Influence of vegetation change on water consumption in watersheds

Water consumption is the water that cannot be directly recycled in the basin. Vegetation water consumption in the basin is the most important form, and reducing water consumption in the basin is the most effective measure to achieve real water saving in the basin. In recent years, many watersheds have carried out afforestation to improve the environment, but it has also directly increased the water consumption of the basin, especially in water-deficient areas, often further exacerbating the tension of water resources. Water is the most important limiting factor for the growth and development of vegetation. What's more, water stress caused crop yield reduction, which can exceed the sum of other environmental stresses. In order to reduce the water pressure of plants, it is necessary to clarify the water consumption rules of different plants, identify the factors affecting the water consumption of plants, and establish the quantitative relationship between the water consumption of plants and the change rate of plants, so as to formulate a vegetation restoration plan with low water consumption and suitable for regional water resources, avoiding the phenomenon of planting high water-consuming plants in water-deficient areas, which not only reduces the pressure of regional water resources but also reduces the pressure of water shortage on plants. Therefore, in order to formulate a plant restoration policy suitable for water resources in the region, it is of great significance to study the impact of vegetation change on water consumption for regional sustainable development (Pereira et al., 2021).

Plant transpiration and soil evaporation, which are also known as evapotranspiration (ET), account for more than 90% of water consumption and are the main body of water consumption. The calculation of vegetation water consumption is the primary basis for formulating policies for the rational utilization of water resources (Lopez et al., 2019). Vegetation water consumption varies greatly in different phases and at different spatial scales in a watershed, which is affected by many factors. It was found that vegetation coverage and growth period were the most critical factors affecting vegetation water consumption in arid Arab. Wind speed was the main driving force in a 50-year (1961–2010) vegetation water consumption study in the Iranian region. The change of vegetation distribution in the Yanhe River Basin may be the main factor affecting the change of vegetation water consumption. Valipour and his team's monitoring of 18 sites found the complexity of climate change driving vegetation water consumption changes (Valipour et al., 2020). On the Loess Plateau from 2001 to 2020, the most important factors affecting vegetation water consumption were the normalized difference vegetation index (NDVI) and wind speed (Zhang et al., 2022). In the Pearl River Basin from 1980 to 2019, the main factor affecting vegetation water consumption was the average temperature (Zuo et al., 2022). The increase in vegetation water consumption in humid areas of China is caused by 91% of the climate and 9% by vegetation greening (Zhang et al., 2020).

Studies have shown that changes in vegetation (such as species, quantity, and distribution) affect changes in vegetation water consumption. The greater the vegetation density in arid Arab, the greater the water consumption (El-Shirbeny et al., 2022). The natural vegetation area increased at a rate of 12.37%/5a, and the water consumption increased at a rate of 12.82%/5a in the Tarim River Basin (Wang et al., 2022). In the lower reaches of the Yanhe River Basin located in the semi-arid area, the water consumption per unit area from high to low is woodland > shrubland > spare forest land > medium-coverage grassland > cultivated land > low-coverage grassland (Guo et al., 2022).

Accounting for vegetation water consumption is the core of water resource assessment. Therefore, the accurate calculation of vegetation water consumption needs to select the appropriate calculation method according to the actual situation of the study area. Although there are many calculation methods for vegetation water consumption, the FAO-56 Penman–Monteith (PM) equation is the most widely used. The FAO-56 PM method has accuracy, stability, and global acceptability (Pereira et al., 2021). The FAO-56 PM method has been widely used in Asia, America, and other regions (Wang, 2014; Sullivan et al., 2019; Zhang et al., 2021).

The above studies showed that there is a close relationship between vegetation change and water consumption, and there are regional factors affecting vegetation and water consumption in different regions. The PM model is an effective tool for studying regional evapotranspiration. However, few studies have comprehensively analysed the relationship between vegetation change and water consumption, and proposed that there is a moderate threshold in regional vegetation restoration; that is, vegetation restoration must be based on the regional water resource-carrying capacity to guide the formulation and implementation of water policy. This study focuses on the Ziya River Basin (ZYRB), which is primarily composed of croplands, grasslands, and so on. As one of the most important agricultural production areas in China, the ecological environment of the ZYRB is fragile and sensitive to climate change (Yu, 2018). Consequently, the calculation of water consumption is important to socioeconomic development and ecological environmental improvement in the region. In this paper, the land use of different phases is used to analyse interannual changes, and the NDVI is used to analyse annual changes. The objectives of this study are as follows: (1) to analyse vegetation changes and causes in different phases of the ZYRB; (2) to analyse the interannual variability of water consumption associated with dominant land-cover classes across the ZYRB; (3) to evaluate water consumption patterns in terms of crop type and water management strategies; and (4) to explore the influence of vegetation changes on water consumption according to the spatial and temporal distribution characteristics of water consumption in different phases of the basin and to propose that vegetation restoration has a moderate threshold.

The Ziya River Basin (112°20′–117°50′,36°03′–39°35′), which is located in the southern part of the Haihe River Basin in North China, is one of the seven major subbasins of the Haihe River Basin. Based on the administrative division, the basin is located in Hebei, Shanxi, and Tianjin (Xiong & Li, 2007), covering an area of 4.69 × 10⁴ km². The ZYRB has a semi-arid and semi-humid continental monsoon climate in the warm temperate zone (Li et al., 2021). Based on meteorological data from 2008 to 2012, the annual average temperature is 11.8–12.9 °C, and the annual average precipitation is 540 mm. Most precipitation is concentrated in June, July, and August. The precipitation in mountain areas is approximately 620 mm, while it is approximately 500 mm in plain areas. The potential evapotranspiration in the upstream region is approximately 700–1,200 mm, and the actual evapotranspiration is 300–500 mm (Li, 2012).

The data include geographic information data, remote sensing data, meteorological data, and hydrological data (Table 1). According to the availability of data, we choose 2000–2015 as the research period.

• (1)

  Monthly NDVI data in 2001, 2005, 2010, and 2014 were obtained from the data cloud website. NDVI data include MODND1M China NDVI products calculated by MODND1D within 1 month and at a maximum daily temporal resolution, and spatial resolution is monthly and 500 m. Due to the incompleteness of the data in 2015, the NDVI data in 2014 were used as a replacement (Jiao, 2021).

• (2)

  According to the FAO-56 Penman–Monteith formula, meteorological data, including daily average pressure, daily maximum temperature, daily minimum temperature, daily precipitation, daily evaporation, relative humidity, daily average wind speed, and sunshine hours, are needed.

The land-use change index can be expressed by the land-use dynamic degree.

• (1)

  Single land-use type dynamic degree

The land-use dynamic degree quantitatively describes the speed of land-use change, which has a positive effect on predicting the future trend of land-use change (Wang et al., 1999).

Following the F distribution of degrees of freedom (k, n − k − 1), given the explicitness level α (generally 0.05 or 0.1, i.e., 95% confidence or 90% confidence, respectively), the critical value Fα (k, n − k − 1) is obtained by looking up the table. According to the sample, the value of the F statistic is calculated, and F > Fα (k, n − k − 1) is used to reject or accept the original hypothesis H₀ to determine whether the linear relationship of the original equation is obvious in general.

