Snowmelt Duration Research Articles

Snowmelt duration controls red algal blooms in the snow of the European Alps

Algae populate multiple habitats, including snow and ice, where they can form red blooms. These decrease snow albedo, accelerating snowmelt and potentially feeding back on snow and glacier decline caused by climate change. Quantifying this feedback requires the understanding of bloom evolution with climate change. Little, however, is known about the drivers of red snow blooms. Here, we develop an algorithm to analyze 5 y of satellite data from the European Alps and separate bloom occurrences from similarly colored Saharan dust depositions. In a second step, we combine the occurrences of blooms with meteorological data and snow simulations to identify the drivers of blooms. Results show that the upward migration of algae from the ground and blooming requires the presence of liquid water throughout the whole snow column for at least 46 d. Our limited data suggest that moderate dust amounts provide nutrients favorable to bloom, whereas large dust amounts hasten snowmelt and reduce its duration below the threshold required for blooming. Over the period studied, blooms cover 1.3% of the area above 1,800 m elevation, advancing the snow melt-out date by 4 to 21 d in these areas. Under warmer climates, maximum snow mass will decrease whereas snowmelt duration, that controls algal blooms' occurrences, is less sensitive to global temperature increase. In this respect, the impact of bloom on snowmelt will either remain stable (RCP4.5) or decrease (RCP8.5). Algal blooms in the Alps therefore do not constitute a positive climate feedback.

Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China

The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusions of live data of the Chinese Land Data Assimilation System (CLDAS, 0.05° × 0.05°, hourly data) were used as real data, and the Central Meteorological Observatory guidance forecast (SCMOC, 0.05° × 0.05°, forecasting the following 10 days in 3 h intervals) was used as forecast data, both of which were issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products for SCMOC. Based on the conventional temperature forecast information, we designed four temperature-rise prediction algorithms for essential factors affecting snowmelt. The temperature-rise prediction algorithms included the daily maximum temperature algorithm, daily temperature-rise-range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm. Four temperature-rise prediction values were calculated for each prediction product. The root–mean-squared error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution. Comprehensive tests showed that the forecast product revised by the average filter algorithm was superior to the revised dynamic linear regression algorithm as well as the original forecast product. Through these algorithms, the more suitable temperature-rise forecast value for each grid point in the study area could be obtained at different prediction times. The comprehensive and accurate temperature forecast value in the mountainous snowmelt season could provide an accurate theoretical basis for the effective prediction of runoff in snowmelt areas and the prevention of snowmelt flooding.

Duration of the influence of snowmelt on land surface temperature and humidity after snowmelt on the Mongolian Plateau

The impact of snowmelt on surface hydrothermal conditions is a research hot spot given the background of global warming. However, existing remote sensing-based studies have mostly focused on demonstrating the impacts of snow and are based on large time scales. How to measure the duration of snowmelt impact on surface hydrothermal conditions more accurately is a problem that needs to be addressed. We used a method to quantify the impact duration of snowmelt based on the characteristics of the phase change in land surface temperature (LST) and land surface water index (LSWI) after melting. We analyzed the snow factors that have caused the difference in impact duration and the interaction on the impact duration. The results are described as follows: (1) The LST and LSWI changes after snow melting are characterized by distinct phases. (2) The duration of the snowmelt impact on LST ranged from 4.61 days in the south to 21.23 days in the north; the effect of snow on the LSWI ranged from 8.06 days in the south to 25.38 days in the north. (3) The two durations have a significant positive correlation with snow depth and snow melt date. The combination of several snow parameters and other meteorological factors has a significant interaction effect on the duration of snowmelt influence. In most combinations where there is no interaction, the duration is significantly affected only by snow elements. The interaction can change the direction and extent of the effect of a single snow or meteorological element on the duration of snow impact. This research can supplement the theoretical basis for solving ecological problems and production in the study area, such as spring drought, forage mowing, and cold protection of livestock.

Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia

Abstract. Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 synthetic-aperture-radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. Here we present a workflow to combine Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the La Joie Basin, a 985 km2 watershed in the southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow-free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends in terms of elevation and aspect on the watershed scale and reflect snowmelt records from continuous snow water equivalence observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (< 30∘) in open areas. The accuracy of snowmelt duration is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt duration agrees with trends in snow depths observed in the La Joie Basin. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins.

High-resolution spatio-temporal analysis of snowmelt over Antarctic Peninsula ice shelves from 2015 to 2021 using SAR images

ABSTRACT Ice shelves play an essential role in the dynamics of the Antarctic ice sheet. The surface meltwater is important, as it can irreversibly weaken ice shelves by exerting additional hydrostatic pressure. Therefore, high-resolution snowmelt products are urgently needed to accurately analyze melting patterns of ice shelves and further estimate mass loss. In this study, a new high-resolution (40 m) snowmelt dataset over all of the Antarctic Peninsula ice shelves larger than 100 km was developed based on the modified snowmelt detection framework by using a co-orbit normalization method. The dataset provides detailed snowmelt information on each ice shelf, including the image coverage, melting area and melting ratio every 5 days. The melting patterns of three typical ice shelves (George VI, Wilkins and Larsen C Ice Shelves) and the spatio-temporal melting distribution of the Antarctic Peninsula (AP) were further analyzed. The snowmelt information indicates that both the extent and duration of snowmelt have been increasing in the Antarctic Peninsula from 2015 to 2021, and we found that the snowmelt on the Antarctic Peninsula showed a spatial pattern of significantly intense snowmelt on the western side. We believe this study will provide essential data for ice shelf investigation to support other fields of polar research.

Forest impacts on snow accumulation and melt in a semi-arid mountain environment

Snowmelt is complex under heterogeneous forest cover due to spatially variable snow surface energy and mass balances and snow accumulation. Forest canopies influence the under-canopy snowpack net total radiation energy balance by enhancing longwave radiation, shading the surface from shortwave radiation, in addition to intercepting snow, and protecting the snow surface from the wind. Despite the importance of predicting snowmelt timing for water resources, there are limited observations of snowmelt timing in heterogeneous forest cover across the Intermountain West. This research seeks to evaluate the processes that control snowmelt timing and magnitude at two paired forested and open sites in semi-arid southern Idaho, USA. Snow accumulation, snowmelt, and snow energy balance components were measured at a marginal snowpack and seasonal snowpack location in the forest, sparse vegetation, forest edge, and open environments. At both locations, the snow disappeared either later in the forest or relatively uniformly in the open and forest. At the upper elevation location, a later peak in maximum snow depth resulted in more variable snow disappearance timing between the open and forest sites with later snow disappearance in the forest. Snow disappearance timing at the marginal snowpack location was controlled by the magnitude and duration of a late season storm increasing snow depth variability and reducing the shortwave radiation energy input. Here, a shorter duration spring storm resulted in more uniform snowmelt in the forest and open. At both locations, the low-density forests shaded the snow surface into the melt period slowing the melt rate in the forest. However, the forest site had less cold content to overcome before melting started, partially canceling out the forest shading effect. Our results highlight the regional similarities and differences of snow surface energy balance controls on the timing and duration of snowmelt.

Extraction of snow melting duration and its spatiotemporal variations in the Tibetan Plateau based on MODIS product

The Qinghai-Tibet Plateau (TP) is one of the most sensitive areas to climate change, and its ecological environment changes directly or indirectly reflect the global climate change trend. The snow cover ratio (SCR) is one of the important indicators reflecting the climate and environmental changes on the TP. The daily remote sensing data of snow cover on the TP from 2003 to 2014 were used to study the spatiotemporal distribution of snow cover on the TP. This dataset is generated based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) product. The snowmelt days were extracted using the moving average method. The results have shown that the average snowmelt day on the TP starts on the 103rd day and ends on the 223rd day of a year, and the snowmelt duration has a downward trend. Snow is mainly distributed in the Nyainqentanglha Mountains, Karakoram Mountains and Himalayas. The SCR in summer has a downward trend, while in autumn has a rising trend. This shows that the difference in SCR during the year has enlarged, increasing the risk of snowmelt floods. The SCR is highly correlated with temperature, but weakly correlated with precipitation. Using long-term snow cover remote sensing data, the distribution of glacier coverage on the TP can be roughly extracted, of which the glacier area accounts for about 1.1% of the total area of the TP. This study provides an important reference for understanding the spatiotemporal changes of snow cover in the TP.

