Snow Extent Research Articles

The Character and Causes of Elevation-Dependent Warming in High-Resolution Simulations of Rocky Mountain Climate Change

AbstractThe character and causes of elevation-dependent warming (EDW) of surface temperatures are examined in a suite of high-resolution ( km) regional climate model (RCM) simulations of climate change over the Rocky Mountains using the Weather Research and Forecasting Model. A clear EDW signal is found over the region, with warming enhanced in certain elevation bands by as much as 2°C. During some months warming maximizes at middle elevations, whereas during others it increases monotonically with elevation or is nearly independent of elevation. Simulated EDW is primarily caused by the snow albedo feedback (SAF). Warming maximizes in regions of maximum snow loss and albedo reduction. The role of the SAF is confirmed by sensitivity experiments wherein the SAF is artificially suppressed. The elevation dependence of free-tropospheric warming appears to play a secondary role in shaping EDW. No evidence is found for a contribution from elevation-dependent water vapor feedbacks. Sensitivity experiments show that EDW depends strongly on certain aspects of RCM configuration. Simulations using 4- and 12-km horizontal grid spacings show similar EDW signals, but substantial differences are found when using a grid spacing of 36 km due to the influence of terrain resolution on snow cover and the SAF. Simulations using the Noah and Noah-MP land surface models (LSMs) exhibit large differences in EDW. These are caused by differences between LSMs in their representations of midelevation snow extent and in their parameterization of subpixel fractional snow cover. These lead to albedo differences that act to modulate the simulated SAF and its effect on EDW.

Linkages between the South and East Asian summer monsoons: a review and revisit

The relationship between the South Asia monsoon (SAM) and the East Asia monsoon (EAM) possibly modulated by both external forcings and internal dynamics has been a long-standing and controversial issue in climate sciences. This study reviews their linkages as revealed in modern records and model simulations during the past, present and future, and provides a comprehensive explanation of the key mechanisms controlling the diversity of the SAM–EAM relationship. Particular attention is paid to several external forcings that modulate the relationship, including El Niño and Southern Oscillation, Indian Ocean Dipole mode (IODM), boreal summer teleconnections, and Eurasian snow extent on intraseasonal to interdecadal timescales. The major focus is placed on two integral views of the inter-connection between the two monsoon systems: one is the positive inter-correlation, which is associated with decaying El Niño and developing Indian Ocean sea surface temperature (SST) warming anomalies; the other is the negative inter-correlation, resulting from developing El Niño and western Pacific SST cooling. The IODM mode also has a delayed impact on the negative connection by modulating Eurasian snow cover. The observed evidence reveals that the recent intensification of the negative relationship is attributable to the strengthening of the zonal SST gradient along the Indian Ocean, western Pacific, and eastern Pacific. Analysis of experiments in the fifth phase of the Coupled Model Intercomparison Project further indicates a possibility for the negative linkage to be further enhanced under anthropogenic global warming with considerable interdecadal modulation in mid and late twenty-first century.

Effect of spatial variability of wet snow on modeled and observed microwave emissions

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the TB response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50m) energy balance snow model over a 34×34km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the TB signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data.

A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011

Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.

Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent

AbstractSnow and glacial melt processes are an important part of the Himalayan water balance. Correct quantification of melt runoff processes is necessary to understand the region's vulnerability to climate change. This paper describes in detail an application of conceptual GR4J hydrological model in the Tamor catchment in Eastern Nepal using typical elevation band and degree‐day factor approaches to model Himalayan snow and glacial melt processes. The model aims to provide a simple model that meets most water planning applications. The paper contributes a model conceptualization (GR4JSG) that enables coarse evaluation of modelled snow extents against remotely sensed Moderate Resolution Imaging Spectroradiometer snow extent. Novel aspects include the glacial store in GR4JSG and examination of how the parameters controlling snow and glacial stores correlate with existing parameters of GR4J. The model is calibrated using a Bayesian Monte Carlo Markov Chain method against observed streamflow for one glaciated catchment with reliable data. Evaluation of the modelled streamflow with observed streamflow gave Nash Sutcliffe Efficiency of 0.88 and Percent Bias of <4%. Comparison of the modelled snow extents with Moderate Resolution Imaging Spectroradiometer gaveR2of 0.46, with calibration against streamflow only. The contribution of melt runoff to total discharge from the catchment is 14–16% across different experiments. The model is highly sensitive to rainfall and temperature data, which suffer from known problems and biases, for example because of stations being located predominantly in valleys and at lower elevations. Testing of the model in other Himalayan catchments may reveal additional limitations. © 2016 The Authors.Hydrological Processespublished by John Wiley & Sons Ltd.

