Amber Spackman Jones

A sensor network for high frequency estimation of water quality constituent fluxes using surrogates

Characterizing spatial and temporal variability in the fluxes and stores of water and water borne constituents is important in understanding the mechanisms and flow paths that carry constituents to a stream and through a watershed. High frequency data collected at multiple sites can be used to more effectively quantify spatial and temporal variability in water quality constituent fluxes than through the use of low frequency water quality grab sampling. However, for many constituents (e.g., sediment and phosphorus) in-situ sensor technology does not currently exist for making high frequency measurements of constituent concentrations. In this paper we describe how water quality measures such as turbidity or specific conductance, which can be measured in-situ with high frequency, can be used as surrogates for other water quality constituents that cannot economically be measured with high frequency to provide continuous time series of water quality constituent concentrations and fluxes. We describe the observing infrastructure required to make high frequency estimates of water quality constituent fluxes based on surrogate data at multiple sites within a sensor network supporting an environmental observatory. This includes the supporting sensor, communication, data management, and data storage and processing infrastructure. We then provide a case study implementation in the Little Bear River watershed of northern Utah, USA, where a wireless sensor network has been developed for estimating total phosphorus and total suspended solids fluxes using turbidity as a surrogate.

iSAW: Integrating Structure, Actors, and Water to Study Socio-Hydro-Ecological Systems

Urbanization, climate, and ecosystem change represent major challenges for managing water resources. Although water systems are complex, a need exists for a generalized representation of these systems to identify important components and linkages to guide scientific inquiry and aid water management. We developed an integrated Structure-Actor-Water framework (iSAW) to facilitate the understanding of and transitions to sustainable water systems. Our goal was to produce an interdisciplinary framework for water resources research that could address management challenges across scales (e.g., plot to region) and domains (e.g., water supply and quality, transitioning, and urban landscapes). The framework was designed to be generalizable across all human–environment systems, yet with sufficient detail and flexibility to be customized to specific cases. iSAW includes three major components: structure (natural, built, and social), actors (individual and organizational), and water (quality and quantity). Key linkages among these components include: (1) ecological/hydrologic processes, (2) ecosystem/geomorphic feedbacks, (3) planning, design, and policy, (4) perceptions, information, and experience, (5) resource access and risk, and (6) operational water use and management. We illustrate the flexibility and utility of the iSAW framework by applying it to two research and management problems: understanding urban water supply and demand in a changing climate and expanding use of green storm water infrastructure in a semi-arid environment. The applications demonstrate that a generalized conceptual model can identify important components and linkages in complex and diverse water systems and facilitate communication about those systems among researchers from diverse disciplines.

Open source software for visualization and quality control of continuous hydrologic and water quality sensor data

It is common for in situ hydrologic and water quality data to be collected at high frequencies and for extended durations. These data streams, which may also be collected across many monitoring sites require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a standard data model in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, an open source software application that allows users to query and export, visualize, and perform quality control post processing on time series of environmental observations data stored in an ODM database using automated Python scripting that records the corrections and adjustments made to data series in the quality control process and ensures data editing steps are traceable and reproducible. We developed a workflow for scripting of time series data quality control.ODM Tools automatically scripts manual quality control data edits in Python.ODM Tools preserves provenance of quality control edits.ODM Tools is open source and cross platform compatible.ODM Tools demonstrates options for desktop application development and deployment using Python.

A data management and publication workflow for a large-scale, heterogeneous sensor network.

It is common for hydrology researchers to collect data using in situ sensors at high frequencies, for extended durations, and with spatial distributions that produce data volumes requiring infrastructure for data storage, management, and sharing. The availability and utility of these data in addressing scientific questions related to water availability, water quality, and natural disasters relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into usable data products. It also depends on the ability of researchers to share and access the data in useable formats. In this paper, we describe a data management and publication workflow and software tools for research groups and sites conducting long-term monitoring using in situ sensors. Functionality includes the ability to track monitoring equipment inventory and events related to field maintenance. Linking this information to the observational data is imperative in ensuring the quality of sensor-based data products. We present these tools in the context of a case study for the innovative Urban Transitions and Aridregion Hydrosustainability (iUTAH) sensor network. The iUTAH monitoring network includes sensors at aquatic and terrestrial sites for continuous monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. We present the overall workflow we have developed for effectively transferring data from field monitoring sites to ultimate end-users and describe the software tools we have deployed for storing, managing, and sharing the sensor data. These tools are all open source and available for others to use.

Data Management Dimensions of Social Water Science: The iUTAH Experience

Integrating social and hydrologic sciences for understanding water systems is challenged by data management complexities. Contemporary mandates for open science and data sharing necessitate better understanding of the implications of social science data types. In the context of an interdisciplinary water research program that endeavors to integrate and share social science and biophysical data, we highlight the array of data types and issues associated with social water science. We present a multi-dimensional classification of social water science data that provides insight into data management considerations for each data type. Recommendations for cyberinfrastructure, planning, and policy are offered.

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

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.