Hindu-Kush Karakoram Himalaya Research Articles

Analysis of hydrological responses using semi-distributed conceptual models in a mountainous catchment in the Hindu Kush Himalayan region

The Hindukush Karakoram Himalaya region, an important global water unit, is the origin of ten major river basins worldwide. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin’s hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-Day Factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models in Nash Sutcliffe Efficiency, greater than 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights to investigate the sub-basin scale.

Transboundary hazard and downstream impact of glacial lakes in Hindu-Kush Karakoram Himalayas

Glacial Lake Outburst Floods (GLOFs) can generate catastrophic flash floods when the damming structure is breached or overtopped. Some of these glacial lakes are located in transboundary regions where floods originating from the lake in one country could inundate a neighboring country, devastating the population and infrastructure of both nations and influencing socio-political relationships. Therefore, assessing the lakes' hazard is crucial. This study investigates transboundary glacial lakes, considering their GLOF hazard, including potential mass movement intrusion, moraine's stability, upstream and downstream process cascades, downstream flood extents, and the exposure and vulnerability of the downstream infrastructure and affected population. GLOF exposure assessments were carried out to identify exposed buildings, bridges, and hydropower systems in transboundary regions. China currently has the highest number of transboundary lakes, with most of them potentially impacting India and Nepal. Most of the transboundary lakes in China, and many in India and Nepal, are susceptible to mass movements. Among the 230 transboundary glacial lakes in the Hindu Kush Karakoram Himalaya, 55 lakes can potentially impact other glacial lakes along their flow path, creating a cascade of events. Five transboundary lakes could potentially impact over 1000 buildings, and 16 lakes could impact over 500 buildings. A total of 35 lakes can impact at least one hydropower station along their flow path, and 4 lakes can impact two hydropower stations. This research emphasizes the critical importance of conducting comprehensive risk analyses of GLOFs in transboundary regions to inform policy-makers. It calls for investing in broad-scale assessments and data-driven decision-making for mitigating and adapting to GLOF risks effectively. Finally, by raising awareness among policy-makers, the study aims to drive actions that safeguard communities and infrastructure vulnerable to GLOF.

Implications of Accuracy of Global Glacier Inventories in Hydrological Modeling: A Case Study of the Western Himalayan Mountain Range

Alpine glaciers are a fundamental component of the cryosphere and are significantly sensitive to climate change. One such region is the Hindukush Karakoram Himalaya (HKH) and Tibetan Plateau (TP) region, which contains more than 40,000 glaciers. There are more than 12 glacier inventories available covering parts of (or the entire) HKH region, but these show significant uncertainties regarding the extent of glaciers. Researchers have used different glacier inventories without assessing their accuracy. This study, therefore, assessed the implications of the accuracy of global glacier inventories in hydrological modeling and future water resource planning. The accuracy assessment of most commonly used two global glacier inventories (Global Land Ice Monitoring from Space-GLIMS v 2.0 and Randolph Glacier Inventory-RGI v 6.0) has been carried out for three sub-basins of the Upper Indus Basin—the Swat, the Chitral, and the Kabul River basins (combined, this is referred to as the Great Kabul River Basin)—with a total basin area of 94,552.86 km2. Glacier outlines have been compared with various Landsat 7 ETM+, Landsat 8, high-resolution Google Earth images, and manually digitized debris-covered glacier outlines during different years. The total glacier area for the Great Kabul River Basin derived from RGI and GLIMS is estimated to be 2120.35 km2 and 1789.94 km2, respectively, which was a difference of 16.9%. Despite being sub-basins of the Great Kabul River Basin, the Swat, and the Chitral River basins were different by 54.74% and 19.71%, respectively, between the two inventories, with a greater glacierized area provided by RGI, whereas the Kabul River basin was different by 54.72%, with greater glacierized area provided by GLIMS. The results and analysis show that GLIMS underestimates glacier outlines in the Swat and the Chitral basins and overestimates glacier extents in the Kabul River basin. The underestimation is mainly due to the non-representation of debris-covered glaciers. The overestimation in GLIMS data is due to the digitization of seasonal snow as part of the glaciers. The use of underestimated GLIMS outlines may result in 5–10% underestimation of glacier-melt contribution to flows in the Swat River basin, while an underestimation of 7% to 15% is expected in the Chitral River Basin, all compared to RGI v 6.0 outlines. The overestimation of glacier-melt contribution to flows in the Kabul River basin is insignificant (1% to 2%) using GLIMS data. In summary, the use of the GLIMS inventory will lead to underestimated flows and show that the Great Kabul River Basin (particularly the Chitral River Basin) is less sensitive to climate change effects. Thus, the current study recommends the use of RGI v 6.0 (best glacier inventory) to revisit the existing biased hydro-climate studies and to improve future hydro-climate studies with the concomitant rectification of the MODIS snow coverage data. The use of the best glacier inventory will provide the best estimates of flow sensitivity to climate change and will result in well-informed decision-making, precise and accurate policies, and sustainable water resource management in the study area. The methodology adopted in the current study may also be used in nearby areas with similar hydro-climate conditions, as well as for the most recently released RGI v 7.0 data.

