The CyberWater project creates a new cyberinfrastructure with an open data, open modeling framework software. The goal of this project is to reduce the user time and effort needed for hydrologic modeling studies, thereby expediting fundamental knowledge discoveries. In addition to facilitating diverse data and model integration while ensuring reproducible computing, CyberWater also enables on-demand access to high-performance computing facilities.
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
Benefits to the CUAHSI Community
- 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.
- 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
- 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.
- 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 the focus group held virtually in August 2020.
- CyberWater: Overview of CyberWater for User Test Group, Xu Liang
- CyberWater: What is CyberWater? Daniel Luna
- CyberWater: Generic Model Agent Tools, Yao Liang
- CyberWater: High Performance Computing Module, Ranran Chen & Yao Liang
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