Precipitation and Pan Evaporation Patterns in YHH Basin (1950-2023): Analysis and Influencing Factors

doi:10.21203/rs.3.rs-4906966/v1

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Precipitation and Pan Evaporation Patterns in YHH Basin (1950-2023): Analysis and Influencing Factors

https://doi.org/10.21203/rs.3.rs-4906966/v1

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Considering the vital importance of the Yellow River-Huai River-Haihe River (YHH) basin in national food security and water resource management, a thorough investigation into the spatiotemporal distribution of precipitation and pan evaporation, along with the factors that influence these phenomena, is essential for the region's agricultural productivity, economic growth, and cultural sustainability. To this end, daily meteorological data from 159 stations across the YHH basin from 1950 to 2023 were hereby utilized for exploration. Advanced methods such as Kriging spatial interpolation, regression analysis, Mann-Kendall trend analysis, and wavelet analysis were employed to elucidate the spatial distribution, interannual variability, and periodicity of precipitation and evaporation. Furthermore, Pearson correlation analysis was conducted to examine the inter relationships among various meteorological elements, while a multi-year monthly mean model was used to analyze the monthly variation patterns of regional precipitation and evaporation within a year. Moreover, the center of the gravity migration model was introduced to reveal the spatiotemporal evolution patterns of these meteorological elements. Overall, this research breaks new ground by examining the seasonal dynamics between precipitation and pan evaporation in the YHH basin and their implications for agricultural irrigation. The insights gained provide vital scientific backing for developing regional land management strategies, guiding crop cultivation plans, and fostering agricultural resilience in the face of climate change.

Earth and environmental sciences/Climate sciences

Earth and environmental sciences/Environmental sciences

Yellow River-Huai River-Haihe River (YHH) basin

Precipitation

Pan evaporation

Spatiotemporal evolution

Influencing factors

The Yellow River-Huai River-Haihe River (YHH) basin, a key area for comprehensive agricultural development in China, stands to benefit immensely from climate change studies. Such research positively influences the area's agricultural sector by informing strategies to enhance productivity and sustainability ^[1]. Additionally, being a significant agricultural and industrial base in China, the YHH basin holds a strategic position in the country’s economic development and food security. However, the spatial and temporal distribution of water resources across the region is highly uneven, which does not correspond well with the demands of economic and social development ^[2–6]. With the soaring water demand from regional urbanization ^[7], research into the climate, precipitation, and evaporation of the YHH basin has become an inevitable trend. Therefore, conducting meteorological factor research in the YHH region, and exploring the spatial and temporal distribution and evolution patterns of regional precipitation and evaporation are of significant strategic importance for the region’s agriculture, economy, and culture ^[8].

Meanwhile, the climate change in the YHH basin has attracted widespread attention from the academic community. For instance, Leng Shilin ^[9] studied the relationship between the maximum evaporation potential and actual precipitation in the Yellow River-Huai River Plain. Yong ^[10] analyzed the interannual variations in precipitation intensity and coverage area of different precipitation levels and the spatial distribution of extreme rainfall in the YHH basin. Grounded in GIMMS NDVI remote sensing data and meteorological records from 1982 to 2003, Chen Huailiang ^[11] analyzed the spatiotemporal characteristics of vegetation activity responses to climate change in the YHH region through trend analysis, correlation analysis, and singular value decomposition. Yang Zhiyong ^[12] delved into the temporal variation characteristics of drought and flood areas in the YHH basin based on precipitation process data. Additionally, in-depth analyses of climate change and its evolution patterns in the YHH basin have also been frequently conducted by previous researchers, forging a solid foundation for this study. However, influenced by regional factors, rainfall and evaporation exhibit different trends in different areas and time periods, and significant differences in their influencing factors are observed. Besides, these studies based on the YHH region did not dissect individual basins and offered limited analysis on the factors influencing rainfall and evaporation within the YHH basin. Therefore, building on previous research and using the latest meteorological data series, this study investigated the factors affecting rainfall and pan evaporation in the YHH basin. By examining the effects of meteorological elements in different basins as a new research direction, it aimed to gain a deeper understanding of the regularities of regional meteorological element changes affecting rainfall and evaporation and to propose more targeted strategic recommendations for water resources and crops.

