Accuracy of Snow Water Equivalent Estimated From GPS Vertical Displacements: A Synthetic Loading Case Study for Western U.S. Mountains

GPS monitoring of solid Earth deformation due to surface loading is an independent approach for estimating seasonal changes in terrestrial water storage (TWS). In western United States (WUSA) mountain ranges, snow water equivalent (SWE) is the dominant component of TWS and an essential water resource. While several studies have estimated SWE from GPS-measured vertical displacements, the error associated with this method remains poorly constrained. We examine the accuracy of SWE estimated from synthetic displacements at 1,395 continuous GPS station locations in the WUSA. Displacement at each station is calculated from the predicted elastic response to variations in SWE from SNODAS and soil moisture from the NLDAS-2 Noah model. We invert synthetic displacements for TWS, showing that both seasonal accumulation and melt as well as year-to-year fluctuations in peak SWE can be estimated from data recorded by the existing GPS network. Because we impose a smoothness constraint in the inversion, recovered TWS exhibits mass leakage from mountain ranges to surrounding areas. This leakage bias is removed via linear rescaling in which the magnitude of the gain factor depends on station distribution and TWS anomaly patterns. The synthetic GPS-derived estimates reproduce approximately half of the spatial variability (unbiased root mean square error 50%) of TWS loading within mountain ranges, a considerable improvement over GRACE. The inclusion of additional simulated GPS stations improves representation of spatial variations. GPS data can be used to estimate mountain-range-scale SWE, but effects of soil moisture and other TWS components must first be subtracted from the GPS-derived load estimates.

[1]  N. Molotch.  Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model , 2009 .

[2]  Victor Zlotnicki,et al.  Time‐variable gravity from GRACE: First results , 2004 .

[3]  L. Jia,et al.  Load Love numbers and Green's functions for elastic Earth models PREM, iasp91, ak135, and modified models with refined crustal structure from Crust 2.0 , 2012, Comput. Geosci..

[4]  Michael Lehning,et al.  Persistence in intra‐annual snow depth distribution: 1. Measurements and topographic control , 2011 .

[5]  Thomas R. Carroll,et al.  NOHRSC OPERATIONS AND THE SIMULATION OF SNOW COVER PROPERTIES FOR THE COTERMINOUS U.S , 2001 .

[6]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

[7]  Jeongho Baek,et al.  Computation of the Load Love Number and the Load Green's Function for an Elastic and Spherically Symmetric Earth , 2011 .

[8]  Hiroko Kato Beaudoing,et al.  Estimating evapotranspiration using an observation based terrestrial water budget , 2011 .

[9]  E. Small,et al.  Terrestrial water storage response to the 2012 drought estimated from GPS vertical position anomalies , 2014 .

[10]  Duncan Carr Agnew,et al.  Ongoing drought-induced uplift in the western United States , 2014, Science.

[11]  M. Clark,et al.  Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data , 1999 .

[12]  G. Blewitt,et al.  Uplift and seismicity driven by groundwater depletion in central California , 2014, Nature.

[13]  Matthew Rodell,et al.  The potential for satellite-based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US , 2002 .

[14]  Clifford H. Thurber,et al.  Parameter estimation and inverse problems , 2005 .

[15]  B. Scanlon,et al.  Global analysis of approaches for deriving total water storage changes from GRACE satellites , 2015 .

[16]  Duncan Carr Agnew,et al.  SPOTL: Some Programs for Ocean-Tide Loading , 2012 .

[17]  Thomas H. Painter,et al.  The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo , 2016 .

[18]  M. Rodell,et al.  Water in the Balance , 2013, Science.

[19]  K. Verdin,et al.  Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA , 2011 .

[20]  W. Farrell.  Deformation of the Earth by surface loads , 1972 .

[21]  Kelly Elder,et al.  Evaluation of forest snow processes models (SnowMIP2) , 2009 .

[22]  F. Landerer,et al.  Accuracy of scaled GRACE terrestrial water storage estimates , 2012 .

[23]  Srinivas Bettadpur,et al.  High‐resolution CSR GRACE RL05 mascons , 2016 .

[24]  K. Mo,et al.  Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products , 2012 .

[25]  Bridget R. Scanlon,et al.  Evaluation of groundwater storage monitoring with the GRACE satellite: Case study of the High Plains aquifer, central United States , 2009 .

[26]  S. Bettadpur,et al.  Modeling Earth deformation from monsoonal flooding in Bangladesh using hydrographic, GPS, and Gravity Recovery and Climate Experiment (GRACE) data , 2010 .

[27]  Dennis P. Lettenmaier,et al.  Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow , 2010 .

[28]  Felix W. Landerer,et al.  GPS as an independent measurement to estimate terrestrial water storage variations in Washington and Oregon , 2015 .

[29]  J. Famiglietti,et al.  Estimating snow water equivalent from GPS vertical site-position observations in the western United States , 2013, Water resources research.

[30]  Felix W. Landerer,et al.  Seasonal variation in total water storage in California inferred from GPS observations of vertical land motion , 2014 .

[31]  D. Alsdorf,et al.  Seasonal fluctuations in the mass of the Amazon River system and Earth's elastic response , 2005 .

[32]  R. Houborg,et al.  Drought indicators based on model‐assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations , 2012 .

[33]  S. Seneviratne,et al.  Basin scale estimates of evapotranspiration using GRACE and other observations , 2004 .

[34]  Eric E. Small,et al.  Snowpack density modeling is the primary source of uncertainty when mapping basin‐wide SWE with lidar , 2017 .