What drives basin scale spatial variability of snowpack properties in northern Colorado?

This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km²). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest

Snowpack variability across various spatio-temporal resolutions

High-resolution snow depth (SD) maps (1×1m) obtained from terrestrial laser scanner measurements in a small catchment (0.55km2) in the Pyrenees were used to assess small-scale variability of the snowpack at the catchment and sub-grid scales. The coefficients of variation are compared for various plot resolutions (5×5, 25×25, 49×49, and 99×99m) and eight different days in two snow seasons (2011-2012 and 2012-2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small-scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small-scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub-grid variability. Topographic variables including slope, wind sheltering, sub-grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99×99m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25×25m resolution they explained slightly more than 50%. The results highlight the importance of considering small-scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. © 2014 John Wiley and Sons, Ltd.This study was supported by the research projects Hidrología nival en el Pirineo Central Español: Variabilidad espacial, importancia hidrológica y respuesta a la variabilidad y cambio climático (CGL2011-27536/HID, Hidronieve) and CGL2011-27753-C02-01, financed by the Spanish Commission of Science and Technology and FEDER; CTTP1/12 ‘Creación de un modelo de alta resolución espacial para cuantificar la esquiabilidad y la afluencia turística en el Pirineo bajo distintos escenarios de cambio climático’, financed by the Comunidad de Trabajo de los Pirineos; and 844/2013 ‘El glaciar de Monte Perdido: Monitorización y estudio de su dinámica actual y procesos criosféricos asociados como indicadores de procesos de cambio global’, financed by MAGRAMA, National Parks. SRFs and GASs time were funded by the NASA Terrestrial Hydrology Program entitled ‘Improved Characterization of Snow Depth in Complex Terrain Using Satellite Lidar Altimetry’ (Grant # NNX11AQ66G led by PI Dr. M.F. Jasinski).Peer Reviewe

Synergistic Airframe-Propulsion Interactions and Integrations

this paper. Successful solutions to this issue depend on appropriate technology ideas