Young-Don Choi


DOI bib
Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States
Ashley E. Van Beusekom, Lauren Hay, Andrew Bennett, Young-Don Choi, Martyn P. Clark, J. L. Goodall, Zhiyu Li, Iman Maghami, Bart Nijssen, Andrew W. Wood
Journal of Hydrometeorology, Volume 23, Issue 2

Abstract Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.


DOI bib
Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces
Young-Don Choi, Jonathan L. Goodall, Jeffrey M. Sadler, Anthony M. Castronova, Andrew Bennett, Zhiyu Li, Bart Nijssen, Shaowen Wang, Martyn P. Clark, Daniel P. Ames, Jeffery S. Horsburgh, Yi Hong, C. Bandaragoda, M. Seul, Richard Hooper, David G. Tarboton
Environmental Modelling & Software, Volume 135

Cyberinfrastructure needs to be advanced to enable open and reproducible environmental modeling research. Recent efforts toward this goal have focused on advancing online repositories for data and model sharing, online computational environments along with containerization technology and notebooks for capturing reproducible computational studies, and Application Programming Interfaces (APIs) for simulation models to foster intuitive programmatic control. The objective of this research is to show how these efforts can be integrated to support reproducible environmental modeling. We present first the high-level concept and general approach for integrating these three components. We then present one possible implementation that integrates HydroShare (an online repository), CUAHSI JupyterHub and CyberGIS-Jupyter for Water (computational environments), and pySUMMA (a model API) to support open and reproducible hydrologic modeling. We apply the example implementation for a hydrologic modeling use case to demonstrate how the approach can advance reproducible environmental modeling through the seamless integration of cyberinfrastructure services. • New approaches are needed to support open and reproducible environmental modeling. • Efforts should focus on integrating existing cyberinfrastructure to build new systems. • Our focus is on integrating repositories, computational environments, and model APIs. • An example implementation is shown using HydroShare, JupyterHub, and pySUMMA. • We demonstrate how the approach fosters reproducibility using a modeling case study.