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The climate of High Mountain Asia (HMA) has changed in recent decades. While the temperature is consistently increasing at a higher rate than the global warming rate, precipitation changes are inconsistent, with substantial temporal and spatial variation. Climate warming will have enormous consequences for hydroclimatic extremes. For the higher altitudes of the HMA, which are a significant source of water for the large rivers in Asia, often trends are calculated using a limited number of in situ observations mainly observed in valleys. This study explores the changes in mean, extreme, and compound-extreme climate variables and their seasonality along the full altitudinal range in HMA using daily ERA5 reanalysis data (1979–2018). Our results show that winter warming and summer wetting dominate the interior part of HMA. The results indicate a coherent significant increasing trend in the occurrence of heatwaves across all regions in HMA. The number of days with heavy precipitation shows more significant trends in southern and eastern basins than in other areas of HMA. The dry period occurrence shows a distinct demarcation between lower- and higher-altitude regions and is increasing for most basins. Although precipitation and temperature show variable tendencies, their compound occurrence is coherent in the monsoon-dominated basins. These changes in indicators of climatic extremes may imply substantial increases in the future occurrence of hazards such as floods, landslides, and droughts, which in turn impact economic production and infrastructure.

The Tibetan Plateau (TP) is a global warming hotspot, however, the warming status at high elevation (>5000 m) is poorly understood due to very sparse observations. Here we analyze spatial patterns in TP warming rates based on a novel near-surface air temperature dataset of 1980–2014 recently developed by ingesting high-elevation observations and downscaled reanalysis datasets. We show that the high snow cover persistence at high elevation reduces strengthening of positive feedbacks responsible for elevation dependent warming at low-middle elevations, leading to reversed altitudinal patterns of TP warming above and below 5000 m. An important negative feedback is induced by the presence of snow and glaciers at elevations above 5000 m, due to their “buffering” effects by consuming or reflecting energy that would be used for warming in the absence of snow or ice. A further decrease in snow cover and glacier extent at high elevations may thus amplify the warming on the TP.

The Tibetan Plateau (TP) is a global warming hotspot, however, the warming status at high elevation (>5000 m) is poorly understood due to very sparse observations. Here we analyze spatial patterns in TP warming rates based on a novel near-surface air temperature dataset of 1980–2014 recently developed by ingesting high-elevation observations and downscaled reanalysis datasets. We show that the high snow cover persistence at high elevation reduces strengthening of positive feedbacks responsible for elevation dependent warming at low-middle elevations, leading to reversed altitudinal patterns of TP warming above and below 5000 m. An important negative feedback is induced by the presence of snow and glaciers at elevations above 5000 m, due to their “buffering” effects by consuming or reflecting energy that would be used for warming in the absence of snow or ice. A further decrease in snow cover and glacier extent at high elevations may thus amplify the warming on the TP.

This project was funded as part of the National Geographic Society and Rolex partnership to support a Perpetual Planet. Mountains are the water towers of the world, supplying a substantial part of both natural and anthropogenic water demands1,2. They are highly sensitive and prone to climate change3,4, yet their importance and vulnerability have not been quantified at the global scale. Here, we present a global Water Tower Index, which ranks all water towers in terms of their water-supplying role and the downstream dependence of ecosystems and society. For each tower, we assess its vulnerability related to water stress, governance, hydropolitical tension and future climatic and socio-economic changes. We conclude that the most important water towers are also among the most vulnerable, and that climatic and socio-economic changes will affect them profoundly. This could negatively impact 1.9 billion people living in (0.3 billion) or directly downstream of (1.6 billion) mountain areas. Immediate action is required to safeguard the future of the world’s most important and vulnerable water towers. Postprint

Melting snow and glacier ice in the Himalaya forms an important source of water for people downstream. Incoming longwave radiation (LWin) is an important energy source for melt, but there are only few measurements of LWin at high elevation. For the modelling of snow and glacier melt, the LWin is therefore often represented by parameterizations that were originally developed for lower elevation environments. With LWin measurements at eight stations in three catchments in the Himalaya, with elevations between 3,980 and 6,352 m.a.s.l., we test existing LWin parameterizations. We find that these parameterizations generally underestimate the LWin, especially in wet (monsoon) conditions, where clouds are abundant and locally formed. We present a new parameterization based only on near-surface temperature and relative humidity, both of which are easy and inexpensive to measure accurately. The new parameterization performs better than the parameterizations available in literature, in some cases halving the root-mean-squared error. The new parameterization is especially improving existing parameterizations in cloudy conditions. We also show that the choice of longwave parameterization strongly affects melt calculations of snow and ice.

