Snow Cover Variability Research Articles

Sensitivity of active-layer freezing process to snow cover in Arctic Alaska

Abstract. The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R>0.55, p<0.01) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼<0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77, p<0.01); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper (<0.4 m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (>0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss.

Changes in the Relationship Between the Interannual Variation of Eurasian Snow Cover and Spring SAT Over Eastern Eurasia

AbstractChanges in the relationship between interannual variation of spring (March–April–May) surface air temperature (SAT) over eastern Eurasia (SAT_EA) and Eurasian snow during 1972–2009 are investigated. The results show that the interannual variation in SAT_EA is significantly correlated with Eurasian snow cover anomalies (SCA) over two key regions, which include the western (SCAW) and eastern (SCAE) parts of the Eurasian continent. A pronounced climate shift of SAT_EA is observed around the late 1980s, and therefore, the data are divided into two subperiods, that is, 1972–1987 (P1) and 1988–2009 (P2). Examinations show that the relationship between SAT_EA and SCAW is impacted by the Arctic Oscillation. In contrast, the relationship between SCAE and the SAT_EA is Arctic Oscillation independent and is significantly increased from P1 to P2. Further analysis shows that the increased correlation between SAT_EA and SCAE from P1 to P2 cannot be well explained by the snow‐related surface heat flux. A wave train‐like anomalous circulation plays an important role in enhancing their relationship through intensified wind‐induced heat advection in P2, which causes reduced snow and increased SAT_EA. A lead‐lag regression analysis of the SCAE and the circulation anomalies indicates that the changes of the SCAE and SAT_EA are mainly responses to atmospheric circulation anomalies that occur 2 months before spring. Numerical experiments that use a linear baroclinic model indicate that the snow anomalies around Lake Baikal may impact the variation of SAT_EA through modulating atmospheric circulation over the East Asia‐North Pacific area.

How Much Do Clouds Mask the Impacts of Arctic Sea Ice and Snow Cover Variations? Different Perspectives from Observations and Reanalyses

Decreasing sea ice and snow cover are reducing the surface albedo and changing the Arctic surface energy balance. How these surface albedo changes influence the planetary albedo is a more complex question, though, that depends critically on the modulating effects of the intervening atmosphere. To answer this question, we partition the observed top of atmosphere (TOA) albedo into contributions from the surface and atmosphere, the latter being heavily dependent on clouds. While the surface albedo predictably declines with lower sea ice and snow cover, the TOA albedo decreases approximately half as much. This weaker response can be directly attributed to the fact that the atmosphere contributes more than 70% of the TOA albedo in the annual mean and is less dependent on surface cover. The surface accounts for a maximum of 30% of the TOA albedo in spring and less than 10% by the end of summer. Reanalyses (ASR versions 1 and 2, ERA-Interim, MERRA-2, and NCEP R2) represent the annual means of surface albedo fairly well, but biases are found in magnitudes of the TOA albedo and its contributions, likely due to their representations of clouds. Reanalyses show a wide range of TOA albedo sensitivity to changing sea ice concentration, 0.04–0.18 in September, compared to 0.11 in observations.

Snow cover variability at Polar Bear Pass, Nunavut

Information on arctic snow covers is relevant for climate and hydrology studies and investigations into the sustainability of both arctic fauna and flora. This study aims to (1) highlight the variability of snow cover at Polar Bear Pass (PBP) at a range of scales: point, local, and regional using both in situ snow cover measurements and remote sensing imagery products; and (2) consider how snow cover at PBP might change in the future. Terrain-based snow surveys documented the end-of-winter snowpacks over several seasons (2008–2010, 2012–2013), and snowmelt was measured daily at typical terrain types. MODIS products (snow cover) were used to document spatial snow cover variability across PBP and Bathurst and Cornwallis Islands. Due to limited data, no significant difference in snow cover duration can be identified at PBP over the period of record. Locally, end-of-winter snow cover does vary across a range of terrain types with snow depths and densities reflecting polar oasis sites. Aspect remains a defining factor in terms of snow cover variability at PBP. Northern areas of the Pass melt earlier. Regionally, PBP tends to melt out earlier than most of Bathurst Island. In the future, we surmise that snowpacks at PBP will be thinner and disappear earlier.

