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Correlating between evapotranspiration and precipitation provides insights into Xilingol grassland eco-engineering at larger scale
Ecological Engineering 84 (2015) 100–103
Contents lists available at ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
Correlating between evapotranspiration and precipitation provides insights into Xilingol grassland eco-engineering at larger scale Zhang Shengwei a , Shen Rui a , Zhao Hongbin b,∗∗ , Liu Tingxi a , Shao Hongbo c,d,∗ , Zhang Zichang a a
Water Conservancy and Civil Engineering College, Inner Mongolia Agriculture University, Hohhot 010018, China College of Life Sciences, Inner Mongolia Agriculture University, Hohhot 010018, China c Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China d Key Laboratory of Coastal Biology & Bioresources Utilization, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai 264003, China b
a r t i c l e
i n f o
Article history: Received 16 March 2015 Received in revised form 20 June 2015 Accepted 27 July 2015 Keywords: Grassland eco-engineering Grass evapotranspiration Dual crop coefficient Soil water content Precipitation variation
a b s t r a c t The Xilingol Grassland in Northern China is the largest grassland ecosystem of Inner Mongolia and plays an important role in the local ecosystem. However, grassland desertification and degradation are quite severe in this area. Therefore, we simulated the daily actual evapotranspiration (ET) and soil water content in a representative area of the Xilingol Grassland, West Ujimqin Balaguer River basin, during the grass growth period by using the dual crop coefficient model. In addition, we compared the simulation results with actual data and found that the R2 value between the actual measurement data and simulation data for soil water content was 0.662 with an error of 4.14%. The simulated total ET during the entire growth period in 2013 was 332.41 mm, including 215.13 mm grassland transpiration (64.7%) and 117.28 mm soil evaporation. Then we randomly selected precipitation and ET values for different growth periods for analysis. The results showed that the absolute R values are all higher than 0.5, which indicates that variations in ET are highly correlated with precipitation over time. By reducing the precipitation to half the actual values, the model predicts 257.71 mm ET (22.5% less than the value in natural conditions), 176.84 mm transpiration (17.8% less than the value in natural conditions), and 80.87 mm soil evaporation (31% less than the value in natural conditions). By assuming that the precipitation was reduced by half, the model predicts 274.89 mm ET (17.3% less than the actual value), 190.05 mm grassland transpiration (11.7% less than the actual value), and 84.85 mm soil evaporation (27.7% less than the actual value). © 2015 Elsevier B.V. All rights reserved.
1. Introduction The Xilingol Grassland in North Chinais a natural green ecological barrier for a wide inland area. However, continuous changes of natural conditions and the development and utilization of mineral resources in this area over a long period of time have led to continuous grassland degradation of the Xilingol Grassland; therefore, the grassland ecosystem is threatened (Wu et al., 2014). Since 2000, a series of methods have been employed to recover the grassland ecosystem; however, Xilingol Grassland degradation
∗ Corresponding author at: Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China. ∗∗ Corresponding author at: Inner Mongolia Agriculture University, Hohhot 010018, China. E-mail addresses: [email protected] (Z. Hongbin), [email protected] (S. Hongbo). http://dx.doi.org/10.1016/j.ecoleng.2015.07.015 0925-8574/© 2015 Elsevier B.V. All rights reserved.
