The Analysis of Hydrometeorological Characteristics in the Yarlung Tsangpo River Basin

Abstract

Understanding the hydrometeorological processes of the Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau, is crucial for effective water resource management and climate change adaptation. This study provides a comprehensive analysis of the basin’s hydrometeorological characteristics using long-term observational data from six representative stations across the upper, middle, and lower reaches. We examined trends, periodicity, variability, and correlations of key elements—precipitation, temperature, evaporation, and discharge—employing methods such as linear regression, Mann–Kendall tests, wavelet analysis, and Kendall rank correlation coefficient tests. The results indicated that precipitation and discharge exhibited non-significant upward trends, with fluctuations across decades, while temperature showed a significant increase of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, and a negative correlation between evaporation and discharge. The hydrometeorological characteristics are significantly influenced by climatic factors, especially precipitation and temperature, with the warming trend potentially affecting water’s availability and distribution. These findings offer valuable insights for water resource management and highlight the need for continuous monitoring to understand hydrological responses to climatic and anthropogenic changes in this critical region.

1. Introduction

Understanding hydrometeorological processes is essential for effective water resource management, environmental protection, and socio-economic development. The Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau due to its extensive ice fields and glaciers, is a critical area for studying the impacts of climate change and tectonic activities on hydrometeorological processes [1]. The basin serves as the headwater region for many of Asia’s major rivers and provides critical freshwater resources to nearly one-third of the world’s population [2]. The Yarlung Tsangpo River, originating from the Jiemayangzong Glacier on the southwestern Qinghai–Tibet Plateau at 5390 m, spans 2058 km within China before becoming the Brahmaputra River. It traverses seven administrative regions, draining an area of 242,000 square kilometers, with a total elevation drop of 5.44 km and an average gradient of 2.64‰. Its middle and upper reaches are characterized by lake basins and valleys, contrasting sharply with the steep gorges in the lower reaches, forming a distinct longitudinal profile with significant geomorphic implications [3]. The Yarlung Tsangpo River Basin exhibits a unique combination of climatic zones, ranging from subtropical in the lower reaches to plateau temperate, sub-frigid, and frigid zones in the upper reaches [4]. This climatic gradient is influenced by the river’s extensive elevation range and complex topography, resulting in diverse weather patterns and significant spatial variability in precipitation, temperature, and discharge. The hydrometeorological characteristics of the basin are influenced by both natural factors and human activities.

Previous studies have extensively explored the Yarlung Tsangpo River Basin’s hydrology, geology, and hydrochemistry. Research has focused on the basin’s tectonic evolution and geomorphological characteristics influenced by tectonic activities [3,5], as well as hydrochemical processes dominated by rock weathering and affected by anthropogenic activities like mining [2,6,7]. Isotopic analyses have traced the sources of river water, emphasizing the contributions of precipitation and snowmelt [6,8]. However, comprehensive long-term analyses of hydrometeorological elements—such as precipitation, temperature, evaporation, and discharge—in this region are relatively scarce. This gap limits our understanding of the climatic influences on the basin’s hydrological processes and underscores the need for integrated studies examining these elements over extended periods. Recent advancements have started to fill this gap by investigating the changes in precipitation, temperature, and vegetation. For instance, the Yarlung Tsangpo River Basin has experienced notable increases in precipitation and temperature over the past few decades, along with significant changes in the Leaf Area Index (LAI), which have impacted actual evapotranspiration (AET) and streamflow patterns. Specifically, increases in precipitation have been identified as the dominant driver of AET changes, while the LAI-induced vegetation changes have had a more complex effect, both enhancing transpiration and intercepting evaporation but reducing soil evaporation, ultimately leading to only marginal changes in AET in some areas [9]. Additionally, the relationship between precipitation and streamflow has shown considerable variation, with wetter periods often leading to increased runoff, while drier periods result in streamflow decreases. The influence of human activities such as land-use changes, particularly reforestation and urbanization, has further complicated this relationship by affecting both local evapotranspiration and runoff dynamics [10]. In some cases, reforestation has been found to increase runoff during dry seasons due to increased transpiration, while in other areas the degradation of grasslands has led to water dissipation during the wet season [11]. Another significant finding from recent research is the impact of vegetation dynamics, particularly vegetation greening, on the hydrology of the basin. Vegetation greening, driven by increased temperatures and changing precipitation patterns, has led to an expansion of plant cover, which has had both positive and negative effects on streamflow. While this greening has increased evapotranspiration, reducing streamflow, it has also enhanced soil moisture retention during dry periods. Studies have shown that the reduction in snow and ice cover in the Tibetan Plateau has led to a reduction in seasonal runoff in some regions, despite increases in precipitation and temperature [12]. Moreover, land-use changes have also had a notable effect on the runoff patterns, with increases in tree plantation areas leading to changes in the seasonality of streamflow. These findings underscore the complexity of the interactions between climate, vegetation, and human activities, highlighting the need for integrated approaches to assess and predict hydrological changes in the region [13,14].

