The role of passive microwaves in characterizing snow cover in the Colorado river basin

GeoJournal ◽  
1992 ◽  
Vol 26 (3) ◽  
pp. 381-388 ◽  
Author(s):  
A. T. C. Chang ◽  
J. L. Foster ◽  
A. Rango
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Colorado River ◽  
Colorado River Basin

The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin

2017 ◽  
Vol 31 (26) ◽  
pp. 4705-4718 ◽  
Author(s):  
Christine A. Rumsey ◽  
Matthew P. Miller ◽  
Gregory E. Schwarz ◽  
Robert M. Hirsch ◽  
David D. Susong
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Colorado River Basin ◽  
Dissolved Solids ◽  
Upper Colorado River Basin

scholarly journals The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin

2019 ◽  
Vol 34 (1) ◽  
pp. 150-152
Author(s):  
Christine A. Rumsey ◽  
Matthew P. Miller ◽  
Gregory E. Schwarz ◽  
Robert M. Hirsch ◽  
David D. Susong
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Colorado River Basin ◽  
Dissolved Solids ◽  
Upper Colorado River Basin

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

1989 ◽  
Vol 20 (2) ◽  
pp. 73-84 ◽  
Author(s):  
Edward G. Josberger ◽  
Edouard Beauvillain
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Colorado River ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Colorado River Basin ◽  
Upper Colorado River Basin ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Cover Extent

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.

scholarly journals Variability of low flow magnitudes in the Upper Colorado River Basin: identifying trends and relative role of large-scale climate dynamics

2014 ◽  
Vol 11 (7) ◽  
pp. 8779-8802 ◽  
Author(s):  
M. Pournasiri Poshtiri ◽  
I. Pal
Keyword(s):  
River Basin ◽  
Large Scale ◽  
River Flow ◽  
Colorado River ◽  
Colorado River Basin ◽  
Low Flow ◽  
Anthropogenic Factors ◽  
Relative Role ◽  
Water Abstraction

Abstract. Low flow magnitude in a head water basin is important for planners because minimum available amount of water in a given time period often leads to concerns regarding serious repercussions, in both up and downstream regions. This is a common scenario in arid region like Colorado River basin located in the southwestern US. Low flow variability in Colorado River is due to complex interactions between several natural and anthropogenic factors; but we aim to identify the relative role of climate on varying low flow magnitudes at different spatial locations. The research questions we aim to answer are: Is there a systematic variability in water availability during the driest time of a year or season? How does that vary across locations and is there a link between large-scale climate and low flow variations? Towards that aim we select 17 stream gauge locations, which are identified as "undisturbed" meaning that these stations represent near-natural river flow regimes in the headwater region of Colorado River, which provides a useful resource for assessment of climate and hydrology associations without the confounding factor of major direct (e.g. water abstraction) or indirect (e.g. land-use change) human modification of flows. A detailed diagnostic analysis gives us fair understanding on the variability of low flow magnitude that is explained by climate. We also present spatial heterogeneity of hydro-climatological linkages that is important for suitable adaptive management measures.

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

2012 ◽  
Vol 13 (2) ◽  
pp. 539-556 ◽  
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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