Problem Statement

Snowpack, measured in the form of snow-water equivalent (SWE), is a fundamental component of the water supply in mountainous regions and the hydro-connected downstream environments. SWE represents the quantity of liquid water within a snowpack and is one of the key parameters in predicting snowmelt runoff and corresponding water supply forecasting. Characterizing seasonal SWE dynamics, including peak SWE and the timing of snowmelt, supports estimates of in-stream peak flow timing, duration, and intensity. Beyond environmental considerations, there is significant economic motivation to quantify available water stored as snowpack. The value of the annual snowpack in the western U.S. can approach the order of a trillion U.S. dollars due to the resulting meltwater supporting agriculture, residential, and commercial purposes. In spite of the economic and environmental factors, accurate estimation of SWE on a broad scale remains a longstanding challenge.

Recognizing the value of widespread SWE estimates and mountainous basin hydroclimate, there are several tools and methods for characterizing the quantity of frozen water stored in a snowpack to enhance seasonal water supply forecasting. The U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) has installed over 900 automated snow telemetry (SNOTEL) in situ monitoring stations throughout the western U.S. Many of the SNOTEL stations now exhibit a long-term historical observation record greater than 30 years, monitoring snow depth, SWE, and a number of climatic conditions such as precipitation, temperature, and wind speed. Recent advancements in Light Detection and Ranging (LiDAR) on aircraft and uncrewed aerial vehicles (UAV) complement existing in situ SWE observations by extending snowpack characterization to the catchment scale. Physics-based modeling techniques, such as in the National Oceanic and Atmospheric Administration's (NOAA) National Water Model (NWM), can demonstrate high accuracy in modeling various snowpack characteristics by simulating the mass and energy balance from inputs of solar radiation, wind, temperature, precipitation, and other variables.

Module Overview

This module will provide the learner with fundamental snow-hydrology terminalogy and the technical skills needed to conduct a seasonal snow analysis pursuant to water supply forecasting, covering snow terminology, snow observation systems, and snow modeling basics. Section 1 is an introduction to key snow terminology relevant to water supply forecasting. This section provides the foundation to work through the following sections and to successfully complete the module. Sections 2 focuses on snow observation systems from the USDA National Resource Conservation Service (NRCS) SNOTEL network and spatial data products from the NASA Airborne Snow Observatory (ASO) mission. Learners will undergo technical training on data retrieval, data processing, visualization, and analysis. Section 3 introduces the concept of a physically-based snow model, using NOAA's NWM NOAH-OWP-Modular as an example. The section will describe the fundamentals of the model including parameterizations, forcings, and outputs. The modeling section will conclude with technical training on data retrieval, data processing, visualization, and analysis. Section 4 is a comprehensive learning activity where the learner will apply the technical skills and knowledge gained throughout the module to evaluate the state-of-the-snowpack for the Hetch Hetchy Reservoir on the Tuolumne river in the Sierra Nevada mountains, a key water supply watershed for the greater San Franscico area.