The main land-use types in the plain area were croplands, followed by some urban and built-up lands. Mountain areas had mainly grasslands and forests. The dynamic degree of land-use types in the ZYRB changed spatially from 2001 to 2015. Table 2 shows that the dynamic degree of different land-use types in the ZYRB was obviously different. The dynamic degree of cropland/natural vegetation mosaics was large, up to nearly 100%, increasing by 0.22 km² from 2001 to 2015, and the area was doubled. Second, mixed forests, permanent wetlands, and deciduous broadleaf forests grew faster, with growth rates of 66.83, 66.64, and 64.27%, respectively, and their areas increased by 112.69, 13.34, and 247.75 km², respectively. The growth rate of savannas was 46.64%, with a total increase of 920.94 km². The total area of barren land, cropland/natural vegetation mosaics, and water bodies changed little to less than 3 km², and the change in barren land showed that the development and utilization of barren land increased. The grassland area decreased the most, showing a continuous decreasing trend. The dynamic degrees in the three periods were −3.06, −4.26, and −3.41%, respectively. The dynamic degree in the whole period was −10.34%, and the total area decreased by 1,595.60 km². With the development of the economy and urbanization, human demand for land increased gradually, and the area of urban and built-up lands increased continuously in the three periods, with an increase of 438.78 km² in 2001–2015. The area of permanent wetlands continuously increased in the whole period from 2001 to 2015, with growth rates of 2.14, 41.05, and 15.67%, respectively. The ecological environment improved slightly with the increase in wetlands. Forests and savannas increased by 360.44 and 920.94 km², respectively, with an average annual growth rate of approximately 66%. The area of croplands increased by 617.6 km² from 2001 to 2010 and decreased by 698.53 km² from 2010 to 2015. The area of deciduous broadleaf forests increased rapidly from 2001 to 2010, the area of mixed forests increased rapidly after 2005, and the area of savannas began to increase in large areas after 2010. This shows that in the early stage of returning croplands to forests and grasslands, the policy had just begun to advance, and the vegetation recovery was slow. After 2010, more remarkable results have been achieved. The dynamic degree of the water area was the smallest, and the area changed little (Zhang, 2005; Zhao et al., 2017; Li & Liu, 2022).

Table 2

Land-use changing range and dynamics in the ZYRB from 2001 to 2015.

Note: Red indicates a conspicuous decrease, and yellow indicates a conspicuous increase.

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From the perspective of topography, the land-use area change in the plain area in the ZYRB was smaller than that in the mountainous area from 2001 to 2015. The main land-use types in the plain area were croplands, followed by urban and built-up lands. From 2001 to 2015, the urban and construction land area expanded from 3,603 to 3,942 km², an increase of approximately 439 km². The cropland area increased to 24,837 km² in 2010 and decreased to 24,138 km² in 2015. Mountain areas had mainly grasslands and forests. From 2001 to 2015, grassland areas decreased by 1,595.6 km², mixed forests increased by 112.8 km², and deciduous broadleaf forests increased by 247.75 km².

To analyse the transformation of areas between land-use types, the land-transfer matrix of four periods, 2000–2005, 2005–2010, 2010–2015, and 2001–2015, was obtained by using ArcGIS technology. The area of grasslands, woody savannas, and croplands increased from 2001 to 2005, and the area of other land-use types remained basically the same. In terms of land inflow, the increase in the grassland area was mainly due to the conversion of croplands (365.88 km²) and savannas (232.08 km²). The increase in urban and built-up lands was mainly due to the conversion of agricultural land (82.46 km²). In terms of land outflow, grasslands were converted mainly to croplands (754.85 km²) and savannas (261.33 km²). Croplands were partially converted into grasslands (365.88 km²). Closed shrublands were partially transformed into the deciduous broadleaf forest (50.76 km²) and savannas (50.11 km²). From 2005 to 2010, grassland, urban and construction land, and farmland areas increased; savanna areas decreased; and the remaining land-type areas did not change much. From the inflow of land, the increase in the grassland area was transformed from croplands (301.76 km²). In terms of land outflow, grasslands were mainly converted into croplands (691.46 km²), savannas (432.95 km²), and closed shrublands (116.79 km²). From 2010 to 2015, the increase in the grassland area in terms of land inflow was mainly due to the conversion of croplands (676.10 km²) and savannas (99.16 km²). The increase in urban and built-up lands was mainly due to the conversion of croplands (213.58 km²). From the conversion out of land, grasslands were mainly converted to closed shrublands (83.66 km²), croplands (217.41 km²), and savannas (930.62 km²). Closed shrublands were mainly converted to deciduous broadleaf forests (66.03 km²) and savannas (200.01 km²). From 2001 to 2015, the types of land with greater internal conversions were grasslands, agricultural lands, dense scrubs, and savannas. The conversion types of grasslands were croplands (930.33 km²) and savannas (456.62 km²), and the conversion types of closed shrublands (159.59 km²) were croplands (1274.61 km²) and savannas (1487.25 km²). The decrease in the area of croplands was mainly due to the conversion into grasslands (930.33 km²) and urban and building lands (421.13 km²). It is inferred that the increase in urban and built-up lands was mainly due to urbanization and socioeconomic development, which made the original development space insufficient to meet human activities and thus occupied arable land, according to the land-use planning of the People's Government of Shanxi Province and Hebei Province (Shanxi, 2010; Hebei, 2015). The increase in the area of forest and grassland indicated that the policy of restoring agricultural land to forests and grasslands had been effective, with a significant increase in vegetation cover.

