On the Increased Precipitation Recycling by Large-Scale Irrigation over the Haihe Plain

Irrigation plays a vital role in global food security and also in the welfare of a large share of the world’s population (De Fraiture and Wichelns, 2010; Aeschbach-Hertig and Gleeson, 2012) as irrigated agriculture accounts for approximately 40% of global crop production (Siebert et al., 2005). Furthermore, irrigation currently uses up approximately 70% of the freshwater resources used by humans (Wada et al., 2013). The demand for irrigation water is expected to increase in the future with a growing global population (De Fraiture and Wichelns, 2010) and projected climate change (Wada et al., 2013; Chaturvedi et al., 2015). Irrigation affects not only the hydrological cycle (Leng et al., 2014), but also aspects of the regional climate, such as precipitation (Guimberteau et al., 2012), which in turn influences regional water resources and future water availability (Saha, 2015). Therefore, understanding the effects of irrigation on precipitation is important for regional climate change research, and for assessing resource use and management.

Research on the effects of irrigation on precipitation has often been conducted by using observational data analysis and numerical simulations. Observational studies have used long-term data to analyze changes in precipitation and investigate the influence of irrigation on precipitation (Chen et al., 2018). Fowler and Helvey (1974) used climate data from before and after irrigation periods in the Columbia Basin to study the effect of irrigation on precipitation, and showed that the changes in precipitation were statistically insignificant. Barnston and Schickedanz (1984) obtained precipitation and irrigation data for a period of 40 yr (1931–1970) in the Southern Great Plains, and statistically identified an approximately 25% increase in precipitation due to irrigation. Although the observational data analysis approach is simple and convenient, the uncertainties associated with the surrounding topography and differences in the location of weather stations have prevented more rigorous analyses (Chen et al., 2018).

Global and regional climate models have recently become popular tools for assessing the effects of irrigation on precipitation (Cook et al., 2015; Harding et al., 2015; Krakauer et al., 2016; Mahalov et al., 2016; Bohn and Vivoni, 2018; Fowler et al., 2018). Puma and Cook (2010) used an atmospheric general circulation model to investigate the global impact of irrigation in the 20th century. They showed that precipitation increased primarily in the downwind areas of irrigated lands, and significantly increased in the midlatitudes and tropics of the Northern Hemisphere from around 1950. Pei et al. (2016) incorporated a dynamic irrigation scheme into the Weather Research and Forecasting (WRF) model to quantify the influence of irrigation on precipitation during the summer of 2012 in the United States. They found that precipitation was slightly reduced over the irrigated area of the high plains and the central United States, but increased in the downwind areas of irrigated lands. Zhang et al. (2017) investigated the effects of irrigation on precipitation in the Heihe Basin in Northwest China by incorporating an irrigation scheme into the WRF model. The results showed no significant change in precipitation by irrigation, although increased precipitation has been observed in this region in recent decades (Su et al., 2020). This is because the cooling and wetting effects from irrigation offset their positive impacts on convective precipitation, and precipitation primarily increases in the remote southeastern portion of the basin. It should be noted that hydrometeorological processes, especially atmospheric convection, occur at scales smaller than the resolved resolution in current climate models; therefore, they need to be parameterized, which results in high levels of uncertainties in assessing this type of land–atmosphere interaction (Donner and Large, 2008; Yin and Porporato, 2017; Gentine et al., 2019).

The contribution to precipitation can be determined by the precipitation recycling method, which quantifies the contribution of local evaporation to total precipitation as the precipitation recycling ratio (PRR; Eltahir and Bras, 1996; Arnault et al., 2016). However, the PRR can only determine the contribution of the total local evaporation to precipitation without identifying different evaporation sources, such as irrigation. To solve this problem, we propose a method to quantify the moisture contribution of irrigation to precipitation, while retaining the simplicity of the PRR method. A case study from the Haihe Plain was used to demonstrate how to distinguish the amount of evaporation from irrigation and evaluate its impacts on regional water resource management.

Thus, the objectives of this study are (1) to propose a method to quantify the contribution of irrigation to precipitation based on the precipitation recycling research method, and (2) to study the changes in the contribution of irrigation to precipitation in the Haihe Plain from 1961 to 2016.

