Seasonal Variations of Precipitation Concentration Index and Characteristic Quantities in the Yarlung Zangbo River Basin from 1981 to 2024

DU Jun¹,²,³, GAO Jiajia²,³,⁴, CHEN Tao¹,², Tsewang¹, Pakgordolma²,⁵

¹Xizang Autonomous Region Climate Centre, Lhasa 850001, Xizang, China
²Field Science Experiment Base for Comprehensive Observation of Atmospheric Water Cycle in Mêdog, CMA/Mêdog National Climate Observatory/Xizang Mêdog Field Scientific Observation and Research Station for Atmospheric Water Cycle, Mêdog 860700, Xizang, China
³Xizagê National Climate Observatory, CMA, Xizagê 857000, Xizang, China
⁴Xizang Institute of Plateau Atmospheric and Environmental Sciences/Xizang Open Laboratory for Plateau Atmospheric Environment, Lhasa 850001, Xizang, China
⁵Nyingchi Meteorological Service of Xizang Autonomous Region, Nyingchi 860000, Xizang, China

Abstract

The Precipitation Concentration Index (PCI) effectively characterizes intra-annual precipitation concentration and has been widely applied in related research. Based on monthly precipitation and mean temperature observations from 15 meteorological stations in the Yarlung Zangbo River Basin (YZRB) from 1981 to 2024, this study analyzes spatiotemporal variation characteristics of PCI, seasonal precipitation amount, frequency, and intensity over the past 44 years, and investigates causes of PCI changes using linear regression, Pearson correlation, and five mutation detection methods including Mann-Kendall and Cramer tests. Results show that: (1) PCI increases from east to west across the YZRB, while annual precipitation, precipitation frequency, and precipitation intensity decrease from east to west. (2) Over the past 44 years, PCI has decreased at a rate of -0.26 per decade (P < 0.05), indicating increasingly uniform monthly precipitation distribution throughout the year. Precipitation shows increasing trends from January to July and in October (fastest increase in July), while decreasing in other months (largest decrease in September). The proportion of monthly precipitation to annual precipitation (MPAP) increases in February and April–July (largest increase in May), while decreasing in other months (largest decrease in September). (3) Increased precipitation in spring, summer, and winter is primarily attributable to enhanced precipitation intensity, whereas decreased precipitation frequency is the main cause of autumn precipitation reduction. The increase in annual precipitation intensity results from significant increases in both the Tibetan Plateau index and Western Pacific Warm Pool intensity index. The decrease in PCI is related to reduced seasonal differences under a warming background. (4) PCI was lower only in the 2000s but higher in the other three decades, with an abrupt change occurring in the early 1990s. Abrupt changes in annual precipitation, precipitation frequency, and precipitation intensity occurred in the early 2000s and mid-to-late 1990s, respectively.

Keywords: precipitation concentration; precipitation characteristic quantity; variation characteristics; atmospheric circulation index; SST index; Yalung Zangbo River Basin

1 Introduction

Under global climate warming, the global water cycle has become anomalous, altering spatiotemporal precipitation patterns. Precipitation represents the most critical component of the water cycle, and intra-annual precipitation variation is crucial for crop growth, water resource conservation, and management. Precipitation concentration characteristics reflect the combined effects of precipitation amount, duration, and processes, serving as an important indicator for evaluating whether regional precipitation distribution is uniform within a year. An et al. summarized common methods for calculating precipitation concentration characteristics, including the Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP) defined by Martin and Oliver based on daily precipitation data, and the Precipitation Concentration Index (PCI) and PCP defined by Zhang based on monthly precipitation data. Domestic scholars have applied these indices to analyze spatiotemporal variation characteristics across China, North China, Southwest China, and Northwest China. Overall, PCI offers more intuitive physical meaning and simpler calculation compared to other precipitation concentration indices.

