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

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Title: An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model.
Authors: Raney, Austin, Maghami, Iman, Feng, Yenchia, Mandli, Kyle, Cohen, Sagy, Goodall, Jonathan
Source: Journal of the American Water Resources Association; Feb2022, Vol. 58 Issue 1, p75-85, 11p
Subject Terms: PYTHON programming language, METEOROLOGICAL research, HYDROLOGIC models, HYDROLOGICAL forecasting, WEATHER forecasting
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Biomedical Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:1093474X
DOI:10.1111/1752-1688.12973