Abstract

The hydrological process of evapotranspiration (ET) plays an important role in water circulation in wetlands, and understanding the contributions of wetland ET to local and regional water cycles can help in designing effective wetland management strategies. In this paper, a numerical model, vegetation indexes, and stable isotopes were integrated to partition ET in the Momoge Wetland to understand hydrological processes and calculate the contribution of wetland ET to local hydrological cycling. The results of the non-steady state (NSS) model indicated clear deviation of leaf water enrichment (δLb) from an isotopic steady state (ISS) for Phragmites australis, and the model accuracy improved particularly in the early morning and evening when air moisture was highest during the day. The isotopic mass balance showed that E and T contributed approximately 62% and 38% to ET, respectively. Using the estimated proportion of T to ET, in combination for the measured leaf transpiration, total ET was estimated at approximately 8.76 mm d−1. Additionally, the amount of ET clearly changed on an hourly scale, with most primarily occurring at approximately noon. Based on comparison among internationally important wetlands distributed in northeast China, the results in this study are reasonable and will provide theoretical data for wetland water resources management.

INTRODUCTION

Wetlands are important ecosystems that provide many valuable ecological services, including habitat for species, flood peak attenuation, water purification and climate change mitigation, among others (Woodward & Wui 2001). Hydrological properties are the driving factors of a wetland ecosystem (Mitsch & Gosselink 2007), and particularly, evapotranspiration (ET) plays an important role in the ecosystem water budget and energy balance (Hu et al. 2009) and affects the composition, species diversity and succession of wetland vegetation communities. In wetlands, ET causes major losses of water from the open water surface by evaporation and from the transpiration of emergent plants, which influence regional water cycling (Pauliukonis & Schneider 2001; Chen et al. 2002); therefore, accurate estimates of ET rates for wetland management and water resource assessment are essential. In the past, accurate water balance estimates could not be obtained easily because ET information from vegetation was lacking (Fermor et al. 2001). As a result, the effectiveness of many wetland management practices was low (Peacock & Hess 2004). Currently, worldwide, much research is focused on the contribution of Phragmites australis to ET, including in Australia (Headley et al. 2012), America (Drexler et al. 2008), Spain (Sánchez-Carrillo et al. 2004), Italy (Borin et al. 2011), England (Fermor et al. 2001; Peacock & Hess 2004), Germany (Herbst & Kappen 1999), Canada (Lafleur 1990) and China (Zhou & Zhou 2009; Yao et al. 2010; Xu et al. 2011). Multiple technologies have been used and include the Bowen Ratio Energy Budget (BREB) method (Peacock & Hess 2004), measurements of sap flow (Moro et al. 2004), the eddy covariance (EC) method (Zhou & Zhou 2009), remote sensing (Yao et al. 2010) and modelling (Herbst & Kappen 1999). Nevertheless, little research has focused on the hourly partitioning of ET using a stable isotope method in a P. australis marsh wetland.

Motivated by this gap in research, the daily consumptive use of water by P. australis and the partitioning of ET in the Momoge National Nature Reserve, a semiarid marsh wetland in northeastern China, were determined using a stable isotope method. The objectives in the current study were to (1) investigate the isotopic characteristics of the water pools and fluxes at the site, (2) determine the relative importance of different components to ET and the daily water use by P. australis and (3) estimate the daily ET distribution on an hourly scale.

MATERIALS AND METHODS

Site and study description

The Momoge National Nature Reserve (45.9359° N, 123.6839° E) is located in the northern Songliao Plain and at the western edge of the Songnen Plain in the northwest of Jilin Province, China. The total area is approximately 1,440 km2, of which 90% is wetland. The climate is semiarid with an average annual rainfall of 392 mm. The annual average temperature is approximately 4.2°C. The summers are hot and rainy, and the winters are extremely cold. Eighty per cent of the reserve is composed of the inland saline wetlands that are typical of this region of China (Figure 1) (Ming et al. 2007). The sampling site was dominated by a dense stand of P. australis of approximately 10 ha (50 m width and 2,000 m length), within which all vegetation, leaf area index (LAI) and water depth were measured in a vegetation survey to determine species composition, plant height and plant density.

Figure 1

The location of the Momoge Natural Reserve and the experimental site.

Figure 1

The location of the Momoge Natural Reserve and the experimental site.

In this study, many biological and meteorological factors were measured, and the parameters of P. australis included the leaf water content (W), specific leaf area (SLA), LAI and leaf-level gas exchange properties. The meteorological factors included water level fluctuation, in addition to air temperature and relative humidity and water surface temperature, which were monitored using an HOBO series temperature and humidity recorder (ProV2, America) and water thermometer, respectively.

The SLA was calculated from the dry weights of 10 pieces of 0.9 cm2 punched from leaves during the day of sampling. The W was determined from the difference between the fresh and the dry weights of the leaves from five plants and the measured mean SLA of foliage. The LAI was determined by counting the number of individual plants within three replicate 1 × 1 m plots, sampling 20 individual plants, determining the dry weight and then calculating the leaf areas using dry weights and SLAs. Leaf-level gas exchange properties, including stomatal conductance, transpiration and leaf temperature, were measured using a LI-6400 analyser (Li-Cor Inc., Lincoln, NE, USA) on three leaves of individual plants. Table 1 shows the results.

