Snow Cover Depletion Research Articles

Rain-on-snow responses to warmer Pyrenees: a sensitivity analysis using a physically based snow hydrological model

Abstract. Climate warming is changing the magnitude, timing, and spatial patterns of mountain snowpacks. A warmer atmosphere may also induce precipitation phase shifts, resulting in a decreased snowfall fraction (Sf). The combination of Sf and snowpack directly influences the frequency and intensity of rain-on-snow (ROS) events, a common cause of flash-flood events in snow-dominated regions. In this work, we investigate ROS patterns and their sensitivity to temperature and precipitation changes in the Pyrenees by modeling ROS through a physically based snow model. This model is forced with reanalysis climate data for elevations of 1500, 1800, and 2400 m perturbed using a range of temperature and precipitation values consistent with 21st century climate projections. ROS patterns are characterized by their frequency, rainfall quantity, and snow ablation. The highest ROS frequency for the historical climate period (1980–2019) is found in the 2400 m zones of the southwest Pyrenees (17 d yr−1). The maximum ROS rainfall amount is detected in 1800 m areas of the southeast (45 mm d−1, autumn), whereas the highest ROS ablation is found in the 2400 m zones of the northwest (−10 cm d−1, summer). When air temperature increases from 1 to 4 ∘C compared to the historical climate period, ROS rainfall amount and frequency increase at a constant rate during winter and early spring for all elevation zones. For the rest of the seasons, non-linear responses of ROS frequency and ablation to warming are found. Overall, ROS frequency decreases in the shoulders of the season across eastern low-elevation zones due to snow cover depletion. However, ROS increases in cold, high-elevation zones where long-lasting snow cover exists until late spring. Similarly, warming induces greater ROS ablation (+10 % ∘C−1) during the coldest months of the season, 2400 m elevations, and northern sectors, where the deepest snow depths are found. In contrast, small differences in ROS ablation are found for warm and marginal snowpacks. These results highlight the different ROS responses to warming across the mountain range, suggest similar ROS sensitivities in near-mid-latitude zones, and will help anticipate future ROS impacts in hydrological, environmental, and socioeconomic mountain systems.

The signal of snowmelt in streamflow and stable water isotopes in a high mountain catchment in Central Spain

Study regionPeñalara catchment, in the mountains of the Central System in Spain, Study focusFor the first time, we investigated the streamflow and streamwater isotopes during the snow accumulation and melting periods and over subsequent months in two snow seasons. The aim is to better understand the hydrological processes linked to snowmelt; to describe the temporal evolution and the interannual differences in isotopic streamwater; and to improve the understanding about the hydrological functioning of snowmelt water across the catchment. New hydrological insights for the regionThe isotopic signal of the streamwater progressively became isotopically depleted from the beginning of the melt period until the snow cover depletion of the catchment. Higher snowfall led to depleted isotopic values in the stream compared to a year with low snowfall. The interannual variability of the isotopic signature of streamwater during snowmelt may represent a difficulty to establishing reference values to be used in mixing models for hydrograph separation. The streamflow isotopic values had very limited sub-daily variation and showed slow temporal changes, suggesting a central role for alpine aquifers in explaining the hydrological functioning of the catchment, pointing to piston flow as a key process in streamflow generation.

Impact of climate change on snow covered area over the transboundary Jhelum River basin of Himalayas region using Remote Sensing

Climate change is one of the pertinent issues for the 21st century mainly for the transboundary river basins because it has parallel impacts on the hydrosphere and cryosphere. The temperature changes impacted the snow-covered area (SCA) of the river basins. MODIS MOD10A2 product data was used to delineate the SCA for the Jhelum River basin. The catchment is further divided into elevation zones using DEM to delineate the SCA for every zone. The temperature changes were plotted against the SCA, and correlation analysis was used to see the impact of climate change on the SCA. The seasonal trend of SCA has depicted an increase in SCA during the spring and summer seasons and decreasing trend during autumn while winter shows stability. The snow cover depletion curve (SCDC) showed that snow is maximum in December, January, and February while its minimum during July and August. The correlation analysis of temperature and SCA for the period of 2001-2017 illustrated the inverse relationship, which depicted that a slight increase in temperature may trigger the fastest depletion in the snow-covered areas generating water issues for snow/glacier-fed river basins.

Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland

Abstract. Snow cover (SC) and timing of snowmelt are key regulators of a wide range of Arctic ecosystem functions. Both are strongly influenced by the amplified Arctic warming and essential variables to understand environmental changes and their dynamics. This study evaluates the potential of Sentinel-1 (S-1) synthetic aperture radar (SAR) time series for monitoring SC depletion and snowmelt with high spatiotemporal resolution to capture their understudied small-scale heterogeneity. We use 97 dual-polarized S-1 SAR images acquired over northeastern Greenland and 94 over southwestern Greenland in the interferometric wide swath mode from the years 2017 and 2018. Comparison of S-1 intensity against SC fraction maps derived from orthorectified terrestrial time-lapse imagery indicates that SAR backscatter can increase before a decrease in SC fraction is observed. Hence, the increase in backscatter is related to changing snowpack properties during the runoff phase as well as decreasing SC fraction. We here present a novel empirical approach based on the temporal evolution of the SAR signal to identify start of runoff (SOR), end of snow cover (EOS) and SC extent for each S-1 observation date during melt using backscatter thresholds as well as the derivative. Comparison of SC with orthorectified time-lapse imagery indicates that HV polarization outperforms HH when using a global threshold. The derivative avoids manual selection of thresholds and adapts to different environmental settings and seasonal conditions. With a global configuration (threshold: 4 dB; polarization: HV) as well as with the derivative, the overall accuracy of SC maps was in all cases above 75 % and in more than half of cases above 90 %. Based on the physical principle of SAR backscatter during snowmelt, our approach is expected to work well in other low-vegetation areas and, hence, could support large-scale SC monitoring at high spatiotemporal resolution (20 m, 6 d) with high accuracy.

Analysis of topographic controls on depletion curves derived from airborne lidar snow depth data

Abstract The annual consistency of spatial patterns of snow accumulation and melt suggests that the evolution of these patterns, known as depletion curves, is useful for estimating basin water content and runoff prediction. Theoretical snow cover depletion curves are used in models to parameterize fractional snow-covered area (fSCA) based on modeled estimates of snow accumulation and snowmelt. Directly measuring the spatio-temporal snow distribution, characterization of depletion curves, and understanding how they vary across mountainous landscapes was not possible until the recent U.S. National Aeronautics and Space Administration (NASA) Airborne Snow Observatory (ASO). Herein, for the first time, high-resolution spatio-temporal snow depth information from the ASO is used to derive observation-based snow cover depletion curves across physiographic gradients by estimating the slope of the fSCA–snow depth relationship (i.e. depletion slope). The depletion slope reveals important insights into snow processes as it is strongly related to snow depth variability (r2 = 0.58). Regression tree analysis between observed depletion slopes and physiography, particularly vegetation height and terrain roughness, displays clear nonlinear dynamics and explains 31% of the variance in depletion slope. This unique observation-based analysis of snow cover depletion curves has implications for energy and water flux calculations across many earth system models.

Inter‐annual variability in snow cover depletion patterns and atmospheric circulation indices in the Upper Irtysh basin, Central Asia

AbstractThe Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large‐scale processes that influence the timing of these snow‐induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub‐basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate‐resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer‐term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km2/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large‐scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long‐term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.

Shifting Hydrological Processes in a Canadian Agroforested Catchment due to a Warmer and Wetter Climate

This study examines the hydrological sensitivity of an agroforested catchment to changes in temperature and precipitation. A physically based hydrological model was created using the Cold Regions Hydrological Modelling platform to simulate the hydrological processes over 23 years in the Acadie River Catchment in southern Québec. The observed air temperature and precipitation were perturbed linearly based on existing climate change projections, with warming of up to 8 °C and an increase in total precipitation up to 20%. The results show that warming causes a decrease in blowing snow transport and sublimation losses from blowing snow, canopy-intercepted snowfall and the snowpack. Decreasing blowing snow transport leads to reduced spatial variability in peak snow water equivalent (SWE) and a more synchronized snow cover depletion across the catchment. A 20% increase in precipitation is not sufficient to counteract the decline in annual peak SWE caused by a 1 °C warming. On the other hand, peak spring streamflow increases by 7% and occurs 20 days earlier with a 1 °C warming and a 20% increase in precipitation. However, when warming exceeds 1.5 °C, the catchment becomes more rainfall dominated and the peak flow and its timing follows the rainfall rather than snowmelt regime. Results from this study can be used for sustainable farming development and planning in regions with hydroclimatic characteristics similar to the Acadie River Catchment, where climate change may have a significant impact on the dominating hydrological processes.

