# Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014

Authors: Wanlin Dong; Chao Li; Qi Hu; Feifei Pan; Jyoti Bhandari; Zhigang Sun
Journal: Advances in Meteorology
 (2020)

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## Abstract

Climate change has caused uneven changes in hydrological processes (precipitation and evapotranspiration) on a space-temporal scale, which would influence climate types, eventually impact agricultural production. Based on data from 61 meteorological stations from 1961 to 2014 in the North China Plain (NCP), the spatiotemporal characteristics of climate variables, such as humidity index, precipitation, and potential evapotranspiration (ET0), were analyzed. The sensitivity coefficients and contribution rates were applied to ET0. The NCP has experienced a semiarid to humid climate from north to south due to the significant decline of ET0 (−13.8 mm decade−1). In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. Relative humidity had the most negative influence on ET0, while wind speed, sunshine hours, and air temperature had a positive effect on ET0. Wind speed and sunshine hours contributed the most to the spatiotemporal variation of ET0, followed by relative humidity and air temperature. Overall, the key climate factor impacting ET0 was wind speed decline in the NCP, particularly in Beijing and Tianjin. The crop yield in Shandong and Henan provinces was higher than that in the other regions with a higher humidity index. The lower the humidity index in Hebei province, the lower the crop yield. Therefore, potential water shortages and water conflict should be considered in the future because of spatiotemporal humidity variations in the NCP.


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## Body

## 1. Introduction

Hydrological processes and crop water requirements have been modified by climate change on local, regional, and global scales [1, 2]. The modification of climate change has coincided with surface air temperature increase.In the hydrological cycle, actual evapotranspiration (ET) and potential evapotranspiration (ET0) played important roles [3], particularly in soil evaporation and crop transpiration, eventually impact crop productivity. ET is measured as the quantity of water evaporating from an area under existing atmospheric conditions [4]. ET is controlled by two processes occurring simultaneously: evaporation from the soil and transpiration from the leaf surface [5]. ET0 is calculated as the maximum quantity of water that can be lost as water vapor in a given climate, by a continuous, extensive stretch of vegetation covering the ground when there is no shortage of water [6]. ET0 is determined by the meteorological conditions and the surface type [7]. Because ET0 is computed from precipitation, temperature, relative humidity, wind speed, and sunshine hours [8–10], any change in these variables is likely to change the ET0. Furthermore, these changes created more benign or stressful conditions for ET0 [11, 12]. ET0 had a significant impact on the availability of water resources [13], consequently influencing agricultural productivity. Plant growth planning often requires information on ET0 [14, 15] to estimate crop transpiration. Therefore, the study of ET0 under climate change has become an interesting research issue to scientists around the world. Also, it is important to identify the changes in ET0 on a regional scale.The humidity index (K), change in precipitation, and ET0 were applied to estimate dry-wet variations. Previous research studies on climate type only considered the influence of temperature and precipitation [16, 17] without including the influence of relative humidity, solar radiation, wind speed, and sunshine hours. Therefore, to understand the changing characteristics of climate variations, it is important to integrate water resource management. Furthermore, K can be applied to predict model scenarios that would persist in critical agricultural areas. Therefore, assessing ET0 and K distribution would explain the relationships between climate change and hydrological processes. This would lead to reasonable water regulation and management to maintain the ecohydrological system.In the NCP, summer maize (Zea mays L.) represents 33% of the national grain yield, while winter wheat represents 50% of the national grain yield [18]. Increasing temperature and decreasing precipitation are likely to reduce the yields of several primary crops over the next two decades [19]. Water shortage would be aggravated in the main grain production belt of North China [20, 21]. Bergamaschi et al. [22] indicated that crop yields would reduce by 10–20% up to 2050 because of warming and drying. Hence, understanding the hydrological distribution in these regions is critical for managing agricultural water resources and adjusting the planting pattern.At present, there are few studies on the spatiotemporal variations in climate type by integrating the input (precipitation) and output (evapotranspiration) of atmospheric water vapor in the NCP. Therefore, the objectives of this study were to (1) quantify the changes in spatial and temporal variations in ET0 and K in the NCP from 1961 to 2014, (2) quantitatively explain the reasons for the changes in ET0 by analyzing the sensitivity coefficients and contribution rates, and (3) analyse the relationship between ET0 and the crop yield. The results might be useful to agricultural planning and layout.