Despite these contributions, comprehensive analyses covering long-term trends, periodicity, variability, and correlations among different hydrometeorological elements across the entire basin are limited. The complex terrain and harsh environmental conditions have led to sparse monitoring networks, resulting in data gaps and uncertainties in understanding the basin’s hydrometeorological behavior [15]. Moreover, the impact of climate change on the hydrometeorological processes in the Yarlung Tsangpo River Basin, especially in terms of discharge response, has been inadequately explored [4]. Most existing studies have focused on specific aspects or short-term observations, lacking an integrated approach to examine the interrelationships among precipitation, temperature, evaporation, and discharge over extended periods. For instance, Li [4] examined the impact of climate change on discharge using rainfall–discharge models but noted the scarcity of data in the upstream areas. Liu [16] investigated hydroclimatic characteristics since the Last Glacial Maximum, highlighting the significant impact of global climate changes on discharge and glacier coverage. However, there is a need to address these knowledge gaps by conducting comprehensive, long-term analyses using more extensive datasets to improve predictions of water availability and assess the impacts of climate change on the basin’s hydrology.

Therefore, this study aims to conduct a comprehensive analysis of the hydrometeorological characteristics of the Yarlung Tsangpo River Basin using long-term data from six representative stations. We employ statistical and analytical methods, including linear regression, wavelet analysis, Pettitt change-point tests, and Kendall rank correlation coefficient tests, to examine the trends, periodicity, variability, and correlations of key hydrometeorological elements. By focusing on the entire basin and utilizing extended datasets, this research provides a more complete understanding of the hydrometeorological processes in this critical region. The findings of this study are expected to enhance the knowledge of hydrometeorological trends and patterns in the Yarlung Tsangpo River Basin, offering valuable insights for water resource management, climate change impact assessments, and sustainable development planning. Furthermore, the results can serve as a reference for future research in similar high-altitude, complex terrain environments, contributing to the global understanding of hydrometeorological responses to climatic and anthropogenic influences.

2. Study Area, Data, and Methodology

2.1. Study Area

The Yarlung Tsangpo River originates from the Jiemayangzong Glacier at an elevation of 5390 m, as shown in Figure 1. It is located at the border between Zhongba County in Shigatse Prefecture and Burang County in Ngari Prefecture. Flowing from west to east across the southern Qinghai–Tibet Plateau, it turns northeast near Pai Town. After receiving its second-largest tributary, the Parlung Tsangpo River, it makes a sharp turn southward, forming the famous Great Bend of the Yarlung Tsangpo River. It then passes through Baxika Township in Motuo County before entering India, where it is renamed the Brahmaputra River. Within China, the river spans approximately 2058 km, with a drop of 5.44 km and an average gradient of 2.64‰. The basin spans 82°0′0″ E to 97°7′0″ E and 28°0′0″ N to 31°16′0″ N, covering an area of 242,000 km². The maximum east–west length is about 1500 km, and the maximum north–south width is about 290 km, with an average width of approximately 166 km. The basin has a narrow, willow-leaf-like shape extending east–west and encompasses seven administrative regions: Ngari, Shigatse, Shannan, Lhasa, Nagqu, Nyingchi, and Chamdo. The river system is well developed but highly asymmetrical in distribution between the left and right banks; the right bank area accounts for only 30% of the total basin area.

2.1.1. Climate

The basin’s diverse and unique topography creates a complex and variable climate, with significant temperature differences between the upper and lower reaches, and pronounced vertical variations. From the lower to the upper reaches, the climate zones include tropical, subtropical, plateau temperate, plateau sub-frigid, and plateau frigid zones, covering all climatic zones of the Tibetan Plateau. Temperatures decrease from southeast to northwest, showing a clear vertical trend of decreasing temperature with increasing altitude. In the river source and high-altitude areas, the annual average temperature ranges from 0.0 to 3.0 °C, while in the middle reaches’ valley areas it is about 5.0 to 9.0 °C. Regions such as Shigatse, Lhasa, and Shannan experience the highest monthly average temperatures in June to July, around 10.0 to 17.0 °C; the lowest monthly averages occur in January or December, at approximately −2.0 to −17.0 °C. Relative humidity ranges between 35% and 70%. The annual frost-free period generally ranges from 110 to 320 days.

2.1.2. Precipitation

The basin includes almost all precipitation zones of Tibet. From the lower to the upper reaches, it can be divided into extremely humid (rainy) zones, humid zones, semi-humid zones, semi-arid zones, and arid zones. Precipitation is mainly influenced by warm and humid air currents from the Bay of Bengal. The annual precipitation in Motuo County in the lower reaches is about 3500 mm (reaching over 5000 mm near Pasighat), around 600 mm in Milin County in the middle reaches, approximately 420 mm in Shigatse, about 310 mm in Lazi County in the upper–middle reaches, and about 280 mm in Zhongba County, showing a significant gradient change. Between 60% and 90% of the annual precipitation is concentrated from June to September.

