4.1. Historical climate observations and trends in the IHR region

Various studies have confirmed that elevated warming negatively affects the glacio-hydrology of snow and ice-fed river catchments, especially in the high-altitude Himalayan region (Shekhar, Singh 2014; Wester et al. 2019). This impact is also seen in the hydrological balance of many watersheds globally (Xu et al. 2009; Immerzeel et al. 2010). It is now well-established that the Earth’s surface has warmed over the past 100 years, primarily due to anthropogenic activities (IPCC 2021). Changes in many components of the climate system, such as precipitation, snow cover, sea ice, and extreme weather events, have also been observed (NOAA 2020). These changes exhibit remarkable regional variations around the globe. Various studies have shown that high-altitude regions are especially sensitive to climate change because they experience higher mean annual rates of warming (IPCC 2007; Xu et al. 2009; Lesher 2011; Pepin et al. 2015). Studies assessing past temperature trends in the Himalayan region indicate that surface air temperatures have increased in the Himalayas since 1970 (Bhutiyani et al. 2007; Immerzeel et al. 2010). Future predictive studies estimate an increase of 0.01°C to 0.75°C per year by 2050 (Xu et al. 2009; Kraaijenbrink et al. 2017). Bhutiyani et al. (2007) observed historical temperatures from three meteorological stations and reported higher rates of warming during winters in the northwestern Himalayas. Temperature trends observed by various studies are given in Table 1.

Assessment of historical precipitation in the Himalayas shows both positive and negative trends (Ye et al. 2007a; Immerzeel et al. 2010) (Table 2). A study by Nepal (2016) based on model assessments showed variation in snowfall patterns, snowmelt, discharge, and evapotranspiration, estimating that these variations are due to sensitivity to climate change. Studies conducted over the entire Indian Himalayan Region (IHR) suggest that increasing human activities have led to unprecedented changes in the earth’s delicate climate system (Sharma et al. 2019). Changes in temperature and precipitation patterns will significantly impact high-altitude hydrology in the region. The net hydrologic impacts of climate change on high-altitude lakes have also been observed, showing dependence on their proximity to glaciers, lake elevation, and whether climate changes are predominantly in temperature or precipitation (Thayyen, Gergan 2010; Ahmed et al. 2022). Our understanding of hydro-meteorological regimes, particularly at the sub-basin scale, is limited by the lack of significant data and information, and knowledge gaps. In the western Himalayas, numerous studies have been conducted to examine recent climate change, project future changes, and model the impacts on the hydrological regime and water availability (Immerzeel et al. 2013; Hasson et al. 2017; Hasson et al. 2019; Lutz et al. 2019; Dahri et al. 2021). However, accurate assessments are challenging because in situ observations are very scarce and the strong influence of the innately complex climate systems interacting with very high orographic features (Dahri et al. 2021).

4.2. Evidence of temperature rise, precipitation changes, and extreme events

Temperature can represent the energy exchange between longwave and shortwave radiation; therefore, temperature change is considered a good indicator of changing climate globally (Bhutiyani et al. 2007). Meteorological observations worldwide show an increase of 0.85°C from 1885 to 2012 and an increasing trend of 0.05°C per decade in global mean temperature (Diaz, Bradley 1997; Easterling et al. 1997; Barry 2001; Folland et al. 2001; IPCC 2013; NOAA 2020). The warming trend is not uniform globally, with inconsistencies observed in mountainous regions due to the unavailability of sufficient long-term in situ observations and complex topographic conditions (Pepin et al. 2015). Studies based on instrumental data suggest that the Himalayan region has experienced a warming trend more than twofold greater than the global average (Shrestha et al. 1999; Kothawale, Rupa Kumar 2005; Bhutiyani et al. 2007; Kothawale et al. 2010; Krishnan et al. 2020). The temperature trend has increased in the past decades, with this unusual warming trend attributed to anthropogenic greenhouse gas (GHG) emissions and deforestation, which are suggested to be dominant climate forcing agents (Shrestha et al. 1999; Kothawale et al. 2010; Zemp et al. 2019). In a study to understand past temperature trends, increases in average annual temperature and average annual maximum temperature have been reported (Huddleston et al. 2003; Bhutiyani et al. 2007; Palazzi et al. 2019). This increase is several-fold higher than the rise in the global average temperature. Another study based on previous meteorological data sets from the Indus and Ganges basins suggested rises of 0.50°C and 0.44°C, respectively (Singh et al. 2008a). A study conducted over the western IHR for the period from 1971 to 2007 suggested a warming rate of 0.46°C per decade, higher than the 0.20°C per decade observed for the rest of India (Kothawale, Rupa Kumar 2005; Sharma, Goyal 2020).

