Calibration of a hydrologic model in data-scarce Alaska using satellite and other gridded products

• Published in: Journal of Hydrology: Regional Studies
• Main Authors: Katie E. Schneider, Terri S. Hogue
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2022
• Subjects: Calibration; Alaska; Sensitivity analysis; Evapotranspiration; Runoff; Water balance; Physical geography; GB3-5030; Geology; QE1-996.5; Canada; permafrost
• Online Access: https://doi.org/10.1016/j.ejrh.2021.100979; https://doaj.org/article/dd1dbbc0d79846cba814e3c6d3fb8df1

Study Region: Alaska and hydrologically connected portions of Canada Study Focus: Rugged terrain and harsh conditions make observational hydroclimatic data, especially runoff data, extremely limited across most of Alaska. Consequently, many previous hydrologic studies in Alaska have focused on smaller-domain trends, where pockets of hydroclimatic observations are clustered. Using a combination of reanalysis and remotely sensed gridded products, we develop a calibration framework for a monthly resolution water budget model, over the entire Alaskan domain, that does not rely on runoff. The domain is comprised of distributed model response units (MRUs), each representing a Hydrologic Unit Code 12 basin. We use a Fourier Amplitude Sensitivity Test (FAST) to inform a two-step calibration process; first calibrating to actual evapotranspiration and then to fractional snow-covered area (fSCA), resulting in a range of parameter solutions for each MRU. New Hydrological Insights for the Region: Using this framework, the model performs best in the moderate to dry Alaskan interior; in the absence of glaciation and where permafrost is generally not continuous. Modelled runoff for recent decades shows increased runoff trends at lower latitudes and slightly decreased runoff trends at higher latitudes within the Alaskan domain. These findings reveal that reliance upon non-runoff fluxes for model calibration is viable in this cold region and that better characterization of hydroclimatic data is necessary to improve confidence in cold region modeling studies.