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Article

Analysis of Precipitation Change and Its Influencing Factors Around the Lop Nor Salt Flat

by
Yuke Wang
1,
Fojun Yao
1,2,3,*,
Chenglin Liu
4,
Xinxia Geng
1,
Yu Shao
1,5 and
Nan Jiang
1,5
1
Key Laboratory of Metallogeny and Resources Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
2
State Key Laboratory of Deep Earth and Mineral Exploration, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100032, China
3
Key Laboratory of Strategic Mineral Resources Mineralization and Evaluation, Taiyuan 030000, China
4
School of Earth Resources, China University of Geosciences (Wuhan), Wuhan, 430074, China
5
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(5), 770; https://doi.org/10.3390/w17050770
Submission received: 11 January 2025 / Revised: 25 February 2025 / Accepted: 1 March 2025 / Published: 6 March 2025
(This article belongs to the Section Water Quality and Contamination)
Browse Figures
Versions Notes

Abstract

:
Known as the “Ear of the Earth”, Lop Nor has become one of China’s four largest uninhabited areas due to environmental changes. Lop Nor is rich in mineral resources, including potassium salt, which has good quality and has been largely mined since 2002. This study focuses on the surrounding area of the Lop Nor Potash Salt Field, which covers an area of 80,036.39 square kilometers, spanning from 39.29° N to 41.84° N and 88.92° E to 92.26° E. The research is based on 1 km resolution precipitation, potential evapotranspiration, temperature data, and 250 m resolution NDVI data spanning 2002–2022. This study is devoted to exploring the trend of precipitation changes in the region surrounding the Lop Nor salt field since the start of the construction of the salt field, exploring the climatic impacts of the construction of the salt field on the surrounding region, and analyzing the correlations related to the changes in precipitation by selected meteorological factors. The Sen and Trend-Free Pre-Whitening Mann–Kendall trend analysis method was used to analyze the trend of precipitation data over the years. Combining with the data of the salt field location, the influence of the development of the salt field on regional precipitation was analyzed both temporally and spatially. The bias correlation analysis method was used to explore the correlation between maximum temperature, potential evapotranspiration, Normalized Difference Vegetation Index, and precipitation. The results of this analysis indicate that between 2002 and 2022, the study area exhibited both increasing and decreasing trends in precipitation. The region experiencing decreasing precipitation is predominantly located in the southwestern part of the study area, encompassing approximately 62% of the total area. Conversely, the area showing increasing precipitation is situated in the northeastern part, accounting for 38% of the total area. Field visits and survey data further corroborated the observed trend of increased precipitation in the northeastern region. Based on these findings, it is hypothesized that the development of salt flats has contributed to the increased precipitation, thereby alleviating regional drought conditions. Additionally, a partial correlation analysis of meteorological factors and precipitation revealed significant correlation. Temperature, potential evapotranspiration (PET), and the Normalized Difference Vegetation Index (NDVI) all exhibited varying degrees of correlation with precipitation. Temperature and potential evapotranspiration were the primary meteorological factors showing significant individual correlations. This study discusses the impact of salt field development and other climatic factors on the drought situation in Lop Nor and quantitatively analyzes the trend of precipitation changes in the study area and the factors affecting it. Water resources are scarce in China’s desert areas, and this research can provide a scientific basis for the state to formulate long-term plans for ecological protection and desert management, and it can also provide guidance for industrial development in desert areas. At the same time, it can provide important data and cases for global climate change research, offering experience and technical support for international cooperation in desertification control.

