydrological model. The NWM predicts streamflow at more than 2.7 million river reaches; and is a subject of growing attention in the hydrological modeling community. Large‐scale computationally distributed models such as the NWM, often require technical knowledge of, and access to, cluster‐based computing environments for model compilation and simulation. User‐friendly tools capable of setting up and running such models to adjust and explore their parameter space generally do not exist. Here we present the Dockerized Job Scheduler (DJS) a Python library that takes a service approach to modeling. The library is capable of (1) generating varied parameter sets and (2) orchestrating concurrent NWM simulations via Docker. DJS is designed to automate the deployment of varied parameter simulations and lower the model usage entrance barrier. In this paper, we use a case study to demonstrate its installation and usage.
ISSN:1093-474X
1752-1688
DOI:10.1111/1752-1688.12973