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Article

Characteristics of Evapotranspiration and Crop Coefficient Correction at a Permafrost Swamp Meadow in Dongkemadi Watershed, the Source of Yangtze River in Interior Qinghai–Tibet Plateau

1
State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
4
China Institute of Water Resources and Hydropower Research, Beijing 100049, China
5
Water Resources Department, Yangtze River Scientific Research Institute, Wuhan 430010, China
*
Author to whom correspondence should be addressed.
Water 2022, 14(21), 3578; https://doi.org/10.3390/w14213578
Received: 20 September 2022 / Revised: 2 November 2022 / Accepted: 4 November 2022 / Published: 7 November 2022
(This article belongs to the Special Issue Hydrometeorological Observation and Modeling)

Abstract

:
The Qinghai–Tibet Plateau (QTP), known as the Earth’s third pole, is highly sensitive to climate change. Various environmental degradation has occurred due to the effects of climate warming such as the degradation of permafrost and the thickening of active layers. Evapotranspiration, as a key element of hydrothermal coupling, has become a key factor of the plateau environment for deciphering deterioration, and the FAO P-M model has a good physical foundation and simple model data requirements as a primary tool to study the plateau evapotranspiration. There has been a large research base, but the estimation of evapotranspiration in alpine regions is still subject to many uncertainties. This is reflected in the fact that the classification of underlying surface types has not been sufficiently detailed and the evapotranspiration characteristics of some special underlying surface types are still unclear. Therefore, in this work, we modified the FAO P-M coefficients based on the characteristics of actual evapotranspiration measured by the Eddy covariance system and the key influencing factors to better simulate the actual evapotranspiration in alpine swamp meadow. The results were as follows: (1) Both ETa measured by the Eddy covariance system and ET0 calculated by FAO P-M showed the same trend at the daily and annual scales and hysteresis was confirmed to exist, so the error caused by hysteresis should be considered in further research. (2) The annual ETa was 566.97 mm and annual ETa/P was 0.76, and about 11.19% of ETa occurred during the night. The ETa was 2.15 during the non-growing seasons, implying that a large amount of soil water was released into the air by evapotranspiration. (3) The evapotranspiration characteristics of alpine swamp meadow are formed under the following conditions: control of net radiation (Rn) affected by VPD during the growing season and affected by soil temperature and humidity during the non-growing season. Precipitation and soil water content are no longer the main controlling factors of evapotranspiration during the growing season at the alpine swamp meadow as the volume soil water content tends to saturate. (4) The basic corrected Kc was 1.14 during the initial and mid-growing season, 1.05 during the subsequent growing season, and 0–0.25 during the non-growing season, and the correction factor process can also provide ideas for correcting the Kc of other vegetation.

1. Introduction

Evapotranspiration refers to the process of surface water transfer to the atmosphere through phase change or the transpiration of vegetation, accompanied by huge latent heat exchange. Studies have shown that about 64% of precipitation on the land surface re-enters into the atmosphere though evapotranspiration; this proportion could reach 90% in arid areas [1], and its energy consumption usually accounts for 48–88% of the net radiation [2]. Therefore, evapotranspiration is not only an important part of the land water cycle, but also an important part of the land surface energy balance [3]. Moreover, both potential evapotranspiration (ETp) and actual evapotranspiration (ETa) had positive responses to temperature [4,5] and water vapor, which returns to the atmosphere through evapotranspiration and is also a major greenhouse gas capable of warming the climate [6]. Reasonable estimation of evapotranspiration is of great significance to regional climate change research [7], the rational utilization of water resources, agricultural irrigation, and water conservation projects [8]. The ETp can be calculated from meteorological observations with a wide distribution and high density. Even with absent local weather stations, a gridded weather dataset can provide information useful for potential crop evapotranspiration calculations [9]. However, it is necessary to determine how to accurately simulate the actual evapotranspiration through ETp [10].
Meanwhile, the widespread existence of hydrothermal coupling [11] made evapotranspiration a key element in understanding the impact of climate warming on ecosystems [12]. The Qinghai–Tibet Plateau (QTP) is known as the third pole and Asia’s water tower [13]. It is also one of the most sensitive areas to climate change and is a highly suitable area for the coupled study of terrestrial ecosystem and climate change [14,15]. Due to global warming, the active layer has obviously warmed and thickened since the last century, which has intensified the hydrothermal exchange between the atmosphere and land surface [16,17]. Consequently, the alpine meadow soil, which covers about 36–40% of the area of QTP, also underwent serious degradation [18]. As it is difficult to establish local weather stations in the QTP and other special environmental areas, the Penman–Monteith (P–M) equation has become the widely used method for evapotranspiration calculation [19,20]. Based on the P–M equation, the FAO P–M was proposed based on reference crops and only requires simple meteorological data input [21], but the crop coefficient Kc could not be easily obtained [22]. Ma et al. used a water-carbon coupled biophysical model, Penman–Monteith-Leuning Version 2 (PML-V2), to evaluate the evapotranspiration in the QTP and indicated that precipitation was considered to be the most important factor at the Yangtze River source [23]. Jia et al. simulated evapotranspiration in swamp land using the FAO P–M with the recommended crop coefficients significantly higher than that measured, and assigning the crop coefficient must be modified if FAO P–M is used to simulate the ETa from swamp land [24]. The alpine swamp meadow in the Yangtze River source area occupied the main position in the hydrological process, but climate change had degraded about 12.9% of the swamp meadow region [25]. However, many studies lacked a proper distinction between the alpine swamp meadow and other alpine plants. Notably, without the limitation of water, evapotranspiration may be greater and precipitation may not be the most important influencing factor in alpine swamp meadows.
Therefore, the world’s first Eddy covariance system on the alpine swamp meadow above 5000 m of the QTP was used to discuss the characteristics of evapotranspiration and its influencing factors. The corrected crop coefficient suitable for an alpine swamp meadow was determined to provide data and method support for subsequent research.

2. Materials and Methods

2.1. Study Area

About 11% of the vegetation types in the source region of the Yangtze River (SRYR) were alpine swamp meadow [26], but alpine swamp meadow has lacked the proper distinction from other alpine vegetations in past studies on evapotranspiration, which has impeded further research on its mechanism. Thus, according to the Vegetation Map of the People’s Republic of China, the vegetation types in the SRYR were clearly distinguished based on the dominant species and soil type, and the alpine swamp meadow was dominated by Kobresia littledalei and Carex moorcroftii (Figure 1a). The world’s first Eddy covariance system (Figure 1c) on the alpine swamp meadow above 5000 m was used to observe evapotranspiration at the Tanggula Mountain Cryosphere Hydrology and Ecology Field Scientific Experiment Station of the Chinese Academy of Sciences (TGL, 33°02′12.48″ N, 92°00′28.08″ E), which is located in the Dongkemadi River Basin in the source region of the Yangtze River on the central Tibetan Plateau. The orientation of the TGL is northeast to southwest (prevailing wind direction), so the areas covered by the Eddy covariance footprint (calculated by FFP) was a uniform alpine swamp meadow [27].

2.2. Data Acquisition and Processing

An integrated observation station was built at this site including an Eddy covariance system, meteorological system, and soil parameter system. The observation items and instruments, the data acquisition unit, and measurement height (depth) are listed in Table 1. Data from January to December 2020 were selected for research, and all data were resampled with a time resolution of 30 min.

2.2.1. Eddy Covariance System

The Eddy covariance method was coupled with the pulsation and vertical wind by covariance to compute the sensible heat flux H and latent heat flux λETa:
H = ρ C p ω θ ¯
λ E T a = λ ρ ω q ¯
where ρ is the density of air (kg/m3); Cp is the specific heat of air at constant pressure [(MJ/(kg·℃)]; λ is the latent heat of vaporization (MJ/kg); ω′ is the pulsation of the vertical component (m/s); θ′ is the pulsation of temperature (℃); and q′ is the pulsation of specific humidity (g/kg).
To ensure the accuracy of the results, raw data acquired at 10 Hz were processed using EddyPro (LI-COR, USA) including the spike removal, lag correction of H2O relative to the vertical wind component, sonic virtual temperature correction, the performance of the planar fit coordinate rotation, corrections for density fluctuation (WPL-correction), and frequency response correction. Output flux data were conducted as follows: (i) data from periods of sensor malfunction were rejected; (ii) those within 1 h before and after precipitation were rejected; (iii) those missing more than 3% of raw data were rejected; (iv) data were rejected when the friction velocity was below 0.1 m/s, and after conducting the above procedure, about 71% of the flux data was available and quality flags were calculated for all fluxes; the flag values qc = 0 and qc = 1 for fluxes suitable for general analysis and qc = 2 for fluxes were discarded from the result dataset.
During long time observation, 17–50% of flux data were missing or rejected [28]. Liu used a look-up table (LUT) and mean diurnal variations (MDV) to fill the gap of flux data [3]. Xu indicated that LUT could obtain a better result when the meteorological observation data were available synchronously and MDV was more suitable for a short time gap-fill [29]. In this study, days of maximum continuous lack of data were less than 5 days; thus, LUT and MDV were required for the gap-fill and added with online flux data calculated by EasyFlux_DL (IRGASON, USA).

2.2.2. Meteorological System

The meteorological system included a two-layer gradient automatic weather system (GAWS) and a four-component radiation system. Wind speed (Ws) and direction (Wd) were measured at 1.5 m by Sensor-05103 (R.M. Young, USA). Mean air temperature (AT, °C) and relative humidity (RH, %) were measured at 1.5 m by a Sensor-109 (Campbell, USA). Precipitation (P, mm) was measured at 1.7 m using T-200B (Geonor, Norway) and the snow depth (Sdp, cm) was measured at 1.7 m by the snow-depth sensor and snow-pillow at the Earth’s surface. The net radiation (Rn, W/m2) was calculated from the downward/upward short-wave radiation and downward/upward long-wave radiation measured independently by NR01 (Hukseflux, The Netherlands) at a height of 1.5 m. It can be formulated as follows:
Rn = Sd − Su + Ld − Lu
where Sd and Su were the downward and upward short-wave radiation, and Ld and Lu were the downward and upward short-wave radiation, respectively. The unit of the above parameters was W/m2.
All data were collected from the data acquisition unit by LoggerNet (Campbell, USA) and because the capture rates of solid and liquid precipitation are different, the precipitation data were corrected using a scheme in this area proposed by He [30].

2.2.3. Soil Parameter System

Soil parameters were measured at eight levels by HydraProbe (Stevens, USA) at a depth of 0.1–1.1 m, which include the volumetric soil water content (VSWC, m3/m3) and soil temperature (ST, °C). Additionally, the soil moisture and ST were used to calculate the soil heat flux (G, W/m2) using the thermal diffusion equation correction (TDEC) proposed by Yang [31], which has been well validated in a typical permafrost region of Naqu. The one-dimensional heat conduction equation of soil is as follows:
ρ s c s T t = G z
Integrating over both sides:
G ( z ) = G ( z r ) + Z r Z ρ s c s T ( z ) t d z
Given the soil temperature profile T(Zi), Equation (5) can be expressed as:
G ( z ) = G ( z r ) + 1 Δ t Z i Z [ ρ s c s ( Z i , t + Δ t ) T s ( Z i , t + Δ t ) ρ s c s ( Z i , t ) T s ( Z i , t ) ]
where ρscs is the soil heat storage [J/(kg·K)]; Ts is soil temperature (℃); t is the time (s); and Gz is the soil heat flux at a depth of z (W/m2) and G(Zr) can be iterated from zero when the soil moisture and soil temperature observations are adequately deep. In this passage, we assumed that the soil heat conduction coefficient was 1.0 W/(m·k); subsequently, the soil diffusion equation was used to solve the temperature profile T(Zi), and the observed temperature profile was used to correct it. Finally, Equation (6) was used to solve the soil heat flux of each layer, and the surface soil heat flux (G0) was selected for future calculations.

2.3. Energy Balance and Calculation of Evapotranspiration

The energy balance of an alpine swamp meadow is expressed as follows:
Rn = H +λET + G0
As has been noted in the literature, the energy balance Equation (4) is not usually closed [29] The energy balance ratio (EBR) and energy balance deficit (EBD) were used to check the turbulence flux observation; this is expressed as follows:
E B R = ( H + λ E ) ( R n G )
E B D = R n H λ E T G 0
In addition to the Eddy covariance to calculate the actual evapotranspiration (ETa), the FAO Penman–Monteith Equation (FAO P–M; [21]) was used to calculate the reference crop evapotranspiration (ET0), and the crop coefficient (Kc) at the alpine swamp meadow was calculated by comparing two approaches. The related formulas are expressed as follows:
E T 0 d a i l y = 0.408 Δ ( R n G 0 ) + γ 900 T a + 273 u 2 ( e s e a ) Δ + γ ( 1 + 0.34 u 2 )
E T 0 h o u r l y = 0.408 Δ ( R n G 0 ) + γ 37 T a + 273 u 2 ( e s e a ) Δ + γ ( 1 + 0.34 u 2 )
K c = E T a / E T 0
where Δ is the rate of change of saturation vapor pressure with temperature (kpa/℃); γis the psychrometric constant (kpa/℃); Ta is the air temperature at 2 m (℃); es and ea represent the saturation vapor pressure at Ta and the actual air vapor pressure (kpa), respectively; and u2 is the wind velocity at 2 m (m/s).
To calculate ETa from ET0 using a corrected Kc at the alpine swamp meadow, the root-mean-square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were selected to evaluate the effectiveness of different approaches; their formulas are expressed as follows:
R M S D = [ 1 n i = 1 n ( E T ec ( i ) E T p m ( i ) ) 2 ] 1 / 2
N S E = 1 1 N ( E T ec ( i ) E T p m ( i ) ) 2 1 N ( E T p m ( i ) E T p m ¯ ) 2
where ETec(i) indicates the actual evapotranspiration calculated by the Eddy covariance; ETpm(i) is the actual evapotranspiration calculated by FAO P-M using the different Kc approach; and the overline denotes the average value.

2.4. Calculations of Parameters Influencing the Characteristics of Evapotranspiration

The daily equilibrium evapotranspiration (ETeq, mm/d) and surface conductance gs (ms−1) were calculated from the Penman–Monteith equation using the following form [32]:
E T e q = Δ Δ + γ ( R n G )
g s 1 = ρ a C p V P D / ( γ λ E ) + ( β Δ / γ 1 ) / g a
where ETeq (mm/d) is the evapotranspiration influenced only by radiative heating; ρa (kg/m3) is the moist air density; Cp [(MJ/(kg·℃)] is the specific heat of air at constant pressure; β is the Bowen ratio, which was computed by H/λE. The aerodynamic conductance ga (m/s) was estimated from the friction velocity u* (m/s) and wind speed u (m/s), and is expressed as:
g a 1 = u / u * 2 + 6.2 u * 0.67

