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The analysis of rainfall variability has significant implications for environmental studies since it influences the agrarian economy of regions such as the western Himalayas. The main objective of this research is to identify future precipitation trends in parts of the Beas River basin using modeled data from three models employed in the Climate Model Intercomparison Project Phase 6. The ACCESS, CanESM, and NorESM models were utilized to obtain modeled meteorological data from 2015 to 2100 (86 years). Data from global climate models were downscaled to the regional level and validated with the India Meteorological Department (IMD). Mention that the modeled data were downscaled from the regional level to the local level. The nonparametric trends test, modified Mann–Kendall, and Sen's slope estimator (Q) were employed to detect the trend and magnitude. Furthermore, the sub-trends of the data series were evaluated utilizing the innovative trend analysis (ITA) approach. Results have shown a significant increasing trend in future timescales, indicating the more frequent extreme events in the basin under all scenarios. The basin has shown a maximum slope of 24.9 (ITA) and 12.2 (Sen's slope).This study findings hold significant implications for policymakers and water resource managers.

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  • Projection of precipitation in future timescale (2015–2100) using CMIP6 model data has been carried out.

  • Trend analysis of precipitation has been carried out using various trend tests, i.e., modified Mann–Kendall, Sen's slope, and innovative trend analysis.

  • The trend tests have revealed a statistically significant increase across the entire basin at a 5% significance level.

  • The results demonstrate a higher frequency/magnitude of extreme events in the Beas River basin.

CMIP6, hydrographic basin, modeled data, precipitation, SSPs, trends
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Climate change is irrefutably harming the environment and negatively impacting the environmental and hydrological processes (Doulabian et al. 2021). Global warming caused by rising greenhouse gas emissions and socioeconomic growth has triggered the climate change phenomena over the planet. Therefore, it signifies one of the paramount challenges that affects billions of people and the natural ecosystem. Various studies have shown a significant increase in extreme events since the mid-19th century (Fanta et al. 2023). In semi-arid and humid regions, precipitation and temperature are regarded as pivotal variables within the domains of climate science and hydrology used to elucidate the scope and magnitude of climatic alterations and fluctuations (Gao et al. 2015).

Past studies have shown that the effects of climate change are the major concern to the ecosystem (Bajracharya et al. 2018). The Intergovernmental Panel for Climate Change (IPCC) has mention in their various reports that there will be a global increase of 2° in surface temperature by the end of the century (van Vuuren et al. 2011). Especially in the Himalayas, transboundary rivers are most vulnerable to the climate change as compared to the other parts of the globe (Bhadwal et al. 2019). The Indus basin with five river basins (Sutlej, Beas, Chenab, Ravi, and Ghaggar), in particular, is becoming increasingly sensitive to climatic changes (Shrestha et al. 2019). Temperature and precipitation are the two climate variables that are widely used for analysis of the climate change (Jaweso et al. 2019). As a result, the alteration of climate variables’ quantity, timing, and intensity has a significant detrimental effect on the economy of developing nations. Therefore, it is crucial for effective management of natural resources to study the spatiotemporal pattern of climatic variables. Since climatic data are not normally distributed, nonparametric trends tests like Mann–Kendall (MK), Sen's slope estimator (Q), and innovative trend analysis (ITA) are widely used for spatiotemporal studies of climate variables (Tabari et al. 2011; Bari et al. 2016; Wang et al. 2016; Bisht et al. 2018; Singh et al. 2020; Kant et al. 2024).

