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Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain
Agricultural and Forest Meteorology 150 (2010) 581–589
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Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain Huimin Lei, Dawen Yang * State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2009 Received in revised form 26 January 2010 Accepted 30 January 2010
Using the eddy covariance technique, three years (October 2005–September 2008) of water and energy flux measurements were obtained for a winter wheat/summer maize rotation cropland in the North China Plain. This region is critical for food production in China, and is prone to significant water shortages and drought. Seasonal and interannual variability in evapotranspiration (ET) were examined in terms of relevant controlling factors. The annual ET was 595 and 609 mm in the periods of 2005–2006 and 2006– 2007, respectively. The average seasonal ET in the wheat and maize field was 401 and 212 mm, respectively. Seasonal variability in ET was primarily explained by the variations in equilibrium evaporation (ETeq) and canopy conductance (Gs). Daily evapotranspiration ranged from 1.0 to 7.8 mm day1 during the wheat season and reached up to 5.1 mm day1 during the maize season. The maximum midday average Gs was 32 mm s1 for wheat and 17 mm s1 for maize. During the rapid growth stages, the average midday LE/Rn (LE is latent heat flux, Rn is net radiation) was 83% for wheat and 57% for maize, indicating a higher water consumption for wheat than for maize. On an annual basis, latent heat flux accounted for about 59% of the net radiation, suggesting that more energy is partitioned into evapotranspiration in this agroecosystem site. Regional irrigation promoted sensible heat advection from the surrounding drier surface during the wheat seasons. Monthly ET totals enhanced by sensible heat advection accounted for 27% of the ETeq during the rapid growing season of wheat. ß 2010 Elsevier B.V. All rights reserved.
Keywords: Flux observation Evapotranspiration Energy partition Priestley–Taylor coefficient Canopy conductance Winter wheat Summer maize
1. Introduction Evapotranspiration is a major component of the energy and water balances in agricultural ecosystems (Burba and Verma, 2005; Steduto and Hsiao, 1998). Understanding the seasonal and interannual variability in evapotranspiration and energy partitioning, based on the long-term measurements of fluxes, is important for identifying weather, soil water, and crop factors which regulate fluxes at different temporal scales, and is vital for modeling crop production and water balance (Burba and Verma, 2005). There is a particular need for in-depth study of evapotranspiration for the improvement of water resource use and management in waterlimited agricultural areas. The North China Plain, the largest agricultural production area in China, is a water-limited region. More than 50% of the nation’s wheat and 33% of its maize production is grown on this plain, through a wheat/maize double cropping system (Wang et al., 2008). However, the area is experiencing serious water shortages (Cai, 2008), primarily due to the high water consumption by the agriculture sector through crop transpiration and soil evaporation.
* Corresponding author. Tel.: +86 10 62796976; fax: +86 10 62796971. E-mail address: [email protected] (D. Yang). 0168-1923/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2010.01.022
The diversion of large amounts of irrigation water diverted from the rivers or pumped from the groundwater have caused serious environmental problems, such as drying up of the river and formation of groundwater funnels (Yang et al., 2004; Kendy et al., 2003). To protect limited water resources, accurate quantification of the water requirements for the two predominant crops is essential. Irrigation of large areas of cropland clearly alters the regional energy and water cycles, creating a unique regional environment (Prueger et al., 1996). Extensive irrigation not only increases evapotranspiration and reduces runoff, but also decreases sensible heat flux. For example, modeling study in the Colorado river basin has demonstrated that irrigation water requirements of 15.4 mm year1 could result in a decrease of 37% in streamflow and a decrease in surface temperature of 0.04 8C (Haddeland et al., 2006). Evaluation of the basic characteristics of evapotranspiration and energy partitioning in response to irrigation is therefore helpful in identifying the effect of irrigation on regional water and energy balances in the North China Plain. The eddy covariance technique (EC) is generally considered to be a highly suitable method for providing direct and trustworthy flux measurements (Baldocchi, 2003). A large number of studies have used this technique to characterize the temporal and spatial variability of canopy-scale carbon dioxide, water vapor, and energy
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fluxes across diverse ecosystems (Baldocchi et al., 2001). However, there have been few examinations of the variations in evapotranspiration and energy fluxes in the irrigated croplands of the North China Plain using this technique (Wang et al., 2006; Qin et al., 2008). Since March 2005, long-term observations of energy and water budgets, in association with hydrometeorological elements, have been carried out over an irrigated wheat/maize rotation cropland in the central North China Plain, as one of the reference sites for the Coordinated Energy and water cycle Observations Project (CEOP, http://monsoon.t.u-tokyo.ac.jp/ceop2/index.html). In the present study, we used three years of consecutive measurement data, covering the period from October 2005 to September 2008, to analyze variations in evapotranspiration and energy partitioning in this region. This included three full winter wheat seasons and three full summer maize seasons. Interannual variability was determined using a crop year defined from October to September of the following year. The main objectives were to examine the seasonal and interannual variations in evapotranspiration in terms of relevant controlling factors (weather, soil moisture, canopy conductance), and to quantify the seasonal and interannual distributions of evapotranspiration (latent heat flux) and energy partitioning. 2. Material and method 2.1. Site description The Weishan flux site (N368390 , E1168030 ) is located near the center of the Weishan Irrigation District (area: 4448 km2, approximately 36.148–37.018N and 115.438–116.518E) along the downstream of the Yellow River in the North China Plain (Fig. 1). Its regional climate is temperate and semi-humid with 532 mm (average: 1984–2007) of mean annual precipitation and 1950 mm (average period: 1961–2005) of mean annual pan evaporation (20 cm diameter evaporation pan). Approximately 70% of its annual precipitation is concentrated in the wet season from June to September. The mean annual air temperature is 13.3 8C. Annually, about 215 mm (average period: 1984–2007) of water is diverted
Fig. 1. Weishan Irrigation District (WID) and flux site location.
from the Yellow River for the supplementary irrigation of winter wheat. The ground surface is very flat with 1:7500 of average gradient. The major soil type is silt loam with a saturated soil water content value of 0.43 m3 m3. The soil consists of approximately 32% sand and 10% clay. The groundwater table ranges between 1.0 and 3.5 m within a year. Winter wheat and summer maize are the two predominant crops and are cultivated in rotation. The growing season of winter wheat is from mid-October to late May, while maize is planted in late June and harvested in early October. June and October are the intermittent months for the two crops. There is a large area of cotton field located to the north of the Weishan Irrigation District (Fig. 1), with a growing season usually from midApril to early September. Irrigation water from the Yellow River cannot reach this region, so limited irrigation water is made available by pumping the groundwater. 2.2. Measurements A 10 m-high tower was erected for mounting flux and meteorological instruments. The EC system was placed 3.7 m above the ground. Wind velocity and virtual temperature fluctuations were measured using a three-dimensional (3D) sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA). An open path infrared gas analyzer (LI7500, LI-COR, Inc., Lincoln, NE, USA) was used to measure the fluctuations in CO2 and H2O density. The sampling frequency of the EC system was 10 Hz. Spectra and co-spectra computations show that these sampling rates are adequate for measuring fluxes with this configuration. Hydrometeorological variables were recorded at 10-min intervals on several data loggers (CR10X, Campbell Scientific, Inc., Logan, UT, USA). Air temperature and humidity (HMP45C, Vaisala Inc., Helsinki, Finland) were measured at 1.6, 3.6 and 6.0 m above ground level. Downward and upward solar and longwave radiation (CNR-1, Kipp & Zonen, Delft, the Netherlands) and photosynthetically active radiation (PAR) (LI190SB, LICOR, Lincoln, NE, USA) were measured at 3.5 m above ground level. A tipping bucket rain gauge (TE525MM, Campbell Scientific Inc., Logan, UT, USA) was mounted at 1.5 m above the ground. Wind speed and direction (05103, Young Co., Traverse City, MI, USA) were measured at 10.0 m above ground level. Soil temperature (Campbell-107, Campbell Scientific Inc., Logan, UT, USA) and soil water content (TRIME-EZ/IT, IMKO, Ettlingen, Germany) were measured at 0.05, 0.10, 0.20, 0.40, 0.80, and 1.6 m below the ground. Soil heat flux (HFP01SC, Hukseflux, the Netherlands) was measured at 0.03 m below the ground. Soil temperature, soil water content, and soil heat flux were determined by averaging two measurement profiles obtained west and east of the tower. Groundwater level was measured using an automatic water depth sensor (CS420-10, Campbell Inc., Logan, UT, USA) installed in a groundwater well close to the tower. Irrigation was estimated from the change in soil water storage for the 160 cm depth within 1 day when each irrigation event occurred. Since the measured saturated hydraulic conductivity at the top soil and deep soil of 100 cm was 132 and 7.7 cm day1, respectively, irrigation water can all be stored in the soil layer of 0–160 cm within 1 day. Leaf area index (LAI) was estimated by directly measuring the leaf area sampled from the crop field using an optical area meter. However, only a few measurements of LAI were available during the study period. Based on the non-linear relationship between the observed LAI and Normalized Differential Vegetation Index (NDVI) data (8-day temporal resolution and 250 m spatial resolution, MODIS/Terra MOD09Q1), consecutive leaf area index values derived from the NDVI data from the grid containing our flux tower were used for the analysis.
