Climatic Trends And Their Implications For Cryospheric Change In The Upper Alaknanda River Basin, Central Himalaya (1981–2024)

The Hindu Kush Himalayan (HKH) region, often referred to as the "Third Pole," holds the largest volume of ice outside the polar regions and serves as a vital water source for over a billion people downstream [1,2]. This region is experiencing climate change at an alarming rate, with observed warming exceeding the global average. This phenomenon, known as elevation-dependent warming (EDW), makes high-altitude ecosystems particularly susceptible to even minor shifts in temperature and precipitation [3,4]. The consequences are already visible in the form of accelerated glacier retreat, permafrost degradation, and alterations in river discharge patterns, posing long-term threats to water, energy, and food security [5] .

The Upper Alaknanda River Basin (UARB), located in the Garhwal Himalaya of Uttarakhand, India, is a critical part of the Ganges headwaters. This basin is characterized by a high concentration of glaciers and glacial lakes, making it a natural laboratory for studying climate-cryosphere interactions. The region has also witnessed catastrophic flood events, such as the 2013 Kedarnath disaster, which, while primarily triggered by an extreme precipitation event, highlighted the region's vulnerability to hydrological hazards [6]. More recently, growing concerns have centered on the risk of Glacial Lake Outburst Floods (GLOFs), where moraine-dammed lakes fail catastrophically, releasing large volumes of water and sediment. Understanding the climatic drivers behind glacier melt and lake expansion is therefore not just a scientific pursuit but a critical requirement for disaster risk reduction [7,8].

Previous studies in the western and central Himalaya have documented rising temperatures and variable precipitation trends [9,10]. However, most basin-specific studies have either focused on a single climate variable, used shorter time series, or lacked the spatial resolution needed to capture the complex orographic controls on climate in a rugged terrain like the UARB. Coarse-resolution studies often fail to resolve the climatic heterogeneity that exists between adjacent sub-basins, which can be the difference between a stable and a rapidly growing glacial lake. Furthermore, while temperature trends are generally consistent, precipitation trends in the Himalaya are known to be highly non-uniform and poorly understood, often showing no significant trend or even a slight decline in winter precipitation from western disturbances [11].

This study aims to fill this gap by conducting a detailed, high-resolution climatic trend analysis for the UARB from 1981 to 2024. By subdividing the basin into 13 distinct hydrological sub-basins, this research seeks to achieve the following objectives: (1) to quantify the long-term trends (magnitude and significance) in Tmean, Tmax, Tmin, and annual precipitation using robust non-parametric statistical methods; (2) to map the spatial heterogeneity of these trends across the basin; and (3) to discuss the implications of the observed climatic changes for glacier health and GLOF risk [12]. The central hypothesis is that the UARB is experiencing a statistically significant warming trend, with daytime temperatures (Tmax) rising faster than nighttime temperatures (Tmin), alongside a spatially variable but overall declining precipitation trend. This combination is hypothesized to be a primary driver of negative glacier mass balance and proglacial lake expansion. The novelty of this work lies in its combined use of high-quality ERA5 and CHIRPS datasets, its sub-basin scale of analysis, and its explicit link to cryospheric response and hazard assessment. The findings presented here will provide the essential climatic foundation for subsequent assessments of glacier dynamics and GLOF vulnerability in the region [13].

2. STUDY AREA

The Upper Alaknanda River Basin (UARB) is located in the Chamoli district of Uttarakhand, India, forming the headwater region of the Alaknanda River, one of the two main source streams of the Ganges River (Figure 1). Geographically, the basin extends from approximately 30°15' N to 31°05' N latitude and 79°20' E to 80°05' E longitude. The basin is entirely within the Central Himalaya and is characterized by extremely rugged topography, with elevations ranging from about 1,500 meters above sea level (masl) in the lower valleys to over 7,700 masl at the summit of peaks like Kamet (7,756 m). The terrain is dominated by high Himalayan crystalline rocks, steep slopes, and deep river valleys carved by glacial and fluvial action [6].

For the purpose of this analysis, the UARB was subdivided into 13 hydrological sub-basins, each representing a major tributary system: Chamro, AnderDip, Khulia Garvya, Kagbhusand, Khriao Ganga, Rishi Ganga, Baibala, Kamet, Pushpavati, Nagthvai, Alaknanda (the main stem), Arwa, and Sarasvati. This subdivision is critical for capturing spatial heterogeneity in climatic trends, as the orographic effect of the Himalaya creates significant local variations in precipitation and temperature lapse rates. The region experiences a typical Himalayan climate, dominated by the Indian Summer Monsoon (June-September), which accounts for approximately 70-80% of the annual precipitation. The winter months (December-March) also receive precipitation in the form of snow from western disturbances. The high-altitude zones of the basin are home to numerous glaciers of varying sizes and a growing number of proglacial lakes, making it a highly sensitive region for climate change impact studies and GLOF risk assessment [14].

Figure 1. Location Map of the Upper Alaknanda River Basin showing elevation zones and the 13 delineated sub-basins.

Figure 2. Spatial Grid Maps of Temperature Trends in the Upper Alaknanda Basin (1981–2024)

Figure 3. Spatial Grid Map of Precipitation Trend in the Upper Alaknanda Basin (1981–2024).