Analysing the elevation-distributed hydro-climatic regime of the snow covered and glacierised Hunza Basin in the upper Indus

In the high altitude Hindukush Karakoram Himalaya (HKH) mountains, the complex weather system, inaccessible terrain and sparse measurements make the elevation-distributed precipitation and temperature among the most significant unknowns. The elevation-distributed snow and glacier dynamics in the HKH region are also little known, leading to serious concerns about the current and future water availability and management. The Hunza Basin in the HKH region is a scarcely monitored, and snow- and glacier-dominated part of the Upper Indus Basin (UIB). The current study investigates the elevation-distributed hydrological regime in the Hunza Basin. The Distance Distribution Dynamics (DDD) model, with a degree day and an energy balance approach for simulating glacial melt, is forced with precipitation derived from two global datasets (ERA5-Land and JRA-55). The mean annual precipitation for 1997–2010 is estimated as 947 and 1,322 mm by ERA5-Land and JRA-55, respectively. The elevation-distributed precipitation estimates showed that the basin receives more precipitation at lower elevations. The daily river flow is well simulated, with KGE ranging between 0.84 and 0.88 and NSE between 0.80 and 0.82. The flow regime in the basin is dominated by glacier melt (45%–48%), followed by snowmelt (30%–34%) and rainfall (21%–23%). The simulated snow cover area (SCA) is in good agreement with the MODIS satellite-derived SCA. The elevation-distributed glacier melt simulation suggested that the glacial melt is highest at the lower elevations, with a maximum in the elevation 3,218–3,755 masl (14%–21% of total melt). The findings improve the understanding of the local hydrology by providing helpful information about the elevation-distributed meltwater contributions, water balance and hydro-climatic regimes. The simulation showed that the DDD model reproduces the hydrological processes satisfactorily for such a data-scarce basin.

Exploring the Spatiotemporal Variation in Light-Absorbing Aerosols and Its Relationship with Meteorology over the Hindukush–Himalaya–Karakoram Region

Light-absorbing aerosols such as black carbon (BC), organic carbon (OC), and dust can cause the warming and melting of glaciers by absorbing sunlight. Further research is needed to understand the impact of light-absorbing aerosols on the Hindukush–Karakoram–Himalaya region in northern Pakistan. Therefore, spatiotemporal variation in absorbing surface mass concentration retrieved from Modern-Era Retrospective analysis for Research and Applications, optical properties such as aerosol optical depth (AOD) and absorption aerosol optical depth (AAOD) from the ozone monitoring instrument, and meteorological parameters from the European Centre for Medium-Range Weather Forecasts Reanalysis were investigated over northern Pakistan from 2001 to 2021. The BC concentration was lowest in May and highest in November, having a seasonal maximum peak in winter (0.31 ± 0.04 µg/m3) and minimum peak in spring (0.17 ± 0.01 µg/m3). In addition, OC concentration was found to be greater in November and smaller in April, with a seasonal higher peak in autumn (1.32 ± 0.32 µg/m3) and a lower peak in spring (0.73 ± 0.08 µg/m3). The monthly and seasonal variabilities in BC and OC concentrations are attributed to solid fuels, biomass burning, changes in vegetation, agricultural activities, and meteorology. In contrast, the dust concentration was high in July and low in December, with a seasonal average high concentration in summer (44 ± 9 µg/m3) and low concentration in winter (13 ± 2 µg/m3) due to drier conditions, dust activity, long-range transport, and human activities. Moreover, the seasonal variation in AOD and AAOD was identical and higher in the summer and lower in the winter due to dust aerosol loading and frequent dust activities. AOD and AAOD followed a similar pattern of spatial variation over the study area. Meteorological parameters greatly impact light-absorbing aerosols; therefore, low temperatures in winter increase BC and OC concentrations due to shallow boundary layers, while severe precipitation in spring decreases concentrations. During summer, dry conditions cause soil erosion and increase the amount of dust suspended in the atmosphere, leading to higher AOD and AAOD values. Conversely, higher precipitation rates and speedy winds disperse the dust aerosols in winter, resulting in lower AOD and AAOD values.

