Water Data Science: Data Driven Techniques, Training, and Tools for Improved Management of High Frequency Water Resources Data

Electronic sensors can measure water and climate conditions at high frequency and generate large quantities of observed data. This work addresses data management challenges associated with the volume and complexity of high frequency water data. We developed techniques for automatically reviewing data, created materials for training water data managers, and explored existing and emerging technologies for sensor data management. Data collected by sensors often include errors due to sensor failure or environmental conditions that need to be removed, labeled, or corrected before the data can be used for analysis. Manual review and correction of these data can be tedious and time consuming. To help automate these tasks, we developed a computer program that automatically checks the data for mistakes and attempts to fix them. This tool has the potential to save time and effort and is available to scientists and practitioners who use sensors to monitor water. Scientists may lack skillsets for working with sensor data because traditional engineering or science courses do not address how work with complex data with modern technology. We surveyed and interviewed instructors who teach courses related to “hydroinformatics” or “water data science” to understand challenges in incorporating data science techniques and tools into water resources teaching. Based on their feedback, we created educational materials that demonstrate how the articulated challenges can be effectively addressed to provide high-quality instruction. These materials are available online for students and teachers. In addition to skills for working with sensor data, scientists and engineers need tools for storing, managing, and sharing these data. Hydrologic information systems (HIS) help manage the data collected using sensors. HIS make sure that data can be effectively used by providing the computer infrastructure to get data from sensors in the field to secure data storage and then into the hands of scientists and others who use them. This work describes the evolution of software and standards that comprise HIS. We present the main components of HIS, describe currently available systems and gaps in technology or functionality, and then discuss opportunities for improved infrastructure that would make sensor data easier to collect, manage, and use. In short, we are trying to make sure that sensor data are good and useful; we’re helping instructors teach prospective data collectors and users about water and data; and we are making sure that the systems that enable collection, storage, management, and use of the data work smoothly