Relationship between Spatiotemporal Variations of Climate, Snow Cover and Plant Phenology over the Alps—An Earth Observation-Based Analysis

Alpine ecosystems are particularly sensitive to climate change, and therefore it is of significant interest to understand the relationships between phenology and its seasonal drivers in mountain areas. However, no alpine-wide assessment on the relationship between land surface phenology (LSP) patterns and its climatic drivers including snow exists. Here, an assessment of the influence of snow cover variations on vegetation phenology is presented, which is based on a 17-year time-series of MODIS data. From this data snow cover duration (SCD) and phenology metrics based on the Normalized Difference Vegetation Index (NDVI) have been extracted at 250 m resolution for the entire European Alps. The combined influence of additional climate drivers on phenology are shown on a regional scale for the Italian province of South Tyrol using reanalyzed climate data. The relationship between vegetation and snow metrics strongly depended on altitude. Temporal trends towards an earlier onset of vegetation growth, increasing monthly mean NDVI in spring and late summer, as well as shorter SCD were observed, but they were mostly non-significant and the magnitude of these tendencies differed by altitude. Significant negative correlations between monthly mean NDVI and SCD were observed for 15–55% of all vegetated pixels, especially from December to April and in altitudes from 1000–2000 m. On the regional scale of South Tyrol, the seasonality of NDVI and SCD achieved the highest share of correlating pixels above 1500 m, while at lower elevations mean temperature correlated best. Examining the combined effect of climate variables, for average altitude and exposition, SCD had the highest effect on NDVI, followed by mean temperature and radiation. The presented analysis allows to assess the spatiotemporal patterns of earth-observation based snow and vegetation metrics over the Alps, as well as to understand the relative importance of snow as phenological driver with respect to other climate variables.

Snow–atmosphere coupling in the Northern Hemisphere

Local and remote impacts of seasonal snow cover on atmospheric circulation have been explored extensively, with observational and modelling efforts focusing on how Eurasian autumn snow-cover variability potentially drives Northern Hemisphere atmospheric circulation via the generation of deep, planetary-scale atmospheric waves. Despite climate modelling advances, models remain challenged to reproduce the proposed sequence of processes by which snow cover can influence the atmosphere, calling into question the robustness of this coupling. Here, we summarize the current level of understanding of snow–atmosphere coupling, and the implications of this interaction under future climate change. Projected patterns of snow-cover variability and altered stratospheric conditions suggest a need for new model experiments to isolate the effect of projected changes in snow on the atmosphere. Using the ‘Can it? Has it? Will it?’ framework, this Review synthesizes current understanding on Eurasian snow–atmosphere coupling, outlining observational and modelling evidence for their dynamical connection and discussing possible changes in the future.

Snow cover variability in North-West Himalaya during last decade

Snow is an essential component of the cryosphere and spatio-temporal variability of snow cover over large Himalayan region is important for climate change studies, regional and global energy budget, aquatic cycles and managing water resources, etc. In the present paper, analysis of snow cover area (SCA) variation for more than a decade period from 2001 to 2016 in North-West Himalaya (NWH) and its climatic zones, i.e., lower Himalayan zone (LHZ), middle Himalayan zone (MHZ), and upper Himalayan zone (UHZ), has been presented. SCA has been estimated using 10-day maximum snow cover product derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images. Large inter and intra-annual variation in snow cover of NWH and its climatic zones have been observed during the data period. SCA in NWH varied from ~ 13,180 km2 (August, 2001) to ~ 2, 11,000 km2 (February, 2004) during the data period. Mean of annual and seasonal SCA has been estimated for entire NWH and its climatic zones. Mean annual SCA of NWH, LHZ, MHZ, and UHZ were estimated to be ~ 92,482 km2, ~ 8150 km2, ~ 35,078 km2, and ~ 21,190 km2 respectively. SCA was observed to be decreasing in NWH, LHZ, and MHZ at the rate of 840 km2 year−1, 31 km2 year−1, and 74 km2 year−1 respectively during 2001–2010, although the trend was statistically non-significant. Statistically significant increasing trend in SCA has been observed in UHZ at the rate of 241 km2 year−1 during 2001–2016. The paper highlights a shift in SCA trends after 2010 in NWH, LHZ, and MHZ and slowdown in snow/ice cover shrinkage during recent years. Additionally, variation in snowline elevation, snow cover duration and effect of topography on snow cover has been explored during different seasons in the NWH and its climatic zones.

