Jeffery S. Horsburgh, Ph.D. - US grants

0610075
Stevens
This project is one of several "test-bed" projects funded to evaluate issues that need to be resolved for cost-effective design of environmental observatories for research on interactions among hydrological, physical, chemical, and biological processes at the watershed scale. This project seeks to develop better and simpler methods to quantify pollutant loadings in storm-water runoff from urban areas. The PIs will use Bayesian modeling techniques to develop relationships between easily measured surrogates and key water quality variables and will evaluate ways to optimize the timing and frequency of sampling to minimize pollutant loading uncertainties. This topic is of considerable interest nationwide because of the inherent costs of accurately measuring loadings from sources like urban storm water, which are subject to high frequency variations in both concentrations and flows. The project will involve both under-graduate and graduate students and includes a broad set of outreach activities to researchers, students and practitioners working water pollution problems in the Great Salt Lake Basin. Results of the project could have important practical applications nationally in the area of storm-water management.

NSF 1038973

WSC CATEGORY 1: Hydrologic and Ecological Impacts of Changes in Human Water Resource Management in Response to Climate Change and Urbanization.

Human responses to rapid demographic and climatic changes in the Intermountain West have major ramifications for how water flows through this landscape, which in turn affect patterns of water availability, water quality, and the provision of ecological services. Existing scientific understanding of hydrologic and ecological processes in this region tends to be fragmented by discipline, fails to account for many important human elements, and lacks an integrated social-engineering-geoscience-ecological theoretical framework. This project supports a team of interdisciplinary scientists and applied water resource managers to develop a scientific research plan to study complex water systems in the transitioning irrigated landscapes of this Intermountain region. A core scientific question underlying the project is: "What are the intended and unintended consequences of changes in water availability, water allocation and water use efficiency in response to anticipated climate change and urbanization?" To answer that question, the project will produce an analytical framework for modeling patterns of climate change, human water use and their impacts on local and watershed-scale hydrologic processes and ecological systems. A major focus is the identification of ways to integrate human infrastructure and behavior within existing hydrologic and ecological models, and the interactions among the various components. Project activities include weekly workgroup sessions for researchers and water resource managers to share expertise and sensitize team members to the accomplishments and limitations of one another's work. The project also uses in-depth interdisciplinary research methodology workshops to build a unified vision among team members that will guide future research design, data analysis, and modeling approaches. Field trips and meetings in local watersheds help ensure that the research plan is designed with full awareness of the complex social, economic, and political realities that constrain water resource management strategies in this landscape. A key project output will include a synthesis paper that outlines plans for a water systems research observatory and modeling program that integrates human, hydrologic, engineering and ecological components.

Projected climate change and population growth in the Intermountain West will require intensive management of water resources. This project will develop an improved science framework for understanding the impacts of management decisions on water availability, water quality, and ecosystem health in the region. By supporting interdisciplinary collaboration and integrating human and natural science models, the project contributes to better scientific understanding of complex water system dynamics. Meanwhile, systematic interactions between scientists and stakeholders ensure that future research will address the information needs and real-world constraints of applied water resource managers. The project provides important educational benefits in two ways. First, graduate students are receiving valuable training and experience by collaborating in an interdisciplinary work environment and helping design an integrated water systems research plan. Second, water resource managers and users are gaining a deeper understanding of the strengths and gaps in the existing science base, and are improving their ability to work with regional scientists.

1224638
Horsburgh

This EAR Geoinformatics Program grant supports a two year project to develop a community information model and related software to enable web based interoperability of earth observations derived from sensors and samples that span now discrete data and informatics initiatives for multiple communities. The system would target specific existing web service data repositories in order to demonstrate how the information model can support federation of earth observations data across multiple data publication systems. Specific repositories include the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), EarthChem, the Critical Zone Observatory (CZO) Integrated Data Management System (CZOData), the Integrated Ocean Observing System (IOOS) and the Data Observations Network for Earth (DataONE). The plan calls for collaboration with the related Pis of these projects (through subward support) to improve capture, sharing, and archival these data and associated metadata by building ontologies for describing, encoding and publishing data in common formats to allow interoperability. The plan involves community workshops to engage stakeholders and students in the design of the model. The model would incorporate international standards for data description and publishing utilizing Open Geospataial Consortium standards and domain specific markup languages. It is hoped that the results will feed directly into the larger EarthCube cyberinfrastructure initiative.

