[1] Inverse weighted distance and regression nonexact techniques were evaluated for interpolating methods snow water equivalent (SWE) across the entire Colorado River Basin of the western United States. A 1-km spacing was used for the gridding of snow telemetry (SNOTEL) measurements for the years 1993, 1998, and 1999, which on average, represented higher than average, average, and lower than average snow years. Because of the terrain effects, the regression techniques (hypsometric elevation and multivariate physiographic parameter) were found to be superior to the weighted distance approaches (inverse distance weighting squared, and optimal power inverse distance weighting). A regression detrended inverse weighted distance method was developed for the hypsometric and multivariate techniques in order to preserve the point SNOTEL data. On the basis of root mean square error analysis and estimates of SWE volumes in different elevation zones for the entire basin and for subbasins the elevation detrended method with a point by point regression was found to be the most appropriate technique. Various search radii and anisotropies of the search ellipse were tested with the hypsometric method, producing only small difference in the root mean square error and SWE volumes.
Abstract:The USGS precipitation-runoff modelling system (PRMS) hydrologic model was used to evaluate experimental, gridded, 1 km 2 snow-covered area (SCA) and snow water equivalent (SWE) products for two headwater basins within the Rio Grande (i.e. upper Rio Grande River basin) and Salt River (i.e. Black River basin) drainages in the southwestern USA. The SCA product was the fraction of each 1 km 2 pixel covered by snow and was derived from NOAA advanced very high-resolution radiometer imagery. The SWE product was developed by multiplying the SCA product by SWE estimates interpolated from National Resources Conservation Service snow telemetry point measurements for a 6 year period (1995)(1996)(1997)(1998)(1999)(2000). Measured SCA and SWE estimates were consistently lower than values estimated from temperature and precipitation within PRMS. The greatest differences occurred in the relatively complex terrain of the Rio Grande basin, as opposed to the relatively homogeneous terrain of the Black River basin, where differences were small. Differences between modelled and measured snow were different for the accumulation period versus the ablation period and had an elevational trend. Assimilating the measured snowfields into a version of PRMS calibrated to achieve water balance without assimilation led to reduced performance in estimating streamflow for the Rio Grande and increased performance in estimating streamflow for the Black River basin. Correcting the measured SCA and SWE for canopy effects improved simulations by adding snow mostly in the mid-to-high elevations, where satellite estimates of SCA are lower than model estimates.
Temporal and spatial differences in snow-water equivalent (SWE) at 240 snow telemetry (SNOTEL) and at 500 snow course sites and a subset of 93 collocated sites were evaluated by examining the correlation of site values over the snow season, interpolating point measurements to basin volumes using hypsometry and a maximum snow extent mask, and variogram analysis. The lowest correlation at a point (r = 0.79) and largest interpolated volume differences (as much as 150 mm of SWE over the Gunnison basin) occurred during wet years (e.g., 1993). Interpolation SWE values based on SNOTEL versus snow course sites were not consistently higher or lower relative to each other. Interpolation rmse was comparable for both datasets, increasing later in the snow season. Snow courses correlate over larger distances and have less short-scale variability than SNOTEL sites, making them more regionally representative. Using both datasets in hydrologic models will provide a range of predicted streamflow, which is potentially useful for water resources management.
The increasing expression of human activity, climate variability, and climate change on humid, terrestrial hydrologic systems has made the integrated nature of large river basins more apparent. However, to date, there is no instrument platform sufficient to characterize river basins' hydrologic couplings and feedbacks, with many processes and impacts left almost entirely unobserved (e.g., snowmelt floods). Characterization at the river basin scale will require a more holistic vision and a far greater commitment from the environmental science community. It will require new designs and implementation of integrated instrumentation, a new generation of models, and a management framework that clearly addresses the human‐climate‐terrestrial interactions impacting our watersheds and river basins. Initially, we propose that existing “similarity classifications” (e.g., regional soil, geologic, ecologic, hydrographic digital products) can provide a starting point for organizing historical data and initiating a long‐term adaptive, multiscale observing strategy. This vision paper outlines instrumentation platforms for point, plot, reach, and hillslope scales that could be located within the “characteristic” landscapes of river basins. The network of observing platforms then forms the basis of a “Hydro‐Mesonet” that can potentially support multiscale, multiprocess scientific studies necessary to understand and improve forecasts of our water resources at the river basin scale. This paper concludes with a discussion of how a network of such sites can support research at the level of the individual researcher and scale to the level of community‐wide initiatives.
The relation between snow water equivalent (SWE) and 28 variables (27 topographically-based topographic variables and canopy density) for the Colorado River Basin, USA was explored through a multi-variate regression. These variables include location, slope and aspect at different scales, derived variables to indicate the distance to sources of moisture and proximity to and characteristics of obstacles between these moisture sources and areas of snow accumulation, and canopy density. A weekly time step of snow telemetry (SNOTEL) SWE data from 1990 through 1999 was used. The most important variables were elevation and regional scale (81 km 2) slope. Since the seasonal and inter-annual variability is high, a regression relationship should be formulated for each time step. The inter-annual variation in the relation between SWE and topographic variables partially corresponded with the amount of snow accumulated over the season and the El Niño Southern Oscillation cycle.
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