3. Result and Discussion

3.1. Characteristics of Environmental Elements

Meteorology, underlying surface, and radiation conditions are critical parameters affecting evapotranspiration. Therefore, the automatic meteorological system (AWS), soil parameter system, and radiation system of the TGL station was used to analyze the basic meteorological, underlying surface and radiation characteristics of the typical alpine swamp meadow and was the basis for subsequent research analysis. Figure 2 shows the variations in the meteorological conditions. The mean annual air temperature was −5.5 °C; the highest and lowest temperatures were 7.7 °C (9 August) and −26.96 °C (24 January), respectively. According to Wang [33], when the temperature was steady above 3 °C, the alpine swamp meadow entered the Ini-growing season. When the temperature was steady above 5 °C, the alpine swamp meadow entered the mid-growing season. When the temperature was steady under 5 °C but above 3 °C, the alpine swamp meadow entered the late-growing season. Thus, the Ini-growing season, mid-growing season, late-growing season were from 11 June–12 July, 13 July–15 August, and 16 August–21 September, respectively. The average wind speeds during the non-growing and growing seasons were 3.32 and 2.43 m/s, respectively, and the fluctuation was lower during the growing season. The mean values of the vapor pressure deficit (VPD) during the growing and non-growing seasons were 0.19 and 0.17 kpa, respectively. Precipitation occurred on 212 days throughout the year, with 741.7 mm of accumulated precipitation; the single maximum daily precipitation reached 21.4 mm (9 July). According to the snow depth sensor, solid precipitation was dominant in the non-growing season, and the snow-free period was from 13 May–31 December.
Figure 3 shows the variations in the ST and VSWC conditions. A distinct freezing–thawing cycle could be observed; the completely frozen stage (1 January–14 May) occurred when the ST was completely lower than 0 °C and the VSWC was stable. When the temperature of the surface soil was above 0 °C and the VSWC was saturated, the thawed stage began (2 September–27 October); when the VSWC increased, the thawing stage occurred (15 May–1 September); when the VSWC decreased, the freezing stage occurred (28 October–31 December).
Figure 4 shows the radiation condition and characteristics of the energy budget at this site. As the site is located at a high altitude, seasonal variation in the daily-mean net radiation (Rn) was significant, ranging from −32.64 to 222.77 W/m2 (Figure 5a), and the annual average daily net radiation was 79.78 W/m2. The maximum and minimum values occurred on 18 July–11 January, respectively. The average daily EBD and EBR were −2.47 W/m2 and 1.078, respectively. Therefore, the energy balance in this site almost closed, and thus the λET values measured by the Eddy covariance system were considered reliable and the reason for the worse EBD and EBR values was the advection effect by the higher wind speed during the non-growing season.

3.2. Seasonal and Diurnal Variation of Evapotranspiration

Figure 5 shows the ETa measured by the Eddy covariance system and reference evapotranspiration (ET0) computed by the FAO P–M equation. ETa and ET0 had similar trends both on the day and annual scales, and all values showed unimodal variation (Figure 6a). ETa was close to ET0 during the growing season, but ET0 was higher than ETa during the freezing and completely frozen periods. ET0 was higher than ETa between 8:00 and 16:00 during the day, and the maximum ET0 and ETa values occurred at about 14:00 and 15:00, respectively. (Figure 5b). There was a hysteresis effect between the Eddy covariance and FAO Penman–Monteith methods and this phenomenon was also observed in an underlying surface with exposed water [34]. The ETa measured by the Eddy covariance system indicated that nocturnal evapotranspiration existed at alpine swamp meadows, but the FAO P–M method underestimated it. Additionally, the nocturnal evapotranspiration phenomenon in the northern Utah region accounted for 1.7% of the total during a complete growing cycle of alfalfa [35]; that in the north Qinghai–Tibet Plateau region accounted for 9.8–15.8% from May to September in an alpine desert, alpine steppe, alpine meadow steppe, and alpine meadow [36], but the nocturnal evapotranspiration of alpine swamp meadows is still unclear.
Figure 6 shows the annual distribution of evapotranspiration. There was an obvious distribution characteristic: evapotranspiration after 12:00 accumulated 443.77 mm, and accounted for 78.3% of the annual actual evapotranspiration. Nocturnal evapotranspiration occurring during the growing season accumulated 63.45 mm, and accounted for 11.19% of the annual actual evapotranspiration.

3.3. Hydrologic Balance

Figure 7 shows the seasonal variation in evapotranspiration and precipitation and its cumulative value in the alpine swamp meadow. During the period of observation, the cumulative precipitation (Pc) reached 741.7 mm, of which 476.3 mm occurred in the growing season, accounting for 64% of the annual precipitation (Figure 7a). The ratio of ETa to P (ETa/P) is an important parameter to depict the hydrological balance [37]. The annual actual evapotranspiration is 566.97 mm; thus, the annual ETa/P was 0.76 and this value was close to that of an alpine steppe (0.51–0.77; [38]) but lower than that of a degraded alpine meadow (0.97; [37]). There were significant differences in the different periods. The ETa/P values were 0.63 and 2.15 during the growing and during the non-growing seasons. Thus, from the ETa/P, we inferred that the precipitation was recharged in air through evapotranspiration in the alpine swamp meadow; and in addition to precipitation, a mass of soil water entered the air through evapotranspiration during the non-growing season. Cumulative annual ETeq (ETeqc) and cumulative annual ET0 (ET0c) values were 917.50 and 889.84 mm, which were higher than the cumulative annual ETa (ETac) and cumulative annual precipitation (Pc). The ETeqc partly reflects the maximum possible evapotranspiration of an ecosystem influenced only by radiation [20], but the ET0c was higher than ETeqc before the growing season. Additionally, despite the marked increase in ETeq and Pc during the growing season, there were no significant increases in ETac and ET0c during the growing season. ETeqc, Pc, and ETac were reduced, but ET0c still had a rapid grow rate after the growing season (Figure 7b).

