Spatial and Temporal Distribution of Cloud Water in the Yellow River Basin, China

Abstract

The Yellow River Basin is essential to China’s economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m⁻²), while the low-value area appeared on the Loess Plateau (2 g·m⁻²). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m⁻²). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m⁻²). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin.

1. Introduction

Precipitation is the key link in the Earth’s water cycle, and is also the most important input parameter for hydrological process simulation in river basins [1] (Zeng et al., 2019). Water resources are key factors that restrict economic development in northern China, and air–water resources are an important part of water resources [2,3] (Li et al., 2008; Wang et al., 2012). Scientific evaluation of air–water resources has great potential value and strategic significance for effectively alleviating the shortage of water resources, improving the ecological environment, and ensuring the sustainable development of the social economy [4] (Liu et al., 2019). Cloud water resources and water vapor content are indispensable components of air–water resources. Hence, it is critical to study their spatial and temporal distribution characteristics to improve regional water resources.

Nevertheless, due to the constraints of climatic conditions and hydrological processes, the water resources, food, and ecological security of the Yellow River Basin are at greater risk [5] (Huang et al., 2015). Determining how to effectively manage the decreasing water resources has become a significant challenge for the sustainable development of the Yellow River Basin [6] (Zhang et al., 2014). Cloud water resources are water resources stored in clouds that can be used for natural precipitation or artificial precipitation. The water content in clouds plays an essential role in the growth process of cloud droplets and the formation and intensity of the precipitation. It is not only an important parameter for numerical simulation of global climate, but also a key to the influence of clouds on climate [7] (Chen et al., 2013). The water content in clouds is affected by many factors, among which atmospheric circulation and topography are important factors affecting the spatial differentiation of cloud water content. Horizontal atmospheric circulation determines the transport channel and budget of water vapor in the air, while the downward flow in vertical circulation leads to the aggravation of drought in the region and affects the formation of medium and low clouds, which has a great influence on cloud water resources in the air [8] (Liu et al., 2018). The distribution of cloud water content and cloud ice content is influenced by atmospheric circulation and topography, which have strong regional characteristics [9] (Heng et al., 2011). Plateau topography, in particular, has a complex influence on water vapor convergence and divergence, due to each plateau layer’s unique water vapor transport in summer. The water vapor transport flux in the surface layer of the plateau is much larger than that in the middle and upper layers, and the maximum value area of water vapor transport is located on the southeast part of the plateau [10] (Wang et al., 2014). The cloud water resources in the plain areas show a weak, increasing trend with the increase years [11] (Dai et al., 2009).

The NCEP/NCAR reanalysis data and relatively mature satellite data, such as ISCCP and CloudSat data, have been used to study the water vapor flux many times, but it is difficult to analyze the temporal and spatial changes and laws of cloud water resources on a longer time scale due to time scale and spatial resolution limitations. ERA-Interim is one of the better-performing reanalysis products [12] (Beck et al., 2017) and is widely used to assess the impact of climate change on the atmospheric cycle, ecosystem carbon, and water cycle [13] (Zhang et al., 2022). Many scholars have confirmed the high accuracy and applicability of ECMWF reanalysis information globally or regionally through their studies [14] (Balsamo et al., 2015). Guan et al. used ERA-Interim reanalysis data to analyze the spatial distribution, annual seasonal variability, and transport characteristics of Central Asia and the arid region of China (CA–AC) water vapor during 1979–2012 and concluded that CA-AC is the region with the lowest water vapor content at low and mid-latitudes in Asia [15] (Guan et al., 2018). Xu et al. used the zenith trophic delay (ZTD) data of seven regional International GNSS Service (IGS) stations in China in 2018, combined with the meteorological reanalysis data of ERA-Interim in 2018, compared and analyzed the accuracy and reliability of calculating ZTD from ERA5 data, and analyzed the variation characteristics of the deviation and root mean square error of ERA5 data time series, in addition to the influence of the long time and high spatial resolution characteristics of ERA5 data on calculating ZTD. The results showed that the accuracy of calculating ZTD from ERA5 data is better than that from ERA-Interim data in some stations. The accuracy of calculating ZTD from the two data sources has a certain relationship with the spatial position of the stations. Moreover, the deviation of ZTD calculated by ERA5 data is slightly smaller than that of ERA-Interim data with the same spatial and temporal resolution and has high accuracy and stability [16] (Xu et al., 2020). Gong et al. investigated the applicability of the numerical model information of the European Center for Medium-Range Numerical Forecasts (ECWMF), the Japan Meteorological Agency (JMA), and National Oceanic and Atmospheric Administration’s Global Forecast System (GFS) from July 2010 to July 2013 using the national automatic stations of the China Meteorological Administration, and showed that ECWMF is closer to the actual observations than JMA and GFS [17] (Gong et al., 2015). Therefore, we conducted a scientific and in-depth analysis of the spatial and temporal characteristics of air cloud water in the Yellow River Basin based on ERA5 reanalysis data with longer time scales and higher spatial resolution and analyzed the effects of global warming on the changes in local atmospheric water vapor content, cloud liquid water, and cloud ice water. This research can be used to quantify the temporal and spatial changes in cloud water in the Yellow River Basin, analyze the impact of climate change on cloud water, and evaluate the development potential of cloud water in the Yellow River Basin, thus providing scientific support for the rational development and utilization of cloud water in the Yellow River Basin.

