High Mountain Asia Research Articles

Regional differences in global glacier retreat from 1980 to 2015

Global warming triggers shrinking and thinning of glaciers worldwide, with potentially severe implications for human society. However, regional differences in glacier retreat and its relationship with climatic characteristics have not been conclusively demonstrated. In this study, regional changes in global glaciers based on two primary features, area change and mass balance, were investigated on the basis of data collected from published research on glacier changes. Results show that during the period 1980–2015, the rate of global glacier area shrinkage was 0.18% per year and that of global glacier mass loss was 0.25 m w.e. per year. Retreat of glaciers located at low and middle latitudes was characterized by severe area shrinkage and mass loss. Correspondingly, in the Arctic, deglaciation was characterized by ice thinning due to a low area reduction but relatively high mass loss rate. However, glaciers in high southern latitudes were in a relatively stable status. High Mountain Asia exhibited the lowest rate of area shrinkage and mass loss among glaciers located at low and middle latitudes, and a slower rate of mass loss compared with the global average. Glaciers in the Tropical Andes exhibited the fastest rate of glacier area shrinkage (−1.6% per year), whereas Antarctic and Subantarctic glaciers showed the lowest rate (−0.11% per year). For mass balance, the most negative occurred at Southern Andes (−0.81 m w.e. per year), followed by Alaska (−0.74 m w.e. per year). Only the Antarctic and Subantarctic experienced small mass gain (0.04 m w.e. per year). High levels of correlation are found between the rates of glacier retreat and annual average temperature and annual total precipitation instead of their trends. The variability of the surface climate conditions in the glacier environment plays a key role in driving these regional differences in global glacier retreat.

Large hydropower and water-storage potential in future glacier-free basins.

Climate change is causing widespread glacier retreat1, and much attention is devoted to negative impacts such as diminishing water resources2, shifts in runoff seasonality3, and increases in cryosphere-related hazards4. Here we focus on a different aspect, and explore the water-storage and hydropower potential of areas that are expected to become ice-free during the course of this century. For roughly 185,000 sites that are glacierized at present, we predict the potentially emerging reservoir storage volume and hydropower potential. Using a climate-driven glacier-evolution model5 and topographical analysis6, we estimate a theoretical maximal total storage and hydropower potential of 875±260 cubic kilometres and 1,355±515 terawatt-hours per year, respectively (95% confidence intervals). A first-order suitability assessment that takes into account environmental, technical and economic factors identifies roughly 40 per cent of this potential (355±105 cubic kilometres and 533±200 terawatt-hours per year) as possibly being suitable for realization. Three quarters of the potential storage volume is expected to become ice-free by 2050, and the storage volume would be enough to retain about half of the annual runoff leaving the investigated sites. Although local impacts would need to be assessed on a case-by-case basis, the results indicate that deglacierizing basins could make important contributions to national energy supplies in several countries, particularly in High Mountain Asia.

Recent glacier and lake changes in High Mountain Asia and their relation to precipitation changes