2.1.3. Evaporation

The annual surface evaporation in the basin is about 1250 mm. In the upper reaches, it ranges between 1200 and 1400 mm. High-evaporation zones in the middle reaches include Lazi, Lhasa, Zedang, and Lang County, with annual surface evaporation exceeding 1600 mm. In the lower reaches, due to higher precipitation, evaporation is below 1000 mm.

2.1.4. Discharge

Discharge in the basin is composed of precipitation, groundwater, and ice–snow meltwater. The multi-year average annual discharge volume is 166.1 billion cubic meters. In the river source region, discharge is mainly replenished by meltwater; the upper reaches are primarily supplied by groundwater. The middle reaches mainly rely on rainfall, and after the confluence of the Nyang River in the lower–middle reaches, the contribution of ice–snow meltwater significantly increases, forming a mixed replenishment type. The lower reaches are predominantly supplied by rainfall. The intra-annual distribution of discharge is uneven; during the high-flow period (June to September), water volume accounts for over 65% of the annual total, while in the low-flow period (October to March) it accounts for 15.3% to 17.6%.

2.2. Data

Considering the basin’s topographical and geomorphological characteristics, as well as the completeness and representativeness of the hydrological station data, this study selected observed data from six national basic stations and first-class accuracy stations located in the upper, middle, and lower reaches of the Yarlung Tsangpo River Basin for analysis, as shown in

Table 1. Basic information sheet for representative stations.

Station Name	River Name	Catchment Area (km²)	Longitude (E)	Latitude (N)	Discharge and Rainfall Timescale	Evaporation and Temperature Timescale	Multi-Year Average Discharge (m³/s)	Multi-Year Average Rainfall (mm)	Multi-Year Average Evaporation (mm)	Multi-Year Average Temperature (°C)
Lazi	Yarlung Tsangpo River	49,370	87°31′49″	29°07′49″	1980–2011	1980–2011	161.7	310.6	2989.1	7.0
Yangcun	Yarlung Tsangpo River	153,191	91°49′17″	29°16′05″	1956–2011	1956–2011	932.7	406.9	2757.4	8.8
Nuxia	Yarlung Tsangpo River	189,843	94°33′34″	29°27′00″	1956–2011	1975–2011	1853.0	550.4	992.3	8.9
Shigatse	Nianchu River	11,121	88°53′42″	29°17′00″	1980–2011	1980–2011	35.9	446.0	1861.0	6.8
Lhasa	Lhasa River	26,225	91°08′00″	29°38′08″	1956–2011	1956–2011	292.9	451.6	2031.2	7.9
Gengzhang	Nyang River	15,581	94°04′37″	29°43′42″	1979–2011	1979–2011	475.7	833.9	1133.0	8.7

. Additionally, the multi-year average values for discharge, rainfall, evaporation, and temperature at each station are provided in Table 1. The mainstem control stations are the Lazi, Yangcun, and Nuxia hydrological stations. The major tributary control stations are the Shigatse hydrological station on the Nianchu River (a primary right-bank tributary), the Lhasa hydrological station on the Lhasa River (a primary left-bank tributary), and the Gengzhang hydrological station on the Nyang River (a primary left-bank tributary).

2.3. Research Methods

2.3.1. Trend Analysis Method

In analyzing the hydrometeorological characteristics of the Yarlung Tsangpo River Basin, this study employs linear regression, moving averages, and the Mann–Kendall non-parametric test to investigate the annual average discharge, precipitation, evaporation, and temperature at six representative stations. This approach aims to better understand the interannual variability in these factors within the basin.

Linear regression and moving averages are fundamental methods frequently utilized to detect and visualize long-term trends in time-series data. Linear regression fits a straight line (y = a + bx) to the data, where b (the slope) indicates whether the variable is increasing (b > 0) or decreasing (b < 0) over time. Meanwhile, the moving average smooths short-term fluctuations by averaging data points within a specified window size n, allowing underlying long-term changes to be more clearly observed. These two approaches complement one another, with linear regression quantifying the trend and the moving average highlighting the general pattern by reducing random noise.

The Mann–Kendall test is a non-parametric statistical test widely used to identify trends in time series without requiring the data to follow a normal distribution. Its statistic S is defined as follows:

$$S=\sum _{i=1}^{n-1} \sum _{j=i+1}^n \mathit{sgn} (x_j-x_i)$$

(1)

where sgn is the sign function:

$$sgn(x)=\left\{\begin{array}{c}1,x>0\\ 0,x=0\\ -1,x<0\end{array}\right.$$

(2)

The Mann–Kendall test evaluates whether there is a statistically significant increasing or decreasing trend in the time series. If the test indicates significance, it suggests that the variable exhibits a clear upward or downward trend over time. The Mann–Kendall test is robust and has been broadly applied in the hydrometeorological field to reveal long-term changes in climate or hydrological elements.