The Yalung Zangbo River is a major international river in Asia, and water resource issues in its basin have long been a focus of attention. It is also one of China's richest rivers in hydropower potential, with abundant natural reserves of approximately 24.2×10⁸ kW, second only to the Yangtze River, accounting for about 1/6 of China's total hydropower potential. The basin serves as an important ecological security barrier and biodiversity conservation area in China. Spanning 2,057 km within China with a drainage area of 460,000 km², the basin exhibits distinct vertical vegetation zonation. Due to complex terrain and diverse climates, its ecosystem is extremely sensitive and vulnerable under global climate change. Since the 1980s, most of the YZRB has experienced increasing precipitation, with significant reductions in snow cover, profoundly affecting water cycle processes and mechanisms. Precipitation changes in the basin also impact the evolution of water systems, ecosystems, and mountain disaster systems on the Tibetan Plateau. While domestic scholars have conducted extensive research on spatiotemporal precipitation variation characteristics in the YZRB using meteorological observations, studies on intra-annual precipitation distribution characteristics, precipitation concentration, and seasonal variations in precipitation frequency and intensity remain scarce.

This study analyzes seasonal variations of precipitation concentration and characteristic quantities in the YZRB from 1981 to 2024 based on monthly precipitation and precipitation days data. The proportion of monthly precipitation to annual total (MPAP) is used to characterize intra-annual precipitation distribution. Precipitation frequency refers to the proportion of days with daily precipitation ≥0.1 mm in a year, while precipitation intensity is the ratio of cumulative daily precipitation ≥0.1 mm to precipitation days.

1.1 Data Sources

Monthly precipitation and precipitation days data from 15 meteorological stations in the YZRB from 1981 to 2024 were provided by the Xizang Autonomous Region Meteorological Information Network Center and underwent strict quality control. Atmospheric circulation indices including the Asian polar vortex, Western Pacific subtropical high, Indo-Burma trough, Tibetan Plateau index, and Tibetan Plateau T index, as well as sea temperature indices including NINO 3.4 sea surface temperature anomaly, Indian Ocean warm pool area and intensity, Western Pacific warm pool area and intensity, and warm pool and cold tongue types, were obtained from the National Climate Center. The study area and meteorological station distribution are shown in [FIGURE:1].

1.2 Research Methods

1.2.1 Precipitation Concentration Index Calculation

PCI is calculated using the method proposed by Oliver and improved by De Luís:

$$
PCI = \frac{\sum_{i=1}^{12} P_i}{\sum_{i=1}^{12} P_i} \times 100
$$

where $P_i$ is monthly precipitation (mm) and $i$ is the month.

1.2.2 Climate Tendency Rate

The climate tendency rate is calculated using linear regression:

$$
Y = a + bt
$$

where $Y$ represents precipitation characteristics (PCI, precipitation amount, frequency, or intensity), $t$ is time, $a$ is the regression constant, and $b$ is the regression coefficient. The climate tendency rate per decade is represented as $10b$, with significance tested using the correlation coefficient between $t$ and $Y$.

1.2.3 Mutation Test Methods

Five mutation detection methods were applied: Mann-Kendall test, Cramer test, moving t-test, Pettitt test, and Yamamoto test. These methods were used to examine abrupt changes in precipitation characteristics in the YZRB.

1.2.4 Data Processing and Mapping

Seasons are defined as spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). Data processing and mapping were performed using DPS V19.05 software for trend analysis and ArcGIS 10.8 for spatial distribution maps. The Inverse Distance Weighted (IDW) interpolation method was used for average value spatial distribution maps, which has been widely applied for meteorological elements on the Tibetan Plateau. Trend spatial distribution maps were generated directly using ArcGIS.

2 Results

2.1 Precipitation Concentration Index Variation Characteristics

2.1.1 Spatial Distribution of PCI

Annual PCI values in the YZRB range from 13.5 to 31.0, generally increasing from east to west, with the lowest value in Bomi and highest in Lhatse. PCI values are below 20 in Nyingchi City and Lhari, indicating seasonal precipitation with moderate intra-annual concentration. Other areas show PCI > 20, indicating extremely concentrated intra-annual precipitation with large monthly variations. The climate tendency rate shows that PCI decreases at most stations, particularly in Nyingchi City and the eastern and central parts of Lhasa, with rates of -1.1 to -0.07 per decade (P < 0.05). Lhatse shows the largest decrease [-0.83·(10a)⁻¹], followed by Lhaze.