## 2. Materials and Methods

### 2.1. Study Area and Data

The study area, located in the NCP (31–43°N and 110–123°E), has a warm, temperate monsoon climate. The precipitation changes significantly in summer. The main crops are summer maize and winter wheat. The mean annual temperature and average annual precipitation were 13.0°C and 586 mm, respectively [23]. The soil has a silt-loam texture in the cultivated layer in general. This study was based in Beijing, Tianjin, Hebei province, Henan province, and Shandong province.In this study, daily meteorological data from January 1961 to December 2014 were obtained from 60 stations in the NCP (Table1). These data contained daily mean, minimum and maximum temperature, sunshine hours, wind speed, precipitation, and relative humidity provided by the National Climatic Centre of China Meteorological Administration (http://cdc.cma.gov.cn). The wind speed at 10 m height was converted to wind speed at 2 m height using the wind profile relationship introduced in Allen et al. [24], as shown in equation (1). The observed dataset has been subjected to strict quality and homogenization control. The geographical location of the stations is shown in Figure 1.(1)u2=uz4.87In67.8z−5.42,where u2 is the wind speed at 2 m above the ground surface (m·s−1), uz is the wind speed at z m above the surface (m·s−1), and z is the height of measurement above the ground surface (m).Table 1
Geographic characteristic information of each meteorological station in the study.
No.
Province
Site
Latitude (°)
Longitude (°)
Elevation (m)
1
Beijing
Huairou
40.72
116.55
487.9
2
Miyun
40.38
116.87
71.8
3
Beijing
39.80
116.47
31.3
4
Hebei
Zhangbei
41.15
114.70
1393.3
5
Weixian
39.83
114.57
909.5
6
Shijiazhuang
38.03
114.42
81.0
7
Xingtai
37.07
114.50
77.3
8
Fengning
41.22
116.63
661.2
9
Weichang
41.93
117.75
842.8
10
Zhangjiakou
40.78
114.88
724.2
11
Huailai
40.40
115.50
536.8
12
Chengde
40.98
117.95
385.9
13
Zunhua
40.20
117.95
54.9
14
Qinglong
40.40
118.95
227.5
15
Qinhuangdao
39.85
119.52
2.4
16
Langfang
39.12
116.38
9.0
17
Tangshan
39.67
118.15
27.8
18
Leting
39.43
118.88
10.5
19
Baoding
38.85
115.52
17.2
20
Raoyang
38.23
115.73
19.0
21
Huanghua
38.37
117.35
6.6
22
Nangong
37.37
115.38
27.4
23
Henan
Anyang
36.05
114.40
62.9
24
Xinxiang
35.32
113.88
73.2
25
Sanmengxia
34.80
111.20
409.9
26
Lushi
34.05
111.03
568.8
27
Mengjin
34.82
112.43
333.3
28
Luanchuang
33.78
111.60
750.3
29
Zhengzhou
34.72
113.65
110.4
30
Xuchang
34.03
113.87
66.8
31
Kaifeng
34.78
114.30
73.7
32
Xixia
33.30
111.50
250.3
33
Nanyang
33.03
112.58
129.2
34
Baofeng
33.88
113.05
136.4
35
Xihua
33.78
114.52
52.6
36
Nanyang
32.61
113.67
153.0
37
Zhumadian
33.00
114.02
82.7
38
Xinyang
32.13
114.05
114.5
39
Shangqiu
34.45
115.67
50.1
40
Gushi
32.17
115.62
42.9
41
Shandong
Huiminxian
37.48
117.53
11.7
42
Gaoqing
37.12
117.88
122.3
43
Changdao
37.93
120.72
39.7
44
Longkou
37.62
120.32
4.8
45
Chengshantou
37.40
122.68
47.7
46
Chaoyang
36.23
115.67
37.8
47
Jinan
36.60
117.05
170.3
48
Qiyuan
36.18
118.15
305.1
49
Yantai
37.23
120.49
48.6
50
Weifang
36.75
119.18
22.2
51
Qingdao
36.07
120.33
76.0
52
Haiyang
36.77
121.18
40.9
53
Gunzhou
35.57
116.85
51.7
54
Feixian
35.25
117.95
121.2
55
Juxian
35.58
118.83
107.4
56
Rizhao
35.43
119.53
36.9
57
Linyi
34.96
118.51
36.2
58
Tianjin
Jixian
40.17
117.45
5.1
59
Tianjin
39.08
117.07
2.5
60
Tanggu
39.05
117.72
4.8Figure 1
Distribution of meteorological stations in China (a) and the study area (b).
(a)
(b)