Climate models like the global climate model (GCM) and regional climate model (RCM) are globally used in climate projections studies (Jehanzaib et al. 2020). However, due to the coarser resolution and model formulation of the GCMs, it cannot be used at the regional scale. Hence, with the help of statistical downscaling, GCM models are converted into RCM in the finer scale resolution (Reda et al. 2015; Mishra et al. 2020).The aforementioned research primarily relied on data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Now, the World Climate Research Programme (WCRP) has just begun Phase 6 of the Climate Model Intercomparison Project (CMIP6) (Meehl et al. 2014). The shared socioeconomic pathways (SSPs) emission scenarios are recommended by CMIP6 for climate projection. SSP-based scenarios describe and quantify emissions trajectories and land use changes differently from the representative concentration pathway scenarios used for CMIP5 future forecasts (Riahi et al. 2017). Various SSP scenarios – SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 – have been given under the CMIP6 project (Gidden et al. 2019). According to past studies, a considerable warming in the second half of the 20th century is anticipated, which will further exacerbate the hydrological regime of an agrarian country such as India (Sonali & Kumar 2013). India has varying topography and land–sea contrast with the Indian Ocean and Himalayan Mountain range. Hence, this complex topography and geography of the Indian peninsula have caused climate change at a local-scale convective process (Krishnan et al. 2020). The study reported by Nepal & Shrestha (2015) used climate models to estimate changes in climate variables in the basins, which would affect the hydrological regimes of the Indus, Brahmaputra, and Ganges rivers in the coming years. Furthermore, the Beas river, the major tributary of Indus river, has faced many extreme events recently (Prasad et al. 2016). Various studies have found that the severity and frequency of erratic change in climate variables are going to be increased across the Southeast Asian region. The study conducted by Kant & Meena (2024) in parts of the Beas river basin has shown the vulnerability of the basin to the rainfall extremes.

The Beas river basin, which serves the sustenance of about 1 billion people, requires rigorous planning and management of water resources, considering the perpetually shifting characteristics of the surrounding ecosystem. Nonetheless, there is a gap in the research related to the spatiotemporal pattern of precipitation under the influence of climate change. As per the reports (https://disasterscharter.org/), Beas river basin was severely affected by the flash flood in July 2023. Early preparedness and developing the local-scale adaptation solutions solely rely on correct regional-scale information of climatic data. The principal aim of this research is to employ the modified Mann–Kendall (mMK), Sen's slope (Q), and ITA techniques to analyze the rainfall patterns and their magnitudes spanning an 86-year period (2,015–2,100) in designated areas of the Beas river basin, utilizing data from the latest climate model CMIP6.

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Study area

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The Beas river basin, in Himachal Pradesh, India, is the subject of the current research. It contributes to the Indus river system, which flows through the lower Indian Himalayas (Figure 1) and originates from the Beas Kund glacier. The Beas river has a catchment area of around 11,280 km2 (up to Pong Dam) of which only 778 km2 are covered by perennial ice and glaciers. The elevation ranges from 380 to 6,585 m from average mean sea level and is located between 31°45′ N–32°41′ N latitude and 75°97′ E–77°86′ E longitude. Kant et al. (2022) calculated the geomorphologic and morphometric parameters, i.e., stream order of 1–8, form factor ratio 0.32, shape factor ratio 2.79, and drainage density of 2.52 km/km2, which indicates the region as a flood-prone one. The Winter season of the Beas river basin has an average maximum temperature of 14.1 °C to a minimum of 0.22 °C. The mean annual precipitation is 1,217 mm, of which 70% occurs in the monsoon season from July to September (Bhattacharya et al. 2020).
Figure 1
Study area map.
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Study area map.

CMIP6 data

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In this study, the authors simulated data from three CMIP6 models, i.e., ACCESS, CanESM, and NorESM, from 2015 to 2100 as shown in Figure 2. Coarser resolution of the CMIP6 climate model may not be able to capture the intricate topography and local-scale features, which influences the local-scale rainfall patterns. Furthermore, climate models have inherent biases and uncertainties in their parameterizations of physical processes like cloud formation and precipitation. This can lead to inaccuracies in simulating rainfall trends, particularly for extreme events. Although CMIP6 data offer vital insights into future climate patterns, researchers must acknowledge and tackle these limitations to enable rigorous and dependable trend analysis. This process includes utilizing multimodel ensembles (CMIP5, 6, and 7), implementing various bias correction approaches, downscaling where required, and conducting comprehensive evaluations and integrating climate models with land surface models can enhance the accuracy of extreme events projections by including the impacts of anticipated changes in land use. These general circulation models (GCMs) provide carefully designed settings for studying the effects of different future greenhouse gas emissions on Earth's climate. The downscaled model output is more accurate than the GCM output for direct use in regional-scale studies of climate change. However, climate models have some uncertainties associated with it due to their coarser resolutions.
Figure 2
Flowchart of the methodology.
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Flowchart of the methodology.