continues via resource development and urban construction (Wu et al., 2013; Huang et al., 2013). Variations in evapotranspiration (ET) can reflect grassland degradation to a certain degree (Wang et al., 2012) because ET decreases with decreases in the vegetation coverage and density of degraded grasslands (Wang et al., 2008a,b). Grassland ET plays an important role in determining the local hydrological and water circulation processes (Valiantzas, 2013). Although numerous methods can be employed to study ET, such as the Bowen ratio method and Eddy correlation method, they all have certain limitations. For example, when the ET rate is small, the Bowen ratio method produces huge errors, whereas the Eddy correlation method underestimates transpiration (Li et al., 2008). Fan et al. (2002) and He et al. (2002) calculated the crop water demand in Yangling, Shaanxi and the corn water demand during summer in Anyang, North Henan using single and dual crop coefficient models, respectively, and compared the simulation results with the data measured by lysimeters, and their results indicated that the dual
Z. Shengwei et al. / Ecological Engineering 84 (2015) 100–103
crop coefficient model produced more accurate results. In addition, Wang et al. (2008a,b) studied the variation of ET during the grass growth period in a meadow steppe using the dual crop coefficient model and demonstrated that the dual crop coefficient model is quite reliable for calculating the crop water demands of different plants. Thus, we have evaluated grass ET and related factors that might affect calculations of ET based on the dual crop coefficient model. Zhang et al. (2012) applied the dual crop coefficient model and found that the yearly peak ET values related to seasonal variations in the desert steppe in Inner Mongolia are always different from the peak values of adjacent years, and this finding can be used in the analysis of negative effects of climate change on grass. Hu et al. (2009) studied vegetation ET and found that vegetation growth and the hydrological processes are more likely to be negatively affected in dry environments compared with humid environments. In recent years in northern China, grass ET has gradually decreased because of descending underground water levels (Yang et al., 2012). Thus, the grass ET of the Xilingol Grassland can be affected by drought and lowered underground water levels. Miao et al. (2009) found that during drought years in the Xilingol Grassland, the soil moisture level decreases because of reduced precipitation; therefore grass growth is limited, canopy areas are reduced, and grass transpiration is decreased. Hao et al. (2007) found that precipitation and its frequency along the Inner Mongolian Plateau have important impacts on grass ET. The above studies indicate that precipitation has a great impact on grass ET; therefore, this topic is of significance and should be studied in detail. This study aims to determine the correlation between the actual ET of the Xilingol Grassland and crop coefficients and identify the main influencing factors, estimate the water demand during grass growth and analyze grass growth conditions under different precipitation environments to provide support for and to help maintain and recover the grassland ecosystem at larger scale. 2. Materials and methods 2.1. General conditions of the studied area The study area is located along the Xilingol West Ujimqin Balaguer River watershed within the representative Inner Mongolian grassland at 44◦ 19 56.96 N, 117◦ 51 24.52 E (Fig. 1). The typical steppe studied here is the main component of the Xilingol Grassland (Liu et al., 2003; Zhang et al., 2014). The study area has an arid and semi-arid temperature continental climate. Spring is arid and windy; summer is semi-arid, and
101
precipitation is heterogeneously distributed; fall is cooler, but frost and snow arrive early; winter is cold and lasts for an extended period. There are two subtypes of dark chestnut soil in this area: thin dark chestnut loess and thin dark chestnut flax soil. 2.2. Introduction of the SIMDualKc model The SIMDualKc model is used to evaluate crop ET based on the dual crop coefficient method and develop an irrigation management model (Rosa et al., 2008; Paredes et al., 2014; He et al., 2002). Crop ET (ETc ) calculations based on the dual crop coefficient method include two parts: water loss because of soil surface evaporation and water loss because of crop transpiration; these values are obtained by multiplying the base crop coefficient (Kcb ) and soil evaporation coefficient (Ke ) by the reference crop transpiration, respectively. ETc = (Kcb + Ke )ET0
(1)
The general steps to calculate crop ET using the dual crop coefficient method are as follows: (1) calculate the reference ET (ET0 ); (2) set the time during the crop growth period; (3) choose the crop coefficient for the initial, mid and end stages of crop growth; (4) adjust the crop coefficients during the mid and end stages of crop growth based on the local temperature, crop density, and management level; (5) determine daily soil evaporation coefficients according to the soil evaporation; (6) calculate the actual crop coefficient using Kc act = Ks Kcb + Ke ; and (7) calculate the actual crop ETa using ETa = Kc act ET0 (Rosa et al., 2008). 3. Model input data The soil data used in this study are from a Chinese soil database, which indicates that the soil in the research area is a thin dark chestnut loess. The data required by the dual crop coefficient model include the soil layer thickness of different layers, field capacity (FC), and wilting point (WP). To test the model results, we measured the soil water contents at 5 cm, 10 cm, 15 cm, and 30 cm from the surface every 15 min using a soil moisture probe (CS616, Campbell Scientific Inc., USA) to determine the varying soil water content in the different layers. The crop growth period is divided into four different stages in the dual crop coefficient model: initial-growth period, fast-growth period, mid-growth period, and end-growth period. Based on the characteristics of crop growth periods and actual conditions, the starting data for the different growth periods of the Xilingol Grassland in 2013 can be determined. The crop residual, land surface covered by dead organics, or land surface covered by dead and aged crops that are not harvested are close to the value of the total covered land surface (Mollard et al., 2014). Thus, prior to green herbage burning, the dead herbage can be treated as a covering and is required when inputting the covering information in the coverage interface. 4. Results 4.1. Simulation and verification of soil water content
Fig. 1. Location of the study area and types of vegetation.
