Hydrologic Information Systems: Advancing Cyberinfrastructure for Environmental Observatories

Recently, community initiatives have emerged for the establishment of large-scale environmental observatories. Cyberinfrastructure is the backbone upon which these observatories will be built, and scientists\u27 ability to access and use the data collected within observatories to address research questions will depend on the successful implementation of cyberinfrastructure. The research described in this dissertation advances the cyberinfrastructure available for supporting environmental observatories. This has been accomplished through both development of new cyberinfrastructure components as well as through the demonstration and application of existing tools, with a specific focus on point observations data. The cyberinfrastructure that was developed and deployed to support collection, management, analysis, and publication of data generated by an environmental sensor network in the Little Bear River environmental observatory test bed is described, as is the sensor network design and deployment. Results of several analyses that demonstrate how high-frequency data enable identification of trends and analysis of physical, chemical, and biological behavior that would be impossible using traditional, low-frequency monitoring data are presented. This dissertation also illustrates how the cyberinfrastructure components demonstrated in the Little Bear River test bed have been integrated into a data publication system that is now supporting a nationwide network of 11 environmental observatory test bed sites, as well as other research sites within and outside of the United States. Enhancements to the infrastructure for research and education that are enabled by this research are impacting a diverse community, including the national community of researchers involved with prospective Water and Environmental Research Systems (WATERS) Network environmental observatories as well as other observatory efforts, research watersheds, and test beds. The results of this research provide insight into and potential solutions for some of the bottlenecks associated with design and implementation of cyberinfrastructure for observatory support

Effect of Alkyl Chain Length and Linker Atom on the Crystal Packing in 6,12-Dialkoxy- And 6,12-Dialkylsulfanyl-Benzo[1,2- b:4,5- b′]bis[ b]benzothiophenes

The effect of varying the chain length on the solid state conformation and packing of 6,12-dialkoxy- and 6,12-dialkylsulfanyl-benzo[1,2-b:4,5-b′]bis[b]benzothiophenes has been studied. The compounds were prepared by SNAr reaction of 6,12-difluorbenzo[1,2-b:4,5-b′]bis[b]benzothiophene with alkoxides or alkanethiolates derived from C7-C10 alcohols and alkanethiols. Single crystal X-ray diffraction analysis revealed that all but two compounds crystallize in the triclinic space group P1. Two compounds were obtained as monoclinic crystals with space group P21/c. The alkoxy substituted compounds adopted a molecular conformation with a step from the core and a gauche conformation about the C1′-C2′ bond placing the alkyl chains close to parallel with the pentacyclic arene ring system, whereas in the alkylsufanyl derivatives, the alkyl chains were arranged strongly deviated from the plane of the ring, with the sulfur atom antiperiplanar to C3′ of the alkyl chain. NMR measurement of T1 relaxation in CDCl3 showed both the alkoxy and alkylsulfanyl substituents to be freely rotating at ambient temperature in solution, indicating the orientation of the chains in the solid state was due to packing interactions during crystallization

ODM Tools Python: Open Source Software For Managing Continuous Sensor Data

Hydrologic and water quality data is being collected at high frequencies, for extended durations, and with spatial distributions that 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 framework in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, which is an open source software application that allows ODM users to query and export, visualize, and edit data stored in an ODM database. Previous versions of ODM Tools included functionality to export data series and associated metadata, plot and summarize single data series, generate derivative data series, and edit data series using a set of simple tools. We have developed a new version of ODM Tools in Python that adds a modernized graphical user interface, multiple platform support (Windows, Linux, and Mac), multiple database support (Microsoft SQL Server and MySQL), and support for automated scripting of quality control edits performed on data series through an integrated Python script editor and console. Scripting records the corrections and adjustments made to data series in the quality control process, ensuring that the steps are traceable and reproducible. Additional improvements to ODM Tools Python include customizable queries for data selection and export, the ability to plot multiple data series simultaneously with various plot types, and user-defined functions for data series editing and derivation

ODM Tools Python: Open Source Software For Managing Continuous Sensor Data

Hydrologic and water quality data is being collected at high frequencies, for extended durations, and with spatial distributions that 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 framework in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, which is an open source software application that allows ODM users to query and export, visualize, and edit data stored in an ODM database. Previous versions of ODM Tools included functionality to export data series and associated metadata, plot and summarize single data series, generate derivative data series, and edit data series using a set of simple tools. We have developed a new version of ODM Tools in Python that adds a modernized graphical user interface, multiple platform support (Windows, Linux, and Mac), multiple database support (Microsoft SQL Server and MySQL), and support for automated scripting of quality control edits performed on data series through an integrated Python script editor and console. Scripting records the corrections and adjustments made to data series in the quality control process, ensuring that the steps are traceable and reproducible. Additional improvements to ODM Tools Python include customizable queries for data selection and export, the ability to plot multiple data series simultaneously with various plot types, and user-defined functions for data series editing and derivation

Assessing Subjectivity in Environmental Sensor Data Post Processing via a Controlled Experiment

Collection of high resolution, in situ data using environmental sensors is common in hydrology and other environmental science domains. Sensors are subject to drift, fouling, and other factors that can affect the quality of the measurements and their subsequent use for scientific analyses. The process by which sensor data are reviewed to verify validity often requires making edits in post processing to generate approved datasets. This quality control process involves decisions by technicians, data managers, or data users on how to handle problematic data. In this study, an experiment was designed and conducted where multiple participants performed quality control post processing on the same datasets using consistent guidelines and tools to assess the effect of individual technician on the resulting datasets. The effect of technician experience and training was also assessed by conducting the same procedures with a group of novices unfamiliar with the data and compared results to those generated by a group of experienced technicians. Results showed greater variability between outcomes for experienced participants, which we attribute to novice participants\u27 reluctance to implement unfamiliar procedures that change data. The greatest variability between participants\u27 results was associated with calibration events for which users selected different methods and values by which to shift results. These corrections resulted in variability exceeding the range of manufacturer-reported sensor accuracy. To reduce quality control subjectivity and variability, we recommend that monitoring networks establish detailed quality control guidelines and consider a collaborative approach to quality control in which multiple technicians evaluate datasets prior to publication

A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters

We present a low-cost (≈$150) monitoring system for collecting high temporal resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically driven, positive displacement, residential water meters and can collect data at a variable time resolution interval. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah, for a period of over 1 month. Battery life for the device was estimated to be over 5 weeks with continuous data collection at a 4 s time interval. Data collected using this system, under ideal installation conditions, was within 2% of the volume recorded by the register of the meter on which they were installed. Results from field deployments are presented to demonstrate the accuracy, functionality, and applicability of the system. Results indicate that the device is capable of collecting data at a temporal resolution sufficient for identifying individual water use events and analyzing water use at coarser temporal resolutions. This system is of special interest for water end use studies, future projections of residential water use, water infrastructure design, and for advancing our understanding of water use timing and behavior. The system’s hardware design and software are open source, are available for potential reuse, and can be customized for specific research needs

Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs