Hindu Kush Himalayan Research Articles

Climate change scenarios predict reduction in suitable habitats and range shifts for Ophiocordyceps sinensis (Berk.) in Hindu Kush Himalaya

The Hindu Kush Himalayan region has seen a faster pace of anthropogenic climate warming than the global average during the last 50 years. Since 1980s this region is experiencing intense climatic events notably elevation-dependent warming. Given its unique evolutionary background, rich variety of species, and significant endemism, it is crucial to comprehend the effects of climate change on species distributions in this area. Of particular interest are the fungi, which have been the subject of much fewer studies on how they will respond to climate change, despite the fact that they may have a significant role in the functioning and stability of ecosystems. We therefore selected Ophiocordyceps sinensis an alpine fungus species for predicting the effects of climate change on its distribution in Hindu Kush Himalaya. Regarded as one of the most expensive natural resources used in oriental medicine, Ophiocordyceps and its surrounding habitats are under threat from various ecological and anthropogenic factors. We used Species Distribution Modeling software Maxent 3.3.4 and a set of uncorrelated climatic (temperature and precipitation) and topographical (elevation, slope and aspect) variables at a spatial resolution of 2.5 arc minutes to model the suitable habitats. To predict the future distribution of O. sinensis we used future climate data from BCCCSM2- HR global circulation model for three emission scenarios of the shared socioeconomic pathways (SSPs) (SSP126, SSP245 and SSP585). Maxent model predicted current and future habitats with high accuracy. Current potential distribution map of O. sinensis shows that high suitability areas occur in India, China, Nepal and Bhutan. Prediction maps under all three scenarios showed a large reduction in suitable habitats as compared to current climatic conditions. Analysis of range change reveals that species exhibits both range expansion and range contraction under climate change scenarios. Range contraction is noticeably more than range expansion causing overall reduction in the suitable habitats occupied by O. sinensis. ‘Centroid Range Shift’ analysis revealed potential suitable habitats will shift to South-West direction under all future scenarios with almost overlapping centroids.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Evaluating the dynamics of climate and anticipated snow cover changes on the hydrological response of rivers in the Hindu Kush Himalaya region

ABSTRACT The research aims to assess the interplay between climate dynamics and snow cover changes, informing water resource management. Utilizing the SWAT model with historical and future data, hydrological responses in the Marshyangdi River Basin are examined. The model's performance confirms its ability to simulate runoff components effectively. Projections indicate a concerning decrease in snow cover area for 2030, 2050, and 2090, with declines of 2.03, 4.03, and 14.20%, respectively, attributed to global warming and human activities. Isolating climate change reveals substantial increases in stream flow, notably pre-monsoon surges under different scenarios, with projected increments of 84 and 130% under SSP245 and SSP585, respectively. Analysis of snow cover impact shows a slight increase in annual stream flow, with a 3.54% anticipated decrease during the post-monsoon period, raising concerns for water availability in the dry season. Comparing both impacts reveals no significant differences in stream flow or precipitation. Annual stream flow increases under different scenarios, driven by precipitation, with varying impacts. Climate change emerges as the dominant driver, affecting surface runoff, groundwater, and evapotranspiration. This research unravels complex relationships between climate change, snow cover, and hydrological responses, providing valuable insights for water resource management in mountainous regions like the Hindu Kush Himalaya.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Livelihood impacts of community-based forest landscape restoration in the Hindu Kush Himalaya, Pakistan

Livelihood impacts of community-based forest landscape restoration in the Hindu Kush Himalaya, Pakistan

Empowering young farmers' voices in climate change extension programs: An in-depth analysis of decision-making dynamics and social media engagement

Effective integration of rural communities in climate change extension programs requires a detailed examination of the key factors that shape their participation. This study examines various variables and assesses their influence on young farmers' decision making within climate change extension programs and social media engagement for climate change awareness dissemination in the Hindu Kush Himalayas (HKH), Pakistan. Data were collected from young farmers aged 18–34 years through household surveys and from various government department officials through key informant interviews (KIIs). A bivariate probit regression model and thematic analysis were used to analyze the data. The bivariate probit regression shows that perceived risk and access to climate information positively influence young farmers' decision making in climate change extension programs, while years of education have a negative association. For social media engagement, the bivariate probit regression shows that government program benefits and the number of male members positively influence farmers' participation, while household size has a negative association. The results of our KIIs revealed that positive factors influencing young farmers' engagement in climate change extension programs include geographic ties, dependence on livestock, attitudes toward climate change, and positive perceptions of agriculture. Conversely, barriers include low acreage and productivity, reluctance of elders, lack of political support, and adherence to tradition. This study recommends that to improve young farmers' decision-making in climate change extension programs, there is a need to improve access to climate information and strengthen government support to promote social media engagement.

