Reproducible hydrological modeling with CyberGIS-Jupyter: A case study on summa

Fangzheng Lyu, Dandong Yin, Anand Padmanabhan, Youngdon Choi, Jonathan L. Goodall, Anthony Castronova, David Tarboton, Shaowen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

CyberGIS-Jupyter is a cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. As a cutting-edge hydrological modeling framework, the Structure for Unifying Multiple Modeling Alternative (SUMMA) functions as a unified approach to process‐based modeling. The purpose of this research is to investigate the feasibility of coupling CyberGIS-Jupyter with SUMMA to realize reproducible hydrological modeling. CyberGIS-Jupyter is employed to systematically integrate advanced cyberinfrastructure, including two high-performance computers – Virtual ROGER and XSEDE Comet, data management, and execution and visualization of SUMMA-based modeling. By taking advantage of CyberGIS-Jupyter, users can easily tune different parameters for a SUMMA model and submit computational jobs for executing the model on HPC resources without having to possess in-depth technical knowledge about cyberGIS or cyberinfrastructure. Computational experiments demonstrate that the integration of CyberGIS-Jupyter and SUMMA achieves a high-performance and easy-to-use implementation for reproducible SUMMA-based hydrological modeling.

Original languageEnglish (US)
Title of host publicationProceedings of the Practice and Experience in Advanced Research Computing
Subtitle of host publicationRise of the Machines (Learning), PEARC 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450372275
DOIs
StatePublished - Jul 28 2019
Event2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019 - Chicago, United States
Duration: Jul 28 2019Aug 1 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019
CountryUnited States
CityChicago
Period7/28/198/1/19

Fingerprint

Information management
Visualization
Experiments

Keywords

  • CyberGIS-Jupyter
  • Geographic Information Science and Systems (GIS)
  • High-Performance Computing
  • Hydrological Modeling
  • Science Gateway

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Lyu, F., Yin, D., Padmanabhan, A., Choi, Y., Goodall, J. L., Castronova, A., ... Wang, S. (2019). Reproducible hydrological modeling with CyberGIS-Jupyter: A case study on summa. In Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019 [3333052] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3332186.3333052

Reproducible hydrological modeling with CyberGIS-Jupyter : A case study on summa. / Lyu, Fangzheng; Yin, Dandong; Padmanabhan, Anand; Choi, Youngdon; Goodall, Jonathan L.; Castronova, Anthony; Tarboton, David; Wang, Shaowen.

Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019. Association for Computing Machinery, 2019. 3333052 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lyu, F, Yin, D, Padmanabhan, A, Choi, Y, Goodall, JL, Castronova, A, Tarboton, D & Wang, S 2019, Reproducible hydrological modeling with CyberGIS-Jupyter: A case study on summa. in Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019., 3333052, ACM International Conference Proceeding Series, Association for Computing Machinery, 2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019, Chicago, United States, 7/28/19. https://doi.org/10.1145/3332186.3333052
Lyu F, Yin D, Padmanabhan A, Choi Y, Goodall JL, Castronova A et al. Reproducible hydrological modeling with CyberGIS-Jupyter: A case study on summa. In Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019. Association for Computing Machinery. 2019. 3333052. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3332186.3333052
Lyu, Fangzheng ; Yin, Dandong ; Padmanabhan, Anand ; Choi, Youngdon ; Goodall, Jonathan L. ; Castronova, Anthony ; Tarboton, David ; Wang, Shaowen. / Reproducible hydrological modeling with CyberGIS-Jupyter : A case study on summa. Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019. Association for Computing Machinery, 2019. (ACM International Conference Proceeding Series).
@inproceedings{7aeb0769dad4458fa3804bf40860648e,
title = "Reproducible hydrological modeling with CyberGIS-Jupyter: A case study on summa",
abstract = "CyberGIS-Jupyter is a cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. As a cutting-edge hydrological modeling framework, the Structure for Unifying Multiple Modeling Alternative (SUMMA) functions as a unified approach to process‐based modeling. The purpose of this research is to investigate the feasibility of coupling CyberGIS-Jupyter with SUMMA to realize reproducible hydrological modeling. CyberGIS-Jupyter is employed to systematically integrate advanced cyberinfrastructure, including two high-performance computers – Virtual ROGER and XSEDE Comet, data management, and execution and visualization of SUMMA-based modeling. By taking advantage of CyberGIS-Jupyter, users can easily tune different parameters for a SUMMA model and submit computational jobs for executing the model on HPC resources without having to possess in-depth technical knowledge about cyberGIS or cyberinfrastructure. Computational experiments demonstrate that the integration of CyberGIS-Jupyter and SUMMA achieves a high-performance and easy-to-use implementation for reproducible SUMMA-based hydrological modeling.",
keywords = "CyberGIS-Jupyter, Geographic Information Science and Systems (GIS), High-Performance Computing, Hydrological Modeling, Science Gateway",
author = "Fangzheng Lyu and Dandong Yin and Anand Padmanabhan and Youngdon Choi and Goodall, {Jonathan L.} and Anthony Castronova and David Tarboton and Shaowen Wang",
year = "2019",
month = "7",
day = "28",
doi = "10.1145/3332186.3333052",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the Practice and Experience in Advanced Research Computing",

