scholarly journals Impact of forcing on sublimation simulations for a high mountain catchment in the semiarid Andes

2020 ◽  
Vol 14 (1) ◽  
pp. 147-163 ◽  
Author(s):  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Simon Gascoin ◽  
Christophe Kinnard ◽  
Stef Lhermitte ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Meteorological Data ◽  
Wrf Model ◽  
Annual Rainfall ◽  
Cold Bias ◽  
High Mountain ◽  
Mountain Catchment ◽  
Depth Sensitivity ◽  
Sublimation Rates

Abstract. In the semiarid Andes of Chile, farmers and industry in the cordillera lowlands depend on water from snowmelt, as annual rainfall is insufficient to meet their needs. Despite the importance of snow cover for water resources in this region, understanding of snow depth distribution and snow mass balance is limited. Whilst the effect of wind on snow cover pattern distribution has been assessed, the relative importance of melt versus sublimation has only been studied at the point scale over one catchment. Analyzing relative ablation rates and evaluating uncertainties are critical for understanding snow depth sensitivity to variations in climate and simulating the evolution of the snowpack over a larger area and over time. Using a distributed snowpack model (SnowModel), this study aims to simulate melt and sublimation rates over the instrumented watershed of La Laguna (513 km2, 3150–5630 m a.s.l., 30∘ S, 70∘ W), during two hydrologically contrasting years (i.e., dry vs. wet). The model is calibrated and forced with meteorological data from nine Automatic Weather Stations (AWSs) located in the watershed and atmospheric simulation outputs from the Weather Research and Forecasting (WRF) model. Results of simulations indicate first a large uncertainty in sublimation-to-melt ratios depending on the forcing as the WRF data have a cold bias and overestimate precipitation in this region. These input differences cause a doubling of the sublimation-to-melt ratio using WRF forcing inputs compared to AWS. Therefore, the use of WRF model output in such environments must be carefully adjusted so as to reduce errors caused by inherent bias in the model data. For both input datasets, the simulations indicate a similar sublimation fraction for both study years, but ratios of sublimation to melt vary with elevation as melt rates decrease with elevation due to decreasing temperatures. Finally results indicate that snow persistence during the spring period decreases the ratio of sublimation due to higher melt rates.

scholarly journals Uncertainties in the spatial distribution of snow sublimation in the semi-arid Andes of Chile

2019 ◽  
Author(s):  
Marion Réveillet ◽  
Shelley MacDonell ◽  
Simon Gascoin ◽  
Christophe Kinnard ◽  
Stef Lhermitte ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Meteorological Data ◽  
Wrf Model ◽  
Relative Increase ◽  
Annual Rainfall ◽  
Cold Bias ◽  
Depth Sensitivity ◽  
Sublimation Rates ◽  
Semi Arid

Abstract. In the semi-arid Andes of Chile, farmers and industry in the cordillera lowlands depend on water from snowmelt, as annual rainfall is insufficient to meet their needs. Despite the importance of snow cover for water resources in this region, understanding of snow depth distribution and snow mass balance is limited. Whilst the effect of wind on snow cover pattern distribution has been assessed, the relative importance of melt versus sublimation has only been studied at the point-scale over one catchment. Analyzing relative ablation rates and evaluating uncertainties are critical for understanding snow depth sensitivity to variations in climate and simulating the evolution of the snow pack over a larger area and over time. Using a distributed snowpack model (SnowModel), this study aims to simulate melt and sublimation rates over the instrumented watershed of La Laguna (3150–5630 m above sea level, 30° S), during two hydrologically contrasted years. The model is calibrated and forced with meteorological data from nine Automatic Weather Stations (AWS) located in the watershed, and atmospheric simulation outputs from the Weather Research and Forecasting (WRF) model. Results of simulations indicate first a large uncertainty in sublimation ratios depending on the forcing. The melt/sublimation ratios increased by 100 % if forced with WRF compared to AWS data due to the cold bias and precipitation over-estimation observed in WRF output in this region. Second, the simulations indicate similar sublimation ratios for both years, but ratios vary with elevation with a relative increase in melt at higher elevations. Finally results indicate that snow persistence has a significant impact on the sublimation ratio due to higher melt rates.

