Rain versus Snow in the Sierra Nevada, California: Comparing Doppler Profiling Radar and Surface Observations of Melting Level

2008 ◽  
Vol 9 (2) ◽  
pp. 194-211 ◽  
Author(s):  
Jessica D. Lundquist ◽  
Paul J. Neiman ◽  
Brooks Martner ◽  
Allen B. White ◽  
Daniel J. Gottas ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Spatial Location ◽  
Snow Accumulation ◽  
Mountain Range ◽  
Diurnal Cycles ◽  
Heating And Cooling ◽  
Wide Range ◽  
C 50 ◽  
Melting Level

Abstract The maritime mountain ranges of western North America span a wide range of elevations and are extremely sensitive to flooding from warm winter storms, primarily because rain falls at higher elevations and over a much greater fraction of a basin’s contributing area than during a typical storm. Accurate predictions of this rain–snow line are crucial to hydrologic forecasting. This study examines how remotely sensed atmospheric snow levels measured upstream of a mountain range (specifically, the bright band measured above radar wind profilers) can be used to accurately portray the altitude of the surface transition from snow to rain along the mountain’s windward slopes, focusing on measurements in the Sierra Nevada, California, from 2001 to 2005. Snow accumulation varies with respect to surface temperature, diurnal cycles in solar radiation, and fluctuations in the free-tropospheric melting level. At 1.5°C, 50% of precipitation events fall as rain and 50% as snow, and on average, 50% of measured precipitation contributes to increases in snow water equivalent (SWE). Between 2.5° and 3°C, snow is equally likely to melt or accumulate, with most cases resulting in no change to SWE. Qualitatively, brightband heights (BBHs) detected by 915-MHz profiling radars up to 300 km away from the American River study basin agree well with surface melting patterns. Quantitatively, this agreement can be improved by adjusting the melting elevation based on the spatial location of the profiler relative to the basin: BBHs decrease with increasing latitude and decreasing distance to the windward slope of the Sierra Nevada. Because of diurnal heating and cooling by radiation at the mountain surface, BBHs should also be adjusted to higher surface elevations near midday and lower elevations near midnight.

scholarly journals Comparison of Methods to Estimate Snow Water Equivalent at the Mountain Range Scale: A Case Study of the California Sierra Nevada

2017 ◽  
Vol 18 (4) ◽  
pp. 1101-1119 ◽  
Author(s):  
Melissa L. Wrzesien ◽  
Michael T. Durand ◽  
Tamlin M. Pavelsky ◽  
Ian M. Howat ◽  
Steven A. Margulis ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Snow Accumulation ◽  
Energy Budgets ◽  
Mountain Range ◽  
Order Of Magnitude ◽  
Snow Water

Abstract Despite the importance of snow in global water and energy budgets, estimates of global mountain snow water equivalent (SWE) are not well constrained. Two approaches for estimating total range-wide SWE over Sierra Nevada, California, are assessed: 1) global/hemispherical models and remote sensing and models available for continental United States (CONUS) plus southern Canada (CONUS+) available to the scientific community and 2) regional climate model simulations via the Weather Research and Forecasting (WRF) Model run at 3, 9, and 27 km. As no truth dataset provides total mountain range SWE, these two approaches are compared to a “reference” SWE consisting of three published, independent datasets that utilize/validate against in situ SWE measurements. Model outputs are compared with the reference datasets for three water years: 2005 (high snow accumulation), 2009 (average), and 2014 (low). There is a distinctive difference between the reference/WRF datasets and the global/CONUS+ daily estimates of SWE, with the former suggesting up to an order of magnitude more snow. Results are qualitatively similar for peak SWE and 1 April SWE for all three years. Analysis of SWE time series indicates that lower SWE for global and CONUS+ datasets is likely due to precipitation, rain/snow partitioning, and ablation parameterization differences. It is found that WRF produces reasonable (within 50%) estimates of total mountain range SWE in the Sierra Nevada, while the global and CONUS+ datasets underestimate SWE.

scholarly journals Impact of North Atlantic Oscillation on the Snowpack in Iberian Peninsula Mountains