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2.3. Data post-processing The half-hourly latent and sensible heat fluxes were calculated by computing the mean covariance of water and temperature fluctuations with the fluctuating vertical velocity observed by the EC system. The TK2 software package (Mauder et al., 2006) was employed for the quality assurance and quality control (QA/QC) of the EC data. This included the coordinate rotation by the planar fit method (Wilczak et al., 2001), spectral loss correction (Moore, 1986), sonic virtual temperature conversion (Mauder et al., 2006), density fluctuations correction (Webb et al., 1980), and spike detection (Vickers and Mahrt, 1997). In the spike detection algorithm, any value that exceeded the mean value 5.5 times the standard deviations in a window of 10 values was labeled as spike. The data gaps were then filled to calculate the daily mean values. Short gaps (2 h) were filled with linear interpolation, and larger data gaps were filled using the mean diurnal average method (Falge et al., 2001). The missing daily average temperature, daily average relative humidity, and daily total precipitation data were spatially interpolated from nearby national meteorological stations (Yang et al., 2004). 2.4. Calculations of bulk parameters To interpret the interannual and seasonal variability in ET, we used two bulk parameters: canopy conductance and the Priestley– Taylor coefficient. The canopy conductance (Gs) was calculated by inverting the Penman–Monteith equation as Gs ¼
g LEg a DðRn GÞ þ rcp VPD LEðD þ g Þ
(1)
where LE is the latent heat flux, Rn the net radiation, G the soil heat flux, r the air density (1.2 kg m3), cp is the specific heat of the air (1004.7 J kg1 K1), D is the slope of relation between saturation vapor pressure and temperature, g is the psychrometric constant, and VPD is the air vapor pressure deficit. The aerodynamic conductance (ga) was approximately estimated by the Monteith– Unsworth equation (Monteith and Unsworth, 1990): ga ¼
u þ 6:2u2=3 u2
1
(2)
where u is the mean wind velocity, and u* is the friction velocity estimated by the EC system. The Priestley–Taylor coefficient (a) is defined as the ratio of ET to the equilibrium evaporation (ETeq) which is calculated as follows (Priestley and Taylor, 1972):
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where D and P are similarity constants and D = 0.28, P = 0.59 for unstable condition (Hsieh et al., 2000), k (=0.4) the von Karman constant, x the horizontal coordinate, L the Obukhov length and zu is a length scale, defined as: zm z0 zu ¼ zm ln 1þ z0 zm
(5)
where zm is the measurement height, and z0 is the zero displacement height. 3. Results and discussion 3.1. Energy balance closure One measure for testing data quality is to test for closure of surface energy balance (Wilson et al., 2002). Using half-hourly data from June 2005 to December 2008, the slope between available energy flux (Rn–G) and the sum of sensible and latent heat fluxes for this site was 0.74, the intercept was 11.87 W m2, and the coefficient of determination (R2) was 0.83. The regression statistics of LE + Hs on Rn–G for half-hourly data are shown in Table 1. For the three-year period, the slope of the regression was less than one, and the intercept was slightly greater than zero, which indicates that the EC measurements underestimated the sensible heat and/ or latent heat fluxes. Many other long-term EC studies have reported the lack of energy balance closure for reasons that are complicated and still not fully understood (Foken, 2008). Thus, it is difficult to evaluate the causes of energy imbalance at this site. Measurement error of the EC system, representation of the measured soil heat flux, horizontal energy advection, energy used in photosynthesis, and energy storage in the canopy and top soil could all contribute to energy imbalance (Foken, 2008; Li and Yu, 2007; Meyers and Hollinger, 2004). For FLUXNET sites, Wilson et al. (2002) reported that the slope values range from 0.53 to 0.99 with a mean of 0.79 0.01, and the intercept values range from 32.9 to 36.9 W m2 with a mean of 3.7 2.0 W m2. For ChinaFLUX sites, Li et al. (2005) reported that the slope values range from 0.49 to 0.81 with a mean of 0.67, and the intercept values range from 10.8 to 79.9 W m2 with a mean of 28.9 W m2. The energy balance closure statistics at our site were reasonable compared with these ranges. Although not perfect, the energy balance closure during the three-year period was similar, indicating the measurements system provides reliable estimates of the surface energy balance components. 3.2. Footprint analysis
D ðR GÞ ET eq ¼ Dþg n
(3)
To avoid the occurrence of numerical instability when the denominator approaches zero, the midday data of Gs and a (10:00– 14:00) were used to calculate the midday average values. Additionally, data in precipitation and low PAR (<25 mmol m2 s1) periods were excluded from the calculations. 2.5. Footprint model Scalar flux footprint estimation is commonly used to quantify the contributing source areas to scalar flux measurement. In this study, an approximate analytical footprint model was used for footprint estimation (Hsieh et al., 2000), giving the footprint (f) as follows: f ðx; zm Þ ¼
1 P 1P 1P P Dz Dz L exp L j j j j u u 2 2 k x2 k x 1
(4)
Two typical periods were selected as the case study to estimate the average footprint for this site (Table 2). All the parameters were estimated using the data in daytime during 10:00–14:00. At the unstable condition, our analysis indicated that approximately 90% of the measured flux was expected to come from within the nearest 420 and 166 m during wheat and maize periods, respectively. Around the flux tower in a radius of 420 m, only a small fraction of ground surface is covered by trees, road and irrigation channel, indicating that the measured fluxes were primarily contributed by the cropland. Table 1 Slope, intercept and R2 of the half-hourly energy balance closure. Year
Slope
Intercept (W m2)
R2
Data number
2006 2007 2008
0.74 0.76 0.75
10.85 8.04 3.82
0.84 0.82 0.86
1173 1630 1348
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Table 2 Typical values (u*: friction velocity; Hs: sensible heat flux; L: Obukhov length) for footprint estimation in unstable condition. Period
Crop
u* (m s1)
Hs (W m2)
Crop height (m)
L (m)
April August
Wheat Maize
0.32 0.25
35.3 45.1
0.5 1.8
83 33
3.3. Hydrometeorological conditions and leaf area index Over the study period, the total precipitation values were 360 (2005–2006), 480 (2006–2007), and 464 mm (2007–2008), which were all significantly below the 553 mm normal level (1990– 2008). Most of the rainfall occurred from June through September when maize was growing. There was relatively little rainfall in March to May when winter wheat was in its period of rapid growth (Fig. 2a). Irrigation totals ranged from 101 to 297 mm in wheat seasons but equaled to zero in maize seasons (Fig. 2e). The mean annual daily mean temperature in the three crop years was 13.5, 13.9, and 13.1 8C, respectively (normal: 13.8 8C during 1990– 2008). Seasonal variation in vapor pressure deficit indicated that
June had the highest evaporative demand, while the growing seasons for both crops had a relatively low atmospheric water demand (Fig. 2c). Soil water content was kept at a higher level during the wheat seasons than that during the maize seasons as a result of irrigation despite the uneven seasonal distribution of precipitation (Fig. 2e). Although soil water content reached a minimum in June or July, it rarely fell below a ‘‘moisture stress threshold’’ (assumed here as 50% of the extractable soil water). Since wheat was harvested before mid-June, the canopy would not experience any moisture stress. Only the summer maize experienced a slight moisture stress in July of 2006 due to low precipitation. Leaf area index was shown to have a clear ‘‘two peak’’ pattern in each crop year corresponding to the crop rotation (Fig. 2d). According to the general crop phenology, LAI increased slightly in November due to the emergence of winter wheat, and then decreased when winter wheat went into dormancy. It increased quickly from early March because of the greening of winter wheat. Wheat reached its peak LAI of about 5–6 m2 m2 in late April or early May (the period from late April to early May is referred to as the ‘‘rapid growth season’’ for wheat), and rapidly senesced by early June. Followed by the planting of summer maize in mid-June,
Fig. 2. Summaries of weekly (a) total precipitation (P), (b) average air temperature (Ta) at 1.6 m, (c) average vapor pressure deficit (VPD) at 1.6 m, (d) average leaf area index (LAI), and (e) volumetric soil water content (SWC, 0–80 cm averaged) from October 2005 to September 2008. Irrigation (I) events were also presented.
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Table 3 Annual and seasonal summaries of total evapotranspiration (ET) and hydrometeorological elements, including total net radiation (Rn), mean air temperature (Ta), mean vapor pressure deficit (VPD), total equilibrium evaporation (ETeq), total precipitation (P), total irrigation (I), mean volumetric soil water content (0–80 cm averaged, SWC), mean groundwater depth (GWL), and the number of days (Nd) with leaf area index greater than 2.5 m2 m2. Crop seasons
Periods
ET (mm)
Rn (MJ m2)
Ta (8C)
VPD (kPa)
ETeq (mm)
P (mm)
I (mm)
P+I (mm)
SWC (m3 m3)
GWL (cm)
Annual (10.1–9.30)
2005–2006 2006–2007 2007–2008a
609 595 554
2285.2 2279.7 2173.0
13.47 13.88 19.07
0.621 0.655 0.69
644 656 614
360 480 459
101 297 237
461 777 696
0.30 0.27 0.27
222 277 263
– – –
Winter wheat (10.1–6.14)
2005–2006 2006–2007 2007–2008a
412 391 320
1285.4 1271.7 1126.7
8.93 9.84 14.65
0.524 0.618 0.689
362 357 304
145 114 135
101 297 237
246 411 372
0.31 0.27 0.28
193 294 233
62 96 64
2006 2007 2008
198 204 234
999.8 1008.0 1046.3
24.49 23.63 23.50
0.853 0.745 0.690
282 299 310
215 366 324
0 0 0
215 366 324
0.27 0.28 0.26
270 242 293
45 68 58
Summer maize (6.15–9.30)
a
Nd (day)
Data during 2007.11.13–2008.02.29 were missing.
