View previously supported collaborations and partnerships.
Interested in collaborating on a project with CUAHSI? Contact firstname.lastname@example.org.
Building Infrastructure to Prevent Disasters
On September 20, 2017, Hurricane Maria made landfall in Puerto Rico as a first Category 4 storm. With sustained winds of 155 miles per hour and three times the rainfall of Hurricane Harvey, Hurricane Maria decimated Puerto Rico’s infrastructure, putting 3.1 million people without power or access to clean water. Researchers now seek to develop and advance open-source software infrastructure to support scientific investigation and data-driven decision-making following natural disasters like Hurricane Maria, with a pilot project focused on drinking water.
This project was initiated with the support of the National Science Foundation Software Infrastructure for Sustained Innovation (SI2) program with a Rapid Response Research grant (RAPID; 1810647). Related funding is acknowledged on the Github HydroShare Puerto Rico Water Studies Repository Wiki.
Widespread disruption of drinking water distribution systems in Puerto Rico following Hurricane Maria poses a significant risk to human health. Thus, it is necessary to strategically archive and disseminate water resources data relevant to public health and environmental concerns. This project seeks to design and test a prototype scientific cyberinfrastructure that integrates existing hardware and software platforms for the storage and curation of water resources data and analytical tools following natural disasters that cause loss of public utilities.
Project management details can be found on the Github HydroShare Puerto Rico Water Studies Repository Wiki.
1. Field Collection of Spatially- and Temporally-Resolved Water Quality and Public Health Data. We will execute a drinking water sampling campaign to quantify microbial and inorganic contaminants in various water distribution system networks across Puerto Rico and develop educational materials for the interpretation of this data.
2. Demonstrate the usability of HydroShare as a centralized cyberinfrastructure to house selected datasets related to disaster response. We will assemble baseline and hurricane recovery datasets in HydroShare, an online, collaborative hydrologic information system.
3. Use the prototype cyberinfrastructure of develop testable hypotheses about earth system processes and population health trends. Integrated data may be used to drive analytical tools like the Observation Data Model (ODM2) and Landlab. Research products based on our cyberinfrastructure will foster streamlined disaster preparedness, recovery efforts, and population health research.
This project aims to promote open and Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, following Stall et al., 2017. Especially in Earth and space sciences, data curation and documentation practices are uneven and inconsistent. We will strive to provide clean, curated data with adequate documentation and easy discoverability.
HydroShare will serve as the archival system for project data, which will promote the sharing and reuse of project data. All data will be publicly and freely available using a Creative Commons License, where applicable. All resources will be described with metadata that conform to the Dublin Core metadata standard (DCMI 2012) and the Open Archives Initiatives’ Object Reuse and Exchange (OAI-ORE) standard (Lagoze et al., 2008). Curated research products (e.g., datasets, models, etc.) will be citable using a digital object identifier (DOI). All source code developed in this project will be openly shared in GitHub repositories associated with HydroShare.
Contact / Get Involved:
To share data you have for any of these hurricanes, add it to HydroShare, and use the keyword “Maria2017” to associate it with this project.
To publish data:
You may formally publish your data in HydroShare which assigns it a citable digital object identifier. It will be reviewed for completeness by CUAHSI. Join the CUAHSI 2017 Hurricane Data Community group or the Puerto Rico Water Studies Group in Hydroshare and share your data with these groups to make it visible to group members. If you would like your data to be added to one of the curated collections above, contact email@example.com. CUAHSI will then forward the request to the collection curators.
Contact firstname.lastname@example.org with questions.
CUAHSI works with university researchers and stakeholders to support and expand community projects. This project is led by the contributors listed below.
Christina Bandaragoda1, Jimmy Phuong2, Sean Mooney1, Kari Stephens1, Erkan Istanbulluoglu3, Julia Hart3, Kelsey Pieper4, William Rhoads4, Marc Edwards4, Amy Pruden5, Virginia Riquelme5, Ishi Keenum5, Ben Davis5, Matthew Blair5, Greg House5, Jerad Bales6, Emily Clark7, Liza Brazil8, Miguel Leon9, William G McDowell10, Jeffery S Horsburgh11, David G Tarboton11, Amber Spackman Jones11, Eric Hutton12, Gregory E Tucker13, Lynn McCready14, Scott Dale Peckham14, W. Christopher Lenhardt15, Ray Idaszak15, Graciela Ramirez-Toro16, Melitza Crespo Medina16, Tim Ferguson-Sauder17.
