Item: SWE Estimates in the Hindu Kush and the Sierra Using Passive Microwave and Reconstruction
-
-
Title: SWE Estimates in the Hindu Kush and the Sierra Using Passive Microwave and Reconstruction
Proceedings: International Snow Science Workshop 2014 Proceedings, Banff, Canada
Authors:
- Edward H. Bair [ US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH, USA ]
- Jeff Dozier [ Bren School of Environmental Science & Management, University of California Santa Barbara, CA, USA ]
- Karl Rittger [ National Snow and Ice Data Center, Boulder, CO, USA ]
- Carrie Vuyovich [ US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH, USA ]
- Robert E. Davis [ US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH, USA ]
Date: 2014-09-29
Abstract: 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 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 SWE estimates. Recently, SWE reconstruction has been shown to be accurate at estimating basin-wide SWE in the Sierra Nevada and elsewhere. Reconstruction combines a melt model with snow covered area (SCA) measurements to retroactively build the snowpack, from disappearance back to its peak. Reconstruction can only be used retrospectively, so we examine prior reconstructed 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 Afghanistan's Hindu Kush, where neither streamflow nor ground-based SWE measurements are available. In both regions, most of the runoff comes from snowmelt, and both experience occasional drought. In the Sierra Nevada, PM SWE estimates are an order of magnitude smaller than reconstructed SWE estimates and appear to suffer from biases caused by a dense canopy and a deep snowpack. In the Hindu Kush, PM SWE estimates are smaller than reconstructed estimates in basins with deep snow (>250 mm SWE) but greater than reconstructed estimates in basins with shallow snow (< 90 mm). We hypothesize that AMSR-E underestimates the snowpack when the PM SWE signal saturates, and overestimates it when the snowpack has a significant depth hoar layer. To aid operational snow assessments in Afghanistan, we develop regression relationships to predict SWE using PM SWE and SCA as independent variables, and reconstructed SWE as the dependent variable.
Object ID: ISSW14_paper_P2.36.pdf
Language of Article: English
Presenter(s):
Keywords: SWE, microwave, reconstruction
Page Number(s): 837-838
-