Abstract. We present an updated, spatially resolved estimate of 2003–2008 glacier surface elevation changes for the entire region of High Mountain Asia (HMA) from ICESat laser altimetry data. The results reveal a diverse pattern that is caused by spatially greatly varying glacier sensitivity, in particular to precipitation availability and changes. We introduce a spatially resolved zonation where ICESat samples are grouped into units of similar glacier behaviour, glacier type and topographic settings. In several regions, our new zonation reveals local differences and anomalies that have not been described previously. Glaciers in the Eastern Pamirs, Kunlun Shan and central TP were thickening by 0.1–0.7 m a−1, and the thickening anomaly has a crisp boundary in the Eastern Pamirs that continues just north of the central Karakoram. Glaciers in the south and east of the TP were thinning, with increasing rates towards southeast. We attribute the glacier thickening signal to a stepwise increase in precipitation around ∼1997–2000 on the Tibetan Plateau (TP). The precipitation change is reflected by growth of endorheic lakes in particular in the northern and eastern TP. We estimate lake volume changes through a combination of repeat lake extents from Landsat data and shoreline elevations from ICESat and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for over 1300 lakes. The rise in water volume contained in the lakes corresponds to 4–25 mm a−1, when distributed over entire catchments, for the areas where we see glacier thickening. The precipitation increase is also visible in sparse in situ measurements and MERRA-2 climate reanalysis data but less visible in ERA-Interim reanalysis data. Taking into account evaporation loss, the difference between average annual precipitation during the 1990s and 2000s suggested by these datasets is 34–100 mm a−1, depending on region, which can fully explain both lake growth and glacier thickening (Kunlun Shan) or glacier geometry changes such as thinning tongues while upper glacier areas were thickening or stable (eastern TP). The precipitation increase reflected in these glacier changes possibly extended to the northern slopes of the Tarim Basin, where glaciers were nearly in balance in 2003–2008. Along the entire Himalaya, glaciers on the first orographic ridge, which are exposed to abundant precipitation, were thinning less than glaciers in the dryer climate of the inner ranges. Thinning rates in the Tien Shan vary spatially but are rather stronger than in other parts of HMA.

Deriving Bias and Uncertainty in MERRA-2 Snowfall Precipitation Over High Mountain Asia

A Bayesian approach to estimate bias and uncertainty in snowfall precipitation from MERRA-2 and other precipitation products was applied over High Mountain Asia (HMA), using a newly developed snow reanalysis method. Starting from an ‘uninformed’ prior probability distribution, a posterior scaling factor applied to MERRA-2 snowfall was derived by constraining model-based estimates of seasonal snow accumulation and ablation over the water year (WY) with fractional snow covered area (fSCA) measurements derived from Landsat and MODIS (MODSCAG). Several sub-domains (nine representative 1° by 1° tiles) across HMA were examined over the period WYs 2001-2015 and compiled into an uncertainty parameterization where a lognormal distribution was fitted to the empirical posterior distribution with a mean of 1.54 (median of 1.19) and coefficient of variation of 0.83, indicating that MERRA-2 underestimates snowfall on average by ~54% with sizeable uncertainty. For reference, the uncertainties in snowfall precipitation from the ERA5 and APHRODITE-2 precipitation produces were also evaluated, and these products were found to underestimate snowfall, on average by a factor around 1.78 and 3.34 (with median scaling factors of 1.42 and 2.51) respectively. The results indicate that snowfall precipitation at high-elevations dominated by snowfall is underestimated in most existing products, especially in the gauge-based APHRODITE-2 product, where the biases were also found to exhibit geographical variations with the largest underestimation in monsoon-influenced high-elevation tiles. The derived MERRA-2 uncertainty model is being used to develop a full domain-wide HMA snow reanalysis, which will shed further light onto the space-time variations in snowfall biases in these products.

A Joint Landsat- and MODIS-Based Reanalysis Approach for Midlatitude Montane Seasonal Snow Characterization

A new snow reanalysis method is presented that is designed to jointly assimilate Landsat- and MODIS-derived (MODSCAG) fractional snow covered area (fSCA) to characterize seasonal snow in remote regions like High Mountain Asia (HMA) where in situ data is severely lacking. The method leverages existing readily available global datasets for forcing a snow model and uses the fSCA retrievals along with the ensemble prior model estimates to derive posterior estimates using a Bayesian framework. The method addresses MODIS viewing-geometry effects on the fSCA retrievals by accounting for viewing angle dependent measurement errors and using a CDF-matching technique to improve the joint fSCA measurement consistency before assimilation. The method was verified through comparison with the Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates over the Tuolumne River watershed in California. The posterior SWE estimates were shown to be much more consistent with the independent ASO estimates across the three WYs examined. Tests over Tuolumne showed that in cases where sufficient Landsat observations are available (i.e. with multiple sensors and in areas of overlapping Landsat tiles), assimilation of only Landsat data may be optimal, which is attributable primarily to the higher spatial resolution of the raw Landsat data, but that in cases with fewer Landsat measurements (i.e. with single Landsat tiles and/or significant reduction due to clouds), the additional screened and CDF-matched MODIS-based measurements can have a positive (albeit marginal) impact. Illustrative results are presented for nine HMA test tiles to illustrate how the method can provide posterior estimates of the space-time climatology of SWE storage in areas where in situ data does not generally exist. Ongoing work is being conducted to use the method outlined herein to generate an HMA-wide reanalysis dataset that will provide an opportunity for a more thorough characterization of HMA seasonal snow storage and dynamics over the joint Landsat-MODIS era. The method is generalizable to any midlatitude montane region where seasonal snow is important.