}

TY - GEN

T1 - Reproducible hydrological modeling with CyberGIS-Jupyter

T2 - A case study on summa

AU - Lyu, Fangzheng

AU - Yin, Dandong

AU - Padmanabhan, Anand

AU - Choi, Youngdon

AU - Goodall, Jonathan L.

AU - Castronova, Anthony

AU - Tarboton, David

AU - Wang, Shaowen

PY - 2019/7/28

Y1 - 2019/7/28

N2 - CyberGIS-Jupyter is a cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. As a cutting-edge hydrological modeling framework, the Structure for Unifying Multiple Modeling Alternative (SUMMA) functions as a unified approach to process‐based modeling. The purpose of this research is to investigate the feasibility of coupling CyberGIS-Jupyter with SUMMA to realize reproducible hydrological modeling. CyberGIS-Jupyter is employed to systematically integrate advanced cyberinfrastructure, including two high-performance computers – Virtual ROGER and XSEDE Comet, data management, and execution and visualization of SUMMA-based modeling. By taking advantage of CyberGIS-Jupyter, users can easily tune different parameters for a SUMMA model and submit computational jobs for executing the model on HPC resources without having to possess in-depth technical knowledge about cyberGIS or cyberinfrastructure. Computational experiments demonstrate that the integration of CyberGIS-Jupyter and SUMMA achieves a high-performance and easy-to-use implementation for reproducible SUMMA-based hydrological modeling.

AB - CyberGIS-Jupyter is a cyberGIS framework for achieving data-intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on advanced cyberinfrastructure. As a cutting-edge hydrological modeling framework, the Structure for Unifying Multiple Modeling Alternative (SUMMA) functions as a unified approach to process‐based modeling. The purpose of this research is to investigate the feasibility of coupling CyberGIS-Jupyter with SUMMA to realize reproducible hydrological modeling. CyberGIS-Jupyter is employed to systematically integrate advanced cyberinfrastructure, including two high-performance computers – Virtual ROGER and XSEDE Comet, data management, and execution and visualization of SUMMA-based modeling. By taking advantage of CyberGIS-Jupyter, users can easily tune different parameters for a SUMMA model and submit computational jobs for executing the model on HPC resources without having to possess in-depth technical knowledge about cyberGIS or cyberinfrastructure. Computational experiments demonstrate that the integration of CyberGIS-Jupyter and SUMMA achieves a high-performance and easy-to-use implementation for reproducible SUMMA-based hydrological modeling.

KW - CyberGIS-Jupyter

KW - Geographic Information Science and Systems (GIS)

KW - High-Performance Computing

KW - Hydrological Modeling

KW - Science Gateway

UR - http://www.scopus.com/inward/record.url?scp=85070986346&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85070986346&partnerID=8YFLogxK

U2 - 10.1145/3332186.3333052

DO - 10.1145/3332186.3333052

M3 - Conference contribution

AN - SCOPUS:85070986346

T3 - ACM International Conference Proceeding Series

BT - Proceedings of the Practice and Experience in Advanced Research Computing

PB - Association for Computing Machinery

ER -