Table 1

Primary biotic variables and water depth during the sampling period

Leaf water content (mol/m2LAI Plant height (cm) Plant density (number/m2Vegetation coverage (%) Water depth (cm) SLA (cm/g) 
5.4 3.03 203 141 65 85 135.2 
Leaf water content (mol/m2LAI Plant height (cm) Plant density (number/m2Vegetation coverage (%) Water depth (cm) SLA (cm/g) 
5.4 3.03 203 141 65 85 135.2 

Sample collection and measurement

In the wetland ecosystem, samples of different water pools were collected five times on two consecutive days, 20th and 21st June 2013, which included surface (0–5 cm) standing water, stem water below the water level and leaf water for P. australis, and atmospheric water vapour at three heights (20, 150 and 210 cm) above standing water. Additionally, the atmospheric background water vapour was collected at the height of 4 m above the ground with no vegetation approximately 100 m from the sampling site.

Standing water samples were collected directly using 2-mL glass vials 0–5 cm below the water level. Stems of P. australis were sampled near the roots and below the surface of the standing water. For the collection of leaf samples, the upper and lower leaves of the plants were mixed. Standing water, stem water and leaf water samples were all collected in triplicate, and all samples were sealed immediately with Parafilm in 2-mL glass vials and then frozen below 4°C in the laboratory.

Based on the methods of Helliker et al. (2002), atmospheric water vapour was cryogenically captured and analysed for δ18O. Water vapour was collected at three levels above the standing water through a low absorption plastic tube at a flow rate of 0.5 L min–1 for 30 min and condensed in a 15-cm-long glass tube placed in a −80°C ethanol/liquid nitrogen bath. The melted water was poured into 2-ml glass vials, which were sealed with Parafilm and stored in a refrigerator until analysis. Each sample was approximately 0.5–1 mL, which is sufficient for δ18O analysis.

Stem and leaf water samples were extracted in the laboratory using a cryogenic vacuum distillation apparatus (Ehleringer et al. 2000). Then, all samples, including water vapour samples, were analysed for δ18O composition using a Liquid Water Isotope Analyser (Model DLT-100; Los Gatos Research Inc., USA). The precision for δ18O and δD analyses was better than 0.3‰ and 2‰, respectively (Sturm & Knohl 2010).

Calculation of T/ET

In this study, we used the isotopic mass balance of water vapour to determine the contributions of standing water evaporation (E) and transpiration from P. australis (T) to total ET fluxes in the marsh canopy. Assuming horizontal advection of water vapour was negligible and E and T were the primary sources of vapour in the marsh, the mass balance equations were developed as follows (Lai et al. 2006; Bijoor et al. 2011):  
formula
(1)
 
formula
(2)
where f is defined as the fractional contribution, δ represents the isotopic composition, and the subscripts c, b, E and T represent canopy vapour, background air, standing water evaporation and plant transpiration, respectively. When the isotopic composition of the ‘end members’ or components (E, T and mixed background vapour) of total canopy vapour are known from measurements or modelling methods, the fractional contributions of each source can be solved by the equations (Phillips & Gregg 2003), and the frequency and range of potential source contributions can be determined. The δ18O composition of end member δc was the water vapour measured at 210 cm above the standing water. The mixed isotopic composition of background air (δb) was assumed to be equivalent to the measured δ18O values of water vapour at the height of 4 m above the ground without vegetation approximately 100 m from the sampling site. The end member δE was calculated using the following model (Craig & Gordon 1965):  
formula
(3)
where δe is the isotopic composition of liquid water at the surface, δv is the isotopic composition of the atmospheric vapour above the water surface, αeq is the temperature-dependent equilibrium fractionation factor, and ɛeq is the equilibrium isotopic enrichment defined as ɛeq = αeq − 1. For molecular diffusion in air, the kinetic fractionation factor is ɛk, and in this study, the kinetic fractionation factors were approximately 21‰ for oxygen and 11‰ for hydrogen (Bijoor et al. 2011), which included the effects of a turbulent boundary layer (Cappa et al. 2003). The h is relative humidity, which was normalized to the surface temperature of the standing water.
Generally, δT is calculated by assuming the attainment of isotopic steady state (ISS), which indicates that the δT equals that of the xylem water (δs) (Flanagan & Ehleringer 1991). However, plant transpiration is not always in an ISS at time scales of several hours, particularly at night-time (Farquhar & Cernusak 2005; Welp et al. 2008), which could translate into an error in partitioning of ET under the assumption of ISS (Yepez et al. 2005). Thus, the δT from modelled values of leaf water enrichment was calculated at the evaporating sites under non-steady state (NSS) conditions (ΔLe) (Farquhar & Cernusak 2005):  
formula
(4)
where wi is defined as the water vapour mixing ratio at the temperature of the water at the sites of evaporation (mol mol−1), g is the leaf (stomata and boundary layer) conductance (mol m−2s−1), αk is the kinetic fractionation factor for molecular diffusion in air, W is the water storage in the leaf lamina (mol m−2 leaf), and t is the time in seconds. ΔLe is defined as ΔLe = (δLeδs)/(1 + δs/1,000) (in ‰), where δLe is the isotopic composition of leaf water at the evaporating sites in δ notation. P is the Péclet number, which describes the ratio of advection to diffusion of enrichment within the leaf. The leaf water enrichment, ΔLes, at the evaporating sites under steady state conditions was calculated as follows (Cernusak et al. 2002):  
formula
(5)
where Δv = (δvδs)/(1 + δs/1,000) (in ‰) and is the isotopic enrichment of water vapour relative to that of the source (stem) water and h is the relative humidity normalized to leaf temperature. The isotopic composition of transpiration relative to the source water (ΔT) was calculated as follows (Farquhar & Cernusak 2005):  
formula
(6)
where ΔT = (δTδs)/(1 + δs/1,000) (in ‰). Using the estimated fractional contributions of transpiration and measured leaf-level transpiration flux, the total ET flux was calculated as follows:  
formula
(7)
In this estimation, we assumed that each sampling represented the mean of approximately 3 hours.