LAI started another new cycle, reaching its peak LAI of about 4– 6 m2 m2 in late July or early August (the period from August to early September is referred to as the ‘‘rapid growth season’’ for maize), and senescing by late September. 3.4. Characteristics of the interannual variability in evapotranspiration The seasonal and annual summaries of measured evapotranspiration and other hydrometeorological elements during the three years of our study are shown in Table 3. On an annual basis, the cumulative evapotranspiration values were 609 and 595 mm in the periods of 2005–2006 and 2006–2007, respectively. Growing
season ET in wheat and maize averaged 401 and 212 mm, respectively. On the average, cumulative ET during the wheatgrowing season was higher than the maize ET by 189 mm (89%). Cumulative ET during the wheat-growing seasons was much higher than the cumulative precipitation in the corresponding periods, while it was lower than the total precipitation during the maize-growing seasons. A large amount of irrigation (161% of total precipitation on average) was carried out to meet winter wheatgrowing demands. The total amount of precipitation and irrigation was larger than the total evapotranspiration during the wheat seasons, except the period of 2005-2006 when soil water storage was sufficient (Table 4) because of the high amount of precipitation in the previous year (annual precipitation: 680 mm). ETeq can
Table 4 Monthly summaries of total evapotranspiration (ET), total net radiation (Rn), mean air temperature (Ta), mean vapor pressure deficit (VPD), total equilibrium evaporation (ETeq), total precipitation (P), total irrigation (I), mean volumetric soil water content (0–80 cm averaged, SWC), mean groundwater depth (GWL), and mean leaf area index (LAI) for the periods from October, 2005 to September, 2008. Y
M
Crop season
ET (mm)
Rn (MJ m2)
Ta (8C)
VPD (kPa)
ETeq (mm)
P (mm)
I (mm)
SWC (m3 m3)
GWL (cm)
LAI (m2 m2)
2005
10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
Crop rotation Winter wheat
36.5 25.5 6.7 3.5 21.9 58.1 95.7 120.8 64.8 44.7 78.6 52.7 23.6 28.1 12.9 6.3 21.0 48.2 101.9 129.0 34.7 71.5 59.2 58.6 28.9 – – – – 51.4 83.4 122.3 47.5 67.9 83.0 63.4
136.1 49.8 3.5 25.4 88.0 195.9 264.7 350.8 335.5 288.3 309.2 238.3 117.0 59.6 15.9 30.5 85.1 173.2 288.3 374.8 260.3 327.6 281.0 266.3 102.3 – – – – 206.4 264.6 381.2 309.9 308.4 324.7 249.2
14.06 8.94 1.31 0.88 1.32 8.74 13.96 19.08 25.88 26.89 25.32 19.65 17.68 7.92 0.36 1.39 5.14 7.57 14.04 21.05 24.76 25.68 24.54 19.95 13.81 6.80 1.63 2.63 0.23 9.20 14.41 20.06 23.63 25.62 24.23 19.77
0.56 0.47 0.28 0.18 0.30 0.63 0.58 0.71 1.60 0.87 0.62 0.66 0.77 0.47 0.19 0.28 0.43 0.43 0.74 1.17 1.36 0.85 0.51 0.67 0.48 0.38 0.20 0.17 0.31 0.61 0.68 0.90 1.14 0.65 0.54 0.61
47.1 19.9 9.9 8.7 18.5 43.5 67.1 97.6 92.3 79.4 89.4 71.0 38.1 22.9 11.5 12.6 18.8 38.7 72.6 106.9 71.1 94.8 85.1 83.1 33.3 – – – – 45.6 66.9 107.1 87.2 89.7 97.6 77.0
22 6 1 0 5 0 11 78 43 59 118 18 1 16 3 0 7 51 20 15 66 149 80 72 22 2 5 0 0 6 45 40 46 188 41 69
0 0 0 0 0 101 0 0 0 0 0 0 0 0 0 0 0 0 142 155 0 0 0 0 0 0 0 0 0 130 107 0 0 0 0 0
0.35 0.33 0.32 0.32 0.31 0.32 0.30 0.29 0.26 0.25 0.29 0.28 0.28 0.27 0.27 0.27 0.27 0.28 0.28 0.29 0.25 0.28 0.29 0.26 0.29 – – – – 0.29 0.30 0.28 0.24 0.28 0.27 0.26
92 151 201 220 229 161 196 249 311 321 255 276 258 287 309 317 321 279 253 208 312 281 218 285 250 – – – – 200 183 244 318 274 264 322
1.3 0.9 1.0 0.7 0.8 1.7 4.1 3.7 1.2 2.2 3.3 1.7 1.0 2.2 2.3 1.4 1.8 3.0 4.6 3.5 1.4 2.8 4.9 3.1 0.9 0.9 1.0 0.7 0.7 1.7 4.2 4.1 1.4 2.4 4.3 2.4
2006
2007
2008
Crop rotation Summer maize
Crop rotation Winter wheat
Crop rotation Summer maize
Crop rotation Winter wheat
Crop rotation Summer maize
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Fig. 3. Seasonal variations in daily total evapotranspiration (ET) and daily total equilibrium evaporation (ETeq) for the years 2005–2008.
The seasonal variations in daily evapotranspiration and daily equilibrium evaporation for three consecutive years are shown in Fig. 3. After the planting of winter wheat in mid-October, ET showed a gradual increase from about 1.0 to 2.5 mm day1 because of the canopy emergence in November. During winter from December to January, ET was very low because of both low equilibrium evaporation and the dormancy of winter wheat. Afterward, ET varied closely with ETeq until late May when winter wheat started to senesce. The ET in June was around 1.0 mm day1, corresponding to the crop harvest, despite the high evaporative demand (Fig. 2c). Summer maize grew rapidly from late June, leading to a rapid increase in ET until August. There was a rapid decrease in ET from late August, following both the senescence of summer maize and decrease in ETeq. For both crops, wheat
generally reached a peak ET value between 6.6 and 7.8 mm day1, while maize reached a peak value between 4.0 and 5.1 mm day1. The peak ET for maize was much lower than that of wheat even though ETeq reached a similar magnitude (around 5 mm day1) when the peak values in ET were reached for both crops. This can be explained by the more efficient water use of the C4 maize (Tong et al., 2009) as compared to the C3 wheat. On a daily basis, equilibrium evaporation explained 78% of the seasonal variability in daily ET during the wheat seasons, but explained 54% of the seasonal variability in daily ET during the maize seasons (Table 4). Canopy conductance was analyzed to explore the biological control of the seasonal variations in ET (Baldocchi et al., 2004). The seasonal variation in midday Gs is given in Fig. 4. It was greatly reduced in the periods when the crops were rotated. Midday Gs ranged from near 1 mm s1 when winter wheat was dormant up to 21–32 mm s1 in April for winter wheat and up to 13–17 mm s1 in August for summer maize. The maximum Gs values for both wheat and maize were substantially higher than that for a meadow in the Tibetan Plateau (11 mm s1, Gu et al., 2005), which was possibly due to the ample water and high leaf nitrogen (Schulze et al., 1994) at our site. The average canopy conductance in the winter wheat season from March to May was around two times greater than that in the maize season from July to September, which is in agreement with the results presented by Baldocchi (1994) that Gs in a closed wheat canopy is typically two to five times greater than the values from a sparse corn canopy. To eliminate the effect of weather conditions on ET, the midday ET (10:00–14:00) values were normalized with midday equilibrium evaporation (the normalized ET is also referred to as the Priestley–Taylor coefficient a). In Fig. 5, the data for both crops suggested that ET is greatly influenced by canopy conductance
Fig. 4. Seasonal variations in midday (10:00–14:00) averages of canopy surface conductance for the years 2005–2008.
