New snow metrics for a warming world. Issue 6 (23rd June 2021)

• Record Type: Journal Article
• Authors: Nolin, Anne W.; Sproles, Eric A.; Rupp, David E.; Crumley, Ryan L.; Webb, Mariana J.; Palomaki, Ross T.; Mar, Eugene
• Is Part Of: Hydrological processes. Volume 35:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-23
• Subjects: climate change -- climate model -- remote sensing -- SNOTEL -- snow cover -- SnowCloudMetrics; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14262
• Languages: English
• ISSNs: 0885-6087

Abstract

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. Abstract : New snow metrics Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), Frequency of a Warm Winter (FWW) and At‐Risk Snow (ARS), are produced on Google Earth Engine (GEE). We applied these metrics to three U.S. watersheds: the Truckee, Colorado Headwaters, and Upper Connecticut. SCF and SDD are sensitive to yearly changes in snow cover. Future winters in the Truckee will be warmer with little snow whereas the other watersheds will have longer duration snow cover, lower FWW, and less ARS.