Table 3 shows the annual and interannual NDVI values of the ZYRB in 2001, 2005, 2010, and 2014. The MODIS NDVI time series contains a large amount of information, which can reflect the changes in vegetation over time. In this paper, the traditional maximum value composite (MVC) method was used to smooth and reduce noise (Li et al., 2019).

The NDVI value in the plain area was higher than that in the mountainous area since the main vegetation in the plain area was croplands and the main vegetation in the mountain area was grasslands and forests, also indicating that the growth period of croplands was the main contributor to the NDVI value.

From 2001 to 2005, the total vegetation water consumption was positively correlated with precipitation (R² = 0.887, DW = 1.818, and p = 0.001). From 2005 to 2010, the total vegetation water consumption was negatively correlated with precipitation and wind speed and negatively correlated with relative humidity (R² = 0.990, DW = 2.288, and p = 0.002). From 2010 to 2015, the total water consumption of vegetation was negatively correlated with precipitation (R² = 0.606, DW = 2.796, and p = 0.039). In the analysis of the correlation between the total amount of vegetation water consumption and precipitation in the ZYRB, because the water consumption of farmland was the highest, it played a leading role. The total amount of cropland water consumption was consistent with precipitation, and there was a positive correlation trend, and vice versa. The correlation between the total amount of water consumption and the overall trend of precipitation from 2001 to 2015 was negative (Figure 10(b)). From 2001 to 2015, the total water consumption of vegetation was positively correlated with sunshine hours (R² = 0.871, DW = 1.995, and p = 0.002). Among them, R² values indicated that the independent variable could explain the degree of the dependent variable. A DW close to 2 showed that the sample was independent, p < 0.05; that is, the level of the sample was less than 0.05, there were significant differences, and the sample was statistically significant.

In short, among the driving factors, the correlation between vegetation water consumption and meteorological factors was mainly annual precipitation and sunshine time, followed by relative humidity, wind speed, and pressure. The total water consumption was highly correlated with precipitation, and the impact of climate change on water consumption was mainly through precipitation and sunshine in regard to climatic factors.

In a calculation interval of 15 years, the water consumption of vegetation in the basin showed a decreasing trend from 2001 to 2015. The calculation results were the same as those of Feng (2020), who used the Fubaopu model based on the Budyko hypothesis to estimate the actual evapotranspiration in the upper reaches of the ZYRB from 1961 to 2013, which verified the correctness of the data calculations in this paper. Feng (2020) believed that climate was the main factor affecting the evapotranspiration of vegetation in the basin, and the underlying surface was the secondary factor affecting evapotranspiration. Croplands and urban and construction lands and even barren lands have a great impact on the actual water consumption of vegetation in the ZYRB. The increase in barren land would also reduce the water consumption of vegetation in the basin. For example, the barren area increased by 5.12% from 2001 to 2015, suggesting that the increase in the barren area in the ZYRB was an influencing factor in the reduction in vegetation water consumption in the basin.