2. Study Area and Methods

2.1. Study Area

As the second-largest river in China, the Yellow River has a total length of 5464 km, providing domestic water for about 9% of China’s population and irrigating 17% of China’s cultivated land [18] (Giordano et al., 2004) (Figure 1). The terrain of the Yellow River Basin is undulating, with elevations ranging from 65 to 6813 m, with an average annual rainfall of 466 mm. The upper reaches of the Yellow River in the west are located in the Qinghai-Tibet Plateau, which is the main water-producing area and provides freshwater resources to meet agriculture, industry, and ecological requirements in the whole Yellow River basin. The main land types in this area are grassland, lake, and desert. In the middle and upper reaches, the Loess Plateau is one of the regions with the most serious soil erosion in the world [19] (Lu et al., 2012), and the main land types are grassland, desert, forest, and farmland [20] (Su et al., 2013). The lower reaches are plain areas, mainly cultivated land, providing food production services [21] (Wang et al., 2019). In the basin, the upstream precipitation lasts for a long time with low intensity, resulting in small flood peaks and large runoff. Midstream precipitation occurs for a short time with high intensity, resulting in high flood peaks and small runoff, and mostly develops into rainstorms and floods causing great harm. There is a huge gap in the socio-economic development level between the northwest and southeast of the Yellow River Basin [22] (Treacy et al., 2018). The Yellow River provides abundant water energy for industrial and agricultural development in the flowing areas.

2.2. Research Methods

2.2.1. Data Source

This study used the new generation monthly ERA5 reanalysis data released by the European Numerical Forecasting Center (ECWMF) with global resolution. ECMWF is based on its prediction model and data assimilation system to “reanalyze” archived observations, thus creating a global data set describing the recent history of atmosphere, land, and ocean. The accuracy of the data was improved by making full use of the four-dimensional assimilation system. In recent years, the performance of ERA5 numerical prediction products has been generally recognized [23] (Pan et al., 2013), and the data have also been adopted to study water vapor transport [24] (Xie et al., 2018) and cloud water content distribution [25] (Liu et al., 2018) in summer on the plateau and surrounding areas.

2.2.2. Research Methods

In this study, the ERA5 reanalysis data were selected for the Total Column Water Vapor (TCWV), Cloud Liquid Water Content (CLWC), Cloud Ice Water Content (CIWC), Total Precipitation (TP), 850 hpa Specific humidity, U-component of wind, and V-component of wind. The temporal resolution is hourly and the spatial resolution is 0.25° × 0.25°. The cloud water content is the sum of cloud liquid water and cloud ice water. The four seasons are divided according to the meteorological division method: March, April, and May are spring; June, July, and August are summer; September, October, and November are autumn; and December, January, and February are winter. In calculations, the atmospheric water vapor content (TCWV), cloud liquid water content (CLWC), and cloud ice water content (CIWC) are the water vapor measures of each barometric layer. Among them, the multi-year statistical value was calculated by the average of the annual statistical value. The annual average was calculated by the average of each month, and the average of each month was calculated by the average of each day. TCWV, CLWC, CIWC, and TP were averaged using the mean function in Python, and the water vapor fluxes were also implemented in Python and calculated as follows:

