CyberWater

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.

Project Description

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.

Project Objectives

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.

Publications

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

Contact/Get Involved

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