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Understanding the Distribution of Snow Using Remotely Sensed Snow Covered Area.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Understanding the Distribution of Snow Using Remotely Sensed Snow Covered Area./
作者:	Schneider, Dominik.
面頁冊數:	1 online resource (145 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
標題:	Hydrologic sciences. -
電子資源:	click for full text (PQDT)
ISBN:	9781369784954

Understanding the Distribution of Snow Using Remotely Sensed Snow Covered Area.
Schneider, Dominik.

Understanding the Distribution of Snow Using Remotely Sensed Snow Covered Area. - 1 online resource (145 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2017.

Includes bibliographical references

Snowmelt makes up a large portion of the streamflow in the mountainous western United States. The spatial distribution of snow water equivalent (SWE) can affect the magnitude and tim- ing of the spring and summer runoff represented in the hydrograph. Hence, efforts to improve our understanding of the spatial distribution of SWE are vital for good management of our ecological and water resources. SWE is traditionally monitored at measuring stations spread across the western United States, but these stations have been shown to poorly represent the unsampled ar- eas. Remote sensing from satellites has existed since the 1960s but is still unable to measure SWE at scales relevant for water resources. This research utilizes spatio-temporal datasets to promote the use of historical observations of fractional snow covered area (fSCA) to improve estimates of SWE. First, I show that retrospective models of historical SWE distributions from observed fSCA depletion patterns augment existing ground observations of SWE to improve real-time estimates of SWE in unsampled locations. Second, I show that remotely sensed observations of fSCA improve the temporal transferability of the relationship between topography and SWE. Third, a high reso- lution spatio-temporal dataset is used to observe depletion curves for the first time and evaluate the topographic controls on the relationship between fSCA and snow depth inherent in these depletion curves. Each of these chapters leverages fSCA as an important component and together imply that fSCA has historically been an underutilized observation. Observations of fSCA are available glob- ally for about three decades but necessitate spatially explicit observations of snow depth or SWE to make the most of this long record. Emerging technologies, such as Light Detection and Ranging (LiDAR) and Ground Penetrating Radar (GPR), that provide high resolution spatio-temporal ob- servations of the snowpack and other environmental variables should continue to be exploited to provide insights regarding the physical processes controlling snow dynamics and more generally our water resources. Future adaptions to climate change rely on improving our understanding of the controlling processes and our ability to monitor them at the relevant scales.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369784954Subjects--Topical Terms:

1179176
Hydrologic sciences.
Index Terms--Genre/Form:

554714
Electronic books.

Understanding the Distribution of Snow Using Remotely Sensed Snow Covered Area.
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504 $a Includes bibliographical references
520 $a Snowmelt makes up a large portion of the streamflow in the mountainous western United States. The spatial distribution of snow water equivalent (SWE) can affect the magnitude and tim- ing of the spring and summer runoff represented in the hydrograph. Hence, efforts to improve our understanding of the spatial distribution of SWE are vital for good management of our ecological and water resources. SWE is traditionally monitored at measuring stations spread across the western United States, but these stations have been shown to poorly represent the unsampled ar- eas. Remote sensing from satellites has existed since the 1960s but is still unable to measure SWE at scales relevant for water resources. This research utilizes spatio-temporal datasets to promote the use of historical observations of fractional snow covered area (fSCA) to improve estimates of SWE. First, I show that retrospective models of historical SWE distributions from observed fSCA depletion patterns augment existing ground observations of SWE to improve real-time estimates of SWE in unsampled locations. Second, I show that remotely sensed observations of fSCA improve the temporal transferability of the relationship between topography and SWE. Third, a high reso- lution spatio-temporal dataset is used to observe depletion curves for the first time and evaluate the topographic controls on the relationship between fSCA and snow depth inherent in these depletion curves. Each of these chapters leverages fSCA as an important component and together imply that fSCA has historically been an underutilized observation. Observations of fSCA are available glob- ally for about three decades but necessitate spatially explicit observations of snow depth or SWE to make the most of this long record. Emerging technologies, such as Light Detection and Ranging (LiDAR) and Ground Penetrating Radar (GPR), that provide high resolution spatio-temporal ob- servations of the snowpack and other environmental variables should continue to be exploited to provide insights regarding the physical processes controlling snow dynamics and more generally our water resources. Future adaptions to climate change rely on improving our understanding of the controlling processes and our ability to monitor them at the relevant scales.
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