Large‐diameter trees affect snow duration in post‐fire old‐growth forests

AbstractSnow duration in post‐fire forests is influenced by neighbourhoods of trees, snags, and deadwood. We used annually resolved, spatially explicit tree and tree mortality data collected in an old‐growth, mixed‐conifer forest in the Sierra Nevada, California, that burned at low to moderate severity to calculate 10 tree neighbourhood metrics for neighbourhoods up to 40 m from snow depth and snow disappearance sampling points. We developed two linear mixed models, predicting snow disappearance timing as a function of tree neighbourhood, litter density, and simulated incoming solar radiation, and two multiple regression models explaining variation in snow depth as a function of tree neighbourhood. Higher densities of post‐fire large‐diameter snags within 10 m of a sampling point were related to higher snow depth (indicating reduced snow interception). Higher densities of large‐diameter trees within 5 m and larger amounts of litter were associated with shorter snow duration (indicating increased longwave radiation emittance and accelerated snow albedo decay). However, live trees with diameters >60 cm within 10 m of a snow disappearance sampling point were associated with a longer‐lasting spring snowpack. This suggests that, despite the local effects of canopy interception and emitted longwave radiation from boles of large trees, shading from their canopies may prolong snow duration over a larger area. Therefore, conservation of widely spaced, large‐diameter trees is important in old‐growth forests because they are resistant to fire and can enhance the seasonal duration of snowmelt.

ПРОСТРАНСТВЕННО-ВРЕМЕННЫЕ ЗАКОНОМЕРНОСТИ СНЕГОТАЯНИЯ НА РЕЧНЫХ ВОДОСБОРАХ ВЕРХНЕЙ КАМЫ

The paper deals with the spatio-temporal dynamics of the snowmelt process in the catchments of the Upper Kama, differing in size and relief features and located both on the plains – gauging station (g/s) Kosa-Kosa (A=6,221 km2), g/s Kama-Gainy (A=27,822 km2) and in a mountainous territory – g/s Vishera-Ryabinino (A=31,084 km2). The calculations were carried out using the method of temperature coefficients integrated into the GIS for years with different meteorological conditions and duration of snowmelt: 2004, 2010, and 2015. An analysis of the calculation results showed that the average duration of snowmelt in the studied catchments is 32 days. The duration of snowmelt and the daily amount of melted snow are influenced by the intensity of the increase in positive air temperatures. The timing of the start of snowmelt varies much less than the timing of its end. The plain catchments are freed from snow cover earlier than the mountain one by an average of 20 days. This is due to the latitudinal and altitudinal zonality, as well as the significant dissection of the relief of the g/s Vishera-Ryabinino catchment. The process of intense snowmelt begins here, on average, 9 days later than in the plain catchments. The g/s Kosa-Kosa catchment is freed from snow cover earlier than the others, which is due to its small area and weak dissection. The influence of latitudinal zonality during the melting of snow cover is best seen in the meridional catchment g/s Kama-Gainy. The southern and central parts of the catchment area are freed from snow first, later followed by the northern part.

Earlier winter/spring runoff and snowmelt during warmer winters lead to lower summer chlorophyll-a in north temperate lakes.