In Fig. 2, the actual measured soil water contents are shown as points, the simulated soil water contents are shown as a curve, and the histograms represent the corresponding precipitation. The figure indicates that most of the points fall on the curve, which suggests that the simulation results are consistent with the actual conditions of the Xilingol Grassland (Fig. 3). Fig. 2 shows the effect of precipitation on soil water content throughout the study period. According to the curve in Fig. 2, the
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5
0.25
10 0.2
15 20
0.15
25
0.1
30 0.05 0 30-Jun
Table 1 Correlation of actual grass ET with precipitation over different time periods.
0
Precipitation (mm)
Soil water content (cm3/cm3)
0.3
Precipitation (mm) Initial day Actual ET (mm)
Pearson −0.064 correlation Significance 0.547 (both sides)
1 day after
2 days after
3 days after
−0.052
0.181
0.178
0.624
0.089
0.098
35 20-Jul
9-Aug
29-Aug
40 8-Oct
18-Sep
Date Fig. 2. Comparison between simulated (curve) and observed (points) soil water content and precipitation (bar).
soil water content usually increases with precipitation. For the periods from 07/10–07/17 and 08/02–08/25, the decrease in actual soil water content was greater than the decrease predicted by the simulation, which might have been caused by flocks and herds grazing the grass, which reduces the effective surface coverage, promotes absorbed solar radiation by the soil surface, and increases soil water evaporation. The R2 between the simulation results and actual measured data is 0.62, which indicates that the results are well correlated and consistent. 4.2. Analysis of ET variation The crop ET quantity obtained from the dual crop coefficient model includes the soil evaporation quantity (Ke ET0 ) and crop transpiration quantity (Kcb ET0 ). The actual grassland ET during the initial-growth period is generally below 1 mm and increases during the fast-growth period. During the mid-growth and end-growth periods, the actual grassland ET barely passed 4 mm, which was related to the characteristics of the grass in this area, including a small leaf area index (LAI). The precipitation distribution indicate that precipitation is of great importance for natural grass growth in this area. In the research area, when the groundwater depth exceeded 3.5 m, the underground water level was low, and the ascending water transportation to the root layer via the capillarity effect was weakened, which also decreased the actual grassland ET. 4.3. Correlation between ET and meteorological elements
temperature, relative humidity, wind speed, and precipitation, and the corresponding R values are 0.439, 0.445, 0.445, 0.371, and 0.186, respectively. These values are all lower than 0.5, which means that the actual grass ET is not well correlated with any of these meteorological elements. Table 1 presents a correlation analysis between precipitation and the actual grass ET during randomly chosen threeday growth periods. For all time periods, precipitation only occurs on the first day. According to our analysis, precipitation has little effect on the actual grass ET on the same day and presents a negative correlation. However, precipitation greatly affects the actual grass ET on the day following the precipitation event. 5. Discussion Zheng et al. (2012) investigated the local herders that utilize the Xilingol Grassland and concluded that reduced precipitation and over utilization are the main causes of grassland degradation. Therefore, two difference scenarios are designed for insights into grassland eco-engineering in Xilingol. 5.1. Variation in ET with precipitation reduced by half During the research period, there were 56 precipitation events. By reducing the number of the precipitation events to 28 and defining the events as random, the simulations produces results for grass ET of 257.71 mm, which is 22.5% less than in the normal conditions. The grass transpiration is 176.84 mm, and the soil evaporation is 80.87 mm. According to the results, there are only 13 days when the grass ET exceeds 3 mm. The mean grass ET under the assumed conditions is decreased by 0.45 mm, therefore, the number of precipitation events determines the pattern of grass growth. 5.2. Variation in ET with precipitation reduced to half of the actual amount
By analyzing the correlation between the actual grass evaporation and different meteorological elements, we can determine the importance of different meteorological elements for actual grass ET. The correlation analysis of actual grass ET with solar irradiation,
When the precipitation is reduced to half of the actual amount during the grass growth period, the model predicts that grass ET will be 274.893 mm, which is 17.3% less than the actual amount,
Hypothetical data
Vegetation root depth, height and soil water consumption ratio
Kcb
Air temperature, wind speed and relative humidity
ET0
Soil layer thickness, field capacity, and wilting point
Ke
Grass transpiration
ETa
Hypothetical ETa
Grassland ecological engineering
Soil evaporation
Precipitation Historical data
vegetation physiological process
Hypothetical precipitation
Fig. 3. Diagram of this work for insights into Xilingol grassland eco-engineering.