1.1 Data sources

The meteorological data utilized in this study were sourced from the China Meteorological Data Network (https://data.cma.cn/), encompassing daily rainfall, evaporation, and other variables to ensure data authority and reliability. A total of 178 meteorological stations within the YHH basin were included, and the data comprised daily pan evaporation (diameter = 20 mm) (mm), daily air temperature (°C), daily average wind speed at 10 m above ground level (m/s), daily average relative humidity (%), daily average solar radiation (MJ/m²), and daily rainfall (mm). To ensure the continuity and validity of the collected data, a further screening of the meteorological stations was performed. For daily data with consecutive missing values not exceeding 5 days, linear interpolation was employed to replace the missing values ^[13]. Data with more than 5 consecutive missing days were excluded, yielding the retention of daily observational data for 159 meteorological stations in the YHH basin from 1950 to 2023.

The spatial distribution of the collected stations is depicted in Fig. 1. The stations are distributed as follows: 85 in the Yellow River basin, 40 in the Huai River basin, and 34 in the Haihe River basin, exhibiting a consistent coverage across these regions.

1.2 Research methodology

The study employed Kriging interpolation for spatial estimation of meteorological variables to predict the spatial distribution of meteorological elements. Regression analysis was performed to determine the relationship between each meteorological element and precipitation and evaporation, and Mann-Kendall trend analysis was conducted to test the significance of trends in time series. Furthermore, the periodic characteristics of rainfall and evaporation were explored utilizing wavelet analysis. In addition to Pearson correlation analysis, Spearman’s rank correlation analysis was also employed to assess potential nonlinear relationships. A multi-year monthly mean model was established to analyze the monthly variation patterns of regional rainfall and evaporation within a year.

Beyond these common analytical methods, the center of gravity migration model ^{[14, 15]} was also introduced to further reveal the spatiotemporal evolution characteristics of various meteorological elements. The specific formulas are as follows:

\(\bar {X}=\frac{{\sum\nolimits_{{i=1}}^{n} {{X_i}{R_i}} }}{{\sum\nolimits_{{i=1}}^{n} {{R_i}} }}\)

\(\bar {Y}=\frac{{\sum\nolimits_{{i=1}}^{n} {{Y_i}{R_i}} }}{{\sum\nolimits_{{i=1}}^{n} {{Y_i}} }}\)

where, \(\bar {X}\)and\(\bar {Y}\)denote the coordinates of the center of gravity, while\({X_i}\)and\({Y_i}\)represent the latitude and longitude coordinates of the i-th meteorological station. Additionally, \({R_i}\)refers to the monthly or annual meteorological data for the i-th station.

2.1 Spatial distribution

To investigate the spatial distribution patterns of rainfall and pan evaporation in the YHH basin, Kriging spatial interpolation was employed for analysis. The spatial distribution of long-term average rainfall and pan evaporation in the YHH basin is illustrated in Figs. 2 and 3, respectively.

Based on the Kriging interpolation results (see Figs. 2 and 3), the long-term average spatial distribution of rainfall and pan evaporation is higher in the eastern and southern parts of the basin, contrasting with the lower values observed in the western and northern parts. Marked disparities in spatial distribution are noted among the individual basins. The Huai River basin exhibits the highest values, succeeded by the Haihe River basin, while the Yellow River basin records the lowest values.

2.2 Multi-year trend

To investigate the intra-annual variation trends and distribution patterns of regional rainfall and pan evaporation, the researchers processed the data collected from the YHH basin to obtain multi-year monthly mean datasets. Subsequently, the intra-annual distribution of these multi-year monthly mean values for the entire basin were compared with those for each individual basin. The results are presented in Figs. 4 and 5.

