Special Issue: Water Resources in Beijing-Tianjin-Hebei Region

Effects of different land use types on potential evapotranspiration in the Beijing-Tianjin-Hebei region, North China

  • HAN Jingyan 1, 2 ,
  • ZHAO Yong , 2* ,
  • WANG Jianhua 2 ,
  • ZHANG Bing 3 ,
  • ZHU Yongnan 2 ,
  • JIANG Shan 2 ,
  • WANG Lizhen 2
  • 1. Institute of Hydrology and Water Resources, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
  • 2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • 3. Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
*Corresponding author: Zhao Yong (1977-), PhD, E-mail:

Author: Han Jingyan (1984-), PhD, specialized in hydrology and water resource. E-mail:

Received date: 2018-05-12

  Accepted date: 2018-11-15

  Online published: 2019-06-25

Supported by

National Key Research and Development Program of China, No.2016YFC0401407

National Natural Science Foundation of China, No.51379216

National Science Foundation for Distinguished Young Scholars, No.51625904

International Science & Technology Cooperation Program of China, No.2016YFE0102400


Journal of Geographical Sciences, All Rights Reserved


Potential evapotranspiration (ET0) is vital for hydrologic cycle and water resource assessments as well as crop water requirement and irrigation demand assessments. The Beijing-Tianjin-Hebei region (Jing-Jin-Ji)-an important, large, regional, economic community in China has experienced tremendous land use and land cover changes because of urbanisation and ecological restoration, affecting the hydrologic cycle and water resources of this region. Therefore, we analysed ET0 in this region using climate data from 22 meteorological stations for the period 1991-2015 to understand this effect. Our findings show that ET0 increased significantly at a rate of 7.40 mm per decade for the region. Based on the major land use type surrounding them, the meteorological stations were classified as urban, farmland, and natural stations using the 2015 land use dataset. The natural stations in the northern mountainous area showed a significant increase in ET0, whereas most urban and farmland stations in the plain area showed a decrease in ET0, with only a few of the stations showing an increase. Based on the different ET0 trends for different land use types, these stations can be ranked as follows: urban stations (trend value: -4.663 to -1.439) > natural stations (trend value: 2.58 to 3.373) > farmland stations (trend value: -2.927 to -0.248). Our results indicate that land use changes affect meteorological parameters, such as wind speed and sunshine duration, which then lead to changes in ET0. We noted that wind speed was the dominant parameter affecting ET0 at all the natural stations, and wind speed and sunshine duration were the dominant parameters affecting ET0 at most of the urban stations. However, the main controlling parameters affecting ET0 at the farmland stations varied. These results present a scope for understanding land use impact on ET0, which can then be applied to studies on sustainable land use planning and water resource management.

Cite this article

HAN Jingyan , ZHAO Yong , WANG Jianhua , ZHANG Bing , ZHU Yongnan , JIANG Shan , WANG Lizhen . Effects of different land use types on potential evapotranspiration in the Beijing-Tianjin-Hebei region, North China[J]. Journal of Geographical Sciences, 2019 , 29(6) : 922 -934 . DOI: 10.1007/s11442-019-1637-7

