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Using Remote Sensing Data Fusion Modeling to Track Seasonal Snow Cover in a Mountain Watershed

Using Remote Sensing Data Fusion Modeling to Track Seasonal Snow Cover in a Mountain Watershed
Author: Allison N. Vincent
Publisher:
Total Pages: 186
Release: 2021
Genre: Mountain watersheds
ISBN:

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"Seasonal snowfall is the largest component of the water budget in many mountain headwater regions around the world. In addition to sustaining biological water needs in drier, lower elevation areas throughout the year, mountain snowpack also provides essential water inputs to the Critical Zone (CZ) - the outer layer of the Earth’s surface, which hosts a variety of biogeochemical processes responsible for transforming inorganic matter into forms usable for life. Water is a known driver of CZ activity, but uncertainty exists in its spatial and temporal interactions with CZ processes, particularly in the complex terrain of heterogeneous mountain areas. Increasing pressure on the CZ due to climate change and human land use needs creates an urgency to better understand the CZ system and how it may change in the future. An important variable for water driven CZ behaviors in mountain areas is the spatial extent of snow, also known as snow-covered area (SCA). SCA in mountain areas can change quickly over small scales of time and space with large impacts on the rest of the system. It has been difficult historically, however, to measure snowpack extent for large areas on very fine spatial and temporal scales due to a lack of remote sensing datasets with both of these fine scale characteristics. In this study we use the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to fill this historic knowledge gap for the East River watershed in Colorado, USA. By fusing low spatial and high temporal resolution data from MODIS (500-m, daily) with high spatial and low temporal resolution data from Landsat (30-m, 16 days), a fine resolution, 30-m daily dataset can be created. This study is one of the first to use this model with the primary intent of monitoring SCA in a mountain watershed."--Boise State University ScholarWorks.


Remote Sensing in Snow Hydrology

Remote Sensing in Snow Hydrology
Author: Klaus Seidel
Publisher: Springer Science & Business Media
Total Pages: 200
Release: 2004-04-07
Genre: Mathematics
ISBN: 9783540408802

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The authors of this monograph initially provide an assessment of the role of snow and ice in the global water balance, and methods of snow measurements are detailed. Periodical satellite snow-cover mapping enabling the regional distribution of snow and water equivalent is evaluated, enhancing runoff forecasts.


Remote Sensing of Snow and Its Applications

Remote Sensing of Snow and Its Applications
Author: Ali Nadir Arslan
Publisher: MDPI
Total Pages: 190
Release: 2021-03-17
Genre: Science
ISBN: 3036500707

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The reprint book of the “Remote Sensing of Snow and Its Applications” Special Issue provides recent studies on all aspects of remote sensing of snow, from retrieving the data to the application. These studies mainly address the following: (a) New opportunities (Copernicus Sentinels) and emerging remote sensing methods, (b) use of snow data in modeling, and (c) characterization of snowpack.


Applications Systems Verification and Transfer Project

Applications Systems Verification and Transfer Project
Author: Herbert H. Schumann
Publisher:
Total Pages: 68
Release: 1981
Genre: Hydrological forecasting
ISBN:

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Both LANDSAT and NOAA satellite data were used in improving snowmelt runoff forecasts. When the satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident.


Fractional Snow Cover Estimation in Complex Alpine-forested Environments Using Remotely Sensed Data and Artificial Neural Networks

Fractional Snow Cover Estimation in Complex Alpine-forested Environments Using Remotely Sensed Data and Artificial Neural Networks
Author: Elzbieta Halina Czyzowska-Wisniewski
Publisher:
Total Pages: 258
Release: 2014
Genre:
ISBN:

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There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ̃0.09; mean error ̃0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA.


Snow and Glacier Melt Runoff Modeling Using Remote Sensing and GIS

Snow and Glacier Melt Runoff Modeling Using Remote Sensing and GIS
Author: Gopinadh Rongali
Publisher:
Total Pages: 0
Release: 2023-01-15
Genre: Technology & Engineering
ISBN: 9785667417637