GLAM Bio-Lith RT: A Tool for Remote Sensing Reflectance Simulation and Water Components Concentration Retrieval in Glacial Lakes

A new open--source software tool, called GLAM BioLith--RT (Glacier Lakes Assisted Melting Biological Lithological Radiative Transfer), has been developed for modeling of Radiative Transfer (RT) in water bodies containing suspended lithic particles , phytoplankton, dissolved salts, and colored dissolved organic matter. Although our objective is an application to glacial lakes of High Mountain Asia, the model has potential application for the study of seawater, organic-rich lakes, rivers, etc. The tool is built on a solid foundation of an existing published open-source code called WASI, which has been reviewed and augmented with new capabilities, notably the addition of a suspended lithic particle grain size parameterization. GLAM BioLith-RT operates in both a forward modeling and inverse modeling mode. The forward mode is specifically designed to compute the reflectance spectra of glacier lakes from inherent optical water properties. Conversely, in the inverse mode, measured spectral reflectance is employed with other inputs to derive best fitting water component properties (e.g., suspended particles concentration). The inverse modeling includes Bayesian Optimization of the output which is a significant advance over the existing software. We have tested the code for sensitivity to noise, and uncertainties in input parameters. The model succeeds in nearly reproducing the hyperspectral reflectance of some glacial lakes in Nepal as observed by the EO-1 Hyperion hyperspectral imager. The inverse modeling approach, when supported up by validated forward modeling, offers a means for remote sensing characterization of suspended sediment load in glacial lakes and rivers and hence, use of suspended sediment as a proxy for glacial activity; and many other potential applications in other thematic areas.

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

An Automated Approach for Estimating Snowline Altitudes in the Karakoram and Eastern Himalaya From Remote Sensing

The separation of fresh snow, exposed glacier ice and debris-covered ice on glacier surfaces is needed for hydrologic applications and for understanding the response of glaciers to climate variability. The end-of-season snowline altitude is an indicator of the equilibrium line altitude of a glacier and is often used to infer the mass balance of a glacier. Regional snowline estimates are generally missing from glacier inventories for remote, high-altitude glacierized areas such as High Mountain Asia. In this study, we present an automated, decision-based image classification algorithm implemented in Python to separate snow, ice and debris surfaces on glaciers and to extract glacier snowlines at monthly and annual time steps and regional scales. The method was applied in the Hunza basin in the Karakoram and the Trishuli basin in eastern Himalaya. We automatically partitioned the various types of surfaces on glaciers at each time step using band ratios combined with topographic criteria based on two versions of the Shuttle Radar Topography Mission elevation dataset. Snowline altitudes were extracted on a pixel-by-pixel basis using a “buffer” method adapted for each elevation dataset. Over the period studied (2000 to 2016), end-of-the-ablation season annual ELAs fluctuated from 4,917 m a.s.l. to 5,336 m a.s.l. for the Hunza, with a 16-year average of 5,177 ±108 m, and 5,395 m a.s.l. to 5,565 m a.s.l. for the Trishuli, with an average of 5,444 ± 63 m a.s.l. Snowlines were sensitive to the manual corrections of the partition, the topographic slope, the elevation dataset and the band ratio thresholds particularly during the spring and winter months, and were not sensitive to the size of the buffer used to extract the snowlines. With further refinement and calibration with field measurements, this method can be easily applied to Sentinel-2 data (5 days temporal resolution) as well as daily PlanetScope to derive sub-monthly snowlines.