3.4. Relationship between Evapotranspiration and Environmental Elements

In this section, stepwise regression and path analysis were used to find the similarities and differences in the relationship between evapotranspiration and environmental elements during the growing and non-growing seasons. Table 2 presents the results of stepwise regression; we can recognize that the major influence factors during the growing season (MFG) were the net radiation (Rn) and VPD, and those during the non-growing season (MFNG) were the VSWC, net radiation (Rn), ST, P, and wind speed (Ws). The adjusted R2 shows that the MFG and MFNG could explain about 66 and 79% of the evapotranspiration characteristics during the growing and non-growing seasons, respectively.
Figure 8 shows the structure of MFG and MFNG given by path analysis. The structure of MFG and MFNG given by path analysis reflects that the ETa was mainly influenced by the radiation (Rn) and atmospheric (VPD) conditions during the growing season (Figure 8a); in addition to the radiation and atmospheric conditions, the soil condition also had a notable influence on the ETa during the non-growing season. Evapotranspiration increased by 0.780 mm/d for every 1 W/m2 increase in net radiation, wherein 0.682 mm/d was directly influenced by changes in the radiation condition and 0.098 mm/d occurred by the interaction between the radiation and atmospheric conditions (Path.1, Rn-VPD-ETa: 0.098 mm/d) during the growing season (Figure 8a). Evapotranspiration increased by 0.749 mm/d for every 1 W/m2 increase in net radiation, wherein 0.381 mm/d was directly influenced by changes in the radiation condition, 0.382 mm/d was through the positive interaction between the radiation and soil conditions (Path.1, Rn-VSWC-ETa: 0.242 mm/d; Path.2 Rn-ST-ETa: 0.119 mm/d; Path.3 Rn-Ws-ETa: 0.021 mm/d), and −0.015 mm/d was through the negative interaction between the radiation and atmospheric conditions (Path.1 Rn-P-ETa:−0.015 mm/d) during the non-growing season (Figure 8b).
From the above relationship, we inferred that ETa was restrained when precipitation occurred during the non-growing season because the solid precipitation blocked the water exchange between the air and ground surface. Soil conditions were no longer major factors influencing the ETa at the alpine swamp meadow during the growing season because the soil surface water content had already saturated during the onset of the growing season; this also explained why the jump in precipitation had not brought notable changes in the ETa. This conclusion was similar to that of Zhang, who highlighted that the positive effect between VSWC and ETa was stronger during drought [39]. Although the results of stepwise regression show that the direct effect of wind speed on evapotranspiration is negative (path.1 Ws-ETa), the combined effect exerted by other environmental elements through wind speed increases the actual evapotranspiration (e.g., Path1.Rn-Ws-ETa).
As presented in Table 2 and shown in Figure 8a, regardless of whether the adjusted R2 was lower or er was higher during the growing season, other factors influenced the ETa. Burenina et al. pointed out that the characteristics of evapotranspiration is determined by the general climatic characteristics of the research area and different species composition [40]. Thus, the surface conductance gs and aerodynamic conductance ga were incorporated into the path analysis model to evaluate the influence of the alpine swamp meadow on evapotranspiration. The results shown in Figure 9 indicate that after considering factor gs, which represents the effect of vegetation, the adjusted R2 and er increased from 0.656 to 0.790 and 0.56 to 0.46, respectively. Although gs had a negative relationship with ETa, the effect of gs was two-sided: one way was evapotranspiration influenced by the increase in atmospheric conditions from 0.519 to 0.547 mm/d for every 1 kpa increase in VPD (Path.1 VPD-gs-ETa), and another way is evapotranspiration influenced by the change in radiation conditions from 0.780 to 0.747 mm/d for every 1 W/m2 increase in net radiation (Path.1 Rn-gs-ETa). This may partly explain why the ETeqc had a more rapid growth rate than that of ETac during the growing season in Figure 7b.