Winter conditions, such as ice cover and snow accumulation, are changing rapidly at northern latitudes and can have important implications for lake processes. For example, snowmelt in the watershed-a defining feature of lake hydrology because it delivers a large portion of annual nutrient inputs-is becoming earlier. Consequently, earlier and a shorter duration of snowmelt are expected to affect annual phytoplankton biomass. To test this hypothesis, we developed an index of runoff timing based on the date when 50% of cumulative runoff between January 1 and May 31had occurred. The runoff index was computed using stream discharge for inflows, outflows, or for flows from nearby streams for 41lakes in Europe and North America. The runoff index was then compared with summer chlorophyll-a (Chl-a) concentration (a proxy for phytoplankton biomass) across 5-53years for each lake. Earlier runoff generally corresponded to lower summer Chl-a. Furthermore, years with earlier runoff also had lower winter/spring runoff magnitude, more protracted runoff, and earlier ice-out. We examined several lake characteristics that may regulate the strength of the relationship between runoff timing and summer Chl-a concentrations; however, our tested covariates had little effect on the relationship. Date of ice-out was not clearly related to summer Chl-a concentrations. Our results indicate that ongoing changes in winter conditions may have important consequences for summer phytoplankton biomass and production.

Snow Phenology and Hydrologic Timing in the Yukon River Basin, AK, USA

The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins.

Spatio-temporal changes of snowmelt in Greenland ice sheet based on SSM/I (SSMIS) data (1988-2016)

The snowmelt of the Greenland ice sheets is of great significance to the study of global climate change. This paper is based on the 19.35 GHz horizontal polarization data and 37.00 GHz vertical polarization data of the Special Sensor Microwave/ Imager Sounder (SSMIS) and Special Sensor Microwave/ Image (SSM/I) carried by National Defense Meteorological Satellite Program (DMSP) from 1988 to 2016, by cross-polarization ratio (XPGR) algorithm (threshold value is -0.0158). The inter-annual trends of snowmelt area, annual average snowmelt onset, end date and duration in Greenland were studied. The results showed that the maximum snowmelt area was 2,080,000 km2 in 2012, and the minimum was 1,115,000 km2 in 1992. From 1988 to 2016, the snowmelt area of the Greenland ice sheets was increased by 2.8×105 km2, with a growth rate of 9.66×103 km2/year. In the annual average change rate, there were earlier snowmelt onset date (0.16 days earlier each year), longer snowmelt duration (0.36 days longer each year) and later snowmelt end date (0.06 days later each year), and the snowmelt area was in the marginal region. The snowmelt area of the southern margin is the largest, and there are obvious regional differences. The snowmelt of Greenland ice sheets changes greatly and shows a periodic change rule in the annual mean snowmelt variation.

Effects of Temperature Variability and Warming on the Timing of Snowmelt Events in Southern Finland during the Past 100 Years

This study analyses the first and last days of snowmelt events and the number of days (duration) between those throughout a water year (September-August). The snowmelt duration (SD) as well as its first (SFD) and last (SLD) days were estimated using daily precipitation and temperature measurements at the Kaisaniemi meteorological station in southern Finland during 1909-2008 as input datasets to a temperature-index snowmelt model. As snowmelt is a sensitive hydrological variable to temperature, this study also evaluated historical variations and trends in November-May (SDt), November-January (SFDt), and March-May (SLDt) temperatures corresponding to SD, SFD, and SLD at Kaisaniemi. The trends in all these parameters as well as their correlations with the well-known climate teleconnections over Finland were investigated. Long-term average values indicated the longest SD was about 131 days between 15 December and 25 April at Kaisaniemi. The SD significantly (p<0.05) shortened by 0.37 (days/year) at Kaisaniemi during 1909-2008 mainly due to the earlier (0.32 days/year) SLD. Such trends in SD and SLD were principally associated with century-long significant warming trends (0.02 °C/year) in both SDt and SLDt. The Arctic Oscillation (AO) was the most influential climate teleconnection for historical variations in SD, SLD, SDt, SFDt, and SLDt at Kaisaniemi.