The Haihe Plain (35°–41°N, 114°–118°E) is located in the eastern part of northern China, with an approximate area of 152,078 km². It covers the two municipalities of Beijing and Tianjin, most of Hebei Province, and the northern parts of Henan and Shandong provinces (Fig. 1). It is a sub-humid and semi-arid continental monsoon region with an annual precipitation between 400 and 700 mm. The Haihe Plain is an important grain production area, and is the main production area for winter wheat and summer maize in China (Fang et al., 2010; Weng et al., 2010). Only approximately 30% of the annual local precipitation occurs during the winter wheat growing period, which cannot meet even one-third of the crop water demand in these areas (Liu et al., 2001; Sun and Ren, 2014). Consequently, irrigation is essential for sustainable high-yield grain production and the water used for winter wheat accounts for approximately 70% of the agricultural water used in the region (Li et al., 2008; Yang et al., 2014).

Figure 1.  Location of the study area and distribution of the meteorological stations.

In a region, precipitation is supplied by local evaporation and horizontally advected atmospheric moisture from outside the region (Trenberth, 1999). Some of the locally evaporated water vapor falls back to the ground, which is then called recycled precipitation. The ratio (ρ) of recycled precipitation to total precipitation is denoted as the PRR. According to its definition, ρ can be expressed as follows:

where P_E is the precipitation formed by local evaporation, and P_I is the precipitation formed by the externally advected moisture.

Two assumptions were made to derive the general recycling formula (Eltahir and Bras, 1996). The first assumption states that the atmosphere is vertically well-mixed with respect to moisture fractions of different origins (Hua et al., 2016). Based on this assumption, PRR can be defined as follows:

where O_E is the outflow of atmospheric moisture from local evaporation and O_I is the outflow of atmospheric moisture that comes from externally advected moisture.

The general balance equation for atmospheric moisture can be expressed as follows:

where ∆S is the change in atmospheric moisture storage, I is the inflow of atmospheric moisture, and E is evaporation.

The second assumption states that the change in the storage of atmospheric moisture vapor is small compared to the atmospheric moisture vapor flux, and evaporation can be neglected at monthly or longer time scales. Therefore, the conservation balance equation for atmospheric moisture can be simplified as follows:

Finally, the formula for calculating the PRR can be obtained as follows:

During the main irrigation period, the local evaporation within a large region comes from precipitation, irrigation, and other water sources, and can be expressed as follows:

where E_P is evaporation from precipitation, E_irri is evaporation from irrigation, and E_other is evaporation from other water sources.

Therefore, the proportion of evaporation produced by irrigation relative to local evaporation is as follows:

where φ is the ratio of evaporation produced by irrigation relative to local evaporation, which is the sum of evaporation from all water sources.

The contribution ratio of irrigation to precipitation can be expressed as follows:

where ω_irri is the contribution ratio of irrigation to precipitation.

The contribution of irrigation to precipitation can be expressed as follows:

where P_irri is the contribution of irrigation to precipitation.

In the Haihe Plain, local evaporation during the main irrigation period (April to June) comes mainly from two sources: precipitation and irrigation water. The irrigation water is usually obtained from surface water formed in hilly areas and groundwater. During this period, there is less domestic and industrial water consumption, and thus, it is temporarily ignored. Simultaneously, the Haihe Plain had little precipitation and high evaporation, which led to more water consumption and a shortage of soil moisture. Furthermore, the precipitation and irrigation of the month were almost completely consumed by evaporation (Wang et al., 2013). Therefore, local evaporation can be expressed as follows:

In the Haihe Plain, local evaporated water vapor is mainly derived from agricultural irrigation water and direct local precipitation during the main irrigation period. The agricultural irrigation water is composed of two parts: mountain surface water intercepted by the reservoir and groundwater; precipitation produces almost no runoff during this period. Because the monthly evaporation mainly comes from irrigation and precipitation, the ratio of the evaporation from irrigation to local precipitation is the same as the ratio of irrigation water to local precipitation. The ratio of evaporation produced by irrigation to local evaporation can be expressed as follows:

where $ {W_{\rm{irri}}} $ is the amount of irrigation water.

The Mann–Kendall (MK) test has commonly been used to identify the significance of hydrologic and climatic trends (Wang et al., 2016; Han et al., 2018). In this study, the MK test was used to identify the significance of the linear trends of precipitation, the amount of irrigation water, and other variables.