1 Introduction

Water is the vital basic resource for the sustainable development of any region’s agriculture, society and economy. However, in current times, water scarcity has become a serious global issue with a dramatic increase in water demand. Furthermore, water deficiency has become increasingly evident with increasing land and water use (Liu et al., 2008).
With the rise of water deficiency, potential evapotranspiration (ET0)-an important component of the hydrological cycle-has been widely used for water deficiency evaluation and water resource management (Huang et al., 2014). Additionally, evapotranspiration (ET) is a key variable of the climate system, as it affects the energy and water balance of a surface (Boisier et al., 2014). Global and regional land surfaces rapidly undergo changes with increasing urbanisation, agricultural practices and ecological restoration projects (Li et al., 2017). Such land use changes influence the water balance of a region, affecting the available water supply and ET (Kundu et al., 2017). Changes in ET reflect the potential effects of different land use types on the climate, hydrological cycle and water resources of a region.
Land use or land use practices have different effects on ET in different parts of a region. Deforestation may result in a decrease in ET (Olchev et al., 2008), and agricultural land is noted to exhibit higher actual ET values than urban land (Liu et al., 2010). In China, ET in farmland areas has been observed to be more than that in urban areas, showing a significant increase of 98 mm per year, because of cropland management measures, such as irrigation (Liu et al., 2008). While agricultural activities have contributed to a significant increase in ET (Zou et al., 2017), a decrease in ET0 was noted in farmland areas that are highly dependent on irrigation, such as that in Northwest China (Han et al., 2016). Thus, ET0 may vary for different land use types on a regional scale.
China has experienced rapid land use changes during the last century, and this process is expected to continue in the future (Liu et al., 2008). The Jing-Jin-Ji region (Beijing Municipality, Tianjin Municipality and Hebei Province), North China, is a semi-humid and semi-arid region, facing water resource issues because of rapid urbanisation, extensive agricultural activities and a dramatic increase in water demand (Dong et al., 2008). Therefore, water security has become more important in this region. Although the water supply capacity of the Jing-Jin-Ji region has improved as the implementation of the South-North Water Transfer Project, water shortage crisis still exists due to the continuous increase of population in the region (Feng and Liu, 2006). Consequently, an analysis of ET0 for different land use types in this region is required to understand the regional water resource behaviour and for water resource management.
Little is known about changes in ET0 for different land use types in the Jing-Jin-Ji region. Therefore, in this study, we (1) analyse the variation in ET0 for different land use types, (2) study the relationship between land use and ET0 at meteorological stations and (3) discuss the response of meteorological parameters and ET0 to different land use types. The conclusions will enhance our understanding of the impact of land use on ET0, thus providing a basis for regional water resource management.

2 Materials and methods

2.1 Study area

The Jing-Jin-Ji region (36°02°-42°38°N, 113°25°-119°51°E) is located in North China (Figure 1a). Its altitude ranges from 2 to 2836 m and its general topography consists of mountains, plateaus and plains, with high-relief areas in the northwest and low-relief areas in the southeast. The western part of this region contains the Loess Plateau and the Taihang Mountains, the northern part contains the Mongolian Plateau and the Yanshan Mountains, and the eastern and southeastern parts contain the North China Plain. The Jing-Jin-Ji region is a large, regional, economic community and the fastest-developing region in China. Climatically, it is a semi-humid and semi-arid region, part of the East Asian monsoon region. The annual mean precipitation is about 539 mm, and the annual evaporation is about 470 mm (Liu et al., 2010; Zhao et al., 2014).
Figure 1 Location of the meteorological stations (a) and land use patterns in 2015 (b) for the Jing-Jin-Ji region

2.2 Meteorological data

Daily meteorological data of 22 national meteorological stations from 1991 to 2015 were downloaded from the National Climate Center of the China Meteorological Administration (http://data.cma.cn). The original variables used to calculate ET0 were mean air temperature (Tmean), minimum air temperature (Tmin), maximum air temperature (Tmax), sunshine duration (SD), relative humidity (RH) and wind speed (U10).