### 2.2. Data Analyses

#### 2.2.1. Estimation of Humidity Index (K)

Humidity index is the ratio of precipitation to potential evapotranspiration and is calculated by(2)K=PET0,where P is the daily precipitation (mm·d−1) and ET0 is the daily potential evapotranspiration (mm·d−1). The classification of climate region based on humidity index is listed in Table 2 [25].Table 2
Humidity index (K).
Humidity index
Climate region
K < 0.03
Extremely arid climate region
0.03K < 0.2
Arid climate region
0.2K < 0.5
Semiarid climate region
0.5K < 1.0
Semihumid climate region
K > 1.0
Humid climate regionET0 is calculated by the Penman–Monteith formula [24]:(3)ET0=0.408Rn−G+γ900/T+273U2es−eaΔ+γ1+0.34U2,where Rn is the net radiation at the surface, MJ·m−2·d−1, G is soil heat flux density, MJ·m−2·d−1, γis the psychrometric constant, kPa °C−1, T is the mean daily air temperature, °C, U2 is the wind speed at a height of 2 m, m·s−1, es is the saturation vapor pressure, kPa, ea is the actual vapor pressure, kPa, and Δ is the slope of the saturated water-vapor pressure curve, kPa °C−1. The computation of all data required for calculating ET0 followed the method and procedure given in Chapter 3 of FAO-56 [24].


#### 2.2.2. Sensitivity Analysis and Sensitivity Coefficient

Sensitivity analysis of the ET0 equation is an effective way to analyze the effect of meteorological factors on ET0 [26]. Previous studies showed the usage of nondimensional relative sensitivity coefficients to explain climate variables influence on ET0 [27]:(4)SVi=limΔVi⟶0=ΔET0/ET0ΔVi/V=∂ET0∂ViViET0,where SVi is the sensitivity coefficient of the ith climate variable, ET0 is the potential evapotranspiration, mm·d−1, ΔET0 is the daily change of ET0, Vi is the ith climate variable, and ΔVi is the change of Vi. A positive/negative SVi of a variable indicated that ET0 would increase/decrease as climate variables. The greater the SVi, the greater effect of the climate factor on ET0.


#### 2.2.3. Calculation of Attribution Rate

The attribution rateGvi is used to link the climate variable to ET0:(5)Gvi=Svi×Rvi,where Gvi is the contribution of the ith climate variable to ET0, Svi is the sensitivity coefficient, and Rvi is the relative change rate for the ith climate variation, which was given by equation (5). The meaning of Gvi is the same as Svi.In this study,Svi and Gvi for daily air temperature, solar radiation, relative humidity, and wind speed were estimated to quantify the contribution of each factor to the variation of ET0.(6)RVi=ΔViV¯i=n×TrendViV¯i,where TrendVi is the climate tendency rate for the ith climate variation and is calculated by equation (6), Vi¯ is the mean value for the climate variation, and n is the time in years. In this study, n = 54.


#### 2.2.4. Climate Trend

Climate tendency rate (TrendVi) was calculated by the least square method:(7)Xi=at+b,t=1,2,3…n,where Xi is the ith climate variation, t is the time in years, a is the regression coefficient, 10×a is the climate tendency rate, and b is the constant parameter.


