Snow Cover of the Upper Colorado River Basin from Satellite Passive Microwave and Visual Imagery

A comparison of passive microwave images from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and visual images from the Defense Meteorological Satellite Program (DMSP) of the Upper Colorado River Basin shows that passive microwave satellite imagery can be used to determine the extent of the snow cover. Eight cloud-free DMSP images throughout the winter of 1985-1986 show the extent of the snowpack, which, when compared to the corresponding SMMR images, determine the threshold microwave characteristics for snow-covered pixels. With these characteristics, the 27 sequential SMMR images give a unique view of the temporal history of the snow cover extent through the first half of the water year. Beginning mid-November, the snow-covered area rapidly increases from near zero to 80 percent by the middle of January. During late February the snow-covered area decreases as a result of basin-wide warming. The microwave determinations initially overestimate the decrease in snow cover, as a result of liquid water in the snowpack, but the return of cooler temperatures restores the veracity of the passive microwave determinations.

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Snow conditions and hydrology of the upper Colorado River basin from satellite passive microwave observations

Annals of Glaciology ◽

10.1017/s0260305500013045 ◽

1993 ◽

Vol 17 ◽

pp. 322-326 ◽

Cited By ~ 8

Author(s):

Edward G. Josberger ◽

William J. Campbell ◽

Per Gloersen ◽

Alfred T.C. Chang ◽

Al Rango

Keyword(s):

River Basin ◽

Colorado River ◽

Microwave Radiometer ◽

Passive Microwave ◽

Colorado River Basin ◽

Small Scale ◽

Upper Colorado River Basin ◽

Average Maximum ◽

Elevation Data ◽

Snowpack Properties

Satellite passive microwave observations can provide unique mesoscale (25 km) information on snowpack properties; however, the mountainous terrain of the upper Colorado River basin compounds the difficulty of the problem. Nevertheless, observations of this region from the Scanning Multichannel Microwave Radiometer (SMMR) have provided unique, synoptic, mesoscale snowpack information from 1979 to 1987 on the snowpack extent. For this nine-year period, the SMMR 18 and 37 GHz brightness temperature observations, combined to form a parameter called NGR, show the average maximum snowpack extent covers 70% of the basin and occurs on water year day 130 (mid-February). The minimum snowpack extent took place in 1981 and covered 35% of the basin. The maximum snowpack extent took place in 1979 and covered 99% of the basin. Summation of the NGR values from each SMMR mesoscale pixel within the basin provides an index of the regional snowpack properties on both an intra- and inter-annual basis and exhibits behavior similar to the snowpack extent. When compared to the nine-year average, 1981 is the minimum year and 1979 is the maximum year. Furthermore, the sum over the basin of the annual maximum NGR from each pixel correlates with the annual discharge,r= 0.6. This correlation increases to 0.8 when digital elevation data are used to characterize each SMMR pixel and only the April through July discharge is used in the regression. Hence, this study combines the small scale elevation data with the mesoscale SMMR observations to investigate the basin-wide or regional snowpack characteristics and its hydrology.

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Snow conditions and hydrology of the upper Colorado River basin from satellite passive microwave observations

Annals of Glaciology ◽

10.3189/s0260305500013045 ◽

1993 ◽

Vol 17 ◽

pp. 322-326 ◽

Cited By ~ 1

Author(s):

Edward G. Josberger ◽

William J. Campbell ◽

Per Gloersen ◽

Alfred T.C. Chang ◽

Al Rango

Keyword(s):

