CyberWater

Scientists need to use diverse data and integrate models outside their own disciplines with sufficient model accuracy and predictability. This is currently difficult to accomplish because (1) a vast quantity of diverse data are not readily accessible to models; and (2) diverse models developed individually by different research groups are difficult to share an integrate between disciplines. 

The goal of this project is to build an open data, open modeling framework software that enables easy and incremental integration of diverse data and models for knowledge discovery and interdisciplinary team-work, as well as reproducible computing and seamless and on-demand access to HPC resources. Our project team includes hydrologists, climate experts, meteorologists, computer scientists and CI experts, from multiple universities and CUAHSI, who collaborate closely to ensure CyberWater will engage the broad communities for domain scientists' benefits. 

CyberWater can lower the learning curve for modelers with limited skills in computer and technology, and enable them to quickly employ models from other domains. It advances the state-of-the-art by making it possible to quickly, effectively and reliably connect diverse models together to form a comprehensive integrated modeling framework for tackling emerging complex interdisciplinary problems using diverse sources of data. 

Currently, a large interdisciplinary team must be formed to modify model code to couple the diverse domain models together. The innovations of CyberWater include: 

1. Significantly reducing model integration complexity, and allowing easy coupling of diverse models without coding.

2. Facilitating the use of diverse data by automatically ingesting data of heterogeneous types, formats, and access protocols, etc.

3. Automating the flow from data to model to output visualization in real- or near real-time

4. Supporting reproducible computing

5. Enabling high power computing on demand

• Scientists can use the CyberWater framework to greatly simplify their data access, coupled model simulations, reproducible computing, and access to HPC facilities, all of which significantly enhance research productivity.

• CyberWater will be a valuable education tool to teach and train university students on how to conduct interdisciplinary and reproducible research, thus greatly enhancing university STEM education capability for future workforce.

An open-data open-model framework for hydrological models’ integration, evaluation and application.

• Salas, D., Liang, X., Navarro, M., Liang, Y., and Luna, D. An open-data open-model framework for hydrological models’ integration, evaluation and application. Environmental Modelling and Software, 126 (April 2020), 104622. https://doi.org/10.1016/j.envsoft.2020.104622 

Cyberwater - An open and sustainable framework for diverse data and model integration.

• Luna, D., Chen, R., Yuan, C., Liang, Y., Liang, X., Bales, J., Castronova, A.M., Demir, I., Hooper, R.P., Krajewski, W.F. and Lin, L., 2019. CyberWater—An open and sustainable framework for diverse data and model integration. AGU Fall Meeting, 2019, pp.IN11B-02.

Collaborative Research: CyberWater - An open and sustainable framework for diverse data and model integration with provenance and access to HPC.

• Liang, X., Liang, Y., Luna, D., Chen, R., Cao, Y., Fu, Y., Pamidighantam, S.,Song, F., Bales, J., Castronova, A., Demir, I., Hooper, R., Krajewski, W., Lin, L., Mantilla, R., Zhang, Y., 2020. Collaborative Research: CyberWater - An open and sustainable

Watch the videos below to learn more about our project. These lectures were a part of our 2021 virtual cyberseminar.

• CyberWater 2021 Workshop Welcome and Overview
• Overview of the CyberWater Project, Platform and Features
• The CyberWater Interface and Modeling Workflow
• Using the Generic Model Agent to Port Models into CyberWater
• Leveraging High Performance Computing in CyberWater
• CyberWater’s Geographic Information System Capabilities
• Overview of 2nd User Testing Workshop - August 2021

Please contact Xu Liang (xuliang@pitt.edu) or Anthony Castronova (acastronova@cuahsi.org) if you would like to be involved or have any questions on the project.

Contributors                                            

• Xu Liang (University of Pittsburgh), Project lead PI
• Yao Liang (Indiana University - Purdue University Indianapolis), IUPUI PI
• Fengguang Song (Indiana University - Purdue University Indianapolis), IUPUI Co-PI
• Sudhakar Pamidighantam (Indiana University), IUPUI Co-PI
• Dimuthu Upeksha (Indiana University), IUPUI/IU Programmer
• Ibrahim Demir (University of Iowa), Iowa PI
• Witold Krajewski (University of Iowa), Iowa Co-PI
• Ricardo Mantilla (University of Iowa), Iowa Co-PI
• Yang Zhang (North Carolina State University), NC State PI
• Lan Lin (Ball State University), BSU PI
• Anthony Castronova (CUAHSI), CUAHSI PI
• Richard Hooper (Tufts University), Paid Consultant through CUAHSI
• Jennifer Adam (Washington State University), Unpaid Collaborator
• Jonathan L. Goodall (University of Virginia), Unpaid Collaborator
• Nancy Wilkins-Diehr (San Diego Supercomputer Center), Unpaid Consultant
• Ge Sun (U.S. Department of Agriculture), Unpaid Consultant
• Yingping Wang (CSIRO, Australia), Unpaid Consultant
• Dave Meyer (NASA), Unpaid Consultant