Figure 2
Flowchart of the methodology.
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Flowchart of the methodology.

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Further analysis of climate data, i.e., regridding and bias correction, were done using empirical quantile mapping (EQM) and data from the India Meteorological Department (IMD). The IMD dataset were developed using data from 6,955 rain gauge stations (Pai et al. 2014). Climate model data were rescaled to 0.25 × 0.25 (IMD) scale, and bias correction were carried out with the help of a Python code (https://www.spyder-ide.org/). EQM is a very effective technique for mitigating bias in climate modeling (Mishra et al. 2020; Enayati et al. 2021; Robertson et al. 2023). The quantile mapping (QM) technique combines the cumulative distribution function (CDF) of precipitation forecasts with that of observed precipitation and corrects the biases present in the outputs of numerical models (Enayati et al. 2021; Campos et al. 2022; Xue et al. 2022; Li et al. 2024). The QM technique often utilizes either empirical or theoretical CDFs. This work utilizes EQM to rectify the biases in precipitation forecasts by employing an empirical CDF as given in Equation (1):
(1)
where x denotes the precipitation forecasts, xcor denotes the corrected precipitation forecasts using EQM, Ffore(x) denotes the empirical CDF of x, and Ftrans() represents the transfer function of the empirical CDFs between the forecasts and observations.
For this study, SSP245 and SSP585 were selected. SSP2-4.5 (middle of the road) was carefully chosen because of its central position of the key metrics of the mitigation and adaptation challenges (Fricko et al. 2017) and SSP585 (high emission scenarios) was chosen because it is characterized by the challenges (high and low) for mitigation and adaptation (Kriegler et al. 2017). Details of the models used in this study are summarized in Table 1. More information about CMIP6 models can be found in Eyring et al. (2016). Eight stations data from eight stations have been utilized in this study on the basis of proximity to the river as shown by Figure 3. After regridding and bias corrections to the same scale (0.25 × 0.25) as that of the IMD, CMIP6 climate data were compared and the correlation coefficient was calculated using the Taylor diagram as shown in Figure 4. A correlation coefficient ranging from 0.4 to 0.95 has been computed for the bias-corrected climate data, and this variation may be due to the complexity of rough terrain at the regional level.
Table 1

GCMs used for the analysis

ModelOriginResolution (longitude/latitude)
CanESM ESM 2 Canada 2.8 × 2.8 
ACCESS CM2 Australia 1.8 × 1.2 
NorESM Norway 2.5 × 1.8 
ModelOriginResolution (longitude/latitude)
CanESM ESM 2 Canada 2.8 × 2.8 
ACCESS CM2 Australia 1.8 × 1.2 
NorESM Norway 2.5 × 1.8 
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Figure 3
Location of the stations.
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Location of the stations.

Figure 3
Location of the stations.
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Location of the stations.

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Figure 4
Comparison of CMIP6 and IMD datasets used in the study. A, ACCESS; N, NorESM; C, CanESM; 2, SSP245; 5, SSP585.
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Comparison of CMIP6 and IMD datasets used in the study. A, ACCESS; N, NorESM; C, CanESM; 2, SSP245; 5, SSP585.

Figure 4
Comparison of CMIP6 and IMD datasets used in the study. A, ACCESS; N, NorESM; C, CanESM; 2, SSP245; 5, SSP585.
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Comparison of CMIP6 and IMD datasets used in the study. A, ACCESS; N, NorESM; C, CanESM; 2, SSP245; 5, SSP585.

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Methodology

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Nonparametric trend test

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The main difference between parametric and nonparametric trend test is that the former uses only normally distributed data, whereas the latter accepts all types of data (climate data). Here, in this study, MK, Sen's slope, and ITA methods are used for the trend analysis of precipitation, and the complete methodology is shown in Figure 4.