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ABSTRACT
This EAGER award creates an interoperability test bed to identify the components of an effective layered architecture for geoscience and environmental science research. In a layered architecture, every layer consists of different technologies, each of which uses different interaction protocols. The proposed project will examine a wide variety of existing technologies in terms of their effectiveness in working across present data silos. These technologies include data grids, workflow systems, policy management systems, web visualization services, and security protocols that work with various repository catalogs. Project goals are focused on developing cyberinfrastructure tools and approaches that allow geoscience data repositories to enable new science and more effectively make their data holdings discoverable and available to the public. Essential elements of the project include the collection and comparision of various approaches and existing tools to check effectiveness in handling and integrating geoscience data, and by automating processes needed to integrate various databases and data types. The project is led by a team of experts in cyberinfrastructure and geoscience data management and employs a spiral softwar3ee development approach. Broader impacts of the work include building infrastructure for science in order to facilitate data-enabled science in the geosciences. It will also produce results that are likely to be applicable to fields outside of the geosciences. The effort supports a larger NSF effort to establish a new paradigm in the development of an integrative and interoperable data and knowledge management system for the geosciences for a new NSF initiative called EarthCube.

1552444 (Horsburgh)

To overcome challenges in using smart water metering as an effective tool for sustainably managing urban water supplies, an integrated research and education plan called Cyberinfrastructure for Intelligent Water Supply (CIWS) is being implemented. CIWS will investigate novel cyberinfrastructure and analytics to advance smart metering technology, enable detailed characterization of residential water use behavior, and build the scientific data and knowledge base for sustainably managing urban water supplies. CIWS will enable transitioning of existing conventional "dumb" water meters (the vast majority of water meters in use today) into low-cost (~$100) "smart" and computationally-capable devices that can collect high frequency data, use onboard processing capability to "shrink" the collected data by extracting the timing and volume of individual water end uses, and then transmit actionable data products to water managers for analysis - all without replacing or affecting the functionality of the meter. CIWS will be capable of closing critical gaps in understanding of and ability to quantify water use behavior at the household and water system level. It will also enable identification of alternative water management strategies and opportunities for water conservation and increased efficiency.

CIWS and associated residential water use studies offer a way to characterize residential water use and generate new knowledge about: 1) how water use behavior varies across socio-demographic groups and neighborhood types; 2) the timing of water demand and how this information can be used by water providers to ensure water availability and efficiency, plan for related energy demand, and improve customer satisfaction; and 3) how water consumers change their behavior given detailed information about their water use. This information is critical in identifying opportunities for conservation, forecasting demand, and determining how water use patterns may change over time in response to population growth, demographic shifts, and technology improvements. CIWS will advance understanding of water use behavior, the cyberinfrastructure for smart metering, and the pool of "cyber-savvy" professionals and students capable of implementing smart metering, all of which are critical for realizing the promises of smart metering. Mentorship will be provided for a new generation of engineers and scientists who will receive training and engage in research that prepares them to leverage new cyberinfrastructure. Water users will be directly engaged in data collection and information transfer. Hundreds of USU students will engage in a campus "Water Wars" competition aimed at water conservation and sustainability using cyberinfrastructure developed by this project. Logan City and USU Facilities will be integral partners in data collection. Finally, graduate and undergraduate students will be engaged in a visualization challenge using continuous flow data from residential water meters. These efforts will involve participants from underrepresented groups in undergraduate research through targeted recruiting using existing programs at USU.