2.3.2. Periodic Analysis Method

Periodic behaviors in time series can be investigated using methods such as spectral analysis and wavelet analysis. Spectral analysis is effective for revealing the frequency components and overall periodicity of a time series over its entire duration, but it does not capture time-localized oscillations [17]. Its core lies in the Fourier transform:

$$\mathit{F}(\mathsf{\omega })={\int }_{-\infty }^{\infty }\mathit{f}\left(\mathit{t}\right){\mathit{e}}^{-\mathit{i}\mathsf{\omega }\mathit{t}}\mathit{dt}$$

(3)

where f(t) is the original time-domain signal, F(ω) is its frequency-domain representation, and ω is the angular frequency.

In contrast, wavelet analysis provides a more advanced framework by decomposing a signal across multiple scales and time intervals. This approach not only highlights frequency components but also reveals the local time-domain characteristics, earning wavelet analysis the title of a mathematical “microscope” [18]. Owing to its ability to handle non-stationary signals, transient changes, and multi-scale properties, wavelet analysis has significant advantages over classical spectral methods. Multiple wavelet functions have been developed, such as the Morlet, Haar, and Meyer wavelets. The core idea of the wavelet transform is to obtain localized time–frequency information by convolving the signal with a set of scaled and translated wavelet basis functions:

$$\mathit{W}(\mathit{a},\mathit{b})={\displaystyle \frac{1}{\sqrt{\mathit{a}}}}{\int }_{-\infty }^{\infty }\mathit{f}(\mathit{t})\mathsf{\psi }\left({\displaystyle \frac{\mathit{t}-\mathit{b}}{\mathit{a}}}\right)\mathit{dt}$$

(4)

where f(t) is the signal to be analyzed, ψ(t) is the wavelet basis function, and a and b represent scale and translation parameters, respectively. These parameters control how the wavelet is stretched (scale) and shifted in time (translation) to probe different time–frequency characteristics in the data. In this study, continuous wavelet transform was performed using the complex Morlet wavelet.

2.3.3. Variability Analysis Method

Hydrometeorological elements are often influenced by both natural factors and human activities, potentially leading to abrupt transitions in their time series [19]. Common methods for detecting such variability include the moving t-test, Hurst coefficient, Cramer method, Yamamoto method, Mann–Kendall rank test, and Pettitt test. In this study, we employed the non-parametric Pettitt change-point test [20], which is outlined below.

The Pettitt test is widely used to detect potential change points in a time series, especially when the data do not satisfy normality assumptions. Its core principle is to compare different segments of the series to locate a position where the statistical characteristics (e.g., mean, variance) significantly change. Concretely, this test evaluates the rank-sum differences for each possible change point and compares them against a critical value to determine whether the change is statistically significant.

Suppose the time series is $X=(x_1,x_2,...,x_n)$. The goal is to identify whether there exists a change point k where the statistical properties notably differ before and after k. For each candidate k, divide the series into two parts: the first half $X_1=(x_1,x_2,...,x_k)$, and the second half $X_2=(x_{k+1},x_{k+2},...,x_n)$. Then, compute the rank of each data point within these subseries. For any two points $x_i$ and $x_j$, where $i<j$, define

$$\mathrm{sign}\left(x_i-x_j\right)=\left\{\begin{array}{c}1,x_i>x_j\\ 0,x_i=x_j\\ -1,x_i<x_j\end{array}\right.$$

(5)

The Pettitt test statistic $U_k$ for the potential change point k is

$$U_k=\sum _{i=1}^k \sum _{j=k+1}^n \mathrm{sign} (x_i-x_j)$$

(6)

A large absolute value of $U_k$ indicates a substantial difference between the two segments. The test then identifies the position k that maximizes $\left|U_k\right|$ and compares this maximum against a critical value to assess its significance. If it exceeds the critical threshold, the null hypothesis of “no change-point” is rejected, implying a significant change at k. Although highly applicable to non-normal data, the Pettitt test can detect only a single change point at a time. If multiple change points are suspected, other methods or combined analyses are necessary to identify them comprehensively.

2.3.4. Correlation Analysis Method

Correlation analysis measures the degree of association between variables, although it does not imply causality. In this study, we employ the Kendall rank correlation to assess the relationship between meteorological variables and discharge. This non-parametric test is robust against non-normal data distributions, outliers, and potential non-linearities.

The Kendall rank correlation coefficient $\tau$ is computed by comparing concordant and discordant pairs of observations. Let $C$ be the number of concordant pairs, $D$ the number of discordant pairs, and n the total number of observations:

$$\tau ={\displaystyle \frac{2(C-D)}{n\left(n-1\right)}}$$

(7)

Values of $\tau$ range from −1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no correlation. For large samples, a significance test approximates $\tau$ by a normal distribution:

$$Z={\displaystyle \frac{3\left(C-D\right)}{\sqrt{n\left(n-1\right)\left(2n+5\right)/18}}}$$

(8)

If $\left|Z\right|$ exceeds the critical value at a given significance level $\alpha$, the null hypothesis of “no correlation” is rejected, indicating a statistically significant association.

3. Results and Discussion

3.1. Trend Analysis

The results of the trend analysis are shown in

Table 2. Rate of change and significance of each parameter at representative stations.