2.3 Land use data and classification of stations

We obtained land use data from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences. The resolution of the land use map was 1 km, and land use datasets for the year 2015 were used to identify different land use types (Figure 1b). Depending on the area ratio of a land use type within a 5-km radius around the station site, the stations were classified into three types: natural, farmland and urban stations. Natural stations had the sum of farmland and urban land with a percentage less than 50%, In other words, the percentage of water bodies, forests and grasslands in this region was >50%. The percentage of cultivated land with respect to farmland stations was >50% and that of urban land with respect to urban stations was >50% (Han et al., 2012; Xu et al., 2015).
Table 1 Location of 22 national meteorological stations in the Jing-Jin-Ji region
Station No. Station Latitude (°N) Longitude (°E) Elevation (m) Dominant land use type
53399 Zhangbei (ZB) 41.15 114.70 1393.3 Farmland
53593 Yuxian (YX) 39.83 114.57 909.5 Farmland
53698 Shijiazhuang (SJZ) 38.03 114.42 81.0 Urban
53798 Xingtai (XT) 37.07 114.50 77.3 Urban
54308 Fengning (FN) 41.22 116.63 661.2 Natural
54311 Weichang (WC) 41.93 117.75 842.8 Natural
54401 Zhangjiakou (ZJK) 40.78 114.88 724.2 Urban
54405 Huailai (HL) 40.40 115.50 536.8 Farmland
54423 Chengde (CD) 40.98 117.95 385.9 Natural
54429 Zunhua (ZH) 40.20 117.95 54.9 Urban
54436 Qinglong (QL) 40.40 118.95 227.5 Natural
54449 Qinhuangdao (QHD) 39.85 119.52 2.4 Urban
54511 Beijing (BJ) 39.80 116.47 31.3 Urban
54518 Langfang (LF) 39.12 116.38 9.0 Farmland
54527 Tianjin (TJ) 39.08 117.07 2.5 Urban
54534 Tangshan (TS) 39.67 118.15 27.8 Urban
54539 Laoting (LT) 39.43 118.88 10.5 Farmland
54602 Baoding (BD) 38.85 115.52 17.2 Urban
54606 Raoyang (RY) 38.23 115.73 19.0 Farmland
54623 Tanggu (TG) 39.05 117.72 4.8 Urban
54624 Huanghua (HH) 38.37 117.35 6.6 Farmland
54705 Nangong (NG) 37.37 115.38 27.4 Farmland

2.4 Calculation of ET 0

The FAO56 Penman-Monteith equation, considered to be the most accurate, was applied to calculate daily ET0. The annual ET0 value was obtained by summing up the daily values. The equation is expressed as follows (Allen et al., 1998):
where ET0 is the daily reference ET rate (mm day-1), Rn denotes net radiation at the ground surface (MJ m-2 day-1), G represents soil heat flux density (MJ m-2 day-1), T denotes mean daily air temperature at a 2-m height (℃), U2 denotes wind speed at a 2-m height (m s-1), es represents saturation vapour pressure (kPa), ea represents actual vapour pressure (kPa), Δ denotes the slope of the vapour pressure curve (kPa ℃-1) and γ is the psychometric constant (kPa ℃-1).
The original wind speed data was obtained at a 10-m height, which then required conversion to wind speed at a 2-m height. The equation for this conversion is expressed as follows:
${{u}_{2}}={{u}_{z}}\frac{4.87}{\ln (67.8z-5.42)}$ (2)
where z is the measurement height above the ground surface (m), uz is the wind speed at a z-m height (m s-1).

2.5 Trend and relative contribution analysis

The Mann-Kendall (MK) trend test is widely used for long-term time series trend analysis in hydrology and meteorology (Jhajharia et al., 2009; Huo et al., 2013; Wang et al., 2015). In this method, the equations for calculating the test statistic S and the standardised test statistic Z are as follows:
$S=\sum\limits_{i=1}^{n-1}{\sum\limits_{j=i+1}^{n}{sgn \left( {{x}_{j}}-{{x}_{i}} \right)}}$ (3)
$sgn ({{x}_{j}}-{{x}_{i}})=\left\{ \begin{matrix} 1\begin{matrix} {} & if({{x}_{j}}-{{x}_{i}})>0 \\\end{matrix} \\ 0\begin{matrix} {} & if({{x}_{j}}-{{x}_{i}})=0 \\\end{matrix} \\ -1\begin{matrix} {} & if({{x}_{j}}-{{x}_{i}})<0 \\\end{matrix} \\\end{matrix} \right.$ (4)
$\sigma _{s}^{2}=\frac{n(n-1)(2n+5)-\sum\limits_{p=1}^{q}{{{t}_{p}}({{t}_{p}}-1)(2{{t}_{p}}+5)}}{18}$ (5)
$Z=\left\{ \begin{matrix} \begin{matrix} \frac{s-1}{\sigma _{s}^{{}}} & if\begin{matrix} s>0 & {} \\\end{matrix} \\\end{matrix} \\ 0\begin{matrix} {} & if\begin{matrix} s=0 & {} \\\end{matrix} \\\end{matrix} \\ \begin{matrix} \frac{s+1}{\sigma _{s}^{{}}} & if\begin{matrix} s<0 & {} \\\end{matrix} \\\end{matrix} \\\end{matrix} \right.$ (6)
where Z is used to estimate the statistical trend. A positive Z value indicates an increasing trend in the time series, while a negative Z value indicates a decreasing trend. There is a significant trend in the time series when |Z| > Z1-α/2. The tested significance level α was set to 0.05 in this study, and at this significance level, the value of Z1-α/2 was 1.96.
To assess the effect of meteorological variables on ET0, linear regression was used to establish a multiple regression equation, and the relative contribution of each meteorological variable to be ET0 was noted to be a ratio of each variable’s regression coefficient to the sum of all of the variables’ regression coefficients (Li et al., 2014; Wang et al., 2016; Han et al., 2018).