Fig. 5. Relationship between the midday Priestley–Taylor coefficient (a) and midday canopy conductance (Gs).
explain 98% and 79% of the variability in seasonal ET totals of wheat and maize, respectively (Table 3). Seasonal ET totals were found to have a non-significant relationship with the total amount of precipitation and irrigation, as well as the number of days in each season when the LAI was greater than 2.5 m2 m2 (Table 3). Compared with other ecosystems, these annual ET values were larger than those measured in natural forest (493 mm, Wu et al., 2007) and steppe ecosystems (345 mm, Hao et al., 2007). Seasonal ET total during the 2008 maize season was comparable to the observed ET total of 253 mm in 2008 at a rainfed maize field (184 km northwest of our site) in the region (Y. Shen, personal communication, 2009). However, it tended to be a little lower than that reported from other agricultural crops near similar latitudes in North America (Burba and Verma, 2005; Suyker and Verma, 2008). 3.5. Seasonal variations in evapotranspiration and its control factors
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stress in the maize season in July of 2006 (ESW less than 0.5) (see Fig. 2e), the a values were reduced by soil moisture. Since the ESW values were rarely less than 0.5, we concluded that soil water availability exerted a very limited control on ET in the maize season during the study period. 3.6. Characteristics of energy partitioning
Fig. 6. Relationship between the midday Priestley–Taylor coefficient (a) and extractable soil water (ESW). Dots denote the weekly average values.
when the Gs values are below approximately 15–20 mm s1. Because Gs of maize was much lower than that of wheat, the control of canopy conductance on ET during the maize seasons was stronger than that during the wheat seasons. This explained why equilibrium evaporation explained a lower variability in ET during the maize seasons than during the wheat seasons. The occurrence of threshold Gs is consistent with the observations of Suyker and Verma (2008) in maize. A theoretical study on a fully developed canopy also indicates that a becomes insensitive to canopy conductance when it exceeds about 16 mm s1 (McNaughton and Spriggs, 1986). The effect of moisture stress on ET was further examined via the relationship between normalized ET and the extractable soil water (ESW) (Fig. 6). No significant relationship was found between ESW and a (R2 < 0.15). However, during a short period of soil moisture
Seasonal variations in the daily total sensible heat flux (Hs) during the three years are examined in Fig. 7. The peak values of daily Hs (between 7 and 8 MJ m2 day1) occurred during June when the two crops were rotated. However, another rotation period in mid-October met a much lower Hs because of the lower net radiation (see Table 4). The sensible heat flux decreased rapidly following crop emergence each year. In the period of rapid growth for winter wheat, negative daily total sensible heat fluxes were observed, reaching up to 5 MJ m2 day1. However, Hs rarely reached negative values during the rapid growing season for summer maize. The ratios of midday fluxes to midday net radiation (LE/Rn, Hs/ Rn) are presented in Fig. 8a to examine the seasonal variations in energy partitioning related to canopy phenology. There were three peaks of LE/Rn in each crop year, occurring in November and April/ May, for the emergence and the full development of winter wheat, and in August for the full development of summer maize. For winter wheat, the values of LE/Rn typically ranged from 0.05 in the dormant season to 0.8–0.9 after the canopy was fully developed. Following canopy senescence, the LE/Rn values rapidly decreased
Fig. 7. Seasonal variations in daily total sensible heat flux (Hs) during the years 2005–2008.
Fig. 8. Seasonal variations in (a) midday energy partitioning terms (LE/Rn, Hs/Rn) and (b) the midday Bowen ratio during the years 2005–2008. Rn is the net radiation, LE is the latent heat flux, and Hs is the sensible heat flux. The leaf area index (LAI) is also included.
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to about 0.05–0.12. For summer maize, the maximum values of LE/ Rn were up to about 0.65–0.8, which was smaller than that for wheat. The values of Hs/Rn presented an opposite trend with canopy development, reaching minimum values of 0.1 for wheat and about 0.05 for maize. The Bowen ratio (b) showed similar opposite trends with the canopy phenology (Fig. 8b). The day-today variability in b was large in the two rotation periods as well as during the dormancy period in the winter wheat season, ranging from about 0.5 to 15. The b values decreased to relatively steady levels when both crops were fully developed, with the average value of 0.13 (averaged for April and May) for wheat and 0.43 (averaged for August and September) for maize. However, b was negative, falling to about 0.1 in the rapid growing days during winter wheat seasons, because of the negative sensible heat flux. On an annual scale, the latent heat flux was the largest consumer of net radiation (59%), which was comparable with a maize-soybean agroecosystem (Suyker and Verma, 2008), but higher than a Tibetan meadow ecosystem (24%, Gu et al., 2005) and a temperatemixed forest ecosystem (52%, Wu et al., 2007). During the rapid growing season of winter wheat from March to May, the average midday LE/Rn was about 83%, while it was about 57% during the rapid growing season of summer maize from July to September, indicating that wheat is more water-consuming than maize. 3.7. Impact of irrigation on energy fluxes and evapotranspiration The negative daily sensible heat flux was observed during the winter wheat seasons. This can be explained by sensible heat advection (Prueger et al., 1996), which would indicate that Hs was a source of energy for evapotranspiration during that period. Since there was sufficient irrigation in the Irrigation District, the occurrence of sensible heat advection during April and May most probably resulted from the drier soil regimes in the north upwind areas (Fig. 1). The northern cotton fields had a much drier land surface as they were irrigated by a limited pumping of groundwater. An estimated spatial distribution of the latent heat flux in this region has demonstrated that the latent heat flux at the north part of the Weishan Irrigation District was much lower than that within the Irrigation District (Yi, 2008). A similar phenomenon was also reported for a nearby winter wheat site (Li and Yu, 2007). Previous studies have shown that change of actual evaporation is dominated by change in precipitation, rather than potential evaporation in natural non-humid regions (including the North China Plain), but is controlled by change in potential evaporation rather than precipitation in humid regions (Yang et al., 2006, 2007). However, in this study, the variability in evapotranspiration was largely explained by equilibrium evaporation on a seasonal and a daily basis, implying that regional irrigation has shifted the pattern of annual water-energy balance from that in non-humid region to that in humid region. The sensible advection caused by regional irrigation enhanced the evapotranspiration and affected the local energy budget. This can be evaluated by the relationship between ET and ETeq (Table 4). Monthly ET totals in excess of the ETeq totals ranged from 2 to 29 mm from February to May during the wheat seasons, accounting for 27% of the equilibrium evaporation. 4. Conclusions Using the EC technique, evapotranspiration and energy fluxes were measured over a typical irrigated wheat/maize rotation cropping field in the North China Plain for a three-year period. Although annual precipitation was significantly below the normal, the soil water content rarely fell below the threshold for moisture stress because of the sufficient irrigation during the wheat seasons and sufficient precipitation during the maize seasons. The annual
ET ranged from 595 to 609 mm, and the ET total for irrigated wheat season was 89% higher than that for rainfed maize season on average. The interannual variability in the seasonal ET totals could be primarily explained by the variations in equilibrium evaporation, and the seasonal variability in daily ET could be largely explained by the variations in equilibrium evaporation and canopy conductance below a threshold of 15 mm s1. Soil water availability exerted only a very limited effect on ET. The average midday value of LE/Rn was 83% during the rapid growing season of wheat and about 57% during the rapid growing season of maize. On an annual basis, the latent heat flux consumed about 59% of the net radiation, showing a relatively higher proportion than that in natural ecosystems. Regional irrigation not only shifted the pattern of annual water-energy balance from that in non-humid region to that in humid region, but also caused sensible heat advection from the surrounding drier surface during the wheat seasons. The evapotranspiration, enhanced by sensible heat advection, accounted for 27% of the equilibrium evaporation during the rapid growing season of wheat. Acknowledgements This research was supported by the National 973 Project of China (Project No. 2006CB403405), the National Natural Science Foundation of China (Project No. 50939004), and the Doctoral Program Foundation of Institutions of Higher Education of China (Project No. 2007000307). We thank Dr. Matthias Mauder at University of Bayreuth for the support for the TK2 software package. We would like to express our appreciation to one anonymous reviewer, whose comments and suggestions led to significant improvements in the manuscript. References Baldocchi, D.D., 1994. A comparative study of mass and energy exchange over a closed C3 (wheat) and an open C4 (corn) canopy: I. The partitioning of available energy into latent and sensible heat exchange. Agric. For. Meteorol. 67, 191– 220. Baldocchi, D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol. 9, 479–492. Baldocchi, D.D., Falge, E., Gu, L., et al., 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434. Baldocchi, D.D., Xu, L., Kiang, N., 2004. How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oakgrass savanna and an annual grassland. Agric. For. Meteorol. 123, 13–39. Burba, G.G., Verma, S.B., 2005. Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems. Agric. For. Meteorol. 135, 190–201. Cai, X., 2008. Water stress, water transfer and social equity in Northern ChinaImplications for policy reforms. J. Environ. Manage. 87, 14–25. Falge, E., Baldocchi, D.D., Olson, R., Anthoni, P., et al., 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric. For. Meteorol. 107, 43–69. Foken, T., 2008. The energy balance closure problem: an overview. Ecol. Appl. 18, 1351–1367. Gu, S., Tang, Y., Cui, X., et al., 2005. Energy exchange between the atmosphere and a meadow ecosystem on the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 129, 175–185. Haddeland, I., Lettenmaier, D.P., Skaugen, T., 2006. Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. J. Hydrol. 324, 210–223. Hao, Y., Wang, Y., Huang, X., et al., 2007. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agric. For. Meteorol. 146, 57–69. Hsieh, C., Katul, G., Chi, T., 2000. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Adv. Water Resour. 23, 765–772. Kendy, E., Ge´rard-Marchant, P., Walter, M.T., et al., 2003. A soil–water-balance approach to quantify groundwater recharge from irrigated cropland in the North China Plain. Hydrol. Process. 17, 2011–2031. Li, L., Yu, Q., 2007. Quantifying the effects of advection on canopy energy budgets and water use efficiency in an irrigated wheat field in the North China Plain. Agric. Water Manage. 89, 116–122.