In recent decades, several studies have been conducted for the northwestern Himalayas using reanalysis datasets; the result shows an increased rate of warming in the region (Dimri, Dash 2012; Kumar et al. 2014). A study conducted in the central Himalayas for the period 1967-2007 using a meteorological dataset suggests an increase in the annual mean temperature of 0.49°C (Singh et al. 2013). A study conducted by Singh et al. (2016) found that precipitation is a relatively less-studied parameter for assessment of climate change. Since the precipitation data is limited no visible trend is observed for IHR precipitation, unlike temperature. Few studies suggest that, in comparison to moderate rainfall events, extreme rainfall events have increased significantly in IHR. Whereas in the northwestern Himalayas in the Indus basin (IHR) a significant increase in precipitation has been observed (Shrestha et al. 2000; Goswami et al. 2006; Nandargi, Dhar 2011). In another study conducted by (Bhutiyani et al. 2009) in northwestern IHR using the meteorological dataset from 10 weather stations suggests a decrease in annual and monsoon precipitation and an increase in winter precipitation. Whereas another study (Dimri, Dash 2012) reported a contrasting trend, indicating an insignificant decrease in winter precipitation. The study conducted for Uttarakhand & Himachal Pradesh shows a significant decrease in annual precipitation. Whereas the study for the western (Jammu & Kashmir, J&K) and eastern parts of IHR shows an increasing trend in precipitation (Singh, Goyal 2016). Studies conducted by (Kothawale et al. 2010), have also suggested temperature variations in the region. The temperature trends across the central Himalayan region (Nepal) exhibit significant spatial variation. There is a visible warming trend over the majority of the Himalayan and middle mountainous areas. On the contrary, the Terai and Siwalik regions display comparatively smaller warming trends, and in some cases, there are even observable cooling trends. This spatial distribution of maximum temperature trends, as highlighted by Shrestha et al. (1999), underscores the complexity of climate patterns in the central Himalayas, with distinct warming patterns in mountainous terrains and varying trends in the lowland regions. A study conducted in the central Himalayas (Nepal) from 1971 to 1994 using the temperature data from 49 stations shows a warming trend of 0.068 to 0.128°C in most of the middle mountain and Himalayan regions. The study revealed that the southern plain region shows a warming trend of 0.038°C per year, along with increased variation in rainfall patterns in the region. Global warming and its impact on the hydrological cycle and nature of hydrological events have posed an additional threat to the region (Mall et al. 2006).

Extreme events like flash floods, flooding from monsoon rainfall, lake outbursts, landslides, and avalanches may be induced by climate change (National Research Council 2012). Mainly, the monsoon flood is caused by heavy rains, but other factors such as snowmelt and changes in land use may enhance the scale of the event, making it more disastrous (Pramanik, Bhaduri 2016). The water resources of snow-dominated regions such as the Himalayas are adversely impacted by climate change (Barnett et al. 2005; Piao et al. 2010; Chen et al. 2016; Roe et al. 2017; Huss, Hock 2018; Chandel, Ghosh 2021). Increasing warmth and humidity in daily weather, erratic rainfall patterns, extension of the normal winter season, and increasing incidences of floods, drought, and cyclones are some of the common observations, reflecting changes in normal atmospheric circulation and climate change predictions (IPCC 2007). Many studies have been conducted to observe a particular trend in precipitation patterns over this region. The results suggest that precipitation trends are sensitive to location and the methodology used for assessment, along with the elevation of the meteorological stations that have been used for collection of data. It has been observed that the region has sparse meteorological stations, and the available stations are at low altitudes (Palazzi et al. 2013). This situation results in low-altitude bias, and snow measurements at all altitudes are typically fairly error-prone (Winiger et al. 2005; Hunt et al. 2020).