RESULTS AND DISCUSSION

Isotopic characters

The scatter plot of δD versus δ18O is shown in Figure 2, and the correlation between δD and δ18O was described by δD = 3.53 δ18O−53.03 (r2 = 0.88, n = 70) for the water pools and fluxes in the marsh wetland ecosystem. The data for the liquid water (standing water, stem water and leaf water) were all below the local meteoric water line (LMWL), and the data of vapour were all above the LMWL. These results suggested that the kinetic fractionation of H18O was higher than that of HDO (hydrogen + deuterium + oxygen) and that strong evaporation occurred at the study site because of the dry weather conditions, as indicated by the low slope of the scatter plot. The results for δ18O stable isotope were focused in this study to avoid redundancy. The primary biotic and environmental variables for the two sampling days are shown in Table 2, and the temporal variations of δ18O values of the water pools and fluxes are presented in Figure 3. The δ18O values of the surface standing water were nearly constant, with an arithmetic average (±standard deviation) of −4.5 ± 0.73‰. The stem water δ18O values (−5.50 ± 0.61‰) of P. australis were similar to those of the standing water. However, the leaf water showed a clear isotopic enrichment relative to the source (stem) water (Figure 3).

Figure 2

δ18O–δD relationships for the water pools and fluxes in the wetland ecosystem during the sampling period. The global and local meteoric water line (GMWL) are also shown (grey and black dashed lines, respectively).

Figure 2

δ18O–δD relationships for the water pools and fluxes in the wetland ecosystem during the sampling period. The global and local meteoric water line (GMWL) are also shown (grey and black dashed lines, respectively).

Table 2

Primary biotic and environmental variables at different times during the study

Time W (mol m−2g (mol m−2s−1wi (mmol mol−1Tr (mmol m−2s−1Tw (°C) (160 cm) Ta (°C) (160 cm) RH (%) (160 cm) 
20 Jun 0710 5.9 0.005 27.9 0.13 23.7 22.9 68.1 
20 Jun 1000 5.5 0.118 33.1 5.70 24.6 28.2 50.7 
20 Jun 1230 6.0 0.111 33.3 5.33 26.1 31.6 39.7 
20 Jun 1530 5.4 0.081 30.9 3.77 27.3 32.1 43.8 
20 Jun 1830 5.9 0.056 18.8 1.58 26.5 25.4 53.5 
21 Jun 0700 5.7 0.053 30.2 1.67 23.5 22.8 76.6 
21 Jun 0930 3.6 0.054 38.5 2.46 24.4 28.5 54.2 
21 Jun 1240 5.6 0.060 39.0 2.70 26.4 31.3 40.8 
21 Jun 1530 5.1 0.045 38.9 2.04 27.7 32.0 37.4 
21 Jun 1830 5.3 0.027 23.1 0.68 27.3 27.5 41.9 
Time W (mol m−2g (mol m−2s−1wi (mmol mol−1Tr (mmol m−2s−1Tw (°C) (160 cm) Ta (°C) (160 cm) RH (%) (160 cm) 
20 Jun 0710 5.9 0.005 27.9 0.13 23.7 22.9 68.1 
20 Jun 1000 5.5 0.118 33.1 5.70 24.6 28.2 50.7 
20 Jun 1230 6.0 0.111 33.3 5.33 26.1 31.6 39.7 
20 Jun 1530 5.4 0.081 30.9 3.77 27.3 32.1 43.8 
20 Jun 1830 5.9 0.056 18.8 1.58 26.5 25.4 53.5 
21 Jun 0700 5.7 0.053 30.2 1.67 23.5 22.8 76.6 
21 Jun 0930 3.6 0.054 38.5 2.46 24.4 28.5 54.2 
21 Jun 1240 5.6 0.060 39.0 2.70 26.4 31.3 40.8 
21 Jun 1530 5.1 0.045 38.9 2.04 27.7 32.0 37.4 
21 Jun 1830 5.3 0.027 23.1 0.68 27.3 27.5 41.9 
Figure 3

Temporal variations of δ18O isotopic composition of water pools and fluxes during the study period.

Figure 3

Temporal variations of δ18O isotopic composition of water pools and fluxes during the study period.

Daily ET partitioning

Using the measured or modelled isotopic end members (i.e., δc, δb, δE and δT; Table 2) and the measured leaf-level transpiration, T/ET was estimated based on the mass balance equations (Table 3). To evaluate the results of ET partitioning, two different methods were used to estimate the ratio of transpiration to ET: (1) based on the steady state estimates of δT and the P&G method (ISS method) and (2) based on the NSS estimates δT and the P&G method (NSS method). Using the NSS method, T/ET ranged from 0.19 ± 0.12 to 0.67 ± 0.39 with a mean of 0.38 ± 0.18 (Table 4). The ISS and NSS methods produced very similar results, and the T/ET estimate from the NSS method was approximately 0.05-fold higher than that from the ISS method. Using the estimated T/ET from the NSS method, the average estimated total water loss from E, T and ET was 6.29, 2.46 and 8.76 mm d−1, respectively (Figure 4).

