Stephanie L. Reeder

Data visualization and analysis within a Hydrologic Information System: Integrating with the R statistical computing environment

This paper presents a prototype software system for visualization and analysis of hydrologic data that provides interoperability between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the R statistical computing environment. By linking these two systems within a single desktop software application, an integrated hydrologic data management and analysis environment has been created that simplifies the process used by scientists and engineers to find, access, organize, and analyze the hydrologic data needed in modeling and managing hydrologic and environmental systems. The implementation of this work is a software plug-in for the CUAHSI HIS HydroDesktop software system called HydroR. We describe the design, graphical user interface, and implementation of the HydroR plug-in. An example application of HydroR is presented in which total suspended solids concentrations are modeled for the Little Bear River using a regression developed from turbidity and total suspended solids observations downloaded from the CUAHSI HIS using HydroDesktop. Finally, we conclude with a summary of our experience in developing interoperability between HIS and R and suggest future developments that can extend the capabilities we have developed. We developed a software system that integrates the CUAHSI HIS and R.HydroR combines data discovery and management with analysis and visualization in a single software environment.HydroR reduces effort required to access data and transfer it into an analysis environment.HydroR promotes repeatable analyses and may reduce potential errors.

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.