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 105 ◽  
Author(s):  
Esteban Alonso-González ◽  
Juan I. López-Moreno ◽  
Francisco M. Navarro-Serrano ◽  
Jesús Revuelto
Keyword(s):  
North Atlantic Oscillation ◽  
Iberian Peninsula ◽  
Sierra Nevada ◽  
North Atlantic ◽  
Snow Water Equivalent ◽  
Mountain Range ◽  
Winter Climate ◽  
Mountain Ranges ◽  
Iberian Range ◽  
The Impact

The North Atlantic Oscillation (NAO) is considered to be the main atmospheric factor explaining the winter climate and snow evolution over much of the Northern Hemisphere. However, the absence of long-term snow data in mountain regions has prevented full assessment of the impact of the NAO at the regional scales, where data are limited. In this study, we assessed the relationship between the NAO of the winter months (DJFM-NAO) and the snowpack of the Iberian Peninsula. We simulated temperature, precipitation, and snow data for the period 1979–2014 by dynamic downscaling of ERA-Interim reanalysis data, and correlated this with the DJFM-NAO for the five main mountain ranges of the Iberian Peninsula (Cantabrian Range, Central Range, Iberian Range, the Pyrenees, and the Sierra Nevada). The results confirmed that negative DJFM-NAO values generally occur during wet and mild conditions over most of the Iberian Peninsula. Due to the direction of the wet air masses, the NAO has a large influence on snow duration and the annual peak snow water equivalent (peak SWE) in most of the mountain ranges in the study, mostly on the slopes south of the main axis of the ranges. In contrast, the impact of NAO variability is limited on north-facing slopes. Negative (positive) DJFM-NAO values were associated with longer (shorter) duration and higher (lower) peak SWEs in all mountains analyzed in the study. We found marked variability in correlations of the DJFM-NAO with snow indices within each mountain range, even when only the south-facing slopes were considered. The correlations were stronger for higher elevations in the mountain ranges, but geographical longitude also explained the intra-range variability in the majority of the studied mountains.

scholarly journals Nutrient and mercury deposition and storage in an alpine snowpack of the Sierra Nevada, USA

2015 ◽  
Vol 12 (12) ◽  
pp. 3665-3680 ◽  
Author(s):  
C. Pearson ◽  
R. Schumer ◽  
B. D. Trustman ◽  
K. Rittger ◽  
D. W. Johnson ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Dry Deposition ◽  
Wet Deposition ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Organic N ◽  
Lake Tahoe Basin ◽  
Inorganic N ◽  
Elevation Gradients ◽  
Tahoe Basin

Abstract. Biweekly snowpack core samples were collected at seven sites along two elevation gradients in the Tahoe Basin during two consecutive snow years to evaluate total wintertime snowpack accumulation of nutrients and pollutants in a high-elevation watershed of the Sierra Nevada. Additional sampling of wet deposition and detailed snow pit profiles were conducted the following year to compare wet deposition to snowpack storage and assess the vertical dynamics of snowpack nitrogen, phosphorus, and mercury. Results show that, on average, organic N comprised 48% of all snowpack N, while nitrate (NO3--N) and TAN (total ammonia nitrogen) made up 25 and 27%, respectively. Snowpack NO3--N concentrations were relatively uniform across sampling sites over the sampling seasons and showed little difference between seasonal wet deposition and integrated snow pit concentrations. These patterns are in agreement with previous studies that identify wet deposition as the dominant source of wintertime NO3--N deposition. However, vertical snow pit profiles showed highly variable concentrations of NO3--N within the snowpack indicative of additional deposition and in-snowpack dynamics. Unlike NO3--N, snowpack TAN doubled towards the end of winter, which we attribute to a strong dry deposition component which was particularly pronounced in late winter and spring. Organic N concentrations in the snowpack were highly variable (from 35 to 70%) and showed no clear temporal, spatial, or vertical trends throughout the season. Integrated snowpack organic N concentrations were up to 2.5 times higher than seasonal wet deposition, likely due to microbial immobilization of inorganic N as evident by coinciding increases in organic N and decreases in inorganic N in deeper, aged snow. Spatial and temporal deposition patterns of snowpack P were consistent with particulate-bound dry deposition inputs and strong impacts from in-basin sources causing up to 6 times greater enrichment at urban locations compared to remote sites. Snowpack Hg showed little temporal variability and was dominated by particulate-bound forms (78% on average). Dissolved Hg concentrations were consistently lower in snowpack than in wet deposition, which we attribute to photochemically driven gaseous re-emission. In agreement with this pattern is a significant positive relationship between snowpack Hg and elevation, attributed to a combination of increased snow accumulation at higher elevations causing limited light penetration and lower photochemical re-emission losses in deeper, higher-elevation snowpack. Finally, estimates of basin-wide loading based on spatially extrapolated concentrations and a satellite-based snow water equivalent reconstruction model identify snowpack chemical loading from atmospheric deposition as a substantial source of nutrients and pollutants to the Lake Tahoe Basin, accounting for 113 t of N, 9.3 t of P, and 1.2 kg of Hg each year.