Comparative assessment of spatiotemporal variability in cryosphere and hydro-climatic regime of the Hunza, Astore and Shigar Basins (Hindukush-Karakoram–Himalaya Region) in Pakistan

Comparative assessment of spatiotemporal variability in cryosphere and hydro-climatic regime of the Hunza, Astore and Shigar Basins (Hindukush-Karakoram–Himalaya Region) in Pakistan

Climatic and seismic data-driven deep learning model for earthquake magnitude prediction

The effects of global warming are felt not only in the Earth’s climate but also in the geology of the planet. Modest variations in stress and pore-fluid pressure brought on by temperature variations, precipitation, air pressure, and snow coverage are hypothesized to influence seismicity on local and regional scales. Earthquakes can be anticipated by intelligently evaluating historical climatic datasets and earthquake catalogs that have been collected all over the world. This study attempts to predict the magnitude of the next probable earthquake by evaluating climate data along with eight mathematically calculated seismic parameters. Global temperature has been selected as the only climatic variable for this research, as it substantially affects the planet’s ecosystem and civilization. Three popular deep neural network models, namely, long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transformer models, were used to predict the magnitude of the next earthquakes in three seismic regions: Japan, Indonesia, and the Hindu-Kush Karakoram Himalayan (HKKH) region. Several well-known metrics, such as the mean absolute error (MAE), mean squared error (MSE), log-cosh loss, and mean squared logarithmic error (MSLE), have been used to analyse these models. All models eventually settle on a small value for these cost functions, demonstrating the accuracy of these models in predicting earthquake magnitudes. These approaches produce significant and encouraging results when used to predict earthquake magnitude at diverse places, opening the way for the ultimate robust prediction mechanism that has not yet been created.

Half-a-century (1971–2020) of glacier shrinkage and climatic variability in the Bhaga basin, western Himalaya

Glacier shrinkage is a globally occurring phenomena. High-resolution change detection based on frequent mapping and monitoring of high-altitude glaciers is necessary to precisely evaluate future water availability and to understand glacier evolution under different climatic scenarios in the Hindukush-Karakoram-Himalayan (HKH) region. This also holds true for the Bhaga basin of the western Himalaya. This study investigates glacier and glacier lake changes in the Bhaga basin, over the last five decades based on satellite imagery including Corona KH4 (1971), Landsat 7 Enhanced Thematic Mapper Plus (ETM+; 2000), Linear Imaging Self-Scanning Sensor (LISS IV; 2013), and Sentinel 2 (2020). Regional temperature and precipitation trends were evaluated from gridded climatic datasets (1900–2020). In the Bhaga basin 306 glaciers (>0.2 km2) were mapped with a total area of 360.3 ± 4.0 km2, of which 55.7 ± 0.6 km2 was covered with debris in 2013. The total glacier covered area decreased by ∼8.2 ± 1.5 % (0.16 ± 0.03 % yr−1) during the entire observation period 1971–2020, with noticeable heterogeneity between tributary watersheds. In the past two decades (2000–2020), the deglaciation rate has increased significantly (0.25 % yr−1) compared to the previous decades (1971–2000; 0.12 % yr−1). Glacier lake area increased by 0.6 ± 0.1 km2 (0.012 km2 yr−1) between 1971 and 2020. The NCEP/NCAR climatic data reveals an increase of 0.63°C in temperature and a decrease of 6.39 mm in precipitation for the period 1948–2018. In comparison, APHRODITE data shows an increasing trend in temperature of 1.14°C between 1961 and 2015 and decreasing trend in precipitation of 31 mm between 1951 and 2007. Both NCEP/NCAR and APHRODITE data reveal significant temperature increase and precipitation decrease since the 1990s, which have probably augmented ice loss in the Bhaga basin during the early 21st century.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data.