Monthly atmospheric moisture flux and evapotranspiration data for the 1.25° × 1.25° grid (1961–2016) are obtained from the Japanese 55-yr Reanalysis (JRA-55) project conducted by the Japan Meteorological Agency (https://jra.kishou.go.jp/JRA-55/index_en.html). Even though some uncertainties exist owing to the use of only one reanalysis (Gerber and Martineau, 2018; Cao et al., 2019), the data accuracy of JRA-55 in East Asia is relatively high compared to other reanalysis data. JRA-55 is the first comprehensive reanalysis to cover data from the last half-century (Tsujino et al., 2018). Monthly precipitation observation datasets are provided by the National Meteorological Information Center of China Meteorological Administration (http://data.cma.cn/). Data on the irrigation amounts are obtained from Water Resource Bulletins for Beijing, Tianjin, Hebei, Henan, and Shandong.

Figure 2 shows the precipitation changes in the Haihe Plain throughout the year, flood season (July and August), and main irrigation period (April, May, and June) from 1961 to 2016. The annual mean precipitation is 560.76 mm and the maximum annual precipitation is 953.55 mm in 1994; thereafter, the precipitation reaches a minimum of 358.52 mm in 2002. Annual precipitation shows a decreasing trend from 1961 to 2016, with a linear rate of 14.07 mm decade⁻¹. The average precipitation in the flood season is 311.69 mm, accounting for 55.57% of the annual precipitation, while the average precipitation of the irrigation period is 131.79 mm, accounting for 23.50% of the annual precipitation. The precipitation in the flood season decreases significantly (p < 0.05), by −20.64 mm decade⁻¹, from 1961 to 2016. The precipitation in the irrigation period increases with a propensity rate of 4.54 mm decade⁻¹, which is consistent with findings of Su et al. (2020).

Figure 2.  Precipitation changes in the Haihe Plain for the entire year, flood season, and irrigation period from 1961 to 2016.

Figure 3 shows the precipitation change slope for each month on the Haihe Plain from 1961 to 2016. Changes in precipitation mainly occurred from April to September during 1961–2016 and the slope of the precipitation changes in the other months was almost zero. The precipitation changes in July and August were the biggest and showed a decreasing trend from 1961 to 2016, with a linear slope of −8.56 and −12.09 mm decade⁻¹, respectively. During the irrigation period, precipitation values in May and June showed an increasing trend from 1961 to 2016, with linear slopes of 2.77 and 3.03 mm decade⁻¹, respectively; whereas precipitation in April showed a decreasing trend with a linear slope of 1.26 mm decade⁻¹. The precipitation in September showed an increasing trend from 1961 to 2016 with a linear slope of 1.92 mm decade⁻¹.

Figure 3.  Changes in the slope of monthly precipitation values in the Haihe Plain from 1961 to 2016.

Figure 4 shows the spatial distributions of the trend in annual precipitation change during the entire year, flood season, and irrigation period in the Haihe Plain from 1961 to 2016. The annual precipitation showed declining trends over most regions of the Haihe Plain, while a decreasing trend in annual precipitation was observed in 169 out of 175 stations (or 97% of stations). The areas with large reductions in annual precipitation (> 15 mm decade⁻¹) are mainly in the eastern and northern plain areas, whereas the stations showing increasing trends and relatively small reduction trends (< 5 mm decade⁻¹) are mainly in the western plain. The change in precipitation in the flood season showed an almost decreasing trend throughout the region from 1961 to 2016. This decreasing trend was clearly larger than that of the annual precipitation and only one station showed a slight increasing trend in precipitation. A precipitation reduction trend of greater than 20 mm decade⁻¹ during the flood season can be seen at 82 out of 175 stations (or 47% of the stations), which are mainly located in the north and east plain areas. Furthermore, a decreasing trend of more than 20 mm decade⁻¹ in annual precipitation was observed at 22 out of 175 stations (or 13% of the stations). The stations with a relatively small declining trend (< 10 mm decade⁻¹) in precipitation during the flood season are mainly located in the western plain. In contrast to the main trends in precipitation changes observed annually and during the flood season, the precipitation changes during the irrigation period showed an increasing trend in most areas of the plain. An increasing trend in precipitation change during the irrigation period can be seen in 171 out of 175 stations (or 98% of the stations), and the slope of the precipitation change in most regions was generally lower than 10 mm decade⁻¹. The area in which the precipitation increase rate was less than 5 mm decade⁻¹ during the irrigation period is mainly in the southwestern plain, whereas the area in which the precipitation increase rate was greater than 5 mm decade⁻¹ is mainly in the northwestern plain.

Figure 4.  Spatial distributions of annual trends in precipitation changes in the Haihe Plain during (a) the entire year, (b) flood season, and (c) irrigation period from 1961 to 2016.