Comparative Analysis of Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques

The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging a Temporal Convolutional Network (TCN), for snowmelt forecasting of the Hindu Kush Himalayan (HKH) region. To evaluate the performance of our proposed model, we conducted a comparative analysis with other popular models, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Transformer models. Furthermore, nested cross-validation (CV) was used with five outer folds and three inner folds, and hyperparameter tuning was performed on the inner folds. To evaluate the performance of the model, the Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), R square (R2), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) were computed for each outer fold. The average metrics revealed that the TCN outperformed the other models, with an average MAE of 0.011, RMSE of 0.023, R2 of 0.991, KGE of 0.992, and NSE of 0.991 for one-day forecasts of streamflow. The findings of this study demonstrate the effectiveness of the proposed deep learning model as compared to traditional machine learning approaches for snowmelt-driven streamflow forecasting. Moreover, the superior performance of this TCN highlights its potential as a promising deep learning model for similar hydrological applications.

Ice cover loss and debris cover evolution in the Afghanistan Hindu Kush Himalaya between 2000 and 2020

ABSTRACT Glaciers in Afghanistan are crucial elements for water resource and summer river flows. They are also threatened by rapid climate warming. This study presents an up-to-date assessment of ice cover loss for the entire country over two periods, 2000–2008 and 2008–2020, using newly developed remote sensing indices that include a more reliable determination of changing debris cover. The results suggest an estimated ice-covered area of 2,684 ± 101 km2 in 2020, that was 75 ± 0.7% clean ice and 25 ± 3.0 percent debris-covered ice. Total ice-covered area retreated by −0.15 ± 0.01 percent yr−1 between 2000 and 2008 and −0.43 ± 0.05 percent yr−1 between 2008 and 2020. Notably, 60 percent of ice cover loss (2000–2020) related to ice cover extents with a size ≤ 2.5 km2, comprising 60 percent of the total ice-covered area in 2000. Higher altitude accumulation zones also exhibited mass loss. However, there was also substantial spatial variation in these rates of loss based on geographical region, glacier size, and climate zones. In the north-eastern regions that are geographically close to or part of the north-west Karakoram ice cover was declining at a substantially lower rate, stable, or even increasing slightly, as compared with the northern and central regions of Afghanistan.

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

ABSTRACT The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. 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 the 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 with Nash-Sutcliffe efficiency > 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 for investigations at the sub-basin scale.

Population ecology and habitat suitability modelling of an endangered and endemic medicinal plant Meconopsis aculeata Royle under projected climate change in the Himalaya

The Himalayan region is home to the endangered and endemic medicinal plant, Meconopsis aculeata. Habitat degradation due to overexploitation and unscientific collection of the species in recent years has caused the restricted distribution range of species across its range. Therefore, the present study was conducted in the Hindu Kush Himalayan region (4.5 million km2 area) to study population ecology and identify suitable habitats for the habitat distribution modelling under projected climate change to monitor and restore the dwindling populations. Ten sites/populations (20 × 20 m plot) of M. aculeata have been studied. East aspect (5 sites) and rocky habitat (04 sites) represented the maximum number of sites. The density of M. aculeata has a significant negative correlation with slope and available potassium in soil. The MaxEnt algorithm, species occurrence data and environmental variables were used in the current study. CMIP6 project's MIROC6 and BCC-CSM2-MR climate change models were used to identify the potential distribution area for the future under the SSP245 and SSP370 scenarios for the period 2081–2100. The results showed the satisfactory quality of models based on the True-Skill-Statistic (0.838 ±0.149), Area-Under-Curve values (0.958±0.038), and the Receiver-Operating-Characteristic curves. MaxEnt predicted that 1,78,993 km2 (4.10 %) area is favourable for the occurrence of the species. The precipitation of the driest quarter, precipitation of the coldest quarter and elevation were the most influential variables for habitat suitability prediction. The distribution area with an increase in greenhouse gases shows a decrease in the period 2081–2100 in both SSPs and GCMs. The study predicted highly suitable areas that would play a vital role in in situ conservation and improving the status of species. The predicted distribution maps of the species would help policymakers and decision-makers in developing suitable management plans, which will ultimately help in the conservation of species.