Z. Shengwei et al. / Ecological Engineering 84 (2015) 100–103
grass transpiration will be 190.05 mm, and soil evaporation will be 84.85 mm. The grass transpiration value of 190.05 mm is 13.21 mm higher than the value from reducing the number of the precipitation, which indicates that the precipitation frequency has a strong effect on grass growth. 6. Conclusions Based on the analysis presented here, we concluded that in the arid and semi-arid continental climate conditions of the West Ujimqin Banner watershed, precipitation plays an important role in determining grass ET. Based on the data simulated by the dual crop coefficient model, we found that the grass ET rarely reached 4 mm/d throughout the study period. Grass transpiration was higher than soil evaporation throughout the study period. When the number of precipitation events is reduced to half the actual number, the precipitation frequency has a greater effect on the water demand during the grass growth period; therefore, precipitation frequency determines the pattern of grass growth. Simulations of grass ET were performed to obtain the grassland water demands during different growth periods and for arid regions. By considering the actual conditions, the grass growth conditions were obtained under the different simulated environmental conditions. Currently, the Xilingol Grassland suffers from severe degradation, and the grassland quality has been greatly decreased. Because water conditions strongly affect grassland vegetation growth and production, grassland ET processes must be studied to provide support for and to help maintain the grasslands and determine the most efficient method to mitigate the degradation. Acknowledgements This research was supported by the National Natural Science Foundation of China (No. 51269014, 31360113, 51139002) and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13069). References Fan, Y.Q., Cai, H.J., 2002. Comparison of crop water requirements computed by single crop coefficient approach and dual crop coefficient approach. J. Hydraul. Eng. 3, 50–54. Hao, Y.B., Wang, Y.F., Huang, X.Z., 2007. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agric. For. Meteorol., 57–69.
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He, J., Li, F., Liu, Z.J., 2002. Comparison of summer corn water requirements computed by single crop coefficient approach and dual crop coefficient approach. J. Anhui Agric. Sci. 41, 12830–12910. Huang, L., Xiao, T., Zhao, Z.P., Sun, C.Y., Liu, J.Y., Shao, Q.Q., Fan, J.W., Wang, J.B., 2013. Effects of grassland restoration programs on ecosystems in arid and semiarid China. J. Environ. Manag. 117, 268–275. Hu, Z.M., Yu, G.R., Sun, X.M., 2009. Partitioning of evapotranspiration and its controls in four grassland ecosystems: application of a two-source model. Agric. For. Meteorol., 1410–1420. Li, S.E., Kang, S.Z., Zhu, Z.L., 2008. Research progress of measurement of land surface evapotranspiration based on Eddy covariance technology. Sci. Agric. Sin. 41, 20–26. Liu, J.Y., Liu, M.L., Zhuang, D.F., Zhang, Z.X., Deng, X.Z., 2003. Study on spatial pattern of land-use change in China during 1995–2000. Sci. China (D) 46 (4), 373–384. Miao, H.X., Chen, S.P., Chen, J.Q., 2009. Cultivation and grazing altered evapotranspiration and dynamics in Inner Mongolia steppes. Agric. For. Meteorol., 1810–1819. Mollard, F.P.O., Naeth, M.A., Cohen-Fernandez, A., 2014. Impacts of mulch on prairie seedling establishment: facilitative to inhibitory effects. Ecol. Eng. 64, 377–384. Paredes, P., de Melo-Abreu, J.P., Alves, I., Pereira, L.S., 2014. Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focuson model parameterization. Agric. Water Manag. 144 (2014), 81–97. Rosa, R.D., Paredes, P., Rodrigues, G.C., Alves, I., Fernandoa, R.M., Pereira, L.S., Allen, R.G., 2008. Implementing the dual cropcoefficient approach in interactive software.1. Background and computational strategy. Agric. Water Manag. 103, 8–24. Valiantzas, G.D., 2013. Simplified forms for the standardized FAO-56 Penman–Monteith reference evapotranspiration using limited weather data. J. Hydrol. 505, 7–21. Wang, G.X., Liu, G.S., Li, C.G., 2012. Effects of changes in alpine grassland vegetation cover on hillslope hydrological processes in a permafrost watershed. J. Hydrol. 