Station	Annual Discharge Rate Change (m³/s/10a)	Precipitation Rate Change (mm/10a)	Evaporation Rate Change (mm/10a)	Temperature Rate Change (°C/10a)	Discharge Significance	Precipitation Significance	Evaporation Significance	Temperature Significance
Lazi	6.562	0.858	158.19	0.516	Insignificant	Insignificant	Significant *	Significant *
Yangcun	14.958	2.344	−101.79	0.324	Insignificant	Insignificant	Significant *	Significant *
Nuxia	11.313	15.235	−37.942	0.321	Insignificant	Insignificant	Insignificant	Significant *
Shigatse	3.051	30.178	−69.942	0.402	Insignificant	Insignificant	Significant *	Significant *
Lhasa	6.387	3.592	−11.219	0.382	Insignificant	Insignificant	Significant *	Significant *
Gengzhang	21.267	9.415	−60.750	0.375	Insignificant	Insignificant	Significant *	Significant *

Note: * indicates significance at the 0.05 confidence level.

. All representative stations exhibit good synchronicity, with discharge showing a non-significant upward trend overall, ranging from 3.051 to 21.267 m³/s/10a. Similarly, precipitation shows an insignificant upward trend across the stations, with the rate of change varying between 0.858 and 30.178 mm/10a. The observed temperatures at all stations exhibited a significant increasing trend and were notably higher than both the national (China) and global temperature growth rates. The analysis results in this paper indicate that the average annual temperature growth rate in the Yarlung Tsangpo River Basin has reached an increase of 0.39 °C per decade. The evaporation volume decreased with the increase in precipitation. The evaporation at Lazi Station in the upper mainstream of the Yarlung Tsangpo River showed a significant increase, related to the low precipitation in the station’s control area and the continuously rising temperature that caused a decrease in relative humidity. The evaporation at the Yangcun, Nuxia, Shigatse, Lhasa, and Gengzhang hydrological stations exhibited a significant decreasing trend.

Taking Gengzhang Station as an example (Figure 2), the trends of the long series of hydrometeorological elements are presented in the figures provided. The annual average discharge at Gengzhang Station shows substantial interannual variability. A generally increasing trend can be observed, represented by a linear relationship with a slope of 2.13. This suggests a gradual increase in discharge over the observed period, although the increase may not be statistically significant due to considerable fluctuations. These variations are likely influenced by annual precipitation changes and other basin-specific hydrological factors. The annual precipitation at Gengzhang Station also shows variability over the years. The fitted trend line, characterized by a slope of 0.94, indicates a gradual upward trend. This pattern may be indicative of an evolving regional precipitation regime, potentially linked to broader climatic changes impacting the area. Regarding annual evaporation, a notable decreasing trend is evident. The trend line has a slope of −7.55, indicating a steady decline in evaporation over the years. This reduction may be attributed to changes in temperature, precipitation, and relative humidity. The combination of increased precipitation and potential changes in atmospheric humidity may be contributing to this observed decrease. The annual average temperature at Gengzhang Station exhibits a clear upward trend, consistent with global warming. The fitted trend line shows a slope of 0.03, representing a steady increase in temperature throughout the observed period. This warming trend is particularly evident in recent years, aligning with broader global temperature rise patterns. The increase in temperature may have significant implications for the hydrological cycle, affecting both evaporation rates and the timing and magnitude of precipitation events in the region.

Adding Nuxia Station as another example, the long-term hydrometeorological trends are presented in Figure 3. The annual average discharge at Nuxia Station exhibits moderate interannual variability and a generally increasing tendency. The fitted linear relationship shows a slope of approximately 1.15, indicating a gradual upward trend in discharge over the observed period. Similar to Gengzhang Station, the variability in discharge at Nuxia is likely influenced by fluctuations in precipitation and the broader hydrological dynamics within the basin. The annual precipitation at Nuxia Station also demonstrates an upward trend, with a slope of around 1.34. This gradual increase could be tied to regional climatic shifts, such as changes in large-scale atmospheric circulation patterns that bring more moisture to the area. Nonetheless, as with any basin-scale analysis, local topographic effects may also play a role in modulating precipitation. In contrast, the annual evaporation at Nuxia Station exhibits a negative slope (−3.49), reflecting a noticeable downward trend over the study period. This decrease may stem from factors such as rising relative humidity or a shift in the seasonal distribution of precipitation. The evolving balance between temperature, precipitation, and humidity likely contributes to the observed decline in evaporation. Finally, the annual average temperature at Nuxia Station shows a positive slope of 0.026, suggesting a steady increase in temperature throughout the observed years. This warming aligns with broader regional and global trends, reinforcing the notion that rising temperatures could influence hydrological processes by affecting both evaporation rates and precipitation regimes. Such changes may have important ramifications for water resource planning and ecological sustainability in the region.