Southern Hemisphere Origins for Interannual Variations of Snow Cover over the Western Tibetan Plateau in Boreal Summer

The climate response to the Tibetan Plateau (TP) snow cover (TPSC) has been receiving extensive concern. However, relatively few studies have been devoted to revealing the potential factors that can contribute to the TPSC interannual variability, especially during boreal summer. This study finds that the May Southern Hemisphere (SH) annular mode (SAM), the dominating mode of atmospheric circulation variability in the SH extratropics, exhibits a significant positive relationship with the interannual variations in western TPSC during boreal summer. Observational analysis and numerical experiments manifest that the signal of the May SAM can be “prolonged” by a meridional Indian Ocean tripole (IOT) sea surface temperature anomaly (SSTA) via atmosphere–ocean interaction. The IOT SSTA pattern persists into the following summer and excites anomalous local-scale zonal–vertical circulation. Subsequently, a tropical dipole rainfall (TDR) mode is induced with precipitation anomalies between the tropical western Indian Ocean and the eastern Indian Ocean–Maritime Continent. Rossby wave ray tracing diagnosis reveals that the wave energies, generated by the latent heat release of the TDR mode, can propagate northward into the western TP. As a response, abnormal cyclone (or anticyclone) and upward (or downward) movement are triggered over the western TP, providing favorable dynamical conditions for more (or less) TPSC. Moreover, the strong May SAM is usually followed by a cold air temperature anomaly over the western TP in summer, which is unfavorable for snow-cover melting, and vice versa. In brief, the IOT SSTA plays an “ocean bridge” role and the TDR mode plays an “atmosphere bridge” role in the process of the May SAM impacting the following summer TPSC variability. The results may provide new insight into the cross-equatorial propagation of the SAM influence.

Snow Cover Change as a Climate Indicator in Brunswick Peninsula, Patagonia

Snow cover changes are assessed for the Brunswick Peninsula in southern Patagonia (52.9oS to 53.5oS), located on the transition between the wet Pacific Ocean area and the drier leeward side of the Andes. We use the Normalized Difference Snow Index (NDSI) and a new index which we call SnowPower, combining the NDSI and the Melt Area Detection Index (MADI), to reconstruct the snow cover extent and its temporal distribution for the period 2000-2016, based on Moderate Resolution Imaging Spectroradiometer Sensor (MODIS) MOD09GA products. Comparison of these satellite products with available snow duration and snow height data for 2010-2016 at the local Club Andino ski centre of Punta Arenas shows that the NDSI exhibits the best results. A reasonable and significant linear correlation is found between the MODIS NDSI snow cover extent and the mean monthly temperature at Punta Arenas Airport combined with the monthly snow accumulation at Jorge Schythe station at Punta Arenas city for the extended winter period (April to September) from 2000 to 2016. Snow cover changes within this time series are extended to 1972 and 1958 based on historical climate data of Jorge Schythe and Punta Arenas airport, repectively. The results show a significant decreasing trend of snow extent of 19% for Brunswick Peninsula for the 45-year period (1972-2016), which can be attributed to a statistically significant long-term warming of 0.71oC at Punta Arenas during the extended winter (April to September) in the same period. Multiple correlation with different climate variables indicates that solid precipitation has a relevant role on short-term snow cover variability, but is not related to the observed long-term snow cover decrease.