An evaluation of high‐resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow‐albedo feedback

AbstractThe snow‐albedo feedback (SAF) strongly influences climate over midlatitude mountainous regions. However, over these regions the skill of regional climate models (RCMs) at simulating properties such as snow cover and surface albedo is poorly characterized. These properties are evaluated in a pair of 7 year long high‐resolution RCM simulations with the Weather Research and Forecasting model over the central Rocky Mountains. Key differences between the simulations include the computational domain (regional versus continental) and land surface model used (Noah versus Noah‐MP). Simulations are evaluated against high‐resolution satellite estimates of snow cover and albedo from the Moderate Resolution Imaging Spectroradiometer. Both simulations generally reproduce the observed seasonal and spatial variability of snow cover and also exhibit important biases. One simulation substantially overpredicts subpixel fractional snow cover over snowy pixels (by up to 0.4) causing large positive biases in surface albedo, likely due in part to inadequate representation of canopy effects. The other simulation exhibits a negative bias in areal snow extent (as much as 19% of the analysis domain). Surface measurements reveal large positive biases in snow albedo (exceeding 0.2) during late spring caused by neglecting radiative effects of impurities deposited onto snow. Semi‐idealized climate change experiments show substantially different magnitudes of SAF‐enhanced warming in the two simulations that can be tied to the differences in snow cover in their control climates. More confident projections of regional climate change over mountains will require further work to evaluate and improve representation of snow cover and albedo in RCMs.

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000–2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

A New Method Comparing Snowmelt Timing with Annual Area Burned

The interactions between climate and wildland fire are complex. To better understand these interactions, we used ArcMap 10.2.2 to examine the relationships between early spring snowmelt and total annual area burned within a defined region of the Rocky Mountains of the western United States. Our research methods used Monitoring Trends in Burn Severity (MTBS) fire perimeter data and weekly snow extent provided by the Rutgers Global Snow Lab analysis of National Oceanic and Atmospheric Administration (NOAA) daily snow maps. Our results indicated a significant correlation between early spring snowmelt and total annual area burned (P = 0.0497), providing further evidence that snowmelt timing may be a driving factor for wildland fires. This project builds on the findings of previous studies and provides a novel method for making general predictions about the upcoming fire season months in advance, using freely available remotely sensed data in real time. Further research should apply our model to a broader geographic area, and incorporate higher resolution snowmelt timing data.

Variations of Annual Minimum Snow and Ice Extent over Canada and Neighboring Landmass Derived from MODIS 250-m Imagery for 2000–2014

. Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our previous results derived from the MODIS Circumpolar Arctic clear-sky composites, generated for the end of melting season, and showed smaller MSI extent by 24%, on average. Produced MSI distributions were also compared with the permanent snow and ice maps available from 6 global land cover datasets: (i) Global Land Cover GLC-2000, (ii & iii) European Space Agency's (ESA) Globcover circa 2005 and 2009, (iv–vi) land cover maps derived under the ESA Climate Change Initiative (CCI) for 2000, 2005, and 2010. Significant biases were discovered between various land cover datasets and our results. For example, GLC-2000 overestimated snow/ice extent by 194% (325,400 km2) for the Canadian Arctic. The biases over the entire landmass (excluding Greenland) are 135% (3.7 × 105 km2), 113% (3.0 × 105 km2), 89% (2.2 × 105 km2), and 28% (0.8 × 105 km2) between our results and GLC-2000, ESA Globcover 2005, ESA Globcover 2009, and ESA CCI datasets, correspondingly. The derived MSI extent was compared with Randolph Glacier Inventory (RGI) 4.0 and showed much better consistency (ranging from 1% to 15%).