The MK test is a nonparametric test that detects monotonic increasing or decreasing trends. However, the results of the test may contain error if autocorrelation exists in the data series. To avoid this problem, a pre-whitening procedure is performed to remove the autocorrelation in the time series. If the autocorrelation is statistically significant and the coefficient of autocorrelation is r1, then MK test is applied on ‘pre-whitened’ series obtained as given in Equation (2).

MK statistics:
(2)

The MK test was used for the analysis of trends for the whole time period (Mann 1945; Kendall 1948). Here, x1, x2, x3, …, xn are the data points of the series (Mann 1945). The null hypothesis, H0, states that there is no monotonic trend, and this is tested against one of three possible alternatives hypothesis, Hα:

  • I. There is a upward trend (monotonic).

  • II. There is a downward trend (monotonic).

  • III. There is either a (I) or (II).

Sen'sslope estimator is a nonparametric method that is used to identify the magnitude of trend in a time series (Sen 1968). Sen's slope can be calculated as in Equation (3).
(3)
where Xj and Xk are data values of Ti that give the Sen's slope estimator with positive value as increasing and negative value as decreasing trends.

Innovative trends analysis

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The graphical ITA approach was devised by Şen (2012) as a means of doing trend analysis on hydrometeorological data. The initial phase involves the categorization of hydrometeorological time series data into two equal sub-series, followed by the independent ranking of the data in ascending order. The ITA trend test was carried out using MS Excel 2015. This method does not depend upon assumptions such as serial correlation and non-normality of data such as MK and Sen's slope. However, Sen's slope estimator computes the trend of rainfall and temperature using the sample's median value. The ITA method does not contemplate the influence of outliers (higher and lower) of climatic variables (Alashan 2020). However, the ITA is not affected by the sample size, distribution, and autocorrelation. The ITA also considers all the time series data values during trend investigation (Machiwal et al. 2019). The ITA is also a nonparametric statistical technique that does not require a normally distributed sample (Fanta et al. 2023). ITA slope is calculated using Equation (4) and is shown in Figure 5.
(4)
where St is the ITA slope and m2 and m1 are the arithmetic mean of second half and first half of the time series divided by half-length (n/2) of the over whitened series, respectively.
Figure 5
Template of ITA trend test.
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Template of ITA trend test.

Figure 5
Template of ITA trend test.
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Template of ITA trend test.

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The annual rainfall series of eight stations belonging to the Beas river basin were analyzed using three climate models, i.e., ACCESS, CanESM, and NorESM, for the period 2015–2100, as shown in Figure 6. Furthermore, trend analysis has shown the significant increasing trends for most of the stations under SSP245 and SSP585. The study outcomes indicate the basin's vulnerability to more frequent extreme events in future timescales. All the tests are computed at a 95% confidence limit, as shown in Table 2 for ITA and Table 3 for complete analysis of trends tests.
Table 2

ITA trends test (showing first half of the series in y-axis and second half in the x-axis)

 
 
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Table 3

Trends test results (5% significance level)

