scholarly journals A Comparative Evaluation of Snow Depth and Snow Water Equivalent Using Empirical Algorithms and Multivariate regressions

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Arash Zaerpour ◽  
◽  
Arash Adib ◽  
Ali Motamedi ◽  
◽  
...  
Keyword(s):  
Snow Depth ◽  
Comparative Evaluation ◽  
Snow Water Equivalent ◽  
Snow Water ◽  
Empirical Algorithms

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

scholarly journals Improving the snow physics of WEB-DHM and its point evaluation at two SnowMIP alpine sites

2010 ◽  
Vol 7 (3) ◽  
pp. 3481-3519 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
Y. Xue ◽  
Y. Hirabayashi
Keyword(s):  
Surface Temperature ◽  
Liquid Water ◽  
Snow Depth ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Bias Error ◽  
Snow Albedo ◽  
Basin Scale ◽  
Snow Water ◽  
Snow Physics

Abstract. The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

scholarly journals Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Implicit Scheme ◽  
Snow Water ◽  
Irtysh River ◽  
Simulation Results ◽  
The Irtysh River

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

scholarly journals Snow-Core Analysis at Mid- and High Altitude in the Snow-Pack (Abstract)

1988 ◽  
Vol 10 ◽  
pp. 217
Author(s):  
Yves Page
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Analytical Techniques ◽  
Chemical Pollution ◽  
Data Archive ◽  
Snow Pack ◽  
Micro Structure ◽  
Mountain Area ◽  
Winter Climate ◽  
Snow Water

Since 1977, we have been studying the weather and climate of a mountain area, and particularly the winter snow-pack. The area is situated in the French Alps, at an altitude of 1450-2500 m. Since winter 1982-83, we have been studying the snow-pack each week and every time we collected snow cores. We collected them at different test sites (area 1: 1850 m NW; area 2: 1800 m SE; area 3: 1600 m NW; area 4: 2000 m). The following parameters were collected at the test sites on a continuous basis throughout the winter: new and total snow depth, wind direction and precipitation (solid and liquid), snow-water equivalent (precipitation and total snow depth), standard snow profiles, and structure of the snow-pack. After data analysis, many different questions were investigated, in two respects: (1) Analytical techniques: problems of sampling snow cores (transport, conservation, evolution), and analysis of the micro-structure of the different snow layers in correlation with the macro-structure of the snow-pack. (2) Results and interpretations: do changes in the structure (macro- and micro-structure) of the snow cores take place during their conservation? Can we consider snow cores as preserving the record of the winter climate (precipitation, chemical pollution)? (3) Data archive: Constitution of snow-core data archive, in relation to the climatology of the area.

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