444–445, 22–33. Wang, L.F., Chao, L.B.G., Wang, Y.J., Wang, Z.Q., 2008a. Study on evapotranspration variation law of Leymus Chinensis in growth period in meadow grassland. Water Sav. Irrig. 2008 (5), 1–5. Wang, Y.H., Zhou, G.S., Jia, B.R., 2008b. Modeling SOC and NPP responses of meadow steppe to different grazing intensities in Northeast China. Ecol. Model. 217, 72–78. Wu, J., Liu, Z.M., Qian, J.Q., 2013. Non-linear effect of habitat fragmentation on plant diversity: evidence from a sand dune field in a desertified grassland in northeastern China. Ecol. Eng. 54, 90–96. Wu, X., Li, Z.S., Fu, B.J., Zhou, W.M., Liu, H.F., Liu, G.H., 2014. Restoration of ecosystem carbon and nitrogen storage and microbial biomass after grazing exclusion in semi-arid grasslands of Inner Mongolia. Ecol. Eng. 73, 395–403. Yang, Y.T., Shang, S.H., Jiang, L., 2012. Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China. Agric. For. Meteorol. 164, 112–122. Zhang, W.Y., Nie, F.J., Liu, S.W., Zuo, L.Y., Yao, X.F., Jia, D.L., Liu, J.T., 2014. Characteristics and genesis of mineral deposits in East Ujimqin Banner, western segment of the Great Xingan Mountains, NE China. J. Asian Earth Sci. 97, 459–471. Zhang, F., Zhou, G.S., Wang, Y., Yang, F.L., Nilsson, C., 2012. Evapotranspiration and crop coefficient for a temperate desert steppe ecosystem using eddy covariance in Inner Mongolia, China. Hydrol. Process. 26, 379–386. Zheng, S.H., Huang, G.A., Wang, Y., Bao, L.D., Du, S.Y., Hu, Y.X., Chen, X.M., 2012. Analysis of the causes of grassland degradation for the East Ujimqin grassland. Inner Mong. Prataculture 24, 1–4.
Contents lists available at ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
Correlating between evapotranspiration and precipitation provides insights into Xilingol grassland eco-engineering at larger scale Zhang Shengwei a , Shen Rui a , Zhao Hongbin b,∗∗ , Liu Tingxi a , Shao Hongbo c,d,∗ , Zhang Zichang a a
Water Conservancy and Civil Engineering College, Inner Mongolia Agriculture University, Hohhot 010018, China College of Life Sciences, Inner Mongolia Agriculture University, Hohhot 010018, China c Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China d Key Laboratory of Coastal Biology & Bioresources Utilization, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Yantai 264003, China b
a r t i c l e
i n f o
Article history: Received 16 March 2015 Received in revised form 20 June 2015 Accepted 27 July 2015 Keywords: Grassland eco-engineering Grass evapotranspiration Dual crop coefficient Soil water content Precipitation variation
a b s t r a c t The Xilingol Grassland in Northern China is the largest grassland ecosystem of Inner Mongolia and plays an important role in the local ecosystem. However, grassland desertification and degradation are quite severe in this area. Therefore, we simulated the daily actual evapotranspiration (ET) and soil water content in a representative area of the Xilingol Grassland, West Ujimqin Balaguer River basin, during the grass growth period by using the dual crop coefficient model. In addition, we compared the simulation results with actual data and found that the R2 value between the actual measurement data and simulation data for soil water content was 0.662 with an error of 4.14%. The simulated total ET during the entire growth period in 2013 was 332.41 mm, including 215.13 mm grassland transpiration (64.7%) and 117.28 mm soil evaporation. Then we randomly selected precipitation and ET values for different growth periods for analysis. The results showed that the absolute R values are all higher than 0.5, which indicates that variations in ET are highly correlated with precipitation over time. By reducing the precipitation to half the actual values, the model predicts 257.71 mm ET (22.5% less than the value in natural conditions), 176.84 mm transpiration (17.8% less than the value in natural conditions), and 80.87 mm soil evaporation (31% less than the value in natural conditions). By assuming that the precipitation was reduced by half, the model predicts 274.89 mm ET (17.3% less than the actual value), 190.05 mm grassland transpiration (11.7% less than the actual value), and 84.85 mm soil evaporation (27.7% less than the actual value). © 2015 Elsevier B.V. All rights reserved.