The figures above indicate that the YHH basin exhibits significant intra-annual variation trends in multi-year monthly mean rainfall and pan evaporation. There is a distinct seasonal distribution of rainfall, with the minimum value of 10.66 mm observed in December and the maximum value of 171.3 mm in July. The basin experiences a significant disparity in monthly average rainfall, with a range spanning 16 times the minimum. The boxplot variations of rainfall show that the variability is greatest in summer, reaching its peak in July. In January, evaporation is notably minimal at just 10.54 mm, whereas it peaks in July at 99.93 mm, showcasing a substantial 9.48-fold difference from the lowest to the highest value. The boxplot variations of evaporation reveal that the most pronounced changes occur in May, June, and July, characterized by the highest frequency of outliers and the largest disparity between the maximum and minimum values observed within this timeframe.

The distribution patterns of these two meteorological factors consistently show that the Huai River basin has higher values than the Haihe River basin, which again surpasses the Yellow River basin.

To further explore the long-term variation trends of rainfall and evaporation in the basin, the Mann-Kendall trend analysis and linear fitting were hereby employed to study their evolutionary patterns. The results are presented in Figs. 6 and 7.

The Mann-Kendall trend analysis indicates that the multi-year average rainfall in the YHH basin has experienced several distinct periods of decline since 1950, particularly during the years 1950–1952, 1960–1962, and 1966–2023. This downward trend may be associated with changes in atmospheric circulation patterns caused by global warming, along with regional changes in land use and urbanization. During the intervals of 1953–1959 and 1963–1965, upward trends are observed. Furthermore, the trend after 2005 is statistically significant, passing the 0.05 significance test. Analyzing the linear regression fitting of the multi-year average rainfall reveals an overall declining trend in the YHH basin from 1950 to 2023, with a trend coefficient of -2.391 mm/year. Notably, the multi-year average rainfall from 1951 to 1968 exhibits notable fluctuations, characterized by abrupt increases and decreases. These fluctuations coincide with the years when the U statistic curve in the Mann-Kendall trend analysis intersects the X-axis. The mutation points in the annual average rainfall in the YHH basin are observed in the years 2000 and 2003, as indicated by the intersection of the U statistic and the variance of the U statistic curves.

The multi-year average pan evaporation in the YHH basin shows declining trends in the periods 1950–1955, 1965–1977, 1981–1990, and 2015–2023, and increasing trends in the periods 1956–1964, 1978–1980, and 1991–2014. However, these trends do not pass the 0.05 significance test. The linear regression fitting of the multi-year average evaporation suggests an overall non-significant declining trend in the YHH basin from 1950 to 2023, with a trend coefficient of -0.180 mm/year. The mutation points in the annual average evaporation are observed in the years 1952 and 2013, as indicated by the intersection of the U statistic and the variance of the U statistic curves.

2.3 Periodic analysis

To further investigate the evolutionary patterns of multi-year rainfall and pan evaporation within the YHH basin, wavelet analysis was hereby conducted on the relevant elements. This analysis yields the time-frequency distribution contours of the real part of the Morlet wavelet and wavelet variance for the periodic variations of rainfall and evaporation in the YHH basin^[16]. The results are presented in Figs. 8 and 9.

Analysis of the wavelet variance diagrams reveals that the multi-year average rainfall in the YHH basin exhibits four distinct peaks, corresponding to periodicities of approximately 5 years, 10 years, 24 years, and 32 years. A further examination of these periodicities indicates that the rainfall series has the largest oscillation amplitude at around the 10-year cycle, which can be considered the primary dominant period. Concurrently, the 5-year, 32-year, and 24-year cycles are identified as the second, third, and fourth dominant periods, respectively. A comprehensive analysis incorporating the real part of the wavelet analysis reveals that aside from the primary and secondary dominant periods, the other periodic variations are not significantly pronounced. The cyclical fluctuations around the 10-year period predominantly influence the periodic characteristics of annual rainfall in the YHH basin throughout the entire time series.

