scholarly journals Over-winter persistence of supraglacial lakes on the Greenland Ice Sheet: results and insights from a new model

2020 ◽  
Vol 66 (257) ◽  
pp. 362-372 ◽  
Author(s):  
Robert Law ◽  
Neil Arnold ◽  
Corinne Benedek ◽  
Marco Tedesco ◽  
Alison Banwell ◽  
...  
Keyword(s):  
Snow Cover ◽  
Thermal Stratification ◽  
Greenland Ice Sheet ◽  
Ice Melt ◽  
Supraglacial Lakes ◽  
Lake Model ◽  
Lake Formation ◽  
Snow And Ice Melt ◽  
Water Depths

AbstractWe present a newly developed 1-D numerical energy-balance and phase transition supraglacial lake model: GlacierLake. GlacierLake incorporates snowfall, in situ snow and ice melt, incoming water from the surrounding catchment, ice lid formation, basal freeze-up and thermal stratification. Snow cover and temperature are varied to test lake development through winter and the maximum lid thickness is recorded. Average wintertime temperatures of −2 to $-30^{\circ }{\rm C}$ and total snowfall of 0 to 3.45 m lead to a range of the maximum lid thickness from 1.2 to 2.8 m after ${\sim }250$ days, with snow cover exerting the dominant control. An initial ice temperature of $-15^{\circ }{\rm C}$ with simulated advection of cold ice from upstream results in 0.6 m of basal freeze-up. This suggests that lakes with water depths above 1.3 to 3.4 m (dependent on winter snowfall and temperature) upon lid formation will persist through winter. These buried lakes can provide a sizeable water store at the start of the melt season, expedite future lake formation and warm underlying ice even in winter.

scholarly journals Investigating Controls on the Formation and Distribution of Wintertime Storage of Water in Supraglacial Lakes

2020 ◽  
Vol 8 ◽  
Author(s):  
Derrick Julius Lampkin ◽  
Lora Koenig ◽  
Casey Joseph ◽  
Jason Eric Box
Keyword(s):  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Lake Area ◽  
Channel Networks ◽  
Supraglacial Lakes ◽  
Lake Model ◽  
Temporal Occurrence ◽  
Atmospheric Factors ◽  
And Behavior ◽  
Percolation Zone

Supraglacial lakes over the Greenland Ice Sheet can demonstrate multi-model drainage states. Lakes can demonstrate incomplete drainage, where residual melt can become buried under ice and snow and survive throughout the winter. We evaluate atmospheric factors that influence the propensity for the formation of buried lakes over the ice sheet. We examine the spatial and temporal occurrence and behavior of buried lakes over the Jakobshavn Isbrae and Zachariae Isstrøm outlet basins and assess the magnitude of insolation necessary to preserve melt water using a numerical lake model from 2009 to 2012. Buried lakes tend to occur at higher elevations within the ablation zone and those present at elevations > 1000 m tend to reoccur over several seasons. Lakes without buried water are relatively small (∼1 km2), whereas lakes with buried water are larger (∼6–10 km2). Lake area is correlated with the number of seasons sub-surface water persists. Buried lakes are relatively deep and associated with complex supraglacial channel networks. Winter stored water could be a precursor to the formation of supraglacial channels. Simulations of the insulation potential of accumulated snow and ice on the surface of lakes indicate substantial regional differences and inter-annual variability. With the possibility of inland migration of supraglacial lakes, buried lakes could be important in the evolution of ablation/percolation zone hydrology.

scholarly journals Formation and development of supraglacial lakes in the percolation zone of the Greenland ice sheet

2017 ◽  
Vol 63 (241) ◽  
pp. 847-853 ◽  
Author(s):  
CHRISTINE CHEN ◽  
IAN M. HOWAT ◽  
SANTIAGO DE LA PEÑA
Keyword(s):  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Low Permeability ◽  
Near Surface ◽  
Observable Change ◽  
Supraglacial Lakes ◽  
Run Off ◽  
Lake Formation ◽  
And Storage ◽  
Percolation Zone