A comparison of decadal averages reveals several shared patterns across the two stations, despite differences in their record lengths. Both Gengzhang (1979–2011) and Nuxia (1956–2011) show rising decadal discharge. While Gengzhang’s discharge increased from roughly the early 1980s into the 2000s, Nuxia’s data—extending back to the 1950s—underscore a similarly steady climb across multiple decades. This consistent upward trend suggests that regional-scale factors (e.g., increased precipitation or glacier melt contributions) may be influencing flow in the Yarlung Tsangpo Basin. Although precipitation displays considerable interannual variability at both stations, the decadal averages indicate a generally increasing trajectory at Gengzhang and Nuxia. This finding aligns with the observed rise in discharge, implying that enhanced rainfall regimes—or more effective moisture transport—could be driving a portion of the runoff increases. Despite station-specific differences in absolute evaporation values, both Gengzhang and Nuxia have exhibited a decreasing trend over recent decades. This decline could result from several factors, such as changes in relative humidity, shifts in atmospheric circulation, or local land-use modifications. The similar downward directions at both sites hint at a broader regional process affecting evaporative losses. In line with global and Tibetan Plateau warming, both stations have experienced a decadal rise in mean temperature. This warming trend is consistent whether one examines Gengzhang’s data, from 1979 onward, or Nuxia’s longer record, starting in 1956. Such sustained temperature increases may carry implications for snow/ice melt dynamics and subsequent runoff contributions.

Taken together, these shared decadal patterns underscore a coherent regional signal across the Yarlung Tsangpo River Basin—rising discharge and precipitation, declining evaporation, and persistent warming. While local factors undoubtedly shape year-to-year fluctuations, the consistently observed directions of change at both Gengzhang Station and Nuxia Station suggest a broader climatic or hydrological shift within the basin.

3.2. Periodic Analysis

Taking Gengzhang Station as an example, the periodic analysis of hydrometeorological elements is presented using wavelet transform coefficients across different frequency domains, as shown in Figure 4. Discharge and precipitation each demonstrate pronounced oscillations on a 2–5 year timescale, reflecting frequent transitions between comparatively wet and dry conditions. A 10-year cycle emerges in both variables as well, indicating decadal “dry–wet” fluctuation patterns likely influenced by regional climatic variability. Furthermore, wavelet peaks exceeding 20 years suggest even longer-term, synchronous variability between discharge and precipitation. Meanwhile, evaporation exhibits notable periodic features at the 3-year and 10-year scales, although its overall stability appears weaker than that of discharge or precipitation, possibly due to the combined effects of temperature variations, land-surface changes, and relative humidity fluctuations. Temperature itself shows a robust 10-year oscillation, consistent with the decadal variability often observed in plateau regions, and weaker spectral components at periods beyond 20 years, hinting at a superimposed long-term warming trend. Overall, these results highlight the dynamic interplay among hydrometeorological factors at multiple temporal scales, underscoring the complexity of climate and water-cycle processes at Gengzhang Station.

At Nuxia Station, the wavelet analyses (Figure 5) reveal multi-scale periodicity in discharge, precipitation, evaporation, and temperature, echoing the patterns observed at Gengzhang Station. Short-term cycles (around 3–9 years) stand out in both discharge and precipitation, indicating a recurring alternation between relatively wet and drier phases. A mid-range cycle (near 10–15 years) further highlights decadal-scale oscillations, while a longer periodic component (beyond 20 years) suggests broader climatic shifts impacting the flow and rainfall patterns over extended intervals. Evaporation exhibits somewhat irregular short-term fluctuations but still aligns with notable decadal rhythms, likely influenced by local atmospheric conditions and temperature anomalies. Meanwhile, temperature displays periodic components on both the shorter (3–9 years) and longer (potentially 20–30 years) timescales, hinting at a mix of transient climatic variations and progressive warming trends in the basin. Taken together, these results underscore a common suite of short-term and decadal rhythms in key hydrometeorological elements at Nuxia Station, reinforcing the notion that both localized processes and broader climatic drivers act in concert over multiple temporal scales across the Yarlung Tsangpo River Basin.

3.3. Variability Analysis

To better understand the abrupt change characteristics of precipitation, discharge, evaporation, and temperature at different temporal scales and at various hydrological stations, this study systematically analyzed these meteorological and hydrological variables using change-point detection methods.

Table 3. Abrupt change points of hydrometeorological elements at the representative stations.

		Average Annual Discharge	Annual Precipitation	Annual Evaporation	Average Annual Temperature
Lazi	Abrupt change year	1998	2001	2001	1998
	Abrupt change rate	11.40%	−0.2%	11.70%	14.60%
	Significance	Not significant	Not significant	Significant *	Significant *
Yangcun	Abrupt change year	1998	1996	2002	1984
	Abrupt change rate	23.4%	9.5%	−13.2%	11.9%
	Significance	Not significant	Not significant	Significant *	Significant *
Nuxia	Abrupt change year	1998	1977	2002	1994
	Abrupt change rate	16.9%	15.0%	−3.26%	1.62%
	Significance	Not significant	Not significant	Not significant	Significant *
Shigatse	Abrupt change year	1998	1998	1990	1998
	Abrupt change rate	33.0%	20.0%	−7.7%	10.8%
	Significance	Not significant	Not significant	Significant *	Significant *
Lhasa	Year	1996	1990	1995	1984
	Rate	19.1%	10.5%	3.2%	14.0%
	Significance	Not significant	Not significant	Significant *	Significant *
Gengzhang	Year	1995	1985	1996	1998
	Rate	12.9%	17.2%	−17.5%	7.6%
	Significance	Not significant	Not significant	Significant *	Significant *