The State of Remote Sensing Capabilities of Cascading Hazards over High Mountain Asia.

Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward towards improved characterization of evolving hazards and risk across HMA.

Change of snow and ice melting time in High Mountain Asia

High Mountain Asia (HMA) is very sensitive to climate changes. In HMA, air temperature and precipitation shifts or increases are reflected in the timing of snowmelt onset. In this study, a long-term series (1979–2018) of snow melt onset time is first derived using spaceborne microwave radiometer data, following which the long-term trend of snow and ice-melt time of Tienshan, Altay, Karakorom, Hindu Kush, and Pamir is analyzed. Previous studies proposed many algorithms for detecting snowmelt onset and freeze-up using microwave remote sensing. The previously proposed algorithms were extensively applied to polar regions (including the Antarctic, Siberia, Greenland, and sea ice surface), where the impact of topography or mixed pixel problem is relatively small. However, for high mountain regions, complex topography and a potential mixed pixel issue would result in a very complicated and noisy satellite-observed active and passive microwave remote sensing signal. This study thus proposes a recently developed snow and ice-melt detection algorithm in which, for passive microwave data, a median filter is first applied to the original signal (brightness temperature) to suppress random, small, or short-duration signal variations. The differential average derivative of a particular date is then calculated using the time series first-order derivative as the average first-order derivative of a specific count of observations after this particular date. Generally, the differential average derivative is an indicator of sudden changes in time series brightness temperature. Results show that the melt onset time in the majority of HMA occurs earlier, except in the case of the Karakorom Mountains and a part of the West Kunlun Mountains. Moreover, the melt onset time derived from satellite morning (6:00 local time) pass data shows that the melt onset time of Karakorom, the West Kulun Mountains, and southeast Tibet remains stable, or even occurs later. The trend of southeast Tibet is unique, with its earlier melt based on afternoon (18:00 local time) pass data and its later melt based on morning pass data. This suggests that the melt–refreeze period of southeast Tibet is increasing together with the increasing diurnal temperature difference. Then, a 2-m air temperature in ERA5 reanalysis data is used for comparison with melt onset time for validation and analysis. ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface, and sea-state parameters. A strong correlation exists between monthly average air temperatures and melt onset time, with the maximum linear fit R 2 of 0.76. This strong correlation indicates the good data quality of ERA5 reanalysis and melt onset time. The unique trend of southeast Tibet can also be explained using ERA5 reanalysis data, which show that the monthly mean daily maximum air temperature is increasing, but the monthly mean daily minimum air temperature is decreasing. This renders a slope of the linear fit of melt onset data in the period of 1988–2018 in the whole HMA region. Because the satellite overpasses time difference, data from only 1988 to 2018 is used for trend analysis. The analysis of the relationship between the melt onset time change rate and elevation shows that the areas with an earlier melt are almost all located in low-elevation regions, and the rate of melt time change is positively correlated with elevation. This suggests that low-elevation regions are more affected by climate changes. This study provides objective evidence of the impact of climate change on the cryospheric system in HMA.

A dataset of spatial distribution of degree-day factors for glaciers in High Mountain Asia

A dataset of spatial distribution of degree-day factors for glaciers in High Mountain Asia

Terrain Induced Biases in Clear-Sky Shortwave Radiation Due to Digital Elevation Model Resolution for Glaciers in Complex Terrain