1. Introduction

Research on arid zones has always been a matter of great interest. Given the rising frequency of extreme weather events in recent years, the significance of arid zone research has grown. Drought is usually recognized as an abnormal climatic event caused by a shortage of precipitation over an extended period of time [1]. As one of the more serious natural disasters, drought can have negative impacts on the environment and human activities [2]. The occurrence of drought affects local water supply, agricultural production, ecological environment, and socio-economic development [3]. Depending on the causes, droughts are usually categorized into four types: meteorological drought, agricultural drought, hydrological drought, and socio-economic drought [4]. All aspects of human life are affected by drought because drought is characterized by a high frequency of occurrence, a large area of impact, and a long impact duration [5,6,7]. Data show that droughts have caused far greater economic losses each year than other natural disasters in recent years. More seriously, global climate change has also led to a higher frequency of events such as drought [8,9,10,11].
In 1965, the Palmer Drought Index (PDSI) was proposed by Pallmer in the United States based on precipitation and temperature data and has been widely used [12]. After that, the Standardized Precipitation Index (SPI) was proposed in 1993, and its use was recommended by the World Meteorological Organization (WMO) to other countries around the world in 2009 [13,14]. The Standardized Precipitation Evapotranspiration Index (SPEI) was proposed in 2010 [15]. The Composite Drought Index (CDI) was proposed in 2012 [16], and the Standardized Soil Moisture Index (SSMI) was proposed in 2014 [17]. The standardized drought index is applied in various aspects of drought research such as drought detection [18,19,20].
Among the climatic variables influencing drought, precipitation plays a critical role in its development and persistence [21]. Precipitation data are usually relatively easy to obtain, which allows researchers to easily collect large amounts of data for analysis. Precipitation trends are more directly related to other climatic factors such as temperature and atmospheric circulation. Analyzing precipitation trends can reveal the causes and mechanisms of drought in relation to temperature changes and atmospheric circulation patterns. In contrast, the SPI index is mainly a statistical indicator based on precipitation data, and the direct connection with other climate factors is relatively weak. In some practical application scenarios, the precipitation trend is easier to be understood by decision-makers and the public. When government departments are formulating measures, visualized precipitation trend data can more easily provide the basis for decision making. This study assessed rainfall trends. While many studies use statistical data from meteorological sites and analyze it with software like SPSS 26, this methodology has some limitations. This study combines two robust nonparametric statistical methods, Sen trend analysis and the Mann–Kendall test of significance, to reduce the impact of the data. Sen trend analysis was used to determine the trend, and then the Mann–Kendall test was used to test the significance of trend change [22,23,24]. Theil–Sen Median and Mann–Kendall test have been widely used in combination to analyze trends [25,26,27]. Taking into account the influence of the autocorrelation of the data series, some studies have used the trend-free pre-whitening Mann–Kendall test to replace the Mann–Kendall test [28].
In the field of research on the impact of salt field construction and regional climate, the previous work mainly focused on the structural changes of the ecosystem of salt fields and the impact on the salinization of the surrounding soil [29,30]. There are preliminary discussions on the key issue of how salt field construction affects regional precipitation, but there are still many shortcomings. Moreover, existing assessment methods tend to ignore the synergistic effects of multiple environmental factors, leading to uncertainties in the attribution analysis.
In this study, Mori’s slope analysis of precipitation in the area around the Lop Nor Salt Flats from 2002 to 2022 was conducted using publicly available raster data. The annual precipitation in the study area was trend analyzed and the TFPW-MK test was used to determine its statistical significance, and then the results were classified accordingly. Partial correlation analysis was carried out on temperature, evapotranspiration, Normalized Vegetation Index (NDVI) and precipitation data for the years 2002–2022 to investigate the relationship between these factors and precipitation.
The core objective of this study is to investigate the impact of salt field construction on drought conditions in the surrounding areas. Through comprehensive and systematic data analysis, this study precisely fills the gap in the field of precipitation research on the impact of salt field construction on climate. In addition, this study also innovatively proposes new strategies to mitigate regional drought conditions, which provides a solid scientific basis for drought management practices and is expected to contribute to the sustainable development of the regional ecosystem.

2. Materials and Methods

2.1. Overview of the Research Area

2.1.1. Geographic Location

Lop Nor, known as the “Ear of the Earth”, is located in the southeastern part of China’s Xinjiang Uygur Autonomous Region in the eastern part of the Tarim Basin. Being the lowest part of the Tarim Basin, it was once a convergence point for rivers like the Tarim, Peacock, Chechen, and Shule. It has gradually dried up, transforming into an uninhabitable region. Lop Nor is rich in natural resources, such as gold, copper, and iron deposits, and it is significant as a potash base in China.
The broader Lop Nor region is located in the northern part of Ruoqiang County, Xinjiang Province, China. It consists of the Hexi Corridor in the east, the Taklamakan Desert in the west, the Kuruktag Mountains in the north, and the Altun Mountains in the south.
As shown in Figure 1, the research area selected for this research is the surrounding area of the salt field in the north of the “Big Ear” in Lop Nor. The research area is 39.29° N–41.84° N, 88.92° E–92.26° E with an area of 80,036.39 square kilometers and 351.92 square kilometers of salt field. The salt field has been under large construction since 2002.

2.1.2. Terrain and Geomorphology

The internal elevation of the research area is 688–2244 m, the southwest and central parts of the terrain are low, about 700 m above sea level; the northwest and southeast corners of the terrain are high, about 2000 m above sea level; the eastern part of the elevation is between the lowest elevation and the highest elevation of the research area. The research area features higher elevations in the north, south, and east with lower elevations in the west. The salt field is located in the center of the research area with lower elevation. In addition to this, most of the northern and central part of the study area has south-facing slopes, and most of the southern part has north-facing slopes. Slopes are lower in the center.

2.1.3. Climatic Characteristics

The research area is located in the interior of the Eurasian continent, far from the sea, and it has a temperate continental climate. Summer temperatures can exceed 45 °C; winter temperature is usually below −20 °C, and the lowest can reach −30 °C. The annual and daily temperature differences are large. The climate of the study area is arid, and rainfall is scarce and concentrated in summer. The annual evapotranspiration is greater than 3000 mm. The climate of the research area is characterized by significant temperature differences, limited precipitation, elevated evaporation rates, and vigorous winds.

2.1.4. Type of Land Use

As shown in Figure 2, the research area has 14 land use types. They are shrublands, open woodland, high-cover grassland, medium-cover grass-land, low-cover grassland, lake, beach, other building land, sandy land, Gobi, slat and alkaline land, marshy land, bare land, and bare rocky terrain. The land use type is dominated by sandy land in the southwestern and southeastern part of the study area, Gobi and bare rocky texture in the northern part, and slat and alkaline land in the central area where the salt fields are located.