Note: * indicates significance at the 0.05 confidence level.

summarizes the abrupt change year, change rate, and significance level of precipitation, discharge, evaporation, and temperature for six representative hydrological stations. It can be observed from the table that the abrupt change characteristics vary across stations, although some common trends can be noted. Generally, the abrupt changes in discharge and precipitation occurred mostly in the 1980s and 1990s, but the significance test results for most stations did not reach a significant level. This suggests that, although there were trends of abrupt changes in discharge and precipitation at these stations, the magnitude of these changes was insufficient to be deemed statistically significant. In contrast, abrupt changes in evaporation and temperature were more prominent. At most stations, temperature showed a significant abrupt increase, especially at stations such as Lazi, Lhasa, and Yangcun, where the rate of temperature change was high, and the results of the significance tests confirmed the statistical significance of these increases. Meanwhile, evaporation mostly exhibited significant abrupt decreases, particularly at Gengzhang Station, where the change rate reached −17.5%, with high significance levels. These changes in evaporation may be related to the rising temperature and reduced precipitation, affecting the evaporation conditions at that station. Furthermore, the table reflects spatial differences among the stations. For instance, Nuxia Station showed relatively low change rates and significance in both temperature and evaporation, whereas other stations, such as Gengzhang and Yangcun, exhibited more significant abrupt changes. These differences are likely related to the geographical location, topographical characteristics, and local climate conditions of each station.

Figure 6 presents the Pettitt change-point test statistic sequence for the hydrometeorological series at Gengzhang Station. From the figure, it can be observed that the test statistic exhibits significant peaks in specific years, representing abrupt points in the hydrometeorological series. Specifically, the discharge at Gengzhang Station experienced a significant abrupt change in 1995, indicating a substantial change in discharge at that time. Figure 7 shows the Pettitt change-point test statistic sequence for the hydrometeorological data at Nuxia Station, where distinct peaks occur in several specific years, indicating abrupt shifts within the time series. In particular, the discharge at Nuxia Station underwent a marked change during one of these peak years, signifying a significant shift in river flow behavior. Similar abrupt points also appear in the precipitation, evaporation, and temperature series, suggesting simultaneous or interrelated changes in the broader hydrological regime. As with Gengzhang Station, these identified change points are consistent with the results in Table 3, underscoring the reliability of the Pettitt test in detecting statistically significant transitions in the dataset.

From the wavelet analysis figures, local characteristic changes of each variable at different timescales can also be observed. By combining the results of the Pettitt test, the temporal dynamic changes of each element can be better understood, as well as the potential impact of these changes on the regional hydrological cycle. Particularly in the analysis of abrupt changes in temperature and evaporation, a coupling relationship can be found, where a significant increase in temperature corresponds to a significant decrease in evaporation. This relationship highlights the important influence of climate change on the regional hydrological process.

Based on the analyses of both the tables and figures, it can be concluded that the hydrological stations exhibit certain abrupt change characteristics in precipitation, discharge, evaporation, and temperature at various temporal scales, with spatial variability in these changes. Discharge and precipitation generally display non-significant abrupt changes, whereas evaporation and temperature exhibit more significant abrupt changes. These results reveal the changing trends and abrupt characteristics of regional hydrometeorological elements against the backdrop of global warming, which will be crucial for understanding future changes in the hydrological cycle of this region.

3.4. Correlation Analysis

The increase in temperature accelerates evaporation from water surfaces, which is likely to reduce river discharge. Moreover, increased precipitation leads to a reduction in evaporation. This suggests that the factors are interrelated and influence one another.

Table 4. Correlation between meteorological elements and discharge.

Parameters	Precipitation	Temperature	Evaporation	Relative Humidity
Discharge of Lazi	0.560 **	0.268	−0.048	0.308
Discharge of Yangcun	0.872 **	0.254	−0.247	0.723 **
Discharge of Nuxia	0.821 **	0.246	−0.460 *	0.694 **
Discharge of Shigatse	0.859 **	0.327	−0.619 **	0.603 **
Discharge of Lhasa	0.917 **	−0.149	−0.697 **	0.557 **
Discharge of Gengzhang	0.911**	0.190	−0.587 **	0.421 **

Note: * indicates significance at the 0.05 confidence level, and ** indicates significance at the 0.01 confidence level.

presents the correlation coefficients between various meteorological factors and discharge. According to the results, the correlation coefficient between precipitation and discharge is 0.82, indicating a strong positive correlation. This implies that precipitation is the primary factor influencing discharge, and that an increase in precipitation significantly drives an increase in discharge. The correlation coefficient between evaporation and discharge is −0.44, showing a negative correlation. This suggests that, in the study area, evaporation and discharge are inversely related, particularly in dry seasons, where higher evaporation reduces surface water flow. The correlation coefficient between relative humidity and discharge is 0.55, indicating a weak positive correlation, which suggests that changes in relative humidity have a limited direct impact on discharge. Notably, the Lazi Station’s data, where no hydrological stations exist in the upstream basin area of 49,370 km², use only meteorological data from the control station as a basin average, resulting in lower representativeness.