## 2.1. Study Area and Data

The study area, located in the NCP (31–43°N and 110–123°E), has a warm, temperate monsoon climate. The precipitation changes significantly in summer. The main crops are summer maize and winter wheat. The mean annual temperature and average annual precipitation were 13.0°C and 586 mm, respectively [23]. The soil has a silt-loam texture in the cultivated layer in general. This study was based in Beijing, Tianjin, Hebei province, Henan province, and Shandong province.In this study, daily meteorological data from January 1961 to December 2014 were obtained from 60 stations in the NCP (Table1). These data contained daily mean, minimum and maximum temperature, sunshine hours, wind speed, precipitation, and relative humidity provided by the National Climatic Centre of China Meteorological Administration (http://cdc.cma.gov.cn). The wind speed at 10 m height was converted to wind speed at 2 m height using the wind profile relationship introduced in Allen et al. [24], as shown in equation (1). The observed dataset has been subjected to strict quality and homogenization control. The geographical location of the stations is shown in Figure 1.(1)u2=uz4.87In67.8z−5.42,where u2 is the wind speed at 2 m above the ground surface (m·s−1), uz is the wind speed at z m above the surface (m·s−1), and z is the height of measurement above the ground surface (m).Table 1
Geographic characteristic information of each meteorological station in the study.
No.
Province
Site
Latitude (°)
Longitude (°)
Elevation (m)
1
Beijing
Huairou
40.72
116.55
487.9
2
Miyun
40.38
116.87
71.8
3
Beijing
39.80
116.47
31.3
4
Hebei
Zhangbei
41.15
114.70
1393.3
5
Weixian
39.83
114.57
909.5
6
Shijiazhuang
38.03
114.42
81.0
7
Xingtai
37.07
114.50
77.3
8
Fengning
41.22
116.63
661.2
9
Weichang
41.93
117.75
842.8
10
Zhangjiakou
40.78
114.88
724.2
11
Huailai
40.40
115.50
536.8
12
Chengde
40.98
117.95
385.9
13
Zunhua
40.20
117.95
54.9
14
Qinglong
40.40
118.95
227.5
15
Qinhuangdao
39.85
119.52
2.4
16
Langfang
39.12
116.38
9.0
17
Tangshan
39.67
118.15
27.8
18
Leting
39.43
118.88
10.5
19
Baoding
38.85
115.52
17.2
20
Raoyang
38.23
115.73
19.0
21
Huanghua
38.37
117.35
6.6
22
Nangong
37.37
115.38
27.4
23
Henan
Anyang
36.05
114.40
62.9
24
Xinxiang
35.32
113.88
73.2
25
Sanmengxia
34.80
111.20
409.9
26
Lushi
34.05
111.03
568.8
27
Mengjin
34.82
112.43
333.3
28
Luanchuang
33.78
111.60
750.3
29
Zhengzhou
34.72
113.65
110.4
30
Xuchang
34.03
113.87
66.8
31
Kaifeng
34.78
114.30
73.7
32
Xixia
33.30
111.50
250.3
33
Nanyang
33.03
112.58
129.2
34
Baofeng
33.88
113.05
136.4
35
Xihua
33.78
114.52
52.6
36
Nanyang
32.61
113.67
153.0
37
Zhumadian
33.00
114.02
82.7
38
Xinyang
32.13
114.05
114.5
39
Shangqiu
34.45
115.67
50.1
40
Gushi
32.17
115.62
42.9
41
Shandong
Huiminxian
37.48
117.53
11.7
42
Gaoqing
37.12
117.88
122.3
43
Changdao
37.93
120.72
39.7
44
Longkou
37.62
120.32
4.8
45
Chengshantou
37.40
122.68
47.7
46
Chaoyang
36.23
115.67
37.8
47
Jinan
36.60
117.05
170.3
48
Qiyuan
36.18
118.15
305.1
49
Yantai
37.23
120.49
48.6
50
Weifang
36.75
119.18
22.2
51
Qingdao
36.07
120.33
76.0
52
Haiyang
36.77
121.18
40.9
53
Gunzhou
35.57
116.85
51.7
54
Feixian
35.25
117.95
121.2
55
Juxian
35.58
118.83
107.4
56
Rizhao
35.43
119.53
36.9
57
Linyi
34.96
118.51
36.2
58
Tianjin
Jixian
40.17
117.45
5.1
59
Tianjin
39.08
117.07
2.5
60
Tanggu
39.05
117.72
4.8Figure 1
Distribution of meteorological stations in China (a) and the study area (b).
(a)
(b)


## 2.2. Data Analyses

### 2.2.1. Estimation of Humidity Index (K)

Humidity index is the ratio of precipitation to potential evapotranspiration and is calculated by(2)K=PET0,where P is the daily precipitation (mm·d−1) and ET0 is the daily potential evapotranspiration (mm·d−1). The classification of climate region based on humidity index is listed in Table 2 [25].Table 2
Humidity index (K).
Humidity index
Climate region
K < 0.03
Extremely arid climate region
0.03K < 0.2
Arid climate region
0.2K < 0.5
Semiarid climate region
0.5K < 1.0
Semihumid climate region
K > 1.0
Humid climate regionET0 is calculated by the Penman–Monteith formula [24]:(3)ET0=0.408Rn−G+γ900/T+273U2es−eaΔ+γ1+0.34U2,where Rn is the net radiation at the surface, MJ·m−2·d−1, G is soil heat flux density, MJ·m−2·d−1, γis the psychrometric constant, kPa °C−1, T is the mean daily air temperature, °C, U2 is the wind speed at a height of 2 m, m·s−1, es is the saturation vapor pressure, kPa, ea is the actual vapor pressure, kPa, and Δ is the slope of the saturated water-vapor pressure curve, kPa °C−1. The computation of all data required for calculating ET0 followed the method and procedure given in Chapter 3 of FAO-56 [24].


### 2.2.2. Sensitivity Analysis and Sensitivity Coefficient

Sensitivity analysis of the ET0 equation is an effective way to analyze the effect of meteorological factors on ET0 [26]. Previous studies showed the usage of nondimensional relative sensitivity coefficients to explain climate variables influence on ET0 [27]:(4)SVi=limΔVi⟶0=ΔET0/ET0ΔVi/V=∂ET0∂ViViET0,where SVi is the sensitivity coefficient of the ith climate variable, ET0 is the potential evapotranspiration, mm·d−1, ΔET0 is the daily change of ET0, Vi is the ith climate variable, and ΔVi is the change of Vi. A positive/negative SVi of a variable indicated that ET0 would increase/decrease as climate variables. The greater the SVi, the greater effect of the climate factor on ET0.


