Int J Biometeorol DOI 10.1007/s00484-015-1016-8
ORIGINAL PAPER
Water and carbon dioxide fluxes over an alpine meadow in southwest China and the impact of a spring drought event Lei Wang 1 & Huizhi Liu 1 & Jihua Sun 2 & Jianwu Feng 1
Received: 2 October 2014 / Revised: 23 March 2015 / Accepted: 11 May 2015 # ISB 2015
Abstract Based on the eddy covariance measurements from June 2011 to December 2013, the seasonal variations and the controls of water and CO2 fluxes were investigated over an alpine meadow in Lijiang, southwest China. The year 2012 had the largest total precipitation among years from 2011 to 2013 (1037.9, 1190.4, and 1066.1 mm, respectively). A spring drought event occurred from March to May 2012, and the peak normalized difference vegetation index (NDVI) in 2012 was the lowest. Throughout the whole year, net radiation (Rn), vapor pressure deficit, and air temperature (Ta) were the primary controls on evapotranspiration (ET), and Rn is the most important factor. The influence of Rn on ET was much more in the wet season (R2 =0.93) than in the dry season (R2 = 0.28). In the wet season, the ratio of ET to equilibrium ET (ETeq) (0.92±0.14; mean±S.D.) did not show a clear seasonal pattern with NDVI when the soil water content (SWC) was usually more than 0.25 m3 m−3, indicating that ET could be predicted well by ETeq (or radiation and temperature). On half-hourly and daily scales, photosynthetic active radiation (PAR) and air temperature were the main meteorological factors in determining the net ecosystem production (NEP). The seasonal trends of NEP were closely related with the change of NDVI. The integrated NEP in the 2012 wet season (157.8 g C m−2 year−1) was 19.5 and 23.8 % lower than in the 2011 and 2013 wet season (207.0 and 196.1 g C m−2 year−1). The mean
* Huizhi Liu [email protected] 1
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 100029
2
Yunnan Institute of Meteorological Sciences, Kunming, China 650034
ET/ETeq for each of the wet seasons from 2011 to 2013 was 0.88. The 2012 spring drought and its reduction in NDVI decreased the total NEP significantly but had little effect on the total ET in the wet season. The different response of NEP and ET to the spring drought was attributed to the high SWC and small vapor pressure deficit during the wet season. Keywords Alpine meadow . Energy partitioning . Evapotranspiration . Carbon dioxide exchange . Net ecosystem production
Introduction The alpine meadow is one of the main types of grassland ecosystems in China, and it is mainly distributed in the Qinghai-Tibetan Plateau and its outer area. Alpine meadow covers 58.83 million ha, which is 17.77 % of the total area of the grassland in China (DAHV and CISNR 1996). This alpine meadow ecosystem is very sensitive and vulnerable to global climate change (Klein et al. 2004). The warming trend of the air temperature at high-altitude areas has been observed to be above average at the global scale, such as in the Swiss Alps and the Tibetan Plateau (Beniston and Rebetez 1996; Fan et al. 2011; Liu et al. 2006). Gu et al. (2005) reported that both the phenology of the vegetation and soil water content (SWC) were the main factors influencing the seasonal variation of the energy partitioning over the Haibei alpine meadow in the north of the Qinghai-Tibetan Plateau. Net ecosystem production (NEP) in alpine grasslands increased rapidly with low photosynthetic active radiation (PAR), while it was independent of PAR when the canopy was light saturated (Fu et al. 2006; Gu et al. 2003). In the alpine meadow, the annual NEP is comprehensively controlled by temperature environment, including its effects on biomass growth (Kato et al. 2006).