Figure 5 shows the variation in irrigation water volume on the Haihe Plain. According to the changes in the irrigation water volume, the time series can be divided into three periods. The amount of irrigation water increased significantly (p < 0.01) from 1961 to 1979, with a linear rate of 5.27 × 10⁹ m³ decade⁻¹. During this period, because of the construction of water conservancy projects and the large-scale construction of underground wells, mountain surface water and over-exploited groundwater increased the amount of irrigation water in this area, guaranteeing a rapid increase in agricultural water demand. The amount of agricultural irrigation water peaked and remained stable from 1980 to 2000, with an average irrigation water volume of 15.4 × 10⁹ m³. After 2001, the amount of irrigation water showed a significant (p < 0.01) decreasing trend, with a slope of 2.87 × 10⁹ m³ decade⁻¹. Unrestricted groundwater exploitation had led to serious over-exploitation of groundwater and a rapid decline in groundwater levels. Because of this, the construction of water-saving projects and the comprehensive management of groundwater over-exploitation reduced the amount of agricultural irrigation water used.

Figure 5.  Changes in the quantity of irrigation water in the Haihe Plain from 1961 to 2016.

It is also imperative to understand the seasonal pattern of irrigation water use across the Haihe Plain, because the contribution of irrigation to precipitation is likely to be largest during the period of the year with the heaviest irrigation (DeAngelis et al., 2010). We quantified monthly crop water use based on seasonal patterns of irrigation habits. The main crops grown in the plain area are wheat and corn. The water requirement period for wheat growth is mainly from April to May, whereas that of corn is from June to August. Precipitation in the Haihe Plain is mainly concentrated in July and August, but because crops also require water in April, May, and June, less precipitation during the growing period necessitates extensive agricultural irrigation. Figure 6 shows the average monthly precipitation and irrigation water used in the Haihe Plain. The irrigation period was mainly concentrated in April, May, and June, and the irrigation amounts were 15, 37, and 17 mm, respectively. During the irrigation period, the amount of precipitation was relatively low and the amount of irrigation exceeded the amount of precipitation in May. Given the amounts of precipitation and irrigation, the total water volume in May and June exceeded 75 mm. In July and August, the amount of irrigation was much less than the amount of precipitation, whereas the amount of irrigation was little or almost zero in other months.

Figure 6.  Mean monthly change in precipitation, irrigation water, and proportion of irrigation water to the sum of irrigation water and local precipitation in the Haihe Plain from 1961 to 2016.

Figure 6 also shows the change in the ratio of irrigation water to the sum of irrigation water and local precipitation. This ratio was relatively high from March to June, with a ratio greater than 20%. The proportion of irrigation water, which accounted for 53% in May, was the highest, followed by that in April. In March, the amount of irrigation water was small, but because the amount of precipitation was also small, the irrigation water still accounted for a large proportion of the total. In contrast, irrigation water accounted for only 21% of the total in June because the amounts of irrigation water and precipitation were large. In the remaining months, the proportion of irrigation water relative to the sum was relatively small and the proportion was less than 5%.

Figure 7 shows the annual variation in evaporation in the Haihe Plain in April, May, and June from 1961 to 2016. The trends in evaporation in all 3 months were generally similar. During 1961–1979, evaporation in April, May, and June showed an increasing trend with linear slopes of 0.12, 0.08, and 0.36 mm day⁻¹ decade⁻¹, respectively. During 1980–2000, evaporation in April, May, and June showed no obvious trend and the slope was almost zero, with evaporation averages of 1.50, 2.10, and 2.63 mm day⁻¹, respectively. During 2001–2016, evaporation in April and June showed a decreasing trend, with linear slopes of 0.03 and 0.17 mm day⁻¹ decade⁻¹, whereas evaporation in May showed an increasing trend with linear slopes of 0.13 mm day⁻¹ decade⁻¹.

Figure 7.  Changes in evaporation in April, May, and June from 1961 to 2016.

Figure 8 shows the changes in atmospheric moisture inflow in the Haihe Plain in April, May, and June from 1961 to 2016. In April, the atmospheric moisture inflow was stable from 1961 to 2016 with an average value of 16.96 mm day⁻¹. In May, the atmospheric moisture inflow was stable from 1961 to 1979 and the average value during this period was 19.65 mm day⁻¹. Atmospheric moisture inflow showed a decreasing trend from 1980 to 2000, with a linear slope of 2.00 mm day⁻¹ decade⁻¹. Atmospheric moisture inflow increased from 2001 to 2016 and its linear tendency rate was 4.18 mm day⁻¹ decade⁻¹. In June, the atmospheric moisture inflow increased from 1961 to 1979, with a linear slope of 5.40 mm day⁻¹ decade⁻¹. The atmospheric moisture inflow decreased from 1980 to 2000 and its linear slope was −4.54 mm day⁻¹ decade⁻¹. The atmospheric moisture inflow showed an increasing trend from 2001 to 2016 and its linear slope was 1.48 mm day⁻¹ decade⁻¹.

