Journal
WATER RESOURCES RESEARCH
Volume 53, Issue 2, Pages 1735-1745Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2016WR019993
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Funding
- National Science Foundation [IIS 1344272, ICER-1517823]
- U.S. Geological Survey
- Directorate For Geosciences
- ICER [1517823] Funding Source: National Science Foundation
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Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information. Plain Language Summary The Water Quality Portal (http://www.waterqualitydata.us/) makes available more than 297,000,000 water quality records from all 50 states. Users can search for records at, upstream or downstream of their favorite river or lake location. Water quality Big Data is accompanied with opportunities and challenges for computing, data analysis, and interdisciplinary collaboration.
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