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Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain Huimin Lei, Dawen Yang * State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2009 Received in revised form 26 January 2010 Accepted 30 January 2010
Using the eddy covariance technique, three years (October 2005–September 2008) of water and energy flux measurements were obtained for a winter wheat/summer maize rotation cropland in the North China Plain. This region is critical for food production in China, and is prone to significant water shortages and drought. Seasonal and interannual variability in evapotranspiration (ET) were examined in terms of relevant controlling factors. The annual ET was 595 and 609 mm in the periods of 2005–2006 and 2006– 2007, respectively. The average seasonal ET in the wheat and maize field was 401 and 212 mm, respectively. Seasonal variability in ET was primarily explained by the variations in equilibrium evaporation (ETeq) and canopy conductance (Gs). Daily evapotranspiration ranged from 1.0 to 7.8 mm day1 during the wheat season and reached up to 5.1 mm day1 during the maize season. The maximum midday average Gs was 32 mm s1 for wheat and 17 mm s1 for maize. During the rapid growth stages, the average midday LE/Rn (LE is latent heat flux, Rn is net radiation) was 83% for wheat and 57% for maize, indicating a higher water consumption for wheat than for maize. On an annual basis, latent heat flux accounted for about 59% of the net radiation, suggesting that more energy is partitioned into evapotranspiration in this agroecosystem site. Regional irrigation promoted sensible heat advection from the surrounding drier surface during the wheat seasons. Monthly ET totals enhanced by sensible heat advection accounted for 27% of the ETeq during the rapid growing season of wheat. ß 2010 Elsevier B.V. All rights reserved.
Keywords: Flux observation Evapotranspiration Energy partition Priestley–Taylor coefficient Canopy conductance Winter wheat Summer maize
1. Introduction Evapotranspiration is a major component of the energy and water balances in agricultural ecosystems (Burba and Verma, 2005; Steduto and Hsiao, 1998). Understanding the seasonal and interannual variability in evapotranspiration and energy partitioning, based on the long-term measurements of fluxes, is important for identifying weather, soil water, and crop factors which regulate fluxes at different temporal scales, and is vital for modeling crop production and water balance (Burba and Verma, 2005). There is a particular need for in-depth study of evapotranspiration for the improvement of water resource use and management in waterlimited agricultural areas. The North China Plain, the largest agricultural production area in China, is a water-limited region. More than 50% of the nation’s wheat and 33% of its maize production is grown on this plain, through a wheat/maize double cropping system (Wang et al., 2008). However, the area is experiencing serious water shortages (Cai, 2008), primarily due to the high water consumption by the agriculture sector through crop transpiration and soil evaporation.
* Corresponding author. Tel.: +86 10 62796976; fax: +86 10 62796971. E-mail address: [email protected] (D. Yang). 0168-1923/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2010.01.022
The diversion of large amounts of irrigation water diverted from the rivers or pumped from the groundwater have caused serious environmental problems, such as drying up of the river and formation of groundwater funnels (Yang et al., 2004; Kendy et al., 2003). To protect limited water resources, accurate quantification of the water requirements for the two predominant crops is essential. Irrigation of large areas of cropland clearly alters the regional energy and water cycles, creating a unique regional environment (Prueger et al., 1996). Extensive irrigation not only increases evapotranspiration and reduces runoff, but also decreases sensible heat flux. For example, modeling study in the Colorado river basin has demonstrated that irrigation water requirements of 15.4 mm year1 could result in a decrease of 37% in streamflow and a decrease in surface temperature of 0.04 8C (Haddeland et al., 2006). Evaluation of the basic characteristics of evapotranspiration and energy partitioning in response to irrigation is therefore helpful in identifying the effect of irrigation on regional water and energy balances in the North China Plain. The eddy covariance technique (EC) is generally considered to be a highly suitable method for providing direct and trustworthy flux measurements (Baldocchi, 2003). A large number of studies have used this technique to characterize the temporal and spatial variability of canopy-scale carbon dioxide, water vapor, and energy
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fluxes across diverse ecosystems (Baldocchi et al., 2001). However, there have been few examinations of the variations in evapotranspiration and energy fluxes in the irrigated croplands of the North China Plain using this technique (Wang et al., 2006; Qin et al., 2008). Since March 2005, long-term observations of energy and water budgets, in association with hydrometeorological elements, have been carried out over an irrigated wheat/maize rotation cropland in the central North China Plain, as one of the reference sites for the Coordinated Energy and water cycle Observations Project (CEOP, http://monsoon.t.u-tokyo.ac.jp/ceop2/index.html). In the present study, we used three years of consecutive measurement data, covering the period from October 2005 to September 2008, to analyze variations in evapotranspiration and energy partitioning in this region. This included three full winter wheat seasons and three full summer maize seasons. Interannual variability was determined using a crop year defined from October to September of the following year. The main objectives were to examine the seasonal and interannual variations in evapotranspiration in terms of relevant controlling factors (weather, soil moisture, canopy conductance), and to quantify the seasonal and interannual distributions of evapotranspiration (latent heat flux) and energy partitioning. 2. Material and method 2.1. Site description The Weishan flux site (N368390 , E1168030 ) is located near the center of the Weishan Irrigation District (area: 4448 km2, approximately 36.148–37.018N and 115.438–116.518E) along the downstream of the Yellow River in the North China Plain (Fig. 1). Its regional climate is temperate and semi-humid with 532 mm (average: 1984–2007) of mean annual precipitation and 1950 mm (average period: 1961–2005) of mean annual pan evaporation (20 cm diameter evaporation pan). Approximately 70% of its annual precipitation is concentrated in the wet season from June to September. The mean annual air temperature is 13.3 8C. Annually, about 215 mm (average period: 1984–2007) of water is diverted
Fig. 1. Weishan Irrigation District (WID) and flux site location.
from the Yellow River for the supplementary irrigation of winter wheat. The ground surface is very flat with 1:7500 of average gradient. The major soil type is silt loam with a saturated soil water content value of 0.43 m3 m3. The soil consists of approximately 32% sand and 10% clay. The groundwater table ranges between 1.0 and 3.5 m within a year. Winter wheat and summer maize are the two predominant crops and are cultivated in rotation. The growing season of winter wheat is from mid-October to late May, while maize is planted in late June and harvested in early October. June and October are the intermittent months for the two crops. There is a large area of cotton field located to the north of the Weishan Irrigation District (Fig. 1), with a growing season usually from midApril to early September. Irrigation water from the Yellow River cannot reach this region, so limited irrigation water is made available by pumping the groundwater. 2.2. Measurements A 10 m-high tower was erected for mounting flux and meteorological instruments. The EC system was placed 3.7 m above the ground. Wind velocity and virtual temperature fluctuations were measured using a three-dimensional (3D) sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA). An open path infrared gas analyzer (LI7500, LI-COR, Inc., Lincoln, NE, USA) was used to measure the fluctuations in CO2 and H2O density. The sampling frequency of the EC system was 10 Hz. Spectra and co-spectra computations show that these sampling rates are adequate for measuring fluxes with this configuration. Hydrometeorological variables were recorded at 10-min intervals on several data loggers (CR10X, Campbell Scientific, Inc., Logan, UT, USA). Air temperature and humidity (HMP45C, Vaisala Inc., Helsinki, Finland) were measured at 1.6, 3.6 and 6.0 m above ground level. Downward and upward solar and longwave radiation (CNR-1, Kipp & Zonen, Delft, the Netherlands) and photosynthetically active radiation (PAR) (LI190SB, LICOR, Lincoln, NE, USA) were measured at 3.5 m above ground level. A tipping bucket rain gauge (TE525MM, Campbell Scientific Inc., Logan, UT, USA) was mounted at 1.5 m above the ground. Wind speed and direction (05103, Young Co., Traverse City, MI, USA) were measured at 10.0 m above ground level. Soil temperature (Campbell-107, Campbell Scientific Inc., Logan, UT, USA) and soil water content (TRIME-EZ/IT, IMKO, Ettlingen, Germany) were measured at 0.05, 0.10, 0.20, 0.40, 0.80, and 1.6 m below the ground. Soil heat flux (HFP01SC, Hukseflux, the Netherlands) was measured at 0.03 m below the ground. Soil temperature, soil water content, and soil heat flux were determined by averaging two measurement profiles obtained west and east of the tower. Groundwater level was measured using an automatic water depth sensor (CS420-10, Campbell Inc., Logan, UT, USA) installed in a groundwater well close to the tower. Irrigation was estimated from the change in soil water storage for the 160 cm depth within 1 day when each irrigation event occurred. Since the measured saturated hydraulic conductivity at the top soil and deep soil of 100 cm was 132 and 7.7 cm day1, respectively, irrigation water can all be stored in the soil layer of 0–160 cm within 1 day. Leaf area index (LAI) was estimated by directly measuring the leaf area sampled from the crop field using an optical area meter. However, only a few measurements of LAI were available during the study period. Based on the non-linear relationship between the observed LAI and Normalized Differential Vegetation Index (NDVI) data (8-day temporal resolution and 250 m spatial resolution, MODIS/Terra MOD09Q1), consecutive leaf area index values derived from the NDVI data from the grid containing our flux tower were used for the analysis.