1. Introduction The Xilingol Grassland in North Chinais a natural green ecological barrier for a wide inland area. However, continuous changes of natural conditions and the development and utilization of mineral resources in this area over a long period of time have led to continuous grassland degradation of the Xilingol Grassland; therefore, the grassland ecosystem is threatened (Wu et al., 2014). Since 2000, a series of methods have been employed to recover the grassland ecosystem; however, Xilingol Grassland degradation
∗ Corresponding author at: Institute of Agro-biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China. ∗∗ Corresponding author at: Inner Mongolia Agriculture University, Hohhot 010018, China. E-mail addresses: [email protected] (Z. Hongbin), [email protected] (S. Hongbo). http://dx.doi.org/10.1016/j.ecoleng.2015.07.015 0925-8574/© 2015 Elsevier B.V. All rights reserved.
continues via resource development and urban construction (Wu et al., 2013; Huang et al., 2013). Variations in evapotranspiration (ET) can reflect grassland degradation to a certain degree (Wang et al., 2012) because ET decreases with decreases in the vegetation coverage and density of degraded grasslands (Wang et al., 2008a,b). Grassland ET plays an important role in determining the local hydrological and water circulation processes (Valiantzas, 2013). Although numerous methods can be employed to study ET, such as the Bowen ratio method and Eddy correlation method, they all have certain limitations. For example, when the ET rate is small, the Bowen ratio method produces huge errors, whereas the Eddy correlation method underestimates transpiration (Li et al., 2008). Fan et al. (2002) and He et al. (2002) calculated the crop water demand in Yangling, Shaanxi and the corn water demand during summer in Anyang, North Henan using single and dual crop coefficient models, respectively, and compared the simulation results with the data measured by lysimeters, and their results indicated that the dual
Z. Shengwei et al. / Ecological Engineering 84 (2015) 100–103
crop coefficient model produced more accurate results. In addition, Wang et al. (2008a,b) studied the variation of ET during the grass growth period in a meadow steppe using the dual crop coefficient model and demonstrated that the dual crop coefficient model is quite reliable for calculating the crop water demands of different plants. Thus, we have evaluated grass ET and related factors that might affect calculations of ET based on the dual crop coefficient model. Zhang et al. (2012) applied the dual crop coefficient model and found that the yearly peak ET values related to seasonal variations in the desert steppe in Inner Mongolia are always different from the peak values of adjacent years, and this finding can be used in the analysis of negative effects of climate change on grass. Hu et al. (2009) studied vegetation ET and found that vegetation growth and the hydrological processes are more likely to be negatively affected in dry environments compared with humid environments. In recent years in northern China, grass ET has gradually decreased because of descending underground water levels (Yang et al., 2012). Thus, the grass ET of the Xilingol Grassland can be affected by drought and lowered underground water levels. Miao et al. (2009) found that during drought years in the Xilingol Grassland, the soil moisture level decreases because of reduced precipitation; therefore grass growth is limited, canopy areas are reduced, and grass transpiration is decreased. Hao et al. (2007) found that precipitation and its frequency along the Inner Mongolian Plateau have important impacts on grass ET. The above studies indicate that precipitation has a great impact on grass ET; therefore, this topic is of significance and should be studied in detail. This study aims to determine the correlation between the actual ET of the Xilingol Grassland and crop coefficients and identify the main influencing factors, estimate the water demand during grass growth and analyze grass growth conditions under different precipitation environments to provide support for and to help maintain and recover the grassland ecosystem at larger scale. 2. Materials and methods 2.1. General conditions of the studied area The study area is located along the Xilingol West Ujimqin Balaguer River watershed within the representative Inner Mongolian grassland at 44◦ 19 56.96 N, 117◦ 51 24.52 E (Fig. 1). The typical steppe studied here is the main component of the Xilingol Grassland (Liu et al., 2003; Zhang et al., 2014). The study area has an arid and semi-arid temperature continental climate. Spring is arid and windy; summer is semi-arid, and