(1)$\mathrm{Q}=\frac{1}{g}{\displaystyle {\int }_{Ps}^{Pt}(u,v)}qdp$

where Q is the water vapor flux, q is the specific humidity, Ps is the surface pressure, Pt is the atmospheric top pressure, u and v are the longitudinal wind component and the latitudinal wind component, respectively, and g is the acceleration of gravity.

3. Results and Analysis

3.1. Spatial Distribution Characteristics of Atmospheric Water Vapor Content

3.1.1. Annual Average Distribution

Atmospheric water vapor content indicates the amount of liquid water obtained when all the water vapor in the whole air column condenses, which is closely related to the length and altitude of the air column. It can be seen from the distribution of the annual average water vapor content in the Yellow River Basin (Figure 2a) that the water vapor content is mainly between 3 and 21 mm in the Yellow River Basin. The high-value area is distributed primarily in the central part of Henan Province, which is rich in water vapor content, with the highest water vapor content being 21 mm. The Guanzhong Plain in central Shaanxi and Shandong Provinces has low terrain and a long air column, and the second-highest water vapor content in the Yellow River Basin. The water vapor content is between 12 and 21 mm. The area with low water vapor content is Qinghai Province, the birthplace of the Yellow River, with a water vapor content of only 3 mm. This area has a high altitude, relatively short air column, thin atmosphere thickness, and low density, so the total water vapor content of the air column is relatively small.

3.1.2. Seasonal Distribution

From Figure 2, it can be seen that the seasonal spatial distribution of water vapor content was the same as that of the annual average. The water vapor content was the highest in summer, followed by autumn and winter, and the least in spring. In spring (Figure 2b), the high-value center of water vapor content was in Henan Province, reaching 21 mm, while the Loess Plateau and Qinghai Province were the low-value centers, with the water vapor content being less than 12 mm. In summer (Figure 2c), the water vapor content increased obviously in the Yellow River Basin, and the areas with high water vapor content increased. There was high water vapor content in Henan and Shandong Provinces, and the water vapor content in the central area was up to 24 mm. The low water vapor content was similar to that in spring, but the low water vapor content decreased obviously. In autumn (Figure 2d), the high-value areas decreased, and the low-value areas increased, compared with summer. The high-value central regions were concentrated in Henan and Shandong Provinces, with the highest water vapor content of 24 mm, while the low-value areas were in Qinghai Province and Loess Plateau, with the lowest water vapor content of 6 mm. In winter (Figure 2e), the high-value areas were still in Henan and Shandong Provinces, with the highest water vapor content of 21 mm, while the low-value areas in the Loess Plateau reduced, and the low-value areas were mainly concentrated in Qinghai Province, with the lowest value of only 3 mm. Generally speaking, the high-value areas of water vapor content were distributed on the plains, basins, and hilly areas at low altitudes, while the low-value areas were distributed above plateau areas at higher altitudes.

3.2. Spatial and Temporal Distribution Characteristics of Cloud Water Content

3.2.1. Spatial Distribution Characteristics

Cloud liquid water content and cloud ice water content are commonly adopted indicators to measure the amount of liquid water and solid water in clouds. In order to understand the distribution of cloud liquid and cloud ice water content, the annual average distribution of cloud ice and cloud liquid water content was calculated from 1980 to 2019 (Figure 3a,f). It can be seen that the distribution situation is different from that of atmospheric water vapor content. The area with high ice water content was located in Maqu County, Gansu Province, with a maximum value of 0.45 g·m⁻², while the low-value area was concentrated on the Loess Plateau, with a cloud ice water content of 0.005 g·m⁻².

