GIS- and field data based modeling of snow water equivalent in shrub tundra

Dvornikov, Yury; Khomutov, Artem; Mullanurov, Damir; Ermokhina, Ksenia; Gubarkov, Anatoly; Leibman, Marina

doi:10.15356/2076-6734-2015-2-69-80

Cited by 13 publications

(20 citation statements)

References 19 publications

(25 reference statements)

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“…The dominant plants are mosses (Dicranum elongatum, Sphagnum), lichens and vascular plants (such as Carex aquatilis); plant distribution at the site is governed by surface moisture variability (e.g., Hinkel et al, 2003;Zona et al, 2011). There are currently no tall shrubs or woody plants established within the study site, and therefore complex topography is most likely to control the snow depth distribution within the study domain (Sturm et al, 2005;Dvornikov et al, 2015).…”

Section: Study Sitementioning

confidence: 99%

“…At each spatial scale, we can compute micro-and macrotopographic metrics such as slope and curvature as well as their correlations with corresponding probe-measured snow depth. The curvature is of particular interest, since Dvornikov et al (2015) reported strong correlations between snow surface curvature and snow depth as well as a dependency of this correlation on the DEM resolution (the lower resolution led to lower correlation coefficients). Note that the DEM resolution (0.5 m) in this study is much higher than the one (25 m) in Dvornikov et al (2015).…”

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

“…The curvature is of particular interest, since Dvornikov et al (2015) reported strong correlations between snow surface curvature and snow depth as well as a dependency of this correlation on the DEM resolution (the lower resolution led to lower correlation coefficients). Note that the DEM resolution (0.5 m) in this study is much higher than the one (25 m) in Dvornikov et al (2015). We compute a wind factor in a similar manner as Dvornikov et al (2015), with a slight modification.…”

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

“…Snow depth characterization in Arctic tundra environments has traditionally been performed using snow depth probes (Benson and Sturm, 1993;Hirashima et al, 2004;Derksen et al, 2009;Rees et al, 2014;Dvornikov et al, 2015) or modeled using terrain and vegetation information (Sturm and Wagner, 2010;Liston and Sturm, 1998;Pomeroy et al, 1997). Recently, there have been several new techniques for estimating snow depth in high resolution and in a noninvasive and spatially extensive manner.…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian methods also permit systematic incorporation of expert knowledge or process-specific information, such as the relationships between datasets and parameters. In particular, snow depth is known to be affected by topography and wind direction (e.g., Benson and Sturm, 1993;Anderson et al, 2014;Dvornikov et al, 2015). To our knowledge, such Bayesian data integration methods have never been applied to estimate end-ofwinter snow variability using multiple types of datasets.…”

Section: Introductionmentioning

confidence: 99%

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Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

et al. 2017

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Abstract. This paper compares and integrates different strategies to characterize the variability of end-of-winter snow depth and its relationship to topography in ice-wedge polygon tundra of Arctic Alaska. Snow depth was measured using in situ snow depth probes and estimated using groundpenetrating radar (GPR) surveys and the photogrammetric detection and ranging (phodar) technique with an unmanned aerial system (UAS). We found that GPR data provided highprecision estimates of snow depth (RMSE = 2.9 cm), with a spatial sampling of 10 cm along transects. Phodar-based approaches provided snow depth estimates in a less laborious manner compared to GPR and probing, while yielding a high precision (RMSE = 6.0 cm) and a fine spatial sampling (4 cm × 4 cm). We then investigated the spatial variability of snow depth and its correlation to micro-and macrotopography using the snow-free lidar digital elevation map (DEM) and the wavelet approach. We found that the end-of-winter snow depth was highly variable over short (several meter) distances, and the variability was correlated with microtopography. Microtopographic lows (i.e., troughs and centers of low-centered polygons) were filled in with snow, which resulted in a smooth and even snow surface following macrotopography. We developed and implemented a Bayesian approach to integrate the snow-free lidar DEM and multiscale measurements (probe and GPR) as well as the topographic correlation for estimating snow depth over the landscape. Our approach led to high-precision estimates of snow depth (RMSE = 6.0 cm), at 0.5 m resolution and over the lidar domain (750 m × 700 m).

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Section: Study Sitementioning

confidence: 99%

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

Section: Spatial Variability Analysis Of Topography and Snow Depthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

et al. 2017

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Measurement of the physical properties of the snowpack

Kinar

Pomeroy

2015

Reviews of Geophysics

190

202

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This paper reviews measurement techniques and corresponding devices used to determine the physical properties of the seasonal snowpack from distances close to the ground surface. The review is placed in the context of the need for scientific observations of snowpack variables that provide inputs for predictive hydrological models that help to advance scientific understanding of geophysical processes related to snow in the near-surface cryosphere. Many of these devices used to measure snow are invasive and require the snowpack to be disrupted, thereby precluding the possibility for multiple measurements to be made at the same sampling location. Moreover, many devices rely on the use of empirical calibration equations that may not be valid at all geographic locations. The spatial density of observations with most snow measurement devices is often inadequate. There is a need for improved automation of snowpack measurement instrumentation with an emphasis on field-based feedback of measurement validity in lieu of postprocessing of samples or data at a lab or office location. The scientific future of snow measurement instrumentation thereby requires a synthesis between science and engineering principles that takes into consideration geophysics and the physics of device operation.

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The High–Low Arctic boundary: How is it determined and where is it located?

Ermokhina,

Terskaia,

Ivleva

et al. 2023

Ecology and Evolution

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Geobotanical subdivision of landcover is a baseline for many studies. The High–Low Arctic boundary is considered to be of fundamental natural importance. The wide application of different delimitation schemes in various ecological studies and climatic scenarios raises the following questions: (i) What are the common criteria to define the High and Low Arctic? (ii) Could human impact significantly change the distribution of the delimitation criteria? (iii) Is the widely accepted temperature criterion still relevant given ongoing climate change? and (iv) Could we locate the High–Low Arctic boundary by mapping these criteria derived from modern open remote sensing and climatic data? Researchers rely on common criteria for geobotanical delimitation of the Arctic. Unified circumpolar criteria are based on the structure of vegetation cover and climate, while regional specifics are reflected in the floral composition. However, the published delimitation schemes vary greatly. The disagreement in the location of geobotanical boundaries across the studies manifests in poorly comparable results. While maintaining the common principles of geobotanical subdivision, we derived the boundary between the High and Low Arctic using the most up‐to‐date field data and modern techniques: species distribution modeling, radar, thermal and optical satellite imagery processing, and climatic data analysis. The position of the High–Low Arctic boundary in Western Siberia was clarified and mapped. The new boundary is located 50–100 km further north compared to all the previously presented ones. Long‐term anthropogenic press contributes to a change in the vegetation structure but does not noticeably affect key species ranges. A previously specified climatic criterion for the High–Low Arctic boundary accepted in scientific literature has not coincided with the boundary in Western Siberia for over 70 years. The High–Low Arctic boundary is distinctly reflected in biodiversity distribution. The presented approach is appropriate for accurate mapping of the High–Low Arctic boundary in the circumpolar extent.

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GIS- and field data based modeling of snow water equivalent in shrub tundra

Cited by 13 publications

References 19 publications

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Mapping snow depth within a tundra ecosystem using multiscale observations and Bayesian methods

Measurement of the physical properties of the snowpack

The High–Low Arctic boundary: How is it determined and where is it located?

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