Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

Kim, Rhae Sung; Kumar, Sujay V.; Vuyovich, Carrie; Houser, Paul R.; Lundquist, Jessica D.; Mudryk, Lawrence; Durand, Michael; Barros, Ana P.; Kim, Edward J; Forman, B. A.; Gutmann, E. D.; Wrzesien, Melissa L.; Garnaud, Camille; Sandells, Melody; Marshall, Hans‐Peter; Cristea, Nicoleta; Pflug, Justin M.; Johnston, Jeremy; Cao, Yueqian; Mocko, David M.; Wang, Shugong

doi:10.1002/essoar.10504007.1

Cited by 15 publications

(27 citation statements)

References 53 publications

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“…about the magnitude of snowpack in a given location and year. Such a finding is consistent with the expectation of significant biases in individual data products (Kim et al 2021;Mudryk et al 2015;Mortimer et al 2020), and emphasizes the potential sensitivity of research claims about SWE magnitudes to data choices.…”

supporting

confidence: 86%

“…Furthermore, estimates based on passive microwave retrievals, which provide the longest-running record of spatially continuous daily SWE over a hemispheric or global domain, suffer from issues of interference from vegetation and a saturation effect that makes it difficult to accurately quantify deep snowpacks (Dietz et al 2012). Finally, reanalyses that statistically or dynamically estimate SWE allow for spatiotemporally-consistent and high-resolution estimates of snowpack, but these products are highly sensitive to the forcing data and snow physics schemes they employ (Kim et al 2021;Mudryk et al 2015). These issues notwithstanding, both satellite remote sensing observations and reanalysis products are difficult to validate against in situ data.…”

mentioning

confidence: 99%

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Observing, Measuring, and Assessing the Consequences of Snow Drought

Gottlieb

Mankin

2022

Bulletin of the American Meteorological Society

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Warmer and shorter winters from climate change will reduce snowpacks in most seasonally snow-covered regions of the world, with consequences for freshwater availability in spring and summer when people and ecosystems demand water most. Recent record low snowpacks, such as those in the winters of 2013/14 and 2014/15 in the Western United States, have led to a surge in research on ‘snow droughts,’ which are pointed to as harbingers of global warming that pose significant societal hazards. Yet despite the importance of understanding snow droughts to best prepare for their attendant impacts, the concept remains amorphous, with no agreed-upon definition of what they are, how best to measure them, and how such snow droughts connect to warm-season impacts. These knowledge gaps limit our understanding of the risks posed by snow droughts in the present and future, and thus our preparedness for their differential impacts on freshwater resources. To address these issues, we compile a hemispheric ensemble of in situ, satellite, and reanalysis snowpack datasets. We use this ensemble to evaluate the scientific challenges and uncertainties arising from differences in defining and measuring snow droughts, and identify opportunities to leverage this information to better understand the significance of snow droughts. We show that a clearer quantification of what constitutes a snow drought, including its uncertainties, improves our ability to anticipate costly and disruptive warm-season droughts, which is vital for informing risk management and adaptation to changing snow regimes.

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confidence: 86%

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confidence: 99%

Observing, Measuring, and Assessing the Consequences of Snow Drought

Gottlieb

Mankin

2022

Bulletin of the American Meteorological Society

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“…This indicates that at scales of < 0.5 km 2 , the mean of snow depth is sufficient to represent snow in SMRT (passive mode), but as the spatial coverage increases variability also increases and suggests a second parameter ( ) is needed to represent snow variability. The link between spatial coverage and snow depth variability can be used to improve land data assimilation (Kim et al, 2021) depending on the scale used for the application.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Meloche

Langlois

Rutter

et al. 2021

Preprint

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Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.

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“…SWE estimates derived from models at continental scale are subject to uncertainties in both meteorologic forcing data and model parameterizations (e.g. Kim et al, 2021). SWE is highly sensitive to changing temperature and precipitation in a warming climate; confident projections of resultant changes are uncertain because we lack baseline SWE estimates.…”

Section: Scientific Objectives Of Global Remote Sensing Of Swe and Spatial And Temporal Requirementsmentioning

confidence: 99%

“…Land surface models driven by meteorology from atmospheric reanalysis can produce hemisphericscale SWE information at coarse spatial resolutions (e.g. Kim et al, 2021), but there is a large spread between products due to differences in the meteorological forcing data (especially precipitation) and a pronounced negative bias in mountain areas (Wrzesien et al, 2019a;Cao and Barros, 2020;Lundquist et al, 2019). Differential airborne and ground-based lidar altimetry Meyer et al, 2021) and spaceborne stereo photogrammetry (Deschamps-Berger et al, 2020) can provide snow depth information at high resolution by differencing repeat digital elevation models but are limited to small spatial domains and sparse temporal sampling.…”

Section: Introductionmentioning

confidence: 99%

Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

Tsang

Durand

Derksen

et al. 2021

Preprint

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Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

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Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

Abstract: Shugong (2021) Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15 (2). pp. 771-791.

Cited by 15 publications

References 53 publications

Observing, Measuring, and Assessing the Consequences of Snow Drought

Observing, Measuring, and Assessing the Consequences of Snow Drought

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Review Article: Global Monitoring of Snow Water Equivalent using High Frequency Radar Remote Sensing

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