Researchers across the country and around the world expend tremendous resources to gather and analyze vast stores of hydrologic data and populate a myriad of models to better understand hydrologic phenomena and find solutions to vexing water problems. Each of those researchers has limited money, time, computational capacity, data storage, and ability to put that data to productive use. What if they could combine their efforts to make collaboration easier? What if those collected data sets and processed model outputs could be used collaboratively to help advance hydrologic understanding beyond their original purpose? HydroShare is a system to advance hydrologic science by enabling the scientific community to more easily and freely share products resulting from their research, not just the scientific publication summarizing a study, but also the data and models used to create the scientific publication. HydroShare supports the sharing and publication of hydrologic data and models. This capability is necessary for community model development, execution, and evaluation and to improve reproducibility and community trust in scientific findings through transparency. As a platform for collaboration and running models on advanced computational infrastructure, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. HydroShare is designed to help researchers easily meet the sharing requirements of data management plans while at the same time providing value added functionality that makes metadata capture more effective and helps researchers improve their work productivity. This project will extend the capabilities of the HydroShare cyberinfrastructure to enhance support for scientific methods, advance the social capabilities of HydroShare to enable improved collaborative research, integrate with 3rd party consumer data storage systems to provide more flexible and sustainable data storage. and establish an application testing environment to empower researchers to develop their own computer programs to act on and work with data in HydroShare. Empowering HydroShare users with the ability to rapidly develop web application programs opens the door to unforeseen, innovative combinations of data and models. WRF-Hydro, the framework for the NOAA National Water Model, will be used as a use case for collaboration on model development. Since WRF-Hydro is used by NOAA as part of the National Water Model (NWM), this collaboration opens possibilities for transfer of research to operations. Collectively, this functionality will provide a computing framework for transforming the practice of broad science communities to leverage advances in data science and computation and accelerate discovery.

HydroShare is a system for sharing hydrologic data and models aimed at giving hydrologists the cyberinfrastructure needed to manage data, innovate and collaborate in research to solve water problems. It addresses the challenges of sharing data and hydrologic models to support collaboration and reproducible hydrologic science through the publication of hydrologic data and models. With HydroShare users can: (1) share data and models with colleagues; (2) manage who has access to shared content; (3) share, access, visualize and manipulate a broad set of hydrologic data types and models; (4) use the web services interface to program automated and client access; (5) publish data and models to meet the requirements of research project data management plans; (6) discover and access data and models published by others; and (7) use web apps to visualize, analyze, and run models on data. This project will extend the capabilities of HydroShare to: (1) enhance support for scientific methods enabling systematic data and model analysis and hypothesis testing; (2) advance the social capabilities of HydroShare to enable improved collaborative research; (3) integrate with 3rd party consumer data storage systems to provide more flexible and sustainable data storage; and (4) establish an application testing environment to empower researchers to develop their own computer programs to act on and work with data in HydroShare. Under development since 2012 and first released in 2014, HydroShare supports the sharing and publication of hydrologic data and models. This capability is necessary for community model development, execution, and evaluation. As a platform for collaboration and cloud based computation on network servers remote from the user, HydroShare enhances the capability for data intensive research in hydrology and other aligned sciences. HydroShare is innovative from a computer science and CI perspective in the way computation and data sharing are framed as a network computing platform that integrates data storage, organization, discovery, and programmable actions through web applications (web apps). Support for these three key elements of computation allows researchers to easily employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving ability to collaborate and reproduce results. The generation of new understanding, through integration of information from multiple sources and reuse and collaborative enrichment of research data and models, will be enhanced. Structured and systematic model process intercomparisons and alternative hypothesis testing will be enabled, bringing, through user friendly CI, the latest thinking in advancing hydrologic modeling to a broad community of earth science researchers, thereby transforming research practices and the knowledge generated from this research. Interoperability with consumer cloud storage will greatly ease entry of content into HydroShare and support its sustainability. This meshing of the rigorous metadata model of HydroShare with consumer file sharing will enhance reproducibility as well as provide an innovative mechanism for sharing and collaboration. Empowering HydroShare users with the ability to rapidly develop web apps opens the door to unforeseen, innovative combinations of data and models. WRF- Hydro will be used as a use case for collaboration on model development. WRF-Hydro provides a reach-based high resolution representation of hydrologic processes, and offers the potential to bring together scientists working at scales from research catchments on the order of 1 to 100s of square kilometers as well as those working at regional to continental scales and cut across disciplines from environmental engineering to aquatic ecologists. Since WRF-Hydro is used by NOAA as part of the National Water Model (NWM), this collaboration opens possibilities for transfer of research to operations. This project will adapt current best practices in CI for interoperability and extensibility to serve this multidisciplinary community of scientists. HydroShare has already had a broader impact, with documented rapid growth in use and uptake by other projects including in EarthCube. It will become sustainable community CI through operation as part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Water Data Center (WDC) facility. The use of WRF- Hydro/NWM, as a driving use case, will advance CI for community based model improvement. Through the Summer Young Innovators Program at the National Water Center (NWC), supported by the National Weather Service (NWS) and operated by CUAHSI, a pathway already exists to translate research findings to the operational needs of federal agencies participating in the NWC. HydroShare already touches a broad and diverse community, with user base including Native American tribes, hydrologic science students, and faculty researchers across the U.S. This proposal builds on the success of HydroShare to extend its capabilities and broaden model hypothesis testing, collaborative data sharing, and open app development across earth science research and education.

