Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States

Erickson, Tyler; Williams, Mark W.; Winstral, A. H.

doi:10.1029/2003wr002973

Cited by 223 publications

(251 citation statements)

References 43 publications

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“…In fact, wind has been shown to be the dominant influence on spatial variability of snow in complex terrain (Pomeroy et al, 1993;Winstral et al, 2002;Sturm and Wagner, 2010). Without prior knowledge of the spatial snow distribution in a given area, arbitrary manual snow measurements will not provide accurate estimates of snow depth over large alpine regions (Elder et al, 1991;Anderton et al, 2004;Erickson et al, 2005).…”

Section: A Hedrick Et Al: Lidar Validation Of Snodasmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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Abstract. Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km 2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated groundbased observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage.Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty.

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Section: A Hedrick Et Al: Lidar Validation Of Snodasmentioning

confidence: 99%

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

et al. 2015

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“…In years with high dust inputs, such as 2006 when the DOC input from wet deposition was 4300 kg C yr −1 (19 kg C ha −1 yr −1 ), the total atmospheric input could be > 5800 kg C yr −1 (24 kg C ha −1 yr −1 ) ( Table 5). Whereas atmospheric deposition occurs year long and across the entire landscape, primary production in barren soils is more patchy, occurring mainly during the summer snow-free period (approximately 90 d) (Freeman et al, 2009b) and over an area that is about half the talus area or roughly 20 % of the watershed area (Erickson et al, 2005). Our calculations show that soil primary production inputs of approximately 10 800 kg C yr −1 (calculated using the production rate of 240 kg C ha −1 yr −1 from Freeman et al (2009b) over 20 % of the watershed) are indeed higher than atmospheric inputs, as suggested by Freeman et al (2009b).…”

Section: Relevance Of Atmospheric Wet and Dry Deposition Inputs For Tmentioning

confidence: 99%

Atmospheric deposition as a source of carbon and nutrients to an alpine catchment of the Colorado Rocky Mountains

et al. 2012

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Abstract. Many alpine areas are experiencing deglaciation, biogeochemical changes driven by temperature rise, and changes in atmospheric deposition. There is mounting evidence that the water quality of alpine streams may be related to these changes, including rising atmospheric deposition of carbon (C) and nutrients. Given that barren alpine soils can be severely C limited, atmospheric deposition sources may be an important source of C and nutrients for these environments. We evaluated the magnitude of atmospheric deposition of C and nutrients to an alpine site, the Green Lake 4 catchment in the Colorado Rocky Mountains. Using a long-term dataset (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) of weekly atmospheric wet deposition and snowpack chemistry, we found that volume weighted mean dissolved organic carbon (DOC) concentrations were 1.12 ± 0.19 mg l −1 , and weekly concentrations reached peaks as high at 6-10 mg l −1 every summer. Total dissolved nitrogen concentration also peaked in the summer, whereas total dissolved phosphorus and calcium concentrations were highest in the spring. To investigate potential sources of C in atmospheric deposition, we evaluated the chemical quality of dissolved organic matter (DOM) and relationships between DOM and other solutes in wet deposition. Relationships between DOC concentration, fluorescence, and nitrate and sulfate concentrations suggest that pollutants from nearby urban and agricultural sources and organic aerosols derived from sub-alpine vegetation may influence high summer DOC wet deposition concentrations. Interestingly, high DOC concentrations were also recorded during "dust-in-snow" events in the spring, which may reflect an association of DOM with dust. Detailed chemical and spectroscopic analyses conducted for samples collected in 2010 revealed that the DOM in many late spring and summer samples was less aromatic and polydisperse and of lower molecular weight than that of winter and fall samples. Our C budget estimates for the Green Lake 4 catchment illustrated that wet deposition (9.9 kg C ha −1 yr −1 ) and dry deposition (6.9 kg C ha −1 yr −1 ) were a combined input of approximately 17 kg C ha −1 yr −1 , which could be as high as 24 kg C ha −1 yr −1 in high dust years. This atmospheric C input approached the C input from microbial autotrophic production in barren soils. Atmospheric wet and dry deposition also contributed 4.3 kg N ha −1 yr −1 , 0.15 kg P ha −1 yr −1 , and 2.7 kg Ca 2+ ha −1 yr −1 to this alpine catchment.

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“…For example, several hydrological models have been applied to test the effect of the spatial distribution of a hydrological variable (e.g. specific discharge, soil moisture, or groundwater 70 recharge) (Erickson et al, 2005;Gómez-Plaza et al, 2001;Li et al, 2014). However, the effects of topography alone on hydrology are not usually addressed in those studies.…”

Section: Introductionmentioning

confidence: 99%

Topography significantly influencing low flows in snow-dominated watersheds

Li¹,

Wei²,

Yang³

et al. 2017

Preprint

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and other environmental fields. Flow variables include annual mean flow (Qmean), Q10%, Q25%, Q50%, Q75%, Q90%, and annual minimum flow (Qmin), where Qx% is defined as flows that at the percentage (x) occurred in any given year. Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, stepwise regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important 30 role in low flows than high flows. However, the effects of TIs on flow variables are not consistent.Our analysis also determines five significant TIs including perimeter, surface area, openness, terrain characterization index, and slope length factor, which can be used to compare watersheds when low flow assessments are conducted, especially in snow-dominated regions. 35

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Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States

Cited by 223 publications

References 43 publications

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Atmospheric deposition as a source of carbon and nutrients to an alpine catchment of the Colorado Rocky Mountains

Topography significantly influencing low flows in snow-dominated watersheds

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