Table 3

δ18o isotopic composition of end members used for distinguishing between evaporation (E) and transpiration (T) in canopy vapour at different times during the study

Time δc δb δE δT δx 
20 Jun 0710 −24.00 −27.41 0.05 −12.90 −6.48 
20 Jun 1000 −26.82 −27.41 −7.45 −6.83 −5.22 
20 Jun 1230 −24.63 −27.41 −22.40 −6.58 −5.78 
20 Jun 1530 −23.68 −27.41 −20.19 −5.23 −4.83 
20 Jun 1830 −24.30 −27.41 −26.49 −7.13 −5.97 
21 Jun 0700 −22.13 −27.41 −9.99 −3.81 −5.36 
21 Jun 0930 −24.05 −27.41 −0.68 −8.72 −6.18 
21 Jun 1240 −22.86 −27.41 −19.76 −7.46 −5.28 
21 Jun 1530 −22.76 −27.41 −21.52 −3.51 −4.49 
21 Jun 1830 −23.82 −27.41 −24.90 −20.00 −5.44 
Time δc δb δE δT δx 
20 Jun 0710 −24.00 −27.41 0.05 −12.90 −6.48 
20 Jun 1000 −26.82 −27.41 −7.45 −6.83 −5.22 
20 Jun 1230 −24.63 −27.41 −22.40 −6.58 −5.78 
20 Jun 1530 −23.68 −27.41 −20.19 −5.23 −4.83 
20 Jun 1830 −24.30 −27.41 −26.49 −7.13 −5.97 
21 Jun 0700 −22.13 −27.41 −9.99 −3.81 −5.36 
21 Jun 0930 −24.05 −27.41 −0.68 −8.72 −6.18 
21 Jun 1240 −22.86 −27.41 −19.76 −7.46 −5.28 
21 Jun 1530 −22.76 −27.41 −21.52 −3.51 −4.49 
21 Jun 1830 −23.82 −27.41 −24.90 −20.00 −5.44 

Note: The end members were canopy vapour collected at a height of 150 cm above the marsh water surface (δc), atmospheric background (δb), E from standing water (δE), and T from Phragmites (δT). δT was calculated from the transpiration δ18O values of Phragmites weighted using the measured leaf-level transpiration, LAI and vegetation coverage (Vc). The stem water δ18O values for Phragmites (δx) are also shown.

Table 4

Estimated E/ET and Tph/ET fractions using two different methods during the sampling periods: (1) ISS method: from the steady state δT estimates and the E/ET and Tph/ET fractions determined directly by the iterative calculation method proposed by Phillips & Gregg (2003) (herein P&G); (2) NSS method: from the non-steady state δT estimates and the P&G method (NSS)

Time ISS
 
NSS
 
E/ET T/ET E/ET T/ET 
20 Jun 0700 0.41 ± 0.27 0.59 ± 0.35 0.33 ± 0.21 0.67 ± 0.39 
20 Jun 1000 0.56 ± 0.41 0.44 ± 0.36 0.50 ± 0.41 0.50 ± 0.41 
20 Jun 1230 0.82 ± 0.49 0.18 ± 0.11 0.81 ± 0.49 0.19 ± 0.12 
20 Jun 1530 0.76 ± 0.45 0.24 ± 0.14 0.76 ± 0.45 0.24 ± 0.15 
20 Jun 1830 0.78 ± 0.45 0.22 ± 0.02 0.77 ± 0.45 0.23 ± 0.03 
21 Jun 0700 0.56 ± 0.34 0.44 ± 0.27 0.58 ± 0.35 0.42 ± 0.25 
21 Jun 1000 0.44 ± 0.28 0.56 ± 0.35 0.40 ± 0.25 0.60 ± 0.37 
21 Jun 1230 0.75 ± 0.44 0.25 ± 0.15 0.73 ± 0.43 0.27 ± 0.16 
21 Jun 1530 0.80 ± 0.48 0.20 ± 0.12 0.81 ± 0.48 0.19 ± 0.12 
21 Jun 1830 0.81 ± 0.48 0.19 ± 0.06 0.53 ± 0.31 0.47 ± 0.11 
Average 0.67 ± 0.16 0.33 ± 0.16 0.62 ± 0.18 0.38 ± 0.18 
Time ISS
 
NSS
 
E/ET T/ET E/ET T/ET 
20 Jun 0700 0.41 ± 0.27 0.59 ± 0.35 0.33 ± 0.21 0.67 ± 0.39 
20 Jun 1000 0.56 ± 0.41 0.44 ± 0.36 0.50 ± 0.41 0.50 ± 0.41 
20 Jun 1230 0.82 ± 0.49 0.18 ± 0.11 0.81 ± 0.49 0.19 ± 0.12 
20 Jun 1530 0.76 ± 0.45 0.24 ± 0.14 0.76 ± 0.45 0.24 ± 0.15 
20 Jun 1830 0.78 ± 0.45 0.22 ± 0.02 0.77 ± 0.45 0.23 ± 0.03 
21 Jun 0700 0.56 ± 0.34 0.44 ± 0.27 0.58 ± 0.35 0.42 ± 0.25 
21 Jun 1000 0.44 ± 0.28 0.56 ± 0.35 0.40 ± 0.25 0.60 ± 0.37 
21 Jun 1230 0.75 ± 0.44 0.25 ± 0.15 0.73 ± 0.43 0.27 ± 0.16 
21 Jun 1530 0.80 ± 0.48 0.20 ± 0.12 0.81 ± 0.48 0.19 ± 0.12 
21 Jun 1830 0.81 ± 0.48 0.19 ± 0.06 0.53 ± 0.31 0.47 ± 0.11 
Average 0.67 ± 0.16 0.33 ± 0.16 0.62 ± 0.18 0.38 ± 0.18 
Figure 4

