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Hydrologic Micro Services (HMS) Platform Architecture
Citation:
Wolfe, K., R. Parmar, C. Knightes, D. Smith, J. Koblich, J. Sitterson, J. Johnston, AND S. Purucker. Hydrologic Micro Services (HMS) Platform Architecture. 2019 International Society for Ecological Modelling Global Conference, Salzburg, AUSTRIA, October 01 - 05, 2019.
Impact/Purpose:
Presented at the International Society for Ecological Modelling Global Conference 2019.
Description:
Emerging challenges in hydrological and water quality modeling paired with advances in cloud based computational technologies have provided an opportunity to develop a scalable hydro-informatics platform. Large scale assessments in water availability and quality are becoming more common. Rapid response to events such as adverse weather events, wildland fires, and droughts is also increasingly important. Existing models and databases can tackle some of these challenges but may not always suffice. Often existing models don’t precisely match the problem being addressed. There is a need to develop a system where modelers can rapidly construct modeling workflows to match the problem being addressed. Web service accessible data products tend to dispense data in custom formats specific to the providers requirements. This heterogeneity forces data consumers to build processing capabilities for each data source. The hydrological modeling domain has made significant progress in developing its own data conventions and formatting requirements, however, there is still a need to develop data provisioning services which can gather data from disparate sources and pre-process it, at least partially, to meet the conventions and formatting customary to the hydrological modeling domain. In this presentation we describe a computational infrastructure developed by the United States Environmental Protection Agency (EPA) to facilitate solutions to the substantial challenges in hydrological and water quality modeling domain. The infrastructure supports data provisioning services of databases hosted locally as well as by external partners, water quantity and quality modeling components and services, and utility services such as geo-processing and statistical calculations. The infrastructure is built with interoperability as a guiding principle and promotes it by exposing functionality through RESTful web services. The cloud-hosted infrastructure is completely containerized. The technology stack includes Django, Celery, Flask, .NET Core, Python, GDAL, PostGIS, MongoDB, SQLite, Leaflet and D3.js.