Figure 8.  Changes in the atmospheric moisture inflow in April, May, and June from 1961 to 2016.

Figure 9 shows the changes in the PRR in April, May, and June from 1961 to 2016 in the Haihe Plain. In April, the changes in PRR were stable from 1961 to 2016 with an average value of 8.32%. The PRR trends in May and June were similar, but the changes in May were more obvious. In May and June, the average PRR values from 1961 to 2016 were 9.74% and 10.36%, respectively. In May and June, the PRR decreased from 1961 to 1979, with respective linear slopes of 0.21% and 1.60% decade⁻¹; the PRR increased from 1980 to 2000, with respective linear slopes of 0.53% and 3.34% decade⁻¹; and the PRR decreased from 2001 to 2016, with respective linear slopes of 2.48% and 0.79% decade⁻¹. One potential reason for the decreasing PRR from 1961 to 1979 might be aerosols, as indicated by Zhao et al. (2018, 2020) and Yang et al. (2018). An increased quantity of aerosols could decrease the precipitation efficiency and amount. The recycled precipitation can be obtained according to the precipitation amount and the PRR. The average recycled precipitation in April, May, and June from 1961 to 2016 was 0.07, 0.12, and 0.23 mm day⁻¹, respectively (Table 1).

Figure 9.  Changes in the PRR in April, May, and June from 1961 to 2016.

The contribution rate of irrigation to precipitation was derived from the PRR and the proportion of evaporation produced by irrigation during local evaporation. Figure 10 shows the annual change in the contribution rate of irrigation to precipitation in the Haihe Plain in April, May, and June from 1961 to 2016. The trends in the irrigation-to-precipitation contribution rates in April, May, and June from 1961 to 2016 were similar. The contribution rate of irrigation to precipitation increased in April, May, and June from 1961 to 1979, with linear slopes of 1.38%, 0.90%, and 0.04% decade⁻¹, respectively. From 1980 to 2000, the contribution rate of irrigation to precipitation in May and June also showed an increasing trend, with linear slopes of 0.53% and 1.75% decade⁻¹, respectively; whereas, that in April showed a decreasing trend with a linear slope of 0.14% decade⁻¹. The contribution rates of irrigation to precipitation decreased in April, May, and June from 2001 to 2016 with linear slopes of 0.62%, 2.08%, and 0.18% decade⁻¹, respectively. The average contribution rates of irrigation to precipitation in April, May, and June from 1961 to 2016 were 3.76%, 5.12%, and 2.29%, respectively (Table 2). Among them, irrigation contributed the most to precipitation in 1980–2000, with average contribution rates of 4.62%, 5.50%, and 2.99% in April, May, and June, respectively.

Figure 10.  Changes in the contribution ratio of irrigation to precipitation in April, May, and June from 1961 to 2016.

Figure 11 shows the annual changes in the contribution of irrigation to precipitation in April, May, and June in the Haihe Plain from 1961 to 2016. The contribution of irrigation to precipitation in April, May, and June increased from 1961 to 1979, with linear slopes of 0.19, 0.57, and 0.42 mm decade⁻¹, respectively. The contribution of irrigation to precipitation in April, May, and June also increased from 1980 to 2000, with linear slopes of 0.11, 0.12, and 0.34 mm decade⁻¹, respectively. The contribution of irrigation to precipitation in April showed a steady trend from 2001 to 2016, with an average value of 0.71 mm, whereas the contribution of irrigation to precipitation in May and June showed a decreasing trend from 2001 to 2016, with linear slopes of 0.81 and 0.30 mm decade⁻¹, respectively. The average contributions of irrigation to precipitation in April, May, and June from 1961 to 2016 were 0.72, 1.70, and 1.35 mm, respectively. The contribution of irrigation to precipitation in April, May, and June was the largest during 1980–2000, with average values of 0.81, 2.16, and 1.60 mm, respectively.

Figure 11.  Changes in the contribution of irrigation to precipitation in April, May, and June in the Haihe Plain from 1961 to 2016.