Water loss from ET and transpiration using the estimated T/ET during the study periods (the curves show the changes in ET, T and E with time, respectively).

Figure 4

Water loss from ET and transpiration using the estimated T/ET during the study periods (the curves show the changes in ET, T and E with time, respectively).

Hourly ET distribution

The hourly ET variation was expressed as a single-peak curve that was relatively low in the morning and evening and high at approximately noon (Figure 4). With the increase in net radiation, air temperature and water surface temperature, the ET amount dramatically increased from 0.41 mm in the morning to 3.75 mm at noon, an increase of approximately nine-fold. The T/ET ratio change appeared as an ‘N’ line, with the peak value of approximately 0.53 before noon, the second highest value of approximately 0.43 in the morning, and the minimum of approximately 0.21 at noon. The phenomenon indicated that the transpiration of P. australis increased with the increase of environmental factors (such as net radiation, air temperature and water surface temperature) faster than water evaporation. After noon, the water evaporation decreased faster than P. australis transpiration with the descendant coefficients of 1.287 and 0.317, respectively.

The NSS method and model accuracy

Previous studies suggest that not accounting for the NSS of δT can introduce large errors in partitioning of ET (Yepez et al. 2005; Lai et al. 2006). In this study, comparisons between the methods for partitioning ET showed that the results for ISS and NSS methods were very similar in estimating fractional contributions from E and Tph (Table 4). Therefore, although the transpiration was not at an ISS (Table 3; Figure 4), the ISS method also gave reasonable estimations of contributions from E and Tph. However, slight discrepancies (less than 5%) were detected for the early morning and evening samplings between the two methods; therefore, consideration of a NSS δT in modelling of leaf water for the transitional periods of the day would be beneficial.

Before modelling the NSS δT, the leaf water isotopic enrichment model should be validated using the observed values. The temporal variations of the modelled and measured bulk leaf water 18O enrichments (ΔLb) and the scatter plots of modelled versus measured values showed that in spite of a somewhat underestimate, the modelled ΔLb agreed well with the observed ΔLb (Figure 5). Two thresholds can be used to evaluate the departure of leaf water enrichment from a steady state: (1) ΔLe−ΔLes, the difference between NSS leaf water enrichment at the evaporating sites (ΔLe) and the steady state values (ΔLes) (Welp et al. 2008); (2) δT−δs, the difference between transpiration δT and the source water δs (Harwood et al. 1998). The results showed that using the first threshold, either from the forward prediction using Equation (21) in the Farquhar & Cernusak (2005) (herein F&C) model (ΔLe, F&C) or from the back calculation using the observed bulk leaf water Δ18O based on Equation (16) in Farquhar & Cernusak (2005)Le, back), leaf water enrichments were near but deviated from a steady state on both the 20th and 21st June (Figure 5). When using the second threshold, the modelled δT always deviated from the observed δs, indicating the necessity of modelling the NSS δT.

Figure 5

Temporal variation and modelled versus observed scatter plots of bulk leaf water enrichment (ΔLb) and temporal variation of the modelled leaf water enrichment at evaporating sites (ΔLe) during the period of the 20th and 21st June. ΔLb values were modelled using Equation (21) in Farquhar & Cernusak (2005). ΔLe values were estimated using two approaches, based on a steady state assumption (light grey line): from forward prediction using Equation (22) in Farquhar & Cernusak (2005) (black squares) and from back calculation using the observed ΔLb values based on Equation (16) in Farquhar & Cernusak (2005) (black solid dots).

Figure 5

Temporal variation and modelled versus observed scatter plots of bulk leaf water enrichment (ΔLb) and temporal variation of the modelled leaf water enrichment at evaporating sites (ΔLe) during the period of the 20th and 21st June. ΔLb values were modelled using Equation (21) in Farquhar & Cernusak (2005). ΔLe values were estimated using two approaches, based on a steady state assumption (light grey line): from forward prediction using Equation (22) in Farquhar & Cernusak (2005) (black squares) and from back calculation using the observed ΔLb values based on Equation (16) in Farquhar & Cernusak (2005) (black solid dots).

The isotopic composition of leaf water was calculated using the F&C model, which considers both the NSS and Péclet effect, and then compared with that of the observed leaf water. Previous studies show that considering the Péclet effect can improve the agreement between modelled and observed leaf water (Farquhar & Cernusak 2005; Welp et al. 2008). In this study, the modelled dΔLb values were very consistent with the measured dΔLb values, in spite of the underestimations, particularly in the transition periods of evening and morning (Figure 5). In such transitional periods, the observed ΔLb values were higher than those of ΔLe, providing support for the argument that isotopic exchanges of H218O occur between leaves and the air (Farquhar et al. 2007; Welp et al. 2008).

