Getting Hydroinformatic Tools From Research Into Practice: The Watershare Approach

The global Water Sector is faced with significant challenges, including, but not restricted to aging infrastructure, fluctuating populations, new pollutants, more stringent regulations and the need for benchmarking their performance. At the same time the Sector remains very fragmented and hence its R&D often doesn’t have the critical mass to develop the tools and models that are required. Although research institutes and academia do develop such tools, the road from the research environment to the first practical application often proves an insurmountable barrier. Watershare offers a platform for such a transition: Launched as an online placeholder for expert water-related tools, Watershare supports a closer collaboration between knowledge providers and knowledge consumers, within a quality assured environment. The Watershare concept encompasses a variety of benchmarked tools designed for areas like water quality and health, sustainability, water technology, asset design and management, and water systems. This paper briefly describes the main tools that are already in the toolbox and suggests possible gaps that need to be filled, matching them with key areas of concern in the water industry of tomorrow. The paper explains the way Watershare operates as a community of practice as well as one of collaborative research and knowledge co-creation. A knowledge management instrument that has been specifically designed to facilitate collective future visioning in an integrated and intelligent fashion is also presented and discussed. Synergies between the tools are explained and strategies supporting an organic, demand-driven, content creation addressing explicit needs of the Water Sector are outlined. It is suggested that this platform can act as a powerful vehicle to bring to market tools and innovation that has been up to date confined only to research prototypes while allowing for their application globally and providing Water Companies of different scales cost-effective access to high quality, benchmarked software tool

A Data Model to Manage Data for Water Resources Systems Modeling

Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata

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

Advancing Water Resources Systems Modeling Cyberinfrastructure to Enable Systematic Data Analysis, Modeling, and Comparisons

Water resources systems models aid in managing water resources holistically considering water, economic, energy, and environmental needs, among others. Developing such models require data that represent a water system’s physical and operational characteristics such as inflows, demands, reservoir storage, and release rules. However, such data is stored and described in different formats, metadata, and terminology. Therefore, Existing tools to store, query, and visualize modeling data are model, location, and dataset-specific, and developing such tools is time-consuming and requires programming experience. This dissertation presents an architecture and three software tools to enable researchers to more readily and consistently prepare and reuse data to develop, compare, and synthesize results from multiple models in a study area: (1) a generalized database design for consistent organization and storage of water resources datasets independent of study area or model, (2) software to extract data out of and populate data for any study area into the Water Evaluation and Planning system, and (3) software tools to visualize online, compare, and publish water management networks and their data for many models and study areas. The software tools are demonstrated using dozens of example and diverse local, regional, and national datasets from three watersheds for four models; the Bear and Weber Rivers in the USA and the Monterrey River in Mexico

Managing a Community Shared Vocabulary for Hydrologic Observations

The ability to discover and integrate data from multiple sources, projects, and research efforts is critical as scientists continue to investigate complex hydrologic processes at expanding spatial and temporal scales. Until recently, syntactic and semantic heterogeneity in data from different sources made data discovery and integration difficult. The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) was developed to improve access to hydrologic data. A major semantic challenge related to data sharing and publication arose in development of the HIS. No accepted vocabulary existed within the hydrology research community for describing hydrologic observations, making it difficult to discover and synthesize data from multiple research groups even if access to the data was not a barrier. Additionally, the hydrology research community relies heavily on data collected or assembled by government agencies such as USGS and USEPA, each of which has its own semantics for describing observations. This semantic heterogeneity across data sources was a challenge in developing tools that support data discovery and access across multiple hydrologic data sources by time, geographic region, measured variable, data collection method, etc. This paper describes a community shared vocabulary and its supporting management tools that can be used by data publishers to populate metadata describing hydrologic observations to ensure that data from multiple sources published within the CUAHSI HIS are semantically consistent. We also describe how the CUAHSI HIS mediates across terms in the community shared vocabulary and terms used by government agencies to support discovery and integration of datasets published by both academic researchers and government agencies