Dataset Information

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

ABSTRACT: Observation-based long-term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long-term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow-2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow-2 SWE when compared against the gamma SWE. However, GlobSnow-2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested-type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow-2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

SUBMITTER: Cho E

PROVIDER: S-EPMC7375042 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Cho Eunsang E Jacobs Jennifer M JM Vuyovich Carrie M CM

Water resources research 20200115 1

Observation-based long-term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long-term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow-2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal ...[more]

PMID: 32713970

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Project description:Landscape genetic analyses allow detection of fine-scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population (Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan's NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined (i.e., eight land cover classes) outperformed models where land cover was defined more coarsely (i.e., habitat/non-habitat or forest/non-forest). Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.

Dataset Information

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Publications

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Similar Datasets

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