4.3. Impact of climate change on cryosphere dynamics

Himalayan glaciers are valley-type glaciers and account for about 70% of non-polar glaciers (Nandy et al. 2006). Earlier studies have estimated that an area of about 32,000 km² is permanently covered by ice and snow in the Himalayas (Negi 1991). According to the Geological Survey of India, there are 9,575 glaciers in the Indian Himalayas, distributed among the three river basins – Indus, Ganga, and Brahmaputra. Most of these glaciers (approximately 90%) are small to very small, being less than 5 km in length and smaller than 5 km² in area. Only a few glaciers, such as Siachen, Gangotri, Milam, and Zemu, are larger than 10 km². The IHR serves as an important source of freshwater for the densely populated areas downstream. The region is particularly vulnerable to climate change because it is highly dependent on snow and glacier-melt runoff to meet its freshwater demands (Singh, Goyal 2016). Due to their latitudinal and altitudinal positions, the Himalayan glaciers react to even minute changes in temperature and precipitation patterns (Kumar et al. 2009; Messerli et al. 2009; Immerzeel et al. 2010; Bajracharya, Shrestha 2011). In recent decades, climate change has induced various glacio-hydrological changes, such as changes in terminus and areal extent of the glaciers, glacier mass balance, and streamflow in downstream areas. The ablation and accumulation of glaciers are directly affected by changes in temperature and precipitation patterns. Air temperature is crucial since it is a major component of energy exchanges that control snow and ice melts. Among various parameters for assessing climate impact, glacier length is a major parameter for observing such changes. The recession in length or snout of a glacier depends on the increase in temperature, with the warming trend shifting the snout and ELA towards higher altitudes. Pandey and Venkataraman (2013) conducted a study from 1980 to 2010 over the western IHR (Chandra-Bhaga basin) and found a glacier area loss of 2.5%. Another study conducted in the Garhwal Himalayas from 1968 to 2006 reported an overall reduction in glacial area from 4.6% to 2.8% (Bhambri et al. 2011). An increased rate of retreat was observed from 1990-2006. A study conducted by Basnett et al. (2013) in the Teesta River basin showed a reduction in area from 3.3% to 0.8% from 1989-1990 to 2010. Another study by Racoviteanu et al. (2015) in the Sikkim Himalayas observed an areal loss from 20.1% to 8% from 1962 to 2000. Studies in various parts of the IHR suggest a reduction in glacierized area, with an increased rate after 1990 (Bahuguna et al. 2007; Berthier et al. 2007; Chalise et al. 2003; Ye et al. 2007b). A survey conducted by Ageta et al. (2003) over Himalayan glaciers in India, Bhutan, and Nepal reported a concerning trend: nearly all measured glaciers exhibited signs of retreat. This observation is particularly pronounced for summer-accumulation type glaciers in regions characterized by elevated precipitation and warmer temperatures, as highlighted by Ageta et al. (2003). Data from the past 100 years generated from glacier studies show that the glaciers in the Himalayas have been, by and large, shrinking and retreating continuously (IPCC 2013; Pramanik, Bhaduri 2016). The findings underscore the vulnerability of Himalayan glaciers to climate change, emphasizing that not only are these glaciers retreating, but the rate of retreat is notably accelerated in certain glacier types and geographical settings.

A few studies also concluded that the changes are observed only in glacier length and area, showing only the horizontal variation and not representing any volume change in the amount of ice present in the glacier. Hence, it cannot be concluded that observed changes are the effects of climate change; other underlying factors may be responsible for such variations. In addition, there can be several other factors that can change the length of a glacier, including bed topography, aspect, and slope, which primarily control the flow and dynamics of glaciers and hence the changes that occur. Apart from these factors, debris cover, a characteristic of Himalayan glaciers, can also be responsible for anomalous behaviour of the snout position of the glaciers (Singh et al. 2016). It has been evident that variation in precipitation and temperature due to climate change over the Himalayan region plays a dominant role in influencing glacier sensitivity and affects the melting process. Glaciers in the Himalayas are under the influence of westerlies that show a decreasing trend from west to east, along with the influence of the Indian and Asian monsoons, showing a decreasing trend from the eastern Himalayas to the west (Burbank et al. 2003; Bookhagen, Burbank 2006; 2010). This complex climate diversity results in a varying pattern of glacier response to climate variables (Fujita, Nuimura 2011; Scherler et al. 2011; Bolch et al. 2012; Gardelle et al. 2013; Gardner et al. 2013; Tayal, Sarkar 2019). Therefore, it is important to understand the impact of climate change on glacial regions. To understand these impacts, it is essential first to identify the probable issues in terms of climate change drivers and human dependence on Himalayan resources. Once these issues are understood, probable solutions can be assessed by applying various disciplinary sciences and developing suitable approaches that aid in a better and more robust understanding of such systems (Fig. 2).