StationACCESS
CanESM
NorESM
SSP245SSP585SSP245SSP585SSP245SSP585
Aut Z: 0.518 Z: 3.097 Z: 13.62 Z: 13.49 Z: 1.04 Z: 7.60 
τ: 0.013 τ: 0.182 τ: 0.24 τ: 0.30 τ: 0.02 τ: 0.14 
S: 0.22 S: 3.78 S: 4.36 S: 5.44 S: 0.38 S: 2.57 
ITA: −0.22 ITA 9.78 ITA 7.61 ITA: 10.6 ITA: 0.12 ITA: 3.84 
Bhuntar Z: 1.38 Z: 7.5 Z: 12.56 Z: 12.93 Z: 1.38 Z: 7.52 
τ: 0.033 τ: 0.137 τ: 0.24 τ: 0.30 τ: 0.033 τ: 0.13 
S: 0.62 S: 2.58 S: 4.65 S: 5.73 S: 0.62 S: 2.58 
ITA: 0.08 ITA: 10.87 ITA: 7.79 ITA: 11.27 ITA 0.34 ITA 4.10 
Chherna Z: 0.024 Z: 1.89 Z: 12.14 Z: 27.7 Z: 1.64 Z: 7.00 
τ: 0.0008 τ: 0.11 τ: 0.266 τ: 0.46 τ: 0.039 τ: 0.13 
S: 0.0005 S: 2.65 S: 6.61 S: 12.13 S: 0.89 S: 3.35 
ITA: 0.14 ITA: 6.20 ITA: 14.6 ITA: 24.20 ITA: 0.93 ITA: 5.39 
Jawali Z: 1.8 Z: 3.55 Z: 12.64 Z: 13.69 Z: 1.07 Z: 6.30 
τ: 0.04 τ: 0.20 τ: 0.24 τ: 0.32 τ: 0.02 τ: 0.12 
S: 1.14 S: 8.23 S: 9.12 S: 12.4 S: 0.70 S: 5.15 
ITA: 0.80 ITA: 19.93 ITA: 15.3 ITA: 24.9 ITA: 1.10 ITA: 7.86 
Manali Z: 2.87 Z: 4.27 Z: 10.46 Z: 10.26 Z: 2.5 Z: 6.63 
τ: 0.07 τ: 0.244 τ: 0.23 τ: 2.42 τ: 0.05 τ: 0.12 
S: 1.45 S: 6.60 S: 5.07 S: 4.94 S: 1.27 S: 2.80 
ITA: 1.6 ITA: 15.7 ITA: 7.8 ITA: 9.70 ITA: 1.54 ITA: 5.16 
Mandi Z: 0.62 Z: 2.97 Z: 13.52 Z: 16.27 Z: 0.87 Z: 7.25 
τ: 0.016 τ: 0.18 τ: 0.23 τ: 0.34 τ: 0.021 τ: 1.99 
S: 0.45 S: 5.01 S: 5.89 S: 8.10 S: 0.46 S: 3.69 
ITA: −0.10 ITA: 11.8 ITA: 10.45 ITA: 16.1 ITA: 0.16 ITA: 5.20 
Mashiyar Z: 1.55 Z: 3.58 Z: 12.51 Z: 12.5 Z: 1.53 Z: 7.44 
τ: 0.04 τ: 0.20 τ: 0.24 τ: 0.28 τ: 0.03 τ: 1.97 
S: 0.65 S: 4.46 S: 4.29 S: 4.63 S: 0.58 S: 2.65 
ITA: 0.38 ITA: 11.2 ITA: 7.19 ITA: 9.06 ITA: 0.43 ITA: 4.15 
Nadaun Z: 0.69 Z: 3.13 Z: 13.29 Z: 18.30 Z: 1.00 Z: 7.27 
τ: 0.016 τ: 0.19 τ: 0.23 τ: 0.38 τ: 0.023 τ: 0.14 
S: 0.26 S: 4.75 S: 6.39 S: 9.71 S: 0.512 S: 3.85 
ITA: 0.17 ITA: 11.30 ITA: 11.5 ITA: 19.3 ITA: 0.31 ITA: 5.37 
StationACCESS
CanESM
NorESM
SSP245SSP585SSP245SSP585SSP245SSP585
Aut Z: 0.518 Z: 3.097 Z: 13.62 Z: 13.49 Z: 1.04 Z: 7.60 
τ: 0.013 τ: 0.182 τ: 0.24 τ: 0.30 τ: 0.02 τ: 0.14 
S: 0.22 S: 3.78 S: 4.36 S: 5.44 S: 0.38 S: 2.57 
ITA: −0.22 ITA 9.78 ITA 7.61 ITA: 10.6 ITA: 0.12 ITA: 3.84 