Estimating the spatial distribution of snow water equivalent in the world's mountains

Estimating the spatial distribution of snow water equivalent (SWE) in mountainous terrain is currently the most important unsolved problem in snow hydrology. Several methods can estimate the amount of snow throughout a mountain range: (1) Spatial interpolation from surface sensors constrained by remotely sensed snow extent provides a consistent answer, with uncertainty related to extrapolation to unrepresented locations. (2) The remotely sensed date of disappearance of snow is combined with a melt calculation to reconstruct the SWE back to the last significant snowfall. (3) Passive microwave sensors offer real‐time global SWE estimates but suffer from several problems like subpixel variability in the mountains. (4) A numerical model combined with assimilated surface observations produces SWE at 1‐km resolution at continental scales, but depends heavily on a surface network. (5) New methods continue to be explored, for example, airborne LiDAR altimetry provides direct measurements of snow depth, which are combined with modelled snow density to estimate SWE. While the problem is aggressively addressed, the right answer remains elusive. Good characterization of the snow is necessary to make informed choices about water resources and adaptation to climate change and variability. WIREs Water 2016, 3:461–474. doi: 10.1002/wat2.1140This article is categorized under: Science of Water > Methods

Climate Change Impact on Aquifer System of Essaouira Basin during the Past 4 Decades (Morocco)

Since the 50s, many changes are observed in the climate of the globe, the warming of the atmosphere and the ocean, the decrease in the extent of snow and ice, and rising sea levels. In addition to the demographic pressure, the Maghreb countries are suffering from the drought, which accentuates the phenomenon of desertification and soil erosion, as well as their salinity, which leads to the salinization and depletion of water resources. In Morocco, the climate change leads to the erosion of the soil under the effect of desertification resulting in siltation of reservoirs and the increased salinization of soil and water resources. The Essaouira basin, which is the subject of this study, is located at the Western extremity of the High Atlas to the Southeast of the Essaouira city; between 31°20' to 31°30'N and between 9°00' to 9°46'W. The basin is characterized by a semi-arid climate with average annual rainfall of around 300 mm·year-1 and an average temperature of 20°C. Considering the importance of the aquifer in Essaouira in the supply of the region's groundwater, a study was conducted to assess quantitatively and qualitatively the impact of climate change on water resources in this area. The aquifers present in this region are one of cenomano-turonian upstream and plio-quaternary downstream, separated by the diaper of Tidzi. A piezometric map of the basin was made (1990-2009) and the hydrochemical method of the waters shows that they present a facies Na-Cl with an electrical conductivity ranging from 700 to 7000 μs/cm. The isotopic tracers like 18O, 2H, 3H and 14C were analyzed to determine the age of groundwater, and a right local meteoric close to the right meteoric world characterizing the precipitation of oceanic origin has been determined. The Essaouira basin turns out to be very vulnerable and sensitive to climate change because its recharge is entirely dependent on the rainwater. These results should be taken in consideration for the future water management in the region.

Investigation of temporal change in glacial extent of Chitral watershed using Landsat data.