There is an urgent need to understand the impacts of severe flooding and infrastructure damage on public health after natural disasters. One limitation to effective disaster response is easy and rapid access to diverse information about available resources, community resource needs, baseline and current environmental conditions. This project aims to expand access to environmental and drinking water quality disaster response and recovery data in a publicly available format using a widely used collaborative online sharing platform named HydroShare. Curating a central repository of assembled data has the potential to greatly facilitate coordinated disaster responses of all types, and improve the monitoring of the recovery process. The project team will prototype this system with an assessment of drinking water, environment, and public health concerns unique to Puerto Rico in the aftermath of Hurricane Maria. By working directly with public water utilities, the project team intends to characterize and map the severity of impaired water resources and distribution systems in Puerto Rico, inform communities about how to protect themselves against hazards specific to their water, and to contribute to rebuilding so the nation is better prepared for future hurricanes. Developing cyber and social infrastructure to understand the dynamics of drinking water contamination after natural disasters will improve disaster preparedness and response, and contribute to efforts across the nation and the world to build for a resilient future.

Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. This project aims to advance open-source software infrastructure to support scientific investigation and data-driven decision making with a prototype system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. The widespread disruption of water treatment processes and uncertain drinking water quality within distribution systems in Puerto Rico poses risk to human health. However, there is no existing digital infrastructure to scientifically determine the impacts of the hurricane to inform a response to the crisis. After every natural disaster, including hurricane Maria, elementary questions on how to provide high quality water supplies and support basic human health are difficult to answer. This project will archive and make accessible data on environmental variables unique to Puerto Rico and Hurricane Maria, damage caused by the storm, and will begin to address time sensitive needs of citizens. By working directly with drinking water utilities to collect samples of biological and inorganic drinking water quality, this project aims to generate understanding and awareness of the degree to which drinking water systems were impacted by Hurricane Maria and the status of drinking water infrastructure and emergency recovery in Puerto Rico after the storm. The goal of this project is to advance understanding of how the severity of a hazard to human health (e.g., no access to safe culinary water) is related to the sophistication, connectivity, and operations of the physical and related digital infrastructure systems. By rapidly collecting data in the early stages of recovery, the team plans to test the design of an integrated cyberinfrastructure system to increase the accessibility of environmental and health data for understanding the impacts from hurricane-related natural disasters. The team will test and stress the CUAHSI HydroShare data publication mechanisms and capabilities to (1) assess the spatial and temporal presence of waterborne pathogens in public water systems impacted by a natural disaster, (2) demonstrate usability of HydroShare as a clearinghouse to centralize selected datasets related to Hurricane Maria, and (3) develop a prototype cyberinfrastructure to assess environmental conditions and public health impacted by natural disasters. By rapidly collecting data in the early stages of recovery, The team plans to test the design of an integrated cyberinfrastructure system to increase the accessibility of environmental and health data for understanding the impacts from hurricane-related natural disasters. This work will develop a prototype of a software infrastructure system to advance understanding of how data-driven information can reduce the impacts of natural disaster and serve as a platform for future research. The project thus serves to not only document post-disaster conditions, but develops a process to track the impact of recovery over time, as monitored through health, power availability and water quality.

