AUTHOR=Gregory Angela , Chen Chao , Wu Rui , Miller Sarah , Ahmad Sajjad , Anderson John W. , Barrett Hays , Benedict Karl , Cadol Dan , Dascalu Sergiu M. , Delparte Donna , Fenstermaker Lynn , Godsey Sarah , Harris Frederick C. , McNamara James P. , Tyler Scott W. , Savickas John , Sheneman Luke , Stone Mark , Turner Matthew A. TITLE=Efficient Model-Data Integration for Flexible Modeling, Parameter Analysis and Visualization, and Data Management JOURNAL=Frontiers in Water VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/water/articles/10.3389/frwa.2020.00002 DOI=10.3389/frwa.2020.00002 ISSN=2624-9375 ABSTRACT=

Due to the complexity and heterogeneity inherent to the hydrologic cycle, the modeling of physical water processes has historically and inevitably been characterized by a broad spectrum of disciplines including data management, visualization, and statistical analyses. This is further complicated by the sub-disciplines within the water science community, where specific aspects of water processes are modeled independently with simplification and model boundary integration receiving little attention. This can hinder current and future research efforts to understand, explore, and advance water science. We developed the Virtual Watershed Platform to improve understanding of hydrologic processes and more generally streamline model-data integration and data integration with tools for data visualization, analysis, and management. Currently, four models have been developed as components and integrated into the overall platform, demonstrating data prepossessing (e.g., sub gridding), data interaction, model execution, and visualization capabilities. The developed data management technologies provide a suite of capabilities, enabling diverse computation capabilities, data storage capacity, connectivity, and accessibility. The developed Virtual Watershed Platform explored the use of virtual reality and 3D visualization for scientific experimentation and learning, provided web services for the transfer of data between models and centralized data storage, enabled the statistical distribution of hydrometeorological model input, and coupled models using multiple methods, both to each other and to a distributed data management and visualization system.