To further analyze the relationships among precipitation, temperature, evaporation, relative humidity, and discharge, the case of Gengzhang Station was examined. Figure 8 displays scatter plots and fitted lines showing the relationships among precipitation, temperature, evaporation, relative humidity, and discharge. Firstly, from the correlation between annual precipitation and discharge (Figure 8a), it is evident that there is a strong positive correlation between annual precipitation and discharge volume. This indicates that, at Gengzhang Station, variations in annual precipitation are a major driving factor for changes in discharge, with increases in precipitation typically leading to significant increases in discharge. Secondly, the relationship between average annual temperature and discharge (Figure 8b) shows a certain positive correlation, which may be due to higher temperatures promoting melting, thus increasing the surface discharge. Particularly in plateau or mountainous environments, an increase in temperature may significantly impact evaporation and snowmelt processes, thereby affecting discharge. Regarding the relationship between annual evaporation and discharge (Figure 8c), the data show a relatively significant negative correlation. This suggests that, in years with high evaporation, large amounts of water are returned to the atmosphere, resulting in a reduction in the surface discharge available. Evapotranspiration includes both plant transpiration and surface water evaporation, both of which play important roles in regional water balance. Hence, this negative correlation is reasonable. Lastly, the relationship between average annual relative humidity and discharge (Figure 8d) indicates a more complex interaction. From the figure, it can be seen that when relative humidity is high, there is a certain increasing trend in discharge. This may be because an increase in relative humidity is often accompanied by increased precipitation, which, in turn, leads to increased discharge. However, the impact of relative humidity on discharge may be indirect, with a complex mechanism that requires a comprehensive analysis involving precipitation and evaporation factors.

Overall, the discharge characteristics at Gengzhang Station are primarily influenced by annual precipitation, evaporation, temperature, and relative humidity. Among these factors, annual precipitation is the most significant, directly determining the discharge supply, while evaporation and temperature exert a regulatory effect on discharge through water redistribution processes.

In addition, correlation analyses were extended to Nuxia Station, as shown in Figure 9. Overall, a moderate positive relationship emerged between discharge and annual precipitation, indicating that higher rainfall coincides with increased water flow (Figure 9a). By contrast, temperature shows a far weaker direct effect on discharge, implying that any influence of warming may be masked by other processes such as complex basin hydrodynamics or varying snowfall contributions (Figure 9b). Evaporation exhibits a notably negative correlation with discharge, suggesting that intensified evaporative demand can reduce runoff availability over time (Figure 9c). Lastly, the results point to a more nuanced positive linkage between relative humidity and discharge, likely reflecting how elevated moisture levels often coincide with greater rainfall activity (Figure 9d). Taken together, these outcomes from Nuxia Station broadly align with the patterns observed at Gengzhang Station, reinforcing the idea that precipitation and evaporation are key drivers of flow variations in this region, while temperature and humidity exert secondary or indirect impacts.

4. Conclusions

By applying methods such as linear regression, Mann–Kendall tests, wavelet analysis, and correlation analysis, we examined the trends, periodicity, variability, and interrelations among precipitation, temperature, evaporation, and discharge. The key findings are as follows:

The results indicated that precipitation and discharge exhibited non-significant upward trends overall, with fluctuations across different decades. Temperature showed a significant increasing trend, with an average rise of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed that hydrological variables exhibited cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Both precipitation and discharge displayed alternating “dry–wet” patterns at the decadal scale, likely influenced by regional climatic variability. Abrupt change analysis showed significant changes in temperature and evaporation, reflecting the impact of climate change on regional hydrological processes, while changes in discharge and precipitation were not statistically significant. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, indicating that precipitation is the primary factor influencing discharge in the basin. Evaporation had a negative correlation with discharge, suggesting that higher evaporation reduces surface water flow. Temperature exhibited a negative correlation with discharge at some stations, possibly due to increased evaporation rates reducing surface discharge.

Overall, the hydrometeorological characteristics of the Yarlung Tsangpo River Basin are significantly influenced by climatic factors, especially precipitation and temperature. The observed warming trend has important implications for the hydrological cycle, potentially affecting water’s availability and distribution. However, due to the sparse distribution of monitoring stations and limited field data, our analysis relied on data from only six representative stations, which may introduce uncertainties. Therefore, expanding the monitoring network and incorporating more comprehensive field data will be essential for refining these findings. These efforts will provide valuable insights for water resource management and climate change adaptation in the basin. Continuous monitoring and comprehensive analysis are crucial to further understand the hydrological responses to climatic and anthropogenic influences in this critical region.