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snowcover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Solar-powered irrigation system as a nature-based solution for sustaining agricultural water management in the Upper Indus Basin

Rapid deglaciation in form of lowering of glacial surface under growing warm temperatures form a major cause of disruption in meltwater flows to cryosphere-fed irrigation network which usually affects the agriculture productivity and livelihoods in the Upper Indus Basin (UIB). In such hotspots of cryosphere change and other vulnerable areas, solar powered irrigation system (SPIS) can be adopted as a nature-based solution to uplift water from the nearby glacio-fluvial streams for irrigation and domestic use. In the present study, solar energy harnessing potential was investigated in the UIB region of Pakistan based on climate and topographic suitability to adopt solar powered irrigation system (SPIS) in the region. Overall mean temperature and elevation were found suitable for harnessing solar energy in major parts of the Hindu Kush-Karakoram-Himalaya (HKH) ranges of the UIB. The slope index indicated high suitability for harnessing solar energy in about 92.5% area, while aspect exhibited high suitability in about 13.3% area and medium in 25.3% area of the HKH region. The slope-aspect (SLA) suitability was found good in about 13.5% area of the Hindu Kush, 12% of the Karakoram and 10.9% area of the Himalaya range, whereas it was observed medium in about 26.7% area of the Hindu Kush, 21.8% of the Karakoram and 22.2% area of the Himalaya range. Overall the SLA suitability was found good in about 12.1% area and medium in about 23.5% area of the three HKH ranges. However, maximum benefits of the SPIS approach can be acquired through linking it with integrated water resource management approach for sustainable agricultural water management in the region.

Spatiotemporal Analysis of Climatic Extremes over the Upper Indus Basin, Pakistan

The Hindukush-Karakoram-Himalayan (HKH) ranges and their massive cryosphere extend over the Upper Indus Basin (UIB) and are prone to incapacitated water supply due to the proclivity of globally increased temperature. Due to excessive carbon emissions, frequent incursions including extreme climatic events, are likely to happen sooner than expected on a regional scale due to recent climate change. The present study examined the variability of climatic extremes (18 indices) during 1971 to2018 over the UIB. The Mann-Kendall (MK) test and Sen’s methods were applied for statistical analysis as the former deals with the magnitude of trends while the direction of observed trends was identified by the latter in climatological time-series data. The frequency and intensity of summer days (SU25 &gt; 25 °C/year) at 13 out of 27 stations significantly increased, particularly in lower regions. The same warming proclivity was dominant in tropical nights (TR20 &gt; 20 °C/year) at 20 stations including Astore, Bunji, Gilgit, Gupis, Murree and Skardu. Similarly, significant increases were observed in extremes of annual precipitation in western and high northern areas; however, significantly, the highest drops in R25 and R5day were exhibited in Chitral at the rates of 13 and 29 days, respectively. These findings tend to support the accelerated summer warming and a rather stable winter warming while stable winter warming showed that overall the UIB seems to be more sensitive towards warming.

Why are the Builders and Operators of Dams and Hydels in The Hindu Kush–Karakoram–Himalaya so Poorly Prepared for Hydroclimatic Hazards?

Why are the Builders and Operators of Dams and Hydels in The Hindu Kush–Karakoram–Himalaya so Poorly Prepared for Hydroclimatic Hazards?

Flood Hazard Mapping of Rivers in Snow- and Glacier-Fed Basins of Different Hydrological Regimes Using a Hydrodynamic Model under RCP Scenarios

The global warming trends have accelerated snow and glacier melt in mountainous river basins, which has increased the probability of glacial outburst flooding. Recurrent flood events are a challenge for the developing economy of Pakistan in terms of damage to infrastructure and loss of lives. Flood hazard maps can be used for future flood damage assessment, preparedness, and mitigation. The current study focused on the assessment and mapping of flood-prone areas in small settlements of the major snow- and glacier-fed river basins situated in Hindukush–Karakoram–Himalaya (HKH) under future climate scenarios. The Hydrologic Engineering Center-River Analysis System (HEC-RAS) model was used for flood simulation and mapping. The ALOS 12.5 m Digital Elevation Model (DEM) was used to extract river geometry, and the flows generated in these river basins using RCP scenarios were used as the inflow boundary condition. Severe flooding would inundate an area of ~66%, ~86%, ~37% (under mid-21st century), and an area of ~72%, ~93%, ~59% (under late 21st century RCP 8.5 scenario) in the Chitral, Hunza, and Astore river basins, respectively. There is an urgent need to develop a robust flood mitigation plan for the frequent floods occurring in northern Pakistan.