3 Results and discussion

3.1 Urban, farmland and natural stations

The meteorological stations in the Jing-Jin-Ji region were classified into three types-urban, farmland and natural stations. If the area of urban land or farmland in the 5-km radius of the station was equal to or greater than 50%, the associated station was referred to as an urban station or a farmland station, respectively. If non-urban and non-farmland areas in the 5-km radius of the stations were >50%, these stations were included under natural stations. Based on the 2015 land use dataset, we noted 10 urban stations, 8 farmland stations and 4 natural stations (Figure 2). The four natural stations, namely Qinglong, Chengde, Fengning and Weichang, were located in the northern mountainous area of the Jing-Jin-Ji region. The proportions of the natural area for each of these stations were 76.16%, 73.27%, 61.96% and 68.30%, respectively. The forests in the northern mountainous area have been planted under the Three-North Shelter Forest Program (Jiang et al., 2015). Furthermore, the western and northern mountainous areas are being considered as ecological conservation areas under the coordinated development of the Jing-Jin-Ji region.
Figure 2 Urban, farmland and natural stations classified using 2015 land use dataset for the Jing-Jin-Ji region
Most of the urban stations are located in the plain area. The urban area proportions for six of the urban stations were >75% and those for four of the stations were between 50% and 75%. As the most urbanised station, the proportion of the urban area around Beijing station was 100%, and those around Zunhua and Qinhuangdao stations were 50.15% and 50.51%, respectively. The urbanised stations were located in four directions: Beijing to Qinhuangdao, Beijing to Tanggu, Beijing to Shijiazhuang and Beijing to Zhangjiakou.
The farmland stations were distributed in the western, southern and eastern parts of the Jing-Jin-Ji region. The proportions of the farmland area for these stations were between 55% and 75% (Figure 2). Three farmland stations (Zhangbei, Huailai and Yuxian) were located in the northwestern part of the plain and four farmland stations (Langfang, Rongyuan, Huanghua and Nangong) were located in the southern part. The land use type for Laoting station was mainly farmland, located in the northeastern part of the plain (Figure 1).

3.2 Changes in ET0 of stations with different land use types

Figure 3a shows the time series of annual ET0 in the study area. The lowest annual ET0 was 909.7 mm in 2003, whereas the highest annual ET0 was 1050.5 mm in 2009. The 5-year moving average annual ET0 of the 22 selected meteorological stations showed an increasing trend from 1991 to 2015. Furthermore, linear regression analysis indicated that ET0 significantly increased (p < 0.05) at a rate of 7.4 mm per decade in the Jing-Jin-Ji region.
Figure 3 Annual change (a) and spatial change (b) in ET0 in the Jing-Jin-Ji region from 1991 to 2015
The change in ET0 showed a spatial difference with different land use types (Figure 3b). The ET0 of most stations increased in the northern part (north of 40°N), but decreased in the southern (south of 40°N). The ET0 of the four natural stations in the northern Yanshan mountainous area showed an increasing trend. The Z value obtained from the MK trend test for the natural stations ranged from 2.58 to 3.373, located above the red dashed line in Figure 4, indicating a significant increase in ET0. The ET0 of the six farmland stations in the eastern and southern parts showed a decreasing trend, which was not significant except for that related to Raoyang station located on the central plain (Figures 3b and 4). However, ET0 increased at Zhangbei and Yuxian stations in the western mountainous area. Furthermore, ET0 showed a significant decreasing trend at stations that had an urbanisation percentage >80%, while ET0 showed an increasing trend at Zhangjiakou, Zunhua and Tianjin stations. Figure 4 shows that, among the stations for which a decreasing ET0 trend was noted, the trend at urban stations was more significant than that at farmland stations. The Z value obtained from the MK trend test for the urban stations ranged from -4.663 to -1.439, while that for farmland stations ranged from -2.927 and -0.248.
Similar findings have been reported for ET0 trends in northern China (Han and Hu, 2012); an obvious decreasing trend in ET0 was noted as a result of extensive irrigation in the arid/semi-arid areas. Moreover, Xu et al. (2015) observed a decreasing trend in ET0 at agricultural and urban stations in the Jinghe River Basin. However, in the case of natural stations, Xu et al. (2015) noted a decreasing ET0 trend, which is in contrast with our findings.
Figure 4 Relationship between ET0 changes and land use patterns in 2015