International audience; Meteorological studies in high-mountain environments form the basis of our understanding of catchment hydrology and glacier accumulation and melt processes, yet high-altitude (>4000 m above sea level, asl) observatories are rare. This research presents meteorological data recorded between December 2012 and November 2013 at seven stations in Nepal, ranging in elevation from 3860 to 5360 m asl. Seasonal and diurnal cycles in air temperature, vapour pressure, incoming short-wave and long-wave radiation, atmospheric transmissivity, wind speed, and precipitation are compared between sites. Solar radiation strongly affects diurnal temperature and vapour pressure cycles, but local topography and valley-scale circulations alter wind speed and precipitation cycles. The observed diurnal variability in vertical temperature gradients in all seasons highlights the importance of in situ measurements for melt modelling. The monsoon signal (progressive onset and sharp end) is visible in all data-sets, and the passage of the remnants of Typhoon Phailin in mid-October 2013 provides an interesting case study on the possible effects of such storms on glaciers in the region.

The hydrology of high-elevation watersheds of the Hindu Kush-Himalaya region (HKH) is poorly known. The correct representation of internal states and process dynamics in glacio-hydrological models can often not be verified due to missing in situ measurements. We use a new set of detailed ground data from the upper Langtang valley in Nepal to systematically guide a state-of-the art glacio-hydrological model through a parameter assigning process with the aim to understand the hydrology of the catchment and contribution of snow and ice processes to runoff. 14 parameters are directly calculated on the basis of local data, and 13 parameters are calibrated against 5 different datasets of in situ or remote sensing data. Spatial fields of debris thickness are reconstructed through a novel approach that employs data from an Unmanned Aerial Vehicle (UAV), energy balance modeling and statistical techniques. The model is validated against measured catchment runoff (Nash-Sutcliffe efficiency 0.87) and modeled snow cover is compared to Landsat snow cover. The advanced representation of processes allowed assessing the role played by avalanching for runoff for the first time for a Himalayan catchment (5% of annual water inputs to the hydrological system are due to snow redistribution) and to quantify the hydrological significance of sub-debris ice melt (9% of annual water inputs). Snowmelt is the most important contributor to total runoff during the hydrological year 2012/2013 (representing 40% of all sources), followed by rainfall (34%) and ice melt (26%). A sensitivity analysis is used to assess the efficiency of the monitoring network and identify the timing and location of field measurements that constrain model uncertainty. The methodology to set up a glacio-hydrological model in high-elevation regions presented in this study can be regarded as a benchmark for modelers in the HKH seeking to evaluate their calibration approach, their experimental setup and thus to reduce the predictive model uncertainty.

Unraveling the interaction between water users in a river basin is essential for sound water resources management, particularly in a context of increasing water scarcity and the need to save water. While most attention from managers and decision makers goes to allocation and withdrawals of surface water resources, reuse of non-consumed water gets only marginal attention despite the potentially significant volumes. As a consequence, claims of water saving are often grossly exaggerated. It is the purpose of this paper to explore the processes associated with water reuse in a river basin among users of varying nature and review existing methods for directly or indirectly describing non-consumed water, recoverable flow and/or water reuse. First a conceptual representation of processes surrounding water withdrawals and associated definitions is discussed, followed by a section on connectivity between individual withdrawals and the complex dynamics arising from dependencies and tradeoffs within a river basin. The current state-of-the-art in categorizing basin hydrological flows is summarized and its applicability to a water system where reuse occurs is explored. The core of the paper focuses on a selection and demonstration of existing indicators developed for assessing water reuse and its impacts. It is concluded that although several methods for analyses of water reuse and recoverable flows have been developed, a number of essential aspects of water reuse are left out of existing indicators. Moreover, a proven methodology for obtaining crucial quantitative information on recoverable flows is currently lacking. Future studies should aim at spatiotemporal tracking of the recoverable portion of water withdrawals and showing the dependency of multiple water users on such flows to water policy makers.