Glaciers are also known as solid reservoirs, and in this regard, Pakistan is a blessed country to have enriched glaciers. The change in glacial extent becomes very crucial for rivers whose discharges are associated with glacier melt. Even a little change in the glacial extent may bring a significant change in the resulting river flows. Considering climate change scenarios, many researchers have predicted future flows in such catchments. But in almost all such studies, the reduction in the glaciers is not normally based on any rational. Therefore, research is needed in order to estimate how glaciers are actually behaving under the change of temperature and precipitations to better estimate the future flows. For this purpose, Chitral watershed was considered as the study area. The seasonal change in the snow extent was estimated by using MODIS data for various years that helped to identify the month with minimum glacial extent. With the help of remote sensing, unsupervised classification was performed to estimate the glacier area in Chitral watershed. The results show a definite receding trend with respect to time in the glaciers of the region for the past decade.

Change in snow cover area of Brahmaputra river basin and its sensitivity to temperature

The discharge of the River Brah maputra is highly affected by the melting of snow at the upper part of its catchment. Increase in discharge due to significant retreat of snow in turn affects the downstream flow characteristics of the river giving rise to severe catastrophic problems such as flood and erosion. Rising temperature is one of the major reasons of melting of snow at the upper Brahmaputra catchment. Keeping in mind such issue, in this paper, a study has been conducted to see how the snow cover area of the Brahmaputra river basin changes with respect to the change in temperature. MODIS image MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid) of 500 m resolution consisting of seven bands has been taken to prepare the normalized difference snow index maps of the study area. The normalized difference snow index map is then used to obtain the areal extent of snow in the Brahmaputra catchment area. The normalized difference snow index maps were prepared starting from 2002 to 2012 for four different months, viz. January, April, July and October. For temperature data, HadCM3 data of spatial resolution 2.5° × 3.75° (latitude by longitude) has been used. The evaluation of the results shows that the snow cover area of the basin shows decreasing trends with respect to the increasing trend of temperature except for the month of January. Further, this study also shows that MODIS data can efficiently be used for snow cover area variation study. Variation of snow cover area is an indication of climate change as the melting of snow clearly reflects the rise in temperature. The attenuation of river flow due to the melting of snow in the river Brahmaputra basin may affect the downstream discharge of the river giving rise to severe flood and erosion problem. It has also been observed that MODIS data can efficiently be used in mapping snow cover of large areas, because of its good spatial as well as high temporal resolution.

Remote sensing of Andean mountain snow cover to forecast water discharge of Cuyo rivers

In the Argentinian Dry Andes, although the melting of glaciers is seen as a threat for the long-term water availability needed by the piedmont crops, the annual snowmelt is the main source of superficial water and aquifer recharge. In this study, we analyse the link between the seasonal and interannual variations in the discharge measured upstream of the first dams on four rivers (Mendoza, Tunuyan, Diamante, Atuel) of the Argentinian Cuyo region (in the Federal Province of Mendoza) and those of the snow bed extent as mapped by optical remote sensing (MODIS MOD10A2 product) on a weekly basis in the 2001-2014 period, at the scale of each watershed.For the four snow-glacier regime rivers, seasonal variations in the discharge appear directly related to those of the snow bed surface area in each watershed, as shown previously (Masiokas et al., 2006). We observed that the high-water period (September-April) discharge is directly related to the snow extent at the beginning of the snowmelt period, i.e. in September and October, as revealed by a correlation of about 0.8. Moreover, the decreasing trend in the winter snow bed extent from 2001 to 2014 clearly explains the observed decreasing trend in the annual water discharge.Agriculture and human activities in these oases mostly depend on river discharge, which from our results clearly depends on the snow extent. Our research indicates that it is possible to use remote sensing to forecast the average discharge in the September-April period (high-water season) from MOD10A2 images with an average uncertainty of 15%. As MOD10A2 data are freely available ten days after acquisition, it is possible to anticipate in early October the risk of water shortages in the coming summer.

Winter Conditions and Land Cover Structure the Subnivium, A Seasonal Refuge beneath the Snow.