Int J Biometeorol
However, previous alpine meadow studies have mainly focused on the middle-latitude area with cold climate type (Gu et al. 2003; Zhao et al. 2006; Fu et al. 2009). Seasonal drought usually occurred in the past 50 years in the monsoon climate zone of southwest China (Zhang et al. 2013). Drought can substantially affect the development of leaf area and therefore modify the seasonal and inter-annual variations in NEP (Miranda et al. 1997; Meyers 2001). Besides the amounts of precipitation, precipitation distribution may cause seasonal drought and is also a critical factor in determining the ecosystem CO2 uptake of semiarid regions (Hunt et al. 2002; Kwon et al. 2008). The precipitation during winter and spring recharges the deep soil and provides water for shallow-rooted plants in the period of grass germination and even throughout the growing season (Knight 1994). Researchers have found that spring drought impacted the function and productivity of grasslands more than the summer/fall drought in the Northern Great Plains of the America and the northeast China (Heitschmidt and Haferkamp 2003; Du and Liu 2013). Lijiang is located at the east of the Hengduan Mountain and the southeast outer area of the Qinghai-Tibetan Plateau. Compared with the northern temperate zone of the Tibetan Plateau (Gu et al. 2008), Lijiang is located in a subtropical zone while it is much warmer with more annual precipitation. The precipitation mainly occurs from June to October. Then, the annual cycle could be divided to the wet (June to October) and dry seasons (other months). From the climate data set of the local weather station, since the 1990s, the climate warming trend has come clearer (0.55 °C 10 years−1), especially in winter (0.9 °C 10 years−1) in this area. The glacier area at the Yulong Snow Mountain has obviously decreased, and the snow line has risen (He and Zhang 2004). The Tibetan Plateau is an important water vapor transfer platform, which is related to the outbreak of the Asian monsoon and the droughts and floods abnormal in China (Xu et al. 2002). Lijiang is located in this water vapor transfer channel. The water flux exchange processes over this area are crucial to better understand the East Asian water vapor cycle (Xu et al. 2002).
Fig. 1 a Location of Lijiang station (map from Google Earth), b pictures of the measurement tower on November 18, 2011, and c a PC800 picture during the snowmelt period
The eddy covariance technique (Baldocchi 2003) was used to measure energy, water, and CO2 fluxes over an alpine meadow in Lijiang from June 2011 to December 2013. Based on the 2.5-year flux measurement data, the objectives of this paper were to (1) characterize the seasonal variations in energy partitioning, evapotranspiration (ET), and NEP over the alpine meadow; (2) analyze the main environmental factors controlling albedo, ET, and NEP for the wet and dry seasons; and (3) determine the response of ET and NEP to a spring drought event on seasonal and annual scales.
Observation site and methods Observation site The observation site (27° 10′ N, 100° 14′ E, 3560 m a.s.l.) is located at the Maoniuping, in the east of the Yulong Snow Mountain, Lijiang, southwest China, and it belongs to the eastern Hengduan Mountains (Fig. 1a). The 30-year annual total precipitation is 980.3 mm, and the mean annual air temperature is 12.6 °C in Lijiang (data from the Lijiang weather observation station (1981–2010), 2400 m a.s.l.). This site is at a subtropical plateau monsoon climate area with clear wet and dry seasons, and this area is influenced by the southwest and southeast monsoons together. Over 85 % of the precipitation occurs from June to October (wet season). According to the climatic average (1981 to 2010), 32 % of days had precipitation events (>1 mm day−1) for the whole year, while the percentage is 58 % in the wet season. The alpine meadow at this observation site is dominated by Kobresia Willd. grass with a maximum height of 20 cm and Berberis Linn. shrub with a maximum height of over 60 cm in the growing season. Besides the green and dead vegetation, the surface is also covered by the bare soil. The soil type is a dark brown loamy soil with lower shortwave albedo, compared with the grass (Guo et al. 2009). The site has a slope of approximately 10°, with the west higher than the east side. There is a coniferous forest about 350 m away to the north of the site.
