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 regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

2012 ◽  
Vol 6 (6) ◽  
pp. 4637-4671
Author(s):  
K. Klehmet ◽  
B. Geyer ◽  
B. Rockel
Keyword(s):  
Regional Climate Model ◽  
Snow Cover ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Snow Accumulation ◽  
Added Value ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

scholarly journals Assessing Climatic Drivers of Spring Mean and Annual Maximum Flows in Western Canadian River Basins

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1617
Author(s):  
Yonas B. Dibike ◽  
Rajesh R. Shrestha ◽  
Colin Johnson ◽  
Barrie Bonsal ◽  
Paulin Coulibaly
Keyword(s):  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
River Basins ◽  
Snow Accumulation ◽  
Spatial Variations ◽  
Annual Maximum ◽  
Spring Precipitation ◽  
Average Maximum ◽  
Maximum Flows

Flows originating from cold and mountainous watersheds are highly dependent on temperature and precipitation patterns, and the resulting snow accumulation and melt conditions, affecting the magnitude and timing of annual peak flows. This study applied a multiple linear regression (MLR) modelling framework to investigate spatial variations and relative importance of hydroclimatic drivers of annual maximum flows (AMF) and mean spring flows (MAMJflow) in 25 river basins across western Canada. The results show that basin average maximum snow water equivalent (SWEmax), April 1st SWE and spring precipitation (MAMJprc) are the most important predictors of both AMF and MAMJflow, with the proportion of explained variance averaging 51.7%, 44.0% and 33.5%, respectively. The MLR models’ abilities to project future changes in AMF and MAMJflow in response to changes to the hydroclimatic controls are also examined using the Canadian Regional Climate Model (CanRCM4) output for RCP 4.5 and RCP8.5 scenarios. The results show considerable spatial variations depending on individual watershed characteristics with projected changes in AMF ranging from −69% to +126% and those of MAMJflow ranging from −48% to +81% by the end of this century. In general, the study demonstrates that the MLR framework is a useful approach for assessing the spatial variation in hydroclimatic controls of annual maximum and mean spring flows in the western Canadian river basins. However, there is a need to exercise caution in applying MLR models for projecting changes in future flows, especially for regulated basins.

scholarly journals Evaluation of CLASS Snow Simulation over Eastern Canada

2017 ◽  
Vol 18 (5) ◽  
pp. 1205-1225 ◽  
Author(s):  
Diana Verseghy ◽  
Ross Brown ◽  
Libo Wang
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern Canada ◽  
Fractional Snow Cover ◽  
Forest Regions ◽  
Snow Water ◽  
Snow Model

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

scholarly journals A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yun Xu ◽  
Andrew Jones ◽  
Alan Rhoades
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Driving Forces ◽  
Regional Climate ◽  
Model Development ◽  
Regional Climate Models ◽  
The North ◽  
Snow Water ◽  
Regional Downscaling

Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

scholarly journals Evaluation of Snow Water Equivalent in NARCCAP Simulations, Including Measures of Observational Uncertainty

2017 ◽  
Vol 18 (9) ◽  
pp. 2425-2452 ◽  
Author(s):  
Rachel R. McCrary ◽  
Seth McGinnis ◽  
Linda O. Mearns
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Dominant Role ◽  
Regional Climate ◽  
Land Surface Model ◽  
Surface Model ◽  
Observational Uncertainty ◽  
Snow Water ◽  
Noah Land Surface Model

Abstract This study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs’ ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.

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.

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 Evaluation of CLASS 2.7 and 3.5 Simulations of Snow Properties from the Canadian Regional Climate Model (CRCM4) over Québec, Canada*

2014 ◽  
Vol 15 (4) ◽  
pp. 1325-1343 ◽  
Author(s):  
A. Langlois ◽  
J. Bergeron ◽  
R. Brown ◽  
A. Royer ◽  
R. Harvey ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Snow Accumulation ◽  
Canadian Regional Climate Model ◽  
Surface Areas ◽  
Snow Properties ◽  
Surface Scheme

Abstract Snow cover simulations from versions 2.7 and 3.5 of the Canadian Land Surface Scheme (CLASS) coupled to the Canadian Regional Climate Model, version 4 (CRCM4), are evaluated over northern Québec and the larger Québec domain using in situ and remotely sensed datasets. Version 2.7 of CLASS has been used in the operational version of CRCM4 at Ouranos since 2006. Version 3.5 includes a number of improvements to the snow processes as well as a more realistic parameterization of snow thermal conductivity. The evaluation shows that version 3.5 provides improved simulations of snow water equivalent, density, depth, and snowpack temperature values. However, snowpack density still contains systematic biases during the snow season that need to be addressed. The snow albedo parameterization in CLASS was found to be very sensitive to an empirical snowfall rate threshold for albedo refreshment and does not keep track of the snow accumulation history in estimating the snow surface albedo. A modified albedo scheme based on snow-specific surface areas is proposed to address this problem.

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.

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