Advancements in remote sensing, along with greater access to high spatial and temporal resolution imagery, have improved our ability to model glacier surface energy and mass balance in remote regions of complex terrain, such as High-mountain Asia (HMA). In general, net shortwave (SW) radiation accounts for the majority of energy available on a glacier surface during the summer months, suggesting that SW modeling errors can critically impact surface energy balance estimates. In this study, we model the clear-sky SW irradiance for a group of glaciers in the Everest region of HMA using a high-resolution (8-meter) digital elevation model (DEM) composite derived from commercial stereo satellite imagery. We then systematically downsample this DEM and considered the effect on incoming SW irradiance, with a sensitivity analysis for standard terrain attributes. The slope and aspect (combined) and topographic shading have the greatest impact on daily SW irradiance and also introduce a larger SW bias when DEM resolution is downsampled. Our results show that modeled incident SW is overestimated as resolution becomes coarser. For 10 selected glaciers in the Everest region, decreasing spatial resolution from 8 to 30 meters results in a range of average daily biases between +20 to +60 Wm-2 (or ~7 to 20%) at some high and low elevations, and an average bias of more than +100 Wm-2 (~33%) as resolution is coarsened to 500 meters. In order to determine the bearing these results have on surface melt, we explore the diurnal variability of this bias. Additionally, we compare our results with modeled incident SW using several global DEM products (ASTER, SRTM, and ALOS) to evaluate error introduced by lower resolution. Models using the 30-meter products show an overall average daily SW bias of +24 Wm-2 (or ~8%) across elevation with some elevations showing a bias up to +60 Wm-2 (~20%) on multiple glaciers. Taken together, our results demonstrate the value of high-resolution data to correct biases in modeled SW radiation and constrain uncertainties for glacier energy balance modeling in regions of complex terrain.

Analyzing Machine Learning Predictions of Passive Microwave Brightness Temperature Spectral Difference Over Snow-Covered Terrain in High Mountain Asia

“Snow is an important component of the terrestrial freshwater budget in high mountain Asia (HMA) and contributes to the runoff in Himalayan rivers through snowmelt. Despite the importance of snow in HMA, considerable spatiotemporal uncertainty exists across the different estimates of snow water equivalent for this region. In order to better estimate snow water equivalent, radiative transfer models are often used in conjunction with microwave brightness temperature measurements. In this study, the efficacy of support vector machines (SVMs), a machine learning technique, to predict passive microwave brightness temperature spectral difference (∆Tb) as a function of geophysical state variables (snow water equivalent, snow depth, snow temperature, and snow density) is explored through a sensitivity analysis. The use of machine learning (as opposed to radiative transfer models) is a relatively new and novel approach for improving snow water equivalent estimates. The Noah-MP land surface model within the NASA Land Information System framework is used to simulate the hydrologic cycle over HMA and model geophysical states that are then used for SVM training. The SVMs serve as a nonlinear map between the geophysical space (modeled in Noah-MP) and the observation space (∆Tb as measured by the radiometer). Advanced Microwave Scanning Radiometer -Earth Observing System measured passive microwave brightness temperatures over snow-covered locations in the HMA region are used as training data during the SVM training phase. Sensitivity of well-trained SVMs to each Noah-MP modeled state variable is assessed by computing normalized sensitivity coefficients. Sensitivity analysis results generally conform with the known first-order physics. Input states that increase volume scattering of microwave radiation, such as snow density and snow water equivalent, exhibit a plurality of positive normalized sensitivity coefficients. In general, snow temperature was the most sensitive input to the SVM predictions. The sensitivity of each state is location and time dependent. The signs of normalized sensitivity coefficients that indicate physical irrationality are ascribed to significant cross-correlation between Noah-MP simulated states and decreased SVM prediction capability at specific locations due to insufficient training data. SVM prediction pitfalls do exist that serve to highlight the limitations of this particular machine learning algorithm.”