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Measurement instruments and the normalized difference vegetation index The eddy covariance (EC) system (Baldocchi 2003) with a three-dimensional sonic anemometer (CSAT3, Campbell, USA) and an open-path CO2/H2O infrared gas analyzer (LI7500A, LI-COR, USA) was used to measure the momentum, sensible heat, water, and CO2 fluxes at 2.5 m above ground (Fig. 1b). The EC sampling frequency was 10 Hz. The infrared gas analyzer (LI-7500A) was calibrated every half a year. The low-response measurements included air temperature, relative humidity (HMP45C, Campbell, USA), wind speed, and wind direction (034B, Campbell, USA) at heights of 2.5 and 1.5 m. Net radiation (including short- and longwave radiation, CNR4, Kipp&Zonen, Netherlands), ultraviolet radiation (CUV5, Kipp&Zonen, Netherlands), and photosynthetic active radiation (LI190SB, LI-COR, USA) were measured at a height of 1.5 m. The soil heat fluxes (HFP01, Hukseflux, Netherlands) were measured at two depths of 5 and 10 cm below the ground. Two soil heat flux plates were installed at each layer. Soil temperature (109-L, Campbell, USA) and soil moisture (CS616, Campbell, USA) were measured at five depths (5, 10, 20, 50, and 100 cm) below the ground. The rainfall in the growing season was measured by a tipping bucket rain gauge (52202, Young, USA). A weighing bucket precipitation gauge (T-200B, Geonor, Norway) was used to measure the solid precipitation in the winter time. The lowresponse measurements sampling frequency is 1/3 Hz. All the data was recorded by a data logger (CR3000, Campbell Scientific, USA) with a 2-GB CF card. A camera (PC800, Reconyx, USA) was used to photograph the land surface and local weather every 30 min (Fig. 1c). In the same time, four points of the 250×250 m2 gridded normalized difference vegetation index (NDVI) data, where the flux tower was located, were obtained from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite, at 16-day intervals (product name: MOD13Q1). The average over these four points is used as the NDVI of the meadow in this observation site. Data post-processing The EddyPro software (version 5.1, LI-COR, USA) was used for the post-processing of 10 Hz EC raw data to calculate the sensible and latent heat and CO2 fluxes. Each sample is 30 min. First, a spike detection algorithm (Vickers and Mahrt 1997) was applied to remove the raw data related to electrical and physical problems. The coordinate system was transformed using the sector-wise planar fit method due to a terrain slope of approximately 10° (Wilczak et al. 2001). Regression planes were determined for two 180° wind sectors based on the local terrain. The sonic temperature flux was converted into the sensible heat flux according to Schotanus
et al. (1983). Spectral loss correction due to sensor separation was performed according to Moore (1986). The density correction (WPL correction) (Webb et al. 1980) was also used for water vapor and CO2 fluxes. The tilted terrain can induce advection of CO2 near the surface, and this may result in underestimation of CO2 flux especially during nocturnal periods of strong static stability (Wilson et al. 2002). This terrain-induced problem has not been solved in this study. The quality check of half-hourly flux data included the stationary and the integral turbulence characteristics tests, proposed by Foken and Wichura (1996). The critical u* was set at 0.1 m s−1 below which the nighttime CO2 flux was dependent on u*. An analytical footprint model was used to evaluate the relative importance of source locations contributing to flux measurements at a given point and a measurement height (Kormann and Meixner 2001). The mean footprint fetch under unstable conditions was 127 m, which was within the representative alpine meadow. However, the mean footprint fetch under stable conditions was 371 m. When the wind blew from the northern forest and u* was below 0.15 m s−1 during the nighttime, 30-min CO2 fluxes were significantly larger than the average level. They were influenced by the forest and then removed from the data set. After the quality control, approximately 72 % of the halfhourly flux data had a good quality during this observation period. The gaps within 2 h of data were filled using linear interpolation. After that, 16 % gaps longer than 2 h still remained. To fill these gaps, an improved type of Blook up table method^ called the marginal distribution sampling (MDS) was used (Lloyd and Taylor 1994; Falge et al. 2001). Here, the clearness index (CI) is used as an indicator of cloudiness. The CI is defined as the ratio of the solar radiation (Sin) received at the Earth’s surface to the extraterrestrial irradiance (Se) at a plane parallel to the Earth’s surface (Gu et al. 1999), so S in Se h . i S e ¼ S sc 1 þ 0:033cos 360t d 365 sinβ
