A Process-Based Energy Balance Snowmelt Model II: Application

2013 ◽  
Vol 726-731 ◽  
pp. 3346-3352
Author(s):  
Gao Jie
Keyword(s):  
Energy Balance ◽  
Snow Water Equivalent ◽  
Sensible Heat Flux ◽  
Colorado Front Range ◽  
Wave Radiation ◽  
Snow Pack ◽  
Front Range ◽  
Snow Density ◽  
Column Model ◽  
Snow Water

Based on the process-based energy balance snow melting model Snow Column Model, several scenarios are set to study the response of snow pack to climate change according to site-based data in snowpit 006, Niwot Ridge, Colorado, Front Range of Rocky Mountains. Based on an introduction and validation of Snow Column Model by data of 1996, a further validation is made on data during 1997 and 1999. Scenarios are set based on observations of solar radiation, long-wave radiation, air temperature, latent and sensible heat flux during 1996 and 1999. The responses of snow pack to an average temperature fluctuation within 6.2°C are analyzed. The results illustrate that snow density and snow water equivalent accelerated decreases while the variance in snow density does not increase monotonically over time.

A Process-Based Energy Balance Snowmelt Model I: Principle

2013 ◽  
Vol 726-731 ◽  
pp. 3338-3345
Author(s):  
Gao Jie
Keyword(s):  
Energy Balance ◽  
Colorado Front Range ◽  
Snow Pack ◽  
Front Range ◽  
Important Process ◽  
Snow Hydrology ◽  
Snow Melting ◽  
Snow Density ◽  
Niwot Ridge ◽  
Snowmelt Model

Snow melting is an important process of snow hydrology. A process-based energy balance snowmelt model: Snow Column Model is developed to reveal the processes of energy conservation, phase change, mass transfer, compaction and growth of grain size. It could provide the information of snow density, temperature and liquid water held in snow packs varied with snowmelt processes. The observations during April and June, 1996 of snowpit in Niwot Ridge, Colorado, Front Range of Rocky Mountains are used to calculate and compare. The calculated ones are consistent with the observed. The model not only demonstrates the processes happened inside snow pack, but also will offer a better understanding of the response of snow pack to climate change in further studies.

scholarly journals An Index of Regional Snow-Pack Stability based on Natural Slab Avalanches

1985 ◽  
Vol 31 (108) ◽  
pp. 67-73
Author(s):  
Arthur Judson ◽  
Rudy M. King
Keyword(s):  
Real Time ◽  
Testing Procedure ◽  
Statistical Distribution ◽  
Colorado Front Range ◽  
Sequential Testing ◽  
Test Results ◽  
Snow Pack ◽  
Front Range ◽  
Time Evaluation

AbstractAn index of regional snow-pack stability based on occurrences of natural slab avalanches was developed using a statistical distribution and a sequential testing procedure. The study interprets avalanche information on 185 paths in the Colorado Front Range. Results show general agreement with operational hazard estimates; test results have real-time evaluation potential.

scholarly journals Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Accumulation ◽  
Accurate Estimation ◽  
Snow Density ◽  
Snow Stratigraphy ◽  
Flood Estimation ◽  
Snow Water ◽  
Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

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/.

Snowpack energy balance and changes in the frequency and extremity of rain-on-snow events in the warming climate

2021 ◽  
Author(s):  
Ondrej Hotovy ◽  
Michal Jenicek
Keyword(s):  
Energy Balance ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Winter Season ◽  
Catchment Scale ◽  
The Future ◽  
Snow Water ◽  
The Impact ◽  
Catchment Runoff

<p>Seasonal snowpack significantly influences the catchment runoff and thus represents an important input for the hydrological cycle. Changes in the precipitation distribution and intensity, as well as a shift from snowfall to rain is expected in the future due to climate changes. As a result, rain-on-snow events, which are considered to be one of the main causes of floods in winter and spring, may occur more frequently. Heat from liquid precipitation constitutes one of the snowpack energy balance components. Consequently, snowmelt and runoff may be strongly affected by these temperature and precipitation changes.</p><p>The objective of this study is 1) to evaluate the frequency, inter-annual variability and extremity of rain-on-snow events in the past based on existing measurements together with an analysis of changes in the snowpack energy balance, and 2) to simulate the effect of predicted increase in air temperature on the occurrence of rain-on-snow events in the future. We selected 40 near-natural mountain catchments in Czechia with significant snow influence on runoff and with available long-time series (>35 years) of daily hydrological and meteorological variables. A semi-distributed conceptual model, HBV-light, was used to simulate the individual components of the water cycle at a catchment scale. The model was calibrated for each of study catchments by using 100 calibration trials which resulted in respective number of optimized parameter sets. The model performance was evaluated against observed runoff and snow water equivalent. Rain-on-snow events definition by threshold values for air temperature, snow depth, rain intensity and snow water equivalent decrease allowed us to analyze inter-annual variations and trends in rain-on-snow events during the study period 1965-2019 and to explain the role of different catchment attributes.</p><p>The preliminary results show that a significant change of rain-on-snow events related to increasing air temperature is not clearly evident. Since both air temperature and elevation seem to be an important rain-on-snow drivers, there is an increasing rain-on-snow events occurrence during winter season due to a decrease in snowfall fraction. In contrast, a decrease in total number of events was observed due to the shortening of the period with existing snow cover on the ground. Modelling approach also opened further questions related to model structure and parameterization, specifically how individual model procedures and parameters represent the real natural processes. To understand potential model artefacts might be important when using HBV or similar bucket-type models for impact studies, such as modelling the impact of climate change on catchment runoff.</p>