Scientific challenges in hydrology and water resources such as understanding impacts of variable climate, sustainability of water supply with population growth and land use change, and impacts of hydrologic change on ecosystems and humans are increasingly data intensive. The volume of data produced by environmental scientists to study hydrologic systems requires advanced software tools for effective data visualization, analysis, and modeling. Scientists spend much of their time accessing, organizing, and preparing datasets for analyses, which can be a barrier to efficient analyses and hinders scientific inquiries and advances. This project will develop new software that will enhance scientists' ability to apply advanced data visualization and analysis methods (collectively referred to as "data science" methods) in the hydrology and water resources domain. The project will promote standardized software tools and data formats to help scientists enhance the consistency, share-ability, and reproducibility of the analyses they perform - all of which are important in building trust in scientific results. The software developed in the project will make data loading and organization for analysis easier, reducing the time spent by scientists in choosing appropriate data structures and writing computer code to read and parse data. It will enable users to automatically retrieve data from the HydroShare system, which is a hydrology domain data repository, as well as from important national water data sources like the United States Geological Survey's National Water Information System. The software will automatically load data from these sources into standardized and high performance data structures targeted to specific scientific data types and that integrate with visualization, analysis, and other data science capabilities commonly used by scientists in the hydrology and water resources domains. The project will also reduce the technical burden for water scientists associated with creating a computational environment within which to execute their analyses by installing and maintaining the Python packages developed within the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare-linked JupyterHub environment. Finally, the project will demonstrate the functionality and use of the software by producing a set of educational modules based on real water-data science applications that provide a specific mechanism for delivering the software to the community and promoting its use in classroom and research environments.

Scientific and related management challenges in the water domain are inherently multi-disciplinary, requiring synthesis of data of multiple types from multiple domains. Many data manipulation, visualization, and analysis tasks performed by water scientists are difficult because (1) datasets are becoming larger and more complex; (2) standard data formats for common data types are not always agreed upon, and, when they are, they are not always mapped to an efficient structure for visualization and/or analysis within an analytical environment; and (3) water scientists generally lack training in data intensive scientific methods that would enable them to use new and existing tools to efficiently tackle large and complex datasets. This project will advance Data Science and Analytics for Water (DSAW) by developing: (1) an advanced object data model that maps common water-related data types to high performance data structures within the object-oriented Python language and analytical environment based upon standard file, data, and content types established by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare system; (2) two new Python packages that enable users to write Python code for automating retrieval of desired water data, loading it into high performance memory objects specified by the object data model designed in the project, and performing analysis in a reproducible way that can be shared, collaborated around, and formally published for reuse. The project will use domain-specific data science applications to demonstrate how the new Python packages can be paired with the powerful data science capabilities of existing Python packages like Pandas, numpy, and scikit-learn to develop advanced analytical workflows within cloud and desktop environments. The project aims to extend the data access, collaboration, and archival capabilities of the HydroShare data and model repository and promote its use as a platform for reproducible water-data science. The project also aims to overcome barriers associated with accessing, organizing, and preparing datasets for data science intensive analyses. Overcoming these barriers will be an enabler for transforming scientific inquiries and advancing application of data science methods in the hydrology and water resources domains.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

ABSTRACT

The Critical Zone is loosely defined as the region of Earth from the top of the bedrock to the top of the treetops. This is the region that supports all terrestrial life and is constantly evolving as rock, soil, water, air, and living organisms interact to regulate the Earth?s environment. This project will establish the coordinating hub of the Critical-Zone Collaborative Network, which is designed to continue the research advances in the science of the Critical Zone that NSF has supported for the past decade. The Network Coordinating Hub will provide cyberinfrastructure, data management, and community services to support the Critical Zone Thematic Clusters, which are research collaboratiions pursuing new and practical knowledge concerning the functions of the Earth?s surface systems.

The Network Coordinating Hub will: (1) enhance and integrate existing data services operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) and EarthChem to support the Critical-Zone science community; (2) support scientific discovery through community synthesis activities and via access to community data and modeling cyberinfrastructure; (3) broaden the Critical-Zone community and the impact of Critical-Zone science through outreach and education activities; and (4) enhance collaboration among the Critical Zone Thematic Clusters through coordination, sharing, and community events.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.