General overview of the potential effect of extreme temperature change on society and economyin Poland in the 21st century

This work gives an overview on how the projected changes in the extremes in Poland might impact human health and economy. For that purpose, statisti-cally corrected data from 7 regional climate models were used. A significant increase of extreme hot events (i.e. heat waves, tropical nights) is projected for Central and Southern Poland for the end of the 21st century which might seri-ously affect a society living in large urban areas. Less extreme cold events im-prove thermal comfort in winter. The negative impact of the warming will affect energy systems with higher demand for electricity in summer and agriculture: an earlier beginning of the growing season and flower blooming will enhance the risk of frost damages in spring, whereas excessive heat will reduce yields in summer. Polish tourism should benefit from higher thermal comfort (except for hot July and August in the far future and warming in the winter season bring-ing snow cover depletion in the near future).

Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies

Abstract. The spatial distribution of snow water equivalent (SWE) and melt are important for estimating areal melt rates and snow-cover depletion (SCD) dynamics but are rarely measured in detail during the late ablation period. This study contributes results from high-resolution observations made using large numbers of sequential aerial photographs taken from an unmanned aerial vehicle on an alpine ridge in the Fortress Mountain Snow Laboratory in the Canadian Rocky Mountains from May to July in 2015. Using structure-from-motion and thresholding techniques, spatial maps of snow depth, snow cover and differences in snow depth (dHS) during ablation were generated in very high resolution as proxies for spatial SWE, spatial ablation rates and SCD. The results indicate that the initial distribution of snow depth was highly variable due to overwinter snow redistribution; thus, the subsequent distribution of dHS was also variable due to albedo, slope/aspect and other unaccountable differences. However, the initial distribution of snow depth was 5 times more variable than that of the subsequent dHS values, which varied by a factor of 2 between the north and south aspects. dHS patterns were somewhat spatially persistent over time but had an insubstantial impact on SCD curves, which were overwhelmingly governed by the initial distribution of snow depth. The reason for this is that only a weak spatial correlation developed between the initial snow depth and dHS. Previous research has shown that spatial correlations between SWE and ablation rates can strongly influence SCD curves. Reasons for the lack of a correlation in this study area were analysed and a generalisation to other regions was discussed. The following questions were posed: what is needed for a large spatial correlation between initial snow depth and dHS? When should snow depth and dHS be taken into account to correctly model SCD? The findings of this study suggest that hydrological and atmospheric models need to incorporate realistic distributions of SWE, melt energy and cold content; therefore, they must account for realistic correlations (i.e. not too large or too small) between SWE and melt in order to accurately model SCD.

Effects of preferential flow on snowmelt partitioning and groundwater recharge in frozen soils

Abstract. Snowmelt is a major source of groundwater recharge in cold regions. Throughout many landscapes snowmelt occurs when the ground is still frozen; thus frozen soil processes play an important role in snowmelt routing, and, by extension, the timing and magnitude of recharge. This study investigated the vadose zone dynamics governing snowmelt infiltration and groundwater recharge at three grassland sites in the Canadian Prairies over the winter and spring of 2017. The region is characterized by numerous topographic depressions where the ponding of snowmelt runoff results in focused infiltration and recharge. Water balance estimates showed infiltration was the dominant sink (35 %–85 %) of snowmelt under uplands (i.e. areas outside of depressions), even when the ground was frozen, with soil moisture responses indicating flow through the frozen layer. The refreezing of infiltrated meltwater during winter melt events enhanced runoff generation in subsequent melt events. At one site, time lags of up to 3 d between snow cover depletion on uplands and ponding in depressions demonstrated the role of a shallow subsurface transmission pathway or interflow through frozen soil in routing snowmelt from uplands to depressions. At all sites, depression-focused infiltration and recharge began before complete ground thaw and a significant portion (45 %–100 %) occurred while the ground was partially frozen. Relatively rapid infiltration rates and non-sequential soil moisture and groundwater responses, observed prior to ground thaw, indicated preferential flow through frozen soils. The preferential flow dynamics are attributed to macropore networks within the grassland soils, which allow infiltrated meltwater to bypass portions of the frozen soil matrix and facilitate both the lateral transport of meltwater between topographic positions and groundwater recharge through frozen ground. Both of these flow paths may facilitate preferential mass transport to groundwater.

Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export

AbstractThere is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO3 but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO3 run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.