Similarly, the multi-year average evaporation exhibits four peaks corresponding to periodicities of approximately 7 years, 13 years, 24 years, and 32 years. The evaporation series shows the largest oscillation at around the 24-year cycle, which is considered the primary dominant period. The 7-year, 13-year, and 32-year cycles are designated as the second, third, and fourth dominant periods, respectively. By integrating the real part of the wavelet time-frequency distribution, it is observed that with the exception of the fourth dominant period, the other periodic variations are significantly pronounced. The cyclical fluctuations around the 24-year period predominantly shape the periodic characteristics of annual evaporation in the YHH basin across the entire temporal span.

3.1 Correlation analysis

To analyze the primary meteorological factors influencing rainfall and pan evaporation, regression analysis was conducted between the multi-year average rainfall and evaporation in the YHH basin and the multi-year average values of various meteorological elements. The results are presented in the following figures, with the red lines representing the regression curves, the dark red areas indicating the 95% confidence bands, and the light red areas representing the 95% prediction bands.

As shown in Fig. 10, rainfall in the YHH basin is positively correlated with relative humidity and negatively correlated with temperature, wind speed, and solar radiation. The correlation with wind speed is relatively weaker among these factors.

As illustrated in Fig. 11, solar radiation exerts the most significant influence on evaporation in the YHH basin, followed by relative humidity and air temperature, while the impact of wind speed on evaporation is relatively minor. This conclusion forges a significant foundation for a more profound comprehension of the climatic traits and evaporation mechanisms within the basin. It also furnishes beneficial references that can aid in the research and decision-making endeavors across related domains.

To further delve into the effects of meteorological factors on rainfall and pan evaporation across different basins and to provide more informed support for local policies, a Pearson correlation analysis was conducted on each meteorological element in each basin.

Table 1

Correlation analysis between rainfall and meteorological elements in each basin
Drainage basin	Temperature(℃)	Wind speed(m/s)	Relative humidity(%)	Solar radiation(MJ/m²)
Yellow River basin	− .147	− .640^**	.892^**	− .616^**
Huai River basin	− .592^**	− .477^**	.824^**	− .648^**
Haihe basin	− .087	− .375^*	.344^*	− .278
**. The significant level of correlation coefficient is 0.01
*. The significant level of correlation coefficient is 0.05

Table 1 reveals that rainfall in each basin is positively correlated with relative humidity and negatively correlated with air temperature, wind speed, and solar radiation. Specifically, rainfall in the Yellow River basin and the Huai River basin is highly positively correlated with relative humidity, involving Pearson correlation coefficients of 0.892 and 0.824, respectively, both passing the 0.01 significance test. In the Yellow River basin, there is a weak correlation between rainfall and air temperature, evidenced by a Pearson correlation coefficient of -0.147. In contrast, the correlation with wind speed and solar radiation is more pronounced, with Pearson correlation coefficients of -0.640 and − 0.616, respectively, both passing the 0.01 significance test. In the Huai River basin, rainfall is negatively correlated with air temperature, wind speed, and solar radiation. The correlation coefficients are − 0.592, -0.477, and − 0.648, respectively, all passing the 0.01 significance test. In the Haihe River basin, rainfall does not exhibit significant correlations with air temperature, wind speed, relative humidity, or solar radiation. The weakest of these non-significant correlations is with air temperature, characterized by a Pearson correlation coefficient of -0.087.

Table 2

Correlation analysis between pan evaporation and meteorological elements in each basin
Drainage basin	Temperature(℃)	Wind speed(m/s)	Relative humidity(%)	Solar radiation(MJ/m²)
Yellow River basin	.714^**	.333^**	− .856^**	.887^**
Huai River basin	.646^**	.166	− .861^**	.894^**
Haihe basin	.766^**	.212	− .857^**	.909^**
**. The significant level of correlation coefficient is 0.01
*. The significant level of correlation coefficient is 0.05

Table 2 indicates that pan evaporation in each basin is negatively correlated with relative humidity and positively correlated with air temperature, wind speed, and solar radiation. The correlation between pan evaporation and relative humidity, as well as solar radiation, is significant in all basins, all passing the 0.01 significance test. Among these, solar radiation exhibits the strongest correlation with pan evaporation in all basins. The Pearson correlation coefficients between pan evaporation and solar radiation in the YHH basin are 0.887, 0.894, and 0.909, respectively, all passing the 0.01 significance test. In the basins, the relationship between relative humidity and pan evaporation is notably strong and inverse, ranking as the second most significant correlation. The Pearson correlation coefficients are − 0.856, -0.861, and − 0.857, respectively. The correlation between pan evaporation and wind speed is the weakest in all basins, involving Pearson correlation coefficients of 0.333, 0.166, and 0.212, respectively.

