Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

G A Artan,R Lietzow,J P Verdin

doi:10.5194/hess-17-5127-2013

Abstract

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

Highlights

Every year large parts of the globe are seasonally covered by snow; for example, each year as much as half of the land surface in the Northern Hemisphere can be snow-covered (Robinson and Kukla, 1985)
From a practical point of view, we found the MI-estimated snow water equivalent (SWE) and SWE simulated from Tropical Rainfall Measuring Mission (TRMM) data sets to be unreliable sources for mapping SWE in the study area and to have a large underestimation bias compared with the SNOTEL SWE or the SWE estimated by the Snow Data Assimilation System (SNODAS) system
We presented a distributed snow accumulation and ablation model that is built on the Utah Energy Balance (UEB) model that uses data from weather forecast models as forcing input

Summary

Introduction

Every year large parts of the globe are seasonally covered by snow; for example, each year as much as half of the land surface in the Northern Hemisphere can be snow-covered (Robinson and Kukla, 1985). Most of the water supply for those snow-covered areas comes from snowmelt runoff (Daly et al, 2000; Schmugge et al, 2002; Tekeli et al, 2005); over 60 % of the precipitation in the western US falls as snow (Serreze et al, 1999). In the upper Colorado Basin, 63 % of precipitation falls as snow (Fassnacht, 2006), and 70–80 % of total annual runoff comes from snowmelt (Daly et al, 2000; Schmugge et al, 2002). Artan et al.: Comparison of different snow water products in the upper Colorado Basin during winter and spring is important to water resources and disaster management entities

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Conclusion