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 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.

scholarly journals A snow-transport model for complex terrain

1998 ◽  
Vol 44 (148) ◽  
pp. 498-516 ◽  
Author(s):  
Glen E. Liston ◽  
Matthew Sturm
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Snow Water Equivalent ◽  
Transport Model ◽  
Vegetation Type ◽  
Cold Season ◽  
Snow Accumulation ◽  
Surface Shear Stress ◽  
Surface Shear ◽  
Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

A Distributed Snow-Evolution Modeling System (SnowModel)

2006 ◽  
Vol 7 (6) ◽  
pp. 1259-1276 ◽  
Author(s):  
Glen E. Liston ◽  
Kelly Elder
Keyword(s):  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Vegetation Type ◽  
Atmospheric Model ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Canopy Interception ◽  
Meteorological Forcing ◽  
Spatially Distributed ◽  
Evolution Modeling

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

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.

Testing a remotely-sensed snow water equivalent product in the framework of the operational European Drought Observatory (EDO)

2020 ◽  
Author(s):  
Carmelo Cammalleri ◽  
Paulo Barbosa ◽  
Jürgen Vogt
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Cold Season ◽  
Snow Accumulation ◽  
Continental Scale ◽  
Basin Scale ◽  
Coarse Spatial Resolution ◽  
Snow Water

&lt;p&gt;Winter droughts, defined as periods of reduced precipitation and snow accumulation during the cold season, can have significant impacts on the subsequent summer season, especially over areas that strongly rely on stored water resources released during the spring melting.&lt;/p&gt;&lt;p&gt;The Snow Water Equivalent, SWE, represents a reliable means to quantify the amount of liquid water in the snowpack, and its anomalies can be used to evaluate deviations from the amount usually stored. Unfortunately, the use of SWE for operational monitoring of winter droughts is constrained by the limited availability of long time series of ground observations, and the lack of coordinated measuring networks at European continental scale.&lt;/p&gt;&lt;p&gt;Remote sensing data from microwave sensors, therefore, represent a valuable source of continuously-updated SWE data. Products such as the H-SAF (EUMETSAT Hydrology Satellite Application Facility, http://hsaf.meteoam.it/) SNOBS4-H13 are updated in almost near-real time, providing daily maps covering continental Europe and northern Africa. Limitations include data gaps, difficult retrievals over impervious terrain, coarse spatial resolution and a reduced length of the time series.&lt;/p&gt;&lt;p&gt;In this study, we tested the potential inclusion of a drought indicator based on the H-SAF SWE product in the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu), with the aim to fill the current gap faced over mountainous basins in terms of early warning of spring water deficits.&lt;/p&gt;&lt;p&gt;An analysis of the full dataset collected between 2013 and 2019 highlights how, currently, the main drawback of the product seems to be represented by the limited length of the time series, as well as by the difficulties to capture snow accumulation over some mountainous areas (e.g., Pyrenees) likely due to the coarse spatial resolution. Spatial aggregation at water basin scale was also tested, in order to evaluate the possibility to reduce the effects of some of these limitations.&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;

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 A snow-transport model for complex terrain

1998 ◽  
Vol 44 (148) ◽  
pp. 498-516 ◽  
Author(s):  
Glen E. Liston ◽  
Matthew Sturm
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Snow Water Equivalent ◽  
Transport Model ◽  
Vegetation Type ◽  
Cold Season ◽  
Snow Accumulation ◽  
Surface Shear Stress ◽  
Surface Shear ◽  
Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

scholarly journals Winter Inputs Buffer Streamflow Sensitivity to Snowpack Losses in the Salt River Watershed in the Lower Colorado River Basin

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Marcos D. Robles ◽  
John C. Hammond ◽  
Stephanie K. Kampf ◽  
Joel A. Biederman ◽  
Eleonora M. C. Demaria
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Snow Accumulation ◽  
Colorado River Basin ◽  
Scale Model ◽  
Annual Streamflow ◽  
Basin Scale ◽  
Salt River ◽  
Lower Colorado River Basin ◽  
Lower Colorado River

Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.

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