In this study, both the modelled ΔLe (Figure 5) and the calculated δT (Table 3) showed that the leaf transpiration was not at an ISS. The diurnal changes of δT values during the study time increased more rapidly than those of δx, which is similar to the pattern documented in earlier studies (Harwood et al. 1998; Yakir & Sternberg 2000; Welp et al. 2008). However, notably, the calculated δT on the early morning of the 21st June showed very high values during the day, which was caused by the high air humidity (RH= 77.1%) at the time of sampling, because δT can be positively correlated with air humidity instantaneously (Lee et al. 2007). Therefore, at this humid site at which the air was prone to saturation in the early morning and evening, the NSS in the modelling of leaf water and transpiration must be considered.

Precision evaluation of ET

Many factors affect ET, including meteorological factors, plant physiological and ecological characteristics, the underlying surface conditions and other factors, with complex variations (Li et al. 2000). In practice, the possible factors are net radiation, air temperature, surface temperature, relative humidity, wind speed and some others (Si et al. 2005). Because of the short experimental time, the results might include some deviation. Therefore, we compared the meteorological data for 9 consecutive sunny days (Table 5). From the contrast of meteorological factors, the relative humidity in the study period was higher than that for the consecutive days in late June and the air temperature was lower by approximately 2.1°C, which resulted in the evaporation difference. The evaporation difference between the experimental period and late June was approximately 3.3%, less than 5%. Thus, the results for the study period were reasonable and credible.

Table 5

Meteorological factors from the Baicheng station in late June 2013 (the day of precipitation is not shown)

Date Atmospheric pressure (hpa) Relative humidity (%) Air temperature (°C) Wind speed (m/s) Sunshine duration (h) Evaporation (mm) 
6–15 984.6 54 24.3 2.6 10.3 13.3 
6–16 984.1 52 24 7.4 
6–17 981.6 54 23.9 2.1 11.7 9.7 
6–20 989.1 69 20.9 3.1 9.5 8.6 
6–21 991.5 62 22.5 2.2 12.7 9.2 
6–22 989.3 54 23.8 2.1 13.5 7.9 
6–23 985.3 47 25.4 1.8 13.3 9.7 
6–24 984.1 52 26.3 3.6 11.8 10.5 
6–25 984.4 69 23.1 2.9 4.4 7.1 
Average (9 days) 986 57 23.8 2.6 10.5 9.2 
Average (study period) 990.3 66 21.7 2.7 11.1 8.9 
Date Atmospheric pressure (hpa) Relative humidity (%) Air temperature (°C) Wind speed (m/s) Sunshine duration (h) Evaporation (mm) 
6–15 984.6 54 24.3 2.6 10.3 13.3 
6–16 984.1 52 24 7.4 
6–17 981.6 54 23.9 2.1 11.7 9.7 
6–20 989.1 69 20.9 3.1 9.5 8.6 
6–21 991.5 62 22.5 2.2 12.7 9.2 
6–22 989.3 54 23.8 2.1 13.5 7.9 
6–23 985.3 47 25.4 1.8 13.3 9.7 
6–24 984.1 52 26.3 3.6 11.8 10.5 
6–25 984.4 69 23.1 2.9 4.4 7.1 
Average (9 days) 986 57 23.8 2.6 10.5 9.2 
Average (study period) 990.3 66 21.7 2.7 11.1 8.9 

Note: The data were cited from the China Meteorological Data Sharing Service Network (http://cdc.cma.gov.cn/).

To determine the factors influencing ET in wetlands in northeast China and to further validate the results of this study, we contrasted the ET amount and meteorological factors at wetlands of international importance that are distributed in northeast China (Table 6). Based on the amount of ET, these wetlands were divided into two groups: one group (first group), including the Zhalong and Momoge Wetlands, was distributed in a semiarid area, and the other group (second group) contained the Sanjiang and Panjin Wetlands located in a humid region.

Table 6

Comparison of the ET and influencing factors for internationally important wetlands distributed in northeast China (the data are the average values from the same time in late June)

Wetland Location Average air temperature (°C) Relative humidity (%) Wind speed (m/s) ET (mm) Vapour pressure deficit Net radiation (MJ m−2/d) Sunshine duration (h) 
Zhalong Wetland 46°52′ N, 123°47′ E 29.8 43.2 2.8 8.23 None None 9.23 
Sanjiang Wetland 47°35′ N, 133°31′ E 21.1 62.3 2.2 3.34 9.74 15.2 9.12 
Panjin Wetland 41°08′ N, 121°54′ E 20.2 70.4 3.8 3.25 None None 8.05 
Momoge Wetland (this study) 45°56′ N, 123°28′ E 28.1 51.7 4.1 8.76 None None 8.98 
Wetland Location Average air temperature (°C) Relative humidity (%) Wind speed (m/s) ET (mm) Vapour pressure deficit Net radiation (MJ m−2/d) Sunshine duration (h) 
Zhalong Wetland 46°52′ N, 123°47′ E 29.8 43.2 2.8 8.23 None None 9.23 
Sanjiang Wetland 47°35′ N, 133°31′ E 21.1 62.3 2.2 3.34 9.74 15.2 9.12 
Panjin Wetland 41°08′ N, 121°54′ E 20.2 70.4 3.8 3.25 None None 8.05 
Momoge Wetland (this study) 45°56′ N, 123°28′ E 28.1 51.7 4.1 8.76 None None 8.98 

Note: The data were cited from the China Meteorological Data Sharing Service Network (http://cdc.cma.gov.cn/) and other documents (Jia et al. 2007; Sun & Song 2008; Zhou & Zhou 2009; Yao et al. 2010; Zhang et al. 2011).