3.3 Meteorological parameter changes for different land use types

Figure 5 illustrates the spatial distribution of the changing trend of meteorological parameters in the study area. Wind speed was noted to be significantly increased at all the natural stations, leading to significant ET0 increase at these stations (Figure 5a). On the other hand, wind speed at most farmland and urban stations decreased significantly, thus causing ET0 decrease at these stations. With the exception of Zunhua and Hanghua stations, sunshine duration was particularly decreased at 12 of the 20 farmland and urban stations, thus causing a significant ET0 decreasing trend at these stations (Figure 5b). With respect to relative humidity and air temperature, no obvious consistent changes were noted at the stations. In terms of relative humidity at all the stations, we noted that two natural, three farmland and three urban stations showed increasing trend, causing an increasing ET0 trend, while two natural, five farmland and seven urban stations showed a downward trend, causing a decreasing ET0 trend (Figure 5c). The trend of air temperature was roughly opposite to the relative humidity. The air temperature decreased at the stations with upward trend in relative humidity. Therefore, in terms of air temperature, one natural, five farmland and five urban stations showed an increasing trend, whereas three natural, three farmland and five urban stations showed a decreasing trend (Figure 5d).
Figure 5 Changes of meteorological parameters at 22 stations: (a) wind speed, (b) sunshine duration, (c) relative humidity and (d) average air temperature
In addition to human activity, meteorological parameters play a decisive role in spatial variations in ET0 (Zhao et al., 2014). Li et al. (2017) observed climate change to have a more significant effect on ET than land use change. Wind speed is generally expected to have low values in urban areas than in rural areas because wind is blocked by high buildings in urban areas. Jiang et al. (2009) observed that a decline in average wind speed is closely related to not only weakened winter and summer monsoons but also increasing urbanisation in China. Han et al. (2016) demonstrated that surface wind speed is lowered by farmland and that wind speed values were significantly low at farmland stations. With respect to sunshine duration, Zhang et al. (2004) noted that low values can be attributed to increasing air pollution or high concentration of aerosols in the atmosphere. Furthermore, Liu et al. (2010) found that the aerosol index increased significantly after 1989 in the Haihe River Basin and that this index was higher in areas with higher population density than in those with lower population density. They concluded that an increase in aerosol concentrations in the atmosphere caused by human activities results in low values for sunshine duration and solar radiation. Zhang et al. (2006) also indicated that increasing air pollution may result in reduced sunshine duration in eastern China. Additionally, changes in air temperature can be attributed to factors such as reduction in sunshine duration or irrigation, depending on the land use type. Han and Yang (2013) studied the cooling effect of irrigation in Xinjiang, China, and noted this effect to influence changes in air temperature in the region. In the context of this study, low air temperatures for farmland stations may be attributed to irrigation. Thus, different types of topography and land use largely affect the distribution of wind speed, temperature and other meteorological parameters in a region (Jiang et al., 2009; Zhu et al., 2012). Based on this relationship between land use types and meteorological parameters, we proceeded to analyse the effect of these parameters on variations in ET0, using the multiple regression equation mentioned in section 2.5.