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 Evaluation of the Simple Urban Energy Balance Model Using Selected Data from 1-yr Flux Observations at Two Cities

2009 ◽  
Vol 48 (4) ◽  
pp. 693-715 ◽  
Author(s):  
Toru Kawai ◽  
Mohammad Kholid Ridwan ◽  
Manabu Kanda
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Sensible Heat Flux ◽  
Energy Balance Model ◽  
Balance Model ◽  
Wave Radiation ◽  
Urban Energy Balance ◽  
Two Cities ◽  
Urban Energy

Abstract The authors’ objective was to apply the Simple Urban Energy Balance Model for Mesoscale Simulation (SUMM) to cities. Data were selected from 1-yr flux observations conducted at three sites in two cities: one site in Kugahara, Japan (Ku), and two sites in Basel, Switzerland (U1 and U2). A simple vegetation scheme was implemented in SUMM to apply the model to vegetated cities, and the surface energy balance and radiative temperature TR were evaluated. SUMM generally reproduced seasonal and diurnal trends of surface energy balance and TR at Ku and U2, whereas relatively large errors were obtained for the daytime results of sensible heat flux QH and heat storage ΔQS at U1. Overall, daytime underestimations of QH and overestimations of ΔQS and TR were common. These errors were partly induced by the poor parameterization of the natural logarithm of the ratio of roughness length for momentum to heat (κB−1); that is, the observed κB−1 values at vegetated cities were smaller than the simulated values. The authors proposed a new equation for predicting this coefficient. This equation accounts for the existence of vegetation and improves the common errors described above. With the modified formula for κB−1, simulated net all-wave radiation and TR agreed well with observed values, regardless of site and season. However, at U1, simulated QH and ΔQS were still overestimated and underestimated, respectively, relative to observed values.

scholarly journals Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

2013 ◽  
Vol 17 (12) ◽  
pp. 5127-5139 ◽  
Author(s):  
G. A. Artan ◽  
J. P. Verdin ◽  
R. Lietzow
Keyword(s):  
Energy Balance ◽  
Water Content ◽  
Snow Water Equivalent ◽  
Weather Forecast ◽  
Snow Accumulation ◽  
Passive Microwave ◽  
Data Sets ◽  
Snow Water ◽  
Trmm Precipitation ◽  
Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

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.

scholarly journals Snowmelt modelling on signy island, South Orkney Islands

1998 ◽  
Vol 26 ◽  
pp. 161-166 ◽  
Author(s):  
Michael J. Gardiner ◽  
J. Cynan Ellis-Evans ◽  
Malcolm G. Anderson ◽  
Martyn Tranter
Keyword(s):  
Snow Water Equivalent ◽  
Energy Content ◽  
Heat Fluxes ◽  
Geographical Information ◽  
Wave Radiation ◽  
Snow Pack ◽  
State Variables ◽  
Orkney Islands ◽  
Signy Island ◽  
South Orkney Islands

The ability of the Utah energy-balance and snowmelt model (UEB) to simulate decline in snow water equivalent (SWE) at an extreme location was assessed. Field data were collected at Paternoster Valley, Signy Island, South Orkney Islands (60°43′S) during the austral summer of 1996–97. This is the first application of UEB in a maritime Antarctic site. UEB is a physically based snow melt model using a lumped snow-pack representation with primary state variables SWE and snow pack-energy content(U).Meteorological inputs are air temperature, wind speed, humidity, precipitation and total incoming solar and longwave radiation. The Paternoster Valley catchment was subdivided into eight non-contiguous terrain classes for sampling and modelling using a geographical information system (GIS). Simulations of SWE in each of these classes were compared พ with field observations. It is shown that initialUand snow-surface thermal conductance(Ks)affect model simulations. Good approximations of SWE depletion are obtained using measured incoming solar radiation to drive the model but there are shortcomings in the characterization of long wave radiation and sensible-heat fluxes.

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