Abstract
There is an increase in the frequency and intensity of water related natural disasters driven by climate change and human activities. While satellite data provides extensive coverage of many climatic variables, streamflow monitoring remains inadequate for effectively responding to water-related hazards, particularly in developing regions that lack integrated gauge networks. This study evaluates how incorporating local streamflow gauge data enhances bias correction of the GEOGLOWS Hydrologic Model (RFS) in gauged and ungauged basins using the Stream Analysis for Bias Estimation and Reduction (SABER) method. We analyzed approximately 19,200 gauge stations globally, using machine learning, spatial analysis and Flow Duration Curve (FDC) analysis to characterize hydrological regimes. Model performance metrics were compared before and after incorporating additional local gauge data through case studies. SABER significantly improved RFS performance, with median Kling-Gupta Efficiency (KGE) values increasing from 0.083 in raw simulations to 0.391 after bias correction. Improvements were particularly pronounced when incorporating local gauge data, as demonstrated in Togo where median KGE improved from -0.109 without local gauges to 0.292 after adding them. Effectiveness varied by climate region and stream order, with temperate and continental regions showing the best results. Bias-corrected global hydrological models can bridge the streamflow data gap in the in the world especially developing regions, while incorporating local gauge data substantially enhances model performance. This approach empowers water resources managers with accurate streamflow information while emphasizing the importance of maintaining and sharing quality hydrological observations for improved model products.
Degree
MS
College and Department
Ira A. Fulton College of Engineering; Civil and Environmental Engineering
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Baaniya, Yubin, "Empowering Local Water Resources Managers with Global Hydrological Model Information Through Bias Correction of Their In-Situ Data Using SABER" (2025). Theses and Dissertations. 10691.
https://scholarsarchive.byu.edu/etd/10691
Date Submitted
2025-04-03
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd13527
Keywords
GEOGLOWS, SABER, Bias Correction
Language
english