Changes in monthly streamflow in the Hindukush–Karakoram–Himalaya Region of Pakistan using innovative polygon trend analysis

The present study explored the changes in monthly streamflow in the Hindukush–Karakoram–Himalaya (HKH) region within Pakistan using the recently developed trend analysis method known as innovative polygon trend analysis (IPTA). The monthly streamflow data of 34 gauging stations installed in the HKH region was analyzed, and the Pettitt test was applied to check the homogeneity of the time series. The entire study area was divided into 13 sub-basins, and then changes in monthly streamflow of each sub-basin were explored using the IPTA method. The streamflow of high elevated glaciated and snow/ice-covered sub-basins (e.g., Shyok, Astore, and Chitral) have increased in high flow months (June–August), where there is a downward trend for the Hunza sub-basin in these months. In the Gilgit sub-basin, a transition occurred from no trend in May to a decreasing trend from June to August. The upstream gauges of Swat and Kabul sub-basins showed an increasing trend throughout the year, while downstream gauging stations in the same sub-basins exhibited a strong decreasing trend only in high flow months (June–August). The upper reaches of the Indus part in the Upper Indus Basin (UIB) showed increasing trends in high flow months (June–August), however the downstream gauges of UIB showed decreasing trends throughout the year. Overall, only the glaciated and snow/ice-covered sub-basins experienced increasing trends, while most other sub-basins faced decreasing trends in high flow months and increasing trends in low flow months (October–March). The enhancement of the seasonal pattern of streamflow in the upper reaches of UIB is possibly due to the melting of snow and ice that potentially influence floods and hydropower generation. The results of this study can result in a better understanding of the hydrology of the HKH region and support sustainable water management.

Prevalent risk of glacial lake outburst flood hazard in the Hindu Kush–Karakoram–Himalaya region of Pakistan

Prevalent risk of glacial lake outburst flood hazard in the Hindu Kush–Karakoram–Himalaya region of Pakistan

Hydrologic alteration and potential ecosystemic implications under a changing climate in the Chitral River, Hindukush region, Pakistan

Abstract This study investigates changes in the hydrologic regime of the Chitral River, Hindukush–Karakoram–Himalayan (HKH) region, Pakistan. Different statistically based methods were used to assess climate change-induced hydrologic alterations that can possibly impact aquatic habitat in the study region. The hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) was calibrated, validated, and applied to predict streamflow in the Chitral River basin. The HBV model was forced with the ensemble of four general circulation models under different representative concentration pathway emission scenarios to generate future streamflow under climate change conditions in the basin for the mid-twenty-first century. The results of this study show that hydrologic regimes in the study area, expressed by the magnitude, duration, frequency, timing, and rate of streamflow, are likely to alter in the future. Positive (i.e., with increased frequency) hydrologic alteration is projected for most flow parameters under all scenarios for the 2021–2050 period compared with values observed during the historical period (1976–2005). These hydrologic alterations might have impacts on fish and migratory bird species in the study area. This research can be helpful in providing practical information for more effective water resources and aquatic ecosystem management in the HKH region.

Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region)

Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area.

Spatial analysis of temperature time series over the Upper Indus Basin (UIB) Pakistan