Mountain ranges in Asia are important water suppliers, especially if downstream climates are arid, water demands are high and glaciers are abundant. In such basins, the hydrological cycle depends heavily on high-altitude precipitation. Yet direct observations of high-altitude precipitation are lacking and satellite derived products are of insufficient resolution and quality to capture spatial variation and magnitude of mountain precipitation. Here we use glacier mass balances to inversely infer the high-altitude precipitation in the upper Indus basin and show that the amount of precipitation required to sustain the observed mass balances of large glacier systems is far beyond what is observed at valley stations or estimated by gridded precipitation products. An independent validation with observed river flow confirms that the water balance can indeed only be closed when the high-altitude precipitation on average is more than twice as high and in extreme cases up to a factor of 10 higher than previously thought. We conclude that these findings alter the present understanding of high-altitude hydrology and will have an important bearing on climate change impact studies, planning and design of hydropower plants and irrigation reservoirs as well as the regional geopolitical situation in general.

Rivers originating in the high mountains of Asia are among the most meltwater-dependent river systems on Earth, yet large human populations depend on their resources downstream1. Across High Asias river basins, there is large variation in the contribution of glacier and snow melt to total runoff 2, which is poorly quantified.The lack of understanding of the hydrological regimes of High Asias rivers is one of the main sources of uncertainty in assessing the regional hydrological impacts of climate change3. Here we use a large-scale, high-resolution cryospheric-hydrological model to quantify the upstream hydrological regimes of the Indus, Ganges, Brahmaputra, Salween and Mekong rivers. Subsequently, we analyse the impacts of climate change on future water availability in these basins using the latest climate model ensemble. Despite large differences in runoff composition and regimes between basins and between tributaries within basins, we project an increase in runoff at least until 2050 caused primarily by an increase in precipitation in the upper Ganges, Brahmaputra, Salween and Mekong basins and from accelerated melt in the upper Indus Basin. These findings have immediate consequences for climate change policies where a transition towards coping with intra-annual shifts in water availability is desirable.

Groundwater abstraction and depletion were assessed at a 1-km resolution in the irrigated areas of the Indus Basin using remotely sensed evapotranspiration (ET) and precipitation; a process-based hydrological model and spatial information on canal water supplies. A calibrated Soil and Water Assessment Tool (SWAT) model was used to derive total annual irrigation applied in the irrigated areas of the basin during the year 2007. The SWAT model was parameterized by station corrected precipitation data (R) from the Tropical Rainfall Monitoring Mission, land use, soil type, and outlet locations. The model was calibrated using a new approach based on spatially distributed ET fields derived from different satellite sensors. The calibration results were satisfactoryand strong improvements were obtained in the Nash-Sutcliffe criterion (0.52 to 0.93), bias (-17.3% to -0.4%), and the Pearson correlation coefficient (0.78 to 0.93). Satellite information on R and ET was then combined with model results of surface runoff, drainage, and percolation to derive groundwater abstraction and depletion at a nominal resolution of 1km. It was estimated that in 2007, 68km3 (262mm) of groundwater was abstracted in the Indus Basin while 31km3 (121mm) was depleted. The mean error was 41mm/year and 62mm/year at 50% and 70% probability of exceedance, respectively. Pakistani and Indian Punjab and Haryana were the most vulnerable areas to groundwater depletion and strong measures are required to maintain aquifer sustainability.