2.1.2 Intra-Annual Precipitation Distribution Characteristics

Average monthly precipitation in the YZRB shows a single-peak pattern, concentrated in May–September and accounting for 78.2% of annual precipitation. Climate tendency rates indicate that monthly precipitation increases from January to July and in October, with July showing the fastest increase at 3.53 mm·(10a)⁻¹. Other months show decreasing trends, with September having the largest decrease of -2.73 mm·(10a)⁻¹. The proportion of monthly precipitation to annual precipitation (MPAP) increases in February and April–July (largest increase in May at 0.51%·(10a)⁻¹) and decreases in other months (largest decrease in September at -0.72%·(10a)⁻¹). The decreasing PCI indicates more uniform intra-annual precipitation distribution. Analysis of temperature annual range shows a significant positive correlation with PCI (P < 0.01), suggesting that the more uniform precipitation distribution may be related to reduced seasonal differences under warming.

2.2 Seasonal Variations of Precipitation Characteristics

2.2.1 Seasonal Precipitation Amount

Average seasonal precipitation amounts decrease from east to west. Annual and seasonal precipitation averages are: 500.4 mm (annual), 92.0 mm (spring), 321.9 mm (summer), 78.6 mm (autumn), and 7.7 mm (winter). Maximum values occur in Nyingchi City for summer and in Mainling for other seasons, while minimum values occur in Lhatse for spring and winter, and in Gyangzê for summer, autumn, and annual totals. Annual precipitation shows an increasing trend of 6.64 mm·(10a)⁻¹ (P < 0.05), with summer increasing fastest [4.63 mm·(10a)⁻¹], followed by spring [4.37 mm·(10a)⁻¹] and winter [0.33 mm·(10a)⁻¹], while autumn shows a decreasing trend [-2.64 mm·(10a)⁻¹]. Spatially, annual precipitation increases in most areas except Nyingchi City, Lhasa, Gonggar, and Mainling, with Namling showing the fastest increase. Spring precipitation increases at all stations (largest increase in Nakartse). Summer precipitation decreases in Nyingchi City, Mainling, and Gonggar (largest decrease in Bomi). Autumn precipitation decreases at most stations (largest decrease in Mainling), with increases only in Nakartse, Lhari, and Bomi. Winter precipitation increases at most stations (largest increase in Lhari), with decreases in Nyêmo, Gyatsa, and Bomi.

2.2.2 Seasonal Precipitation Frequency

Annual and seasonal precipitation frequencies decrease from east to west. Average frequencies are: 30.3% (annual), 15.6% (spring), 8.3% (summer), 4.7% (autumn), and 1.7% (winter). Maximum frequencies occur in Mainling for spring, summer, and annual totals, in Bomi for autumn, and in Lhari for winter; minimum values occur in Lhatse for all seasons except summer (minimum in Gyangzê). Annual precipitation frequency decreases at -0.30%·(10a)⁻¹ (P < 0.05), with the fastest decrease in autumn [-0.79%·(10a)⁻¹], followed by summer [-0.19%·(10a)⁻¹] and spring [-0.05%·(10a)⁻¹], while winter shows an increasing trend [0.16%·(10a)⁻¹]. Spatially, annual precipitation frequency increases at 6 stations (Mêdog, Gonggar, Nagartse, Nyêmo, Namling, and Lhatse) and decreases at others (largest decrease in Mainling). Spring precipitation frequency increases in Nyingchi City, Xigazê, and Zêtang (largest increase in Lhasa) and decreases elsewhere (largest decrease in Lhasa). Summer precipitation frequency increases in Namling, Nyêmo, Nagartse, Gonggar, and Mêdog (largest increase in Lhasa) and decreases elsewhere (largest decrease in Nagartse). Autumn precipitation frequency decreases at most stations (largest decrease in Mainling), with increases only in Lhatse, Namling, and Bomi. Winter precipitation frequency increases in most of Xigazê, Gonggar, Zêtang, and Nyingchi City (largest increase in Nyingchi) and decreases elsewhere (largest decrease in Mainling).