3.5. A New Correct Scheme of Crop Coefficient

Figure 10 shows the process of evapotranspiration at the alpine swamp meadow. Obvious thawing and freezing processes and exposed water existed during the growing season, thereby leading to a unique evapotranspiration process in the alpine swamp meadow. Thus, we inferred that the reason for a significantly higher ET0 than ETa during the thawed and completely frozen periods is that the FAO P–M only uses radiation and atmospheric conditions to calculate evapotranspiration without sufficiently considering the influence of freeze–thaw cycle to evapotranspiration, and the crop coefficient recommended by the FAO also does not fully consider the impact of the freeze–thaw process. Thus, the crop coefficient (Kc) requires a new correction scheme when using the FAO P–M in the alpine swamp meadow with an obvious freeze–thaw cycle.
Figure 11 shows the parameterization schemes of Kc and the result for the correction of Kc by initially calculating the actual Kc by ETa/ET0. Additionally, MFG and MFNG were used to build the regression equation between the daily actual Kc and to ensure the accuracy of Kc; the piecewise function was established according to the vegetation growth stage and freeze–thaw process. The new Kc was multiplied with the daily ET0 to obtain the daily ETa calculated by ET0 (ETa,Sim). The results showed that the foundation coefficients were between 0 and 0.25 and 1.05 and 1.14 during the non-growing and growing seasons, respectively, and the VSWC was found to be the key parameter in the model during the non-growing season. The modified crop coefficients take into account VSWC to reflect the effects of the freeze–thaw processes on evapotranspiration and can improve the computational accuracy of models for evapotranspiration. The correction scheme proposed in this paper can not only obtain the actual evapotranspiration in the growing season by ET0, but also obtain the actual evapotranspiration in the non-growing season by calculating the crop coefficient by VSWC and RH.
Figure 12 shows the corrected result and ETa measured by the Eddy covariance system. The annual ETa calculated by ET0 was 592.34 mm, which was 25.37 mm larger than the ETa. The NSE of the scheme was 0.87 and the RMSE of the scheme was 0.44 mm/d; thus, this scheme could effectively evaluate the ETa of the alpine swamp meadow. Moreover, we also calculated ETa through the FAO-recommended Kc [21], whose values are as follows: Kc,ini = 0.4; Kc,mid = 1.05 + Kc,FAO; Kc,later = 0.85 + Kc,FAO. The related formula is expressed as:
K c , F A O = K c , r e c o m m e n d + [ 0.04 ( u 2 2 ) 0.004 R H min 45 ] ( h 3 ) 0.3
where h is the canopy height of the alpine swamp meadow (m).
The result showed that the FAO-recommended Kc underestimated the ETa at the Ini-growing season and was similar to the corrected results during mid- and later-growing seasons. The main reasons why the FAO recommended factors underestimate the actual evapotranspiration during the Ini-growing season are: (1) the growth characteristics of alpine swamp meadow are not a perfect match with the reference crops; (2) dramatic changes in soil water content caused by permafrost freeze–thaw cycles and surface micro-fluctuations in alpine swamp meadow cause the aerodynamic resistance and canopy surface resistance to differ significantly from the reference crops (Jia et al., 2014). This new corrected scheme error mainly occurs in the following ways:
  • By providing less consideration to parameters and using a one-dimensional linear relationship;
  • The soil water content of an alpine swamp meadow is high, and the enthalpy of water leads to a phase difference between the ETa and ET0 calculated by FAO P–M [34].

4. Conclusions

The annual correction coefficient of the FAO P–M formula was obtained through comparative observations based on the clear evapotranspiration characteristics and influencing factors of an alpine swamp meadow. Thus, it can be better applied in an alpine swamp meadow area, and our conclusions are as follows:
(1)
ETa measured by the Eddy covariance system and ET0 calculated by FAO P–M showed the same trend on the daily and annual scales, where all values showed unimodal variation, and hysteresis was confirmed between ET0 and ETa. Therefore, ETa can be calculated by ET0 for alpine swamp meadow, but the error due to hysteresis should be considered in subsequent studies.
(2)
The hydrological balance of alpine swamp meadow was different from that of an alpine steppe and alpine meadow, where the annual ETa and annual ETa/P were 566.97 mm and 0.76, with about 11.19% of ETa occurring at night. The ETa during non-growing seasons was 2.15, implying that a large amount of soil water was released into the air by evapotranspiration, and whether is this the cause of alpine meadow degradation also remains to be investigated.
(3)
The main influencing factors during the growing season were Rn, VPD, and gs, and the main influencing factors during the non-growing season were VSWC, Rn, ST, P, and Ws. Therefore, the evapotranspiration characteristics of an alpine swamp meadow are formed under the following conditions: control of net radiation, affected by VPD during the growing season and affected by soil temperature and humidity during the non-growing season. Precipitation and soil water content are no longer the main controlling factors of evapotranspiration during the growing season at an alpine swamp meadow as the volume soil water content tends to saturate.
(4)
The basic corrected Kc was 1.14 during the initial and mid-growing season, 1.05 during the later growing season, and 0–0.25 during the non-growing season. Moreover, not only can this corrected crop coefficient effectively calculate the actual evapotranspiration from ET0 of the alpine swamp meadow, the correction factor process can also provide ideas for correcting the Kc of other vegetation. In fact, in this paper, we only corrected the single-crop coefficients, which could not separate vegetation transpiration and evaporation. Therefore, the segmentation of transpiration and evaporation in alpine swamp meadow is still worth further discussion.

Author Contributions

Conceptualization, X.H. (Xiaobo He) and Y.D.; writing—original draft preparation, H.G.; writing—review and editing, S.W. and Y.F.; resources, H.F. and X.H. (Xiaofeng Hong); All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [the Joint Research Project of Three-River Headwaters National Park, Chinese Academy of Sciences and the People’s Government of Qinghai Province] grant number [LHZX-2020-11, LHZX-2020-10]; [the Science and Technology Project of the Tibetan Autonomous Region] grant number [XZ202101ZD0009G]; [the project of State Key Laboratory of Cryospheric Science] grant number [SKLCS-ZZ-2022]; [the IWHR Research & Development Support Program] grant number [HY110145B0012021]; [National Public Research Institutes for Basic R&D Operating Expenses Special Project] grant number [CKSF2021485].

