Distribution characteristics and influencing factors of water resources in Henan Province

Water is an important raw material for industrial and agricultural production, is a valuable natural resource needed for human survival, and is closely related to the ecological cycle and human economic activities. Water resource shortages are not only a serious problem in the world but also an important factor related to a country's national livelihood and sustainable economic development (Li et al. 2019; Zuo et al. 2020). It is necessary to study the spatial and temporal distributions of water resources, but there is almost no relevant research in Henan Province. In addition, at present, almost all research on water resources in Henan Province has been carried out from a single aspect and thus lacks a comprehensive analysis from multiple perspectives.

As a country with severe water shortages, the situation is even worse in western and northern China, and the total water resources are unevenly distributed among provinces (Song et al. 2018). Located in Central China, Henan Province is the only province of the country that straddles four major river basins: the Yangtze River, the Huai River, the Yellow River, and the Hai River, and its water resource distribution is considered to be a microcosm of China and is representative of the country. In northern China, where water resources are generally scarce, Henan Province has average water resources of 440 m³/capita, equivalent to one-sixth of the national level, which indicates the province has a serious water shortage. In addition, the situation of water resources in Henan Province is very worrying because of its low utilization rate, serious waste, and pollution (Wang et al. 2021). According to the Henan Water Resources Bulletin, the total amount of water resources in 2019 was 16.89 billion m³, which was 58.1% lower than the long-term average annual value and 50.3% lower than that in 2018. The province's precipitation was 529.1 mm in 2019, which was 29.9% less than that in 2018 and 31.4% less than the long-term average annual value. It is estimated that by 2025, under the 95% guarantee rate, the water shortage in Henan Province will reach 2.211 billion m³ (Zhou 2014). The water resources in Henan Province have the characteristics of uneven spatial and temporal distributions, concentrated annual distributions of precipitation and river runoff, and large annual variability (Zhang et al. 2020). For this current situation, some scholars have also studied the interbasin water resource allocation scheme in Henan Province, evaluated the coupled water–energy–food system, and determined the status of groundwater overdraft (Liu et al. 2020; Du & Song 2021; Yu et al. 2021). Some relevant studies have been published, such as Gu et al.’s (2010) analysis of the variation characteristics of precipitation and water resources in Henan Province from 1956 to 2007, and they evaluated the abundance and depletion of the water resource quantity. However, there have been few detailed studies on the spatiotemporal changes in water resources in Henan Province in recent years. In this context, it is necessary to study the spatial and temporal distributions of water resources in Henan Province and its influencing factors, which can support the optimal allocation of water resources and provide recommendations for sustainable water resource use, thus alleviating the increasingly severe water resources in Henan Province.

Water resources can be affected by climate and anthropogenic activities, such as precipitation, temperature, dam construction, consumptive use, and agricultural irrigation (Wang et al. 2020). They can affect water resources on temporal and spatial scales by influencing processes such as the hydrological cycle. To solve the problem of water scarcity caused by agricultural activities and climate fluctuations, Rahmani & Danesh-Yazdi (2022) studied the impact of different patterns on water resources. Man-made pollution affects precipitation and runoff, impairs water quality, and reduces available water resources (Khan et al. 2017, 2020). However, most of the studies on the influencing factors of water have focused mainly on the hydrological process, such as the simulation of the change or composition of the water cycle in future land use and climate scenarios (Freund et al. 2017), the sustainability of water resources (Bhatti et al. 2021), and the influence of reservoirs on runoff (Zhao et al. 2021), while research on the influencing factors of water resources is scarce.

Since hydrological time series have fractal characteristics and generally do not obey a normal distribution, a nonparametric statistical method is often used for trend analysis in hydrology (Yang et al. 2017). The Mann-Kendall (M-K) method focuses on analyzing the trend of time series in a certain time period and detecting mutations from a quantitative perspective; however, this method cannot predict future trends. Rescaled range analysis (R/S) focuses on analyzing the fractal characteristics of time series in the future from a qualitative perspective, which can better reveal whether the future trend is the same as the past trend (Qi 2019).

To expand the research on the quantity of water resources and provide data support for the planning and allocation of water resources, the following research was carried out. The main objectives of this study were to analyze the spatiotemporal variation characteristics of water resources in Henan Province from 1999 to 2019 and predict the future trend of water resource change. Moreover, the impacts of climate and land use types on water resources are discussed.