High‐Resolution Snowline Delineation From Landsat Imagery to Infer Snow Cover Controls in a Himalayan Catchment

AbstractThe snow cover dynamics of High Mountain Asia are usually assessed at spatial resolutions of 250 m or greater, but this scale is too coarse to clearly represent the rugged topography common to the region. Higher‐resolution measurement of snow‐covered area often results in biased sampling due to cloud cover and deep shadows. We therefore develop a Normalized Difference Snow Index‐based workflow to delineate snow lines from Landsat Thematic Mapper/Enhanced Thematic Mapper+ imagery and apply it to the upper Langtang Valley in Nepal, processing 194 scenes spanning 1999 to 2013. For each scene, we determine the spatial distribution of snow line altitudes (SLAs) with respect to aspect and across six subcatchments. Our results show that the mean SLA exhibits distinct seasonal behavior based on aspect and subcatchment position. We find that SLA dynamics respond to spatial and seasonal trade‐offs in precipitation, temperature, and solar radiation, which act as primary controls. We identify two SLA spatial gradients, which we attribute to the effect of spatially variable precipitation. Our results also reveal that aspect‐related SLA differences vary seasonally and are influenced by solar radiation. In terms of seasonal dominant controls, we demonstrate that the snow line is controlled by snow precipitation in winter, melt in premonsoon, a combination of both in postmonsoon, and temperature in monsoon, explaining to a large extent the spatial and seasonal variability of the SLA in the upper Langtang Valley. We conclude that while SLA and snow‐covered area are complementary metrics, the SLA has a strong potential for understanding local‐scale snow cover dynamics and their controlling mechanisms.

Brief communication: Updated GAMDAM glacier inventory over high-mountain Asia

Abstract. The original Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM) glacier inventory was the first methodologically consistent dataset for high-mountain Asia. Nonetheless, the GAMDAM inventory underestimated glacier area, as it did not include steep ice- and snow-covered slopes or shaded components. During revision of the inventory, Landsat imagery free of shadow, cloud, and seasonal snow cover was selected for the period 1990–2010, after which >90 % of the glacier area was delineated. The updated GAMDAM inventory, comprised of 453 Landsat images, includes 134 770 glaciers with a total area of 100 693±11 790 km2.

Contrasting Meteorological Drivers of the Glacier Mass Balance Between the Karakoram and Central Himalaya

There is strong variation in glacier mass balances in High Mountain Asia. Particularly interesting is the fact that glaciers are in equilibrium or even gaining mass in the Karakoram and Kunlun Shan ranges, which is in sharp contrast with the negative mass balances in the rest of High Mountain Asia. To understand this difference, an in-depth understanding of the meteorological drivers of the glacier mass balance is required. In this study, two catchments in contrasting climatic regions, one in the central Himalaya (Langtang) and one in the Karakoram (Shimshal), are modeled at 1 km grid spacing with the numerical atmospheric model WRF for the period of 2011–2013. Our results show that the accumulation and melt dynamics of both regions differ due to contrasting meteorological conditions. In Shimshal, 92% of the annual precipitation falls in the form of snow, in contrast with 42% in Langtang. In addition, 80% of the total snow falls above an altitude of 5000 m a.s.l, compared with 35% in Langtang. Another prominent contrast is that most of the annual snowfall falls between December and May (71%), compared with 52% in Langtang. The melt regimes are also different, with 41% less energy available for melt in Shimshal. The melt in the Karakoram is controlled by net shortwave radiation (r = 0.79 ± 0.01) through the relatively low glacier albedo in summer, while net longwave radiation (clouds) dominates the energy balance in the Langtang region (r = 0.76 ± 0.02). High amounts of snowfall and low melt rates result in a simulated positive glacier surface mass balance in Shimshal (+0.31 ± 0.06 m w.e. year−1) for the study period, while little snowfall, and high melt rates lead to a negative mass balance in Langtang (−0.40 ± 0.09 m w.e. year−1). The melt in Shimshal is highly variable between years, and is especially sensitive to summer snow events that reset the surface albedo. We conclude that understanding glacier mass balance anomalies requires quantification and insight into subtle shifts in the energy balance and accumulation regimes at high altitude and that the sensitivity of glaciers to climate change is regionally variable.