However, the high-value area of cloud liquid water content is in the southern part of the middle reaches of the Yellow River, with a maximum value of 0.12 g·m⁻². The reason for this is that the cloud cover in this area is thicker and, furthermore, due to the uplift of water vapor by the mountains, the water vapor is condensed, and can easily form topographic clouds and increase the cloud liquid water content in the atmosphere. However, as the result of low air pressure, thin air and low cloud cover occurred on the Loess Plateau and Qinghai Province, and the cloud liquid water content was only 0.02 g·m⁻². High-altitude areas have low air pressure, thin air, and few clouds, resulting in less cloud liquid water content.

In conclusion, the cloud ice water and cloud liquid water contents were greater in the low-altitude areas than in the high-altitude areas in the Yellow River Basin. The cloud liquid water content was much higher than cloud ice water content.

3.2.2. Seasonal Distribution

From the seasonal distribution map of cloud ice water content in the Yellow River Basin (Figure 3), it can be seen that the distribution and characteristics of the seasonal high and low values are basically the same. The high-value area of cloud ice water content in spring is in the eastern part of Qinghai Province (Figure 3b), with the highest cloud ice water content of 0.55 g·m⁻², whereas the lowest value was 0.015 g·m⁻² on the Loess Plateau. In summer, the cloud ice water content diminished obviously (Figure 3c). The high-value areas were in Henan and Shandong Provinces, with the highest cloud ice water content of 0.06 g·m⁻², and the low-value areas were in Loess Plateau and the southeastern part of Gansu Province, with the lowest value being 0.015 g·m⁻². Compared with summer, the cloud ice water content in autumn was still declining (Figure 3d). The high-value area was in Maqu County, Gansu Province, with the highest cloud ice water content of 0.05 g·m⁻². Loess Plateau, in central Shaanxi Province, and western Shanxi Province were the low-value areas with the lowest value of 0.005 g·m⁻². In winter (Figure 3e), the cloud ice water content was higher than that in summer and autumn. The highest content of cloud ice water was 0.025 g·m⁻² in the lower reaches of the Yellow River, whereas, in the north-central areas, Loess Plateau was the location of low-value areas with the lowest value of 0.005 g·m⁻².

By comparison, cloud liquid water content distribution had little seasonal difference in the Yellow River Basin. The distribution areas of high and low values in all seasons were the same. The high-value areas were concentrated on Xi’an City and its surrounding areas in Shaanxi Province (Figure 3g–j), with the highest value of 0.2 g·m⁻², while the low-value areas were concentrated in Loess Plateau, eastern Qinghai Province, and central and southern Gansu Province, with the lowest value of only 0.002 g·m⁻².

3.2.3. Time Variations

From the time-variation figure of average water vapor content from 1980 to 2019 (Figure 4), there was no substantial change in atmospheric water vapor content from 2000 to 2019. Nevertheless, during 1980–2000, the atmospheric water vapor content showed an upward trend, with a peak of 16.27 mm in 1988, and began to fall with fluctuations after 2000. It fell to 10.14 mm in 2011, and the atmospheric water vapor content of the Yellow River Basin showed a slight upward trend from 1980 to 2019.

From the time-variation figure of annual average cloud ice water content from 1980 to 2019, the average cloud ice water content shows a downward trend, with small fluctuations. The maximum value appeared in 2002, but the minimum values were 0.0536 g·m⁻² and 0.0026 g·m⁻², respectively, in 2005. From the time-variation figure of annual average cloud liquid water content from 1980 to 2019, the average cloud liquid water shows a downward trend with huge fluctuations. The maximum value appeared in 1988, but the minimum values were 0.0776 g·m⁻² and 0.001 g·m⁻², respectively, in 2007. From the time-variation figure of seasonal water vapor content from 1980 to 2019, the water vapor content was the highest in summer, with an average value around 30 g·m⁻², followed by autumn and spring, and was the lowest in winter. The water vapor content shows a forward movement in spring, with a maximum value of 17.07 g·m⁻² in 2014 and a minimum value of 3.65 g·m⁻² in 1983. In summer, the water vapor content shows an ascendant trend, with a maximum value of 40.24 g·m⁻² in 2006 and a minimum value of 17.02 g·m⁻² in 1983. In autumn, the water vapor content shows a downward trend, with a maximum value of 11.98 g·m⁻² in 1995 and a minimum value of 6.44 g·m⁻² in 2005. In winter, the water vapor content shows an upward trend, with a maximum value of 10.86 g·m⁻² in 1998 and a minimum value of 1.84 g·m⁻² in 1993.

