Karl Rittger

Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory

Author(s): Bair, EH; Rittger, K; Davis, RE; Painter, TH; Dozier, J | Abstract:

A first overview of SnowEx ground-based remote sensing activities during the winter 2016–2017

NASA SnowExs goal is estimating how much water is stored in Earths terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

10 In many mountains, snowmelt provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely-sensed information as predictors and 15 reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer’s lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques—bagged regression trees and feed-forward neural networks— produced similar mean results, with 0–14% bias and 46–48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the 20 methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.