Recent changes to the hydrological cycle of an Arctic basin at the tundra–taiga transition

Abstract. The impact of transient changes in climate and vegetation on the hydrology of small Arctic headwater basins has not been investigated before, particularly in the tundra–taiga transition region. This study uses weather and land cover observations and a hydrological model suitable for cold regions to investigate historical changes in modelled hydrological processes driving the streamflow response of a small Arctic basin at the treeline. The physical processes found in this environment and explicit changes in vegetation extent and density were simulated and validated against observations of streamflow discharge, snow water equivalent and active layer thickness. Mean air temperature and all-wave irradiance have increased by 3.7 ∘C and 8.4 W m−2, respectively, while precipitation has decreased 48 mm (10 %) since 1960. Two modelling scenarios were created to separate the effects of changing climate and vegetation on hydrological processes. Results show that over 1960–2016 most hydrological changes were driven by climate changes, such as decreasing snowfall, evapotranspiration, deepening active layer thickness, earlier snow cover depletion and diminishing annual sublimation and soil moisture. However, changing vegetation has a significant impact on decreasing blowing snow redistribution and sublimation, counteracting the impact of decreasing precipitation on streamflow, demonstrating the importance of including transient changes in vegetation in long-term hydrological studies. Streamflow dropped by 38 mm as a response to the 48 mm decrease in precipitation, suggesting a small degree of hydrological resiliency. These results represent the first detailed estimate of hydrological changes occurring in small Arctic basins, and can be used as a reference to inform other studies of Arctic climate change impacts.

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Abstract. With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment

The spatial heterogeneity of mountain snow cover and ablation is important in controlling patterns of snow cover depletion (SCD), meltwater production, and runoff, yet is not well-represented in most large-scale hydrological models and land surface schemes. Analyses were conducted in this study to examine the influence of various representations of snow cover and melt energy heterogeneity on both simulated SCD and stream discharge from a small alpine basin in the Canadian Rocky Mountains. Simulations were performed using the Cold Regions Hydrological Model (CRHM), where point-scale snowmelt computations were made using a snowpack energy balance formulation and applied to spatial frequency distributions of snow water equivalent (SWE) on individual slope-, aspect-, and landcover-based hydrological response units (HRUs) in the basin. Hydrological routines were added to represent the vertical and lateral transfers of water through the basin and channel system. From previous studies it is understood that the heterogeneity of late winter SWE is a primary control on patterns of SCD. The analyses here showed that spatial variation in applied melt energy, mainly due to differences in net radiation, has an important influence on SCD at multiple scales and basin discharge, and cannot be neglected without serious error in the prediction of these variables. A single basin SWE distribution using the basin-wide mean SWE (SWE‾) and coefficient of variation (CV; standard deviation/mean) was found to represent the fine-scale spatial heterogeneity of SWE sufficiently well. Simulations that accounted for differences in (SWE‾) among HRUs but neglected the sub-HRU heterogeneity of SWE were found to yield similar discharge results as simulations that included this heterogeneity, while SCD was poorly represented, even at the basin level. Finally, applying point-scale snowmelt computations based on a single SWE depth for each HRU (thereby neglecting spatial differences in internal snowpack energetics over the distributions) was found to yield similar SCD and discharge results as simulations that resolved internal energy differences. Spatial/internal snowpack melt energy effects are more pronounced at times earlier in spring before the main period of snowmelt and SCD, as shown in previously published work. The paper discusses the importance of these findings as they apply to the warranted complexity of snowmelt process simulation in cold mountain environments, and shows how the end-of-winter SWE distribution represents an effective means of resolving snow cover heterogeneity at multiple scales for modelling, even in steep and complex terrain.

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

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

Improvement of the Snow Depth in the Common Land Model by Coupling a Two-Dimensional Deterministic Ensemble Model with a Variational Hybrid Snow Cover Fraction Data Assimilation Scheme and a New Observation Operator

Abstract During snow cover fraction (SCF) data assimilation (DA), the simplified observation operator and presence of cloud cover cause large errors in the assimilation results. To reduce these errors, a new snow cover depletion curve (SDC), known as an observation operator in the DA system, is statistically fitted to in situ snow depth (SD) observations and Moderate Resolution Imaging Spectroradiometer (MODIS) SCF data from January 2004 to October 2008. Using this new SDC, a two-dimensional deterministic ensemble–variational hybrid DA (2DEnVar) method of integrating the deterministic ensemble Kalman filter (DEnKF) and a two-dimensional variational DA (2DVar) is proposed. The proposed 2DEnVar is then used to assimilate the MODIS SCF into the Common Land Model (CoLM) at five sites in the Altay region of China for data from November 2008 to March 2009. The analysis performance of the 2DEnVar is compared with that of the DEnKF. The results show that the 2DEnVar outperforms the DEnKF as it effectively reduces the bias and root-mean-square error during the snow accumulation and ablation periods at all sites except for the Qinghe site. In addition, the 2DEnVar, with more assimilated MODIS SCF observations, produces more innovations (observation minus forecast) than the DEnKF, with only one assimilated MODIS SCF observation. The problems of cloud cover and overestimation are addressed by the 2DEnVar.