Spatial-temporal variations analysis of snow cover in China from 1992−2010

Snow cover is an important component of land cover on the Earth’s surface and is also a good indicator of climate change. Hence, monitoring the spatial distribution and temporal variation of snow cover is of great significance to the study of the global water cycle and climate change. Traditional snow cover monitoring has been primarily based on in situ observations; however, the uneven and low-density distribution of meteorological stations made it difficult to reflect the overall picture of snow cover in some regions. To solve this problem, we used the Presence and Background Learning (PBL) algorithm to estimate the snow cover in China and obtained the 5 days (5 d) snow cover maps of the Special Sensor Microwave/Imager (SSM/I). The PBL model is a type of one-class classifier that needs no negative training sample in the training set. The cornerstone of this method is to combine SSM/I Brightness Temperature data and in situ observations to estimate the probability of the existence of snow cover based on the Artificial Neural Network (ANN). The estimation result indicates that the average annual overall accuracy of the PBL model in China is 0.88, which shows good agreement with the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products. Compared with other snow cover products, such as MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid (MOD10A2) and AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids(AE_5DSno), the performance of the PBL model is better at predicting the snow cover distribution in China. After obtaining the SSM/I 5d snow cover maps of China, we analysed the temporal and spatial variations of snow cover in China during 1992−2010 using the Mann-Kendall test, which included the variation of annual snow cover days, seasonal snow cover distribution and change characteristics, and the variation of the stable snow cover area. The results suggest that China’s snow cover is primarily distributed in the Tibetan Plateau, Xinjiang, northeast China, and Inner Mongolia, which are all high altitude or high latitude regions. From 1992−2010, the following occurred. (1) The number of snow cover days decreased significantly in the three major snow cover regions of China due to rising temperatures, while there was an observably upward trend in the northwestern Tibetan Plateau with the increase of precipitation. (2) The snow cover area of Xinjiang and Northeast-Inner Mongolia reached its maximum in winter and was relatively small in the spring and autumn. The Tibetan Plateau’s snow cover area is generally the largest in the spring, and it is also very large in the autumn and winter. Thus, the seasonal variation characteristics of snow cover there is not as obvious as that of the other two major snow regions. (3) In the four years of 1996, 2003, 2004 and 2006, the snow cover area in the spring in Xinjiang was much lower than that in the autumn, which indicates a “warmer winter” event caused by warmer than usual temperatures in the winter. (4) Although no significant change in the snow cover area was found in all regions during the study period, the change in snow cover on the Tibetan Plateau is particularly noteworthy due to its area having increased greatly since 2005, which might be the beginning of the increase of snow cover area in this region. (5) The stable snow cover area of China is 3.39 million km2. In the three major snow cover regions, the stable snow cover area of the Tibetan Plateau is the largest (1.68 million km2), the Northeast-Inner Mongolia region is the second largest (1.05 million km2), and the Xinjiang region is the smallest (0.63 million km2). The stable snow cover area had no significant variation trend in China during 1992−2010—only the snow cover days of the Tibetan Plateau had a larger interannual fluctuation.

Monitoring of Snow Cover Variability in Chenab Basin Using IRS AWiFS Sensor

Monitoring of seasonal snow cover is important for many applications such as melt runoff estimation, climate change studies and strategic requirements. Contribution of seasonal snow melt runoff of Chenab River is significant and important to meet hydrological requirement at foothills. Seasonal snow cover of Chandra, Bhaga, Miyar, Bhut, Warwan and Ravi, six major tributaries of Chenab River, becomes crucial to assess the water availability. In addition, altitudinal distribution of snow cover significantly influences the melt runoff which is highly sensitive to minor variations in atmospheric temperature. In this investigation, remote sensing based Normalized Difference Snow Index technique has been used to generate 10 daily snow cover product. Snow cover monitoring of all the sub-basins were carried out for 10 years from 2004–2005 to 2013–2014 during hydrological year (October to June) using Advanced Wide Field Sensor (AWiFS) of Indian remote sensing satellite (IRS). Accumulation and ablation patterns of snow cover have also been analyzed for the six sub-basins. Accumulation and ablation pattern of snow cover, from 2004 to 2014 which shows slightly increasing trend for all the sub-basins. Meteorological data of Kelong at Bhaga sub-basin was also analysed. Average monthly snow line altitude was estimated for all the sub-basins using hypsographic curve. Chandra and Bhaga sub-basins are at higher altitude and Ravi sub-basin is at lower altitude. It was also observed that areal extent of snow reaches to lower altitude during last 5 years, particularly in Ravi sub-basin.