ET is highly dependent upon net radiation (Rn) and air temperature, and peak values of ET often mirror the peaks in Rn or air temperature (Zhou & Zhou 2009). Moreover, the Rn is significantly positively correlated with the sunshine duration. From Table 6, no clear difference is observed in sunshine duration between the two groups; therefore, Rn may not be the primary reason for the difference in ET values. However, the air temperature of the first group was dramatically higher than that in the second group. In previous studies, the ET increased with increasing air temperatures, and the correlation coefficient was approximately 0.693. Thus, the air temperature was likely one reason for the differences in ET. Additionally, the relative humidity in the first group was 16.8-fold lower than that in the second group, a clear difference. The relative humidity reflects the influence of the vapour pressure deficit (VPD), with the relative humidity expressed as the ratio between the actual water vapour pressure and saturated vapour pressure at the same temperature (Huang & Liu 2000). Therefore, the air temperature and VPD or relative humidity were likely primary factors that caused the difference in ET between the two groups. The wind speed is a factor that also simultaneously affects the ET amount (Wang et al. 2007; Drexler et al. 2008). In the first group, the ET of the Momoge Wetland was larger than that of the Zhalong Wetland by approximately 0.53 mm/d and the wind speed also increased by approximately 1.3 m/s. In the Panjin Wetland, wind speed promoted and accelerated ET, with a correlation coefficient of approximately 0.477. Thus, overall, air temperature, VPD (or relative humidity) and wind speed are likely the primary factors leading to the differences in ET of wetlands in northeast China.

CONCLUSIONS

The use of stable isotope techniques to partition ET fluxes into evaporation and transpiration over a marsh wetland was the focus of this work. An experiment was conducted in the growing season in the semiarid area of the Momoge Natural Reserve. Isotope turbulent mixing relationships, isotopic values of transpired water from plants and those of evaporating water vapour from the wetland were used to estimate the transpiration and evaporation fractions contributing to the total ET. Three important conclusions were derived from this work. (1) The slope of the fitted curve for delta D and delta 18O was approximately 3.53 in the marsh wetland, indicating strong evaporation of surface water in the arid climate (including plant transpiration and water surface evaporation). (2) Using the NSS method, for the proportions of ET, water surface evaporation accounted for approximately 62% and plant transpiration was approximately 38%. The NSS method effectively improved the accuracy of each, particularly in the morning and evening. (3) According to E and T relative contributions and leaf transpiration rate (Tr) measurements, the estimated ET flux was approximately 8.76 mm. In a comparison of wetlands in northeast China, the air temperature, VPD (or relative humidity) and wind speed were likely the primary factors affecting the differences in ET. The water loss from ET is a useful measurement for wetland restoration and wetland water management, and these findings will be helpful in organizing a strict wetland water management plan for semiarid areas.

ACKNOWLEDGEMENTS

The National Science Foundation of China (NSFC41101091, NSFC 41371193, NSFC 41471080 and NSFC 41301606) and the Science Foundation of Jilin Province (No. 20150101080JC) supported this study. We thank the anonymous referees for their valuable time.