2.1.5. Factors Affecting the Climate of the Research Area

  • Geographic location: Lop Nor Eurasia, belonging to the high-latitude region.
  • Frequent occurrence of sandstorms and dust storms: the western part of the research area is the Taklamakan Desert, so the frequency of sandstorms and dust storms in the study area is high. Dust storms bring extreme weather and threaten the survival of plants and animals in the region.
  • Human activities: Water resources are scarce in the research area, vegetation growth is difficult and the ecosystem is extremely fragile. In such an environment, human activities have a great impact on its climate. Water resource misuse, agricultural expansion, and the pollution of water bodies would deteriorate the ecosystem and exacerbate the harshness of the climate.

2.2. Methods

2.2.1. Trend-Free Pre-Whitening Mann–Kendall Test

In 1995, von Storch pointed out that positive autocorrelation amplifies the significance of the trend of the time series when using the MK test and thus proposed to perform a pre-whitening process to eliminate the autocorrelation component in the time series before using this test [31]. Therefore, Yue et al. proposed the use of trend-free pre-whitening (TFPW) procedure before performing the Mann–Kendall test [32]. In this study, the method was used based on PyCharm (2023.3). The TFPW-MK test procedure is as follows:
For the time series X(X1, X2,…Xn), remove its trend term:
β = m e d i a n X j − X i j − i ∀ j > i
X i ′ = X i − β i
Elimination of autocorrelation effects:
X i ″ = X i ′ − r X i − 1 ′
where r is the first-order autocorrelation coefficient of X′.
The trend term was re-added to obtain the final series for the MK test:
X i ‴ = X i ″ + β i

2.2.2. Theil–Sen Median

The Theil–Sen median method is an efficient and robust nonparametric statistical method for determining trends [33]. Sen’s slope estimation is an effective measure of the amount of change in the trend of a time series [34,35]. Thus, it is widely used to calculate the degree of change in hydrological and meteorological time series trends. In this study, the method was used based on MATLAB R2021b. The calculation follows Equation (1).

2.2.3. Partial Correlation Analysis

Partial correlation is a statistical technique used to explore the relationship between two variables while controlling for the potential effects of one or more additional variables (e.g., covariates or confounding factors) [36]. The value of the partial correlation coefficient ranges from −1 to 1, where a coefficient closer to −1 or 1 indicates a stronger direct relationship between the variables, while values near 0 suggest a weak or no direct correlation. In multivariate research, partial correlation analysis is extensively employed to validate direct relationships between variables, uncover underlying causal mechanisms, and refine regression model assumptions. This approach provides significant theoretical and methodological support for examining variable interactions in complex systems.
Partial correlation analysis, unlike Pearson’s correlation coefficient, controls for the influence of other variables, offering a more precise assessment of the direct relationship between two variables. While Pearson’s correlation measures linear association without accounting for external factors, partial correlation isolates the effect of confounding variables, providing a clearer understanding of intrinsic relationships.

2.2.4. Research Flowchart

As illustrated in Figure 3, the study primarily focused on analyzing the characteristics of precipitation changes and the factors influencing precipitation in the study area. A combination of the Theil–Sen median method and the trend-free pre-whitening Mann–Kendall test was employed to analyze the characteristics of precipitation changes and classify precipitation trends. To examine the factors influencing precipitation, partial correlation analysis was conducted to evaluate the relationships between temperature, evapotranspiration, and NDVI with precipitation, individually.

2.3. Materials

2.3.1. Growing Season Precipitation Data

The annual precipitation data were monthly averages of growing season (May to September) precipitation data. The raw monthly precipitation data were uploaded by Peng Shouzhang from the National Tibetan Plateau Science Data Center. The data can be downloaded from https://data.tpdc.ac.cn/zh-hans/data/faae7605-a0f2-4d18-b28f-5cee413766a2. Data is accessed on 20 June 2024. The period of the raw data is 1901–2022, of which the data from 1990 to 2022 were downloaded for this study. The spatial extent of the raw data is the whole of China, and the data of the research area were extracted in this study. The spatial resolution of the raw data is 0.0083333° (about 1 km). The recommended coordinate system for the raw data is WGS84, and the format of the raw data is NC format, which was processed into TIF format and analyzed in this study. In the introduction of the official website, it is pointed out that this dataset is generated by the Delta spatial downscaling scheme in China based on the global 0.5° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. Moreover, the data from 496 independent meteorological observation points were used for validation, and the results were verified to be credible. When these data were downloaded in this study in 20 June 2024, it was widely used and cited in multiple studies

2.3.2. Yearly Maximum Temperature Data

The yearly temperature data were obtained by averaging the monthly maximum temperature data of each year. The month-by-month temperature data, like the month-by-month precipitation data, come from the National Tibetan Plateau Scientific Data Center and are uploaded by Peng Shouzhang. The data can be downloaded from https://data.tpdc.ac.cn/zh-hans/data/35ffff9f-8e1b-4296-801f-d8231e4f8dc3. Data is accessed on 20 June 2024. The period, spatial extent, resolution, format, and processing of the raw data are identical to those of the monthly precipitation data. The same goes for the following ET data.

