The effect of snow drifting on gamma snow survey results

Cork, H. F.; Loijens, Harry S.

doi:10.1016/0022-1694(80)90064-5

Cited by 13 publications

(11 citation statements)

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“…Among the underestimated SWE values with the dense tree fraction, the larger underestimations occurred over flight lines with high slopes and a range of elevations within the flight footprint (Figure 9). Previous studies found that heterogeneous characteristics within a flight line, so-called uneven effect, can cause underestimates of gamma SWE (Carroll & Carroll, 1989b;Cork & Loijens, 1980). This heterogeneity is commonly caused by snow drifting or mountainous environment.…”

Section: Potential Sources Of Error In Gamma Swementioning

confidence: 99%

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

Jacobs

Vuyovich

2020

Water Resources Research

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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.Plain Language Summary Long-term gridded snowpack data are essential for effective water resource management and flood risk assessments under a changing climate in the United States. However, the currently available snowpack data have large uncertainties, and the evaluations have been limited due to the lack of reliable independent data sets. Here, we evaluate three daily long-term (>30 years) snow water equivalent (SWE) products-empirically based SWE from passive microwave, GlobSnow-2 SWE, and University of Arizona SWE from 1982 to 2017 over the conterminous United States-using the historical airborne gamma SWE observations collected by the National Oceanic and Atmospheric Administration. Two key findings are that (1) the UA SWE product is the most reliable and (2) the long-term airborne SWE record is of great value, especially in forested regions.Special Section: Advances in remote sensing, measurement, and simulation of seasonal snow Key Points: • Long-term airborne gamma radiation observations provide reliable SWE observations even over forest regions • University of Arizona (UA) SWE has the strongest agreement with gamma SWE regardless of seasonal snow and land cover class • Large differences occur when the tree cover is 80% or higher, the slope is steeper than 2.5°, and the elevation range exceeds 100 m Supporting Information: • Supporting Information Correspondence to: E. Cho, ec1072@wildcats.unh.edu Citation: Cho, E., Jacobs, J. M., & Vuyovich, C. M. (2020). The value of long-term (40 years) airborne g...

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Section: Potential Sources Of Error In Gamma Swementioning

confidence: 99%

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

Jacobs

Vuyovich

2020

Water Resources Research

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“…Unfortunately, we do not have enough ground-based observations to quantify these potential errors. In addition, the GAMMA system saturates under conditions of very deep snow, and SWE variations over a flight line also produce SWE underestimates (Cork and Loijens 1980;Carroll and Carroll 1989a). Thus, GAMMA SWE is potentially underestimated under flight line 115.…”

Section: Model Resultsmentioning

confidence: 99%

Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX)

Liston

Hiemstra

Elder

et al. 2008

Journal of Hydrometeorology

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The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km ϫ 30 km domains, enveloping the CLPX mesocell study areas (MSAs) in Colorado. The three MSAs have distinct topography, vegetation, meteorological, and snow characteristics. Simulations were performed using a 30-m grid increment and spanned the snow accumulation season (1 October 2002-1 April 2003). Meteorological forcing was provided by 27 meteorological stations and 75 atmospheric analyses grid points, distributed using a meteorological model (MicroMet). The simulations included a data assimilation model (SnowAssim) that adjusted simulated snow water equivalent (SWE) toward ground-based and airborne SWE observations. The observations consisted of SWE over three 1 km ϫ 1 km intensive study areas (ISAs) for each MSA and a collection of 117 airborne gamma observations, each integrating area 10 km long by 300 m wide. Simulated SWE distributions displayed considerably more spatial heterogeneity than the observations alone, and the simulated distribution patterns closely fit the current understanding of snow evolution processes and observed snow depths. This is the result of the MicroMet/SnowModel's relatively finescale representations of orographic precipitation, elevation-dependant snowmelt, wind redistribution, and snow-vegetation interactions.

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“…This is encouraging for the Northern Great Plains, because topographic relief and vegetation cover are low, so there should not be much change in snow type. However, drifting of snow is significant in the region (Cork & Loijens, ; Pomeroy, Gray, & Landine, ), and the presence of vegetation, raised roads, fences, and shelter belts (which catch wind‐blown snow) could create systematic SWE patterns that might not be captured by the USACE data.…”

Section: Discussionmentioning

confidence: 99%

“…In situ ground measurements are the most trusted (i.e., accurate) SWE observations, but the spatial footprint of these measurements is very small (on the scale of centimetres) and daily (or even weekly) data are only available in a handful of locations in the region. Additionally, snow properties can vary considerably over small distances, especially in windy or rugged areas (Carroll & Carroll, ; Cork & Loijens, ), so the “true” SWE value of a large area is often unknown and may not accurately be characterized by a single measurement site (e.g., Clark et al, ).…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of snow water equivalent observations in the Northern Great Plains

Tuttle

Jacobs

Vuyovich

et al. 2018

Hydrological Processes

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In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = −3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = −9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km2 vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.

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The effect of snow drifting on gamma snow survey results

Cited by 13 publications

References 1 publication

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

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

Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX)

Intercomparison of snow water equivalent observations in the Northern Great Plains

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