CI ¼
sinβ ¼ sinψsinδ þ cosψcosδcosω
ð1Þ ð2Þ ð3Þ
where Ssc is the solar constant (1367 W m−2), td is the day of the year, β is the solar elevation angle, ψ is the degree of latitude, δ is the declination of the sun, and ω is the time angle. The equilibrium ET (ETeq) was calculated as follows (Priestley and Taylor 1972): ETeq ¼
ΔðRn −GÞ Δþγ
ð4Þ
where Rn is the net radiation, G is the soil heat flux, γ is the psychrometric constant, and Δ is the slope of the saturation
Int J Biometeorol
vapor pressure curve at the mean wet-bulb temperature of the air. Canopy conductance (gc) was calculated using the inverted Penman-Monteith equation (Monteith and Unsworth 1990): ΔH −1 ρcp VPD γLE 1 þ ¼ ga gc γLE
ð5Þ
where H is the sensible heat flux, LE is the latent heat flux, ρ is air density, cp is the specific heat of air at constant pressure, VPD is the vapor pressure deficit, and gc is the surface conductance; the aerodynamic conductance ga is calculated by the following equation (Monteith and Unsworth 1990): 1 u ¼ 2 þ 6:2u*−0:67 ga u*
ð6Þ
where u* is the friction velocity and u is the wind speed at 2 m above the ground.
Results and discussion Meteorological conditions and NDVI During the three wet seasons, only 28 days (19 days in October) were clear days (defined by CI>0.7) and the days with CI 0.5) and during October 1–15, 2011. The NEP and PAR were averaged with PAR bins of 200 μmol m−2 s−1. b Relationship between half-hourly NEP and Ta from June to October 2011 and c between daily NEP and daily average Ta. The data were averaged with Ta bins of 1 °C. d Relationship between NEP and NDVI, and NEP was 7day average before and after the NDVI sampling day from June to October in 2011 and 2012
Moreover, the increased cloudiness strengthened NEP significantly, which was also observed over grasslands on the northern Qinghai-Tibetan Plateau and the Heihe River Basin in northwestern China (Gu et al. 2003; Bai et al. 2012). The enhanced NEP under cloudy skies can be attributed to the increase in diffuse PAR (Bai et al. 2012). Under different irradiance conditions, half-hourly NEP increased with the increasing Ta below 12 °C, but
decreased when Ta was above 12 °C (Fig. 8b). This indicates that 12 °C of Ta was the optimal value for CO2 uptake at the half-hourly scale, which is the same as that for another alpine meadow (Fu et al. 2006). On the daily scale, Ta affected NEP across the entire year. The canopy released CO2 when Ta 5 °C (Fig. 8c). Moreover, NEP increased linearly with NDVI and NEP became positive when NDVI increased above a critical value (Fig. 8d). Therefore, the CO2 uptake period length in 2012 was shortened (129 days in 2012 and 164 days in 2013) due to the spring drought, and the ecosystem absorbed 31.2 and 24.3 % less CO2 in the 2012 wet season compared with 2011 and 2013. On the annual scale, the annual total NEP for 2013 (158.5 g C m−2 year−1) was also more than that for 2012 (114.2 g C m−2 year−1) (Table 1). The negative effect of seasonal drought on NEP was similar to other grassland ecosystems (Meyers 2001; Hunt et al. 2002; Kwon et al. 2008). On the contrary, the response of the wet season ET to the spring drought was different. The drought directly produced a significant reduction in ET/ETeq (0.43) for May 2012 (ET/ ETeq =0.88 for May 2013), but had little limitation to ET/ETeq for the wet season in 2012 because vegetation development affected ET/ETeq little. NDVI had a small effect on the monthly ET (R2 =0.27), leading to no inter-annual variability in ET/ ETeq for the wet seasons. The monthly ET showed a strong response to Rn (R2 =0.89), and the total ET during the wet seasons differed from 2.8 to 9.0 % (Table 1). Carbon sequestration ability The maximum uptake and release for the monthly average diurnal cycles at the observation site were −8.8 and 2.7 μmol m−2 s−1, respectively. These maximum values were similar to those