2.3.3. Yearly Evapotranspiration (ET) Data

The yearly ET data were obtained by averaging the monthly ET data of each year. The monthly ET data, along with the monthly precipitation data and the monthly maximum temperature data, were obtained from the National Tibetan Plateau Scientific Data Center and uploaded by Peng Shouzhang. The data can be downloaded from https://data.tpdc.ac.cn/zh-hans/data/8b11da09-1a40-4014-bd3d-2b86e6dccad4. Data is accessed on 20 June 2024. The official website states that this dataset is based on the 1 km month-by-month mean, minimum, and maximum temperature dataset in China, which is obtained by using the Hargreaves potential evapotranspiration formula. The formula is as follows: PET = 0.0023 × S0 × sqrt(MaxT − MinT) × (MeanT + 17.8), where PET is the potential evapotranspiration, mm/month; MaxT, MinT, and MeanT are the monthly maximum, minimum, and mean temperatures, respectively; S0 is the theoretical solar radiation reaching the top of the Earth’s atmosphere based on the S0 is the theoretical solar radiation reaching the top of the Earth’s atmosphere, which is calculated based on the solar constant, sun–Earth distance, Julian day, and equatorial latitude.

2.3.4. Year-by-Year NDVI Data

The data source used in this study is MYD13Q1 V6 with a spatial resolution of 250 m. The official description states that the MYD13Q1 V6 product provides vegetation index (VI) values on a per-pixel basis. There are two main vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as a continuum index of the existing National Oceanic and Atmospheric Administration High-Resolution Radiometer (NOAA-AVHRR)-derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI). The MODIS NDVI and EVI products are based on atmospherically corrected bi-directional surface reflectance that has been masked by water, clouds, heavy aerosols, and cloud shadows. The data for the study area were obtained from the GEE platform.

2.3.5. Elevation Data

The elevation data were obtained from the National Tibetan Plateau Science Data Center, and the data can be downloaded at https://data.tpdc.ac.cn/zh-hans/data/12e91073-0181-44bf-8308-c50e5bd9a734. Data is accessed on 19 October 2024. The data are 2000-year elevations with the spatial extent of the whole of China. The map of these data is a 1 km data based on 1:250,000 contour lines and elevation points in China. There are two projections of this data, Albers Conical Equal Area and the WGS84 coordinate system.

2.3.6. Land Use Data

The elevation data came from the National Tibetan Plateau Science Data Center, and the data can be downloaded at https://data.tpdc.ac.cn/zh-hans/data/a75843b4-6591-4a69-a5e4-6f94099ddc2d. Data is accessed on 16 April 2024. The official website describes that the dataset includes the late 1980s, 1990, 1995, 2000, 2005, 2010, and 2015, with Landsat TM/ETM remote sensing images of each period as the main data source, which were generated by manual visual interpretation. The spatial resolution is 1 kilometer and the projection parameters: Albers_Conic_Equal_Area central longitude 105, standard latitude 1: 25, standard latitude 2: 47. The data from 2015 were selected for this study. These data have high accuracy and are widely used in various studies.

3. Results

3.1. Precipitation Statistics for the Study Area

  • Prior to the year 2002
The climate conditions of the Lop Nur region have long been a focal point of academic research. Studies indicate that around 2200 BCE, the ecological environment of Lop Nur and its western shores underwent significant improvement, and during this period, the Loulan culture flourished [37]. By the Tang and Song dynasties, the region’s climate and environmental conditions had further optimized, providing favorable conditions for human activities and societal development. However, over time, the ecological environment of the Lop Nur region gradually deteriorated. Since then, numerous research teams have conducted in-depth investigations into the evolutionary processes of the Lop Nur environment.
The potassium salt research team led by Liu Chenglin from the Institute of Mineral Resources, Chinese Academy of Geological Sciences, has focused on the climatic and environmental evolution of the Lop Nur region, conducting thorough and systematic research [38]. Their findings reveal that since the Holocene, the ancient climate of the region has undergone a complex alternation between relatively wet, dry, very wet, dry, wet, and dry periods. These conclusions provide important scientific evidence and references for a deeper understanding of the climatic changes in the Lop Nur region and for exploring the impact of climate change on the regional ecological environment and the development of human civilizations.
Figure 4 shows the precipitation data for the study area between 1990 and 2001 prior to the large-scale construction of salt pans. The annual precipitation during this period fluctuated between 2.37 and 5.57 mm/year, with the lowest value recorded in 2001 at 2.37 mm, and the highest in 1993 at 5.57 mm. Over the 12 years from 1990 to 2001, there was one year with 2–3 mm precipitation, five years with 3–4 mm precipitation, four years with 4–5 mm precipitation, and two years with 5–6 mm precipitation. As shown in Figure 4, while there was some fluctuation in precipitation from 1990 to 2001, the overall trend shows a decrease.
b.
During the period from 2002 to 2022
The annual precipitation statistics histogram (Figure 5) from 2002 to 2022 showed that the annual precipitation in the study area fluctuated between 2.20 and 5.67 mm/year. The lowest annual precipitation was recorded in 2022 with 2.20 mm, while the highest occurred in 2007 with 5.67 mm. Peaks of annual precipitation were observed in 2007, 2013, and 2017. Out of the total 21 years from 2002 to 2022, there were 5 years with precipitation ranging 2–3 mm, 6 years with precipitation ranging 3–4 mm, 8 years with precipitation ranging 4–5 mm, and 2 years with precipitation ranging 5–6 mm. Annual precipitation was predominantly concentrated in the range of 3–5 mm.

