High Resolution SnowModel Simulations Reveal Future Elevation‐Dependent Snow Loss and Earlier, Flashier Surface Water Input for the Upper Colorado River Basin

Hammond, John C.; Sexstone, G. A.; Rey, David M.; Barnhart, T. B.; McGrath, Daniel; Driscoll, Jessica M.; Liston, Glen E.; Rasmussen, Kristen L.; McGrath, D.; Fassnacht, S. R.; Kampf, Stephanie K.

doi:10.1029/2022ef003092

Cited by 10 publications

(9 citation statements)

References 123 publications

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“…We developed a SnowModel domain with a spatial resolution of 100 m that encompasses the field area for this study (Figure 1). A spatial resolution of 100 m was chosen, as it is a common choice in process-based snow modeling [16,59]. SnowModel simulations were run at a 3-hour timestep for WY 2022 and WY 2023 using the National Land Data Assimilation System-2 atmospheric forcing data [60].…”

Section: Snowmodel and Data Analysis Integrationmentioning

confidence: 99%

“…SnowModel simulations were run at a 3-hour timestep for WY 2022 and WY 2023 using the National Land Data Assimilation System-2 atmospheric forcing data [60]. A description of the collection of SnowModel submodels including the downscaling of input meteorology can be found in other recent SnowModel studies [16,59,61]. To ensure a reasonable representation of input meteorology for the SnowModel simulations, we utilized both the Spud Mountain SNOTEL and CSAS Swamp Angel study plot stations to perform a standard bias correction to the coarser 1/8 degree resolution, from October to April, for NLDAS-2 air temperature, precipitation, and incoming shortwave radiation for both simulation years.…”

Section: Snowmodel and Data Analysis Integrationmentioning

confidence: 99%

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Modeling Forest Snow Using Relative Canopy Structure Metrics

Moeser,

Sexstone,

Kurzweil

2024

Water

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Snow and watershed models typically do not account for forest structure and shading; therefore, they display substantial uncertainty when attempting to account for forest change or when comparing hydrological response between forests with varying characteristics. This study collected snow water equivalent (SWE) measurements in a snow-dominated forest in Colorado, the United States, with variable canopy structure. The SWE measurements were integrated with 1 m Lidar derived canopy structure metrics and incoming solar radiation to create empirical SWE offset equations for four canopy structure groupings (forest gaps, south-facing forest edges, north-facing forest edges, and the interior forest) that varied in size compared to an open area. These simple equations indirectly integrate terrain shading and canopy shading and were able to estimate 40 to 70% of SWE variation in a heterogenous forested environment. The equations were then applied to a snow melt model with a 100 m grid size by applying the area-weighted average of SWE offsets from the four canopy structure groupings in each model cell. This tiled model configuration allowed for the model to better represent the subgrid heterogeneity of a forest environment that can be seen through an ensemble or range of potential outputs rather than a singular estimate.

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Section: Snowmodel and Data Analysis Integrationmentioning

confidence: 99%

Section: Snowmodel and Data Analysis Integrationmentioning

confidence: 99%

Modeling Forest Snow Using Relative Canopy Structure Metrics

Moeser,

Sexstone,

Kurzweil

2024

Water

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“…The extensive research carried out in past decades has highlighted the declining trend in mountain snow cover in many regions through observations and model analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] . Many studies have shown that reduced snow cover leads to earlier snowmelt and reduced snow accumulation, ultimately affecting climate systems and human societies.…”

Section: Mainmentioning

confidence: 99%

Heterogeneous Warming Rates and Decline in Snow Persistence across Mountains Worldwide

Blau²,

Kad

et al. 2023

Preprint

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The warming of mountainous regions has become evident in recent years with a global average warming rate of 1.41 °C from 1979 to 2022. Our study aims to understand observed changes in snow persistence across mountain regions worldwide, using reanalysis and satellite data. By taking a global perspective, we bridge the gap between regional findings and provide a comprehensive understanding of the impact of increases in global warming on mountain snow decline. Here, we demonstrate that the mountain snow persistence is significantly declining over the past 44 years, with a global mean decline of 7.67 %. The trends in regional snow cover persistence exhibit a heterogeneous and non-linear response to its regional warming rate. A decomposition of snow depth reveals the snowfall ratio majorly contributes to high latitudes, and the snowfall accumulation ratio plays a large role where the melting trend exceeds the snowfall trend. The recent Coupled Model Intercomparison Project 6 (CMIP6) has shown limitations in accurately capturing the mountain snow persistence. Our study provides valuable insights into the intricate interplay between global warming and declining snow cover persistence. Our findings attribute the observed reduction in snow persistence and shed light on decreased snowfall with increased snow melting contributions. Disclosure of the impact of this changing mountain snow is critical for informing sustainable development and preparing for the potential consequences of these changes.

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“…Though never intended to represent the average snow conditions across large basins, they are often used as a representative proxy for distributed snowpack studies. In fact, the network only represents a small spatial footprint of the seasonal snow zone and due to the limited elevations the network covers (Bales et al, 2006) and it may become less representative of total snowpack in a less snowy future (Hammond et al, 2023). Remote sensing products can help improve spatial coverage of snowpack estimates but can be hindered by infrequent satellite return time, vegetation cover, complex topography, cloud masking, and require assumptions to produce metrics such as snow water equivalent (SWE) (Gascoin et al, 2024;Rittger et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

Barnhart,

Putman,

Heldmyer

et al. 2024

Water Resources Research

Self Cite

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Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful.

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High Resolution SnowModel Simulations Reveal Future Elevation‐Dependent Snow Loss and Earlier, Flashier Surface Water Input for the Upper Colorado River Basin

Abstract: Globally, snow loss is causing cascading impacts to soil storage, evapotranspiration, streamflow and groundwater recharge; sediment transport and hazards; aquatic and terrestrial ecology; as well as human water use for public

Cited by 10 publications

References 123 publications

Modeling Forest Snow Using Relative Canopy Structure Metrics

Modeling Forest Snow Using Relative Canopy Structure Metrics

Heterogeneous Warming Rates and Decline in Snow Persistence across Mountains Worldwide

Evaluating Distributed Snow Model Resolution and Meteorology Parameterizations Against Streamflow Observations: Finer Is Not Always Better

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