Fig. 2.

Climate change in the Himalayas – issues and probable solutions.

5.1. Glacier melt contributions to river discharge

Glaciers, by acting as buffers, play a crucial role in maintaining the hydrological cycle and ecosystem stability. This role is well documented by studies conducted in the region. Glaciers regulate the water supply from mountains to the plains during both dry and wet spells (Pramanik, Bhaduri 2016). Under a warming climate, glaciers and seasonal snow cover experience changes in their water storage capacity. A negative mass balance leads to an increased contribution of meltwater to river flows. However, this increased melt is not permanent; as the glacier volume decreases, the total meltwater generated from glaciers will ultimately decrease. Various studies have established that, depending on the state of glacier retreat, a warming climate may lead to either rising or decreasing river flows (Pellicciotti et al. 2010; Ragettli et al. 2016). In a related study, Ren et al. (2007) employed three distinct coupled General Circulation Models (GCMs) to estimate glacier melt rates specifically in the Greater Himalaya (GH). Their modeling exercise estimated a spatially averaged glacier depth reduction of approximately 2 meters for the 2001-2030 period in areas located below 4000 meters. Averaged over the entire GH region, the melting rate is accelerating at about 5 mm per year (Lesher 2011). This modeling approach provides valuable insights into dynamic changes within the glacier systems, offering a quantitative understanding of the thinning process. Furthermore, a study conducted in the Koshi River basin provides insights on hydrological implications of glacier melt. The research indicates that, on average, snow and glacier melt contribute approximately 34% to the total discharge annually. Notably, during the pre-monsoon season (March to May), this contribution rises significantly to 63%. Projections from the model used in this study suggest a potential 13% increase in annual discharge by mid-century, followed by a slight decrease in subsequent years. These anticipated changes in discharge underscore the intricate relationship between glacier melt and river basin hydrology, with potential implications for downstream water resources and ecosystems (Nepal 2016). Studies conducted by Immerzeel et al. (2013) and Lutz et al. (2014) have contributed valuable insights into the complex dynamics of water resources in the Himalayan region. Their research indicates that the combined effects of increasing precipitation and glacier melt are expected to lead to an overall increase in annual runoff for Himalayan watersheds. Thus, at least in the near term, there may be a positive impact on water availability in these regions. According to the findings, water availability is projected to remain relatively constant until mid-century. However, it is important to note that stability in overall water availability does not necessarily imply a consistent distribution throughout the year. Extreme events linked to climate change are anticipated to have a notable impact on the seasonal water availability in downstream areas (IPCC 2007; Nepal 2016).

These projections underscore the intricate nature of the hydrological system in the Himalayan region, where the interplay of increased precipitation, glacier melt, and the influence of climate change-induced extremes contributes to a nuanced understanding of water resource dynamics. Balancing these factors becomes crucial for sustainable water management, particularly in the context of potential challenges posed by variability in seasonal water availability in downstream areas.

5.2. Alterations in snowmelt patterns and timing

A study conducted by Gusain et al. (2014) from 1976 to 2011 using the station data showed that the majority of stations below 4000 m recorded declining trends in seasonal snowfall. Similarly, a study from 1987-2009 showed that the majority of stations at 3250 m elevations also showed a decreasing trend in seasonal snowfall (Singh et al. 2015). Another study conducted by Nepal (2016) observed that snowfall is projected to decrease substantially with rising temperature, the basin will lose snow storage capacity, and there will be a marked decrease in snowmelt runoff from non-glaciated areas. Various studies have shown that the contribution of glaciers (snow and glacier melt) in IHR increases from east to west (Barnett et al. 2005; Alford, Armstrong 2010; Armstrong 2010; Immerzeel et al. 2010; Racoviteanu 2011; Nepal 2016).The escalating contribution of glacier melt to the total volume of meltwater raises substantial concerns, particularly when contrasted with the replenishable nature of snowmelt. Unlike the annual renewal of snowpack through seasonal snowfall, glaciers evolve over extended periods and are not easily replenished (Barnett et al. 2005). This fundamental difference underscores the vulnerability of glacier-dependent water resources and highlights the need for a diligent approach to water management in the face of changing climate conditions. In the context of the diverse climatic patterns within the IHR, it becomes evident that the relative contributions of rain, snowmelt, and glacier melt to river discharge will vary significantly. This variability is anticipated, given the wide range of climates present in the region (Pramanik, Bhaduri 2016). Each river system within the IHR is likely to exhibit distinct hydrological responses based on its specific geographical and climatic characteristics. Numerous studies have emphasized the critical role of temperature change in shaping the contributions of snow and glacier melt to river discharge. The impact extends beyond just the quantity; it also affects the form of precipitation, distinguishing between liquid and solid forms, and the timing of their contributions to the overall discharge. These findings collectively highlight the intricate interactions between climate variables and water resource dynamics in the Himalayan region, emphasizing the need for comprehensive assessments to inform adaptive strategies for sustainable water management in this crucial and vulnerable environment.