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2.3. Data post-processing The half-hourly latent and sensible heat fluxes were calculated by computing the mean covariance of water and temperature fluctuations with the fluctuating vertical velocity observed by the EC system. The TK2 software package (Mauder et al., 2006) was employed for the quality assurance and quality control (QA/QC) of the EC data. This included the coordinate rotation by the planar fit method (Wilczak et al., 2001), spectral loss correction (Moore, 1986), sonic virtual temperature conversion (Mauder et al., 2006), density fluctuations correction (Webb et al., 1980), and spike detection (Vickers and Mahrt, 1997). In the spike detection algorithm, any value that exceeded the mean value 5.5 times the standard deviations in a window of 10 values was labeled as spike. The data gaps were then filled to calculate the daily mean values. Short gaps (2 h) were filled with linear interpolation, and larger data gaps were filled using the mean diurnal average method (Falge et al., 2001). The missing daily average temperature, daily average relative humidity, and daily total precipitation data were spatially interpolated from nearby national meteorological stations (Yang et al., 2004). 2.4. Calculations of bulk parameters To interpret the interannual and seasonal variability in ET, we used two bulk parameters: canopy conductance and the Priestley– Taylor coefficient. The canopy conductance (Gs) was calculated by inverting the Penman–Monteith equation as Gs ¼
g LEg a DðRn GÞ þ rcp VPD LEðD þ g Þ
(1)
where LE is the latent heat flux, Rn the net radiation, G the soil heat flux, r the air density (1.2 kg m3), cp is the specific heat of the air (1004.7 J kg1 K1), D is the slope of relation between saturation vapor pressure and temperature, g is the psychrometric constant, and VPD is the air vapor pressure deficit. The aerodynamic conductance (ga) was approximately estimated by the Monteith– Unsworth equation (Monteith and Unsworth, 1990): ga ¼
u þ 6:2u2=3 u2
1
(2)
where u is the mean wind velocity, and u* is the friction velocity estimated by the EC system. The Priestley–Taylor coefficient (a) is defined as the ratio of ET to the equilibrium evaporation (ETeq) which is calculated as follows (Priestley and Taylor, 1972):
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where D and P are similarity constants and D = 0.28, P = 0.59 for unstable condition (Hsieh et al., 2000), k (=0.4) the von Karman constant, x the horizontal coordinate, L the Obukhov length and zu is a length scale, defined as: zm z0 zu ¼ zm ln 1þ z0 zm
(5)
where zm is the measurement height, and z0 is the zero displacement height. 3. Results and discussion 3.1. Energy balance closure One measure for testing data quality is to test for closure of surface energy balance (Wilson et al., 2002). Using half-hourly data from June 2005 to December 2008, the slope between available energy flux (Rn–G) and the sum of sensible and latent heat fluxes for this site was 0.74, the intercept was 11.87 W m2, and the coefficient of determination (R2) was 0.83. The regression statistics of LE + Hs on Rn–G for half-hourly data are shown in Table 1. For the three-year period, the slope of the regression was less than one, and the intercept was slightly greater than zero, which indicates that the EC measurements underestimated the sensible heat and/ or latent heat fluxes. Many other long-term EC studies have reported the lack of energy balance closure for reasons that are complicated and still not fully understood (Foken, 2008). Thus, it is difficult to evaluate the causes of energy imbalance at this site. Measurement error of the EC system, representation of the measured soil heat flux, horizontal energy advection, energy used in photosynthesis, and energy storage in the canopy and top soil could all contribute to energy imbalance (Foken, 2008; Li and Yu, 2007; Meyers and Hollinger, 2004). For FLUXNET sites, Wilson et al. (2002) reported that the slope values range from 0.53 to 0.99 with a mean of 0.79 0.01, and the intercept values range from 32.9 to 36.9 W m2 with a mean of 3.7 2.0 W m2. For ChinaFLUX sites, Li et al. (2005) reported that the slope values range from 0.49 to 0.81 with a mean of 0.67, and the intercept values range from 10.8 to 79.9 W m2 with a mean of 28.9 W m2. The energy balance closure statistics at our site were reasonable compared with these ranges. Although not perfect, the energy balance closure during the three-year period was similar, indicating the measurements system provides reliable estimates of the surface energy balance components. 3.2. Footprint analysis
D ðR GÞ ET eq ¼ Dþg n
(3)
To avoid the occurrence of numerical instability when the denominator approaches zero, the midday data of Gs and a (10:00– 14:00) were used to calculate the midday average values. Additionally, data in precipitation and low PAR (<25 mmol m2 s1) periods were excluded from the calculations. 2.5. Footprint model Scalar flux footprint estimation is commonly used to quantify the contributing source areas to scalar flux measurement. In this study, an approximate analytical footprint model was used for footprint estimation (Hsieh et al., 2000), giving the footprint (f) as follows: f ðx; zm Þ ¼
1 P 1P 1P P Dz Dz L exp L j j j j u u 2 2 k x2 k x 1
(4)
Two typical periods were selected as the case study to estimate the average footprint for this site (Table 2). All the parameters were estimated using the data in daytime during 10:00–14:00. At the unstable condition, our analysis indicated that approximately 90% of the measured flux was expected to come from within the nearest 420 and 166 m during wheat and maize periods, respectively. Around the flux tower in a radius of 420 m, only a small fraction of ground surface is covered by trees, road and irrigation channel, indicating that the measured fluxes were primarily contributed by the cropland. Table 1 Slope, intercept and R2 of the half-hourly energy balance closure. Year
Slope
Intercept (W m2)
R2
Data number
2006 2007 2008
0.74 0.76 0.75
10.85 8.04 3.82
0.84 0.82 0.86
1173 1630 1348
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Table 2 Typical values (u*: friction velocity; Hs: sensible heat flux; L: Obukhov length) for footprint estimation in unstable condition. Period
Crop
u* (m s1)
Hs (W m2)
Crop height (m)
L (m)
April August
Wheat Maize
0.32 0.25
35.3 45.1
0.5 1.8
83 33
3.3. Hydrometeorological conditions and leaf area index Over the study period, the total precipitation values were 360 (2005–2006), 480 (2006–2007), and 464 mm (2007–2008), which were all significantly below the 553 mm normal level (1990– 2008). Most of the rainfall occurred from June through September when maize was growing. There was relatively little rainfall in March to May when winter wheat was in its period of rapid growth (Fig. 2a). Irrigation totals ranged from 101 to 297 mm in wheat seasons but equaled to zero in maize seasons (Fig. 2e). The mean annual daily mean temperature in the three crop years was 13.5, 13.9, and 13.1 8C, respectively (normal: 13.8 8C during 1990– 2008). Seasonal variation in vapor pressure deficit indicated that
June had the highest evaporative demand, while the growing seasons for both crops had a relatively low atmospheric water demand (Fig. 2c). Soil water content was kept at a higher level during the wheat seasons than that during the maize seasons as a result of irrigation despite the uneven seasonal distribution of precipitation (Fig. 2e). Although soil water content reached a minimum in June or July, it rarely fell below a ‘‘moisture stress threshold’’ (assumed here as 50% of the extractable soil water). Since wheat was harvested before mid-June, the canopy would not experience any moisture stress. Only the summer maize experienced a slight moisture stress in July of 2006 due to low precipitation. Leaf area index was shown to have a clear ‘‘two peak’’ pattern in each crop year corresponding to the crop rotation (Fig. 2d). According to the general crop phenology, LAI increased slightly in November due to the emergence of winter wheat, and then decreased when winter wheat went into dormancy. It increased quickly from early March because of the greening of winter wheat. Wheat reached its peak LAI of about 5–6 m2 m2 in late April or early May (the period from late April to early May is referred to as the ‘‘rapid growth season’’ for wheat), and rapidly senesced by early June. Followed by the planting of summer maize in mid-June,
Fig. 2. Summaries of weekly (a) total precipitation (P), (b) average air temperature (Ta) at 1.6 m, (c) average vapor pressure deficit (VPD) at 1.6 m, (d) average leaf area index (LAI), and (e) volumetric soil water content (SWC, 0–80 cm averaged) from October 2005 to September 2008. Irrigation (I) events were also presented.
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Table 3 Annual and seasonal summaries of total evapotranspiration (ET) and hydrometeorological elements, including total net radiation (Rn), mean air temperature (Ta), mean vapor pressure deficit (VPD), total equilibrium evaporation (ETeq), total precipitation (P), total irrigation (I), mean volumetric soil water content (0–80 cm averaged, SWC), mean groundwater depth (GWL), and the number of days (Nd) with leaf area index greater than 2.5 m2 m2. Crop seasons
Periods
ET (mm)
Rn (MJ m2)
Ta (8C)
VPD (kPa)
ETeq (mm)
P (mm)
I (mm)
P+I (mm)
SWC (m3 m3)
GWL (cm)
Annual (10.1–9.30)
2005–2006 2006–2007 2007–2008a
609 595 554
2285.2 2279.7 2173.0
13.47 13.88 19.07
0.621 0.655 0.69
644 656 614
360 480 459
101 297 237
461 777 696
0.30 0.27 0.27
222 277 263
– – –
Winter wheat (10.1–6.14)
2005–2006 2006–2007 2007–2008a
412 391 320
1285.4 1271.7 1126.7
8.93 9.84 14.65
0.524 0.618 0.689
362 357 304
145 114 135
101 297 237
246 411 372
0.31 0.27 0.28
193 294 233
62 96 64
2006 2007 2008
198 204 234
999.8 1008.0 1046.3
24.49 23.63 23.50
0.853 0.745 0.690
282 299 310
215 366 324
0 0 0
215 366 324
0.27 0.28 0.26
270 242 293
45 68 58
Summer maize (6.15–9.30)
a
Nd (day)
Data during 2007.11.13–2008.02.29 were missing.