(1)University of Washington, Seattle, WA, United States, (2)University of Washington Seattle Campus, Biomedical and Health Informatics, Seattle, WA, United States, (3)Univ of Washington, Seattle, WA, United States, (4)Virginia Tech, Blacksburg, VA, United States, (5)Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, (6)CUAHSI, Cambridge, MA, United States, (7)CUAHSI, Cambridge, MA, United States, (8)CUAHSI, Cambridge, MA United States, (9)University of Pennsylvania, Earth & Environmental Science, Philadelphia, PA, United States, (10)Edmonton, AB, Canada, (11)Utah State University, Logan, UT, United States, (12)Community Surface Dynamics Modeling System, Boulder, CO, United States, (13)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (14)University of Colorado, Boulder, CO, United States, (15) Renaissance Computing Institute, Chapel Hill, NC, United States, (16) Center for Environmental Education Conservation and Research of Inter American University of Puerto Rico, (17) Olin College.
Hurricanes 2017 Data Archive
The 2017 Atlantic hurricane season was one of the most catastrophic ever. Hurricanes Harvey, Irma, and Maria devastated parts of the regions they hit. CUAHSI is hosting community data from 2017 Hurricanes Harvey, Irma and Maria to encourage collaborative research to better understand and plan for similar events in the future. Hurricane Harvey produced the largest consecutive 5-day precipitation total ever recorded in the United States. Over 50 inches of rain fell in some places. Flooding and associated damage in the greater Houston area was extensive, with the storm extending across Texas and neighboring states. Shortly after Harvey struck, Hurricane Irma cut across the Caribbean, Florida, and nearby states, also causing widespread devastation and flooding. This was followed by Hurricane Maria that devastated Puerto Rico. In the days following these events, basic questions about flood inundation depths, extents, and impacts could not be answered because we currently lack the ability to collect important data and the ability to assimilate available data into decision relevant information.
Collections of data from these hurricanes have been established within the CUAHSI HydroShare community data repository to make them easily accessible for research. HydroShare is the archival system for results from this project. Curated research products published in HydroShare are citable for use in peer-reviewed journal articles, conference presentations and proceedings, and other formal publications.
Hurricane Data can be accessed in HydroShare by searching one of the keywords Harvey2017, Irma2017, Maria 2017. You can also join the CUAHSI 2017 Hurricane Data Community group in Hydroshare to see all the data shared with this group. Curated collections of data are in the following HydroShare resources:
Hurricane Harvey Collection
- Harvey 2017 Archive Story Map (contextual access to the data)
- Data files on HydroShare (file browser access to the data)
Hurricane Irma Collection
- Irma 2017 Archive Story Map (contextual access to the data)
- Data files on HydroShare (file browser access to the data)
Hurricane Maria Collection
- Maria 2017 Archive Story Map (contextual access to the data)
- Data files on Hydroshare (file browser access to the data)
This page describes the collections for hurricanes Harvey and Irma established through a U.S. National Science Foundation RAPID grant (Proposal Number: 1761673). A separate grant Proposal Number 1810647 has established the collection for hurricane Maria.
These collections serve as a clearing house and go to point for Harvey and Irma hydrologic data and models to foster collaborative research to better understand these events, and to facilitate re-use of the data by multiple researchers.
The data is intended to support evaluation and improvement of operational models as well as advances in research models of the hydrologic impacts of hurricanes. They will also support methods for data assimilation to refine forecasts and better serve emergency response needs and allow for advancement in coupling of upland flood and tidal surge models. Additionally, the diversity and scale of data represented provide a challenging use case to drive ongoing development and innovation in HydroShare cyberinfrastructure, addressing and responding to community needs.
The objective of the project was to assemble, document, and archive data from hurricanes Harvey and Irma within HydroShare to make them easily accessible for the broad hydrologic science community. The following specific objectives guided the work:
- Create a community repository for hydrologic data from Hurricanes Harvey and Irma
- Assemble, document, and archive hydrologic data from these hurricanes in Hydroshare
- Advance computer and information systems technology for the organization, integration and analysis of diverse data types from multiple sources
- Create a definitive and authoritative source of data that can be used for future hydrology, modeling, and forecasting case studies.
Contact / Get Involved
Please contact email@example.com with questions.
To share data you have for any of these hurricanes, add it to HydroShare, and use one of the keywords, Harvey2017, Irma2017, Maria2017 to associate it with these events.
You may formally publish your data in HydroShare which assigns it a citable digital object identifier. It will be reviewed for completeness by CUAHSI. Join the CUAHSI 2017 Hurricane Data Community group in Hydroshare and share your data with this group to make it visible to group members. If you would like your data to be added to one of the curated collections above, contact firstname.lastname@example.org. CUAHSI will then forward the request to the collection curators.
