USGS Washington Water Science Center
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WA09A - Enhancing Snow Hydrology Models
Problem - The availability of EOS-era remote sensing products offers promise for improved temporal and spatial assessments of snow extent and more importantly, snow water equivalent (SWE), which, in the mid- and high-latitudes is a critical part of the Earth's water and energy cycle. Snow strongly moderates the transfer of energy at the land surface, and snow-pack water storage represents a significant term in the inter-seasonal and, in some locations, inter-annual land-surface water budget. Snow cover is important for initializing and updating both numerical weather-prediction and hydrologic-forecasting models, hence improvements in methods for estimating real-time snow cover would translate into improved ability to forecast atmospheric and hydrologic variables in many regions of the world. Hydrologic forecasts depend on accurate real-time estimation of current moisture states. Snow accumulation modeling (based on real-time observed meteorological data) is a widely used method for developing such estimates of current snow extent and water storage by the snowpack. We propose to improve this method and the resulting hydrologic outputs through data assimilation of remote sensing signatures.
Objectives - Our objective is to improve macroscale hydrologic prediction capability by investigating the use of both active- and passive-microwave remote-sensing observations in the updating of snow hydrology models using data assimilation techniques. We propose to: 1) use both active and passive microwave remote sensing data from NASA’s AMSR and QuikScat sensors in hydrologic data assimilation, 2) combine a microwave emissions and scattering model with a spatially distributed model of snow microphysics to provide the basis for producing an assimilated swe product, and incorporate this model within a macroscale hydrology model, and 3) use the assimilated product to produce advanced nowcasts of snow water equivalent, and the initial conditions for improved streamflow forecasts.
Relevance and Benefits - This project addresses the Climate Variability and Change: Clarifying the Record and Assessing Consequences direction of the USGS Science Strategies document “Facing Tomorrow’s Challenges—U.S. Geological Survey Science in the Decade 2007-2017,” U.S. Geological Survey Circular 1309, through use of modern data assimilation techniques to incorporate remotely sensed observations and by improving the understanding of the variability of the terrestrial hydrosphere.
Approach - The hydrological updating process requires coupling a snow-hydrological model with a microwave radiative-transfer model to convert modeled snow depth, temperature, and grain size into estimates of microwave brightness temperatures and radar scattering cross-sections that can be compared with remote sensing observations. To provide all of the modeled snow parameters necessary, this proposal will upgrade the snow algorithm in the Variable Infiltration Capacity (VIC) model developed at the University of Washington and used in the westwide hydrologic forecast system with more detailed snow microphysics representation included in the NOAA/NWS Snow Model (NSM) developed by the National Weather Service. The upgraded VIC model will produce spatially distributed snow water equivalent fields as well as runoff predictions that can be validated by stream flow measurements.
9722-A4P - Glacier and Snowpack Studies Project
Problem - Glaciers, snow, and ice sheets are key components of the earth's hydrologic cycle and climate system that have great spatial and temporal variability. They not only reflect changes in climate; but through changes in the land-surface energy budget, they have a profound impact on global and regional climate. Like climate, variations in the snow and ice cover of the earth occur on local, regional, continental, and global spatial scales, and over seasonal, decade, century and millennia time scales. Measuring changes in extent and mass of glaciers and snow cover, on the appropriate time scale, is a direct way of determining the net effect of current global climate variations. However, because of the great spatial distribution, remote location, rapid variability, and the lack of adequate observations, the relationship between snow and ice and both climatic change and water resources is not well understood.
Changes in glacier size and volume reflect the integrated response of a glacier to fluctuations in precipitation and surface energy balance that result from climatic fluctuations. Hence, long-term records of glacier fluctuations provide unique information on climate variability. Glaciers are also naturally regulated reservoirs that reduce the inter-annual runoff variability by increasing flow during hot, dry periods and by storing water as ice and snow during cool, wet periods. Annually, the mass wastage of glaciers during this century has diminished the volume of water stored as ice in these natural reservoirs and thus has reduced the glacier melt contribution to the base flow in glacierized basins during dry summer months. Changes in basin hydrology also impact stream ecosystems. There is an emerging view that physical environments function like a filter and thus provide a template for biological communities. This is of particular concern for streams fed by glaciers. Glacier recession will result in lower flow, warmer and more variable stream temperatures, and changes in water quality, leading to the disruption of present alpine stream ecosystems.