for the Dangxiong alpine meadow (Shi et al. 2006), but a little lower than those for the Haibei alpine meadow (Kato et al. 2004b). Compared with the low-lying grasslands, these three alpine meadow ecosystems have much lower potential for CO2 uptake at the half-hourly scale. For example, the maximum CO2 uptake at our site was 45 and 28 % of those for a C3 grassland in California and a C4 tallgrass prairie in Oklahoma, respectively (Table 2). The low temperature may be responsible for the difference in half-hourly CO2 flux between alpine ecosystems and low-lying grasslands. On the annual scale, the CO2 uptake for this site (mean value=135.4 g C m−2 year−1) was larger than the Haibei
alpine meadow (mean value=120.9 g C m−2 year−1) located at a comparable elevation (Kato et al. 2004b). The annual CO2 uptake for the Dangxiong alpine meadow was much lower (potentially a CO2 source) due to the higher elevation and the shorter CO2 uptake period. In general, annual CO2 uptake for different C3 grasslands was similar but much less than that for a C4 tallgrass prairie in Oklahoma (Suyker and Verma 2001). Under a semi-humid climate, C3 grasslands can act as a CO2 sink or source due to the inter-annual variation in precipitation (Table 2). Kato et al. (2006) found that air temperature was the dominant factor in determining the annual CO2 uptake at the Haibei alpine meadow. At this alpine meadow, the response of annual total NEP to air temperature for this wet alpine meadow requires a longer period of measurements.
Conclusion The 2.5-year eddy covariance measurements represent clear differences in the response of ET and NEP to the spring drought effects. ET over this alpine meadow was energy limited. In the wet seasons, the seasonal and inter-annual variations in the ET/ETeq were not significant. This enables to accurately describe ET using ETeq to this study site and likely to the similar regions with high SWC and small VPD. The spring drought effect was larger for NEP than for ET. The seasonal trends of NEP were mainly modified by NDVI. Therefore, later leaf emergence in 2012 depressed NDVI and led to a 28 % reduction in NEP. On the half-hourly and daily scales, air temperature had a major influence on NEP. The response of NEP to air temperature on annual scales should be investigated based on the longer measurements. Acknowledgments This study was supported by the National Natural Science Foundation of China (grant No.: 41030106 and 41305012) and the Third Tibetan Plateau Scientific Experiment: Observations for Boundary Layer and Troposphere (GYHY201406001). We also greatly appreciate the staffs from Lijiang Meteorological Administration for their help in the maintenance of the measurements.
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ORIGINAL PAPER
Water and carbon dioxide fluxes over an alpine meadow in southwest China and the impact of a spring drought event Lei Wang 1 & Huizhi Liu 1 & Jihua Sun 2 & Jianwu Feng 1
Received: 2 October 2014 / Revised: 23 March 2015 / Accepted: 11 May 2015 # ISB 2015
Abstract Based on the eddy covariance measurements from June 2011 to December 2013, the seasonal variations and the controls of water and CO2 fluxes were investigated over an alpine meadow in Lijiang, southwest China. The year 2012 had the largest total precipitation among years from 2011 to 2013 (1037.9, 1190.4, and 1066.1 mm, respectively). A spring drought event occurred from March to May 2012, and the peak normalized difference vegetation index (NDVI) in 2012 was the lowest. Throughout the whole year, net radiation (Rn), vapor pressure deficit, and air temperature (Ta) were the primary controls on evapotranspiration (ET), and Rn is the most important factor. The influence of Rn on ET was much more in the wet season (R2 =0.93) than in the dry season (R2 = 0.28). In the wet season, the ratio of ET to equilibrium ET (ETeq) (0.92±0.14; mean±S.D.) did not show a clear seasonal pattern with NDVI when the soil water content (SWC) was usually more than 0.25 m3 m−3, indicating that ET could be predicted well by ETeq (or radiation and temperature). On half-hourly and daily scales, photosynthetic active radiation (PAR) and air temperature were the main meteorological factors in determining the net ecosystem production (NEP). The seasonal trends of NEP were closely related with the change of NDVI. The integrated NEP in the 2012 wet season (157.8 g C m−2 year−1) was 19.5 and 23.8 % lower than in the 2011 and 2013 wet season (207.0 and 196.1 g C m−2 year−1). The mean
