New snow metrics for a warming world

Abstract

Snow is Earth's most climatically sensitive land cover type. Traditional snow metrics may not be able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE) has been an effective index for streamflow forecasting, but it cannot express the effects of midwinter melt events, now expected in warming snow climates, nor can we assume that station‐based measurements will be representative of snow conditions in future decades. Remote sensing and climate model data provide capacity for a suite of multi‐use snow metrics from local to global scales. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic or overly complicated in their interpretation. We describe a suite of spatially explicit, multi‐temporal snow metrics based on global satellite data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and downscaled climate model output for the U.S. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), At‐Risk Snow (ARS), and Frequency of a Warm Winter (FWW). Using these retrospective and prospective snow metrics, we assess the current and future snow‐related conditions in three hydroclimatically different U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. In the two western U.S. watersheds, SCF and SDD show greater sensitivity to annual differences in snow cover compared with data from the ground‐based Snow Telemetry (SNOTEL) network. The eastern U.S. watershed does not have a ground‐based network of data, so these MODIS‐derived metrics provide uniquely valuable snow information. The ARS and FWW metrics show that the Truckee Watershed is highly vulnerable to conversion from snowfall to rainfall (ARS) and midwinter melt events (FWW) throughout the seasonal snow zone. In comparison, the Colorado Headwaters and Upper Connecticut Watersheds are colder and much less vulnerable through mid‐ and late‐century.

Decline in Seasonal Snow During a Projected 20-Year Dry Spell

“…Maintaining water supply reliability amidst these outcomes will benefit from collaborative, locally specific, portfolio-based approaches. By including relevant metrics and creative management strategies, we can assess and build capacity and resiliency to potential impacts related to changes in mountain hydrologic systems [31,36,37,84,97] while simultaneously reducing emissions [98] and increasing equity [22].…”

“…One broad indicator of hydroclimate change in mountains is the seasonality, or persistence, of snowpack. Snow seasonality can be quantified in many ways [27][28][29][30][31]. As part of characterizing snowpack seasonality across the conterminous United States using a gridded snow water equivalent (SWE) product [32] and the snow seasonality metric (SSM; [29], Hatchett performed a thought experiment to explore: (1) how this metric could identify generally seasonal snowpacks that historically demonstrated less seasonal to occasionally ephemeral behaviors (another form of 'at-risk' snowpacks, [34]) and (2) to estimate the volume of water stored in peak SWE in these snowpacks, with the assumption that patterns of runoff will be different in ephemeral compared to seasonal years.…”

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

“…The capability to map snow cover from space was realized early in the era of spaceborne remote sensing (Dozier et al, 1981;Warren, 1982), and 45 spaceborne multispectral instruments are now routinely used to monitor many snow surface properties: the fractional snow covered area (fSCA), snow albedo, snow grain size, reduction in albedo from light-absorbing particles (LAPs), and snow surface temperature (Painter et al, 2009;Painter et al, 2012;Lundquist et al, 2018;Bair et al, 2019;Nolin, 2010). Furthermore, remotely sensed snow cover information can be used to derive a variety of snow metrics that are relevant to the changing climate and to hydrologic systems (Nolin et al, 2021). These metrics and snow surface 50 properties have been used to estimate persistent ice cover (Painter et al, 2012), analyze the impacts of wildfire on snowmelt (Micheletty, 2014), evaluate continental climate models (Minder, 2016), force regional climate models (Oaida, 2019), partition snow and glacier melt (Armstrong et al, 2018), reconstruct snow water equivalent (SWE) (Guan, 2013;Bair et al, 2016;Rittger et al, 2016), quantify anthropogenic LAP impacts on snowmelt timing Bair et al, 2016), and forecast streamflow (Micheletty, 2021).…”

Snow Persistence Explains Stream High Flow and Low Flow Signatures with Differing Relationships by Aridity and Climatic Seasonality

“…While studies have used alternate snow metrics, such as snowfall fraction of precipitation, to examine linkages with streamflow (Berghuijs et al, 2014a), snow persistence does not require regional fine-scale meteorological data or setting and adjusting temperature and humidity thresholds for the separation of rain and snow to match observations. As continued climate warming reduces seasonal snowpacks and changes snowmelt timing, several remotely sensed snow metrics, including snow persistence, have been recommended for tracking these changes as well as their hydrologic 60 consequences (Nolin et al, 2021). between SP and hydrologic behaviour is that it does not require on-the-ground measurements of variables.…”