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

2008 ◽  
Vol 9 (5) ◽  
pp. 957-976 ◽  
Author(s):  
Glen E. Liston ◽  
Christopher A. Hiemstra ◽  
Kelly Elder ◽  
Donald W. Cline
Keyword(s):  
Snow Water Equivalent ◽  
Distribution Patterns ◽  
Snow Accumulation ◽  
Meteorological Model ◽  
Meteorological Forcing ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Snow Water ◽  
Grid Points ◽  
Evolution Modeling

Abstract 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 × 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 × 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.

scholarly journals Investigating the Variability of High-Elevation Seasonal Orographic Snowfall Enhancement and Its Drivers across Sierra Nevada, California

2018 ◽  
Vol 19 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Laurie S. Huning ◽  
Steven A. Margulis
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Mountain Range ◽  
Distributed Data ◽  
Mountainous Regions ◽  
Average Maximum ◽  
Spatially Distributed ◽  
Orographic Enhancement ◽  
Snow Water

Abstract While orographically driven snowfall is known to be important in mountainous regions, a complete understanding of orographic enhancement from the basin to the mountain range scale is often inhibited by limited distributed data and spatial and/or temporal resolutions. A novel, 90-m spatially distributed snow water equivalent (SWE) reanalysis was used to overcome these limitations. Leveraging this SWE information, the interannual variability of orographic gradients in cumulative snowfall (CS) was investigated over 14 windward (western) basins in the Sierra Nevada in California from water years 1985 to 2015. Previous studies have not provided a detailed multidecadal climatology of orographic CS gradients or compared wet-year and dry-year orographic CS patterns, distributions, and gradients across an entire mountain range. The magnitude of seasonal CS gradients range from over 15 cm SWE per 100-m elevation to under 1 cm per 100 m with a 31-yr average of 6.1 cm per 100 m below ~2500 m in the western basins. The 31-yr average CS gradients generally decrease in higher elevation zones across the western basins and become negative at the highest elevations. On average, integrated vapor transport and zonal winds at 700 hPa are larger during wet years, leading to higher orographically driven CS gradients across the Sierra Nevada than in dry years. Below ~2500 m, wet years yield greater enhancement (relative to dry years) by factors of approximately 2 and 3 in the northwestern and southwestern basins, respectively. Overall, the western Sierra Nevada experiences about twice as much orographic enhancement during wet years as in dry years below the elevation corresponding to the 31-yr average maximum CS.

Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions

2022 ◽  
Keyword(s):  
Sierra Nevada ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Maximum Temperature ◽  
Climate Data ◽  
Ecological Processes ◽  
Historical Climate ◽  
Wide Range ◽  
Monthly Streamflow ◽  
Climate Maps

Abstract High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.