Like glaciers, the northern hemisphere seasonal snowpack reflects climatic fluctuations. Unlike glaciers, the seasonal snow pack has a great impact on atmospheric circulation by modifying the land surface albedo and temperature, and snow is a primary source of water in many regions around the world. The snowpack response to climate change may be changes in the length of the snow season, and fluctuations in the amount of snow accumulated through the winter. Furthermore, snow is unevenly distributed over vast regions, and limited surface measurements may not adequately represent the large-scale distribution of snow. While satellite observations in the visible portion of the spectrum have been successfully used to map global snow covered area, they cannot provide information on snow depth or water equivalent. However, passive microwave observations have this potential, which results from the scattering of the upwelling microwave radiation from the substrate by the overlying snow pack.
Objectives - The objectives of this investigation are to:
Relevance and Benefits - An important part of the USGS mission is to provide scientific information on the variability of the water resources of the Nation. Glaciers and the seasonal snowpack are vital components of hydrologic cycle, particularly in the Western United States. The seasonal snowpack, in many cases provides the majority of Basin discharge. Glaciers act as interannual reservoirs and can provide significant amounts of late season flow, a major contributor to base flow conditions. An understanding of the relationship between climatic fluctuations and both glacier mass and seasonal snow pack is critical for watershed managers to design water-supply and land management options that optimize the quantity and quality of water resources for both people and the environment. On a global scale, the seasonal snowpack not only is a vital water resource, but also, it is a key factor in controlling the earth’s climate through its impact on planetary albedo.
Approach - This project will employ a multi-faceted approach of in situ and satellite observations. It will continue the seasonal mass-balance record at South Cascade Glacier. This record, now more than 4 decades long, is the longest such record in North America and is recognized as a unique high- quality record for climate/glacier mass-balance studies. We will investigate and develop new remote-sensing tools to expand the glacier monitoring program that will utilize the Benchmark glacier observations from Washington and Alaska. The response of glacier mass balance to fluctuations in climate will be determined by analyzing the Benchmark glacier observations in conjunction with climate change indices that characterize the spatial and temporal shifts in climate. These indices include but are not limited to the Pacific Decadal Oscillation (PDO), the Southern Oscillation Index (SOI), and the Arctic Oscillation (AO). The project will improve the accuracy of passive microwave snow depth or water equivalent determinations by including the effects of a temporally and spatially varying snow grain size. The grain size effects will include using either physically or statistically based modeling. Field measurements of snowpack properties, depth, density, grain size, and stratigraphy will be acquired, in support of NASA investigations, where possible. Improved algorithms will be used to analyze the 20-year passive microwave satellite record for variations in annual snowpack volume on a continental and regional scale. as well as provide snowpack input for hydrologic models.
9722-A96 - Ice NASA Snowpack
Problem - The seasonal snowpack of the Northern Hemisphere is the largest and most dynamic component of the cryosphere. It both responds to and influences global atmospheric circulation as a result of its profound impact on surface albedo. The seasonal snowpack also plays a critical role in water resources from the local level to the continental scale.
Objectives - The objectives of this project are to develop algorithms to extract snow-depth and snow-covered-area information from satellite passive microwave observations, and to develop techniques to incorporate the satellite information into hydrologic models and assess the improvements made in hydrologic forecasts.
Relevance and Benefits - As a result of its great temporal and spatial variability, and lack of in situ observations, particularly in Eurasia, adequate snowpack information is lacking for hydrologic forecasting and as input for climate models. Satellite passive microwave observations can provide the needed information.
Approach - The project will work cooperatively with NASA, the University of Washington, and the French Space Agency on the development of algorithms to extract snow depth and snow covered area information from satellite passive microwave observations. The joint studies will also develop techniques to incorporate the satellite information into hydrologic models and assess the improvements made in hydrologic forecasts.