TY - Generic T1 - Mapping Snow Grain Size Using LiDAR Intensity (Extended Abstract) T2 - 88th Annual Western Snow Conference Y1 - 2021 A1 - Chelsea Ackroyd A1 - S. McKenzie Skiles KW - intensity KW - LiDAR KW - radiometric correction KW - remote sensing KW - snow grain size AB -

Net solar radiation is the primary driver of snowmelt, which is mainly determined by snow albedo. Controls on snow albedo vary spectrally: in the visible wavelengths it is controlled by light-absorbing particles, including dust and black carbon. For clean snow, snow albedo is dependent upon ice absorption in the near infrared wavelengths, typically characterized using the effective grain size (Warren, 1982). Grain size is currently estimated using radiative transfer inversion methods that leverage reflectance data from passive optical remote sensing imagery. Theoretically, it may also be possible to relate lidar intensity to grain size when the wavelength of the lidar is in the near-infrared wavelengths: smaller grains would reflect more light back to the sensor, larger grains would reflect less. This indicates that lidar could be used to retrieve surface optical properties. Here, we evaluate how well aerial lidar intensity at 1064 nm can be related to the grain size of snow at the basin-scale.

JF - 88th Annual Western Snow Conference CY - Bozeman, MT UR - /files/PDFs/2021Ackroyd.pdf ER - TY - Generic T1 - Snow Cover Trends Over High Mountain Asia from Modscag T2 - 87th Annual Western Snow Conference Y1 - 2019 A1 - Chelsea Ackroyd A1 - S. McKenzie Skiles A1 - Karl Rittger A1 - Joachim Meyer AB -

Snow from the Hindu Kush Himalaya (HKH) significantly contributes to the water resources that millions depend upon downstream (Immerzeel et al., 2010). Several countries, including Bhutan and Pakistan, depend on snowmelt for agriculture and energy-generating purposes, both of which are main components of these local economies (Sharma et al., 2014; Atif et al., 2018). In addition to its socioeconomic impacts, snow cover in this region also contributes to Earth’s intricate feedback systems. A decrease in snow lowers the surface albedo and increases the energy absorbed, known as the snow albedo feedback; a highly effective radiative forcing mechanism
that contributes to climate change (Hall and Qu, 2006). As the HKH encompasses more snow and ice than anywhere else in the world outside of the polar regions (Panday et al., 2011), a decline in snow would alter the earth’s energy budget. Therefore, it is important to understand how snow patterns are changing over time in this region.

JF - 87th Annual Western Snow Conference CY - Reno, NV UR - /files/PDFs/2019Ackroyd.pdf ER - TY - Generic T1 - Determining Trends in Snowline Elevation in the Indus Basin using MODSCAG T2 - 86th Annual Western Snow Conference Y1 - 2018 A1 - Chelsea Ackroyd A1 - S. McKenzie Skiles A1 - Joachim Meyer A1 - NASA-JPL/ICIMOD SERVIR Team AB -

The amount of streamflow within the Indus Basin is highly dependent on snowmelt from the Himalayan mountains, even in comparison to other contributing factors such as glaciers and monsoonal rainfall. Because snow levels are sensitive to changes in climate, the water supply in this region and the millions of people downstream who rely on this finite resource are particularly susceptible to any change in temperature or precipitation. In order to observe the climate patterns in this basin, we produced a snowline elevation using both the MODIS Snow Cover and Grain Size product (MODSCAG) to determine the snow cover extent as well as ASTER data to determine the elevation. The changes in snowline elevation in the Indus Basin were assessed over a large portion of the current MODIS record (2000-2017). Based on these results, we are able to identify major climate trends and determine how such changes have impacted the Indus Basin over approximately the last two decades. (KEYWORDS: snowline elevation, Indus Basin, MODSCAG, remote sensing, snow cover)

JF - 86th Annual Western Snow Conference CY - Albuquerque, New Mexico UR - /files/PDFs/2018Ackroyd.pdf ER -