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You searched for subject:(SNODAS). Showing records 1 – 2 of 2 total matches.

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University of Colorado

1. DePaolo, Michael Robert. A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data.

Degree: MS, 2013, University of Colorado

Snow loads govern roof design in many parts of the United States. These loads are largely prescribed by the American Society of Civil Engineers ASCE 7 Standard for minimum design loads. Where ASCE 7 does not specify snow loads due to extreme local variability, such as in the West, many state jurisdictions have developed individual roof snow load documents and maps. However, among the western states border discrepancies and a general lack of uniformity in the methodology for developing such loads indicates a need for a unified approach. This paper proposes a methodology to develop ground snow loads for the western United States, the application of which is illustrated for the state of Colorado. An innovative approach is taken which utilizes a hydrological snowpack model, Snow Data Assimilation System (SNODAS), developed by NOAA. This model provides estimates of ground snow depth and snow water content, easily convertible into loads, at 588 SNODAS weather stations in Colorado. The methodology proposed here then incorporates statistical techniques such as principal component analysis (PCA) and multivariate cluster analyses to regionalize the SNODAS stations by key shared properties. Several types of cluster analyses are evaluated including agglomerative hierarchical clustering (AHC), k-means, and a PCA-based method. Using various statistical and practical measures of quality, a step-wise hybrid method combining both AHC and k-means techniques is found to be the most statistically sound and robust clustering method. A relationship is then developed between ground snow depths and ground snow loads for each cluster of SNODAS weather stations. This paper proposes the following additional steps. A database of National Weather Service CO-OP stations with snow depth only measurements is gathered for the state of interest. The 50-year ground snow depths are extrapolated by testing the goodness-of-fit of several probability distributions. The ground snow depth-load relationships for each cluster produced by the hybrid method are then coupled with these 50-year ground snow depths to produce 50-year ground snow loads. Finally, these ground snow loads are mapped in GIS software using a Kriging geostatistical interpolation method to create continuous snow load isolines. Advisors/Committee Members: Abbie B. Liel, George Hearn, James Harris.

Subjects/Keywords: climatology; Colorado ground snow loads; SNODAS; snow engineering; snow loads; structural engineering; Atmospheric Sciences; Civil Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

DePaolo, M. R. (2013). A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/cven_gradetds/109

Chicago Manual of Style (16th Edition):

DePaolo, Michael Robert. “A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data.” 2013. Masters Thesis, University of Colorado. Accessed May 08, 2021. https://scholar.colorado.edu/cven_gradetds/109.

MLA Handbook (7th Edition):

DePaolo, Michael Robert. “A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data.” 2013. Web. 08 May 2021.

Vancouver:

DePaolo MR. A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data. [Internet] [Masters thesis]. University of Colorado; 2013. [cited 2021 May 08]. Available from: https://scholar.colorado.edu/cven_gradetds/109.

Council of Science Editors:

DePaolo MR. A Proposal for a Unified Process to Improve Probabilistic Ground Snow Loads in the United States Using SNODAS Modeled Weather Station Data. [Masters Thesis]. University of Colorado; 2013. Available from: https://scholar.colorado.edu/cven_gradetds/109

2. Lv, Zhibang 1986-. Assimilation of snow information into a cold regions hydrological model.

Degree: 2019, University of Saskatchewan

Spring and summer snowmelt runoff from the Canadian Rocky Mountains recharge many rivers and hence provide critical water supplies for a large portion of the population in western Canada. Because of the complex topography and vegetation conditions, the sparse network of observations of climate and snow properties, and the low quality of atmospheric model products, data assimilation (DA) is a potentially useful tool to improve the forecasting and prediction of snow properties and streamflow. To achieve better snowpack and streamflow estimations using DA, this research aims to: 1) evaluate the usefulness of SNODAS SWE data in Canada, and determine the influence of processes missing from the SNODAS model on the accuracy of SNODAS SWE, 2) explore the possibility of using remotely sensed data for detecting snow interception in forest canopies, 3) assimilate in situ measured and remotely sensed snow interception data into CRHM and assess their influence on the simulation of snow interception losses, 4) determine the optimal method to assimilate in situ snow measurements into the CRHM for prediction of basin snowpacks and streamflow. The results illustrate: 1) missing snow processes (blowing snow transport and canopy snow interception and sublimation) in the SNODAS snow model contribute substantially to its overestimation of SWE, 2) canopy intercepted snow can be detected by optical remote sensing data (NDSI and NDVI), 3) automated snow depth data measured from an adjacent forest and clearing can be used in a mass budget to accurately quantify snow interception loss, and assimilation of in situ measured and remotely sensed snow interception information can all improve simulations of snow interception timing and magnitude, 4) assimilating in situ SWE and snow depth into CRHM generally improves the simulation of snowpack properties and streamflow, but the results varied among different assimilation schemes. A better SWE simulation through DA does not always lead to better prediction of streamflow. The advanced snow interception measurement and DA techniques presented here deepens the understanding of cold regions hydrological DA and improve the capacity to forecast and predict the hydrology of headwater river basins in the Canadian Rockies and other similar regions. Advisors/Committee Members: Pomeroy, John, Patrick, Robert, Guo, Xulin, Helgason, Warren, Pietroniro, Alain.

Subjects/Keywords: Data assimilation; Hydrology; Snow; SWE; Snow depth; Snow interception; SNODAS; Remote sensing

…transects with corresponding SNODAS grid cells at all survey locations in western Canada… …and SNODAS (SNODAS_P) precipitation at Marmot Creek Research Basin, Alberta… …Study (BERMS) sites, all in Canada. (b) The extent of SNODAS data and… …27 Figure 2-3. Comparison of observed and SNODAS-predicted SWE for water years 2011-2015 at… …33 Figure 2-4. Time series comparisons of observed and SNODAS SWE and precipitation at… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lv, Z. 1. (2019). Assimilation of snow information into a cold regions hydrological model. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/12307

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lv, Zhibang 1986-. “Assimilation of snow information into a cold regions hydrological model.” 2019. Thesis, University of Saskatchewan. Accessed May 08, 2021. http://hdl.handle.net/10388/12307.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lv, Zhibang 1986-. “Assimilation of snow information into a cold regions hydrological model.” 2019. Web. 08 May 2021.

Vancouver:

Lv Z1. Assimilation of snow information into a cold regions hydrological model. [Internet] [Thesis]. University of Saskatchewan; 2019. [cited 2021 May 08]. Available from: http://hdl.handle.net/10388/12307.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lv Z1. Assimilation of snow information into a cold regions hydrological model. [Thesis]. University of Saskatchewan; 2019. Available from: http://hdl.handle.net/10388/12307

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.