REFERENCES

REFERENCES
Bijoor
N. S.
,
Pataki
D. E.
,
Rocha
A. V.
&
Goulden
M. L.
2011
The application of delta O-18 and delta D for understanding water pools and fluxes in a Typha marsh
.
Plant Cell & Environment
34
(
10
),
1761
1775
.
Borin
M.
,
Milani
M.
,
Salvato
M.
&
Toscano
A.
2011
Evaluation of Phragmites australis (Cav.) Trin. evapotranspiration in Northern and Southern Italy
.
Ecological Engineering
37
(
5
),
721
728
.
Cappa
C. D.
,
Hendricks
M. B.
,
DePaolo
D. J.
&
Cohen
R. C.
2003
Isotopic fractionation of water during evaporation
.
Journal of Geophysical Research
108
(
D16
).
doi:10.1029/2003JD003597
.
Chen
J. H.
,
Kan
C. E.
,
Tan
C. H.
&
Shih
S. F.
2002
Use of spectral information for wetland evapotranspiration assessment
.
Agricultural Water Management
55
,
239
248
.
Craig
H.
&
Gordon
L. I.
1965
Deuterium and oxygen-18 variations in the ocean and the marine atmosphere
. In:
Stable Isotopes in Oceanographic Studies and Paleotemperatures
(
Tongiorgi
E.
, ed.).
Laboratory of Geology and Nuclear Science
,
Pisa
,
Italy
.
Ehleringer
J. R.
,
Roden
J.
,
Dawson
T. E.
2000
Assessing ecosystem-level water relations through stable isotope ratio analysis
. In:
Methods in Ecosystem Science
(
Sala
O.
,
Jackson
R.
,
Mooney
H. A.
&
Howarth
R.
, eds).
Springer-Verlag
,
New York
,
USA
.
Farquhar
G. D.
&
Cernusak
L. A.
2005
On the isotopic composition of leaf water in the non-steady state
.
Functional Plant Biology
32
(
4
),
293
303
.
Farquhar
G. D.
,
Cernusak
L. A.
&
Barnes
B.
2007
Heavy water fractionation during transpiration
.
Plant Physiology
143
,
11
18
.
Fermor
P. M.
,
Hedges
P. D.
,
Gilbert
J. C.
&
Gowing
D. J. G.
2001
Reedbed evapotranspiration rates in England
.
Hydrological Processes
15
,
621
631
.
Harwood
K. G.
,
Gillon
J. S.
,
Griffiths
H.
&
Broadmeadow
M. S. J.
1998
Diurnal variation of D13CO2, DC18O16O and evaporative site enrichment of dH218O in Piper aduncum under field conditions in Trinidad
.
Plant Cell & Environment
21
(
3
),
269
283
.
Helliker
B. R.
,
Roden
J. S.
,
Cook
C.
&
Ehleringer
J. R.
2002
A rapid and precise method for sampling and determining the oxygen isotope ratio of atmospheric water vapor
.
Rapid Communications in Mass Spectrometry
16
(
10
),
929
932
.
Hu
Z. M.
,
Yu
G. R.
,
Zhou
Y. L.
,
Sun
X. M.
,
Li
Y. N.
,
Shi
P. L.
,
Wang
Y. F.
,
Song
X.
,
Zheng
Z. M.
,
Zhang
L.
&
Li
S. G.
2009
Partitioning of evapotranspiration and its controls in four grassland ecosystems: application of a two-source model
.
Agricultural and Forest Meteorology
149
,
1410
1420
.
Huang
X.
&
Liu
X.
2000
Study on large scale evaporation model
.
Journal of Hehai University
28
(
4
),
13
18
(
in Chinese
).
Jia
Z.
,
Song
C.
&
Wang
Y.
2007
Studies on evapotranspiration over mire in the Sanjiang Plain
.
Climatic and Environmental Research
12
(
4
),
496
502
(
in Chinese
).
Lee
X. H.
,
Kim
K.
&
Smith
R.
2007
Temporal variations of the 18O/16O signal of the whole-canopy transpiration in a temperate forest
.
Global Biogeochemical Cycles
21
(
3
).
doi:10.1029/2006gb002871
.
Li
L.
,
Zhang
G.
&
Wang
Q.
2000
Study on evapotranspiration and its impact factors over Yellow River upperstream area
.
Advance in Earth Sciences
15
(
3
),
256
259
(
in Chinese
).
Ming
J.
,
Xian-guo
L.
,
Lin-shu
X.
,
Li-juan
C.
&
Shouzheng
T.
2007
Flood mitigation benefit of wetland soil – a case study in Momoge National Nature Reserve in China
.
Ecological Economics
61
(
2–3
),
217
223
.
Mitsch
W. J.
&
Gosselink
J. G.
2007
Wetlands
,
4th edn
.
John Wiley & Sons Inc.
,
New York
,
USA
.
Moro
M. J.
,
Domingo
F.
&
López
G.
2004
Seasonal transpiration pattern of Phragmites australis in a wetland of semi-arid Spain
.
Hydrological Processes
18
(
2
),
213
227
.
Sánchez-Carrillo
S.
,
Angeler
D. G.
,
Sánchez-Andrés
R.
,
Alvarez-Cobelas
M.
&
Garatuza-Payán
J.
2004
Evapotranspiration in semi-arid wetlands: relationships between inundation and the macrophyte-cover: open-water ratio
.
Advances in Water Resources
27
(
6
),
643
655
.
Si
J. H.
,
Feng
Q.
&
Zhang
X. Y.
2005
Research progress on surveying and calculation of evapotranspiration of plants and its prospects
.
Advances in Water Resources
16
(
3
),
450
459
.
Wang
J. M.
,
Sammis
T. W.
,
Andales
A. A.
,
Simmons
L. J.
,
Gutschick
V. P.
&
Miller
D. R.
2007
Crop coefficients of open-canopy pecan orchards
.
Agriculture Water Management
88
(
1–3
),
253
262
.
Welp
L. R.
,
Lee
X.
,
Kim
K.
,
Griffis
T. J.
,
Billmark
K. A.
&
Baker
J. M.
2008
d18O of water vapour, evapotranspiration and the sites of leaf water evaporation in a soybean canopy
.
Plant Cell & Environment
31
(
9
),
1214
1228
.
Woodward
R. T.
&
Wui
Y.-S.
2001
The economic value of wetland services: a meta-analysis
.
Ecological Economy
37
(
2
),
257
270
.
Yakir
D.
&
Sternberg
L. D. L.
2000
The use of stable isotopes to study ecosystem gas exchange
.
Oecologia
123
(
3
),
297
311
.
Yepez
E. A.
,
Huxman
T. E.
,
Ignace
D. D.
,
English
N. B.
,
Weltzin
J. F.
,
Castellanos
A. E.
&
Williams
D. G.
2005
Dynamics of transpiration and evaporation following a moisture pulse in semiarid grassland: a chamber-based isotope method for partitioning flux components
.
Agricultural and Forest Meteorology
132
(
3–4
),
359
376
.
Zhang
Y.
,
Zheng
X.
,
Wu
C.
&
Zhang
S.
2011
Experimental study of evapotranspiration from Phragmites Australis wetland in Liaohe Estuary
.
Advance in Water Science
22
(
3
),
351
358
(
in Chinese
).