6.1. Types of hydrological models

According to (Moradkhani, Sorooshian 2008), a model is a simplified representation of a real-world system. The best model is the one that gives results close to reality with the fewest parameters and least model complexity. Models are mainly used for predicting system behavior and understanding various hydrological processes. A model consists of various parameters that define the characteristics of the model. A runoff model can be defined as a set of equations that helps in the estimation of runoff as a function of various parameters used for describing watershed characteristics. The two important inputs required for all models are rainfall data and drainage area. Along with these, watershed characteristics such as soil properties, vegetation cover, watershed topography, soil moisture content, and characteristics of groundwater aquifers are also considered. Hydrologic models are simplified, conceptual representations of a part of the hydrologic cycle.

These models are primarily used for hydrologic prediction and for understanding hydrologic processes. They are currently considered important and necessary tools for water and environmental resource management. Three major types of hydrologic models can be distinguished (Fig. 4). The major hydrological models used in various studies and details of their applications are listed in Table 3.

Fig. 4.

Classification of hydrological models along with their characteristics (Source: Devi et al. 2015).

6.2. Integration of remote sensing and GIS data in modeling

With the advent of various GIS-based hydrological models, remote sensing and GIS-based techniques are being integrated widely into studies. To understand the variation of glaciers (snow cover, ELA, snout, GLOFs, etc.) satellite imagery is being used in the studies. Gardelle et al. (2011) used Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery to compare the evolution of lakes throughout the HKH and found that in the wetter, more eastern region, which is influenced by the Indian summer monsoon (India, Nepal, and Bhutan), glacial lakes are more numerous, larger, and are growing in size, while in the more western, drier region (Pakistan and Afghanistan), glacial lakes are decreasing in size. Glacier length is one of the most widely available datasets for the Himalaya (Lesher 2011). This parameter can be easily reported using remote sensing observations. Uttarakhand has the maximum number of observations of changes in glacier length. All the glaciers are retreating with an average rate of about 18 m/year (Singh et al. 2016). Various studies include DEM datasets for snow cover estimation. Surprisingly, apart from the shrinkage reported by various glaciological studies using remote sensing, a study by Bahuguna et al. (2014) using the same method suggests that 86.8% of 2018 glaciers mapped in Karakoram, Himachal Pradesh, Zanskar, Uttarakhand, Nepal, and Sikkim regions of the HKH were stable not retreating between 2000/2001 and 2010/2011 (Singh et al. 2016). Remote sensing has been used extensively for mapping changes in the area covered by glaciers and snow in the IHR. Mapping of Chhota Shigri, Patsio, and Samudra Tapu glaciers in the Chenab basin, Parbati glacier in the Parbati basin, and Shaune Garang glacier in the Baspa basin, has reported an overall deglaciation of 21% from 1962 to 2001 (Kulkarni et al. 2007). A study from western IHR suggests a loss of glacier area of 19 and 9% from two nearby river basins (Brahmbhatt et al. 2012). Moreover, various distributed grid-based hydrological models (VIC, MIKESHE) require DEM datasets, LULC, Soil maps, and glacier area extent. All of these parameters are computed with the help of remote sensing & GIS techniques. The Soil and Water Assessment Tool (SWAT) has been widely accepted as a suitable model for hydrological analysis in Himalayan watersheds (Swain et al. 2022). As a semi-distributed model, SWAT requires elevation-wise area information for glacierized and non-glacierized regions, which have been derived in previous Himalayan studies using GIS spatial analysis. These applications highlight the importance of accurate representation of elevation-dependent land cover and cryospheric processes in mountainous basins.