Bhuntar Z: 1.38 Z: 7.5 Z: 12.56 Z: 12.93 Z: 1.38 Z: 7.52 
τ: 0.033 τ: 0.137 τ: 0.24 τ: 0.30 τ: 0.033 τ: 0.13 
S: 0.62 S: 2.58 S: 4.65 S: 5.73 S: 0.62 S: 2.58 
ITA: 0.08 ITA: 10.87 ITA: 7.79 ITA: 11.27 ITA 0.34 ITA 4.10 
Chherna Z: 0.024 Z: 1.89 Z: 12.14 Z: 27.7 Z: 1.64 Z: 7.00 
τ: 0.0008 τ: 0.11 τ: 0.266 τ: 0.46 τ: 0.039 τ: 0.13 
S: 0.0005 S: 2.65 S: 6.61 S: 12.13 S: 0.89 S: 3.35 
ITA: 0.14 ITA: 6.20 ITA: 14.6 ITA: 24.20 ITA: 0.93 ITA: 5.39 
Jawali Z: 1.8 Z: 3.55 Z: 12.64 Z: 13.69 Z: 1.07 Z: 6.30 
τ: 0.04 τ: 0.20 τ: 0.24 τ: 0.32 τ: 0.02 τ: 0.12 
S: 1.14 S: 8.23 S: 9.12 S: 12.4 S: 0.70 S: 5.15 
ITA: 0.80 ITA: 19.93 ITA: 15.3 ITA: 24.9 ITA: 1.10 ITA: 7.86 
Manali Z: 2.87 Z: 4.27 Z: 10.46 Z: 10.26 Z: 2.5 Z: 6.63 
τ: 0.07 τ: 0.244 τ: 0.23 τ: 2.42 τ: 0.05 τ: 0.12 
S: 1.45 S: 6.60 S: 5.07 S: 4.94 S: 1.27 S: 2.80 
ITA: 1.6 ITA: 15.7 ITA: 7.8 ITA: 9.70 ITA: 1.54 ITA: 5.16 
Mandi Z: 0.62 Z: 2.97 Z: 13.52 Z: 16.27 Z: 0.87 Z: 7.25 
τ: 0.016 τ: 0.18 τ: 0.23 τ: 0.34 τ: 0.021 τ: 1.99 
S: 0.45 S: 5.01 S: 5.89 S: 8.10 S: 0.46 S: 3.69 
ITA: −0.10 ITA: 11.8 ITA: 10.45 ITA: 16.1 ITA: 0.16 ITA: 5.20 
Mashiyar Z: 1.55 Z: 3.58 Z: 12.51 Z: 12.5 Z: 1.53 Z: 7.44 
τ: 0.04 τ: 0.20 τ: 0.24 τ: 0.28 τ: 0.03 τ: 1.97 
S: 0.65 S: 4.46 S: 4.29 S: 4.63 S: 0.58 S: 2.65 
ITA: 0.38 ITA: 11.2 ITA: 7.19 ITA: 9.06 ITA: 0.43 ITA: 4.15 
Nadaun Z: 0.69 Z: 3.13 Z: 13.29 Z: 18.30 Z: 1.00 Z: 7.27 
τ: 0.016 τ: 0.19 τ: 0.23 τ: 0.38 τ: 0.023 τ: 0.14 
S: 0.26 S: 4.75 S: 6.39 S: 9.71 S: 0.512 S: 3.85 
ITA: 0.17 ITA: 11.30 ITA: 11.5 ITA: 19.3 ITA: 0.31 ITA: 5.37 
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Figure 6
Rainfall projections from 2015 to 2100 for Aut station using the CMIP6 climate model: (a) ACCESS, (b) CanESM, and (c) NorESM (Remaining stations have been added in the Supplementary Materials in adherence to page numbers).
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Rainfall projections from 2015 to 2100 for Aut station using the CMIP6 climate model: (a) ACCESS, (b) CanESM, and (c) NorESM (Remaining stations have been added in the Supplementary Materials in adherence to page numbers).

