Snow Water Equivalent Estimates in the Hindu Kush and the Sierra Nevada Using Passive Microwave and Reconstruction

Accurate measurement of spatially distributed snow water equivalent (SWE) in mountain watersheds is perhaps the most significant problem in snow hydrology. We examine SWE measurements from two techniques. The first uses passive microwave estimates, provided by the National Snow and Ice Data Center, from the AMSR-E sensor aboard the Aqua satellite. Passive microwave (PM) has been used to estimate SWE for decades, and while it is subject to numerous problems, it is the only source of global real-time SWE estimates. Recently, SWE Reconstruction has been shown to be accurate at estimating basin-wide SWE in the Sierra Nevada. Reconstruction combines a melt model with snow covered area measurements to retroactively build the snowpack, from disappearance back to its peak. Reconstruction can only be used retrospectively, so it cannot be used to estimate today’s SWE. Thus, we use Reconstruction of prior water years to better understand the strengths and weaknesses of PM SWE estimates. Our test case is California’s Sierra Nevada, where we have full natural flow estimates and a large network of SWE sensors for comparison. Our application area is the Hindu Kush range in Afghanistan, where there are neither stream flow nor ground-based SWE measurements. Both regions are snowmelt dominated and subject to drought. Using annual SWE estimates from Reconstruction for verification, our results show that annual PM SWE estimates are biased in California and Afghanistan. In California, SWE estimates from AMSR-E are too low, by up to 10×. In the Amu Darya, one of the largest basins in Afghanistan, SWE estimates are too low by about 2×, while in 5/8 basins, PM SWE estimates are consistently too high. In terms of ranks, PM performs poorly, having low Spearman rank correlation coefficients. An exception is the Amu Darya, where the Spearman correlation coefficient is 0.81 for the eight years studied. We examine potential sources of error. Consistent with previous studies, we find that PM error is caused by shallow snow, deep snow, and forest cover. The explanation for the relatively low bias in SWE and relatively high correlation of rank for the Amu Darya appears to be a snowpack that was neither shallow nor exceptionally deep in a region that is nearly devoid of tree cover.