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Open AccessArticle

SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions

1
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
2
Department of Earth Sciences, Montana State University, Bozeman, MT 59717, USA
3
Water Resources Science, Oregon State University, Corvallis, OR 77331, USA
4
Department of Geography, University of Nevada, Reno, NV 97331, USA
5
Pyrenean Institute of Ecology, CSIC, 50059 Zaragoza, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1276; https://doi.org/10.3390/rs10081276
Received: 19 June 2018 / Revised: 23 July 2018 / Accepted: 1 August 2018 / Published: 13 August 2018
(This article belongs to the Collection Google Earth Engine Applications)
We tested the efficacy and skill of SnowCloud, a prototype web-based, cloud-computing framework for snow mapping and hydrologic modeling. SnowCloud is the overarching framework that functions within the Google Earth Engine cloud-computing environment. SnowCloudMetrics is a sub-component of SnowCloud that provides users with spatially and temporally composited snow cover information in an easy-to-use format. SnowCloudHydro is a simple spreadsheet-based model that uses Snow Cover Frequency (SCF) output from SnowCloudMetrics as a key model input. In this application, SnowCloudMetrics rapidly converts NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product (MOD10A1) into a monthly snow cover frequency for a user-specified watershed area. SnowCloudHydro uses SCF and prior monthly streamflow to forecast streamflow for the subsequent month. We tested the skill of SnowCloudHydro in three snow-dominated headwaters that represent a range of precipitation/snowmelt runoff categories: the Río Elqui in Northern Chile; the John Day River, in the Northwestern United States; and the Río Aragón in Northern Spain. The skill of the SnowCloudHydro model directly corresponded to snowpack contributions to streamflow. Watersheds with proportionately more snowmelt than rain provided better results (R2 values: 0.88, 0.52, and 0.22, respectively). To test the user experience of SnowCloud, we provided the tools and tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience, which was generally very positive. While these initial results focus on SnowCloud, they outline methods for developing cloud-based tools that can function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in countries where scientific and computational resources are underdeveloped. View Full-Text
Keywords: cloud computing; remote sensing; snow hydrology; water resources; Google Earth Engine; user assessment; MODIS; snow cover cloud computing; remote sensing; snow hydrology; water resources; Google Earth Engine; user assessment; MODIS; snow cover
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MDPI and ACS Style

Sproles, E.A.; Crumley, R.L.; Nolin, A.W.; Mar, E.; Lopez Moreno, J.I. SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions. Remote Sens. 2018, 10, 1276.

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