Figure 6
Rainfall projections from 2015 to 2100 for Aut station using the CMIP6 climate model: (a) ACCESS, (b) CanESM, and (c) NorESM (Remaining stations have been added in the Supplementary Materials in adherence to page numbers).
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Rainfall projections from 2015 to 2100 for Aut station using the CMIP6 climate model: (a) ACCESS, (b) CanESM, and (c) NorESM (Remaining stations have been added in the Supplementary Materials in adherence to page numbers).

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Aut station has shown an increasing trend for the MK test with the max Sen's slope of 5.44 mm/year for the CanESM model under the SSP585 scenario, and ITA test results are also similar with the increasing trend of 10.6 mm/year. ITA has shown decreasing trends for the SSP245 scenario for the ACCESS model, and corresponding to this, both scenarios have shown a slight increase. Furthermore, NorESM has an increasing trend for both scenarios with a max slope of 2.57 under SSP585.

Bhuntar station has shown an increasing trend for all the scenarios, and corresponding to this ITA test has also followed the same trend. The CanESM model under SSP585 has shown a max slope of 5.73 for Sen's slope and 11.7 for the ITA test. A slight increase has been observed for the ACCESS model for SSP245, and SSP585 has a maximum slope of 10.87 mm/year. The CanESM model has shown a significant increase for both the SSPs. The NorESM model has shown an increasing trend for both scenarios. SSP585 under NorESM has a max Sen's slope of 2.58 and an ITA slope of 4.10 mm/year.

Chherna station has shown an increase for each model with a maximum of ITA of 24.2 and Sen's slope of 12.1 mm/year for the CanESM model under the SSP585 scenario. The ACCESS model has shown a slight increase under SSP245, and for SSP585, the max slope of the ITA test and Sen's estimate was observed at 6.2 and 2.6, respectively. Both SSPs have an increasing trend for the CanESM model, with a maximum slope of 24.2 for ITA and 12.4 mm/year for Sen's slope estimator. The NorESM model has followed the same trend with the max value of 5.3 for ITA and 3.3 for Sen's slope 3.3 under SSP585.

Manali station has increased for all models with the maximum increment of 15.7 and 6.6 for the ACCESS model under SSP585. Apart from this, SSP245 in the ACCESS model has a slight increment of 1.4 and 1.6 mm/year as per Sen's slope and ITA test results. Furthermore, the CanESM model has again shown an increase with a maximum Sen's slope of 4.9 and ITA of 9.70 mm/year. The NorESM model has shown an increase for both SSPs with a maximum slope of 5.16 for ITA and 2.18 mm/year for Sen's slope.

Mandi station has shown negative trends for the ACCESS model under SSP245 and positive trends of 11.8 mm per year for SSP585. Mandi station has shown a significant increase with the maximum increase using the CanESM model for SSP585 with a slope of 16.1 and 8.1 mm/year. The NorESM model has shown a maximum slope of 5.2 and 3.6 mm/year for SSP585.

Mashiyar station has shown an increase for all of models under each scenario with the maximum Sen's slope of 4.6 under SSP585 for the CanESM model and a maximum ITA slope of 11.2 mm/year for the ACCESS model under SSP585. Furthermore, Results for the ACCESS model have shown a slight increase for SSP245 and a significant increase for SSP585 with Sen's slope of 4.6 mm/year. The CanESM model has shown an increase for both scenarios with a maximum slope of 9.06 and 4.63 mm/year for ITA and Sen's slope estimator, respectively. The NorESM model has shown a slight increase under SSP245 and a significant increase with a slope of 4.6 and 2.1 mm/year under SSP585.

Nadaun station has similar trends as Mashiyar, showing an increase for all models under each SSP. SSP585 has shown the maximum increase under the CanESM model with the ITA slope of 19.3 and Sen's slope of 9.7 mm/year. Access model has shown the maximum increase under SSP585 with the value of slope for ITA 11.3 and Sen's slope estimator of 4.7 mm/year. The CanESM model under SSP585 has shown a maximum value of 19.3 (ITA) and 9.7 mm/year (Sen's slope). The NorESM model under SSP245 has shown a slight increase, whereas SSP585 has shown a maximum slope of 5.3 and 3.8 mm/year for the ITA test and Sen's slope estimator, respectively.

