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Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions
Author(s) -
Jones Amber Spackman,
Aanderud Zachary T.,
Horsburgh Jeffery S.,
Eiriksson David P.,
Dastrup Dylan,
Cox Christopher,
Jones Scott B.,
Bowling David R.,
Carlisle Jonathan,
Carling Gregory T.,
Baker Michelle A.
Publication year - 2017
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12557
Subject(s) - environmental science , water quality , hydrology (agriculture) , aquatic ecosystem , turbidity , urbanization , impervious surface , ecology , geology , geotechnical engineering , biology
Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions ( GAMUT ) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH , nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐ a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.