101
precipitation is heterogeneously distributed; fall is cooler, but frost and snow arrive early; winter is cold and lasts for an extended period. There are two subtypes of dark chestnut soil in this area: thin dark chestnut loess and thin dark chestnut flax soil. 2.2. Introduction of the SIMDualKc model The SIMDualKc model is used to evaluate crop ET based on the dual crop coefficient method and develop an irrigation management model (Rosa et al., 2008; Paredes et al., 2014; He et al., 2002). Crop ET (ETc ) calculations based on the dual crop coefficient method include two parts: water loss because of soil surface evaporation and water loss because of crop transpiration; these values are obtained by multiplying the base crop coefficient (Kcb ) and soil evaporation coefficient (Ke ) by the reference crop transpiration, respectively. ETc = (Kcb + Ke )ET0
(1)
The general steps to calculate crop ET using the dual crop coefficient method are as follows: (1) calculate the reference ET (ET0 ); (2) set the time during the crop growth period; (3) choose the crop coefficient for the initial, mid and end stages of crop growth; (4) adjust the crop coefficients during the mid and end stages of crop growth based on the local temperature, crop density, and management level; (5) determine daily soil evaporation coefficients according to the soil evaporation; (6) calculate the actual crop coefficient using Kc act = Ks Kcb + Ke ; and (7) calculate the actual crop ETa using ETa = Kc act ET0 (Rosa et al., 2008). 3. Model input data The soil data used in this study are from a Chinese soil database, which indicates that the soil in the research area is a thin dark chestnut loess. The data required by the dual crop coefficient model include the soil layer thickness of different layers, field capacity (FC), and wilting point (WP). To test the model results, we measured the soil water contents at 5 cm, 10 cm, 15 cm, and 30 cm from the surface every 15 min using a soil moisture probe (CS616, Campbell Scientific Inc., USA) to determine the varying soil water content in the different layers. The crop growth period is divided into four different stages in the dual crop coefficient model: initial-growth period, fast-growth period, mid-growth period, and end-growth period. Based on the characteristics of crop growth periods and actual conditions, the starting data for the different growth periods of the Xilingol Grassland in 2013 can be determined. The crop residual, land surface covered by dead organics, or land surface covered by dead and aged crops that are not harvested are close to the value of the total covered land surface (Mollard et al., 2014). Thus, prior to green herbage burning, the dead herbage can be treated as a covering and is required when inputting the covering information in the coverage interface. 4. Results 4.1. Simulation and verification of soil water content
Fig. 1. Location of the study area and types of vegetation.
In Fig. 2, the actual measured soil water contents are shown as points, the simulated soil water contents are shown as a curve, and the histograms represent the corresponding precipitation. The figure indicates that most of the points fall on the curve, which suggests that the simulation results are consistent with the actual conditions of the Xilingol Grassland (Fig. 3). Fig. 2 shows the effect of precipitation on soil water content throughout the study period. According to the curve in Fig. 2, the
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5
0.25
10 0.2
15 20
0.15
25
0.1
30 0.05 0 30-Jun
Table 1 Correlation of actual grass ET with precipitation over different time periods.
0
Precipitation (mm)
Soil water content (cm3/cm3)
0.3
Precipitation (mm) Initial day Actual ET (mm)
Pearson −0.064 correlation Significance 0.547 (both sides)
1 day after
2 days after
3 days after
−0.052
0.181
0.178
0.624
0.089
0.098
35 20-Jul
9-Aug
29-Aug
40 8-Oct
18-Sep
Date Fig. 2. Comparison between simulated (curve) and observed (points) soil water content and precipitation (bar).
