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

Dong, Wanlin; Li, Chao; Hu, Qi; Pan, Feifei; Bhandari, Jyoti; Sun, Zhigang

doi:https://doi.org/10.1155/2020/3691421

Advances in Meteorology

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Abstract Introduction Materials and Methods Results Discussion Conclusions Data Availability Disclosure Conflicts of Interest Acknowledgments References Copyright Related Articles

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Aerosol and Trace Gas Monitoring for Climate Change Studies

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Volume 2020 | Article ID 3691421 | https://doi.org/10.1155/2020/3691421

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

Wanlin Dong,^1,2Chao Li,³Qi Hu,⁴Feifei Pan,⁵Jyoti Bhandari,²and Zhigang Sun^2,6

Guest Editor: Salman Tariq

Received10 Jun 2019

Accepted23 Dec 2019

Published16 Mar 2020

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.

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 (Table 1). 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.where u₂ is the wind speed at 2 m above the ground surface (m·s⁻¹), u_z 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).

(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 bywhere 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].

ET₀ is calculated by the Penman–Monteith formula [24]:where R_n is the net radiation at the surface, MJ·m⁻²·d⁻¹, G is soil heat flux density, MJ·m⁻²·d⁻¹, is 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, e_s is the saturation vapor pressure, kPa, e_a 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]:where S_Vi is the sensitivity coefficient of the ith climate variable, ET₀ is the potential evapotranspiration, mm·d⁻¹, ET₀ is the daily change of ET₀, V_i is the ith climate variable, and ΔV_i is the change of V_i. A positive/negative S_Vi of a variable indicated that ET₀ would increase/decrease as climate variables. The greater the S_Vi, the greater effect of the climate factor on ET₀.

2.2.3. Calculation of Attribution Rate

The attribution rate is used to link the climate variable to ET₀:where is the contribution of the ith climate variable to ET₀, is the sensitivity coefficient, and is the relative change rate for the ith climate variation, which was given by equation (5). The meaning of G_vi is the same as S_vi.

In this study, and 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.where Trend_Vi is the climate tendency rate for the ith climate variation and is calculated by equation (6), 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 (Trend_Vi) was calculated by the least square method:where X_i 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.

(a)

(b)

The tendency rate of K was −0.005 decade⁻¹ (), 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.

(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 (S_T), Relative Humidity (S_RH), Sunshine Hours (S_SH), and Wind Speed (S_WS) 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. S_T 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. S_RH varied from−0.70 to−0.19 (Figure 4(b)), which indicated that ET₀ decreased as the relative humidity increased. The spatial distribution of S_RH showed a downward trend from south to east. The S_RH was higher in East Shandong, with an absolute value above 0.5. In South Hebei and Beijing, the absolute value of S_RH was below 0.4. The S_SH in all regions was above 0, with a mean value of 0.18 (Figure 4(c)). The S_SH showed an upward trend from north to south. S_WS ranged from 0.10 to 0.31 (Figure 4(d)) and showed a downward trend from north to south. The S_WS 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.

(a)

(b)

(c)

(d)

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

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 (G_vT) ranged from−0.5% to 4.0% (Figure 5(a)). G_vT 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 (G_vRH) ranged from−4.7% to 10.1% (Figure 5(b)). G_vRH in North Hebei and Southwest Shangdong was below 0. The attribution rate of sunshine hours to ET₀ (G_vSH) ranged from−8.4% to 0.2% (Figure 5(c)). G_vSH was above 0 in only one site. The spatial distribution of G_vSH showed a downward trend from north to south. The attribution rate of wind speed to ET₀ (G_vWS) ranged from−19.1% to 4.9% (Figure 5(d)). The highest absolute value of G_vWS was in Beijing and Tianjin.

(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 G_vT and G_vRH were different. G_vT was positive when the sensitivity coefficient was positive and the tendency rate (0.24°C decade⁻¹) of air temperature increased (Figure 6(a)). G_vRH 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 () 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 () 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.

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.

Data Availability

The data used to support the findings of this study have been deposited in the 3691421data-2019.xls repository and are included within the article.

Disclosure

The first author is Wanlin Dong.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was supported by The National Key Research and Development Program of China (2017YFC0503805 and 2017YFD0300304).

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Copyright © 2020 Wanlin Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Advances in Meteorology

Aerosol and Trace Gas Monitoring for Climate Change Studies

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

Abstract

1. Introduction

2. Materials and Methods

2.1. Study Area and Data

2.2. Data Analyses

2.2.1. Estimation of Humidity Index (K)

2.2.2. Sensitivity Analysis and Sensitivity Coefficient

2.2.3. Calculation of Attribution Rate

2.2.4. Climate Trend

3. Results

3.1. Annual and Spatial Variation and Tendency of Humidity Index

3.2. Interdecadal Changes in Precipitation and ET0

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

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

4. Discussion

5. Conclusions

Data Availability

Disclosure

Conflicts of Interest

Acknowledgments

References

Copyright

3.2. Interdecadal Changes in Precipitation and ET₀

3.3. Sensitivity Coefficient of Temperature (S_T), Relative Humidity (S_RH), Sunshine Hours (S_SH), and Wind Speed (S_WS) to ET₀ on Annual and Spatial Scales

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