|Marks, Danny - Danny|
Submitted to: Hydrological Processes
Publication Type: Abstract Only
Publication Acceptance Date: 9/13/2004
Publication Date: 9/13/2004
Citation: Johnson, J., and Marks, D. 2004. The detection and correction of snow water equivalent pressure sensor errors. Hydrological Processes, abstract, Vol 18, pgs 3513-3525.
Technical Abstract: Snow water equivalent sensors (SWE sensors) can experience errors when the base of the snow cover is at the melting temperature, the snow can support shear stresses (assumed to occur at densities greater than 200 kg-m-3), and the rate of snowmelt on the sensor is different than on the surrounding ground. Either under measurement or over measurement errors may occur at critical times when the snow cover transitions from winter-to-spring conditions and at the start of periods of rapid snowmelt. Parameters to determine the onset of SWE sensor under measurement errors are defined by a negative rate of change for SWE, a negative rate of change for snow density, and an increasing snow depth. For the onset of over measurement errors, the rate of change for SWE will be positive while snow depth decreases and the snow density rate of change exceeds a defined positive threshold. When snow temperature and density error conditions, and the three under or over measurement error indicator parameters are satisfied at the same time, a SWE sensor error has started. Real-time correction of the errors is done by multiplying the average snow cover density, set at the start of the error, with the snow depth. Once the error event ends, when the corrected SWE and SWE sensor data intersect, SWE is again determined from SWE sensor measurements. SWE sensor errors were accurately detected and corrected for five different sensors located in maritime and intermountain climatic zones when high quality SWE sensor, snow or air temperature, and snow depth measurements were available. Implementation of the error detection and correction method requires simultaneous measurements of SWE, snow depth, and snow temperature near the ground. Improved error correction can be achieved by incorporating precipitation data and estimates of snow density due to retained rain or snow melt.