3.4 Dominant meteorological parameters affecting ET0 of different land use types

In general, the dominant meteorological parameters affecting ET0 at most stations can be ranked as wind speed > sunshine duration > air temperature > relative humidity (Figure 6). However, at each station, the contribution of each parameter to ET0 was different. Wind speed was the most dominant parameter affecting ET0 at the four natural stations, with its contribution ranging from 44% to 55% (Figure 6). The second dominant meteorological parameter affecting ET0 at Weichang and Fengning stations was air temperature, while that at Chengde and Qinglong stations was sunshine duration.
Figure 6 Relative contributions of meteorological parameters to ET0 of different land use types in the study area
As for the farmland stations, the main parameters controlling ET0 changes were different. The dominant parameters at Huanghua, Yuxian and Huailai stations were wind speed and air temperature. For Langfang and Laoting, the contribution of sunshine duration and air temperature to ET0 changes was >65%. Furthermore, sunshine duration and wind speed were the dominant meteorological parameters affecting ET0 at Raoyang and Nangong stations, and relative humidity and air temperature were the dominant parameters affecting ET0 at Zhangbei station.
With respect to the urban stations, the dominant parameters affecting ET0 at most stations were wind speed and sunshine duration. At Tianjin, Zunhua and Zhangjiakou stations, high wind speed values played an important role in the increasing ET0 trend. The dominant meteorological parameters affecting ET0 at Beijing and Tangshan stations were sunshine duration and air temperature, while those at Qinhuangdao station were air temperature and wind speed.
Our findings show that different types of land use significantly impact ET0 in a region; however, quantitatively assessing this impact is not easy. The influence of human activities, such as air pollution and land use change, on ET0 is ultimately reflected through their influence on meteorological parameters (Zhao et al., 2014). A large number of studies have assessed the attribution of ET0 to meteorological parameters; for example, wind speed was found to be the main parameter affecting ET0 in the Haihe River Basin (Wang et al., 2011), the Huanghe River watershed (Ma et al., 2012), southwestern China (Li et al., 2014) and the Jinghe River Basin (Xu et al., 2015). However, few studies have focused on the relationship between different land use types and ET0. Because of this limitation, it was difficult to compare and assess the accuracy of our findings. Therefore, future studies should focus on verifying these findings on a larger spatial scale.

4 Conclusions

Variations in ET0 and meteorological variables in different land use types were analysed using climate data from 22 meteorological stations in the Jing-Jin-Ji region for the period 1991-2015. Furthermore, land use data for 2015 was used to identify the dominant land use pattern at each meteorological station. The following conclusions may be drawn from this study:
(1) The meteorological stations were classified into urban (10 stations), farmland (8 stations) and natural stations (4 stations). The natural stations were located in the northern mountainous area. Most urban stations were distributed in the North China Plain and coastal area. The farmland stations were located in northwestern part of the plain area. Human activities, including urbanisation, agricultural practices and forest restoration, determined the land use type in the study area.
(2) The annual ET0 increased significantly at a rate of 7.4 mm per decade from 1991 to 2015. However, the spatial distribution of ET0 variations was different for different land use types. ET0 at natural stations increased significantly; the Z value obtained from the MK trend test for the four natural stations ranged from 2.58 to 3.373. ET0 at most urban and farmland stations decreased in the coastal and plain areas; the decreasing ET0 trend at the urban stations was greater than that at the farmland stations. The Z value for the urban stations ranged from -4.663 to -1.439, whereas that for the farmland stations ranged from -2.927 and -0.248.
(3) The dominant meteorological parameters affecting ET0 at most stations were ranked as follows: wind speed > sunshine duration > air temperature > relative humidity. The dominant parameters were wind speed at all the natural stations, and wind speed and sunshine duration at most urban stations. However, parameters affecting ET0 at the farmland stations varied.
Thus, our study shows that land use types in a region influence meteorological parameters such as wind speed and air temperature, which in turn influence ET0. This understanding of the relationship between different land use types, meteorological parameters and ET0 provides a basis for understanding a region’s hydrology and for future studies evaluating ET0 on a larger spatial scale. Furthermore, the findings of this study can be used to devise appropriate measures for sustainable land use practice and water resource management.

The authors have declared that no competing interests exist.

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