Effects of A Severe Drought On Summer Abundance, Growth, and Movement of Cutthroat Trout In A Western Oregon Headwater Stream

Winter snowpack depth, snowmelt timing, and snowmelt duration are projected to change in the future, leading to increased frequency and severity of drought in the Pacific Northwest. In summer 2015, stream flows throughout the Pacific Northwest were at record low levels because of low winter snowpack conditions consistent with these climate projections. We explored effects of the 2015 low-snowpack-associated drought on Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) abundance, growth, and movement patterns in two 100m reaches ( 350 y old forests) of an unnamed perennial western Oregon headwater stream before (2 y) and during a severe drought. We found that the abundance of Cutthroat Trout declined substantially during the drought year, regardless of habitat availability, riparian forest age, or stream wood structure. Fish growth during summer was consistently negative during all 3 y of the study in both reaches. During the drought year, estimated abundance and total biomass of Cutthroat Trout declined in both reaches compared to the 2y prior. In all 3 y, the majority (76%) of fish in the reach with a young riparian forest stand moved >2 m from their release point. In contrast, across the 3 study years, only 26% of fish on average moved >2 m from their release point in the old-growth reach, which had more large wood and pool area. Across both reaches, in the non-drought years, most fish moved into pools (32.4%), but some moved to riffles (23.3%). During the drought year, of the fish that were recaptured, only upstream movement to pools were observed. There were no observed movements of recaptured fish to riffles. Overall, study results suggest that increasing severity of summer drought in the Pacific Northwest is likely to reduce the abundance of fish in small headwater streams, and the remaining fish preferentially use pool habitats such as those found in structurally complex streams.

Antarctic ice-sheet near-surface snowmelt detection based on the synergy of SSM/I data and QuikSCAT data

Microwave radiometer SSM/I data and scatterometer QuikSCAT data have been widely used for the ice-sheet near-surface snowmelt detection based on their sensitivity to liquid water present in snow. In order to improve the Antarctic ice-sheet near-surface snowmelt detection accuracy, a new Antarctic ice-sheet near-surface snowmelt synergistic detection method was proposed based on the principle of complementary advantages of SSM/I data (high reliability) and QuikSCAT data (high sensitivity) by the use of edge detection model to automatically extract the edge information to get the distribution of Antarctic snowmelt onset date, snowmelt duration and snowmelt end date. The verification result shows that the proposed snowmelt synergistic detection method improves the detection accuracy from about 75% to 86% based on AWS (Automatic Weather Stations) Butler Island and Larsen Ice Shelf. The algorithm can also be applied to other regions, which provides methodological support and supplement for the global snowmelt detection.

Snow thickness estimation on first-year sea ice using microwave and optical remote sensing with melt modelling

The snow cover on first-year sea ice plays a paramount role in thermodynamics by modulating sea ice ablation and accretion processes. However, meteoric accumulation and redistribution of snow on first-year sea ice is highly stochastic over space and time, which makes it a poorly understood parameter. In this study, a region of late-winter snow thickness on first-year sea ice in the Canadian Arctic Archipelago is estimated using time series spaceborne C-band microwave scatterometer and optical MODIS data and a simple snow melt model. Results show good correspondence between the modeled snow thickness and remotely sensed dates of melt onset and pond onset. The mean snowmelt duration for 20 study sites is 24.6±1.2days, and the estimated mean snow thickness is 14.7±3.0cm. The overall performance of the model reveals a RMSE of 4.0cm and a mean bias of 0.3cm. The methodology shows promise; particularly because it can easily be scaled up in order to estimate snow thickness on seasonal sea ice on a regional basis.

Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

Abstract Snow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

Investigations on physical properties and ablation processes of snow cover during the spring snowmelt period in the headwater region of the Irtysh River, Chinese Altai Mountains