The correlation analysis reveals that among the meteorological factors influencing rainfall and pan evaporation across the basins, relative humidity and solar radiation exhibit the strongest correlations with both variables. This finding aligns with previous analyses.

3.2 Center of gravity transfer model

The ArcGIS spatial interpolation method can yield the distribution patterns of the multi-year average values of meteorological elements in a region. However, this method only reflects the distribution of meteorological elements within the basin, failing to analyze the spatiotemporal evolution patterns of these elements. Moreover, the precision of the element distribution obtained through interpolation is relatively low. Therefore, the center of gravity migration model was hereby introduced to analyze the distribution and multi-year migration characteristics of elements within the basin by calculating the positions of the centers of gravity for each element from 1950 to 2023, as well as the distances and directions of gravity migration between each decade. The multi-year migration coordinates of the centers of gravity are presented in Table 3, where x and y correspond to the decimal degree coordinate system within ArcGIS.

Table 3

Annual mean barycentric migration coordinates of meteorological elements in the Huang-Huai-hai basin
Year	Rainfall		Evaporation		Temperature		Wind speed		Relative humidity		Solar radiation
Year	x	y	x	y	x	y	x	y	x	y	x	y
1950	115.92	38.75	112.31	36.01	115.89	38.36	115.95	38.71	115.83	38.66	115.77	38.69
1960	115.74	38.58	112.26	36.00	115.90	38.39	115.95	38.70	115.79	38.67	115.79	38.72
1970	115.82	38.80	112.23	36.06	115.87	38.33	115.94	38.69	115.82	38.69	115.78	38.68
1980	115.75	38.63	112.41	36.07	115.89	38.35	115.94	38.71	115.81	38.69	115.78	38.68
1990	115.96	38.73	112.41	36.09	115.91	38.42	115.94	38.69	115.79	38.63	115.78	38.70
2000	115.62	38.48	112.52	35.97	115.90	38.40	115.94	38.72	115.77	38.64	115.80	38.71
2010	115.85	38.75	112.40	35.98	115.85	38.37	115.91	38.71	115.82	38.69	115.77	38.70
2023	115.08	38.28	112.38	35.81	115.86	38.36	115.94	38.71	115.75	38.61	115.76	38.70

To grasp a more detailed understanding of the specific distances involved in gravity migration during each time period, the Vincenty formula was employed for calculation. Introduced by Vincenty in 1975, the Vincenty formula is a geodetic method designed to calculate distances between two points on the Earth's ellipsoidal surface. It effectively translates latitude, longitude, and distance into a solution for the geodetic problem ^[17]. The specific formula is as follows.

\({d_f}=R\sqrt {{{({\theta _A} - {\theta _B})}^2}+\cos \left[ {\frac{{{\theta _A}+{\theta _B}}}{2}} \right]\Delta {\lambda ^2}}\)

where, \({d_f}\)represents the planar distance between two locations; \({\theta _A}\)and\({\theta _B}\)are the latitudes of the two locations, measured in radians; \(\Delta \lambda\)is the difference in longitude between the two locations, also measured in radians; and R denotes the average radius of the Earth. The calculation results are presented in Table 4.