* Huizhi Liu [email protected] 1
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 100029
2
Yunnan Institute of Meteorological Sciences, Kunming, China 650034
ET/ETeq for each of the wet seasons from 2011 to 2013 was 0.88. The 2012 spring drought and its reduction in NDVI decreased the total NEP significantly but had little effect on the total ET in the wet season. The different response of NEP and ET to the spring drought was attributed to the high SWC and small vapor pressure deficit during the wet season. Keywords Alpine meadow . Energy partitioning . Evapotranspiration . Carbon dioxide exchange . Net ecosystem production
Introduction The alpine meadow is one of the main types of grassland ecosystems in China, and it is mainly distributed in the Qinghai-Tibetan Plateau and its outer area. Alpine meadow covers 58.83 million ha, which is 17.77 % of the total area of the grassland in China (DAHV and CISNR 1996). This alpine meadow ecosystem is very sensitive and vulnerable to global climate change (Klein et al. 2004). The warming trend of the air temperature at high-altitude areas has been observed to be above average at the global scale, such as in the Swiss Alps and the Tibetan Plateau (Beniston and Rebetez 1996; Fan et al. 2011; Liu et al. 2006). Gu et al. (2005) reported that both the phenology of the vegetation and soil water content (SWC) were the main factors influencing the seasonal variation of the energy partitioning over the Haibei alpine meadow in the north of the Qinghai-Tibetan Plateau. Net ecosystem production (NEP) in alpine grasslands increased rapidly with low photosynthetic active radiation (PAR), while it was independent of PAR when the canopy was light saturated (Fu et al. 2006; Gu et al. 2003). In the alpine meadow, the annual NEP is comprehensively controlled by temperature environment, including its effects on biomass growth (Kato et al. 2006).
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However, previous alpine meadow studies have mainly focused on the middle-latitude area with cold climate type (Gu et al. 2003; Zhao et al. 2006; Fu et al. 2009). Seasonal drought usually occurred in the past 50 years in the monsoon climate zone of southwest China (Zhang et al. 2013). Drought can substantially affect the development of leaf area and therefore modify the seasonal and inter-annual variations in NEP (Miranda et al. 1997; Meyers 2001). Besides the amounts of precipitation, precipitation distribution may cause seasonal drought and is also a critical factor in determining the ecosystem CO2 uptake of semiarid regions (Hunt et al. 2002; Kwon et al. 2008). The precipitation during winter and spring recharges the deep soil and provides water for shallow-rooted plants in the period of grass germination and even throughout the growing season (Knight 1994). Researchers have found that spring drought impacted the function and productivity of grasslands more than the summer/fall drought in the Northern Great Plains of the America and the northeast China (Heitschmidt and Haferkamp 2003; Du and Liu 2013). Lijiang is located at the east of the Hengduan Mountain and the southeast outer area of the Qinghai-Tibetan Plateau. Compared with the northern temperate zone of the Tibetan Plateau (Gu et al. 2008), Lijiang is located in a subtropical zone while it is much warmer with more annual precipitation. The precipitation mainly occurs from June to October. Then, the annual cycle could be divided to the wet (June to October) and dry seasons (other months). From the climate data set of the local weather station, since the 1990s, the climate warming trend has come clearer (0.55 °C 10 years−1), especially in winter (0.9 °C 10 years−1) in this area. The glacier area at the Yulong Snow Mountain has obviously decreased, and the snow line has risen (He and Zhang 2004). The Tibetan Plateau is an important water vapor transfer platform, which is related to the outbreak of the Asian monsoon and the droughts and floods abnormal in China (Xu et al. 2002). Lijiang is located in this water vapor transfer channel. The water flux exchange processes over this area are crucial to better understand the East Asian water vapor cycle (Xu et al. 2002).