Climate change in the High Mountain Asia simulated with CMIP6 models

2021 ◽  
Author(s):  
Mickaël Lalande ◽  
Martin Ménégoz ◽  
Gerhard Krinner
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Climate Models ◽  
Global Climate Models ◽  
Cold Bias ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Model Biases ◽  
Snow Cover Extent

<p>The High Mountains of Asia (HMA) region and the Tibetan Plateau (TP), with an average altitude of 4000 m, are hosting the third largest reservoir of glaciers and snow after the two polar ice caps, and are at the origin of strong orographic precipitation. Climate studies over HMA are related to serious challenges concerning the exposure of human infrastructures to natural hazards and the water resources for agriculture, drinking water, and hydroelectricity to whom several hundred million inhabitants of the Indian subcontinent are depending. However, climate variables such as temperature, precipitation, and snow cover are poorly described by global climate models because their coarse resolution is not adapted to the rugged topography of this region. Since the first CMIP exercises, a cold model bias has been identified in this region, however, its attribution is not obvious and may be different from one model to another. Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.0 °C (± 3.2 °C), associated with an over-extended relative snow cover extent of 53 % (± 62 %), and a relative excess of precipitation of 139 % (± 38 %), knowing that the precipitation biases are uncertain because of the undercatch of solid precipitation in observations. Model biases and trends do not show any clear links, suggesting that biased models should not be excluded in trend and projections analysis, although non-linear effects related to lagged snow cover feedbacks could be expected. On average over 2081-2100 with respect to 1995-2014, for the scenarios SSP126, SSP245, SSP370, and SSP585, the 9 available models shows respectively an increase in annual temperature of 1.9 °C (± 0.5 °C), 3.4 °C (± 0.7 °C), 5.2 °C (± 1.2 °C), and 6.6 °C (± 1.5 °C); a relative decrease in the snow cover extent of 10 % (± 4.1 %), 19 % (± 5 %), 29 % (± 8 %), and 35 % (± 9 %); and an increase in total precipitation of 9 % (± 5 %), 13 % (± 7 %), 19 % (± 11 %), and 27 % (± 13 %). Further analyses will be considered to investigate potential links between the biases at the surface and those at higher tropospheric levels as well as with the topography. The models based on high resolution do not perform better than the coarse-gridded ones, suggesting that the race to high resolution should be considered as a second priority after the developments of more realistic physical parameterizations.</p>

Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters

2014 ◽  
Vol 29 (1) ◽  
pp. 52-65 ◽  
Author(s):  
Fan Zhang ◽  
Hongbo Zhang ◽  
Scott C. Hagen ◽  
Ming Ye ◽  
Dingbao Wang ◽  
...  
Keyword(s):  
Snow Cover ◽  
High Mountain ◽  
Temperature And Precipitation ◽  
Mountain Catchment ◽  
Effects Of Temperature ◽  
Scarce Data

scholarly journals A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models

2016 ◽  
Vol 18 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Kjetil Schanke Aas ◽  
Kjersti Gisnås ◽  
Sebastian Westermann ◽  
Terje Koren Berntsen
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Digital Camera ◽  
Cold Bias ◽  
Surface Model ◽  
Surface Energy Fluxes ◽  
Subgrid Scales ◽  
Moisture Fluxes

Abstract A mosaic approach to represent subgrid snow variation in a coupled atmosphere–land surface model (WRF–Noah) is introduced and tested. Solid precipitation is scaled in 10 subgrid tiles based on precalculated snow distributions, giving a consistent, explicit representation of variable snow cover and snow depth on subgrid scales. The method is tested in the Weather Research and Forecasting (WRF) Model for southern Norway at 3-km grid spacing, using the subgrid tiling for areas above the tree line. At a validation site in Finse, the modeled transition time from full snow cover to snow-free ground is increased from a few days with the default snow cover fraction formulation to more than 2 months with the tiling approach, which agrees with in situ observations from both digital camera images and surface temperature loggers. This in turn reduces a cold bias at this site by more than 2°C during the first half of July, with the noontime bias reduced from −5° to −1°C. The improved representation of subgrid snow variation also reduces a cold bias found in the reference simulation on regional scales by up to 0.8°C and increases surface energy fluxes (in particular the latent heat flux), and it resulted in up to 50% increase in monthly (June) precipitation in some of the most affected areas. By simulating individual soil properties for each tile, this approach also accounts for a number of secondary effects of uneven snow distribution resulting in different energy and moisture fluxes in different tiles also after the snow has disappeared.