Snow Cover Variation and Meteorological Factor Researchin Yarlung Zangbo Basin of Tibet from 2002 to 2015

Snow Cover Variation and Meteorological Factor Researchin Yarlung Zangbo Basin of Tibet from 2002 to 2015

Analysis of past changes in wet bulb temperature in relation to snow making conditions based on long term observations Austria and Germany

Abstract Ski resorts increasingly rely on snow making to ensure good quality skiing regardless of the variability of the natural snow cover. This study aims to quantify past changes in snow making conditions based on an analysis of long-term wet bulb temperature time series from 20 weather stations in Germany and Austria. The weather stations are located at a range of altitudes from 267 m to 3109 m, in the vicinity of ski resorts. The lengths of the time-series range from 54 to 83 years. Using a threshold mean daily wet bulb temperature of −2 °C we define ‘potential snow making days’ and track changes in the number of snow making days in the months of the early skiing season (October, November, December) by computing linear trends, comparing consecutive 20 year periods, and Mann-Kendall trend testing of overlapping sub-periods. The number of snow making days changes least in October and most in December when averaged over all stations. Very high stations show more change in October and less change in December than the lower stations. Several stations show a significant decrease of snow making days per month, particularly in more recent sub-periods, but trends vary strongly between stations and for different sub-periods. Sub-periods with positive trends are present in earlier phases of the time series at some stations and inter-annual variability is generally 1–2 orders of magnitude greater than detected trends. Detailed microclimatic studies are necessary to determine potential future effects of changes in snow making conditions on the scale of a ski resort.

Spatiotemporal variability of snow cover on Tibet, China using MODIS remote-sensing data

ABSTRACTSnow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.

Possible contribution of the inter‐annual Tibetan Plateau snow cover variation to the Madden–Julian oscillation convection variability

The Madden–Julian oscillation (MJO) is the major mode of intra‐seasonal variability in the Tropics, yet researches about factors related to this oscillation have not been well understood, particularly on an inter‐annual timescale. As a crucial index of the thermal condition of the plateau, the Tibetan Plateau snow cover (TPSC) has attracted great attention with its potential climatic effects. In this paper, observational evidences present that the inter‐annual variation in the wintertime TPSC can exert influence on the convection variability associated with the MJO. For the excessive TPSC winters, the MJO convection tends to be stronger in the Indian Ocean (phases 2–3), while it is more vigorous over the western Pacific (phases 6–7) during the reduced TPSC winters. Furthermore, the South Asia High (SAH) is found to be the key system in the potential physical mechanism. A reduced (excessive) TPSC can excite upper‐level anomalous anticyclone (cyclone) over the Tibetan Plateau and eastern China, which is in favour of westwards extension (eastwards withdrawal) and enhancing (weakening) of the SAH over tropical oceans. Through an anomalous tropical zonal‐vertical circulation, the intensified ascending (descending) motions over the tropical western Pacific may subsequently depress (enhances) the convection over the Indian Ocean. Over the Indian Ocean (phases 2–3) and the western Pacific (phases 6–7), the zonal‐vertical circulation associated with the MJO is exactly superposed with the inter‐annual anomalous vertical circulation linked with the TPSC, so the according changes of MJO convection are most significant in these two categorized phases.

VARIABILITY OF THE ICELANDIC LOW AND THE AZORES HIGH IN JANUARY AND THEIR INFLUENCE ON CLIMATIC CONDITIONS IN POLAND

The study analysed long-term changes and the variability of the Icelandic Low and the Azores High in January and their influence on thermal, precipitation and nival conditions in Poland in the period 1901–2000. There were no statistically significant (0.05) trends of analysed centres of action in January in the 20th century. However, in the second half of the century the center of the Icelandic Low in January moved northwards and the sea-level pressure in the center of the Azores High increased significantly. The latter change was the reason for the significant increasing difference between the Azores High and the Icelandic Low. This change caused the intensification of western advection over Europe. Changes in sea-level pressure in the Icelandic Low and the longitude of the Azores High in January explain about 40% of temperature and snow cover variability in Poland. The variability of some features of the centres of action describes the changes of precipitation and snow cover in Poland better than the NAO index.

How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates?