soil water content usually increases with precipitation. For the periods from 07/10–07/17 and 08/02–08/25, the decrease in actual soil water content was greater than the decrease predicted by the simulation, which might have been caused by flocks and herds grazing the grass, which reduces the effective surface coverage, promotes absorbed solar radiation by the soil surface, and increases soil water evaporation. The R2 between the simulation results and actual measured data is 0.62, which indicates that the results are well correlated and consistent. 4.2. Analysis of ET variation The crop ET quantity obtained from the dual crop coefficient model includes the soil evaporation quantity (Ke ET0 ) and crop transpiration quantity (Kcb ET0 ). The actual grassland ET during the initial-growth period is generally below 1 mm and increases during the fast-growth period. During the mid-growth and end-growth periods, the actual grassland ET barely passed 4 mm, which was related to the characteristics of the grass in this area, including a small leaf area index (LAI). The precipitation distribution indicate that precipitation is of great importance for natural grass growth in this area. In the research area, when the groundwater depth exceeded 3.5 m, the underground water level was low, and the ascending water transportation to the root layer via the capillarity effect was weakened, which also decreased the actual grassland ET. 4.3. Correlation between ET and meteorological elements
temperature, relative humidity, wind speed, and precipitation, and the corresponding R values are 0.439, 0.445, 0.445, 0.371, and 0.186, respectively. These values are all lower than 0.5, which means that the actual grass ET is not well correlated with any of these meteorological elements. Table 1 presents a correlation analysis between precipitation and the actual grass ET during randomly chosen threeday growth periods. For all time periods, precipitation only occurs on the first day. According to our analysis, precipitation has little effect on the actual grass ET on the same day and presents a negative correlation. However, precipitation greatly affects the actual grass ET on the day following the precipitation event. 5. Discussion Zheng et al. (2012) investigated the local herders that utilize the Xilingol Grassland and concluded that reduced precipitation and over utilization are the main causes of grassland degradation. Therefore, two difference scenarios are designed for insights into grassland eco-engineering in Xilingol. 5.1. Variation in ET with precipitation reduced by half During the research period, there were 56 precipitation events. By reducing the number of the precipitation events to 28 and defining the events as random, the simulations produces results for grass ET of 257.71 mm, which is 22.5% less than in the normal conditions. The grass transpiration is 176.84 mm, and the soil evaporation is 80.87 mm. According to the results, there are only 13 days when the grass ET exceeds 3 mm. The mean grass ET under the assumed conditions is decreased by 0.45 mm, therefore, the number of precipitation events determines the pattern of grass growth. 5.2. Variation in ET with precipitation reduced to half of the actual amount
By analyzing the correlation between the actual grass evaporation and different meteorological elements, we can determine the importance of different meteorological elements for actual grass ET. The correlation analysis of actual grass ET with solar irradiation,
When the precipitation is reduced to half of the actual amount during the grass growth period, the model predicts that grass ET will be 274.893 mm, which is 17.3% less than the actual amount,
Hypothetical data
Vegetation root depth, height and soil water consumption ratio
Kcb
Air temperature, wind speed and relative humidity
ET0
Soil layer thickness, field capacity, and wilting point
Ke
Grass transpiration
ETa
Hypothetical ETa
Grassland ecological engineering
Soil evaporation
Precipitation Historical data
vegetation physiological process
Hypothetical precipitation
Fig. 3. Diagram of this work for insights into Xilingol grassland eco-engineering.
Z. Shengwei et al. / Ecological Engineering 84 (2015) 100–103
grass transpiration will be 190.05 mm, and soil evaporation will be 84.85 mm. The grass transpiration value of 190.05 mm is 13.21 mm higher than the value from reducing the number of the precipitation, which indicates that the precipitation frequency has a strong effect on grass growth. 6. Conclusions Based on the analysis presented here, we concluded that in the arid and semi-arid continental climate conditions of the West Ujimqin Banner watershed, precipitation plays an important role in determining grass ET. Based on the data simulated by the dual crop coefficient model, we found that the grass ET rarely reached 4 mm/d throughout the study period. Grass transpiration was higher than soil evaporation throughout the study period. When the number of precipitation events is reduced to half the actual number, the precipitation frequency has a greater effect on the water demand during the grass growth period; therefore, precipitation frequency determines the pattern of grass growth. Simulations of grass ET were performed to obtain the grassland water demands during different growth periods and for arid regions. By considering the actual conditions, the grass growth conditions were obtained under the different simulated environmental conditions. Currently, the Xilingol Grassland suffers from severe degradation, and the grassland quality has been greatly decreased. Because water conditions strongly affect grassland vegetation growth and production, grassland ET processes must be studied to provide support for and to help maintain the grasslands and determine the most efficient method to mitigate the degradation. Acknowledgements This research was supported by the National Natural Science Foundation of China (No. 51269014, 31360113, 51139002) and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13069). References Fan, Y.Q., Cai, H.J., 2002. Comparison of crop water requirements computed by single crop coefficient approach and dual crop coefficient approach. J. Hydraul. Eng. 3, 50–54. Hao, Y.B., Wang, Y.F., Huang, X.Z., 2007. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agric. For. Meteorol., 57–69.
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