In seasonally snow-covered environments, many organisms endure winter by using the subnivium, a below-snow thermally stable seasonal refugium. Because the insulation of snow is dependent on snow depth and density, the stability of temperatures within the subnivium varies across land cover types. Additionally, across much of the Northern Hemisphere snow extent, depth and duration are generally decreasing while snow density is increasing due to climate change. These changes are likely to destabilize the thermal profile of the subnivium, although they have not yet been quantified. To explore the effects of land cover and climate change on the subnivium, we measured snow pack characteristics (depth and density), and ambient and subnivium temperatures from three different land cover types (prairie, deciduous forest, and coniferous forest) and within a micro-greenhouse (2.5 x 2.5 x 2 m) that maintained a temperature of 5°C warmer than outdoor ambient temperatures, and automatically opened during snow events throughout the winter of 2013/14. We found that the mean daily subnivium temperature was significantly colder in the deciduous cover type than the prairie cover type, and that prairie had higher maximum subnivium temperatures than both of the other cover types. Our climate change simulation revealed that, although ambient temperatures within the micro-greenhouse were 5°C warmer than outside the greenhouse, the daily minimum subnivium temperature was significantly lower inside the greenhouse. Our findings suggest that climate change could have considerable effects on the refuge quality of the subnivium, and that some cover types appear to be more susceptible to these effects than others.

Predicting alpine headwater stream intermittency: a case study in the northern Rocky Mountains

This investigation used climatic, geological, and environmental data coupled with observational stream intermittency data to predict alpine headwater stream intermittency. Prediction was made using a random forest classification model. Results showed that the most important variables in the prediction model were snowpack persistence, represented by average snow extent from March through July, mean annual mean monthly minimum temperature, and surface geology types. For stream catchments with intermittent headwater streams, snowpack, on average, persisted until early June, whereas for stream catchments with perennial headwater streams, snowpack, on average, persisted until early July. Additionally, on average, stream catchments with intermittent headwater streams were about 0.7°C warmer than stream catchments with perennial headwater streams. Finally, headwater stream catchments primarily underlain by coarse, permeable sediment are significantly more likely to have intermittent headwater streams than those primarily underlain by impermeable bedrock. Comparison of the predicted streamflow classification with observed stream status indicated a four percent classification error for first-order streams and a 21 percent classification error for all stream orders in the study area.

Snow cover setting-up dates in the north of Eurasia: relations and feedback to the macro-scale atmospheric circulation

Variations of snow cover onset data in 1950 – 2008 based on daily snow depth data collected at first-order meteorological stations of the former USSR compiled at the Russia Institute of Hydrometeorological Information are analyzed in order to reveal climatic norms, relations with macro-scale atmospheric circulation and influence of snow cover anomalies on strengthening/weakening of westerly basing on observational data and results of simulation using model Planet Simulator, as well. Patterns of mean snow cover setting-up data and their correlation with temperature of the Northern Hemisphere extra-tropical land presented in Fig. 1 show that the most sensible changes observed in last decade are caused by temperature trend since 1990th. For the most portion of the studied territory variations of snow cover setting-up data may be explained by the circulation indices in the terms of Northern Hemisphere Teleconnection Patterns: Scand, EA – WR, WP and NAO (Fig. 2). Role of the Scand and EA – WR (see Fig. 2, а , в , г ) is recognized as the most significant. Changes of snow cover extent calculated on the base of snow cover onset data over the Russia territory, and its western and eastern parts as well, for the second decade of October (Fig. 3) demonstrate significant difference in variability between eastern and western regions. Eastern part of territory essentially differs by lower both year-to-year and long-term variations in the contrast to the western part, characterized by high variance including long-term tendencies: increase in 1950–70th and decrease in 1970–80 and during last six years. Nevertheless relations between snow cover anomalies and Arctic Oscillation (AO) index appear to be significant exceptionally for the eastern part of the territory. In the same time negative linear correlation revealed between snow extent and AO index changes during 1950–2008 from statistically insignificant values (in 1950–70 and 1996–2008) to coefficient values –0.82 ÷ –0.85 in 1973–1994 (Fig. 4, а , б ). Results of numerical experiments on simulation of observed October snow cover anomaly in 1976 and its impact on Northern Hemisphere sea level pressure in winter months approved potential ability of abrupt increase of albedo caused by snow cover onset to influence on weakening of westerly and negative temperature anomalies in North Eurasia (Fig. 5). Evidently, based on observational data and results of modeling one should conclude that autumn snow cover anomalies in North are able to effect on macro-scale circulation regime in winter, but in condition of weakening of other major factors influencing on circulation, for example sea surface temperature over the oceans. In any case, correlation analysis of earth observations shows that snow cover extent anomalies could not be recognized as cause of negative AO anomalies and severe winters in North Eurasia in last decade.