2.2.3 Seasonal Precipitation Intensity

Annual and seasonal precipitation intensities decrease from east to west, with a center of high values in Namling and Xigazê. Average intensities are: 4.56 mm·d⁻¹ (annual), 2.74 mm·d⁻¹ (spring), 5.63 mm·d⁻¹ (summer), 3.80 mm·d⁻¹ (autumn), and 1.03 mm·d⁻¹ (winter). Maximum intensities occur in Bomi for autumn and annual totals, in Xigazê for summer, and in Bomi for other seasons; minimum intensities occur in Gyangzê for autumn and annual totals, in Lhatse for summer and winter, and in Gyangzê for spring. Annual precipitation intensity increases at 0.15 mm·d⁻¹·(10a)⁻¹ (P < 0.05), with increases in spring, summer, and winter and a decrease in autumn. Spatially, annual precipitation intensity decreases only in Nyêmo and Bomi (largest decrease in Bomi) and increases elsewhere (largest increase in Lhasa). Spring precipitation intensity increases at all stations (largest increase in Nakartse). Summer precipitation intensity decreases only in Nyêmo, Nyingchi, and Bomi (largest decrease in Namling) and increases elsewhere (largest increase in Mainling). Autumn precipitation intensity decreases in most of Xigazê, Nyêmo, Lhasa, Gonggar, and Bomi (largest decrease in Namling) and increases elsewhere (largest increase in Zêtang). Winter precipitation intensity decreases only in Namling and Nyêmo and increases elsewhere (largest increase in Zêtang).

In summary, precipitation amount, frequency, and intensity in the YZRB all decrease from east to west. Over the past 44 years, autumn precipitation amount and intensity have decreased, while other seasons and annual totals have increased. Winter precipitation frequency has increased, while other seasons and annual frequency have decreased, mainly in autumn. The increased precipitation in spring, summer, and winter is primarily caused by increased precipitation intensity (contribution rates of 50.3%, 59.3%, 50.8%, and 64.3% respectively), while decreased autumn precipitation is mainly due to reduced precipitation frequency (contribution rate of 60.9%).

2.3 Decadal Variations

Table 1 shows decadal anomalies of annual and seasonal precipitation amount, frequency, intensity, and PCI. In the 1980s, spring and summer precipitation and frequency were below normal, while autumn and winter precipitation and frequency were low and all seasonal and annual intensities were small. In the 1990s, only spring and winter precipitation and intensity, and spring frequency were above normal; frequency was only slightly low in winter, and intensity was only high in spring and low in summer. In the 2000s, annual precipitation was low due to reduced autumn precipitation; only spring frequency was high, and autumn intensity was low. Overall, the 1980s had low precipitation, frequency, and intensity; the 1990s had high precipitation, frequency, and intensity; the 2000s had low precipitation and intensity but high frequency; and the 2010s had low precipitation, low frequency, but high intensity, with the 2020s showing the most pronounced patterns.

2.4 Mutation Analysis

Mann-Kendall test results show that the UF curve for annual PCI fluctuated during 1981–1992, declined significantly after 1993, and rose after 2010. The UF and UB curves intersected in 1992 within the ±1.96 confidence interval, indicating a mutation from a relatively high period to a low period. Similarly, annual precipitation and intensity mutated in 2003 and 1996, respectively, shifting from relatively low to high periods. Annual precipitation frequency showed no significant mutation. Table 2 validates these mutation points using five methods. PCI mutations were detected by three methods in 1992; precipitation amount mutations were detected by three methods in 2003; precipitation frequency mutations were detected by two methods in 2002; and precipitation intensity mutations were detected by two methods in 1996. Comprehensive analysis indicates that PCI mutated in the early 1990s, precipitation amount and frequency in the early 2000s, and precipitation intensity in the mid-to-late 1990s.