To explore the effects of the underlying surface on snow properties and the “forest effects” on snow accumulation and ablation processes in the headwater area of the Irtysh River in the Chinese Altai Mountains, the physical properties of snow and certain snowmelt processes were investigated during the snowmelt period in 2014. Our observations showed that the diurnal and daily variations of air and snow temperatures, the liquid water contents (LWCs), and the densities of surface snow layers at 0–5 cm depths were in agreement, but differences in snow properties were obvious at the bottom snow layers. The LWCs were divided into three categories according to snow temperature (T s): LWC = 0, T s < −3 °C; LWC <1 %, −3 ≤ T s < −1 °C; LWC <7 %, T s ≥ −1 °C. The snow densities were greatest in the top layers above all underlying surfaces (grassland, concrete, and river ice) during the snowmelt period. The snowmelt experiment indicated that the mean air temperature was the highest on the grassland and lowest in the glade, but the diurnal temperature range was the lowest in the continuous forest and highest on the grassland. The canopy snow interception rate of Abies sibirica reached up to 70 % and the mean value of the continuous forest cover was about 30 %. The snowmelt duration periods were 20 days on the grassland, 35 days in the continuous forest, and 43 days in the forest glade, and the corresponding average snow melt rates (SMRs) were 2.1, 1.2, and 1.4 mm d−1. For one single A. sibirica tree, the SMR in the sub-canopy was only half that on the edge and outside of the forest canopy.

New satellite climate data records indicate strong coupling between recent frozen season changes and snow cover over high northern latitudes

We examined new satellite climate data records documenting frozen (FR) season and snow cover extent (SCE) changes from 1979 to 2011 over all northern vegetated land areas (≥45 °N). New insight on the spatial and temporal characteristics of seasonal FR ground and snowpack melt changes were revealed by integrating the independent FR and SCE data records. Similar decreasing trends in annual FR and SCE durations coincided with widespread warming (0.4 °C decade−1). Relatively strong declines in FR and SCE durations in spring and summer are partially offset by increasing trends in fall and winter. These contrasting seasonal trends result in relatively weak decreasing trends in annual FR and SCE durations. A dominant SCE retreat response to FR duration decreases was observed, while the sign and strength of this relationship was spatially complex, varying by latitude and regional snow cover, and climate characteristics. The spatial extent of FR conditions exceeds SCE in early spring and is smaller during snowmelt in late spring and early summer, while FR ground in the absence of snow cover is widespread in the fall. The integrated satellite record, for the first time, reveals a general increasing trend in annual snowmelt duration from 1.3 to 3.3 days decade−1 (p < 0.01), occurring largely in the fall. Annual FR ground durations are declining from 0.8 to 1.3 days decade−1. These changes imply extensive biophysical impacts to regional snow cover, soil and permafrost regimes, surface water and energy budgets, and climate feedbacks, while ongoing satellite microwave missions provide an effective means for regional monitoring.

Relationships between stream nitrate concentration and spatially distributed snowmelt in high‐elevation catchments of the western U.S.

AbstractThis study compares stream nitrate ( ) concentrations to spatially distributed snowmelt in two alpine catchments, the Green Lakes Valley, Colorado (GLV4) and Tokopah Basin, California (TOK). A snow water equivalent reconstruction model and Landsat 5 and 7 snow cover data were used to estimate daily snowmelt at 30 m spatial resolution in order to derive indices of new snowmelt areas (NSAs). Estimates of NSA were then used to explain the flushing behavior for each basin over a 12 year period (1996–2007). To identify the optimal method for defining NSAs and elucidate mechanisms underlying catchment flushing, we conducted a series of regression analyses using multiple thresholds of snowmelt based on temporal and volumetric metrics. NSA indices defined by volume of snowmelt (e.g., snowmelt ≤ 30 cm) rather than snowmelt duration (e.g., snowmelt ≤ 9 days) were the best predictors of stream concentrations. The NSA indices were better correlated with stream concentration in TOK (average R2= 0.68) versus GLV4 (average R2= 0.44). Positive relationships between NSA and stream concentration were observed in TOK with peak stream concentration occurring on the rising limb of snowmelt. Positive and negative relationships between NSA and stream concentration were found in GLV4 with peak stream concentration occurring as NSA expands. Consistent with previous works, the contrasting flushing behavior suggests that streamflow in TOK was primarily influenced by overland flow and shallow subsurface flow, whereas GLV4 appeared to be more strongly influenced by deeper subsurface flow paths.