Terrain Shadow Interference Reduction for Water Surface Extraction in the Hindu Kush Himalaya Using a Transformer-Based Network

Water is the basis for human survival and growth, and it holds great importance for ecological and environmental protection. The Hindu Kush Himalaya (HKH) is known as the “Water Tower of Asia”, where water influences changes in the global water cycle and ecosystem. It is thus very important to efficiently measure the status of water in this region and to monitor its changes; with the development of satellite-borne sensors, water surface extraction based on remote sensing images has become an important method through which to do so, and one of the most advanced and accurate methods for water surface extraction involves the use of deep learning networks. We designed a network based on the state-of-the-art Vision Transformer to automatically extract the water surface in the HKH region; however, in this region, terrain shadows are often misclassified as water surfaces during extraction due to their spectral similarity. Therefore, we adjusted the training dataset in different ways to improve the accuracy of water surface extraction and explored whether these methods help to reduce the interference of terrain shadows. Our experimental results show that, based on the designed network, adding terrain shadow samples can significantly enhance the accuracy of water surface extraction in high mountainous areas, such as the HKH region, while adding terrain data does not reduce the interference from terrain shadows. We obtained the water surface extraction results in the HKH region in 2021, with the network and training datasets containing both water surface and terrain shadows. By comparing these results with the data products of Global Surface Water, it was shown that our water surface extraction results are highly accurate and the extracted water surface boundaries are finer, which strongly confirmed the applicability and advantages of the proposed water surface extraction approach in a wide range of complex surface environments.

Elevation dependent climate assessment and its influence on snow cover variability in Hindu Kush Himalayan region using Google Earth Engine for the period of 2003–2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003–2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (−0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

Promoting social justice and women's rights: Mitigating the negative impacts of large hydropower projects on tribal women in the Hindu Kush Himalaya region

This study highlights the significance of comprehending the region's caste system and cultural norms and their influence on implementing compensation and resettlement programs in hydropower projects to gain social sustainability. By leveraging Social Justice Theory, we examine how systemic inequalities affect tribal women, particularly in the context of large-scale hydropower projects. Tribal women's social and cultural identities expose them to vulnerabilities, leading to negative consequences concerning large-scale hydropower projects. The lack of access to justice and limited participation in decision-making poses social justice challenges, undermining their fundamental rights, such as basic needs, healthcare, and education. Consequently, their well-being and quality of life suffer, impeding support for such projects. The extent of these adverse effects may vary depending on whether women belong to local (Indigenous) or non-local tribes (non-Indigenous). Our analysis reveals the critical role of tribal affiliation in moderating the relationship between social justice dimensions—such as distributive, procedural, substantive, and recognition justice—and social sustainability, offering new insights into Social Justice Theory. This study explores the potential of careful planning that prioritises the needs of tribal women regardless of tribal affiliation to mitigate these negative social consequences. The study employs a social justice framework to address these challenges. Through the analysis of 511 questionnaires using PLS-SEM, the study revealed that tribal affiliation moderates the relationship between social justice and sustainability. This contribution enriches Social Justice Theory by providing empirical evidence of the intersectionality of social justice issues in the context of development projects. The study further identified that social sustainability and community satisfaction mediate the relationship between social justice and support for hydropower projects, while tribal affiliation moderates this relationship.

Investigating public understanding and actions regarding Aedes mosquitoes and dengue prevention across altitudinal range in central Nepal.

