SnowCloudMetrics: Snow Information for Everyone

Ryan L Crumley,Ross T Palomaki,Anne W Nolin,Eugene J Mar,Eric A Sproles

doi:10.3390/rs12203341

Ryan L Crumley, Ross T Palomaki + Show 3 more

Open Access

https://doi.org/10.3390/rs12203341

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Abstract

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

Highlights

We have developed SnowCloudMetrics as a cloud-computing portal where users can access and customize snow information globally to help address a wide range of challenges
For the Amu Darya Basin (ADB), yearly snow cover frequency (SCF) images were calculated in Google Earth Engine (GEE) from 2001 to 2019
Rsnemoowte Smenes.tr2i0c18p, 5r,oxvFidOeRsPEvEaRluRaEbVlIeEWsp1a1tioafl18information that can augment in situ observations of snow water equivalent and snow depth that may exist within the study area. spnroovwidmesevtraiclupabrolevisdpeastiavlailnufaobrlme astpioantiathl aint fcoarnmaautgiomnetnhtaitncsaintuaoubgsmerevnattiionnssiotuf sonboswerwvaattieornesqoufivsanloenwt wanadtesrneoqwuidveapletnhttahnadt msnaoywexdisetpwthitthhiant tmheaysteuxdisytawreitah. in the study area

Summary

Introduction

Satellite observations show global changes in the spatial extent of snow [8,9], maximum snow water equivalence (SWE), elevational distributions in mountainous regions, and the timing of meltwater production and snow disappearance from the land surface [10,11,12,13]. Data suggest that high elevation mountain regions are warming faster than low elevations, with the most significant trends along the 0 ◦C isotherm level due to the albedo-temperature feedback associated with declining snow cover and glacier extent [14,15]. The annual duration of snow cover is critically important to the winter tourism economy. In spite of snow’s critical importance to multiple sectors, informative in situ snow observations are sparse in most regions of the world

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