Sustainably Providing Accurate Local River Discharge Data with Global Hydrologic Modeling and Bias Corrections
The Global Water Sustainability Initiative of the Group of Earth Observations (GEOGloWS) supported an initiative to develop a global hydrologic model. The purpose of the modeling initiative is to build a high-quality model using the best available datasets and modeling methods with the primary emphasis on accessibility of the model. The goal is to make the model a sustainable source of river discharge information to supplement the capacity of those countries without the local capacity to maintain sufficient gauge networks and local modeling capabilities and cyberinfrastructure. Past research developed a modeling approach and piloted implementations and data and visualization services in many countries. This dissertation presents 1) the implementation of the full global hydrologic model, 2) the design of several data and visualization services for the model as well as supporting code tools and web apps, 3) a new model agnostic bias correction method, SABER, for removing bias in gauged and ungauged subbasins of hydrologic models, and 4) applying the SABER method to the GEOGloWS hydrologic model. I present a series of case studies showing how the model was used for many applications and supporting usage analytics which demonstrate the utility of the services developed. I analyze the results of the bias correction procedure which demonstrate the improvement in model performance and provide guidance on applying the bias corrected data on local scales. The model resulting from this research has provided as sustainable source of river discharge data. I recommend that future work on developing global scale hydrologic models continue to place emphasis on the accessibility of models to maximize the benefit of modeling efforts to both scientists and broader audiences.
College and Department
Ira A. Fulton College of Engineering and Technology
BYU ScholarsArchive Citation
Hales, Riley Chad, "Sustainably Providing Accurate Local River Discharge Data with Global Hydrologic Modeling and Bias Corrections" (2023). Theses and Dissertations. 9826.
hydrologic modeling, watershed classification, bias correction, machine learning, geospatial analysis, web services, scalar flow duration curve, geoglows, saber, capacity building