AbstractWe examine repeat surface altimetry and radio echo observations of two supraglacial lakes in the percolation zone of the Greenland ice sheet to investigate the changes in firn conditions leading to lake formation and implications for meltwater storage within firn. Both lakes formed in 2011, when an anomalously high melt season was followed by low winter accumulation, resulting in reduced infiltration and storage in the near surface. The lakes expanded during the 2012 record melt season and retained liquid meltwater through the following winter. The lakes then contracted, with one lake slowly draining and refreezing and another rapidly draining to the subsurface. The lack of observable change in firn conditions surrounding the lakes indicates increased run-off in the near surface firn, likely along low-permeability ice layers formed during the previous melt seasons. This implies a reduced ability of the firn to absorb increased meltwater.

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 Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

scholarly journals Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

2014 ◽  
Vol 8 (1) ◽  
pp. 845-885 ◽  
Author(s):  
R. K. Scharien ◽  
K. Hochheim ◽  
J. Landy ◽  
D. G. Barber
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Temporal Frequency ◽  
Model Performance ◽  
Free Water ◽  
Light Transmittance ◽  
The Arctic ◽  
Ice Melt ◽  
Melt Pond

Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.

Iron plays a role in nitrate drawdown by phytoplankton in Lake Erie surface waters as observed in lake-wide assessments

2012 ◽  
Vol 69 (2) ◽  
pp. 369-381 ◽  
Author(s):  
Sonya M. Havens ◽  
Christel S. Hassler ◽  
Rebecca L. North ◽  
Stephanie J. Guildford ◽  
Greg Silsbe ◽  
...  
Keyword(s):  
Surface Waters ◽  
Thermal Stratification ◽  
Lake Erie ◽  
Phytoplankton Community ◽  
Sampling Depth ◽  
Phytoplankton Pigment ◽  
Chl A ◽  
Dissolved Iron ◽  
Continuous Surface

Phytoplankton interactions with iron (Fe) were examined in surface waters of Lake Erie during summer thermal stratification. Lake-wide sampling in June and September 2005 was conducted using a continuous surface water sampler (1 m sampling depth) and in July at 18 hydrographic stations (5 m sampling depth). In situ measurements of photosynthetic efficiency (maximum quantum yield of photosystem II) and phytoplankton community composition were measured using fast repetition rate fluorometry and a phytoplankton pigment-specific fluorometer, respectively, during June and September. High ratios (73%–85%) of intracellular Fe to particulate Fe coincident with increases in chlorophyll a (Chl a) concentrations in the western and central basins in June and July imply that the majority of Fe in these regions was associated with intracellular pools. Correlations between intracellular Fe and Chl a were frequently observed when Heterokontophyta and Pyrrophyta dominated the phytoplankton community. Assimilation of Fe by the phytoplankton strongly influenced its partitioning between the dissolved and particulate phase. Dissolved iron (<0.45 µm) concentrations were proportional to Chl a concentrations and both dissolved iron and Chl a were inversely proportional to nitrate concentrations in July and September, suggesting that dissolved iron influenced both nitrate drawdown and Chl a concentrations in Lake Erie surface waters in summer.

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

scholarly journals A snow cover climatology for the Pyrenees from MODIS snow products

2014 ◽  
Vol 11 (11) ◽  
pp. 12531-12571 ◽  
Author(s):  
S. Gascoin ◽  
O. Hagolle ◽  
M. Huc ◽  
L. Jarlan ◽  
J.-F. Dejoux ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Forested Areas ◽  
Land Cover Map ◽  
Hydropower Production ◽  
Snow Cover Duration

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (we) and 105 mm respectively, for both MOD10A1 and MYD10A1. Kappa coefficients are within 0.74 and 0.92 depending on the product and the variable. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both datasets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decreases over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gapfilling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

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