Table 4

Annual mean center of gravity migration distance and direction of various meteorological elements in the Huang-Huai-hai basin (distance unit: km)
Year	Rainfall		Evaporation		Temperature		Wind speed		Relative humidity		Solar radiation
Year	Distance	Direction	Distance	Direction	Distance	Direction	Distance	Direction	Distance	Direction	Distance	Direction
1950–1960	24.53	southwest	4.63	southwest	3.45	northeast	1.11	southwest	3.65	northwest	3.76	northeast
1960–1970	25.43	northeast	7.20	northwest	7.17	southwest	1.41	southwest	3.42	northeast	4.53	southwest
1970–1980	19.85	southwest	16.22	northeast	2.83	northeast	2.22	northwest	0.87	southwest	0.00	southwest
1980–1990	21.35	northeast	2.22	northwest	7.98	northeast	2.22	southwest	6.89	southwest	2.22	northwest
1990–2000	40.57	southwest	16.61	southeast	2.29	southwest	3.34	northwest	2.06	northwest	2.06	northeast
2000–2010	36.06	northeast	10.86	northwest	5.49	southwest	2.83	southwest	7.05	northeast	2.83	southwest
2010–2023	84.97	southwest	18.99	southwest	1.41	southeast	2.60	southeast	10.77	southwest	0.87	southwest

The path of the forward migration of the centers of gravity for meteorological elements in the YHH basin from 1950 to 2023 is depicted in Fig. 12.

Figure 12 reveals that the migration paths of the multi-year average centers of gravity for meteorological elements from 1950 to 2023 display distinct patterns the spatial distribution of long-term average rainfall and pan evaporation in the basin. These patterns highlight the intricate complexities of regional climate change. The migration span of the rainfall center of gravity is notably pronounced, tracing an eastward then westward migration path that spans across an 8-degree change in longitude. From the latitudinal perspective, it initially moves southward, then northward, and finally back southward, with the final latitudinal shift not exceeding the initial study position. The overall migration trend of the rainfall center of gravity exhibits a “southeast-northwest-southeast” pattern. During the period spanning from 2010 to 2023, the migration span of the rainfall center of gravity reaches its maximum of 84.97 km, possibly attributed to the pronounced influence of monsoons on the Huang-Huai-Hai region during this period ^[18]. Changes in monsoon strength and direction significantly affect rainfall distribution and intensity, resulting in substantial shifts in the rainfall center of gravity.

The migration trajectory of the evaporation center of gravity shares some similarities with that of the rainfall center of gravity, presenting a similar “southeast-northwest-southeast” pattern. This phenomenon is speculated to be related to the interaction between rainfall and evaporation, as changes in rainfall quantity will directly affect the surface moisture content, subsequently influencing the evaporation process. In contrast, the temporal migration patterns of the centroids of meteorological elements exhibit distinct regional characteristics. The migration trajectories of temperature and solar radiation generally follow a “northeast-southwest-northeast” path, a trend that aligns with findings from related research. This pattern is speculated to be related to the geographical location and climatic characteristics of the Huang-Huai-Hai region ^[19]. Situated within the East Asian monsoon zone, the Huang-Huai-Hai basin experiences a distribution of temperature and solar radiation that is significantly influenced by monsoonal circulation patterns. Possibly due to the atmospheric circulation and water vapor transport in the region, the migration trajectories of wind speed and relative humidity, on the other hand, exhibit a “southwest-northeast-southwest” pattern.

Notably, the centers of gravity for rainfall, evaporation, wind speed, and relative humidity show consistent migration directions in longitude, while the migration directions for temperature and solar radiation are also consistent. This may be related to the interactions and correlations among these elements. For instance, temperature and solar radiation are important factors affecting evaporation, and their changes will directly influence the amount of evaporation, thereby affecting the distribution and intensity of rainfall. Changes in wind speed and relative humidity are also crucial factors influencing rainfall and evaporation ^[20].

