Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

GPS snow sensing: results from the EarthScope Plate Boundary Observatory

  • Original Article
  • Published:
GPS Solutions Aims and scope Submit manuscript

Abstract

Accurate measurements of snowpack are needed both by scientists to model climate and by water supply managers to predict/mitigate drought and flood conditions. Existing in situ snow sensors/networks lack the necessary spatial and temporal sensitivity. Satellite measurements currently assess snow cover rather than snow depth. Existing GPS networks are a potential source of new snow data for climate scientists and water managers which complements existing snow sensors. Geodetic-quality GPS networks often provide signal-to-noise ratio data that are sensitive to snow depth at scales of ~1,000 m2, a much larger area than for other in situ sensors. However, snow depth can only be estimated at GPS sites when the modulation frequency of multipath signals can be resolved. We use data from the EarthScope Plate Boundary Observatory to examine the potential for snow sensing in GPS networks. Examples are shown for successful and unsuccessful snow retrieval sites. In particular, GPS sites in forested regions typically cannot be used for snow sensing. Multiple-year time series of snow depth are estimated from GPS sites in the Rocky Mountains. Peak snow depths ranged from 0.4 to 1.2 m. Comparisons with independent sensors show strong correlations between the GPS snow depth estimates and the timing of snowstorms in the region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Armstrong RL, Brodzik MJ (2002) Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms. Ann Glaciol 34:1:38–44 (7). doi:10.3189/172756402781817428

    Google Scholar 

  • Bilich A, Larson KM (2007) Mapping the GPS multipath environment using the signal-to-noise ratio (SNR). Radio Sci 42:RS6003. doi:10.1029/2007RS003652

  • Bilich A, Axelrad P, Larson KM (2007) Scientific utility of the signal-to-noise ratio (SNR) reported by geodetic GPS receivers. Proceedings of the 20th international technical meeting of the satellite division of the institute of navigation (ION GNSS 2007), Fort Worth, TX, Sep 2007, pp 1999–2010

  • Bloschl G, Kirnbauer R, Gutknecht D (1991) Distributed snowmelt simulations in an alpine catchment: 1. Model evaluation on the basis of snow cover patterns. Water Resour Res 27:3171–3179

    Article  Google Scholar 

  • Elder K, Dozier J, Michaelsen J (1991) Snow accumulation and distribution in an alpine Watershed. Water Resour Res 27:1541–1552

    Article  Google Scholar 

  • Erickson T, Williams MW, Winstral A (2005) Persistence of topographic controls on the spatial distribution of snow depth in rugged mountain terrain, Colorado, USA. Water Resour Res 41(4):W04014. doi:10.1029/2003WR002973

  • ESA (2008) CoReH2o—COld REgions hydrology high-resolution observatory, ESA SP-1313/3 candidate earth explorer core missions—report for assessment

  • Gutmann E, Larson KM, Williams M, Nievinski FG, Zavorotny V (2011) Snow measurement by GPS interferometric reflectometry: an evaluation at Niwot Ridge, Colorado. Hydrol Process. doi:10.1002/hyp.8329

  • Hristov HD (2000) Fresnel zones in wireless links, zone plate lenses and antennas. Artech House. ISBN 9780890068496, pp 323

  • Jaldehag KRT, Johansson JM, Davis JL, Elósegui P (1996) Geodesy using the Swedish permanent GPS network: effects of snow accumulation on estimates of site positions. Geophys Res Lett 23(13):1601–1604

    Article  Google Scholar 

  • Joseph A (2010) What is the difference between SNR and C/N0? InsideGNSS, Nov/Dec 2010, pp 20–25

  • Katzberg S, Torres O, Grant M, Masters D (2006) Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: results from SMEX02. Rem Sens Environ 100(1):17–28

    Article  Google Scholar 

  • Kind RJ (1981) Snow drifting. In: Gray D, Male D (eds) Handbook of snow: principles, processes, management and use. Elsevier, New York, pp 338–359

    Google Scholar 

  • Larson KM, Gutmann E, Zavorotny V, Braun JJ, Williams M, Nievinski FG (2009) Can we measure snow depth with GPS receivers? Geophys Res Lett 36:L17502. doi:10.1029/2009GL039430