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (a) Map of the vegetation type of the source region of the Yangtze River (SRYR), which is located in in the interior Qinghai–Tibet Plateau (QTP). [Editorial Board of Vegetation Map of China, Chinese Academy of Sciences, 2001]. (b) Proportion of vegetation and underlying surface types in the SRYR: AM, Alpine meadow; AG, Alpine grassland; AD, Alpine desert; SM, Swamp meadow; PM, Patchy meadow; AS, Alpine shrub; AST, Alpine steppe; SD, Stone desert; Gl, Glacier; WL, Woodland; DS, Desert steppe; ASW, Alpine swamp. (c) Distribution of instruments of the observation station and photos of the Eddy covariance system, soil parameter system, automatic weather system, and radiation system.
Figure 1. (a) Map of the vegetation type of the source region of the Yangtze River (SRYR), which is located in in the interior Qinghai–Tibet Plateau (QTP). [Editorial Board of Vegetation Map of China, Chinese Academy of Sciences, 2001]. (b) Proportion of vegetation and underlying surface types in the SRYR: AM, Alpine meadow; AG, Alpine grassland; AD, Alpine desert; SM, Swamp meadow; PM, Patchy meadow; AS, Alpine shrub; AST, Alpine steppe; SD, Stone desert; Gl, Glacier; WL, Woodland; DS, Desert steppe; ASW, Alpine swamp. (c) Distribution of instruments of the observation station and photos of the Eddy covariance system, soil parameter system, automatic weather system, and radiation system.
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Figure 2. Variations in the (a) air temperature (AT) at a height of 2 m; (b) precipitation (bar) and snow depth (shaded graph); (c) vapor pressure deficit (VPD) at 2 m; and (d) wind speed (WS) at 2.5 m. All data are 30-min averages and (a) air temperature, (b) wind speed, and (c) VPD are shown with their respective daily maximums and minimums. The light color divides the growing season and non-growing periods.
Figure 2. Variations in the (a) air temperature (AT) at a height of 2 m; (b) precipitation (bar) and snow depth (shaded graph); (c) vapor pressure deficit (VPD) at 2 m; and (d) wind speed (WS) at 2.5 m. All data are 30-min averages and (a) air temperature, (b) wind speed, and (c) VPD are shown with their respective daily maximums and minimums. The light color divides the growing season and non-growing periods.
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Figure 3. Variations in the (a) soil temperature (ST) and (b) volumetric soil water content (VSWC) at depths of 0.1 to 1.1 m, respectively.
Figure 3. Variations in the (a) soil temperature (ST) and (b) volumetric soil water content (VSWC) at depths of 0.1 to 1.1 m, respectively.
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Figure 4. Variations in the (a) flux density at a height of 2 m, (b) energy balance deficit (EBD), and (c) energy balance ratio (EBR); the purple and pink solid lines represent 7-day running mean values, and others represent the daily mean value.
Figure 4. Variations in the (a) flux density at a height of 2 m, (b) energy balance deficit (EBD), and (c) energy balance ratio (EBR); the purple and pink solid lines represent 7-day running mean values, and others represent the daily mean value.
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Figure 5. Variations in the (a) daily average evapotranspiration; the red squares represent the reference evapotranspiration (ET0) and blue hollow circles represent the actual evapotranspiration (ETa); (b) hourly average evapotranspiration; the red squares represent the reference evapotranspiration (ET0), blue hollow circles represent the actual evapotranspiration (ETa), the red shadow represents the fitted curve of ET0 under a 95% confident level, the blue shadow represents the fitted curve of ETa under a 95% confident level, and error bars denote the standard deviation of hourly data.
Figure 5. Variations in the (a) daily average evapotranspiration; the red squares represent the reference evapotranspiration (ET0) and blue hollow circles represent the actual evapotranspiration (ETa); (b) hourly average evapotranspiration; the red squares represent the reference evapotranspiration (ET0), blue hollow circles represent the actual evapotranspiration (ETa), the red shadow represents the fitted curve of ET0 under a 95% confident level, the blue shadow represents the fitted curve of ETa under a 95% confident level, and error bars denote the standard deviation of hourly data.
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Figure 6. The annual distribution of evapotranspiration at the alpine swamp meadow during 2020. The different colors from blue to pink represent the actual evapotranspiration (ETa) per half hour. The time between 6:00 and 18:00 represents daytime.
Figure 6. The annual distribution of evapotranspiration at the alpine swamp meadow during 2020. The different colors from blue to pink represent the actual evapotranspiration (ETa) per half hour. The time between 6:00 and 18:00 represents daytime.
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Figure 7. (a) Seasonal variation in evapotranspiration and precipitation in the alpine swamp meadow. (b) Cumulative reference evapotranspiration (ET0c), cumulative actual evapotranspiration (ETac), cumulative equilibrium evapotranspiration (ETeqc), and cumulative precipitation (Pc).
Figure 7. (a) Seasonal variation in evapotranspiration and precipitation in the alpine swamp meadow. (b) Cumulative reference evapotranspiration (ET0c), cumulative actual evapotranspiration (ETac), cumulative equilibrium evapotranspiration (ETeqc), and cumulative precipitation (Pc).
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Figure 8. The structures of MFG and MFNG given by path analysis during the (a) growing season and (b) non-growing season. One-way influence was shown with arrows; the interplay of environmental elements is represented by the line without arrows. Additionally, the green and orange colors implied positive and negative relationships between the actual evapotranspiration (ETa) and environmental elements, respectively. er indicates that the residual error was calculated by er = (1-R2)1/2. The asterisk superscript implies that the value passed the 95% significance t-test.