### 2.2.3. Calculation of Attribution Rate

The attribution rateGvi is used to link the climate variable to ET0:(5)Gvi=Svi×Rvi,where Gvi is the contribution of the ith climate variable to ET0, Svi is the sensitivity coefficient, and Rvi is the relative change rate for the ith climate variation, which was given by equation (5). The meaning of Gvi is the same as Svi.In this study,Svi and Gvi for daily air temperature, solar radiation, relative humidity, and wind speed were estimated to quantify the contribution of each factor to the variation of ET0.(6)RVi=ΔViV¯i=n×TrendViV¯i,where TrendVi is the climate tendency rate for the ith climate variation and is calculated by equation (6), Vi¯ is the mean value for the climate variation, and n is the time in years. In this study, n = 54.


### 2.2.4. Climate Trend

Climate tendency rate (TrendVi) was calculated by the least square method:(7)Xi=at+b,t=1,2,3…n,where Xi is the ith climate variation, t is the time in years, a is the regression coefficient, 10×a is the climate tendency rate, and b is the constant parameter.


## 2.2.1. Estimation of Humidity Index (K)

Humidity index is the ratio of precipitation to potential evapotranspiration and is calculated by(2)K=PET0,where P is the daily precipitation (mm·d−1) and ET0 is the daily potential evapotranspiration (mm·d−1). The classification of climate region based on humidity index is listed in Table 2 [25].Table 2
Humidity index (K).
Humidity index
Climate region
K < 0.03
Extremely arid climate region
0.03K < 0.2
Arid climate region
0.2K < 0.5
Semiarid climate region
0.5K < 1.0
Semihumid climate region
K > 1.0
Humid climate regionET0 is calculated by the Penman–Monteith formula [24]:(3)ET0=0.408Rn−G+γ900/T+273U2es−eaΔ+γ1+0.34U2,where Rn is the net radiation at the surface, MJ·m−2·d−1, G is soil heat flux density, MJ·m−2·d−1, γis the psychrometric constant, kPa °C−1, T is the mean daily air temperature, °C, U2 is the wind speed at a height of 2 m, m·s−1, es is the saturation vapor pressure, kPa, ea is the actual vapor pressure, kPa, and Δ is the slope of the saturated water-vapor pressure curve, kPa °C−1. The computation of all data required for calculating ET0 followed the method and procedure given in Chapter 3 of FAO-56 [24].


## 2.2.2. Sensitivity Analysis and Sensitivity Coefficient

Sensitivity analysis of the ET0 equation is an effective way to analyze the effect of meteorological factors on ET0 [26]. Previous studies showed the usage of nondimensional relative sensitivity coefficients to explain climate variables influence on ET0 [27]:(4)SVi=limΔVi⟶0=ΔET0/ET0ΔVi/V=∂ET0∂ViViET0,where SVi is the sensitivity coefficient of the ith climate variable, ET0 is the potential evapotranspiration, mm·d−1, ΔET0 is the daily change of ET0, Vi is the ith climate variable, and ΔVi is the change of Vi. A positive/negative SVi of a variable indicated that ET0 would increase/decrease as climate variables. The greater the SVi, the greater effect of the climate factor on ET0.


## 2.2.3. Calculation of Attribution Rate

The attribution rateGvi is used to link the climate variable to ET0:(5)Gvi=Svi×Rvi,where Gvi is the contribution of the ith climate variable to ET0, Svi is the sensitivity coefficient, and Rvi is the relative change rate for the ith climate variation, which was given by equation (5). The meaning of Gvi is the same as Svi.In this study,Svi and Gvi for daily air temperature, solar radiation, relative humidity, and wind speed were estimated to quantify the contribution of each factor to the variation of ET0.(6)RVi=ΔViV¯i=n×TrendViV¯i,where TrendVi is the climate tendency rate for the ith climate variation and is calculated by equation (6), Vi¯ is the mean value for the climate variation, and n is the time in years. In this study, n = 54.


## 2.2.4. Climate Trend

Climate tendency rate (TrendVi) was calculated by the least square method:(7)Xi=at+b,t=1,2,3…n,where Xi is the ith climate variation, t is the time in years, a is the regression coefficient, 10×a is the climate tendency rate, and b is the constant parameter.