Heterogeneous Influence of Glacier Morphology on the Mass Balance Variability in High Mountain Asia

We investigate the control of the morphological variables on the 2000–2016 glacier‐wide mass balances of 6,470 individual glaciers of High Mountain Asia. We separate the data set into 12 regions assumed to be climatically homogeneous. We find that the slope of the glacier tongue, mean glacier elevation, percentage of supraglacial debris cover, and avalanche contributing area all together explain a maximum of 48% and a minimum of 8% of the glacier‐wide mass balance variability, within a given region. The best predictors of the glacier‐wide mass balance are the slope of the glacier tongue and the mean glacier elevation for most regions, with the notable exception of the inner Tibetan Plateau. Glacier‐wide mass balances do not differ significantly between debris‐free and debris‐covered glaciers in 7 of the 12 regions analyzed. Lake‐terminating glaciers have more negative mass balances than the regional averages, the influence of lakes being stronger on small glaciers than on large glaciers.

Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations

Abstract. Light-absorbing particles (LAPs), mainly dust and black carbon, can significantly impact snowmelt and regional water availability over high-mountain Asia (HMA). In this study, for the first time, online aerosol–snow interactions are enabled and a fully coupled chemistry Weather Research and Forecasting (WRF-Chem) regional model is used to simulate LAP-induced radiative forcing on snow surfaces in HMA at relatively high spatial resolution (12 km, WRF-HR) compared with previous studies. Simulated macro- and microphysical properties of the snowpack and LAP-induced snow darkening are evaluated against new spatially and temporally complete datasets of snow-covered area, grain size, and impurity-induced albedo reduction over HMA. A WRF-Chem quasi-global simulation with the same configuration as WRF-HR but a coarser spatial resolution (1∘, WRF-CR) is also used to illustrate the impact of spatial resolution on simulations of snow properties and aerosol distribution over HMA. Due to a more realistic representation of terrain slopes over HMA, the higher-resolution model (WRF-HR) shows significantly better performance in simulating snow area cover, duration of snow cover, snow albedo and snow grain size over HMA, as well as an evidently better atmospheric aerosol loading and mean LAP concentration in snow. However, the differences in albedo reduction from model and satellite retrievals is large during winter due to associated overestimation in simulated snow fraction. It is noteworthy that Himalayan snow cover has high magnitudes of LAP-induced snow albedo reduction (4 %–8 %) in pre-monsoon seasons (both from WRF-HR and satellite estimates), which induces a snow-mediated radiative forcing of ∼30–50 W m−2. As a result, the Himalayas (specifically the western Himalayas) hold the most vulnerable glaciers and mountain snowpack to the LAP-induced snow darkening effect within HMA. In summary, coarse spatial resolution and absence of snow–aerosol interactions over the Himalayan cryosphere will result in significant underestimation of aerosol effects on snow melting and regional hydroclimate.

Evaluating the uncertainty of terrestrial water budget components over High Mountain Asia.

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff(R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11±0.45, 1.26±0.11, and 0.85±0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Asia's shrinking glaciers protect large populations from drought stress.

About 800 million people depend in part on meltwater from the thousands of glaciers in the high mountains of Asia. Water stress makes this region vulnerable to drought, but glaciers are a uniquely drought-resilient source of water. Here I show that seasonal glacier meltwater is equivalent to the basic needs of 221±59 million people, or most of the annual municipal and industrial needs of Pakistan, Afghanistan, Tajikistan, Turkmenistan, Uzbekistan and Kyrgyzstan. During drought summers, meltwater dominates water inputs to the upper Indus, Aral and Chu/Issyk-Kul river basins. This reduces the risk of social instability, conflict and sudden migrations triggered by water scarcity, which is already associated with the large, rapidly growing populations and hydro-economies of these basins. Regional meltwater production is, however, unsustainably high-at 1.6 times the balance rate-and is expected to increase in future decades before ultimately declining. These results update and reinforce a previous publication in Nature on this topic, which was retracted after an inadvertent error was discovered.