LAI started another new cycle, reaching its peak LAI of about 4– 6 m2 m2 in late July or early August (the period from August to early September is referred to as the ‘‘rapid growth season’’ for maize), and senescing by late September. 3.4. Characteristics of the interannual variability in evapotranspiration The seasonal and annual summaries of measured evapotranspiration and other hydrometeorological elements during the three years of our study are shown in Table 3. On an annual basis, the cumulative evapotranspiration values were 609 and 595 mm in the periods of 2005–2006 and 2006–2007, respectively. Growing
season ET in wheat and maize averaged 401 and 212 mm, respectively. On the average, cumulative ET during the wheatgrowing season was higher than the maize ET by 189 mm (89%). Cumulative ET during the wheat-growing seasons was much higher than the cumulative precipitation in the corresponding periods, while it was lower than the total precipitation during the maize-growing seasons. A large amount of irrigation (161% of total precipitation on average) was carried out to meet winter wheatgrowing demands. The total amount of precipitation and irrigation was larger than the total evapotranspiration during the wheat seasons, except the period of 2005-2006 when soil water storage was sufficient (Table 4) because of the high amount of precipitation in the previous year (annual precipitation: 680 mm). ETeq can
Table 4 Monthly summaries of total evapotranspiration (ET), total net radiation (Rn), mean air temperature (Ta), mean vapor pressure deficit (VPD), total equilibrium evaporation (ETeq), total precipitation (P), total irrigation (I), mean volumetric soil water content (0–80 cm averaged, SWC), mean groundwater depth (GWL), and mean leaf area index (LAI) for the periods from October, 2005 to September, 2008. Y
M
Crop season
ET (mm)
Rn (MJ m2)
Ta (8C)
VPD (kPa)
ETeq (mm)
P (mm)
I (mm)
SWC (m3 m3)
GWL (cm)
LAI (m2 m2)
2005
10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
Crop rotation Winter wheat
36.5 25.5 6.7 3.5 21.9 58.1 95.7 120.8 64.8 44.7 78.6 52.7 23.6 28.1 12.9 6.3 21.0 48.2 101.9 129.0 34.7 71.5 59.2 58.6 28.9 – – – – 51.4 83.4 122.3 47.5 67.9 83.0 63.4
136.1 49.8 3.5 25.4 88.0 195.9 264.7 350.8 335.5 288.3 309.2 238.3 117.0 59.6 15.9 30.5 85.1 173.2 288.3 374.8 260.3 327.6 281.0 266.3 102.3 – – – – 206.4 264.6 381.2 309.9 308.4 324.7 249.2
14.06 8.94 1.31 0.88 1.32 8.74 13.96 19.08 25.88 26.89 25.32 19.65 17.68 7.92 0.36 1.39 5.14 7.57 14.04 21.05 24.76 25.68 24.54 19.95 13.81 6.80 1.63 2.63 0.23 9.20 14.41 20.06 23.63 25.62 24.23 19.77
0.56 0.47 0.28 0.18 0.30 0.63 0.58 0.71 1.60 0.87 0.62 0.66 0.77 0.47 0.19 0.28 0.43 0.43 0.74 1.17 1.36 0.85 0.51 0.67 0.48 0.38 0.20 0.17 0.31 0.61 0.68 0.90 1.14 0.65 0.54 0.61
47.1 19.9 9.9 8.7 18.5 43.5 67.1 97.6 92.3 79.4 89.4 71.0 38.1 22.9 11.5 12.6 18.8 38.7 72.6 106.9 71.1 94.8 85.1 83.1 33.3 – – – – 45.6 66.9 107.1 87.2 89.7 97.6 77.0
22 6 1 0 5 0 11 78 43 59 118 18 1 16 3 0 7 51 20 15 66 149 80 72 22 2 5 0 0 6 45 40 46 188 41 69
0 0 0 0 0 101 0 0 0 0 0 0 0 0 0 0 0 0 142 155 0 0 0 0 0 0 0 0 0 130 107 0 0 0 0 0
0.35 0.33 0.32 0.32 0.31 0.32 0.30 0.29 0.26 0.25 0.29 0.28 0.28 0.27 0.27 0.27 0.27 0.28 0.28 0.29 0.25 0.28 0.29 0.26 0.29 – – – – 0.29 0.30 0.28 0.24 0.28 0.27 0.26
92 151 201 220 229 161 196 249 311 321 255 276 258 287 309 317 321 279 253 208 312 281 218 285 250 – – – – 200 183 244 318 274 264 322
1.3 0.9 1.0 0.7 0.8 1.7 4.1 3.7 1.2 2.2 3.3 1.7 1.0 2.2 2.3 1.4 1.8 3.0 4.6 3.5 1.4 2.8 4.9 3.1 0.9 0.9 1.0 0.7 0.7 1.7 4.2 4.1 1.4 2.4 4.3 2.4
2006
2007
2008
Crop rotation Summer maize
Crop rotation Winter wheat
Crop rotation Summer maize
Crop rotation Winter wheat
Crop rotation Summer maize
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Fig. 3. Seasonal variations in daily total evapotranspiration (ET) and daily total equilibrium evaporation (ETeq) for the years 2005–2008.
The seasonal variations in daily evapotranspiration and daily equilibrium evaporation for three consecutive years are shown in Fig. 3. After the planting of winter wheat in mid-October, ET showed a gradual increase from about 1.0 to 2.5 mm day1 because of the canopy emergence in November. During winter from December to January, ET was very low because of both low equilibrium evaporation and the dormancy of winter wheat. Afterward, ET varied closely with ETeq until late May when winter wheat started to senesce. The ET in June was around 1.0 mm day1, corresponding to the crop harvest, despite the high evaporative demand (Fig. 2c). Summer maize grew rapidly from late June, leading to a rapid increase in ET until August. There was a rapid decrease in ET from late August, following both the senescence of summer maize and decrease in ETeq. For both crops, wheat
generally reached a peak ET value between 6.6 and 7.8 mm day1, while maize reached a peak value between 4.0 and 5.1 mm day1. The peak ET for maize was much lower than that of wheat even though ETeq reached a similar magnitude (around 5 mm day1) when the peak values in ET were reached for both crops. This can be explained by the more efficient water use of the C4 maize (Tong et al., 2009) as compared to the C3 wheat. On a daily basis, equilibrium evaporation explained 78% of the seasonal variability in daily ET during the wheat seasons, but explained 54% of the seasonal variability in daily ET during the maize seasons (Table 4). Canopy conductance was analyzed to explore the biological control of the seasonal variations in ET (Baldocchi et al., 2004). The seasonal variation in midday Gs is given in Fig. 4. It was greatly reduced in the periods when the crops were rotated. Midday Gs ranged from near 1 mm s1 when winter wheat was dormant up to 21–32 mm s1 in April for winter wheat and up to 13–17 mm s1 in August for summer maize. The maximum Gs values for both wheat and maize were substantially higher than that for a meadow in the Tibetan Plateau (11 mm s1, Gu et al., 2005), which was possibly due to the ample water and high leaf nitrogen (Schulze et al., 1994) at our site. The average canopy conductance in the winter wheat season from March to May was around two times greater than that in the maize season from July to September, which is in agreement with the results presented by Baldocchi (1994) that Gs in a closed wheat canopy is typically two to five times greater than the values from a sparse corn canopy. To eliminate the effect of weather conditions on ET, the midday ET (10:00–14:00) values were normalized with midday equilibrium evaporation (the normalized ET is also referred to as the Priestley–Taylor coefficient a). In Fig. 5, the data for both crops suggested that ET is greatly influenced by canopy conductance
Fig. 4. Seasonal variations in midday (10:00–14:00) averages of canopy surface conductance for the years 2005–2008.