## 3. Results

### 3.1. Annual and Spatial Variation and Tendency of Humidity Index

The humidity index (K) showed an upward trend from north to south, changing from 0.34 to 1.20 (Figure 2(a)), which indicated that the climate of the region varied from semiarid to humid from north to south. The climate in Northwest and mid-west Hebei was semiarid, while that in South Henan was humid, with K above 1. The other regions had semihumid climate, with K ranging from 0.5 to 1.0.Figure 2
Spatial distribution of humidity index (a) and the tendency rate of humidity index (b) from 1961 to 2014.
(a)
(b)The tendency rate ofK was −0.005 decade−1 (P=0.63), which showed a slight drying trend from south to north (Figure 2(b)). Thirty-five percent of the sites (total = 60) mainly distributed in southern NCP had a tendency rate of K above 0, which indicated that these regions were wet. The other sites with a tendency rate of K below 0, especially East Shandong and North Hebei, were dry with a tendency rate of K below −0.01 decade−1.


### 3.2. Interdecadal Changes in Precipitation and ET0

The tendency rate of precipitation was −12.4 mm decade−1, which indicated a downward trend of precipitation. The abrupt decline in precipitation tendency rate was mainly observed in Southeast Hebei and Southeast Shandong (Figure 3(a)). Only 10.0% of all the sites had a tendency rate of precipitation over 0.Figure 3
Spatial distribution of precipitation (a) and ET0 (b) tendency rate from 1961 to 2014.
(a)
(b)The ET0 tendency rate was −13.5 mm decade−1 (Figure 3(b)), which showed a downward trend from 1961 to 2014. The ET0 tendency rate was significant at the 0.05 level in 71.0% of the sites, especially in mid-east Hebei and mid-south Shandong.


### 3.3. Sensitivity Coefficient of Temperature (ST), Relative Humidity (SRH), Sunshine Hours (SSH), and Wind Speed (SWS) to ET0 on Annual and Spatial Scales

S
T varied from 0 to 0.15 (Figure 4(a)), which meant that ET0 increased with temperature. ST in the southeast was higher, especially in the Henan province, while it peaked in the mid-region, such as North Shandong, Beijing, Tianjin, and North Hebei. SRH varied from−0.70 to−0.19 (Figure 4(b)), which indicated that ET0 decreased as the relative humidity increased. The spatial distribution of SRH showed a downward trend from south to east. The SRH was higher in East Shandong, with an absolute value above 0.5. In South Hebei and Beijing, the absolute value of SRH was below 0.4. The SSH in all regions was above 0, with a mean value of 0.18 (Figure 4(c)). The SSH showed an upward trend from north to south. SWS ranged from 0.10 to 0.31 (Figure 4(d)) and showed a downward trend from north to south. The SWS in the northern part of the region, e.g., North Hebei, Beijing, and Tianjin, was above 0.21, while in South Henan, it was below 0.18.Figure 4
Spatial distribution of sensitivity coefficient of the main meteorological factors affecting ET0 from 1961 to 2014. (a) Temperature. (b) Relative humidity. (c) Sunshine hours. (d) Wind speed.
(a)
(b)
(c)
(d)


### 3.4. Climate Factor Attribution Rate to ET0 on Annual and Spatial Scales

G
v
i was applied in this study to indicate the relative change in ET0 resulting from each meteorological factor. The attribution rate of air temperature to ET0 (GvT) ranged from−0.5% to 4.0% (Figure 5(a)). GvT in the northern and eastern parts of the NCP was over 1%, while it was less than 1% in the other regions. The attribution rate of relative humidity to ET0 (GvRH) ranged from−4.7% to 10.1% (Figure 5(b)). GvRH in North Hebei and Southwest Shangdong was below 0. The attribution rate of sunshine hours to ET0 (GvSH) ranged from−8.4% to 0.2% (Figure 5(c)). GvSH was above 0 in only one site. The spatial distribution of GvSH showed a downward trend from north to south. The attribution rate of wind speed to ET0 (GvWS) ranged from−19.1% to 4.9% (Figure 5(d)). The highest absolute value of GvWS was in Beijing and Tianjin.Figure 5
Spatial distribution of attribution rate to ET0 of the main meteorological elements from 1961 to 2014. (a) Temperature. (b) Relative humidity. (c) Sunshine hours. (d) Wind speed.
(a)
(b)
(c)
(d)The attribution rate of air temperature and relative humidity to ET0 was positive, which indicated that ET0 increased with an increase in these two climate factors. However, the mechanisms of GvT and GvRH were different. GvT was positive when the sensitivity coefficient was positive and the tendency rate (0.24°C decade−1) of air temperature increased (Figure 6(a)). GvRH was positive when the sensitivity coefficient was negative and the tendency rate (0.44 decade−1) of relative humidity decreased (Figure 6(c)). The attribution rate of sunshine hours and wind speed was negative, which indicated that the change in the two climate factors decreased ET0. The attribution rate of climate factor to ET0 was in the following order: wind speed > sunshine hour > relative humidity > air temperature.