Mass balance observations and reconstruction for Batysh Sook Glacier, Tien Shan, from 2004 to 2016

In this study we present an analysis of measured annual mass balances for the period 2011 to 2016 and a reconstruction of seasonal mass balances from 2004 to 2010 for Batysh Sook Glacier located in the Kyrgyz Tien Shan. Conventional methods and a model-based extrapolation of the point measurements were used to obtain glacier-wide mass balances and to analyze glaciological measurements. Especially at the beginning of the re-established glacier mass balance monitoring program, deviations between the different methods were significant, having a range of 0.40m w.e. a−1. With the improvement of the measurement network in later years, the results of the different extrapolation methods showed better agreement (range of 0.10 to 0.22m w.e. a−1). For 2011 to 2016, the profile method revealed a mass loss of −0.41±28m w.e. a−1. The contour line method yielded a negative mean mass balance of −0.34±20m w.e a−1, whereas the model-based extrapolation clearly resulted in the most negative value of −0.43±16m w.e. a−1 for the same period.The same distributed accumulation and temperature index melt model used to extrapolate point measurements from 2011 to 2016 was applied in order to reconstruct the mass balance from 2004 to 2010. The model was driven by daily air temperature and precipitation data from a nearby meteorological station and the model parameters were calibrated with in-situ measurements of annual mass balances collected from 2011 to 2016. Winter accumulation measurements taken in May 2014 were used for calibration purposes and to deduce snow distribution patterns. Subseasonal model performance was validated based on the snow cover depletion pattern observed on satellite images during the summer months from 2004 to 2016. For Batysh Sook Glacier an average annual mass balance of −0.39±0.26m w.e. a−1 was found for the period 2003/04 to 2015/16.

Response of streamflow to projected climate change scenarios in an eastern Himalayan catchment of India

Snowmelt run-off model (SRM) based on degree-day approach has been employed to evaluate the change in snow-cover depletion and corresponding streamflow under different projected climatic scenarios for an eastern Himalayan catchment in India. Nuranang catchment located at Tawang district of Arunachal Pradesh with an area of 52 km2 is selected for the present study with an elevation range of 3143–4946 m above mean sea level. Satellite images from October to June of the selected hydrological year 2006–2007 were procured from National Remote Sensing Centre, Hyderabad. Snow cover mapping is done using NDSI method. Based on long term meteorological data, temperature and precipitation data of selected hydrological year are normalized to represent present climatic condition. The projected temperature and precipitation data are downloaded from NCAR’s GIS data portal for different emission scenarios (SRES), viz., A1B, A2, B1; and IPCC commitment (non-SRES) scenario for different future years (2020, 2030, 2040 and 2050). Projected temperature and precipitation data are obtained at desired location by spatially interpolating the gridded data and then by statistical downscaling using linear regression. Snow depletion curves for all projected scenarios are generated for the study area and compared with conventional depletion curve for present climatic condition. Changes in cumulative snowmelt depth for different future years are highest under A1B and lowest under IPCC commitment, whereas A2 and B1 values are in-between A1B and IPCC commitment. Percentage increase in streamflow for different future years follows almost the same trend as change in precipitation from present climate under all projected climatic scenarios. Hence, it was concluded that for small catchments having seasonal snow cover, the total streamflow under projected climatic scenarios in future years will be primarily governed by the change in precipitation and not by change in snowmelt depth. Advancing of depletion curves for different future years are highest under A1B and lowest under IPCC commitment. A2 and B1 values are in-between A1B and IPCC commitment.

Mass-balance reconstruction for Glacier No. 354, Tien Shan, from 2003 to 2014

AbstractThis study presents a reconstruction of the seasonal mass balance of Glacier No. 354, located in the Akshiirak range, Kyrgyzstan, from 2003 to 2014. We use a distributed accumulation and temperature-index melt model driven by daily air temperature and precipitation from a nearby meteorological station. The model is calibrated with in situ measurements of the annual mass balance collected from 2011 to 2014. The snow-cover depletion pattern observed using satellite imagery provides additional information on the dynamics of mass change throughout the melting season. Two digital elevation models derived from high-resolution satellite stereo images acquired in 2003 and 2012 are used to calculate glacier volume change for the corresponding period. The geodetic mass change thus derived is used to validate the modelled cumulative glacier-wide balance. For the period 2003–12 we find a cumulative mass balance of –0.40±10mw.e.a-1. This result agrees well with the geodetic balance of –0.48±0.07mw.e.a-1over the same period.

Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow-covered area (SCA) once snow-free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate-resolution imaging spectroradiometer images to produce snow-cover depletion curves. The snow depletion curves were created for an 80 000 km2 domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow-cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter-annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.