Fig. 5. Relationship between the midday Priestley–Taylor coefficient (a) and midday canopy conductance (Gs).
explain 98% and 79% of the variability in seasonal ET totals of wheat and maize, respectively (Table 3). Seasonal ET totals were found to have a non-significant relationship with the total amount of precipitation and irrigation, as well as the number of days in each season when the LAI was greater than 2.5 m2 m2 (Table 3). Compared with other ecosystems, these annual ET values were larger than those measured in natural forest (493 mm, Wu et al., 2007) and steppe ecosystems (345 mm, Hao et al., 2007). Seasonal ET total during the 2008 maize season was comparable to the observed ET total of 253 mm in 2008 at a rainfed maize field (184 km northwest of our site) in the region (Y. Shen, personal communication, 2009). However, it tended to be a little lower than that reported from other agricultural crops near similar latitudes in North America (Burba and Verma, 2005; Suyker and Verma, 2008). 3.5. Seasonal variations in evapotranspiration and its control factors
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stress in the maize season in July of 2006 (ESW less than 0.5) (see Fig. 2e), the a values were reduced by soil moisture. Since the ESW values were rarely less than 0.5, we concluded that soil water availability exerted a very limited control on ET in the maize season during the study period. 3.6. Characteristics of energy partitioning
Fig. 6. Relationship between the midday Priestley–Taylor coefficient (a) and extractable soil water (ESW). Dots denote the weekly average values.
when the Gs values are below approximately 15–20 mm s1. Because Gs of maize was much lower than that of wheat, the control of canopy conductance on ET during the maize seasons was stronger than that during the wheat seasons. This explained why equilibrium evaporation explained a lower variability in ET during the maize seasons than during the wheat seasons. The occurrence of threshold Gs is consistent with the observations of Suyker and Verma (2008) in maize. A theoretical study on a fully developed canopy also indicates that a becomes insensitive to canopy conductance when it exceeds about 16 mm s1 (McNaughton and Spriggs, 1986). The effect of moisture stress on ET was further examined via the relationship between normalized ET and the extractable soil water (ESW) (Fig. 6). No significant relationship was found between ESW and a (R2 < 0.15). However, during a short period of soil moisture
Seasonal variations in the daily total sensible heat flux (Hs) during the three years are examined in Fig. 7. The peak values of daily Hs (between 7 and 8 MJ m2 day1) occurred during June when the two crops were rotated. However, another rotation period in mid-October met a much lower Hs because of the lower net radiation (see Table 4). The sensible heat flux decreased rapidly following crop emergence each year. In the period of rapid growth for winter wheat, negative daily total sensible heat fluxes were observed, reaching up to 5 MJ m2 day1. However, Hs rarely reached negative values during the rapid growing season for summer maize. The ratios of midday fluxes to midday net radiation (LE/Rn, Hs/ Rn) are presented in Fig. 8a to examine the seasonal variations in energy partitioning related to canopy phenology. There were three peaks of LE/Rn in each crop year, occurring in November and April/ May, for the emergence and the full development of winter wheat, and in August for the full development of summer maize. For winter wheat, the values of LE/Rn typically ranged from 0.05 in the dormant season to 0.8–0.9 after the canopy was fully developed. Following canopy senescence, the LE/Rn values rapidly decreased
Fig. 7. Seasonal variations in daily total sensible heat flux (Hs) during the years 2005–2008.
Fig. 8. Seasonal variations in (a) midday energy partitioning terms (LE/Rn, Hs/Rn) and (b) the midday Bowen ratio during the years 2005–2008. Rn is the net radiation, LE is the latent heat flux, and Hs is the sensible heat flux. The leaf area index (LAI) is also included.
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to about 0.05–0.12. For summer maize, the maximum values of LE/ Rn were up to about 0.65–0.8, which was smaller than that for wheat. The values of Hs/Rn presented an opposite trend with canopy development, reaching minimum values of 0.1 for wheat and about 0.05 for maize. The Bowen ratio (b) showed similar opposite trends with the canopy phenology (Fig. 8b). The day-today variability in b was large in the two rotation periods as well as during the dormancy period in the winter wheat season, ranging from about 0.5 to 15. The b values decreased to relatively steady levels when both crops were fully developed, with the average value of 0.13 (averaged for April and May) for wheat and 0.43 (averaged for August and September) for maize. However, b was negative, falling to about 0.1 in the rapid growing days during winter wheat seasons, because of the negative sensible heat flux. On an annual scale, the latent heat flux was the largest consumer of net radiation (59%), which was comparable with a maize-soybean agroecosystem (Suyker and Verma, 2008), but higher than a Tibetan meadow ecosystem (24%, Gu et al., 2005) and a temperatemixed forest ecosystem (52%, Wu et al., 2007). During the rapid growing season of winter wheat from March to May, the average midday LE/Rn was about 83%, while it was about 57% during the rapid growing season of summer maize from July to September, indicating that wheat is more water-consuming than maize. 3.7. Impact of irrigation on energy fluxes and evapotranspiration The negative daily sensible heat flux was observed during the winter wheat seasons. This can be explained by sensible heat advection (Prueger et al., 1996), which would indicate that Hs was a source of energy for evapotranspiration during that period. Since there was sufficient irrigation in the Irrigation District, the occurrence of sensible heat advection during April and May most probably resulted from the drier soil regimes in the north upwind areas (Fig. 1). The northern cotton fields had a much drier land surface as they were irrigated by a limited pumping of groundwater. An estimated spatial distribution of the latent heat flux in this region has demonstrated that the latent heat flux at the north part of the Weishan Irrigation District was much lower than that within the Irrigation District (Yi, 2008). A similar phenomenon was also reported for a nearby winter wheat site (Li and Yu, 2007). Previous studies have shown that change of actual evaporation is dominated by change in precipitation, rather than potential evaporation in natural non-humid regions (including the North China Plain), but is controlled by change in potential evaporation rather than precipitation in humid regions (Yang et al., 2006, 2007). However, in this study, the variability in evapotranspiration was largely explained by equilibrium evaporation on a seasonal and a daily basis, implying that regional irrigation has shifted the pattern of annual water-energy balance from that in non-humid region to that in humid region. The sensible advection caused by regional irrigation enhanced the evapotranspiration and affected the local energy budget. This can be evaluated by the relationship between ET and ETeq (Table 4). Monthly ET totals in excess of the ETeq totals ranged from 2 to 29 mm from February to May during the wheat seasons, accounting for 27% of the equilibrium evaporation. 4. Conclusions Using the EC technique, evapotranspiration and energy fluxes were measured over a typical irrigated wheat/maize rotation cropping field in the North China Plain for a three-year period. Although annual precipitation was significantly below the normal, the soil water content rarely fell below the threshold for moisture stress because of the sufficient irrigation during the wheat seasons and sufficient precipitation during the maize seasons. The annual
ET ranged from 595 to 609 mm, and the ET total for irrigated wheat season was 89% higher than that for rainfed maize season on average. The interannual variability in the seasonal ET totals could be primarily explained by the variations in equilibrium evaporation, and the seasonal variability in daily ET could be largely explained by the variations in equilibrium evaporation and canopy conductance below a threshold of 15 mm s1. Soil water availability exerted only a very limited effect on ET. The average midday value of LE/Rn was 83% during the rapid growing season of wheat and about 57% during the rapid growing season of maize. On an annual basis, the latent heat flux consumed about 59% of the net radiation, showing a relatively higher proportion than that in natural ecosystems. Regional irrigation not only shifted the pattern of annual water-energy balance from that in non-humid region to that in humid region, but also caused sensible heat advection from the surrounding drier surface during the wheat seasons. The evapotranspiration, enhanced by sensible heat advection, accounted for 27% of the equilibrium evaporation during the rapid growing season of wheat. Acknowledgements This research was supported by the National 973 Project of China (Project No. 2006CB403405), the National Natural Science Foundation of China (Project No. 50939004), and the Doctoral Program Foundation of Institutions of Higher Education of China (Project No. 2007000307). We thank Dr. Matthias Mauder at University of Bayreuth for the support for the TK2 software package. We would like to express our appreciation to one anonymous reviewer, whose comments and suggestions led to significant improvements in the manuscript. References Baldocchi, D.D., 1994. A comparative study of mass and energy exchange over a closed C3 (wheat) and an open C4 (corn) canopy: I. The partitioning of available energy into latent and sensible heat exchange. Agric. For. Meteorol. 67, 191– 220. Baldocchi, D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol. 9, 479–492. Baldocchi, D.D., Falge, E., Gu, L., et al., 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434. Baldocchi, D.D., Xu, L., Kiang, N., 2004. How plant functional-type, weather, seasonal drought, and soil physical properties alter water and energy fluxes of an oakgrass savanna and an annual grassland. Agric. For. Meteorol. 123, 13–39. Burba, G.G., Verma, S.B., 2005. Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems. Agric. For. Meteorol. 135, 190–201. Cai, X., 2008. Water stress, water transfer and social equity in Northern ChinaImplications for policy reforms. J. Environ. Manage. 87, 14–25. Falge, E., Baldocchi, D.D., Olson, R., Anthoni, P., et al., 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agric. For. Meteorol. 107, 43–69. Foken, T., 2008. The energy balance closure problem: an overview. Ecol. Appl. 18, 1351–1367. Gu, S., Tang, Y., Cui, X., et al., 2005. Energy exchange between the atmosphere and a meadow ecosystem on the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 129, 175–185. Haddeland, I., Lettenmaier, D.P., Skaugen, T., 2006. Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins. J. Hydrol. 324, 210–223. Hao, Y., Wang, Y., Huang, X., et al., 2007. Seasonal and interannual variation in water vapor and energy exchange over a typical steppe in Inner Mongolia, China. Agric. For. Meteorol. 146, 57–69. Hsieh, C., Katul, G., Chi, T., 2000. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Adv. Water Resour. 23, 765–772. Kendy, E., Ge´rard-Marchant, P., Walter, M.T., et al., 2003. A soil–water-balance approach to quantify groundwater recharge from irrigated cropland in the North China Plain. Hydrol. Process. 17, 2011–2031. Li, L., Yu, Q., 2007. Quantifying the effects of advection on canopy energy budgets and water use efficiency in an irrigated wheat field in the North China Plain. Agric. Water Manage. 89, 116–122.
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