Estimation of snow water equivalent in a mountain range by using a dynamic regression approach

2020 ◽  
Author(s):  
Antonio-Juan Collados-Lara ◽  
David Pulido-Velazquez ◽  
Eulogio Pardo-Igúzquiza ◽  
Esteban Alonso-González ◽  
Juan Ignacio López-Moreno
Keyword(s):  
Sierra Nevada ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Coefficient Of Determination ◽  
Mountain Range ◽  
Snow Cover Area ◽  
Explanatory Variables ◽  
Regression Approach ◽  
Snow Water

<p>The snow dynamics in alpine systems governs the hydrological cycle in these regions. However, snow data are usually limited due to poor accessibility and limited funds. On the other hand, the majority of scientific studies about snow resources are carried out at mountain slope or basin scale. The main goal of this work is to propose a parsimonious methodology to estimate snow water equivalent (SWE) at mountain range scale. A regression model that includes non-steady explanatory variables is proposed to assess snow depth dynamic based on the information coming from snow depth point observations, a digital elevation model, snow cover area from satellite and a precipitation index representative of the area. The main advantages of the method are its applicability in cases with limited information and in mountain ranges scales. In the proposed methodology different regression model structures with different degrees of complexity are calibrated combining steady and non-steady explanatory variables (elevation, slope, longitude, latitude, eastness, northness, maximum upwind slope, radiation, curvature, accumulated snow cover area and precipitation in a temporal window) and four basic mathematical transformations of these variables (square, root square, inverse and logarithm). In the case of the temporal variables different time windows to define the accumulated values of the explanatory indices have been tested too. We have applied the methodology in a case study, the Sierra Nevada mountain range (Southern Spain), where the calibration has been performed by using the snow depth data observation provided by the ERHIN program which have a very low temporal frequency (2 or 3 measurement per year). When only non-steady explanatory variables are considered, the coefficient of determination of the global spatial estimation model is 0.55. When we also include non-steady variables we obtain an approach with a coefficient of determination of 0.62. We have also calibrated a new regression approach by using, in addition to the ERHIN program information, data coming from a detailed temporal series of snow depth in a new specific location, which has allow to obtain models with R² of 0.59 (for steady explanatory variables) and 0.64 (including also non-steady explanatory variables). The dynamic of the snow density in the mountain range has been estimated by means of a physically based simulation driven by WRF data. Combining the snow depth and the density approaches we have estimated the final SWE in Sierra Nevada. </p><p>This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).</p>

scholarly journals Developing a representative snow monitoring network in a forested mountain watershed

2016 ◽  
Author(s):  
Kelly E. Gleason ◽  
Anne W. Nolin ◽  
Travis R. Roth
Keyword(s):  
Land Cover ◽  
Snow Water Equivalent ◽  
Vegetation Type ◽  
Snow Accumulation ◽  
Training Data ◽  
Mountain Range ◽  
Monitoring Networks ◽  
Vegetation Density ◽  
Basin Scale ◽  
Oregon Cascades

Abstract. Current snow monitoring networks may not be representative of basin-scale distributions of snow water equivalent (SWE), especially in areas where forests and snowpacks are changing. A challenge in establishing new ground-based stations for monitoring snowpack accumulation and ablation is to locate the sites in areas that represent the key processes affecting snow accumulation and ablation. This is especially challenging in forested montane watersheds where the combined effects of terrain, climate, and land cover affect seasonal snowpack. The objectives of this research were to identify the key physiographic drivers of SWE, classify the landscape based on those physiographic drivers, and use that classification to identify a parsimonious set of monitoring sites in a forested watershed in the western Oregon Cascades mountain range. We used a binary regression tree (BRT) non-parametric statistical model to classify 1 April SWE. Training data for the BRT classification were derived using spatially distributed estimates of SWE from a validated physically-based model of snow evolution. The optimal BRT model showed that elevation, vegetation type, and vegetation density were the most significant drivers of SWE in the watershed. Geospatial elevation and land cover data were used to map the BRT-derived snow classes across the watershed. Specific snow monitoring sites were selected randomly within the BRT-derived snow classes to capture the range of spatial variability in snowpack conditions in the McKenzie River Basin. The Forest Elevational Snow Transect (ForEST) represents combinations of forested and open land cover types at low, mid, and high elevations. After five years of snowpack monitoring, the ForEST network provides a valuable and detailed dataset of snow accumulation, snow ablation, and snowpack energy balance in forested and open sites from the rain-snow transition zone to upper seasonal snow zone in the western Oregon Cascades.

