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Using Web Services to Construct General Workflows for Simulating Flow and Environmental Contaminants in Streams
Citation:
Knightes, C., R. Parmar, J. Sitterson, B. Avant, D. Smith, J. Koblich, K. Wolfe, Michael Galvin, L. Prieto, S. Purucker, AND J. Johnston. Using Web Services to Construct General Workflows for Simulating Flow and Environmental Contaminants in Streams. The International Society for Ecological Modelling Global Conference 2019, Salzburg, N/A, AUSTRIA, October 01 - 05, 2019.
Impact/Purpose:
Understanding exposure concentrations in surface water systems, so that we can then understand how these relate to human and ecological effects, requires understanding transport and fate of environmental contaminantes. For ecological questions, there is often limited data and time. The problem is that we need a methodology to simulate exposure concentrations in surface water systems that provides model flexibility and ease of use. Our objective is to develop a web-based platform for automated retrieval, processing, and synthesizing of data using web services and modelling components. Our approach in this presentation is to demonstrate the use of EPA’s Hydrologic Micro Services (HMS) to simulate ammonia and nitrate in a representative river using 3 different workflows developed using HMS. Through this effort, we were able to demonstrate the feasibility and utility of having a web-based tool to gather data and create a system to predict streamflow and concentrations of nitrate and ammonia in the river. This tool can use different modeling structures and approaches to investigate how choosing the model type affects results. This work can work in any stream in the US, even where data is limited or non-existent. Additionally, this provides the foundation for future advances as EPA continues to incorporate more data sources and model components. This work will be usable by academics, public sector, and private industry to gather data and to construct models to address issues of water quantity and quality.
Description:
To investigate environmental exposure concentrations in streams, one must capture two different modelling aspects: 1) transport (e.g., hydrodynamics) and 2) fate (e.g., contaminant kinetics). Representing the flow of streams is useful to understand water quantity, how it changes over time, and the impact of floods and droughts. The chemical transformations and processes governing environmental concentrations are additionally important to capture exposure concentrations. For some environmental management questions, one might need to represent these processes in areas that may have limited data or may have limited time to develop models. To assist with these needs, the United States Environmental Protection Agency (USEPA) Office of Research and Development has developed the Hydrologic Micro Services (HMS) platform, a suite of data provisioning components, micro services, and workflows for the hydrological and water quality modelling domain. As part of these services, USEPA has provided some straight-forward options for simulating flow in one-dimensional streams based on hydrodynamic algorithms commonly used in water quality models. Stream hydrodynamic services use workflows within HMS to pull precipitation data combined with runoff services linked to stream routing algorithms. The user can also use observed and forecasted streamflow provided by the National Oceanic and Atmospheric Administration’s National Water Model. Additionally, processes for simulating environmental contaminants are being incorporated as web services. The user can then use provided or self-constructed workflows in an a la carte style. Data and models are linked with other available web services to construct stream flow and water quality forecasts appropriate for a user’s specific needs. The USEPA plans to add data provisioning components and source selection workflows to its suite with the aim of making modelling workflow development more efficient, allowing modellers to spend more time analysing results rather than gathering and pre-processing data. HMS is in beta testing and available at https://qed.epacdx.net/hms/.