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As we know, snow is one of the forms of precipitation; however, hydrologist treats it differently due to the temporal difference between the time of its fall and the time of its runoff, groundwater recharge, and the fact that it is a part of various hydrological processes. The hydrological point of view in relation with the snow is mostly considered in middle to high latitudes and mountainous regions, where often melt period sometimes lasts for months following seasonal accumulation of snowpack. During this accumulation period, there is a very small amount or no snow melt. Precipitation (sometimes rain) falls and is temporarily retained as snowpack until the melt season starts. It is mandatory for the hydrology to record how much amount of water is collected in a basin in the form of snow. For a better knowledge of the hydrology of mountainous terrain, detailed assessment of the areal distribution of snow, its quality, and the presence of liquid water in it is also necessary. All of these snow indications are difficult to quantify and measure, and they will most certainly differ from one location to the next. Remote sensing (RS) provides a new tool for obtaining snow data for predicting snow and glacier melt runoff. Researchers have manually collected snow data manually through snow- related courses, which are labor-intensive, expensive, and potentially dangerous. Even when accessible, snow course data represents simply a location in the region and can only be used as an index of the available snow water content. Despite the fact that measurements are considered automated, the difficulty of a single point measurement or observation of snow being typical of a broader area or basin persists. It is one of the most easily identifiable forms of water resources utilising aerial photography or satellite imaging in the case of remotely sensed snow data. Satellite systems can currently only determine the area covered by snow, the depth of the snow, and the snow water equivalent; physical snow parameters cannot be monitored directly by these satellite systems. The considerable amount of freshwater has been present in the nature in snow and glacier form in the River basins which are, in most of the cases, located in high mountainous areas. Many other water resources like lakes, Rivers, streams etc. are fed by the outflow of water from these glaciers. The estimated glacier area in the world has about 14.9 x 10⁶ km2, which is approximately 10% of the overall land area present on the earth (Singh and Singh, 2001). Although just 3% of this snow is scattered over mountainous regions on many continents and even beyond the polar regions, it serves a critical role in delivering water to the majority of the world's population. It has been observed that the Himalayan mountains have a big contribution in freshwater supply globally. Major Rivers present in south Asia certainly originate from the Himalayan mountain systems. Among them, the Ganga, Indus, and Brahmaputra are said to be the lifeline of the Indian sub-continent. Snow and glacier melt runoff also contribute to the Himalayan Rivers flow.


Principles of Snow Hydrology

Principles of Snow Hydrology
Author: David R. DeWalle
Publisher: Cambridge University Press
Total Pages: 482
Release: 2008-07-03
Genre: Science
ISBN: 1139471600

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Principles of Snow Hydrology describes the factors that control the accumulation, melting and runoff of water from seasonal snowpacks over the surface of the earth. The book addresses not only the basic principles governing snow in the hydrologic cycle, but also the latest applications of remote sensing, and techniques for modeling streamflow from snowmelt across large mixed land-use river basins. Individual chapters are devoted to climatology and distribution of snow, snowpack energy exchange, snow chemistry, ground-based measurements and remote sensing of snowpack characteristics, snowpack management, and modeling snowmelt runoff. Many chapters have review questions and problems with solutions available online. This book is a reference book for practicing water resources managers and a text for advanced hydrology and water resources courses which span fields such as engineering, earth sciences, meteorology, biogeochemistry, forestry and range management, and water resources planning.


Estimating Snow Water Resources from Space

Estimating Snow Water Resources from Space
Author: Dongyue Li
Publisher:
Total Pages:
Release: 2016
Genre:
ISBN:

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Improving the estimation of snow water equivalent (SWE) in the Sierra Nevada is critical for the water resources management in California. In this study, we carried out an experiment to estimate SWE in the Upper Kern Basin, Sierra Nevada, by assimilating AMSR-E observed brightness temperatures (Tb) into a coupled hydrology and radiative transfer model using an ensemble Kalman batch reanalysis. The data assimilation framework merges the complementary SWE information from modeling and observations to improve SWE estimates. The novelty of this assimilation study is that both the modeling and the radiance data processing were specifically improved to provide more information about SWE. With the enhanced SWE signals in both simulations and observations, the batch reanalysis stands a better chance of successfully improving the SWE estimates. The modeling was at a very high resolution (90m) and spanned a range of mountain environmental factors to better characterize the effects of the mountain environment on snow distribution and radiance emission. We have developed a dynamic snow grain size module to improve the radiance modeling during the intense snowfall events. The AMSR-E 37GHz V-pol observed Tb was processed at its native footprint resolution at ~100 square km. In the batch assimilation, the model predicted the prior SWE and Tb; the prior estimate of an entire year was then updated by the dry-season observations at one time. One advantage of this is that the prior SWE of a certain period is updated using the observations both before and after this period, which takes advantage of the temporally continuous signal of the seasonal snow accumulation in the observations. We found the posterior SWE estimates showed improved accuracy and robustness. During the study period of 2003 to 2008, at point-scale, the average bias of the six-year April 1st SWE was reduced from -0.17 m to -0.02m, the average temporal SWE RMSE of the snow accumulation season decreased by 51.2%. The basin-scale results showed that the April 1st SWE bias reduced from -0.17m to -0.11m, and the temporal SWE RMSE of the accumulation season decreased by 23.6%.