scholarly journals LiDAR snow cover studies on glacier surface: significance of snow- and ice dynamical processes

2013 ◽  
Vol 7 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Laser Scanning ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
High Mountain ◽  
Dynamical Processes ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source for runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally only a small number of precipitation measurements deliver precipitation input for modeling in remote mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of Light Detecting And Ranging (LiDAR) techniques from aircraft, so-called airborne laser scanning (ALS), enables to derive surface elevations changes even in inaccessible terrain. Within one snow accumulation season these surface elevation changes can be interpreted as snow depths as a first assumption for snow hydrological studies. However, dynamical processes in snow, firn and ice are contributing to surface elevation changes on glaciers. To evaluate the magnitude and significance of these processes on alpine glaciers in the present state, ALS derived surface elevation changes were compared to converted snow depths from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers in the high alpine region of Ötztal Alps. LANDSAT data were used to distinguish between firn and ice areas of the glaciers. In firn areas submerging ice flow and densification of firn and snow are contributing to a mean relative deviation of ALS surface elevation changes from actually observed snow depths of −20.0% with a mean standard deviation of 17.1%. Deviations between ALS surface elevation changes and GPR snow depth are small along the profiles on the glacier tongues. At these areas mean absolute deviation of ALS surface elevation changes and GPR snow depth is 0.004 m with a mean standard deviation of 0.27 m. Emergence flow leads to distinct positive deviations only at the very front of the glacier tongues. Snow depths derived from ALS deviate less from actually measured snow depths than expected errors of in-situ measurements of solid precipitation. Hence, ALS derived snow depths are an important data source for both, spatial distribution and total sum of the snow cover volume stored on the investigated glaciers and in the corresponding high mountain catchments at the end of an accumulation season.

scholarly journals Change in snow depletion pattern in a river basin of Arunachal Pradesh under projected climatic scenarios

2017 ◽  
Vol 19 (2) ◽  
pp. 199-210
Keyword(s):  
Snow Cover ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
High Mountain ◽  
Present Climate ◽  
Arunachal Pradesh ◽  
Temperature And Precipitation ◽  
Depletion Curve ◽  
Climatic Scenarios ◽  
The Impact

<p>Snow depletion curves (SDCs) are important in hydrological studies for predicting snowmelt generated runoff in high mountain catchments. The present study deals with the derivation of the average snow depletion pattern in the Mago basin of Arunachal Pradesh, which falls in the eastern Himalayan region and the generation of climate affected SDCs in future years (2020, 2030, 2040, and 2050) under different projected climatic scenarios. The MODIS daily snow cover product at 500m resolution from both the Aqua and Terra satellites was used to obtain daily snow cover maps. MOD10A1 and MYD10A1 images were compared to select cloud free or minimum cloud image to obtain the temporal distribution of snow cover area (SCA). Snow accumulation and depletion patterns were obtained by analysing SCA at different days. For most of the years, two peaks were observed in the SCA analysis. The conventional depletion curve (CDC) representing present climate was derived by determining and interpolating the SCA from cloud-free (cloud&lt;5%) images for the selected hydrological year 2007. The investigation shows that the SCA was highest in February and lowest in May. Ten years meteorological data were used to normalize the temperature and precipitation data of the selected hydrological year (2007) to eliminate the impact of their yearly fluctuations on the snow cover depletion. The temperature and precipitation changes under four different projected climatic scenarios (A1B, A2, B1, and IPCC Commitment) were analysed for future years. Changes in the cumulative snowmelt depth with respect to the present climate for different future years were studied by a degree-day approach and were found to be highest under A1B, followed by A2, B1, and IPCC Commitment scenarios. It was observed that the A1B climatic scenario affected the depletion pattern most, making the depletion of snow to start and complete faster than under different scenarios. Advancing of depletion curve for different future years was found to be highest under A1B and lowest under IPCC Commitment scenarios with A2 and B1 in-between them.</p>

scholarly journals Lidar snow cover studies on glaciers in the Ötztal Alps (Austria): comparison with snow depths calculated from GPR measurements

