Event Title

National Water Model Forecasts and USGS Streamflow Observations Acquisition Using IPython

Presenter Information

Irene Garousi-Nejad
Jeffery Horsburgh

Location

Logan Country Club

Streaming Media

Start Date

3-28-2017 3:35 PM

End Date

3-28-2017 3:40 PM

Description

We live in exciting computational era. The combination of inexpensive but advanced coding libraries and new data repositories provide new opportunities for scientists and researchers to share data (either observations from measurements or outputs from running simulation models) from their study and explore research and data created by others. In hydrology, streamflow data is one of the most common and important data types. Historical observations of streamflow are collected by the United States Geological Survey (USGS) at sites throughout the United States. Additionally, it is common for hydrologic models to be used for forecasting streamflow conditions in the future. Among efforts to forecast streamflow, the most recent have led to the development, launch, and unveiling of America’s first National Water Model (NWM) on August 16, 2016. This model forecasts more precise, detailed, frequent, and expanded water information than ever before at a national scale. Results from the NWM are intended for use by various communities to improve water-related decisions. However, given the large volume of data created by the NWM, researchers who aim to use NWM forecast data face challenges in retrieving, managing, and analyzing these data along with comparing them to observational data collected by the USGS. We developed a Python scripting language-based retrieval code that facilitates and automates the process of querying and retrieving forecast data from the NWM and streamflow data from the USGS. We demonstrated its use in a Jupyter IPython Notebook. The retrieval IPython notebook is a powerful web application that enabled us to create a live data retrieval code, visualizations, and descriptive metadata to visually demonstrate the data retrieval and direct comparison process at a local scale. The IPython Notebook is available on HydroShare for public use so that the users can easily read the code and implement it in their research without installing specific soft-wares or libraries on their local machines.

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Mar 28th, 3:35 PM Mar 28th, 3:40 PM

National Water Model Forecasts and USGS Streamflow Observations Acquisition Using IPython

Logan Country Club

We live in exciting computational era. The combination of inexpensive but advanced coding libraries and new data repositories provide new opportunities for scientists and researchers to share data (either observations from measurements or outputs from running simulation models) from their study and explore research and data created by others. In hydrology, streamflow data is one of the most common and important data types. Historical observations of streamflow are collected by the United States Geological Survey (USGS) at sites throughout the United States. Additionally, it is common for hydrologic models to be used for forecasting streamflow conditions in the future. Among efforts to forecast streamflow, the most recent have led to the development, launch, and unveiling of America’s first National Water Model (NWM) on August 16, 2016. This model forecasts more precise, detailed, frequent, and expanded water information than ever before at a national scale. Results from the NWM are intended for use by various communities to improve water-related decisions. However, given the large volume of data created by the NWM, researchers who aim to use NWM forecast data face challenges in retrieving, managing, and analyzing these data along with comparing them to observational data collected by the USGS. We developed a Python scripting language-based retrieval code that facilitates and automates the process of querying and retrieving forecast data from the NWM and streamflow data from the USGS. We demonstrated its use in a Jupyter IPython Notebook. The retrieval IPython notebook is a powerful web application that enabled us to create a live data retrieval code, visualizations, and descriptive metadata to visually demonstrate the data retrieval and direct comparison process at a local scale. The IPython Notebook is available on HydroShare for public use so that the users can easily read the code and implement it in their research without installing specific soft-wares or libraries on their local machines.