The performance of glaciohydrological models which simulate catchment response to climate variability depends to a large degree on the data used to force the models. The forcing data become increasingly important in high-elevation, glacierized catchments where the interplay between extreme topography, climate, and the cryosphere is complex. It is challenging to generate a reliable forcing data set that captures this spatial heterogeneity. In this paper, we analyze the results of a 1 year field campaign focusing on air temperature and precipitation observations in the Langtang valley in the Nepalese Himalayas. We use the observed time series to characterize both temperature lapse rates (LRs) and precipitation gradients (PGs). We study their spatial and temporal variability, and we attempt to identify possible controlling factors. We show that very clear LRs exist in the valley and that there are strong seasonal differences related to the water vapor content in the atmosphere. Results also show that the LRs are generally shallower than the commonly used environmental lapse rates. The analysis of the precipitation observations reveals that there is great variability in precipitation over short horizontal distances. A uniform valley wide PG cannot be established, and several scale-dependent mechanisms may explain our observations. We complete our analysis by showing the impact of the observed LRs and PGs on the outputs of the TOPKAPI-ETH glaciohydrological model. We conclude that LRs and PGs have a very large impact on the water balance composition and that short-term monitoring campaigns have the potential to improve model quality considerably. Key Points Precipitation is variable and uniform precipitation gradients cannot be derived Temperature lapse rates are not constant throughout the year and shallow Temperature lapse rates and precipitation gradients are key inputs in modeling

Himalayan glacier tongues are commonly debris covered and they are an important source of melt water. However, they remain relatively unstudied because of the inaccessibility of the terrain and the difficulties in field work caused by the thick debris mantles. Observations of debris-covered glaciers are therefore scarce and airborne remote sensing may bridge the gap between scarce field observations and coarse resolution space-borne remote sensing. In this study we deploy an Unmanned Aerial Vehicle (UAV) before and after the melt and monsoon season (May and October 2013) over the debris-covered tongue of the Lirung Glacier in Nepal. Based on stereo-imaging and the structure for motion algorithm we derive highly detailed ortho-mosaics and digital elevation models (DEMs), which we geometrically correct using differential GPS observations collected in the field. Based on DEM differencing and manual feature tracking we derive the mass loss and the surface velocity of the glacier at a high spatial accuracy. On average, mass loss is limited and the surface velocity is very small. However, the spatial variability of melt rates is very high, and ice cliffs and supra-glacial ponds show mass losses that can be an order of magnitude higher than the average. We suggest that future research should focus on the interaction between supra-glacial ponds, ice cliffs and englacial hydrology to further understand the dynamics of debris-covered glaciers. Finally, we conclude that UAV deployment has large potential in glaciology and it may revolutionize methods currently applied in studying glacier surface features.

Understanding the spatial and temporal variability of precipitation in tropical high mountain areas remains a key challenge. Point measurements are often not sufficient to capture the strong spatial variability particularly in mountain regions. Satellite remote sensing allows capturing the spatial heterogeneity of precipitation, yet it is generally characterized by significant bias. Rainfall satellite products such as those coming from the Tropical Rainfall Measuring Mission (TRMM) are being continuously improved and an increasing amount of high- and medium-resolution remote sensing data on biophysical surface properties is becoming available. Here we present a methodology that blends two TRMM products with remote sensing data on vegetation and topography to quantify the spatial distribution of precipitation in areas where direct observations are lacking. The approach assumes that vegetation cover, the topography and satellite-derived estimates of rainfall are reasonable indirect measures of ground-based precipitation. The methodology is evaluated for an area in the Andes of Ecuador. The results show that around 40% of the variance in weekly precipitation is explained by these proxies. During the drier periods of the year, vegetation is the strongest proxy. In the very wet areas and during the wet periods vegetation is usually in a climax development phase with no development trends to correlate with rain, and the other proxies dominate precipitation estimation. A cross-validation procedure in which each one of the weather stations is sequentially excluded from the analysis, was applied to test the performance of the methodology. The performance was satisfactory, and as expected it is related to the density of the weather station network and temporal rainfall variability. Overall we conclude that the methodology is useful for areas with very high variable conditions, where sufficient ground-data is available to establish the relationships with the different remote sensing datasets.