## 3.1. Annual and Spatial Variation and Tendency of Humidity Index

The humidity index (K) showed an upward trend from north to south, changing from 0.34 to 1.20 (Figure 2(a)), which indicated that the climate of the region varied from semiarid to humid from north to south. The climate in Northwest and mid-west Hebei was semiarid, while that in South Henan was humid, with K above 1. The other regions had semihumid climate, with K ranging from 0.5 to 1.0.Figure 2
Spatial distribution of humidity index (a) and the tendency rate of humidity index (b) from 1961 to 2014.
(a)
(b)The tendency rate ofK was −0.005 decade−1 (P=0.63), which showed a slight drying trend from south to north (Figure 2(b)). Thirty-five percent of the sites (total = 60) mainly distributed in southern NCP had a tendency rate of K above 0, which indicated that these regions were wet. The other sites with a tendency rate of K below 0, especially East Shandong and North Hebei, were dry with a tendency rate of K below −0.01 decade−1.


## 3.2. Interdecadal Changes in Precipitation and ET0

The tendency rate of precipitation was −12.4 mm decade−1, which indicated a downward trend of precipitation. The abrupt decline in precipitation tendency rate was mainly observed in Southeast Hebei and Southeast Shandong (Figure 3(a)). Only 10.0% of all the sites had a tendency rate of precipitation over 0.Figure 3
Spatial distribution of precipitation (a) and ET0 (b) tendency rate from 1961 to 2014.
(a)
(b)The ET0 tendency rate was −13.5 mm decade−1 (Figure 3(b)), which showed a downward trend from 1961 to 2014. The ET0 tendency rate was significant at the 0.05 level in 71.0% of the sites, especially in mid-east Hebei and mid-south Shandong.


## 3.3. Sensitivity Coefficient of Temperature (ST), Relative Humidity (SRH), Sunshine Hours (SSH), and Wind Speed (SWS) to ET0 on Annual and Spatial Scales

S
T varied from 0 to 0.15 (Figure 4(a)), which meant that ET0 increased with temperature. ST in the southeast was higher, especially in the Henan province, while it peaked in the mid-region, such as North Shandong, Beijing, Tianjin, and North Hebei. SRH varied from−0.70 to−0.19 (Figure 4(b)), which indicated that ET0 decreased as the relative humidity increased. The spatial distribution of SRH showed a downward trend from south to east. The SRH was higher in East Shandong, with an absolute value above 0.5. In South Hebei and Beijing, the absolute value of SRH was below 0.4. The SSH in all regions was above 0, with a mean value of 0.18 (Figure 4(c)). The SSH showed an upward trend from north to south. SWS ranged from 0.10 to 0.31 (Figure 4(d)) and showed a downward trend from north to south. The SWS in the northern part of the region, e.g., North Hebei, Beijing, and Tianjin, was above 0.21, while in South Henan, it was below 0.18.Figure 4
Spatial distribution of sensitivity coefficient of the main meteorological factors affecting ET0 from 1961 to 2014. (a) Temperature. (b) Relative humidity. (c) Sunshine hours. (d) Wind speed.
(a)
(b)
(c)
(d)


## 3.4. Climate Factor Attribution Rate to ET0 on Annual and Spatial Scales

G
v
i was applied in this study to indicate the relative change in ET0 resulting from each meteorological factor. The attribution rate of air temperature to ET0 (GvT) ranged from−0.5% to 4.0% (Figure 5(a)). GvT in the northern and eastern parts of the NCP was over 1%, while it was less than 1% in the other regions. The attribution rate of relative humidity to ET0 (GvRH) ranged from−4.7% to 10.1% (Figure 5(b)). GvRH in North Hebei and Southwest Shangdong was below 0. The attribution rate of sunshine hours to ET0 (GvSH) ranged from−8.4% to 0.2% (Figure 5(c)). GvSH was above 0 in only one site. The spatial distribution of GvSH showed a downward trend from north to south. The attribution rate of wind speed to ET0 (GvWS) ranged from−19.1% to 4.9% (Figure 5(d)). The highest absolute value of GvWS was in Beijing and Tianjin.Figure 5
Spatial distribution of attribution rate to ET0 of the main meteorological elements from 1961 to 2014. (a) Temperature. (b) Relative humidity. (c) Sunshine hours. (d) Wind speed.
(a)
(b)
(c)
(d)The attribution rate of air temperature and relative humidity to ET0 was positive, which indicated that ET0 increased with an increase in these two climate factors. However, the mechanisms of GvT and GvRH were different. GvT was positive when the sensitivity coefficient was positive and the tendency rate (0.24°C decade−1) of air temperature increased (Figure 6(a)). GvRH was positive when the sensitivity coefficient was negative and the tendency rate (0.44 decade−1) of relative humidity decreased (Figure 6(c)). The attribution rate of sunshine hours and wind speed was negative, which indicated that the change in the two climate factors decreased ET0. The attribution rate of climate factor to ET0 was in the following order: wind speed > sunshine hour > relative humidity > air temperature.