4. Discussion

4.1. The Relationship between Cloud Water and Precipitation

Precipitation is the most basic factor of climate change and the main driving force of land hydrological system change. The change in its distribution pattern directly impacts the water resources system [26] (Lu et al., 2021). Precipitation in the Yellow River Basin is mainly in the form of precipitation, and snowfall accounts for a small proportion. The annual average precipitation in the whole basin was 370.1 billion m³, accounting for 6% of China’s total annual average precipitation. According to the annual average total precipitation distribution map of the Yellow River Basin from 1980 to 2019 (Figure 5a), it can be seen that the precipitation is mainly concentrated between 0 and 250 mm, and the high-value areas are mainly distributed in Puyang City, Henan Province, Heze, Shandong Province and Maqu County, Gansu Province, where the highest value is 250 mm. The low-value areas are distributed in the Loess Plateau, having values less than 50 mm.

The terrain is complex in the Yellow River Basin. A substantial body of scholars has studied precipitation’s temporal and spatial characteristics in the Yellow River Basin and Northwest China. They found that the precipitation showed a linear opposite trend [27] (Zhang et al., 2003) in the east and west; that is, the west showed an increasing trend, and the east showed a decreasing trend [28,29,30,31,32] (Huang et al., 2004; Liu et al., 2007; Chen et al., 2009; Wei et al., 2010; Ren et al., 2016). Atmospheric water vapor content is the basis of precipitation and plays an important role in climate and weather in various regions. According to the study of cloud and precipitation, some studies showed that precipitation mainly occurs in cumulus, cumulonimbus, nimbostratus, stratocumulus, and stratus/stratus fractus [33,34] (Li et al., 2003; Li et al., 2015). Additionally, other studies pointed out that the forced uplift of summer topography in the Qilian Mountains affected the precipitation intensity of cumulus clouds and changed the structural characteristics of cloud water content [35,36] (Liu et al., 2007; Chen et al., 2010). Furthermore, other scholars have calculated the correlation coefficient between the occurrence frequency of different clouds and precipitation in the Qilian Mountains and found that the interannual change in precipitation in Northwest China was consistent with the change in stratiform clouds with large cloud water content [37,38] (Chen et al., 2010; Feng et al., 2018).

Comparing the annual average water vapor content (Figure 2a) with the annual average precipitation (Figure 5) in the Yellow River Basin, it was found that the high-value areas are basically the same, both in the northwest of Henan Province, whereas the low-value areas are different. Comparing the average water vapor content (Figure 2b–e) with the average precipitation (Figure 5b–e) in four seasons, the distribution of water vapor content has clear characteristics of high- and low-value distributions. The spatial distribution of the average precipitation of all seasons shows that the distribution of the central and western regions of the Yellow River Basin is low, and that the southern Qinghai Province in autumn has more precipitation. Comparing the annual averages of cloud ice water content (Figure 3a) and precipitation (Figure 5a) in the Yellow River basin, the distribution characteristics are more consistent, with the high-value areas distributed in the upper reaches of the Yellow River in Qinghai Province and the lower reaches in Henan Province and Shandong Province, while the low-value areas are mostly distributed in the Loess Plateau area. The distribution characteristics of cloud-liquid water content and precipitation are also similar. The distribution characteristics of cloud ice water and cloud liquid water content are also similar to those of precipitation. This is consistent with the results of Zhang et al., who studied the direct relationship between cloud liquid water and cloud ice water on precipitation during a single precipitation event [39] (Zhang et al., 2010). Therefore, cloud ice water and cloud liquid water content have a greater effect on precipitation distribution than water vapor content. These results are of great significance for further analysis of the relationship between precipitation and cloud water, understanding the dry–wet conditions in the region, and mastering the distribution of water vapor resources in the region. This knowledge can be used to effectively and rationally develop the cloud water in the northwest region and provide effective scientific support for carrying out artificial precipitation enhancement.