9.1. Uncertainties in modeling climate change impacts

Uncertainties in modeling studies and delicate hydro-meteorological investigations have posed a serious constraint in assessing the impact of climate change on the hydrometeorology (Dahri et al. 2021). Numerous studies have attempted to assess and model the hydrological regime of the high-altitude western Himalayas (Archer 2003; Hasson 2016; Lutz et al. 2016; Hasson et al. 2019), yet there are significant uncertainties, and our understanding of the basin's hydrological regime remains poor. The greatest uncertainties are associated with the use of non-representative (mostly underestimated) precipitation in the higher altitude areas, which receive the bulk of precipitation and make major contributions to streamflow. In the case of a glacierized basin that is predominantly a snow and glacier-fed system, the temperature-index or degree-day based hydrological models may not accurately simulate the prevailing energy balance, which is the main driving force for streamflow generation from the snow and glacier systems. Their application is mainly limited to simulation of melt rates at daily or coarser resolution (Hock 2003; Pellicciotti et al. 2005). Assessment of the future hydrological regime is primarily constrained by very high uncertainties in the GCM outputs for the study domain. Specific knowledge of IHR with respect to climate change indicators is lacking due to both the inaccessibility of the location and the insufficient theoretical attention given to the complex interactions of spatial scales in weather and climate phenomena in mountain areas.

The Ministry of Environment & Forest (MoEF) discussion paper reported that over the past 100 years, the Himalayan glaciers have shown an erratic pattern. A study conducted for Sonapani glacier revealed that the glacier has retreated by about 500 m during the past 100 years, whereas another study for Kangriz glaciers has not shown any reasonable retreat in the same period; the glacier has retreated less than an inch. A study reported that the Siachen glacier increased in length by 700 m from 1862 to 1909, retreated from 1929 to 1958 (400 m), and for the past 50 years, negligible retreat has been observed. Gangotri glacier showed a significant and rapid retreat of 20 m per year, but has slowed more recently (National Research Council 2012). A similar trend has been observed for Bhagirath Kharak and Zemu glaciers. There are numerous underlying factors, physical features, and a complex interplay of climatic factors that may be responsible for such contrasting behavior. Besides such statements, there are a few studies that have reported that one of the major impacts of climate change over the glacierized region has been an increase in the formation of glacier lakes. The remote nature and lack of meteorological stations and stream flow data in many high-altitude lakes in the Himalayas limit the availability of data required to model the water balance of a watershed or lake, and therefore, few lakes in the Himalayas have been modeled (Lesher 2011). To understand the complex glacier and hydrology interaction, glacio-hydrological models are considered indispensable tools. This coupled approach makes it easy to understand the characteristics of a catchment and its response to climate. But this modeling approach also has its fair share of uncertainty and complications that make the application of such models at high altitudes a challenging task. They are subject to several factors that include: (1) a lack of representative data to force the models (Huss et al. 2014; Pellicciotti et al. 2014), (2) simplifications in model structure due to insufficient process understanding and the scarcity of detailed information about glacio-hydrological processes (Huss et al. 2014), and (3) parametric uncertainty due to insufficient quality or quantity of data for model calibration and validation (Ragettli et al. 2013; 2015).

Given the multitude of control factors, often acting at relatively fine scales, current projections of global glacier change models do not adequately constrain the mechanisms underlying the variations in river flow from high-altitude catchments. It is therefore uncertain whether such models can correctly capture the response times and the magnitudes of future changes. (Marzeion et al. 2012; Huss, Hock 2015; Ragettli et al. 2016). Due to a lack of observed meteorological data and extremely complex orography interacting with synoptic-scale atmospheric circulation, it can be concluded that significant uncertainties still exist to hamper precise representation of the basin's hydrometeorological regime (Andermann et al. 2012; Lutz et al. 2014). The greatest uncertainties in the higher-altitude areas are associated with the spatiotemporal distribution of precipitation and the dynamics of glacial ice mass. Structural limitations of the climate and hydrological modeling frameworks, and lack of reliable and representative observations for bias corrections and validations also impose limitations.

