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  • MDPI AG  (3)
  • 1
    Online Resource
    Online Resource
    SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions
    MDPI AG ; 2018
    In:  Remote Sensing Vol. 10, No. 8 ( 2018-08-13), p. 1276-
    Details
    In: Remote Sensing, MDPI AG, Vol. 10, No. 8 ( 2018-08-13), p. 1276-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs10081276
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2513863-7
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  • 2
    Online Resource
    Online Resource
    SnowCloudMetrics: Snow Information for Everyone
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 20 ( 2020-10-13), p. 3341-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 20 ( 2020-10-13), p. 3341-
    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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12203341
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 19 ( 2022-10-07), p. 4979-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 19 ( 2022-10-07), p. 4979-
    Abstract: Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14194979
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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