AbstractWith climate warming, many regions are experiencing changes in snow accumulation and persistence. These changes are known to affect streamflow volume, but the magnitude of the effect varies between regions. This research evaluates whether variables derived from remotely sensed snow cover can be used to estimate annual streamflow at the small watershed scale across the western U.S., a region with a wide range of climate types. We compared snow cover variables derived from MODIS, snow persistence (SP), and snow season (SS), to more commonly utilized metrics, snow fraction (fraction of precipitation falling as snow, SF), and peak snow water equivalent (SWE). Each variable represents different information about snow, and this comparison assesses similarities and differences between the snow metrics. Next, we evaluated how two snow variables, SP and SWE, related to annual streamflow (Q) for 119 USGS reference watersheds and examined whether these relationships varied for wet/warm (precipitation surplus) and dry/cold (precipitation deficit) watersheds. Results showed high correlations between all snow variables, but the slopes of these relationships differed between climates, with wet/warm watersheds displaying lower SF and higher SWE for the same SP. In dry/cold watersheds, both SP and SNODAS SWE correlated with Q spatially across all watersheds and over time within individual watersheds. We conclude that SP can be used to map spatial patterns of annual streamflow generation in dry/cold parts of the region. Applying this approach to the Upper Colorado River Basin demonstrates that 50% of streamflow comes from areas >3,000 masl. If the relationship between SP and Q is similar in other dry/cold regions, this approach could be used to estimate annual streamflow in ungauged basins.

Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers

Alpine vegetation plays a crucial role in global carbon cycle. Snow cover is an essential component of alpine land cover and shows high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000–2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among different biomes, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidence between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

Identifying and extracting a seasonal streamflow signal from remotely sensed snow cover in the Columbia River Basin

In the western United States, meltwater from mountain snowpacks serves as the dominant water supply for many communities. Efficient distribution and use of this renewable, yet temporally and spatially variable resource relies critically on accurate forecasting of future water availability. Here we report on initial efforts to use Interactive Multisensor Snow and Ice Mapping System (IMS) data on snow coverage to forecast flow in six selected watersheds within the Columbia River Basin. Little research has been done on identifying the relationship between seasonal discharge volume and these satellite-derived snow cover data. In the Yakima watershed within the Columbia River Basin, we could explain 52% of the spring discharge (April – July total streamflow volume) variance by selecting specific 24-km grid cells that exhibit both strong correlation with historical flows as well as high inter-annual variation. This approach yielded reasonable success in other watersheds. Of the six Columbia River subbasins examined in this paper, five of them give statistically significant predictors of April – July streamflow volume at the α = 0.05 level. When comparing this optimized specific-cell technique to the overall average across the entire watershed of interest, we observe improvements in each of our six subbasins, although in some regions, improvements were minimal. Clearly, this optimization technique is inherently limited by the role of snow cover variation in determining streamflow discharges in different subbasins. For both mountainous regions with extensive and stable snow cover as well as low-elevation regions with consistently minimal snow, the snow cover variation only accounts for a small inter-annual streamflow discharge variance. Our methodology shows that the IMS provides remotely-sensed data that are ready to “plug and play” into existing streamflow forecast models such as the Natural Resources Conservation Service's (NRCS) Visual Interactive Prediction and Estimation Routines (VIPER).

Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia

Changes in the extent and amount of snow cover in Eurasia are of great interest because of their vital impacts on the global climate system and regional water resource management. This study investigated the spatial and temporal variability of the snow cover extent (SCE) and snow water equivalent (SWE) of the continental Eurasia using the Northern Hemisphere Equal-Area Scalable Earth Grid (EASE-Grid) Weekly SCE data for 1972–2006 and the Global Monthly EASE-Grid SWE data for 1979–2004. The results indicated that, in general, the spatial extent of snow cover significantly decreased during spring and summer, but varied little during autumn and winter over Eurasia in the study period. The date at which snow cover began to disappear in spring has significantly advanced, whereas the timing of snow cover onset in autumn did not vary significantly during 1972–2006. The snow cover persistence period declined significantly in the western Tibetan Plateau as well as partial area of Central Asia and northwestern Russia, but varied little in other parts of Eurasia. “Snow-free breaks” (SFBs) with intermittent snow cover in the cold season were principally observed in the Tibetan Plateau and Central Asia, causing a low sensitivity of snow cover persistence period to the timings of snow cover onset and disappearance over the areas with shallow snow. The averaged SFBs were 1–14 weeks during the study period and the maximum intermittence could even reach 25 weeks in certain years. At a seasonal scale, SWE usually peaked in February or March, but fell gradually since April across Eurasia. Both annual mean and annual maximum SWE decreased significantly during 1979–2004 in most parts of Eurasia except for eastern Siberia as well as northwestern and northeastern China. The possible cross-platform inconsistencies between two passive microwave radiometers may cause uncertainties in the detected trends of SWE here, suggesting an urgent need of producing a long-term, more homogeneous SWE product in future.