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  • 1
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2020
    In:  Water Resources Research Vol. 56, No. 2 ( 2020-02)
    In: Water Resources Research, American Geophysical Union (AGU), Vol. 56, No. 2 ( 2020-02)
    Abstract: Using a freshwater runoff model suited for snow and ice melt, we classified 13 flow regimes across coastal Gulf of Alaska watersheds In a region of rapid environmental change, fuzzy classification identified watersheds with potential transitional streamflow patterns Our results provide context for future studies evaluating the influence of hydrologic change on coastal Gulf of Alaska ecosystems
    Type of Medium: Online Resource
    ISSN: 0043-1397 , 1944-7973
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2020
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    detail.hit.zdb_id: 5564-5
    SSG: 13
    SSG: 14
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2021
    In:  Hydrological Processes Vol. 35, No. 6 ( 2021-06)
    In: Hydrological Processes, Wiley, Vol. 35, No. 6 ( 2021-06)
    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.
    Type of Medium: Online Resource
    ISSN: 0885-6087 , 1099-1085
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
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    SSG: 14
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  • 3
    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
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
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  • 4
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 25, No. 9 ( 2021-08-31), p. 4651-4680
    Abstract: Abstract. A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/, last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2100610-6
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  • 5
    Online Resource
    Online Resource
    Copernicus GmbH ; 2019
    In:  The Cryosphere Vol. 13, No. 6 ( 2019-06-12), p. 1597-1619
    In: The Cryosphere, Copernicus GmbH, Vol. 13, No. 6 ( 2019-06-12), p. 1597-1619
    Abstract: Abstract. A high spatial resolution (250 m), distributed snow evolution and ablation model, SnowModel, is used to estimate current and future scenario freshwater runoff into Glacier Bay, Alaska, a fjord estuary that makes up part of Glacier Bay National Park and Preserve. The watersheds of Glacier Bay contain significant glacier cover (tidewater and land-terminating) and strong spatial gradients in topography, land cover, and precipitation. The physical complexity and variability of the region produce a variety of hydrological regimes, including rainfall-, snowmelt-, and ice-melt-dominated responses. The purpose of this study is to characterize the recent historical components of freshwater runoff to Glacier Bay and quantify the potential hydrological changes that accompany the worst-case climate scenario during the final decades of the 21st century. The historical (1979–2015) mean annual runoff into Glacier Bay is found to be 24.5 km3 yr−1, or equivalent to a specific runoff of 3.1 m yr−1, with a peak in July, due to the overall dominance of snowmelt processes that are largely supplemented by ice melt. Future scenarios (2070–2099) of climate and glacier cover are used to estimate changes in the hydrologic response of Glacier Bay. Under the representative concentration pathway (RCP) 8.5, the mean of five climate models produces a mean annual runoff of 27.5 km3 yr−1, 3.5 m yr−1, representing a 13 % increase from historical conditions. When spatially aggregated over the entire bay region, the projection scenario seasonal hydrograph is flatter, with weaker summer flows and higher winter flows. The peak flows shift to late summer and early fall, and rain runoff becomes the dominant overall process. The timing and magnitudes of modeled historical runoff are supported by a freshwater content analysis from a 24-year oceanographic conductivity–temperature–depth (CTD) dataset from the U.S. National Park Service's Southeast Alaska Inventory and Monitoring Network (SEAN). The hydrographs of individual watersheds display a diversity of changes between the historical period and projection scenario simulations, depending upon total glacier coverage, elevation distribution, landscape characteristics, and seasonal changes to the freezing line altitude.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2393169-3
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 20 ( 2020-10-13), p. 3341-
    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
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 7
    Online Resource
    Online Resource
    Elsevier BV ; 2023
    In:  Remote Sensing of Environment Vol. 285 ( 2023-02), p. 113403-
    In: Remote Sensing of Environment, Elsevier BV, Vol. 285 ( 2023-02), p. 113403-
    Type of Medium: Online Resource
    ISSN: 0034-4257
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1498713-2
    SSG: 11
    SSG: 14
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  • 8
    In: The Cryosphere, Copernicus GmbH, Vol. 13, No. 7 ( 2019-07-04), p. 1767-1784
    Abstract: Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth (h), day of water year (DOY) and climatological (30-year normal) values for winter (December, January, February) precipitation (PPTWT), and the difference (TD) between mean temperature of the warmest month and mean temperature of the coldest month, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE of less than 60 mm. The model is additionally validated against two completely independent sets of data: one from western North America and one from the northeastern United States. Finally, the results are compared with three other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data sets.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2393169-3
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  • 9
    In: The Lancet, Elsevier BV, Vol. 397, No. 10289 ( 2021-05), p. 2049-2059
    Type of Medium: Online Resource
    ISSN: 0140-6736
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
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    detail.hit.zdb_id: 3306-6
    detail.hit.zdb_id: 1476593-7
    SSG: 5,21
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  • 10
    In: JAMA, American Medical Association (AMA), Vol. 326, No. 17 ( 2021-11-02), p. 1690-
    Type of Medium: Online Resource
    ISSN: 0098-7484
    RVK:
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2021
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    detail.hit.zdb_id: 2018410-4
    SSG: 5,21
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