The study was based on 17 cities in Henan Province: Zhengzhou (ZZ), Kaifeng (KF), Luoyang (LY), Pingdingshan (PDS), Anyang (AY), Hebi (HB), Xinxiang (XX), Jiaozuo (JZ), Puyang (PY), Xuchang (XC), Luohe (LH), Sanmenxia (SMX), Nanyang (NY), Shangqiu (SQ), Zhumadian (ZMD), and Jiyuan (JY).

This paper mainly used the annual data of water resources in Henan Province over the past 21 years, which came from the Bulletin of Water Resources in Henan Province from 1999 to 2019 (http://slt.henan.gov.cn/bmzl/szygl/szygb/). Water resources were divided into SWR, GWR, and TWR for research. The geographic situation data of the study area were from the Henan People's Government website (https://www.henan.gov.cn/), the 30 m precision DEM data were from the Geospatial Data Cloud (http://www.gscloud.cn/), the precipitation and temperature data were from the Henan Water Resources Bulletin and China Meteorological Data Network, respectively, and the land use data with a resolution of 300 m were from the European Space Agency and Copernicus Climate Change Service (http://maps.elie.ucl.ac.be/CCI/viewer/).

A geographic information system (GIS) is a data system that can collect, manage, and analyze spatial attribute data and organize and visualize data rationally in computer space (Deng et al. 2020). This paper realized the visual processing and spatial statistical analysis of data and mapping with the help of ArcGIS tools.

• 1.

  Measuring geographic distribution. The mean center, median center, center feature, and directional distribution tools were used in this paper for the analysis. The mean center identifies the geographic center or density center of a group of elements. The central feature is the most centrally located element among point, line, or polygon features. Standard deviation ellipses (SDEs) are created in the directional distribution to summarize the spatial characteristics of geographic elements; one SDE contains approximately 63% of the elements in the cluster, two contain approximately 98%, and three contain approximately 99%. In SDE, the directions of its long and short axes indicate the primary and secondary trend directions of geographic elements distributed in space, and the lengths indicate the dispersion of geographic elements in the corresponding directions (He et al. 2021).

• 2.

  Moran's I. The Spatial Autocorrelation (Global Moran's I) tool measures spatial autocorrelation based on both feature locations and feature values simultaneously. Given a set of features and an associated attribute, this tool evaluates whether the expressed pattern is clustered, dispersed, or random. This tool evaluates the significance of that index by calculating the Moran's I index, p-value, and z-score, where the p is the significance level, limited by the test statistic. The Global Moran's I ranges from −1 to 1. When it is greater than 0, the data are spatially positively correlated, and values less than 0 are negatively correlated; otherwise, the space is random. It should be noted that even if the Global Moran's I is 0, local spatial aggregation cannot be excluded on this basis, and the Cluster and Outlier Analysis (Local Moran's I) tool was used to further analyze the spatial clusters of features with high or low values. Additionally, Local Moran's I can identify spatial outliers.

• 3.

  Hot Spot Analysis. The Hot Spot Analysis tool calculates the Getis-Ord Gi* statistic for each feature in a dataset to obtain the z-scores and p-values. These indices reflect where features with either high or low values are clustered spatially. The data were combined with Moran's I to assess the statistical characteristics of water resources in Henan Province.

The M-K nonparametric method is recommended and widely used by the World Meteorological Organization for trend analysis and mutation detection of sequences (Zhang et al. 2015), and it is based on the following principles (Wei 2007).

In the two-tailed test, for a given confidence level α, the statistic Z is calculated, rejecting H₀ and accepting H₁ if |Z| ≥ Z_1−α/2. Therefore, there is a significant upward or downward trend of the time series data at the confidence level α. Z > 0 indicates an upward trend, and Z < 0 indicates a downward trend. When |Z| is greater than or equal to 1.64, 1.96, and 2.58, it means that the significance test with 90, 95, and 99% confidence levels is passed, respectively.

UF_k′ was obtained in the same way using the inverse series data x_n, x_(n-1), …, x₁ so that UB_k = −UF_k where UB₁ = 0. For a given significance level α = 0.1 and a critical value U_α = 1.64, the UF_k and UB_k curves and ± 1.64 straight lines are plotted on the same graph, and if an intersection occurs and the intersection is between the critical lines, then the intersection is the mutation point, and its corresponding time is the time when the mutation starts.

