Experimental Framework for Testing the National Water Model: Operationalizing the Use of Snow Remote Sensing in Alaska
Katrina Bennett, LANL Staff Scientist, NMC Affiliate, University of Alaska Affiliate
Snow is an important driver to the hydrologic regime, and accurately observing snow is essential to understanding hydrological processes in Alaska. There is a fundamental lack of data to populate, test and validate models to simulate Alaskan snow regimes and, therefore, the physical mechanisms and process parameterizations that are important to predict Alaskan hydrology are not well under-stood. Improvements to the efficiency, effectiveness, and accuracy of obtaining observational datasets, paired with the knowledge and tools to fuse this data into models, will significantly enhance our ability to capture snowpack processes and simulate streamflow in Alaska, as well as in other cold region, data sparse areas around the country.
This project will develop a near-real-time data stream of simple, first order assimilations of remotely sensed snow cover extent and snow water equivalent data in Alaska and operationalize components of this work for use in NOAA’s Community Hydrologic Prediction System (CHPS) framework. We will then transfer this knowledge to the National Water Model (NWM) system and run experiments using the NWM model for key study watersheds representing a range of hydro-climatic regimes in Alaska.
The goal of these experiments is to validate NWM model skill and recommend physics and parameter improvements to better capture the processes critical to hydrologic prediction in Alaska, focused on the modeling challenges of deep snowpack, glaciers, and permafrost. These conditions are not common in the initial NWM CONUS implementation and therefore lack even a baseline evaluation.
Photo by Katrina Bennett