4.2. Effects of Atmospheric Circulation on Atmospheric Water Vapor and Cloud Water Resources

Atmospheric circulation is the most important factor affecting cloud water in the air. The horizontal atmospheric circulation determines the air–water vapor transport channel and the water vapor budget, whereas the downward flow in the vertical circulation leads to the aggravation of drought in the region and affects the formation of medium and low clouds, which has a greater impact on the air cloud water [9] (Heng et al., 2011). Feng Xing’s research on the Yellow River Basin also confirmed the influence of atmospheric circulation on cloud water in the air [38] (Feng et al., 2018). He pointed out that, during 1976–1995, most of the Yellow River Basin had more precipitation. Because of the blocking of high-pressure development at Wula Mountain and the control of the area from Baikal to Northeast Asia by a negative height anomaly, the Yellow River Basin was at the intersection of airflow in front of the ridge and the bottom of the trough. During 1996–2015, the precipitation in the whole basin of the Yellow River was less, which can be ascribed to the low circulation height to the north of the Ural Mountains in the middle and high latitudes, the consistently high height field from the Caspian Sea to Baikal and then to Northeast Asia, and the weakening of the westerly belt. Zhang Qiang et al. [40] (Zhang et al., 2007) analyzed the interaction between horizontal atmospheric circulation and water vapor transport in arid and semi-arid areas of East Asia, and concluded that the East Asian monsoon, southwest monsoon, plateau monsoon, westerly belt, and northwest airflow from West Siberia all have influences on water vapor content in different areas of this region to a certain extent. Qian Zhengan [41] (Qian et al., 2001) concluded that the vertical circulation can also affect the air cloud water in arid and semi-arid areas of East Asia.

In this study, we drew wind direction maps at 10 m above the ground (Figure 6a) and at 850 hpa at high altitude (Figure 6b), and discussed the relationship between water vapor content, cloud ice water, and cloud liquid water content by studying the wind direction at different heights. At 10 m above the ground, the near-surface wind direction in the southeast with large water vapor content was southeast. Influenced by the East Asian monsoon climate, the water vapor came from the northwest Pacific Ocean, so the southeast monsoon brought humid water vapor to the Pacific Ocean, which increased the water vapor content in the southeast of the Yellow River Basin. The near-surface winds in the northwest of the basin were mainly northwest, west, and northeast, and the water vapor source was located in the Asian continent, so the air mass carried less water vapor, which decreased the annual average water vapor content in the northwest of the Yellow River Basin. In addition, the wind speed in the northwest region of the Yellow River Basin was relatively low, while the wind speed in the southeast region was relatively high. Comparing the distribution characteristics of cloud ice water and cloud liquid water with the 10 m wind direction, it can be seen that, in the upper reaches of the Yellow River basin, the wind direction is related to the distribution of high to low values of cloud ice water and cloud liquid water, but in the middle and lower reaches both are less related to the wind direction. For the 850 hPa wind direction, the distribution of water vapor content, cloud ice water, and cloud liquid water content is closely related to the wind direction, and in the whole Yellow River basin, the wind direction is consistent with the decreasing direction from high to low values of water vapor content, cloud ice water, and cloud liquid water content. The above analysis shows that the water vapor content, cloud ice water, and cloud liquid water contents are greatly affected by the high-altitude 850 hpa wind.

Atmospheric circulation affects the region’s water vapor transport and evapotranspiration. In specific weather conditions, air water vapor and cloud water resources rise in the process due to the gradual reduction in surrounding air pressure, volume expansion, and temperature reduction, and gradually become fine water droplets or ice crystals floating in the air to form clouds. When the cloud droplets increase to overcome the resistance of the air and the top of the updraft, water vapor and cloud water form precipitation, thus providing surface water resources. Therefore, in future studies of atmospheric water vapor content and cloud water resources, the influence of high-altitude winds should be fully considered, to provide theoretical support for the development of airborne water vapor and cloud water resources.