The annual water consumption and NDVI values of vegetation in the ZYRB have the same trend. From March to August, there was an increasing trend; in the distribution of a single peak curve, the largest water consumption occurred in summer. The average annual water consumption in the mountainous and plain areas of the ZYRB was calculated (Table 4). The results indicated that the water consumption in plain areas of the ZYRB was greater than that in mountainous areas from 2001 to 2015. The water consumption in mountainous areas increased slightly by 1% from 2001 to 2005, increased by 12% from 2005 to 2010, and decreased by 12% from 2010 to 2015. The water consumption in plain areas increased by 3% from 2001 to 2005, increased by 14% from 2005 to 2010, and decreased by 8% from 2010 to 2015. From 2001 to 2015, the average annual water consumption in mountainous areas decreased by 0.07 mm and that in plain areas increased by 24.38 mm (7%). From the perspective of the changing range, the water consumption in plain areas increased more.

As shown in the table of water consumption calculation results (Table 5 and Figure 6), the water consumption of croplands was the largest by more than 50%, followed by grasslands, accounting for approximately 30%, followed by savannas, closed shrublands, and deciduous broadleaf forests. The woody savannas and mixed forests had the least water consumption, and their water consumption accounted for less than 0.1% of the total water consumption. The annual area of croplands changed greatly; therefore, it had the greatest impact on the total water consumption. The annual water consumption change accounted for 5.59% of the total water consumption from 2001 to 2015 (Table 2 and Table 5). The change in the area of grasslands, closed shrublands, woody savannas, mixed forests, and deciduous broadleaf forests had a positive effect on the change in water consumption, i.e., an increase in total water consumption due to an increase in the area and a decrease in total water consumption due to a decrease in the area. The change in water consumption caused by the cropland area had a negative effect, where the area of croplands decreased but the total water consumption increased because the water consumption per unit area increased by 24.38 mm from 2001 to 2015.

Table 5

Vegetation water consumption changes and dynamics in the ZYRB from 2001 to 2015.

Note: Red indicates a conspicuous decrease, and yellow indicates a conspicuous increase.

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For most vegetation, when the area decreased, the total water consumption tended to decrease. When the vegetation area increased, the total water consumption increased (Figure 11). Vegetation restoration increased the water consumption of the region. Therefore, vegetation restoration has a certain threshold and cannot be blindly expanded. It is necessary to combine the actual water resources of the basin to guide reasonable land-use planning. However, when the area of croplands decreased, the total water consumption increased, indicating that the water consumption of croplands per unit area was increasing. This may be due to the increase in high water-consuming crops. This was closely related to the change in cultivated land-planting structure and climate change. Therefore, cultivated crops and artificial plants have water consumption factors that are different from those of natural vegetation. They were the main water users in the basin and needed much irrigation water, which needs to be focused on in the water resource management of the basin.

The purpose of this paper is to quantitatively analyse the relationship between vegetation change and water consumption by analysing the water consumption of different types of vegetation under the ecological restoration scenario, so as to realize the scientific formulation of regional sustainable development water resource policy. The main conclusions are as follows:

• (1)

  The water consumption per unit area in descending order was mixed forests (660 mm, annual average), croplands (640 mm), closed shrublands (581 mm), deciduous broadleaf forests (528 mm), grasslands (514 mm), savannas (459 mm), and woody savannas (454 mm).

• (2)

  The main factors affecting vegetation water consumption in the ZYRB were vegetation and sunshine. What's more, there was a positive correlation between vegetation coverage changes and water consumption. The specific regression equation from 2001 to 2015 was y = 0.377x + 84.516, where x is the change in the vegetation area, 10⁶ m², and y is the change in vegetation water consumption, 10⁶ m³. When vegetation restoration is increased, the water consumption will increase, which will increase the water pressure in the region.

• (3)

  For water resource policymakers, it is necessary to rationally coordinate water resources and take into account regional ecological environment balance and socially sustainable development. Therefore, it is suggested that formulating water policies should combine the vegetation restoration policies of the ecological environment protection department to achieve the efficient use of water resources.

• (4)

  At the same time, the deep-seated impact of vegetation restoration on water consumption needs to be further studied in the future. For example, what is the quantitative relationship between other factors and water consumption? How to quantitatively determine the reasonable threshold for regional vegetation restoration? These are the kinds of problems that should be solved.

The authors would like to acknowledge the financial support for this work provided by the National Natural Science Foundation of China (grant no. 52239004).

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

The authors declare there is no conflict.