{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T19:13:42Z","timestamp":1770232422892,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,13]],"date-time":"2018-08-13T00:00:00Z","timestamp":1534118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX16AG35G"],"award-info":[{"award-number":["NNX16AG35G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Earth Science Information Partners (ESIP)","award":["Incubator Project"],"award-info":[{"award-number":["Incubator Project"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2019s 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 R\u00edo Elqui in Northern Chile; the John Day River, in the Northwestern United States; and the R\u00edo Arag\u00f3n 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.<\/jats:p>","DOI":"10.3390\/rs10081276","type":"journal-article","created":{"date-parts":[[2018,8,13]],"date-time":"2018-08-13T11:27:13Z","timestamp":1534159633000},"page":"1276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["SnowCloudHydro\u2014A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1245-1653","authenticated-orcid":false,"given":"Eric A.","family":"Sproles","sequence":"first","affiliation":[{"name":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"},{"name":"Department of Earth Sciences, Montana State University, Bozeman, MT 59717, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan L.","family":"Crumley","sequence":"additional","affiliation":[{"name":"Water Resources Science, Oregon State University, Corvallis, OR 77331, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne W.","family":"Nolin","sequence":"additional","affiliation":[{"name":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"},{"name":"Department of Geography, University of Nevada, Reno, NV 97331, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugene","family":"Mar","sequence":"additional","affiliation":[{"name":"College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7270-9313","authenticated-orcid":false,"given":"Juan Ignacio","family":"Lopez Moreno","sequence":"additional","affiliation":[{"name":"Pyrenean Institute of Ecology, CSIC, 50059 Zaragoza, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1029\/2011EO430001","article-title":"Mountain hydrology, snow color, and the fourth paradigm","volume":"92","author":"Dozier","year":"2011","journal-title":"Eos Trans. 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