5. Conclusions

In this study, the spatial distribution, annual average, and seasonal average distribution characteristics of water vapor content and cloud water resources in the Yellow River basin from 1980–2019 were analyzed using ERA5 reanalysis data, and it was found that:

• (1). From the spatial distribution, the distribution of high-value areas of annual average atmospheric water vapor content was mainly concentrated on plains and basins. There were high-value areas in Henan Province and southwestern Shaanxi Province in the Yellow River Basin with values of 21–24 mm. Low-value areas were mainly concentrated on plateau and piedmont areas, in Loess Plateau and Qinghai Province, with values of 3–6 mm. The annual average water vapor content distribution characteristics in all seasons were consistent with the annual average distribution characteristics. The high-value areas were distributed between 21 and 24 mm, but the low-value areas were distributed between 3 and 6 mm, showing the characteristics of high values in the southeast and low values in the northwest.

• (2). The spatial distribution characteristics of annual average cloud ice water and cloud liquid water content showed that the high-value area of cloud ice water content was in Maqu County, Gansu Province, with a maximum value of 4.5 g·m⁻², in contrast to the low-value area in the Loess Plateau at higher altitude, with a minimum value of only 2 g·m⁻². The high-value area of cloud liquid water content was in the south of Shanxi Province in the Yellow River Basin, and the maximum value reached 12 g·m⁻², whereas the low-value area appeared in the Loess Plateau and Qinghai Province, with the value of only 3 g·m⁻². From the perspective of the overall spatial distribution, the cloud water content in low-altitude areas was higher than that in high-altitude areas, and the cloud liquid water content was higher than that in cloud ice water.

• (3). During the period of 1980 to 2019, the annual average atmospheric water vapor content showed a slight upward trend, while the annual average cloud ice water content and cloud liquid water content showed a downward trend.

Author Contributions

G.Z. and K.Z. conceived the idea of the study; L.S. and Y.X. conducted formula analysis and methodological investigation; Y.L. and L.W. conducted data curation; K.Z. wrote the paper and organized software debugging; X.L., H.T. and J.L. carried out relevant calculation validation; W.Z. and L.Y. checked and edited language. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. Water vapor data can be obtained at the European Mid-range Weather Forecast Center (https://cds.climate.copernicus.eu/cdsapp#!/yourrequests?tab=form, accessed on 1 May 2022).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Figure 1. Location overview of the Yellow River Basin.

Figure 2. Annual average and seasonal average distribution of water vapor content in the Yellow River Basin from 1980 to 2019.((a): Annual average water vapour; (b): Spring average water vapour; (c): Summer average water vapour; (d): Autumn average water vapour; (e): Winter average water vapour.)

Figure 3. Annual and seasonal average distributions of cloud ice water and cloud liquid water in the Yellow River Basin from 1980 to 2019. ((a): Annual average cloud ice water; (b): Spring average cloud ice water; (c): Summer average cloud ice water; (d): Autumn average cloud ice water; (e): Winter average cloud ice water; (f): Annual average cloud liquid water; (g): Spring average cloud liquid water; (h): Summer average cloud liquid water; (i): Autumn average cloud liquid water; (j): Winter average cloud liquid water).

Figure 3. Annual and seasonal average distributions of cloud ice water and cloud liquid water in the Yellow River Basin from 1980 to 2019. ((a): Annual average cloud ice water; (b): Spring average cloud ice water; (c): Summer average cloud ice water; (d): Autumn average cloud ice water; (e): Winter average cloud ice water; (f): Annual average cloud liquid water; (g): Spring average cloud liquid water; (h): Summer average cloud liquid water; (i): Autumn average cloud liquid water; (j): Winter average cloud liquid water).

Figure 4. (a) Annual average temporal changes in water vapor content, cloud ice water, and cloud liquid water in the Yellow River Basin for 1980–2019; (b) seasonal mean time change in water vapor content in the Yellow River Basin from 1980 to 2019.

Figure 5. Annual average and seasonal average total precipitation of the Yellow River Basin from 1980 to 2019. ((a): Annual average precipitation; (b): Spring average precipitation; (c): Summer average precipitation; (d): Autumn average precipitation; (e): Winter average precipitation).

Figure 6. Annual average map of 10 m and 850 hPa wind directions in the Yellow River Basin from 1980 to 2019. ((a): Annual average 10m wind direction; (b): annual average 850hpa wind direction).

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