3.2. Sen-Based Precipitation Trend Analysis

Within the research area, annual precipitation data from 2002 to 2022 were analyzed using Sen-based trend analysis, as shown in Figure 6. The yellow portion represents areas with Sen values less than 0, indicating a decreasing trend of annual precipitation, which covers 62% of the total area of the study area. The blue portion represents areas with Sen analysis results greater than 0, indicating an increasing trend of annual precipitation in this region, covering 38% of the total area of the research area. Spatially, the red boundary of annual precipitation change in Figure 6 spreads roughly in a northwest direction. The regions experiencing a decline in annual precipitation are predominantly located in the southwestern part of the study area, whereas areas with increasing annual precipitation are primarily situated in the northeastern part with a minor section in the southeastern corner. It can be seen that the value of Sen is unstable near the annual precipitation cut-off line (Figure 6), indicating that the trend of annual precipitation is unstable. However, away from the annual precipitation cut-off line, the value of Sen is basically stable, either greater than 0 or less than 0.
It has been suggested that there may be a link between human activities and drought risk since the beginning of the 20th century [39]. Since the mid-to-late 1980s, scientists have increasingly focused on the impact of human activities on drought, noting a distinct “warming and wetting” trend in northwest China’s arid regions. Among them, the summer precipitation in the Tarim Basin showed significant interannual variations [40]. In recent decades, summer precipitation in the Tarim Basin has shown an increasing trend [41].
Several studies have highlighted the Tarim Basin’s low pre-precipitation conversion rate, suggesting that there is potential for improvement, and have explored methods to artificially influence the hydrological cycle in the region [42]. The Lop Nor area, located in the eastern part of the Tarim Basin, has long been recognized for its harsh, inhospitable environment, earning it the reputation of a “no-man’s land.” However, recent research led by Lu Shanlong, an associate researcher at the Institute of Air and Space Information Innovation, has revealed a new finding: human activities are playing a role in mitigating the arid conditions of Lop Nor [43].
Building on previous studies, this research analyzed long-term precipitation data from the Lop Nor region. The findings indicate that annual precipitation in the northeastern part of the Lop Nor salt flats has shown an increasing trend. Furthermore, evidence of human activities was found to be widespread throughout the area. It is plausible to hypothesize that human intervention, along with other environmental factors, has influenced the region’s ecology, contributing to the observed increase in annual precipitation.
The research area is dominated by the Yardang landscape with an arid climate and scarce precipitation. Lop Nor completely dried up in 1962, and its groundwater system underwent a transformation from a once unified pool of surface and groundwater to several relatively independent groundwater systems [44]. There is no surface water system in the research area, only many dry riverbeds, alluvial gullies, and alluvial fans formed at the lake entry point, with a small amount of flood water recharge during summer floods [45].
Precipitation is the process involving the phase change in water in the atmosphere. Three essential conditions are required for precipitation to occur: (1) water vapor must be transported from a water source to the precipitation region, (2) water vapor must rise and condense in the precipitation region to form clouds, and (3) cloud droplets must grow sufficiently to become raindrops and fall [46]. As the material basis for precipitation, water vapor content plays a critical role in influencing precipitation. Numerous studies have demonstrated a strong correlation between regional water vapor content and precipitation with higher water vapor content generally associated with increased precipitation [47,48,49]. The evaporation of large brine surfaces during potassium salt field mining can potentially modify the local water vapor cycle and energy balance. Extensive brine surfaces exposed to the atmosphere create significant evaporation zones. Moreover, during potash mining and processing, brines remain in prolonged contact with air, sustaining the evaporation process. This evaporation enhances atmospheric water vapor content, which, under favorable atmospheric circulation conditions, may stimulate the development of localized convective activity, potentially influencing precipitation patterns [50,51]. Additionally, potash field mining often involves extracting substantial amounts of groundwater, which can alter groundwater levels and flow dynamics. Surface water and groundwater are complex systems that are interrelated and interact with each other [52]. Such changes in groundwater may affect surface vegetation and soil moisture, indirectly impacting precipitation. Nevertheless, the scale and extent of these effects warrant further investigation and quantitative analysis.
Field test data also support this argument. As shown in Figure 7, the Chinese Academy of Geological Sciences (CAGS) has observed various hydrological data in the Lop Nor area for many years, and these hydrological data showed the tendency for drought abatement in the northeastern part of the salt flats. The measured data prove the accuracy of the results of this study. At the same time, the conclusion that salt field mining has improved the drought situation in the surrounding areas was also verified through visits and communication with workers who have been in charge of the salt field mining for a long time.
The increasing precipitation trend in the Lop Nor region signifies a notable shift in the regional climate system with broad implications for both ecology and socio-economic development. First, higher precipitation can enhance local water resources, fostering plant growth and ecological recovery, particularly in arid and semi-arid areas. Sufficient rainfall supports vegetation, boosts land productivity, improves soil fertility, and may mitigate desertification. Additionally, increased precipitation could raise the water table, easing water scarcity, promoting biodiversity, and improving ecosystem services. Second, this trend may provide more irrigation water, especially during dry periods, alleviating water shortages and supporting the growth of crops that require substantial water. However, precipitation variability presents challenges, as fluctuations can strain water infrastructure and necessitate more robust water management to address potential flooding or droughts. Furthermore, changes in precipitation may influence groundwater levels, potentially affecting mineral resource extraction.
Due to some objective conditions, there are some shortcomings in this study. First of all, there may be some errors in the remote sensing data, which need to be noticed. In addition, since the large construction of the salt field began in 2002, the duration is not long enough, so the amount of data involved in this study needs to be increased.