4. RESEARCH METHODOLOGY

The Mann-Kendall (MK) test is a widely used non-parametric method for detecting trends in time series data without assuming a specific statistical distribution [18,22]. It is particularly effective for analyzing climatic and hydrological variables, especially when data contain outliers, missing values, or do not follow a normal distribution [23].

The MK test identifies monotonic upward or downward trends by comparing all observation pairs and computing a test statistic (S), which measures the difference between increasing and decreasing pairs. Significance is then assessed using a normal approximation to calculate a Z-value, determining whether the trend is statistically significant at chosen confidence levels. A key advantage of this method is that it neither requires normality nor assumes linearity, making it robust for noisy environmental datasets [18]. The null hypothesis (H₀) assumes no trend, while the alternative hypothesis (Hₐ) indicates an increasing or decreasing trend.

Sen's Slope Estimator complements the MK test by quantifying the trend magnitude. Unlike linear regression, it calculates the median of all pairwise slopes, offering a reliable estimate even in the presence of outliers or irregularities [24]. The trend magnitude (Q) is computed as:

where xáµ¢ and xâ±¼ are data values at times i and j.

In this study, the MK test was applied to annual time series of temperature (Tmean, Tmax, Tmin) and rainfall for 13 sub-basins over 44 years (1981–2024) using XLSTAT and Python libraries (pymannkendall). A key consideration was the independence assumption of the MK test; climatic data often exhibit serial autocorrelation, which can inflate false trend detection. To address this, modified MK versions accounting for autocorrelation were employed to ensure robust results [25,26]. Overall, the MK test combined with Sen's slope provides a rigorous and interpretable framework for detecting climatic trends, particularly suitable for the Himalayan region [18,25].

5. RESULTS AND DISCUSSION

5.1 Temperature Trend Analysis Results

An analysis of temperature trends for the variables Tmean, Tmax, and Tmin was conducted across the 13 sub-basins of the Upper Alaknanda River Basin from 1981 to 2024. The results indicate a consistent, statistically significant warming trend basin-wide, characterized by spatial heterogeneity in the degree of warming.

5.1.1 Mean Temperature (Tmean) Trends

Across all 13 sub-basins, the mean temperature analysis reveals a consistent warming trend, with every sub-basin showing positive Sen's slope values. This warming is statistically significant at the 95% confidence level (p < 0.05) for all sub-basins.

Figure 4. Mean Temperature Trends in Upper Alaknanda Sub-Basins (1981–2024)

Figure 5. Precipitation Trends in Upper Alaknanda Sub-Basins (1981–2024)

The implications of these climatic trends for glacial lake dynamics and GLOF risk are profound:

• Increased glacier melt: Rising temperatures accelerate glacier retreat, contributing to the expansion of existing glacial lakes and the formation of new lakes.
• Reduced snow accumulation: Declining precipitation reduces the mass input to glaciers, exacerbating negative mass balance.
• Enhanced lake expansion: The combination of increased melt and reduced accumulation leads to higher meltwater runoff, directly feeding proglacial lakes.

Elevated GLOF risk: Larger, faster-growing lakes behind unstable moraine dams have higher probability of catastrophic failure.

CONCLUSION

This study conducted a rigorous, sub-basin scale climatic trend analysis for the Upper Alaknanda River Basin over the 44-year period from 1981 to 2024 using ERA5 (temperature) and CHIRPS (precipitation) datasets, and the application of the Mann-Kendall test and Sen's slope estimator has yielded several definitive conclusions. First, the entire Upper Alaknanda Basin is warming unanimously and significantly, with the strongest warming observed for maximum temperatures (+0.037°C/year, total +1.63°C), followed by mean temperatures (+0.030°C/year, +1.32°C) and minimum temperatures (+0.024°C/year, +1.06°C); the rapid rise in daytime temperatures represents the dominant climatic signal in the region. Second, despite considerable spatial heterogeneity, annual precipitation shows a statistically significant basin-wide decline of -1.62 mm/year, resulting in a total rainfall reduction of approximately 71 mm over the study period, thereby confirming a basin-wide drying trend. Third, the spatial analysis reveals that the most significant decreases in precipitation are concentrated in the Alaknanda, Khriao Ganga, and Rishi Ganga sub-basins, while temperature trends remain spatially uniform; this divergence identifies specific sub-basins as being under the greatest combined climatic stress. Fourth, and most critically, the observed combination of accelerated daytime warming and declining precipitation creates a profound hydrological imbalance that accelerates glacier melt, reduces mass accumulation, and drives the rapid expansion of proglacial lakes, which directly elevates the risk of Glacial Lake Outburst Floods (GLOFs). In conclusion, the Upper Alaknanda River Basin is undergoing rapid and profound climatic change, and these findings provide a robust, quantitative climatic context essential for future studies on glacier dynamics, hydrological modeling, and, most critically, for developing evidence-based early warning systems and disaster risk reduction strategies for GLOF events in the Central Himalaya. Continued monitoring of these climatic trends, especially at the sub-basin scale, remains non-negotiable for the safety and water security of downstream communities.

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