In the case of a decrease in annual precipitation in the Haihe Plain, the precipitation during the main irrigation period (April to June) showed an increasing trend, which may be partly due to the rapid increase in irrigation during the last half-century. In this study, we proposed a simple and convenient method to quantify the contribution of irrigation to precipitation, based on the precipitation recycling method. This was accomplished by distinguishing the proportional contribution of evaporation generated by irrigation to local evaporation. We also analyzed the contribution of irrigation to precipitation in the Haihe Plain from 1961 to 2016. The average PRR values in the Haihe Plain during 1961–2016 are 8.32%, 9.74%, and 10.36% in April, May, and June, respectively. The PRR values are highest during the peak irrigation period from 1980 to 2000, at 8.72%, 10.09%, and 11.10% in April, May, and June, respectively. The contribution rates of irrigation to precipitation from 1961 to 2016 in the Haihe Plain are 3.76%, 5.12%, and 2.29% in April, May, and June, respectively. The total contribution of irrigation to precipitation during the irrigation period from 1961 to 2016 is 3.77 mm; this amount partly comprises a maximum contribution of 1.70 mm in May.

The contribution of irrigation to precipitation observed in this study compares reasonably well with the results of previous studies. Hua et al. (2016) used a dynamic recycling model and Japanese 25-yr Reanalysis (JRA-25) data to calculate the regional recycling ratio across China during 1979–2010. They found that the summer average recycling ratio was 0.09 in the Haihe Plain, which is close to the average PRR of 0.10 observed in June in our research. The Haihe Plain has one of the lowest PRR values in China. In comparison, the PRR values in the upper reaches of the Yangtze River (Kang et al., 2004), Yellow River basin (Kang et al., 2005), and Qinghai–Xizang Plateau (Guo and Wang, 2014) are higher than 15%, which is higher than that in the Haihe Plain. The Haihe Plain is located in eastern China and borders the ocean in the east; therefore, precipitation here is mainly derived from advected moisture arriving from the outside. Wu et al. (2018) used the WRF model to simulate irrigation and increase spring precipitation by approximately 3–7 mm in the Haihe Plain. While the results of our research are within this range, the cooling effect of irrigation may inhibit the occurrence of precipitation. Furthermore, the negative effect of spring irrigation on precipitation in the Haihe Plain is weak. Yang et al. (2016) found that irrigation increased the sum of precipitation in the second half of May and June by approximately 2–4 mm in the Huang–Huai–Hai Plain, which was close to the average contribution amount of irrigation to precipitation in May and June (3.05 mm). Thus, our results are in reasonable agreement with those of previous studies.

During the irrigation period, the average irrigation water volume in the Haihe Plain was 71 mm (54% of precipitation), most of which evaporated to form water vapor, while a small component replenished the groundwater. The precipitation produced by evaporation of irrigation only represented 3.77 mm (5.31% of irrigation) and most of the evaporation from irrigation flowed out of the area as water vapor. Moreover, water loss due to evaporation through irrigation was much larger than irrigation-induced precipitation. Similar conclusions have been found in the North American Great Plains (Harding and Snyder, 2012). According to the average PRR values in April, May, and June, the contribution rates of inflowing atmospheric moisture to precipitation in the corresponding periods were 91.67%, 90.26%, and 89.64%, respectively, indicating that the inflow of atmospheric moisture was the main factor affecting local precipitation.

However, the precipitation produced by the evaporation of irrigation water during the irrigation period is critical for relieving water shortages in the Haihe Plain. To alleviate the impacts of water shortages in the Haihe Basin, the Chinese government established the Middle Route of the South-to-North Water Diversion Project (SNWDPC), the world’s largest inter-basin water diversion project, to promote regional coordination and sustainable development. This project delivers 9.5 × 10⁹ m³ of water to Henan and Hebei provinces, as well as to Beijing and Tianjin (Wang et al., 2017; Nong et al., 2019). The average precipitation produced by evaporation of irrigation water is 3.77 mm and is equivalent to 6.02% of the water transferred in the SNWDPC. Therefore, this portion of the precipitation during the irrigation period supplies the water required for sustainable high-yield grain production and alleviates the water shortage in the Haihe Plain to some extent. While evaporation from irrigation adds additional water to precipitation, thereby helping to alleviate the water shortage, the loss of underground water used for irrigation could have negative impacts on local water sources. Therefore, it is necessary to formulate reasonable measures to reduce groundwater over-exploitation in the Haihe Plain in the future.

Acknowledgments. Daily meteorological data are provided by the National Meteorological Information Center of China Meteorological Administration.