3.3. Significance Classification of Precipitation Trends

Based on the Sen trend analysis of precipitation in the research area, we further investigated the significance of the trend with the TFPW-MK test. The absolute values of the MK test were greater than 1.65, 1.96, and 2.58, which indicates that the trend of precipitation change has passed the confidence level of 90%, 95%, and 99%, respectively. The significance classification of the precipitation trend is determined based on the Sen trend value and the TFPW-MK test value.
Based on the Sen slope value and Z value, the precipitation trend in the study area can be categorized into three classes, namely, non-significant decrease, no change, and non-significant increase. The area of each class in the study area in descending order is non-significant decrease, non-significant increase and no change, corresponding to 55%, 25% and 20% of the area (Figure 8).
The Lobo region is located in the eastern part of the Taklamakan Desert. As shown in the map of land use types, the research area that showed a decreasing trend is mainly the Taklamakan Desert area. The desert region has a long period of drought and harsh climatic conditions, and salt fields have less impact on its climate. Thus, it showed a non-significant downward trend, as shown in the figure.
As shown in Figure 8, the mitigation of drought conditions in the surrounding areas by the salt flats is mainly concentrated in the northeastern region. The land use types in the region of no change are mainly saline and sandy with the land use types in the region of no significant increase mainly Gobi and bare rock. Whether the impact of the salt flats on the climate of the surrounding region is related to the land use types needs further consideration.

4. Discussion

4.1. Factors Affecting Precipitation in the Tarim Basin

There are many factors affecting precipitation in the Tarim Basin. Both the Tibetan Plateau summer winds and the South Asian summer winds can affect the summer precipitation in the Tarim Basin [41,53]. The former is a direct effect, while the latter is an indirect effect. Thermal anomalies of the Iranian Plateau sensible heat and tropical Indian Ocean SST in summer can also modulate summer precipitation variations in the Tarim Basin by influencing the 500 h Pa wind field and water vapor transport. Meanwhile, different South Asian plateau patterns can also affect summer precipitation in the Tarim Basin [54]. In addition, the Silk Road atmospheric remote correlation model has increased summer precipitation in the Tarim Basin since 2009 [55].
Extensive research has shown a correlation between NDVI, air temperature, ET, and precipitation [56,57,58]. On this basis, these three factors are selected in this study to investigate their biased correlation with precipitation. Unlike previous studies, due to the special characteristics of the Lop Nor region, this study also combines the effect of salt field construction on precipitation and explores the changes of precipitation in many aspects.

4.2. Statistics on Factors Affecting Precipitation

Figure 9 illustrates the variations in annual mean temperature, ET, and NDVI in the research area over the span of 21 years, which were obtained by analyzing the time series data using one-way linear regression.
The temperature in the research area ranged from 18.18 to 19.82 °C with the lowest temperature year being 2014 and the highest temperature year being 2022; evapotranspiration in the research area ranged from 101.73 to 109.16 mm with the lowest temperature year being 2003 and the highest temperature year being 2022; NDVI in the study area ranged from 0.0468 to 0.0550 with the lowest NDVI year being 2002 and the highest NDVI years being 2020 and 2021.
As illustrated in Figure 9, the temperature, evapotranspiration (ET), and NDVI in the study area exhibited a univariate linear distribution from 2002 to 2022, with p-values less than 0.05, indicating statistical significance. Temperature, ET, and NDVI all demonstrated an increasing trend over time. The rates of increase, in descending order, were evapotranspiration, temperature, and NDVI with corresponding slopes of 0.191, 0.033, and 0.0004, respectively.