A Review on Extraction of Lakes from Remotely Sensed Optical Satellite Data with a Special Focus on Cryospheric Lakes

Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.

Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada

Abstract. The current work evaluates the spatial and temporal variability in snow after a large forest fire in northern California using Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG). MODIS MOD10A1 fractional snow-covered area and MODSCAG fractional snow cover products are utilized to detect spatial and temporal changes in snowpack after the 2007 Moonlight Fire and an unburned basin, Grizzly Ridge, for water years (WY) 2002–2012. Estimates of canopy-adjusted and non-adjusted MODSCAG fractional snow-covered area (fSCA) are smoothed and interpolated to provide a continuous time series of average daily snow extent over the two basins. The removal of overstory canopy by wildfire exposes more snow cover; however, elemental pixel comparisons and statistical analysis show that the MOD10A1 product has a tendency to overestimate snow coverage pre-fire, muting the observed effects of wildfire. The MODSCAG algorithm better distinguishes subpixel snow coverage in forested areas and is highly correlated to soil burn severity after the fire. Annual MODSCAG fSCA estimates show statistically significant increased fSCA in the Moonlight Fire study area after the fire (P < 0.01 for WY 2008–2011) compared to pre-fire averages and the control basin. After the fire, the number of days exceeding a pre-fire high snow-cover threshold increased by 81%. Canopy reduction increases exposed viewable snow area and the amount of solar radiation that reaches the snowpack, leading to earlier basin average melt-out dates compared to the nearby unburned basin. There is also a significant increase in MODSCAG fSCA post-fire regardless of slope or burn severity. Regional snow cover change has significant implications for both short- and long-term water supply for impacted ecosystems, downstream communities, and resource managers.

Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment

The European Space Agency's Data User Element (DUE) project GlobSnow was established to create a global database of Snow Extent and Snow Water Equivalent. The Snow Extent (SE) product portfolio provided within ESA DUE GlobSnow (2008–2014) is introduced and described, with a special focus on the Daily Fractional Snow Cover (DFSC) of the SE version 2.0 and its successor 2.1 released in 2013–2014. The fractional snow retrieval uses the SCAmod method designed ecpecially to enable accurate snow mapping including forests. The basics of the methodology are presented, as well as the cloud screening method applied in SE production. Considerations for future validations together with discussion on some current issues and potential inaccuracies are presented. One focus of the investigation is on the representativeness of reference FSC generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data, with a particular interest in forested areas. Two methods for reference data generation are investigated. When comparing the GlobSnow Daily Fractional Snow Cover to these reference data, we try to identify how the comparison reflects the possible inaccuracies of the DFSC and to define the conditions where the reference data are not representative. It is obvious that the evaluation result strongly depends on the quality of the reference data, and that the two methods investigated cannot provide representative reference data for dense forests. For fully snow-covered dense conifer forest area in Finland, a Root Mean Squared Error of 20–30% was obtained from comparisons although DFSC indicated full snow cover correctly. These first evaluations would indicate a good performance of GlobSnow SE products in forests; however, this does not necessarily show up in validations due to the non-representativeness of the reference data. It is also concluded that GlobSnow SE products are sensitive to the representativeness of the applied SCAmod parameters and that FSC overestimations may occur in dense forests. GlobSnow SE products are available at www.globsnow.info/se/.