The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo

Abstract

39 Snow cover and its melt dominate regional climate and water resources in many of the 40 world's mountainous regions. Snowmelt timing and magnitude in mountains are controlled 41 predominantly by absorption of solar radiation and the distribution of snow water equivalent 42 (SWE), and yet both of these are very poorly known even in the best-instrumented mountain 43 regions of the globe. Here we describe and present results from the Airborne Snow 44 Observatory (ASO), a coupled imaging spectrometer and scanning lidar, combined with 45 distributed snow modeling, developed for the measurement of snow spectral albedo/broadband 46 albedo and snow depth/SWE. Snow density is simulated over the domain to convert snow 47 depth to SWE. The result presented in this paper is the first operational application of remotely 48 sensed snow albedo and depth/SWE to quantify the volume of water stored in the seasonal 49 snow cover. The weekly values of SWE volume provided by the ASO program represent a 50 critical increase in the information available to hydrologic scientists and resource managers in 51 mountain regions. 52 53 3 54 55Introduction 56Snow cover and its melt dominate sources in many of the world's mountainous regions, and 57 in adjacent areas dependent on river flows originating from mountain basins. In the western 58 United States, snowmelt runoff dominates the surface water hydrology, providing more than 59 75% of the total freshwater (Bales et al., 2006). However, the region faces significant water 60 resource challenges due to the intersection of increasing demand from population growth and 61 changes in runoff volume and timing due to climate change (Christensen et al., 2004; 62 Christensen and Lettenmaier, 2007). 63Observations indicate an ongoing reduction in the seasonal duration of mountain snowpacks 64 Mote et al., 2005;Hamlet et al., 2007;Clow, 2010), a trend likely to 65 continue under unimpeded warming associated with climate change (Christensen and 66 Lettenmaier, 2007;Deems et al., 2013b). Moreover, increasing temperatures in desert systems 67 will increase dust loading to mountain snow cover (Munson et al., 2011), thus reducing the 68 snow cover albedo and accelerating snowmelt runoff (Painter et al., 2007;Painter et al., 2010; 69 Skiles et al., 2012). 70The two most critical properties for understanding timing and magnitude of snowmelt runoff 71 are the spatial distributions of snow albedo and snow water equivalent (SWE) (Blöschl, 1991; 72 Kirnbauer et al., 1994). Despite the importance of these properties in controlling volume and 73 timing of runoff, the mountain snowpack remains poorly quantified around the globe (Bales et 74 al., 2006) (Barnett et al., 2005, leaving runoff and climate models poorly constrained and our 75 physical understanding of mountain snowmelt driven systems incomplete. 76 4In the western US, we have relatively sparse measurements of SWE, mostly at lower and 77 middle elevations and only a few per basin (Bales et al., 2006). These measurements are used as 78 in...

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

“…In California, for example, the California Snow Cooperative Survey performs snow-depth and SWE manual courses at monthly time scale over the entire Sierra Nevada and southern Cascade (http://cdec.water.ca.gov/ snow/). Over the same mountain range, the Airborne Snow Observatory measures snow depth every two weeks during the snowmelt season on selected catchments using LiDAR scans [62]. Over European Alps, various agencies routinely manually measure snow depth for avalanche and hydrologic forecasting (http://www.avalanches.org/eaws/en/main.php).…”

“…While SWE is the primary variable of interest for hydrologic applications, snow depth is a key variable for avalanche forecasting [20]. Because snow density is generally less variable in space than snow depth [67,68], knowledge of snow depth distribution can also potentially be converted to SWE via empirical regressions [69] or dynamic models [62]. In this context, portable, low-cost techniques, such as UAS and a MS, can fill an important gap between laborious, manual measurements and large-scale surveys at lower resolution using satellites or manned aircrafts.…”

“…We first performed a cloud to cloud comparison (C1), meaning that the clouds of points from UAS and MS were directly compared. Second, we compared the MS cloud with the UAS-based computed DSM (C2) as DSMs are largely used as final products in snow applications [62]. We avoided a DSM to DSM comparison between MS and UAS as this would have been affected by uncertainties connected to each DSM generation.…”

Ecosystem responses to elevated CO<sub>2</sub> using airborne remote sensing at Mammoth Mountain, California

“…The Airborne Snow Observatory (ASO, http://aso.jpl.nasa.gov) is a coupled lidar (Riegl Q1560) and spectrometer (CASI-1500) mounted on a King Air A90 aircraft, and was originally developed to monitor snow in the mountains for water resource management 35 (Painter et al, 2016). The Riegl Q1560 is a dual scanning lidar with two 1064 nm laser sources; each scanner is tilted in the along-track direction by ±8° and the cross-track direction (max.…”

Retrospective forecasts of the upcoming winter season snow accumulation in the Inn headwaters (European Alps)

“…For hydropower generation it is interesting to know if a winter season is above or below average regarding the accumulation of snow. For water management demands such as efficient hydropower production, large efforts have been made to measure SWE in catchments of reservoirs (Painter et al, 2016;Krajči et al, 2017;Schattan et al, 2017), to simulate distributed SWE in basins of reservoirs and water intakes Hanzer et al, 2016), to improve flood forecasts with distributed SWE data (Schöber et al, 2014), and to model future runoff under climate change conditions in snow-and ice-melt-dominated catchments (Barnett et al, 2005;Finger et al, 2012;Hanzer et al, 2017). Gridded SWE data used for initialization of a process-based hydrological model improved predictions of SWE with lead times up to 1 month (Jörg-Hess et al, 2015).…”