By analyzing relevant literature and data from the YHH basin, the current state of research in the region and its shortcomings were hereby explored. Grounded in daily meteorological data collected from 159 meteorological stations in the basin from 1950 to 2023, including precipitation, pan evaporation, air temperature, wind speed, relative humidity, and solar radiation, a detailed computational and analytical approach was employed to delve into the spatiotemporal evolution characteristics and interactions of regional meteorological elements. This research was conducted primarily to provide scientific references for related fields, and the main research conclusions are as follows:

(1) The multi-year average precipitation and pan evaporation in the YHH basin display a spatial pattern characterized by higher levels in the eastern and southern parts and lower levels in the western and northern parts. This indicates notable disparities in spatial distribution among the different basins within the YHH region, following the trend of the Huai River basin > Haihe River basin > Yellow River basin. The overall trend for both multi-year average precipitation and pan evaporation indicates a decline. Meanwhile, the monthly mean values exhibit significant changes, with distinct seasonal variations following the order of summer > spring > autumn > winter. The multi-year average precipitation and pan evaporation present certain cyclical characteristics. Rainfall wavelet analysis identifies four distinct peaks at approximately 5 years, 10 years, 32 years, and 24 years, with a 10-year main period, and the evaporation wavelet analysis shows four distinct peaks at approximately 7 years, 13 years, 24 years, and 32 years, similar to the rainfall cycle, involving a 24-year main period;

(2) Precipitation is positively correlated with relative humidity and negatively correlated with air temperature, wind speed, and solar radiation. Evaporation is negatively correlated with relative humidity and positively correlated with air temperature, wind speed, and solar radiation. In terms of the correlation coefficients, both precipitation and pan evaporation are significantly influenced by solar radiation and relative humidity, and the impact of wind speed on these variables is comparatively less pronounced. The effects of meteorological elements on precipitation and pan evaporation vary among the basins. In the Yellow River basin, precipitation exhibits a significant positive correlation with relative humidity and a negative correlation with air temperature, wind speed, and solar radiation, with a relatively small correlation with air temperature and a significant correlation with solar radiation and wind speed. Precipitation in the Huai River basin is negatively correlated with air temperature, wind speed, and solar radiation, while being positively correlated with relative humidity. In the Haihe River basin, precipitation shows a relatively small correlation with all meteorological elements. The correlation between evaporation and meteorological elements in the Yellow River basin is opposite to that of precipitation, presenting a similar degree of influence;

(3) Among the meteorological elements, the multi-year average precipitation exhibits a pronounced trend in the migration of its center of gravity. This migration follows a general pattern of “southeast-northwest-southeast.” Particularly during the period from 2010 to 2023, the migration span reaches its maximum value of 84.97 kilometers. The migration trajectory of the center of gravity for the multi-year average pan evaporation is similar to that of precipitation. The migration trajectories of the multi-year average temperature and solar radiation show a pattern of “northeast-southwest-northeast,” while those of the multi-year average wind speed and relative humidity exhibit a pattern of “southwest-northeast-southwest.” Changes in the centers of gravity for evaporation, temperature, wind speed, relative humidity, and solar radiation are relatively limited in latitude and longitude and have not deviated from the initial study position. The centers of gravity for precipitation, evaporation, wind speed, and relative humidity migrate in consistent longitudinal directions, while temperature and solar radiation show parallel migration trends;

(4) The spatiotemporal distribution patterns, evolution trends, and interactions among the meteorological elements discovered in this study hold significant scientific reference value for strategic planning and decision-making in numerous related fields. These findings contribute significantly to a more profound grasp of the Earth's climate system. Moreover, they supply essential data and decision-making support for critical sectors such as agriculture, water resources, energy, and transportation.

Author Contribution

Shengfeng Wang：Funding support; Computational methods; Data provision; Review of manuscript;Xinmiao Xu：First draft writing; calculation; data review；Longwei Lei：Translation; Typesetting; Data Checking

Acknowledgement

The datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.• The data that support the findings of this study are available from [https://www.cma.gov.cn/] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Data Availability

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Precipitation and Pan Evaporation Patterns in YHH Basin (1950-2023): Analysis and Influencing Factors

Status:

Version 1

Abstract

Figures

0 Introduction

1 Research data and methods

1.1 Data sources

1.2 Research methodology

2 Space-time evolution law

2.1 Spatial distribution

2.2 Multi-year trend

2.3 Periodic analysis

3 Analysis of influencing factors of rainfall and pan evaporation

3.1 Correlation analysis

3.2 Center of gravity transfer model

4 Conclusion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1