## 4. Discussion

The change in climate types was due to the sensitivity to various meteorological variables and their attribution to ET0 in the NCP. ET0 was most sensitive to relative humidity, which had a negative effect. This was consistent with the study by Hu et al. [28] in Northeast China. The factor that impacted ET0 significantly varied depending on the location. Huo et al. [3] indicated that ET0 was very sensitive to 2 m wind speed and relative humidity in Northwest China. In southern Spain, ET0 was sensitive to air temperature and radiation in the warmer season and to 2 m wind speed in cooler seasons [29]. In Australia, temperature was found to be the most important factor for ET0, but the second-most important factors differed between dry and humid catchments [30]. Yang et al. [31] showed that the sensitivity of ET0 to climate factors varied from low elevations to high elevations. The sensitivity of ET0 to climate factors is regional variation because climate conditions and climate factors differ with regional variation [30, 31]. In this study, wind speed reduction was the main reason for the decline in ET0 from 1961 to 2014. However, the climate tendency rate was low and resulted in a relatively low attribution rate.In general, warm climates led to an increase in evaporation and evapotranspiration. However, the observation of pan evaporation rate has been declined in most parts of the world in the past several decades [8, 9, 32, 33], which is called the pan evaporation paradox phenomenon [34]. Also, those only considered the influence of air temperature on ET0, without considering other meteorological factors, e.g., wind speed, relative humidity, and sunshine hours. It revealed significant increasing trends in ET0 (P=0.1) during 1961–1990 over the entire West African region [35]. Although the air temperature significantly increased at the rate of 0.24°C decade−1, the effect of decrease in wind speed and sunshine hours was greater than that of the increase in air temperature, which led to a significant decline of ET0 in the NCP. This pattern of variations is in agreement with the findings of Dinpashoh et al. [36] in North-West Iran where most of the stations selected (86% of the sites) also showed increasing trends in ET0 between 1997 and 2016. However, Hou et al. [37] revealed that temperature was the key variable contributing to increasing ET0 due to its sensitivity to ET0 and the significant increase trend.Agriculture accounts for at least 90% of the total water use in the arid and semiarid regions [38]. An important way to alleviate water stress is to improve agricultural water management. Comprehensively understanding an agro-hydrological process lays a foundation for minimizing agricultural water use. In the presence of a shallow water table, groundwater provides an important source for crop water use in arid and semiarid regions [39, 40], which impact crop productive. Climate type depended on the rate of change of precipitation and ET0. The important issue involves the evaluation of drought impacts on agriculture. Crop yields and drought occurrence statistics are closely related [42, 42], but consistency analysis of drought trends derived from humidity index and agricultural drought survey is sparse. Crop yield increased significantly (P≤0.001) in the study area (Figure 7), in accordance with K in each area. The crop yield was greater in Shandong and Henan province, with a K of 0.70 and 0.77, compared with that in Tianjin, Beijing, and Hebei (Table 3). The lowest K (0.53) was in Hebei province, along with the lowest crop yield. Therefore, regional water balance should be considered and drought or flood risk might be reduced in these areas. China has investigated agricultural drought area for decades, so it is important to investigate the degree that K and ET0 with agricultural drought surveys, especially in their climatic trends.Figure 6
Tendency rate of temperature (a), relative humidity (b), sunshine hours (c), and wind speed (d) from 1961 to 2014 in the NCP.Figure 7
Crop yield from 2000 to 2013.Table 3
The annual humidity index (K) from 1961 to 2014 in each region.
Sites
Shandong
Henan
Beijing
Tianjin
Hebei
K-value
0.70
0.77
0.54
0.54
0.53


## 5. Conclusions

The NCP has experienced a semiarid to humid climate from north to south based on the humidity index due to the slight change in precipitation and the significant decline of ET0 on annual and spatial scales. In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. ET0 was the most sensitive to relative humidity, particularly in East Shandong, followed by wind speed. The dominant cause of ET0 decline was wind speed, with the highest attribution rates, particularly in Beijing and Tianjin. The higher the humidity index in Shandong and Henan province was, the higher the crop yield was. The lower the humidity index in Hebei province was, the lower the crop yield was. It is necessary to analyze the influence of ET0 on crop yield at various crop growth stages.



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*Source: 3691421-2020-03-16.xml*