    Article  Google Scholar 

  • Larson KM, Braun JJ, Small EE, Zavorotny V, Gutmann E, Bilich A (2010) GPS multipath and its relation to near-surface soil moisture. IEEE-JSTARS 3(1):91–99. doi:10.1109/JSTARS.2009.2033612

    Google Scholar 

  • Molotch N, Bales R (2006) SNOTEL representativeness in the Rio Grande headwaters on the basis of physiographics and remotely sensed snow cover persistence. Hydrol Process 20:723–739

    Article  Google Scholar 

  • Ozeki M, Heki K (2012) GPS snow depth sensor with geometry-free linear combinations. J Geod 86(3):209–219. doi:10.1007/s00190-011-0511-x

    Article  Google Scholar 

  • Press F, Teukolsky S, Vetterling W, Flannery B (1996) Numerical recipes in Fortran 90: the Art of parallel scientific computing, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  • Serreze MC, Clark MP, Armstrong RL, McGinnis DA, Pulwarty RS (1999) Characteristics of the Western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour Res 35(7):2145–2160

    Article  Google Scholar 

  • Seyfried MS, Wilcox BP (1995) Scale and the nature of spatial variability: field examples having implications for hydrologic modeling. Water Resour Res 31:173–184

    Article  Google Scholar 

  • Woo KT (1999) Optimum semi-codeless carrier phase tracking of L2. In: Proceedings of the 12th international technical meeting of the satellite division of the institute of navigation, Nashville, TN., Sep 14–17, 1999

  • Zavorotny V, Larson KM, Braun JJ, Small EE, Gutmann E, Bilich A (2010) A physical model for GPS multipath caused by ground reflections: toward bare soil moisture retrievals. IEEE-JSTARS 3(1):100–110. doi:10.1109/JSTARS.2009.2033608

    Google Scholar 

Download references

Acknowledgments

This research was supported by NSF EAR 0948957, NSF AGS 0935725, and a CU interdisciplinary seed grant. Mr. Nievinski has been supported by a Capes/Fulbright Graduate Student and a NASA Earth System Science Research Fellowship. Dr. Larson used a Dean’s Faculty Fellowship in 2011 to write the manuscript. All RINEX files used in this study are freely available from UNAVCO. The authors thank Mark Williams, Eric Small, Valery Zavorotny, and Ethan Gutmann for many valuable discussions. Personnel at UNAVCO routinely provided information and support for this project. Some of this material is based on data, equipment, and engineering services provided by the Plate Boundary Observatory operated by UNAVCO for EarthScope (http://www.earthscope.org) and supported by the National Science Foundation (EAR-0350028 and EAR-0732947). We thank PBO for providing the photographs and Google Earth for satellite images. SNOTEL data shown in this paper were retrieved from http://www.wcc.nrcs.usda.gov/nwcc.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering Sciences, University of Colorado, Boulder, Boulder, CO, 80309-0429, USA

    Kristine M. Larson & Felipe G. Nievinski

Authors
  1. Kristine M. Larson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felipe G. Nievinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kristine M. Larson.

Appendix

Appendix

To avoid confusion with Fresnel zones expressions valid for space- and air-borne platforms, here we provide expressions for ground-based installations (Hristov 2000). Start with n = 1 indicating the first Fresnel zone (FFZ), λ for wavelength, h for antenna height, and e and a for satellite elevation angle and azimuth, respectively. Then, the FFZ dimensions are:

  • d = nλ/2;

  • R = h/tan(e) + (d/sin(e))/tan(e)

  • b = (2 d h/sin(e) + (d/sin(e))2)1/2

  • a = b/sin(e)

Its perimeter can be discretized as function of the inner angle θ \( \in \) [0, 2π]:

  • x′ = a cos(θ) + R

  • y′ = b sin(θ)

Finally, the semi-major axis is aligned with the satellite azimuth:

  • x = sin(a) x′ − cos(a) y′

  • y = sin(a) y′ + cos(a) x′

Rights and permissions

Reprints and permissions

About this article

Cite this article

Larson, K.M., Nievinski, F.G. GPS snow sensing: results from the EarthScope Plate Boundary Observatory. GPS Solut 17, 41–52 (2013). https://doi.org/10.1007/s10291-012-0259-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10291-012-0259-7

Keywords

Search

Navigation