4.3. Partial Correlation Coefficient and Significance Analysis

  • Coefficient of partial correlation between temperature and precipitation and analysis of significance
Figure 10a shows the partial correlation coefficients between temperature and precipitation. A partial correlation coefficient greater than 0 indicates a positive correlation, while less than 0 indicates a negative correlation, and the larger the absolute value, the stronger the correlation. The partial correlation coefficients between precipitation and temperature are divided into six levels with the boundaries of −0.3, −0.2, 0, 0.2, and 0.3. As shown in Figure 10a, there are differences in the correlation between precipitation and temperature in the research area with positive correlation dominating in the western region and negative correlation dominating in the eastern region. The positive correlation area has a certain stratification phenomenon, i.e., the closer to the western part of the research area, the more obvious the positive correlation. The area corresponding to each level is shown in Table 1.
The T-test was used to investigate the significance of partial correlation coefficients. Figure 10b shows the bias correlation coefficients that passed the significance test (p < 0.05). The areas with significant partial correlation between temperature and precipitation in the research area are positively correlated. The area of significant positive correlation is 3404.23 square kilometers, which is 4% of the total area of the research area.
b.
Coefficient of partial correlation between ET and precipitation and analysis of significance
Figure 11a shows the partial correlation coefficients between ET and precipitation, with correlation coefficients greater than 0 indicating a positive correlation and less than 0 indicating a negative correlation; the larger the absolute value, the stronger the correlation. The partial correlation coefficients between precipitation and ET are divided into six levels with −0.3, −0.2, 0, 0.2, and 0.3 as the boundaries. As shown in Figure 11a, the correlation between evapotranspiration and precipitation is mainly negative with only a few areas showing a positive correlation. The area corresponding to each level is shown in Table 2.
The T-test was used to investigate the significance of the partial correlation coefficients. Figure 11b shows the bias correlation coefficients that passed the significance test (p < 0.05). The areas of the research area where evapotranspiration was significantly biased and correlated with precipitation were all negatively related. The area of significant negative correlation is 277.89 square kilometers or 0.35% of the total area of the study area.
c.
Coefficient of partial correlation between NDVI and precipitation and analysis of significance
Figure 12a shows the partial correlation coefficients between NDVI and precipitation. The correlation coefficients greater than 0 indicate a positive correlation, while less than 0 indicates a negative correlation; the larger the absolute value, the stronger the correlation. The partial correlation coefficients between precipitation and NDVI are categorized into six levels with the boundaries of −0.3, −0.2, 0, 0.2, and 0.3. As shown in Figure 12a, the area of negative correlation between NDVI and precipitation is larger than that of positive correlation. The negatively correlated areas are mainly concentrated in the southwestern part of the study area, while the positively correlated areas are mainly concentrated in the northeastern part of the research area. Compared with the biased correlation between temperature and precipitation, the correlation between NDVI and precipitation was not significantly stratified, but there was some stratification Both positive and negative correlations exhibited an upward trend toward the northeast and southwest with the main diagonal of the study area as the axis. The area of the region corresponding to each stratification is shown in Table 3.
Figure 12b shows the bias correlation coefficients that passed the significance test (p < 0.05). The areas with significant partial correlation between NDVI and precipitation in the study area are of two types: significant positive and significant negative correlation. The area of significant positive correlation is 1270.11 km2, accounting for 1.59% of the total area of the study area. The area with significant negative correlation is 6097.93 km2, accounting for 7.62% of the total area of the study area.

5. Conclusions

In this study, we conducted Sen trend analysis, the TFPW-MK significance test, and partial correlation analysis. This analysis, based on 2002–2022 data for growing season precipitation, annual maximum temperature, evapotranspiration, and NDVI, showed an increasing precipitation trend in the study area’s northeast. Field observations and prior studies suggest that salt flat construction mitigates local aridity. This may be related to the fact that evaporation increases the water vapor content of the atmosphere. Under favorable atmospheric circulation conditions, the increase in water vapor content may stimulate the development of local convective activity, which could potentially affect precipitation patterns. Subsequently, we conducted a partial correlation analysis between the three influencing factors—temperature, evapotranspiration, and NDVI—and precipitation. We obtained the partial correlation coefficients and significance plots for these three factors and precipitation. Additionally, we analyzed their spatial characteristics.
The results of the present study bridge the gap in this research area and are of great practical importance. This study proposed that the construction of salt fields may improve the drought situation in the neighboring regions and provide new ideas for subsequent related studies. The governance of arid and semi-arid regions has been a matter of great concern, and this study provides relevant data references for subsequent regional environmental governance decisions. Policy makers can formulate policies on water resources, ecological protection and industrial development based on the trend of precipitation growth in Lop Nor, planning water use, constructing water conservancy, protecting ecology, and adjusting industrial structure; while researchers conduct in-depth investigations into the causes and trends of precipitation and carry out technological innovations, which will provide the basis for policies. As a highly significant uninhabited area, the drought conditions in Lop Nor—potentially mitigated through modern potash mining—offer valuable insights for addressing drought in other regions globally as well as for advancing potash mining practices.
It is believed that with the further expansion of the scale of salt field construction and the growth of the salt field over time, the research on the climate impact of salt field construction on the neighboring regions will be further improved based on more climate and related data. Climate change is a long-term process, and the impact of the construction of the salt flats on the regional climate will be monitored over the long term as well as studied in the future.

6. Research Limitations

Since the large construction of the salt field began in 2002, the amount of precipitation data for the calendar year after the beginning of construction is small. Beyond temperature, evapotranspiration, and the Normalized Vegetation Index (NDVI), other natural factors significantly influence precipitation. Topography plays a crucial role, with mountainous regions typically experiencing higher precipitation due to orographic uplift, while plains and basins may receive less precipitation due to airflow disruption and uneven turbulence. Additionally, atmospheric circulation patterns, such as monsoons and tropical cyclones, strongly affect both global and regional precipitation patterns. For instance, the Asian monsoon profoundly impacts precipitation in northwest China with its intensity directly correlating to precipitation levels. Furthermore, temperature gradients between land and sea, as well as ocean surface temperature variations, can induce significant changes in precipitation distribution. These factors must be considered in future research.
Moreover, multi-source data fusion techniques combine remote sensing and meteorological observation data, compensating for the limitations of individual datasets. This approach provides a higher spatial resolution and broader temporal coverage of precipitation data. Finally, advanced statistical methods, including machine learning algorithms, can be employed to analyze the complex relationships between precipitation variability and multiple environmental and anthropogenic factors, leading to more accurate and comprehensive insights.

