Authors: Jorge Bravo, Marouane Temimi – Stevens Institute of Technology
Title: Assessing the use of the Model for Prediction Across Scales Atmosphere (MPAS) forecasts for Streamflow Prediction Using the NextGen in a Box
Presentation Type: Poster Presentation
Abstract: The Model for Prediction Across Scales–Atmosphere (MPAS-A) features a variable-resolution mesh that enables targeted high-resolution refinement, allowing seamless interaction between large-scale atmospheric circulation and local-scale processes. Despite its growing adoption in atmospheric sciences, the application of MPAS-A as forcing data for hydrological models remains limited. This is largely due to challenges associated with its unstructured mesh and the scarcity of methods for generating meshes with resolutions finer than 3 km. Consequently, relatively little literature exists evaluating the performance of MPAS-A for hydrological applications. This study evaluates the performance of MPAS-A as a meteorological forcing dataset for streamflow prediction within the NextGen-in-a-Box (NGIAB) framework. MPAS-A simulations forced by Global Forecast System (GFS) data at 6-hour intervals are analyzed to assess streamflow behavior as forecast lead time decreases toward event occurrence. The study area is the Pascack Brook subregion of the Hackensack River Watershed, which was previously calibrated using NGIAB-CAL. Model performance is evaluated and compared between uncalibrated and calibrated configurations using a consistent codebase. Results from this study contribute to advancing hydrometeorological forecasting by assessing the applicability of next-generation atmospheric modeling for accurate and scalable streamflow prediction.