Dengue fever, transmitted by Aedes mosquitoes, is a significant public health concern in Nepal. An extensive Knowledge, Attitude, and Practice (KAP) survey related to Aedes mosquitoes and prevention of dengue was conducted along the route from Matihani (62 m above sea level) to Kalinchok (3840 m asl) in central Nepal. This research examined residents' awareness of Aedes mosquitoes and the diseases they transmit, aiming to support the World Health Organization's national and global fight against mosquito-borne diseases. The survey included 550 participants aged 18-75 years (249 males and 201 females). Door-to-door campaigns were conducted to gather data on knowledge scores, practices, and attitudes about Aedes mosquitoes using a standard structured questionnaire. Findings of this study revealed a moderate level of knowledge across the study area, with a mean knowledge score of 5.525±2.58 (correct answer rate of 46.05%). While 99.6% of respondents were aware of dengue transmission by mosquitoes, only 64.5% were able to report Aedes mosquitoes as the key transmitter. Less than one-third of the participants (27.8%) were familiar with black-and-white stripes in Aedes. Additionally, only 36.7% were knowledgeable about the day-biting behavior of Aedes mosquitoes. This KAP survey provides valuable insights into community understanding of Aedes mosquitoes and dengue prevention practices associated with various Nepalese communities residing in low to high altitudes of central Nepal. These findings can be adopted to address the increasing health burden faced by countries with low-to-high altitude regions within the Hindu Kush Himalayas.

Anthropogenic and biological activities elevate microplastics pollution in headwater ecosystem of Yangtze tributaries in Hindu Kush-Himalayan region

Microplastic (MP) pollution is widely spread in oceans, freshwater, and terrestrial environments but MPs in mountainous headwater ecosystem are rarely reported. This study focuses on the headwater of Yangtze tributaries of the Hindu Kush-Himalayan (HKH) region. Five streams at elevations of 900 to 3300 m were selected to investigate the distribution of MPs in water and sediments across altitudes. MPs were found in all water and sediment samples from top stream zone nearly in absence of anthropogenic activity, low anthropogenic zone, and high anthropogenic zone, increased from 12–54, 81–185 to 334–847 items/L, and 2–35, 26–84 to 124–428 items/kg, respectively. This elevation-dependent MP distribution indicated that as elevation decreased, anthropogenic activities intensified and increased MPs input and their abundance, size, and diversity. Notably, hydraulic projects, such as damming, were identified as potential barriers to the migration of MPs downstream. Microbiome analyses revealed the presence of bacterial genes associated with plastic biodegradation in all sediment samples. The study indicates that Shangri-la mountainous streams have been polluted with MPs for years with potential risk of generation of nano-sized particles via natural fragmentation and biodegradation, and thus raises concern on MPs pollution in headwaters streams in mountainous regions.

A Systematic Review on the Distribution and Density of <i>Aedes</i> Species in the Hindu-Kush Himalayan Countries

Aedes mosquitoes are effective carriers of virus pathogens such as chikungunya, dengue, yellow fever, zika, and other viruses, leading to high morbidity and mortality. This review compiles information on the geographical distribution of Aedes mosquitoes in the Hindu-Kush Himalayas (HKH). Articles published in SCI-indexed journals from 2000 to 2022 have been reviewed using the key words "Aedes”, "distribution", and "country name (e.g., Nepal)”. A total of 353 articles have been indexed, of which only 52 were ultimately explored. The review highlights a historical prevalence of Aedes spp. in the HKH countries, with their distribution gradually shifting along altitude gradients. Surging dengue cases in the HKH region pose a public health threat. Urgent action is required, including comprehensive nationwide surveys mapping Aedes spp. across diverse altitudes.

Rock glacier distribution and kinematics in Shigar and Shayok basins based on radar and optical remote sensing

AbstractRecent studies have demonstrated the rock glacier destabilisation and permafrost thawing induced by warming climate represent a continuous threat to life, infrastructure and socio‐economic development in the mountainous regions of the Hindu Kush Himalaya. This study presents the first systematic rock glacier inventory for the Shigar and Shayok basins, quantifying rock glacier geomorphology and kinematics based on morphological evidence using Google Earth images and interferometric synthetic aperture radar (InSAR). The certainty index of each inventoried rock glacier is recorded, along with its geomorphological properties and kinematic attributes. The rock glacier velocity is estimated through the InSAR time series analysis of Sentinel‐1 images from 2020 to 2021, with temporal baselines at 12‐day intervals. We developed a rock glacier inventory consisting of 84 rock glaciers covering an area of 29 km2 for the Shigar Basin and 2206 rock glaciers encompassing 369 km2 for the Shayok Basin. Among these rock glaciers, 69% and 52% are categorised as active rock glaciers, respectively. Rock glaciers in both catchments are confined to elevations between 3600 and 5875 m a.s.l., with a mean area of 0.22 km2. The maximum recorded velocity for active rock glaciers in the Shigar Basin is 101 ± 9 cm year−1, with a median of 27 ± 10 cm year−1, and in the Shayok Basin 114 ± 10 cm year−1 (median of 29 ± 9 cm year−1). Temporal variations in the surface velocities of the rock glaciers reveal that they increase with rising temperatures in both catchments, highlighting the seasonality in the rock glacier surface velocity. In total, we recorded the kinematic attributes of 98% of the inventoried rock glaciers in the study area.

Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data

The changing climate directly affects spatial and temporal patterns of snow and ice cover globally and in the Hindu-Kush Himalayan (HKH) region. In the HKH, around 3.3 billion people across 11 countries depend on water originating from mountain glaciers and snowfields, and melting snow cover has a direct impact on their livelihood and well-being. Various studies have shown that the snow and ice cover in the HKH is declining at an alarming rate but have been limited in geographic, spatial and temporal scales. Here, we employed the Global Land Analysis and Discovery analysis ready Landsat time-series data (GLAD ARD) to map changes in perennial snow and ice between 2001 and 2021 at five-year epochs using decision tree ensemble models. These maps were used to create a stratified sampling design for reference data collection to estimate area and map accuracy. All five-year epoch maps have user's accuracies above 90% and producer's accuracies above 91%. Our sample data analysis showed that one-eighth of the extent of perennial snow and ice in the HKH, totalling 15,770 km2 (CI ± 3195 km2), disappeared over the last two decades with 105,935 km2 (CI ± 3396 km2) remaining in 2021. From map-based estimates, the largest decline of perennial snow and ice cover among the mountain ranges was found in the Himalayas with an estimated reduction of 5741 km2. Among HKH countries, China had the largest area of perennial snow and ice reduction of 10,654 km2, Nepal had the highest net perennial snow and ice reduction rate (31%). Among the river basins, the maximum net loss of perennial snow and ice cover between 2001 and 2021 was in the Indus (24.8%), followed by Brahmaputra (18.3%), and Tarim (15.7%). Our results confirm wide-spread loss of perennial snow and ice across the HKH region and provide both regionally consistent maps with derived area estimates at landform, national and basin-levels and methodology to continue monitoring snow and ice melt.

Climate change and rural livelihoods: The potential of extension programs for sustainable development

AbstractAgricultural extension programs promote regenerative land management practices by increasing farmers' awareness of land use, climate risks, and adoption of adaptation practices for sustainable livelihoods. This study focuses on the perceptions of communities that participate in extension programs and those that do not, regarding climate change risks and livelihood sustainability in Pakistan's Hindu Kush Himalayan (HKH) region. The findings indicate that communities participating in extension programs reported poor community land use practices as a cause of negative climate impacts. Farmers identified livestock deaths due to drought and frequent flooding as the main climate risks in their communities. Our study emphasizes the crucial role of extension programs in mitigating and adapting to environmental risks by increasing awareness, promoting a comprehensive understanding of climate risks and adaptation, and planning for land use management. Additionally, our findings underscore the significance of extension initiatives in enhancing livelihoods, such as agriculture, livestock, and forest‐based livelihoods, in the face of climate change challenges. We recommend increasing access to extension programs, strengthening the role of extension initiatives in climate change mitigation and adaptation, and promoting community participation in decision‐making to support sustainable livelihoods.

Climate change challenges to meeting the sustainable development goals in the Hindu Kush Himalayan region

ABSTRACT Understanding the effects of climate change on achieving the Sustainable Development Goals (SDGs) is critical, especially in the Hindu Kush Himalaya (HKH) region, which is experiencing a rate of warming nearly triple the global average. Despite the region’s importance in supporting major ecosystems and a vast population, the impacts of climate change on specific SDGs and their interconnections remain underexplored. This viewpoint article discusses the influence of climate change on the interplay among various SDGs, with a particular emphasis on SDG 6, SDG 7, and SDG 15. By offering insights into these challenges, this article aims to aid in the formulation and achievement of more effective strategies for meeting the SDGs in the Hindu Kush Himalaya (HKH) region.