Figure 8. The structures of MFG and MFNG given by path analysis during the (a) growing season and (b) non-growing season. One-way influence was shown with arrows; the interplay of environmental elements is represented by the line without arrows. Additionally, the green and orange colors implied positive and negative relationships between the actual evapotranspiration (ETa) and environmental elements, respectively. er indicates that the residual error was calculated by er = (1-R2)1/2. The asterisk superscript implies that the value passed the 95% significance t-test.
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Figure 9. (a) The structure of MFG and MFNG given by path analysis without considering ga and gs during the growing season; and (b) that while considering ga and gs during the growing season. One-way influence was denoted by arrows; the interplay of environmental elements was represented by a line without arrows. Additionally, green and orange colors implied positive and negative relationships between the actual evapotranspiration (ETa) and environmental element. er indicates that the residual error was calculated by er = (1 − R2)1/2. The asterisk superscript implies that the value passed the 95% significance t-test.
Figure 9. (a) The structure of MFG and MFNG given by path analysis without considering ga and gs during the growing season; and (b) that while considering ga and gs during the growing season. One-way influence was denoted by arrows; the interplay of environmental elements was represented by a line without arrows. Additionally, green and orange colors implied positive and negative relationships between the actual evapotranspiration (ETa) and environmental element. er indicates that the residual error was calculated by er = (1 − R2)1/2. The asterisk superscript implies that the value passed the 95% significance t-test.
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Figure 10. The process of evapotranspiration at the alpine swamp meadow; the bottom half refers to the VSWC and ST; ST and VSWC are denoted by contours and color strips, respectively. The upper half shows the condition of the underlying surface. The arrow implies evapotranspiration from the surface and the dark blue underlying surface represents the exposed water.
Figure 10. The process of evapotranspiration at the alpine swamp meadow; the bottom half refers to the VSWC and ST; ST and VSWC are denoted by contours and color strips, respectively. The upper half shows the condition of the underlying surface. The arrow implies evapotranspiration from the surface and the dark blue underlying surface represents the exposed water.
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Figure 11. Correction and parameterization schemes of Kc; rectangle with rounded corners and green background implies the used or calculated data. Green arrow implies the processing of data and the dashed rectangle implies the stepwise regression between the data and main influencing factors. Brace represents the result. ETa,Sim represents the actual evapotranspiration calculated by the reference evapotranspiration.
Figure 11. Correction and parameterization schemes of Kc; rectangle with rounded corners and green background implies the used or calculated data. Green arrow implies the processing of data and the dashed rectangle implies the stepwise regression between the data and main influencing factors. Brace represents the result. ETa,Sim represents the actual evapotranspiration calculated by the reference evapotranspiration.
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Figure 12. The corrected ETa (little red square) calculated by ET0 compared with the ETa (blue line) measured by the Eddy covariance system and ETa (gray hollow circle) calculated by the FAO-recommended Kc.
Figure 12. The corrected ETa (little red square) calculated by ET0 compared with the ETa (blue line) measured by the Eddy covariance system and ETa (gray hollow circle) calculated by the FAO-recommended Kc.
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Table 1. List of the observation items and instruments at the TGL site.
Table 1. List of the observation items and instruments at the TGL site.
Observation ItemsSensorData AcquisitionHeight/Depth
Eddy covariance system3D wind velocity
3D wind direction
CSAT3, CampbellCR10002.5 m
Mixing ratio of water vaporLi-7500, Campbell2.5 m
Meteorological systemWind velocity and direction0513, R.M.YoungCR510X1.5 m
Air temperature109, CampbellCR510X1.5 m
PrecipitationT-200B, GeonorCR10001.7 m
Net radiation aNR01, HuksefluxCR10001.5 m
Soil parameter systemSoil moisture b and temperatureHydra, StevensCR10000.1 m, 0.2 m, 0.3 m, 0.4 m
0.5 m, 0.7 m, 0.9 m, 1.1 m
Note(s): a Upward/downward shortwave radiation and upward/downward longwave radiation were measured separately. b Volumetric soil water content was measured and used to represent the soil moisture.
Table 2. Results of the stepwise regression.
Table 2. Results of the stepwise regression.
PeriodInterpretation EquationAdjust R2
Growing seasonETa = 0.696Rn + 0.247VPD0.656
Non-growing seasonETa = 0.447VSWC a + 0.381Rn + 0.211ST a − 0.134P − 0.068Ws0.793
Note(s): a Volume soil water content (VSWC) and soil temperature (ST) were selected as the average value of ground surface 0–10 cm.
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Guo, H.; Wang, S.; He, X.; Ding, Y.; Fan, Y.; Fu, H.; Hong, X. Characteristics of Evapotranspiration and Crop Coefficient Correction at a Permafrost Swamp Meadow in Dongkemadi Watershed, the Source of Yangtze River in Interior Qinghai–Tibet Plateau. Water 2022, 14, 3578. https://doi.org/10.3390/w14213578

AMA Style

Guo H, Wang S, He X, Ding Y, Fan Y, Fu H, Hong X. Characteristics of Evapotranspiration and Crop Coefficient Correction at a Permafrost Swamp Meadow in Dongkemadi Watershed, the Source of Yangtze River in Interior Qinghai–Tibet Plateau. Water. 2022; 14(21):3578. https://doi.org/10.3390/w14213578

Chicago/Turabian Style

Guo, Haonan, Shaoyong Wang, Xiaobo He, Yongjian Ding, Yawei Fan, Hui Fu, and Xiaofeng Hong. 2022. "Characteristics of Evapotranspiration and Crop Coefficient Correction at a Permafrost Swamp Meadow in Dongkemadi Watershed, the Source of Yangtze River in Interior Qinghai–Tibet Plateau" Water 14, no. 21: 3578. https://doi.org/10.3390/w14213578

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