9.2. Data scarcity: implications for model uncertainties and calibration

Hydrological modeling in data-scarce regions such as the Himalayan river basins is fundamentally constrained by the limited availability of reliable meteorological observations. Basic variables such as precipitation, temperature, humidity, and snow parameters are either sparsely measured or completely absent across high-altitude regions due to harsh terrain, inaccessibility, and logistical challenges (Immerzeel et al. 2014; Nepal et al. 2017). As a result, modelers often rely on interpolated or gridded meteorological datasets to drive hydrological models. While these datasets offer spatially continuous coverage, they come with inherent uncertainties related to coarse resolution, interpolation algorithms, and biases arising from sparse ground-truth observations (Huffman et al. 2010; Karger et al. 2017). Interpolated gridded temperature and precipitation products are commonly applied in large-scale hydrological models; however, their quality is typically evaluated only by cross-validation using the limited station network available (Freudiger et al. 2016). This limitation raises critical concerns regarding their representativeness in high-altitude catchments where observation density is extremely low (Freudiger et al. 2016). For example, precipitation in the Himalayas exhibits considerable spatial variability due to elevation, aspect, and orographic effects and patterns that are often poorly captured by coarse-resolution datasets. Conventional gauge-based rainfall estimates may be accurate locally, but interpolations to ungauged regions in complex terrain often yield approximations that deviate substantially from actual rainfall (Bookhagen, Burbank 2010; Palazzi et al. 2013).

Remote sensing products and global reanalysis datasets have emerged as valuable substitutes in data-sparse regions such as the Indian Himalayan Region (IHR). Studies increasingly utilize products such as ERA-Interim, ERA5, JRA-55, and APHRODITE for hydrological simulations. ERA-Interim and JRA-55 precipitation has been used along with observed datasets. It has been reported that these gridded datasets overestimate precipitation because of spatial variability, and this effect transfers to streamflow predictions. JRA-55 produced higher peaks, and ERA-Interim results were comparatively closer to station-driven simulations. Similar findings have been reported across the Himalayas, where reanalysis datasets frequently overestimate winter precipitation and underestimate convective summer rainfall, adversely affecting calibration and validation outcomes (Soncini et al. 2017; Sabin et al. 2020). These biases directly translate into uncertainties in simulated streamflow, snowmelt contributions, and extreme event estimation. Consequently, hydrological models such as SWAT, VIC, and HBV require careful bias-correction and multi-dataset intercomparison to ensure reliable performance in such regions (Ménégoz et al. 2013; Dahri et al. 2018). The scarcity of observed hydro-meteorological data in the IHR continues to be a major source of uncertainty in hydrological modeling. Improving station density, integrating multisource products, and adopting calibration strategies such as regionalization or multi-objective optimization remain essential for reducing uncertainties and strengthening model reliability.

9.3. Application of bias-corrected and downscaled climate projections in Himalayan hydrologic models

The complex spatial climatic characteristics of the Himalayas pose significant challenges for regional climate models (RCMs), making some form of correction essential before applying RCM simulations for hydrological assessments. In recent years, simple bias-correction techniques have been complemented or replaced by more advanced methods. Several studies across the IHR have applied both simple (linear scaling) and more complex (quantile mapping) bias-correction techniques for temperature and precipitation. Bias correction substantially improved the wetter and colder RCM estimates, and hydrological model responses further demonstrated its importance. However, many studies observed no major difference between the outputs of linear scaling and quantile mapping, because both techniques exhibit almost identical performance, suggesting that simpler methods may be sufficient in certain Himalayan applications (Shrestha et al. 2017). SWAT has also been confirmed as a suitable model for hydrological analysis in Himalayan basins at both monthly and daily time scales (Shrestha et al. 2017; Swain et al. 2022). Further work projecting future hydrological changes using a multi-GCM, multi-bias correction method (BCM) and multi-parameter ensembles, identified three major sources of uncertainty: GCM selection, bias collection methods, and model parameterization. Studies highlight that GCM-related uncertainty can be substantially reduced through bias correction, indicating that the number of GCMs used becomes less critical once they are corrected. Although using more GCMs is generally recommended, it also incurs significant computational cost. Notably, Wang et al. (2020) found that uncertainty trends remained largely unchanged when more than two GCMs were included. Similar conclusions were drawn in recent analysis over the western Himalayas, demonstrating that uncertainty from GCMs, bias-correction techniques, and hydrological model structure collectively influences future hydrological projections (Mehboob, Kim 2024).