Runoff generated from the Upper Indus Basin (UIB) mainly originates in the massifs of the Hindukush–Karakoram–Himalaya (HKH) region of Pakistan. Water supply in early spring depends upon the snow accumulation in the winter and the subsequent temperature. Seasonal temperature variations corroborate the contemporary dynamics of snow and glaciers. Recently, there has been increasing evidence of accelerated warming in high mountain areas, termed as elevation-dependent warming (EDW). We have identified trends, analyzed inconsistencies, and calculated changes in the maximum, minimum, mean and diurnal temperature range (Tmax, Tmin, Tmean, and DTR) at 20 weather stations during four-time series: 1961–2013 (first), 1971–2013 (second), 1981–2013 (third), and 1991–2013 (fourth). We employed the Mann–Kendall test to determine the existence of a trend and Sen’s method for the estimation of prevailing trends, whereas homogeneity analysis was applied before trend identification using three different tests. This study revealed that the largest and smallest magnitudes of trends appeared in the winter and summer, respectively, particularly during the fourth data series. Tmax revealed robust warming at ten stations, most remarkably at Gupis, Khunjrab, and Naltar at rates of 0.29, 0.36, and 0.43 °C/decade, respectively, during the fourth series. We observed that Tmin exhibits a mixed pattern of warming and cooling during the second and third series, but cooling becomes stronger during the fourth series, exhibiting significant trends at twelve stations. Khunjrab and Naltar showed steady warming during the fourth series (spring), at rates of 0.26 and 0.13 °C/decade in terms of Tmean. The observed decreases in DTR appeared stronger in the fourth series during the summer. These findings tend to partially support the notion of EDW but validate the dominance of cooling spatially and temporally.

Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region

Snow cover plays a major role in the earth's climate system. The stability of the snow mass over Hindukush-Karakoram-Himalaya (HKH) in contrast to the worldwide retreat of mountainous glaciers and its relation to aerosol concentration remains poorly understood. The proposed study focused on the understanding of this relationship between various snow parameters and the optical properties of atmospheric aerosols over the HKH region of Northern Pakistan between March and June for a prolonged study period from 2005 to 2015. The aerosol's optical properties were retrieved from snow covered pixels in the study area to avoid the contamination of snow albedo (SA) by other features of land surfaces. The results revealed an increasing trend in the snow cover area (SCA) at the rate of 577.3, 1090.6 and 652.3 km2/year in March, May and June, respectively, with a decrease in April due to the uneven distribution of SCA during 2005–2015. The results revealed a strong positive correlation (R = 0.77) between SCA and SA, whereas SCA and SST were negatively correlated (R = −0.82) during the study period. The Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) indicated the presence of scattering and absorbing aerosols (e.g., dust, polluted dust, and smoke) both at high and low altitudes. However, the diminution of aerosol concentration was caused by their short time span in atmosphere and the occurrence of snowfall that washed them out from the snow at high altitudes. The findings indicated an increased SCA, with contrasting behavior in the ablation period. However, the presence of aerosols demands proper attention, to monitor any future threat to the high-altitude cryosphere.

Water availability in Pakistan from Hindukush–Karakoram–Himalayan watersheds at 1.5 °C and 2 °C Paris Agreement targets

Highly seasonal water supplies from the Himalayan watersheds of Jhelum, Kabul and upper Indus basin (UIB) are critical for managing the world's largest contiguous irrigation system of the Indus basin and its dependent agrarian economy of Pakistan. Here, we assess changes in the contrasting hydrological regimes of these Himalayan watersheds, and subsequent water availability under the Paris Agreement 2015 targets that aim of limiting the mean global warming to 1.5 °C (Plus1.5), and further, well below 2.0 °C (Plus2.0) relative to pre-industrial level. For this, calibrated semi-distributed hydrological model setups of the University of British Columbia are coupled with the 80-member ensemble experiments of Plus1.5 and Plus2.0 performed under the half a degree additional warming, projections, prognosis and impacts (HAPPI) framework. Our results suggest that during October to February, HAPPI ensembles project a median warming of up to 1 °C under Plus1.5 and of more than 1.5ºC under Plus2.0 relative to the historical period (2006–2015). Such warming is generally higher in daily minimum than daily maximum temperature and over high altitudes. Both scenarios project drying of the monsoon (July–September) and the main snowpack accumulation/melt seasons (March-June). Results of hydrological modeling suggest significant changes in the timings of contrasting hydrological regimes that collectively reflect on changes in the overall hydrology of the Himalayan watersheds. Median changes generally indicate strengthening of the nival and glacial regimes for all basins under Plus1.5, except for the nival regime of the Jhelum basin, whereas such changes are more pronounced under Plus2.0. Subsequently, annual water availability from three Himalayan watersheds will increase by 34% and 43% under Plus1.5 and Plus2.0, respectively. These findings provide an overall picture of the surface water availability in Pakistan under the aspirations of the Paris Agreement 2015 and can inform on policy decisions together with the country's first-ever national water policy approved recently.