River Basin ◽

Colorado River ◽

Microwave Radiometer ◽

Passive Microwave ◽

Colorado River Basin ◽

Small Scale ◽

Upper Colorado River Basin ◽

Average Maximum ◽

Elevation Data ◽

Snowpack Properties

Satellite passive microwave observations can provide unique mesoscale (25 km) information on snowpack properties; however, the mountainous terrain of the upper Colorado River basin compounds the difficulty of the problem. Nevertheless, observations of this region from the Scanning Multichannel Microwave Radiometer (SMMR) have provided unique, synoptic, mesoscale snowpack information from 1979 to 1987 on the snowpack extent. For this nine-year period, the SMMR 18 and 37 GHz brightness temperature observations, combined to form a parameter called NGR, show the average maximum snowpack extent covers 70% of the basin and occurs on water year day 130 (mid-February). The minimum snowpack extent took place in 1981 and covered 35% of the basin. The maximum snowpack extent took place in 1979 and covered 99% of the basin. Summation of the NGR values from each SMMR mesoscale pixel within the basin provides an index of the regional snowpack properties on both an intra- and inter-annual basis and exhibits behavior similar to the snowpack extent. When compared to the nine-year average, 1981 is the minimum year and 1979 is the maximum year. Furthermore, the sum over the basin of the annual maximum NGR from each pixel correlates with the annual discharge, r = 0.6. This correlation increases to 0.8 when digital elevation data are used to characterize each SMMR pixel and only the April through July discharge is used in the regression. Hence, this study combines the small scale elevation data with the mesoscale SMMR observations to investigate the basin-wide or regional snowpack characteristics and its hydrology.

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The effects of snowpack grain size on satellite passive microwave observations from the Upper Colorado River Basin

Journal of Geophysical Research Atmospheres ◽

10.1029/95jc02959 ◽

1996 ◽

Vol 101 (C3) ◽

pp. 6679-6688 ◽

Cited By ~ 25

Author(s):

Edward G. Josberger ◽

Per Gloersen ◽

Al Chang ◽

Albert Rango

Keyword(s):

Grain Size ◽

River Basin ◽

Colorado River ◽

Passive Microwave ◽

Colorado River Basin ◽

Upper Colorado River Basin

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Assessing the Performance of Multiple Regional Climate Model Simulations for Seasonal Mountain Snow in the Upper Colorado River Basin

Journal of Hydrometeorology ◽

10.1175/2011jhm1371.1 ◽

2012 ◽

Vol 13 (2) ◽

pp. 539-556 ◽

Cited By ~ 30

Author(s):

Nadine Salzmann ◽

Linda O. Mearns

Keyword(s):

Regional Climate Model ◽

Snow Cover ◽

River Basin ◽

North American ◽

Colorado River ◽

Climate Model ◽

Regional Climate ◽

Colorado River Basin ◽

Detailed Knowledge ◽

Upper Colorado River Basin

Abstract This study assesses the performance of the regional climate model (RCM) simulations from the North American Regional Climate Change Assessment Program (NARCCAP) for the Upper Colorado River basin (UCRB), U.S. Rocky Mountains. The UCRB is a major contributor to the Colorado River’s runoff. Its significant snow-dominated hydrological regime makes it highly sensitive to climatic changes, and future water shortage in this region is likely. The RCMs are evaluated with a clear RCM output user’s perspective and a main focus on snow. Snow water equivalent (SWE) and snow duration, as well as air temperature and precipitation from five RCMs, are compared with snowpack telemetry (SNOTEL) observations, with National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) Reanalysis II (R2), which provides the boundary conditions for the RCM simulations, and with North American Regional Reanalysis (NARR). Overall, most RCMs were able to significantly improve on the results from the NCEP–NCAR reanalysis. However, in comparison with spatially aggregated point observations and NARR, the RCMs are generally too dry, too warm, simulate too little SWE, and have a too-short snow cover duration with a too-late start and a too-early end of a significant snow cover. The intermodel biases found are partly associated with inadequately resolved topography (at the spatial resolution of the RCMs), imperfect observational data, different forcing techniques (spectral nudging versus no nudging), and the different land surface schemes (LSS). Attributing the found biases to specific features of the RCMs remains difficult or even impossible without detailed knowledge of the physical and technical specification of the models.

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