2014 ◽  
Vol 8 (1) ◽  
pp. 41-57 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
Absolute Difference ◽  
High Mountain ◽  
Depth Maps ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source of runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally, only a small number of precipitation measurements deliver precipitation input for modelling in mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of lidar techniques from aircraft, so-called airborne laser scanning (ALS), provides surface elevations changes even in inaccessible terrain. These ALS surface elevation changes can be used to derive changes in snow depths of the mountain snow cover for seasonal or subseasonal time periods. However, since glacier surfaces are not static over time, ablation, densification of snow, densification of firn and ice flow contribute to surface elevation changes. ALS-derived surface elevation changes were compared to snow depths derived from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers. With this combination of two different data acquisitions, it is possible to evaluate the effect of the summation of these processes on ALS-derived snow depth maps in the high alpine region of the Ötztal Alps (Austria). A Landsat 5 Thematic Mapper image was used to distinguish between snow covered area and bare ice areas of the glaciers at the end of the ablation season. In typical accumulation areas, ALS surface elevation changes differ from snow depths calculated from GPR measurements by −0.4 m on average with a mean standard deviation of 0.34 m. Differences between ALS surface elevation changes and GPR derived snow depths are small along the profiles conducted in areas of bare ice. In these areas, the mean absolute difference of ALS surface elevation changes and GPR snow depths is 0.004 m with a standard deviation of 0.27 m. This study presents a systematic approach to analyze deviations from ALS generated snow depth maps to ground truth measurements on four different glaciers. We could show that ALS can be an important and reliable data source for the spatial distribution of snow depths for most parts of the here investigated glaciers. However, within accumulation areas, just utilizing ALS data may lead to systematic underestimation of total snow depth distribution.

scholarly journals A one-dimensional model of the evolution of snow-cover characteristics

1993 ◽  
Vol 18 ◽  
pp. 22-26 ◽  
Author(s):  
Takeshi Yamazaki ◽  
Junsei Kondo ◽  
Takashi Sakuraoka ◽  
Toru Nakamura
Keyword(s):  
Diurnal Variation ◽  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Snow Depth ◽  
Meteorological Data ◽  
Dimensional Model ◽  
Snow Density ◽  
One Dimensional ◽  
Snow Temperature

A one-dimensional model has been developed to simulate the evolution of snow-cover characteristics using meteorological data. This model takes into account the heat balance at the snow surface and heat conduction in the snow cover as well as liquid water flow and densification. The basic variables of the model are snow temperature, liquid water content, snow density and the solid impurities density. With these four variables, the model can calculate albedo, thermal conductivity, liquid water flux, snow depth, water equivalent and the amount of runoff.Diurnal variation of profiles of snow temperature, water content and snow density, and meteorological elements were observed at Mount Zao Bodaira, Yamagata Prefecture, Japan. Simulated diurnal variation patterns of each component by the model were in good agreement with the observations. Moreover, the snow-cover characteristics were simulated for three 90-day periods with meteorological data and snow pit observations at Sapporo. It was found that the model was able to simulate long-period variations of albedo, snow depth, snow water equivalent and the snow density profile.

scholarly journals A one-dimensional model of the evolution of snow-cover characteristics

1993 ◽  
Vol 18 ◽  
pp. 22-26 ◽  
Author(s):  
Takeshi Yamazaki ◽  
Junsei Kondo ◽  
Takashi Sakuraoka ◽  
Toru Nakamura
Keyword(s):  
Diurnal Variation ◽  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Snow Depth ◽  
Meteorological Data ◽  
Dimensional Model ◽  
Snow Density ◽  
One Dimensional ◽  
Snow Temperature

A one-dimensional model has been developed to simulate the evolution of snow-cover characteristics using meteorological data. This model takes into account the heat balance at the snow surface and heat conduction in the snow cover as well as liquid water flow and densification. The basic variables of the model are snow temperature, liquid water content, snow density and the solid impurities density. With these four variables, the model can calculate albedo, thermal conductivity, liquid water flux, snow depth, water equivalent and the amount of runoff. Diurnal variation of profiles of snow temperature, water content and snow density, and meteorological elements were observed at Mount Zao Bodaira, Yamagata Prefecture, Japan. Simulated diurnal variation patterns of each component by the model were in good agreement with the observations. Moreover, the snow-cover characteristics were simulated for three 90-day periods with meteorological data and snow pit observations at Sapporo. It was found that the model was able to simulate long-period variations of albedo, snow depth, snow water equivalent and the snow density profile.

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

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