Intercomparison of snow water equivalent observations in the Northern Great Plains

2018 ◽  
Vol 32 (6) ◽  
pp. 817-829 ◽  
Author(s):  
Samuel E. Tuttle ◽  
Jennifer M. Jacobs ◽  
Carrie M. Vuyovich ◽  
Carrie Olheiser ◽  
Eunsang Cho
Keyword(s):  
Great Plains ◽  
Snow Water Equivalent ◽  
Northern Great Plains ◽  
Snow Water

A comparison of modeled, remotely sensed, and measured snow water equivalent in the northern Great Plains

2003 ◽  
Vol 39 (8) ◽  
Author(s):  
Thomas L. Mote ◽  
Andrew J. Grundstein ◽  
Daniel J. Leathers ◽  
David A. Robinson
Keyword(s):  
Great Plains ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Northern Great Plains ◽  
Snow Water

A hybrid Climatology of Snow Water Equivalent Over the Northern Great Plains of the United States

2002 ◽  
Vol 26 (3) ◽  
pp. 187-209 ◽  
Author(s):  
Andrew Grundstein ◽  
Thomas Mote ◽  
Daniel Leathers
Keyword(s):  
United States ◽  
Great Plains ◽  
Snow Water Equivalent ◽  
The United States ◽  
Northern Great Plains ◽  
Snow Water

Characteristics and Predictability of Midwestern United States Drought

2021 ◽  
Author(s):  
Andrew Hoell ◽  
Trent W. Ford ◽  
Molly Woloszyn ◽  
Jason A. Otkin ◽  
Jon Eischeid
Keyword(s):  
United States ◽  
Great Plains ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Northern Great Plains ◽  
Ohio Valley ◽  
Midwestern United States ◽  
Wave Trains

AbstractCharacteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

scholarly journals Return Levels of Northern Great Plains Snow Water Equivalents

2006 ◽  
Vol 45 (7) ◽  
pp. 995-1002 ◽  
Author(s):  
Andrew J. Grundstein ◽  
Qi Qi Lu ◽  
Robert Lund
Keyword(s):  
South Dakota ◽  
Great Plains ◽  
Return Level ◽  
Northern Great Plains ◽  
Return Levels ◽  
Geographical Regions ◽  
Snow Water ◽  
North And South ◽  
Raw Observations ◽  
Individual Grid

Abstract This paper estimates return levels of extreme snow water equivalents (SWE) in the northern Great Plains region, containing North and South Dakota, Iowa, Minnesota, and Nebraska. The return levels are estimated from extreme-value methods using a new hybrid SWE dataset that improves the spatial resolution of existing data in the area. A novel aspect of the methods is the use of standard error margins to spatially smooth the estimated SWE return levels computed on individual grid cells. The end product is a reliable return-level estimate that controls for uncertainties in the raw observations. The methods should prove useful in analyses of other geographical regions.

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