Author Contributions

Conceptualization, F.Y. and Y.W.; methodology, Y.W. and C.L.; software, Y.W. and Y.S.; validation, F.Y. and C.L.; data curation, N.J. and X.G.; writing—original draft preparation, Y.W.; writing—review and editing, F.Y. and C.L.; supervision, Y.S. and X.G.; project administration, N.J. and X.G.; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the third Xinjiang comprehensive scientific investigation subject (2022xjkk1303), National Key R&D Program Subjects (2022YFC2903305), National Natural Science Foundation of China (No. 41102205), Xinjiang major Science and Technology special projects (No. 2022A03009-3) and the Special Funds. Projects for Basic Scientific Research Business Expenses of Mineral Resources Research Institutes in Chinese Academy of Geological Sciences (No. KK2006).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location map of the area around Lobo Salt Flats: (a) China’s position in the world; (b) location of the study area in China; (c) remote sensing images of the study area.
Figure 1. Location map of the area around Lobo Salt Flats: (a) China’s position in the world; (b) location of the study area in China; (c) remote sensing images of the study area.
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Figure 2. Land use map.
Figure 2. Land use map.
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Figure 3. Research flowchart.
Figure 3. Research flowchart.
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Figure 4. Year-by-year growing season precipitation statistics from 1990 to 2001.
Figure 4. Year-by-year growing season precipitation statistics from 1990 to 2001.
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Figure 5. Year-by-year growing season precipitation statistics from 2002 to 2022.
Figure 5. Year-by-year growing season precipitation statistics from 2002 to 2022.
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Figure 6. Trend analysis of precipitation based on Sen’s slope and its dividing line.
Figure 6. Trend analysis of precipitation based on Sen’s slope and its dividing line.
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Figure 7. Field actual measurement map.
Figure 7. Field actual measurement map.
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Figure 8. Precipitation trend hierarchy.
Figure 8. Precipitation trend hierarchy.
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Figure 9. Annual average data: (a) temperature; (b) evapotranspiration; (c) NDVI.
Figure 9. Annual average data: (a) temperature; (b) evapotranspiration; (c) NDVI.
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Figure 10. Temperature and precipitation analysis plot: (a) bias correlation coefficient; (b) significance.
Figure 10. Temperature and precipitation analysis plot: (a) bias correlation coefficient; (b) significance.
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Figure 11. ET and precipitations analysis plot: (a) bias correlation coefficient; (b) significance.
Figure 11. ET and precipitations analysis plot: (a) bias correlation coefficient; (b) significance.
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Figure 12. NDVI and precipitation analysis plot: (a) bias correlation coefficient; (b) significance.
Figure 12. NDVI and precipitation analysis plot: (a) bias correlation coefficient; (b) significance.
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Table 1. Area of each class of temperature and precipitation bias correlation coefficient.
Table 1. Area of each class of temperature and precipitation bias correlation coefficient.
Partial Correlation CoefficientArea (km2)
<−0.30.50
−0.3~−0.222.81
−0.2~014,363.30
0~0.227,789.99
0.2~0.320,468.41
>0.317,405.02
Table 2. Area of each class of ET and precipitation bias correlation coefficients.
Table 2. Area of each class of ET and precipitation bias correlation coefficients.
Partial Correlation CoefficientArea (km2)
<−0.35893.53
−0.3~−0.224,785.32
−0.2~047,019.00
0~0.22321.91
0.2~0.321.93
>0.30.53
Table 3. Area of each class of NDVI and precipitation bias correlation coefficient.
Table 3. Area of each class of NDVI and precipitation bias correlation coefficient.
Partial Correlation CoefficientArea (km2)
<−0.314,462.57
−0.3~−0.28813.14
−0.2~020,105.57
0~0.222,550.27
0.2~0.38748.77
>0.35359.48
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Wang, Y.; Yao, F.; Liu, C.; Geng, X.; Shao, Y.; Jiang, N. Analysis of Precipitation Change and Its Influencing Factors Around the Lop Nor Salt Flat. Water 2025, 17, 770. https://doi.org/10.3390/w17050770

AMA Style

Wang Y, Yao F, Liu C, Geng X, Shao Y, Jiang N. Analysis of Precipitation Change and Its Influencing Factors Around the Lop Nor Salt Flat. Water. 2025; 17(5):770. https://doi.org/10.3390/w17050770

Chicago/Turabian Style

Wang, Yuke, Fojun Yao, Chenglin Liu, Xinxia Geng, Yu Shao, and Nan Jiang. 2025. "Analysis of Precipitation Change and Its Influencing Factors Around the Lop Nor Salt Flat" Water 17, no. 5: 770. https://doi.org/10.3390/w17050770

APA Style

Wang, Y., Yao, F., Liu, C., Geng, X., Shao, Y., & Jiang, N. (2025). Analysis of Precipitation Change and Its Influencing Factors Around the Lop Nor Salt Flat. Water, 17(5), 770. https://doi.org/10.3390/w17050770

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