The rescaled range (R/S) analysis method was originally a time series statistical method proposed by the hydrologist Hurst in 1965 while studying Nile dams (Ye et al. 2018). Currently, it has been applied in many research fields, such as hydrology and ecology, and is usually used to study the fractal characteristics of time series and long-term memory processes (Peng et al. 2012; Xiao et al. 2019). Based on the research of Peng and Xiao, the principle of R/S analysis is as follows:

For a time series ⁠.

• 1.
  Divide the series into n subintervals of length t. The elements in each subinterval are denoted as ⁠. Calculate the mean value of each subinterval.

  

  (7)
• 2.
  Calculate the cumulative deviation of each subinterval element from its corresponding mean and then obtain the range:

  

  (8)

The slope of the regression line is the value of the Hurst index when and lg n are plotted, and linear regression analysis is performed using the least squares method. When H = 0.5, the time series is considered to be independently distributed, that is, the past trend of change has no effect on the future; when 0 < H < 0.5, it means that the series has inverse persistence, that is, the future trend of change is opposite to the past; when 0.5 < H ≤ 1, it means that the series has persistence, that is, the future trend of change is the same as the past. In addition, the more H tends to 0, the stronger the inverse persistence is, and conversely, the more H tends to 1, the stronger the persistence is.

The next step is to divide the data into four time periods and calculate the average value of that time period, as shown in Table 1. The change in spatial characteristics over time is shown in Figure 4(b). GWR indicated no significant changes in the SDE and the mean center, but the central feature shifted from the previous LH to XC in 2010–2014, and then returned to LH after 2014. The changes in SWR and TWR were similar, with no significant change in the shape of the SDE, but its position shifted significantly in 2010–2014. Different from SWR's ZMD, the initial central feature of TWR was located in LH, but it moved to ZMD during 2004–2009. Subsequently, SWR and TWR moved to PDS simultaneously in 2010–2014, and moved back to ZMD in 2015–2019. In addition, the mean centers of SWR and TWR fluctuated significantly during this period (2010–2014), while the mean center of GWR fluctuated less.

Abrupt changes in the same dataset were analyzed using the M-K method, and the significance level was taken as 0.1. Since the time series data were available only for 21 years, abrupt changes may not be clearly observed, and only a few cities with more obvious abrupt changes were listed in this paper. In the SWR time series, the abrupt changes occurred in approximately 2011 in AY, KF, and ZMD and in approximately 2012, 2013, and 2014 in PDS, LH, and PY, respectively, while two abrupt changes occurred in XY in 2008 and 2015. In the GWR time series, only XY, HB, LH, and PDS had abrupt changes. Xinyang had two mutations in 2008 and 2015, while the mutations in the other three cities occurred in approximately 2010, 2011, and 2012. For TWR, abrupt changes occurred in 2009 in ZMD, in 2010 in SQ and ZK, in 2011 in AY and KF, in 2012 in HB and PDS, and in 2013 in PY and ZZ, and two abrupt changes occurred in 2008 and 2015 in XY. Based on the provincial perspective, the abrupt changes of SWR, GWR, and TWR all occurred in approximately 2011.

Comparing the fitting results of several schemes, this paper divided 21 years of water resource data into four subintervals and calculated Hurst exponent (H) for each city and for the entire province. The results are shown in Figure 7(b). The H of GWR was concentrated mainly in the greater than 0.5 interval, showing persistence, but the persistence of GWR in some cities was slightly weaker; even in some areas of southern Henan, H was less than 0.5. The H-values of SWR and TWR were concentrated mainly in the less than 0.5 interval, indicating inverse persistence. Compared with SWR, the H of TWR was closer to 0.5, which means that its inverse persistence is weaker. In other words, for GWR, most cities had the same trend in the future as in the past, while for SWR and TWR, most cities had an opposite trend in the future than that in the past.

Based on the province-wide perspective, the H-values for SWR, GWR, and TWR were 0.52, 0.83, and 0.58, respectively, which were all greater than 0.5. Therefore, these series were all considered to be persistent, but the persistence of SWR and TWR was weaker and much lower than that of GWR. This means that the future trend of GWR will be consistent with the past trend with a high probability, while the future trends of SWR and TWR will be consistent with the past with a low probability. Combining the results with the individual R/S analysis of each city, we can conclude that GWR has obvious persistence, while SWR and TWR do not have obvious persistence or have inverse persistence.

