Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Abstract

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.

Stream Microbial Community Structured by Trace Elements, Headwater Dispersal, and Large Reservoirs in Sub-Alpine and Urban Ecosystems

“…Our project was conducted in three northern Utah, USA watersheds, selected as part of the iUTAH (innovative Urban Transitions and Aridregion Hydro-sustainability) project, funded by the NSF Established Program to Stimulate Competitive Research (EPSCoR) (Jones et al, 2017). The watersheds include Red Butte Creek, Logan River, and Provo River (Figure 2).…”

“…We quantified a wide range of environmental variables concurrent with bacterial sampling to identify which parameters correlated to community changes (see section "Environmental Drivers of Bacterioplankton Communities"). We measured standard water quality parameters (pH, water temperature, dissolved oxygen, conductivity) using either a YSI Quatro multiparameter probe or YSI EXO2 sonde (data downloaded from iUTAH web services using the R package WaterML; Kadlec et al, 2015;Jones et al, 2017). We analyzed nutrients potentially related to bacterial activity, including total nitrogen (TN, persulfate oxidation digestion and cadmium reduction method), total phosphorus (TP, persulfate oxidation digestion and ascorbic acid method), nitrate (EPA 353.2), ammonia (EPA 350.1), and dissolved orthophosphate (EPA 365.1) colorimetrically on an autoanalyzer (Astoria-Pacific).…”

Enabling Collaborative Numerical Modeling in Earth Sciences using Knowledge Infrastructure

“…Open source cyber-infrastructure platforms for research publication are designed to facilitate the use of existing models by making input data and model code publicly available online and providing software tools for pre-and post-processing data, running models, sharing data, and formally publishing with a digital object identifier (Freeman, 2005;Atkins, 2003). Using recent examples in water monitoring (Horsburgh et al, 2017;Jones et al, 2017;Mihalevich, B.A. 2017), landslide modeling (Strauch et al, 2018), and data science (Freire et al, 2016), we identified three critical open-source technology practices supported by KI expected to scientific discoveries:…”

Low-Cost, Open-Source, and Low-Power: But What to Do With the Data?

“…Another major challenge that many projects and data managers face is how to consolidate data from a network of monitoring sites to a centralized location where they can be stored, archived, checked for quality, and then used for scientific analyses or shared with potential users (Rundel et al, 2009;Jones et al, 2017). Potential heterogeneity in the syntax and semantics of the data can complicate this step (Samourkasidis et al, 2018).…”