16 research outputs found
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
Recommended from our members
Monitoring and understanding trends in extreme storms: State of knowledge
Review of the climate science for severe convective storms, extreme precipitation, hurricanes and typhoons, and severe snowstorms and ice storms in the US shows that the ability to detect and attribute trends varies, depending on the phenomenon. A specific subset of extreme weather and climate types affecting the country is discussed to examine these extreme weather conditions. The categories of storms described were selected as they caused property damage and loss of life. The identification of an extreme occurrence was based on meteorological properties in place of the destructiveness. The primary purpose was to examine the scientific evidence for the prevailing capability to detect trends and understand their causes for certain weather types, including severe convective storms and hurricanes and typhoons
Modelling the dynamics of snow cover, soil frost and surface ice in Norwegian grasslands
Studying the winter survival of forage grasses under a changing climate requires models that can simulate the dynamics of soil conditions at low temperatures. We developed a simple model that simulates depth of snow
cover, the lower frost boundary of the soil and the freezing of surface puddles. We calibrated the model against independent data from four locations in Norway, capturing climatic variation from south to north (Arctic) and from coastal to inland areas. We parameterized the model by means of Bayesian calibration, and identified the least important model parameters using the sensitivity analysis method of Morris. Verification of the model suggests that the results are reasonable. Because of the simple model structure, some overestimation occurs in snow and frost depth. Both the calibration and the sensitivity analysis suggested that the snow cover module could be simplified with respect to snowmelt and liquid water content. The soil frost module should be kept unchanged, whereas the surface ice module should be changed when more detailed topographical data become available, such as better estimates of the fraction of the land area where puddles may form