According to Madhura et al. (2015), the increase in temperatures in the middle layer of the Earth's atmosphere over the last few decades has led to the instability of winds blowing from west to east, causing more fluctuations in weather disturbances in the western regions and an increased chance of heavy rainfall events. The Beas river basin has experienced a notable influence from hydrometeorological disasters, particularly in the form of increasingly frequent extreme weather occurrences such as cloudbursts and flash floods over the past 30 years (Gupta et al. 2024; Kant et al. 2024; Raaj et al. 2024). The outcomes of the study corroborate well with the study findings from various researchers in the Indian subcontinent showing the significant increasing trends of climate variables (Sharma & Singh 2017; Chandrashekar & Shetty 2018; Meena et al. 2019). Gajbhiye et al. (2016) studied the spatial variability of the Sindh river basin using a nonparametric trend test (MK, Sen's slope, and ITA), and the results showed significant increasing trends for each model under both SSP245 and SSP585 scenarios. A similar study was carried out by Sharma & Singh (2017) for 18 districts of Jharkhand state in India using mMK and Sen's slope test, which showed both increasing and decreasing trends in annual rainfall. Kant et al. (2024) used CMIP5 climate models for evaluation of climate variables in Beas river basin and showed significantly increasing trends. The reliability of the trends test can be understood from the fact that diversification of the trend test does not affect the results. Both MK and ITA tests give more or less similar trends. These occurrences can result in the erosion of soil, the occurrence of landslides, and the destruction of agricultural lands and towns.

To summarize, the Beas river basin in Western Himalayas is prone to severe hydrometeorological occurrences as a result of its distinctive geographical and climatic characteristics. The convergence of monsoon disturbances and western disturbances can result in intense rainstorms and flooding, resulting in substantial consequences for the region's infrastructure, economy, and human life (Nagamani et al. 2024). Gaining comprehension and effectively reducing these hazards is essential for ensuring long-term and environmentally friendly progress in the basin.

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In this study, we employed state-of-the-art climate models, specifically the CMIP6 models, including CanESM, ACCESS, and NorESM, to conduct a rigorous analysis of trends in climate data. Our results have shown an increase in the future rainfall events for almost every scenario under ACCESS CanESM and NorESM models at a 5% significance level. The ACCESS model under SSP245 has shown a negligible decrease/insignificant trend in rainfall with ITA slope of −0.22 and Sen's slope of 0.22 mm/year for the Aut station, and ITA of −0.10 Sen's slope of 0.4 mm/year for the Mandi station. In contrast, Chherna, Jawali, and Nadaun have shown the most significant trends with the highest magnitudes. Chherna has shown the highest trend with an ITA of 24.2 mm/year and Sens's slope of 12.13 mm/year for CanESM under SSP585; Jawali station has shown the highest trend for CanESM under SSP585 with an ITA slope of 24.9 mm/year and Sen's slope of 12.4 mm/year. Similarly, Nadaun station has shown the highest trend for CanESM under SSP585 with an ITA slope of 19.3 mm/year and Sen's slope of 9.7 mm/year. The remaining station has shown a moderate increase with positive trends ranging from 0.1 to 16 mm/year for both scenarios.

Our investigation utilized statistical methods such as the MK test and ITA to assess and interpret temporal patterns within the datasets. These methodologies allowed for a comprehensive understanding of climate trends, enabling us to discern significant patterns and variations in the data. The findings presented in this research contribute valuable insights into the field of climate science, offering a robust foundation for further studies and policy decisions in climate change mitigation and adaptation. In addition, fluctuations in precipitation impact the accessibility and timing of water supplies, which is crucial for hydroelectric production, and influence the overall effectiveness and dependability of hydroelectric power generation. However, climate models have some limitations due to varying resolutions. In addition, to overcome these limitations, an ensemble of climate models with latest CMIP7 (warp-cmip.org) and station data can be used for better results.

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Data cannot be made publicly available; readers should contact the corresponding author for details.

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The authors declare there is no conflict.

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