A few studies have applied CMIP6 global climate model outputs in Himalayan regions, where projections have been statistically downscaled using tools such as SDSM, and bias-corrected with CMhyd to improve their suitability for hydrological applications (IPCC 2021; Alasqah et al. 2025). Although downscaling provides a reasonable fit, CMIP6 models still exhibit uncertainty-related biases, particularly in precipitation, due to strong variability in spatial patterns and emission scenarios. Projections generally indicate rising temperatures, with potential decreases in rainfall in arid regions and increases in humid areas, offering useful insights into future water resource conditions. However, studies across the Himalayas have shown that non-linear bias correction techniques significantly improve precipitation estimates, especially for extremes, which are often underrepresented in widely used gridded datasets. Overall, while CMIP5 and CMIP6 projections are valuable for climate and hydrological assessments in the Himalayas, their effective use requires robust downscaling and bias correction to reduce persistent uncertainty in complex mountain terrain (Alasqah et al. 2025).

11.1. Future research opportunities

Climate change is expected to exert significant impacts on snowmelt and glacier runoff across the Himalayas (Brock et al. 2000; Immerzeel et al. 2014; Ragettli et al. 2016). Minimum temperature, an important control on precipitation type in mountainous terrain, is among the least studied parameters in the IHR, with limited long-term observations except for a few studies (Bhutiyani et al. 2007). The rapid rise in minimum temperature observed, particularly in the western IHR, warrants continued investigation using extended instrumental records. Future studies should further examine the relationships between climatic variables, glacier discharge, and basin-scale hydrology, along with improved projections of streamflow variability (Singh et al. 2016). High-resolution, process-based modeling efforts remain essential for capturing the complex hydro-climatic behaviour of high-altitude catchments (Viviroli et al. 2011; Fernández, Mark 2016; Ragettli et al. 2016). Strengthening coordination among research organizations is also necessary to guide climate adaptation, forest conservation, and soil erosion control efforts across the region (Pramanik, Bhaduri 2016).

Glaciological studies continue to face major challenges from limited accessibility and sparse field data; hence, most predictive assessments still rely on secondary datasets. Establishing long-term monitoring stations and standardizing measurement protocols are critical for reducing uncertainties in hydrological research outcomes (Pramanik, Bhaduri 2016). Despite advances in climate modeling, substantial uncertainty persists in simulating current and future climate processes in the Himalayas, marking this as an ongoing research priority (Knutti, Sedláček 2013; Dahri et al. 2021).

In addition, emerging opportunities lie in applying machine learning (ML) and hybrid physics-ML models for snow cover mapping, glacier mass balance reconstruction, streamflow forecasting, and gap-filling sparse hydro-meteorological datasets. ML approaches – when combined with physically based models and remote sensing inputs – can significantly enhance predictive skill in data-scarce Himalayan basins and help reduce model uncertainties (Ouyang et al. 2025).

11.2. Recommended models for Himalayan basins

Comparing fully distributed and semi-distributed hydrological models such as SPHY, HBV, and HECHMS provides a robust basis for discharge estimation in Himalayan catchments. Semi-distributed models like HBV-Light and HEC-HMS often outperform SPHY in data-scarce regions because their parameterization requirements are relatively modest, making them more suitable for the limited observational network available. Future simulations can be further improved by integrating satellite-based products and reanalysis datasets to supplement sparse in situ observations (Abbaspour et al. 2015; Arora et al. 2024). With ongoing climatic changes affecting snow and glacier energy balance, the need for enhanced ground-based measurements and models that can incorporate such observations is increasingly emphasized (Rohrer et al. 2013; Ragettli et al. 2015).

While semi-distributed glacio-hydrological models have yielded satisfactory results across many Himalayan basins, the region's high spatial heterogeneity and sensitivity to climate forcing underscore the importance of adopting coupled approaches. These approaches integrate field observations, physically based modeling, and advanced Artificial Intelligence (AI), Machine Learning (ML)/Deep Learning (DL), capture complex, nonlinear processes, and handle diverse, multi-source datasets: capabilities that traditional models often lack (Maity et al. 2024). Emerging research demonstrates promising applications of AI/ML/DL in precipitation forecasting, evapotranspiration estimation, groundwater variability assessment, and extreme event prediction (floods or compound events).

Furthermore, the adoption of Explainable AI (XAI) and transfer learning can enhance interpretability, improve cross-basin adaptability, and support stakeholder trust in these advanced models (Ouyang et al. 2025). Overall, integrating AI/ML/DL with conventional process-based hydrological models represents a transformative shift toward more accurate, scalable, and reliable hydrological predictions for the Himalayan region.