2.5 Relationships with Atmospheric Circulation and SST Indices

Pearson correlation analysis between precipitation characteristics and atmospheric circulation/SST indices shows that precipitation intensity is significantly positively correlated with the Tibetan Plateau index (P < 0.05) and significantly negatively correlated with the Asian polar vortex intensity index (P < 0.05). PCI is not significantly correlated with these indices. Precipitation intensity is also significantly positively correlated with Western Pacific Warm Pool intensity and Indian Ocean Warm Pool intensity indices (P < 0.05). Stepwise regression analysis indicates that the Tibetan Plateau index and Western Pacific Warm Pool intensity index have the greatest impact on precipitation intensity, with contribution rates of 57.7% and 42.3%, respectively. Both indices show significant increasing trends over the past 44 years, driving the increase in annual precipitation intensity in the YZRB.

3 Discussion

The Second Tibetan Plateau Scientific Expedition shows that the Tibetan Plateau has experienced significant warming and a "wet north, dry south" precipitation pattern. This study reveals a "warm-wet west, warm-dry east" pattern in the YZRB. Under this background, PCI shows an increasing trend in the east and decreasing trend in the west, indicating increased climate risks of summer flooding and spring/autumn droughts in the east, while reducing spring/autumn drought probability in the west.

Compared with the Three Rivers Source region in northern Tibetan Plateau, the YZRB has higher PCI values (>20), indicating extremely concentrated intra-annual precipitation distribution, while the Three Rivers Source region has PCI < 20, showing moderate concentration. Both regions show decreasing PCI trends, with rates of -1.11%·(10a)⁻¹ and -1.71%·(10a)⁻¹, respectively, possibly related to reduced seasonal differences under warming background, consistent with Duan et al. Meteorological stations on the Tibetan Plateau are mainly concentrated in the east and southeast, with sparse coverage in the central and northwest regions. Zhu et al. evaluated multiple precipitation datasets and found that the IGSNRR dataset shows good consistency with observations and small spatial differences, which has been used to identify spatial patterns of precipitation seasonal distribution on the Tibetan Plateau. Du et al. analyzed precipitation concentration and intra-annual distribution in the Three Rivers Source region using CN05.1 daily gridded data. This study reveals seasonal distribution characteristics and trends in the YZRB based on station observations, but the lack of long-term stations in the upper YZRB limits comprehensive understanding of basin-wide water cycle characteristics. Higher-resolution satellite fusion data are needed for further research on spatial heterogeneity mechanisms of precipitation seasonal distribution in the YZRB.

4 Conclusions

(1) Spatially, annual precipitation amount, frequency, and intensity in the YZRB decrease from east to west, while PCI increases from east to west, with most areas showing extremely concentrated intra-annual precipitation distribution. Over the past 44 years, PCI has decreased at -0.26·(10a)⁻¹, mainly in Nyingchi City and eastern/central Lhasa, while other stations show decreasing trends.

(2) Temporally, average annual PCI has decreased significantly (P < 0.05), indicating more uniform intra-annual precipitation distribution. Monthly precipitation increased from January to July and in October (fastest increase in July at 3.53 mm·(10a)⁻¹) and decreased in other months (largest decrease in September at -2.73 mm·(10a)⁻¹). MPAP increased in February and April–July (largest increase in May) and decreased in other months (largest decrease in September). Autumn precipitation amount and intensity decreased, while other seasons and annual totals increased. Winter precipitation frequency increased, while other seasons and annual frequency decreased, mainly in autumn.

(3) The increases in spring, summer, winter, and annual precipitation amounts are mainly caused by increased precipitation intensity, while the decrease in autumn precipitation is primarily due to reduced precipitation frequency. The increase in precipitation intensity is mainly caused by significant increases in the Tibetan Plateau index and Western Pacific Warm Pool intensity index.

(4) In terms of decadal variations, annual precipitation, frequency, and intensity were all below normal in the 1980s; the 1990s showed above-normal values; the 2000s had low precipitation and intensity but high frequency; and the 2010s showed low precipitation and frequency but high intensity, with the 2020s being most pronounced.

(5) PCI mutated in the early 1990s, while annual precipitation and frequency mutated in the early 2000s, and precipitation intensity mutated in the mid-to-late 1990s.

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