scholarly journals Atmospheric River–Induced Precipitation and Snowpack during the Western United States Cold Season

2019 ◽  
Vol 20 (4) ◽  
pp. 613-630 ◽  
Author(s):  
Hisham Eldardiry ◽  
Asif Mahmood ◽  
Xiaodong Chen ◽  
Faisal Hossain ◽  
Bart Nijssen ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Extreme Precipitation ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Cold Season ◽  
Snow Accumulation ◽  
Precipitation Frequency ◽  
Extreme Precipitation Events ◽  
Snow Water ◽  
Precipitation Events

Abstract Atmospheric rivers (ARs) are narrow, elongated corridors of high water vapor content transported from tropical and/or extratropical cyclones. We characterize precipitation and snow water equivalent associated with ARs intersecting the western U.S. coast during the cold season (November– March) of water years 1949–2015. For each AR landfalling date, we retrieved the precipitation and relevant hydrometeorological variables from dynamically downscaled atmospheric reanalyses (NCEP–NCAR) using the WRF mesoscale numerical weather prediction model. Landfalling ARs resulted in higher precipitation amounts throughout the domain than did non-AR storms. ARs contributed the most extreme precipitation events during January and February. Daily snow water equivalent (SWE) changes during ARs indicate that high positive net snow accumulation conditions accompany ARs in December, January, and February. We also assess the historical impact of AR storm duration and precipitation frequency by considering the precipitation depth estimated during a 72-h window bounding the AR landfall date. More extreme precipitation amounts are produced by ARs in the South Cascades and Sierra Nevada ranges compared with ARs with landfall farther north. Most AR extreme precipitation events (and lower SWE accumulations) are produced during warm AR dates, especially toward the northern end of our domain. Analysis of ARs during dry and wet years reveals that ARs during wet years are more frequent and produce heavier precipitation and snow accumulation as compared with dry years. Such conditions are evident in drought events that are associated with a reduced frequency of ARs.

scholarly journals Developing a representative snow-monitoring network in a forested mountain watershed

2017 ◽  
Vol 21 (2) ◽  
pp. 1137-1147 ◽  
Author(s):  
Kelly E. Gleason ◽  
Anne W. Nolin ◽  
Travis R. Roth
Keyword(s):  
Land Cover ◽  
Spatial Variability ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Training Data ◽  
Mountain Range ◽  
Coupled Modeling ◽  
Landscape Characteristics ◽  
Modeling Approach ◽  
Oregon Cascades

Abstract. A challenge in establishing new ground-based stations for monitoring snowpack accumulation and ablation is to locate the sites in areas that represent the key processes affecting snow accumulation and ablation. This is especially challenging in forested montane watersheds where the combined effects of terrain, climate, and land cover affect seasonal snowpack. We present a coupled modeling approach used to objectively identify representative snow-monitoring locations in a forested watershed in the western Oregon Cascades mountain range. We used a binary regression tree (BRT) non-parametric statistical model to classify peak snow water equivalent (SWE) based on physiographic landscape characteristics in an average snow year, an above-average snow year, and a below-average snow year. Training data for the BRT classification were derived using spatially distributed estimates of SWE from a validated physically based model of snow evolution. The optimal BRT model showed that elevation and land cover type were the most significant drivers of spatial variability in peak SWE across the watershed (R2  =  0.93, p value  <  0.01). Geospatial elevation and land cover data were used to map the BRT-derived snow classes across the watershed. Specific snow-monitoring sites were selected randomly within the dominant BRT-derived snow classes to capture the range of spatial variability in snowpack conditions in the McKenzie River basin. The Forest Elevational Snow Transect (ForEST) is a result of this coupled modeling approach and represents combinations of forested and open land cover types at low, mid-, and high elevations. After 5 years of snowpack monitoring, the ForEST network provides a valuable and detailed dataset of snow accumulation, snow ablation, and snowpack energy balance in forested and open sites from the rain–snow transition zone to the upper seasonal snow zone in the western Oregon Cascades.

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