- David Arctur, University of Texas at Austin
- David Tarboton, Utah State University
- Christina Bandaragoda, University of Washington
- David Maidment, University of Texas at Austin
- Ray Idaszak, RENCI, University of North Carolina at Chapel Hill
- Liza Brazil, CUAHSI
- Martin Seul, CUAHSI
- Tony Castronova, CUAHSI
- Erika Boghici, University of Texas at Austin
INSPIRE – CUAHSI-NCAR Collaboration to Improve Hydrology in ESMs
This project is funded by an NSF INSPIRE grant to facilitate collaborations between hydrologists/CZ scientists and global Earth system modelers to improve hydrologic process representations in ESMs.
Our limited ability to predict the water cycle in weather, climate, and Earth System models is at least partially attributed to inadequate representations of the land branch of the water cycle. The hydrology community has traditionally focused on plot, hillslope and catchment scales, and thus their collective wisdom has not been fully tapped to advance large-scale water cycle research. To bridge this community and knowledge gap, we began a collaborative project between CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.) representing the academic hydrology community, and NCAR (National Center for Atmospheric Research) leading weather, climate and ESM development and applications.
The heart of the project is community synthesis activities, through a series of workshops, meetings at AGU, white paper contributions, webinars, writing synthesis papers with recommendations, and experimenting/testing different approaches to represent hillslope hydrology concepts in the NCAR version of the Community Land Model (CLM).
A first synthesis paper, led by Martyn Clark at NCAR, reviewed the hydrologic schemes in current global climate and ESMs, articulated the scientific needs for a community level effort to improve some of the elements in current schemes, and argued for the need for systematic model testing ad benchmarking.
Clark, M. P., Y. Fan, D. M. Lawrence, J. C. Adam, D. Bolster, D. J. Gochis, R. P. Hooper, M. Kumar, L. R. Leung, D. S. Mackay, R. M. Maxwell, C. Shen, S. C. Swenson, and X. Zeng (2015), Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, doi:10.1002/2015WR017096.
In the fall of 2015, we held a first synthesis workshop at NCAR with >40 scientists from the hydrology and atmospheric science communities; the objective was to develop state-of-science synthesis of hydrologic processes and recommend best ways to represent them in large-scale models. A major recommendation from the workshop is to test the importance of lateral surface and groundwater flow from uplands to lowlands to simulating evapotranspiration (ET) fluxes.
Preliminary tests in CLM, using Reynolds Creek CZO as the test bed, suggest implementing lateral flow improves the simulated ET fluxes in the dry season, allowing continued ET from the riparian forests as observed by the flux tower. These results were presented to the community at the workshops held at the 2016 and 2017 AGU meeting.
After the 2016 AGU meeting, CUAHSI initiated a call for white paper contributions from the broader community, and over 20 contributions were received. Based on these contributions, CUAHSI facilitated a series of five webinars, where the white paper contributors presented their approaches to represent hillslope hydrology in large ESM grids. These activities have led to the development of a second synthesis paper to prioritize the processes that are deemed most basic and well understood yet still missing in ESMs, such as lateral flow from high to low lands (Fig 1), and the slope aspect difference in energy and water balance (Fig 2). The manuscript, led by Ying Fan Reinfelder at Rutgers University and includes over 50 coauthors, is in preparation.
Fig 1. Google Earth image of trees growing along drainage lines, CA
Fig 2. Google Earth image of trees growing on shady slopes, CA
Our objectives are:
(1) Energize the hydrology and CZ communities to synthesize our best process understanding at hillslope and catchment scales, and to recommend the best ways to represent them in global models.
(2) Implement synthesis recommendations in the Community Land Model (CLM), and test the sensitivities of model simulated ET globally to the inclusion of hillslope hydrology concepts.
(3) Test/benchmark progress in CLM water cycle simulations with observations, bringing in the rich set of hydrologic observations yet to be tapped for testing large-scale water cycle models, e.g., from the NSF-funded Critical Zone Observatories (CZOs), and the research watersheds of USDA, USGS and USFS.
(4) Conduct CLM/CESM simulations to demonstrate new model capabilities in addressing long-standing science questions related to global water, energy and carbon cycles.
(5) Hold a final synthesis workshop; based on the results of benchmarking and demonstration of new science capabilities (or lack of), the synthesis team will analyze model deficiencies and recommend future model development and observation priorities.
Contact/ Get Involved
Over 75 members from the hydrology, CZ and ESM communities have participated in our workshops, webinars, contributed white papers and presentations, and contributed to synthesis papers and general discussions.
We thank NSF for support through CUAHSI cooperative agreement (NSF-EAR-0753521) and an INSPIRE grant (NSF-EAR-1528298). We thank NCAR leadership for their support of the project, and we thank CUAHSI leadership for their support and CUAHSI staff for coordinating the many workshops and webinars.