The temperature data from 17 meteorological stations in Henan Province were interpolated to obtain the annual average temperature of the province as well as the average temperature in each city. The annual average temperature in Henan Province reached its lowest point within a decade in approximately 2011. The M-K method showed that the temperature and precipitation in Henan Province changed abruptly in approximately 2012 and 2011, respectively. The general trend of the temperature change in each city during this 21-year period was similar to that at the provincial scale. In terms of time, the temperature and precipitation coincided with the mutation time of water resources mentioned above. In terms of space, the geographical distribution of the temperature and precipitation decreased from south to north, which was somewhat consistent with the geographical distribution of water resources described in the above section.

SPSS (Statistical Product Service Solutions) is a series of software used in statistical analysis and calculation, data mining, and so on. The correlation between climatic factors and water resource series was determined with linear regression analysis in SPSS software, and the results are shown in Table 2. These two climate factors were significantly correlated with water resources, among which water resources are negatively correlated with temperature and positively correlated with precipitation. The correlation passed the significance test, so the results are reliable. However, the correlation between water resources and temperature was much lower than that of precipitation, with the R² between temperature and water resources being approximately 0.35, while the R² between precipitation and water resources reached approximately 0.9. On the one hand, temperature affects water resources by influencing the degree of evapotranspiration, especially of surface water, and on the other hand, it can affect water resources by influencing precipitation. Precipitation falls to the ground to form surface runoff or infiltrates to become subsurface runoff, and it is one of the main sources of water resources. In contrast, precipitation has a more direct impact on water resources. Considering that the positive effect of precipitation on water resources is much greater than the negative effect of temperature on water resources, the decreasing water resources from south to north are well understood.

Geographically, cropland is distributed mainly in the central and eastern parts of Henan Province, forest is distributed mainly in the western part of Henan Province, water is distributed mainly in the southwest part of Henan Province, and settlement is distributed mainly in several central and northern cities with Zhengzhou at the center. In the past 20 years, the areas of arable land, grassland, and undeveloped land have gradually decreased, while the areas of water and construction land has gradually increased, and the area of forestland has been maintained at approximately 16.25%. However, as a large agricultural province, the proportion of arable land in Henan Province has always been greater than 70%. The land use change matrix calculated by ArcGIS from 1999 to 2019 is organized as follows (Table 3).

The correlation between each land cover area and the amount of water resources was determined using SPSS based on 21 years of land use data (Table 2), and the results showed that there was no significant correlation between each land use type and water resources. Different land use types have different underlying surfaces that affect the infiltration process and surface runoff. SWR is mainly natural river runoff, and the change in land use type may briefly affect the SWR at that time, but from the annual scale, land use type has had little impact on surface water resources, which is understandable. Similarly, land use has little impact on GWR and TWR. However, we cannot completely eliminate the impact of human activities such as land use on water resources, and this topic requires more detailed data for further research.

In this paper, through the analysis of the spatial and temporal distribution characteristics and influencing factors of water resources in Henan Province, the following conclusions were drawn.

Based on the analysis results of available information, we concluded that the spatial distribution and statistical characteristics of SWR were relatively similar to those of TWR. In the past 21 years, the water resources in Henan Province have been declining. Based on the statistical examination, XY, NY, and ZMD in the southeastern of Henan Province have abundant water resources, while several cities in the northeast have lower water resources.

The time period from 2010 to 2014 showed a clear spatial fluctuation in water resources with a tendency to migrate northward, especially in SWR and TWR. The time series of water resources also showed a mutation, and the time was concentrated from 2008 to 2015. In addition, three cities, PDS, ZMD, and KF, showed a more obvious sudden temporal change in the water resource time series.

Combining the M-K nonparametric test and the R/S analysis method, it is clear that in the GWR time series, cities that have had a decreasing trend in the past will also continue to decline in the future, while several cities in the SWR and TWR series that have had a decreasing trend in the past will have the opposite upward trend in the future.

Among the factors influencing water resources, precipitation had the greatest degree of influence on water resources, followed by temperature, while land use type had no significant influence on water resources. Accordingly, the temporal variation in climate factors could reasonably explain the variation in water resources over the past 21 years.

All relevant data are included in the paper or its Supplementary Information.

This study was funded by the Science and Technology Project in Henan Province (232102320026 and 232102320032), the National Natural Science Foundation of China (Grant Nos. 51509222 and 51909091) and Project of China Geological Survey (NO.DD20211256).

The authors declare there is no conflict.