Fig. 1 a Location of Lijiang station (map from Google Earth), b pictures of the measurement tower on November 18, 2011, and c a PC800 picture during the snowmelt period
The eddy covariance technique (Baldocchi 2003) was used to measure energy, water, and CO2 fluxes over an alpine meadow in Lijiang from June 2011 to December 2013. Based on the 2.5-year flux measurement data, the objectives of this paper were to (1) characterize the seasonal variations in energy partitioning, evapotranspiration (ET), and NEP over the alpine meadow; (2) analyze the main environmental factors controlling albedo, ET, and NEP for the wet and dry seasons; and (3) determine the response of ET and NEP to a spring drought event on seasonal and annual scales.
Observation site and methods Observation site The observation site (27° 10′ N, 100° 14′ E, 3560 m a.s.l.) is located at the Maoniuping, in the east of the Yulong Snow Mountain, Lijiang, southwest China, and it belongs to the eastern Hengduan Mountains (Fig. 1a). The 30-year annual total precipitation is 980.3 mm, and the mean annual air temperature is 12.6 °C in Lijiang (data from the Lijiang weather observation station (1981–2010), 2400 m a.s.l.). This site is at a subtropical plateau monsoon climate area with clear wet and dry seasons, and this area is influenced by the southwest and southeast monsoons together. Over 85 % of the precipitation occurs from June to October (wet season). According to the climatic average (1981 to 2010), 32 % of days had precipitation events (>1 mm day−1) for the whole year, while the percentage is 58 % in the wet season. The alpine meadow at this observation site is dominated by Kobresia Willd. grass with a maximum height of 20 cm and Berberis Linn. shrub with a maximum height of over 60 cm in the growing season. Besides the green and dead vegetation, the surface is also covered by the bare soil. The soil type is a dark brown loamy soil with lower shortwave albedo, compared with the grass (Guo et al. 2009). The site has a slope of approximately 10°, with the west higher than the east side. There is a coniferous forest about 350 m away to the north of the site.
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Measurement instruments and the normalized difference vegetation index The eddy covariance (EC) system (Baldocchi 2003) with a three-dimensional sonic anemometer (CSAT3, Campbell, USA) and an open-path CO2/H2O infrared gas analyzer (LI7500A, LI-COR, USA) was used to measure the momentum, sensible heat, water, and CO2 fluxes at 2.5 m above ground (Fig. 1b). The EC sampling frequency was 10 Hz. The infrared gas analyzer (LI-7500A) was calibrated every half a year. The low-response measurements included air temperature, relative humidity (HMP45C, Campbell, USA), wind speed, and wind direction (034B, Campbell, USA) at heights of 2.5 and 1.5 m. Net radiation (including short- and longwave radiation, CNR4, Kipp&Zonen, Netherlands), ultraviolet radiation (CUV5, Kipp&Zonen, Netherlands), and photosynthetic active radiation (LI190SB, LI-COR, USA) were measured at a height of 1.5 m. The soil heat fluxes (HFP01, Hukseflux, Netherlands) were measured at two depths of 5 and 10 cm below the ground. Two soil heat flux plates were installed at each layer. Soil temperature (109-L, Campbell, USA) and soil moisture (CS616, Campbell, USA) were measured at five depths (5, 10, 20, 50, and 100 cm) below the ground. The rainfall in the growing season was measured by a tipping bucket rain gauge (52202, Young, USA). A weighing bucket precipitation gauge (T-200B, Geonor, Norway) was used to measure the solid precipitation in the winter time. The lowresponse measurements sampling frequency is 1/3 Hz. All the data was recorded by a data logger (CR3000, Campbell Scientific, USA) with a 2-GB CF card. A camera (PC800, Reconyx, USA) was used to photograph the land surface and local weather every 30 min (Fig. 1c). In the same time, four points of the 250×250 m2 gridded normalized difference vegetation index (NDVI) data, where the flux tower was located, were obtained from the Moderate Resolution Imaging Spectrometer (MODIS) on the EOS-1 Terra satellite, at 16-day intervals (product name: MOD13Q1). The average over these four points is used as the NDVI of the meadow in this observation site. Data post-processing The EddyPro software (version 5.1, LI-COR, USA) was used for the post-processing of 10 Hz EC raw data to calculate the sensible and latent heat and CO2 fluxes. Each sample is 30 min. First, a spike detection algorithm (Vickers and Mahrt 1997) was applied to remove the raw data related to electrical and physical problems. The coordinate system was transformed using the sector-wise planar fit method due to a terrain slope of approximately 10° (Wilczak et al. 2001). Regression planes were determined for two 180° wind sectors based on the local terrain. The sonic temperature flux was converted into the sensible heat flux according to Schotanus
et al. (1983). Spectral loss correction due to sensor separation was performed according to Moore (1986). The density correction (WPL correction) (Webb et al. 1980) was also used for water vapor and CO2 fluxes. The tilted terrain can induce advection of CO2 near the surface, and this may result in underestimation of CO2 flux especially during nocturnal periods of strong static stability (Wilson et al. 2002). This terrain-induced problem has not been solved in this study. The quality check of half-hourly flux data included the stationary and the integral turbulence characteristics tests, proposed by Foken and Wichura (1996). The critical u* was set at 0.1 m s−1 below which the nighttime CO2 flux was dependent on u*. An analytical footprint model was used to evaluate the relative importance of source locations contributing to flux measurements at a given point and a measurement height (Kormann and Meixner 2001). The mean footprint fetch under unstable conditions was 127 m, which was within the representative alpine meadow. However, the mean footprint fetch under stable conditions was 371 m. When the wind blew from the northern forest and u* was below 0.15 m s−1 during the nighttime, 30-min CO2 fluxes were significantly larger than the average level. They were influenced by the forest and then removed from the data set. After the quality control, approximately 72 % of the halfhourly flux data had a good quality during this observation period. The gaps within 2 h of data were filled using linear interpolation. After that, 16 % gaps longer than 2 h still remained. To fill these gaps, an improved type of Blook up table method^ called the marginal distribution sampling (MDS) was used (Lloyd and Taylor 1994; Falge et al. 2001). Here, the clearness index (CI) is used as an indicator of cloudiness. The CI is defined as the ratio of the solar radiation (Sin) received at the Earth’s surface to the extraterrestrial irradiance (Se) at a plane parallel to the Earth’s surface (Gu et al. 1999), so S in Se h . i S e ¼ S sc 1 þ 0:033cos 360t d 365 sinβ
CI ¼
sinβ ¼ sinψsinδ þ cosψcosδcosω
ð1Þ ð2Þ ð3Þ
where Ssc is the solar constant (1367 W m−2), td is the day of the year, β is the solar elevation angle, ψ is the degree of latitude, δ is the declination of the sun, and ω is the time angle. The equilibrium ET (ETeq) was calculated as follows (Priestley and Taylor 1972): ETeq ¼
ΔðRn −GÞ Δþγ
ð4Þ
where Rn is the net radiation, G is the soil heat flux, γ is the psychrometric constant, and Δ is the slope of the saturation
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vapor pressure curve at the mean wet-bulb temperature of the air. Canopy conductance (gc) was calculated using the inverted Penman-Monteith equation (Monteith and Unsworth 1990): ΔH −1 ρcp VPD γLE 1 þ ¼ ga gc γLE
ð5Þ
where H is the sensible heat flux, LE is the latent heat flux, ρ is air density, cp is the specific heat of air at constant pressure, VPD is the vapor pressure deficit, and gc is the surface conductance; the aerodynamic conductance ga is calculated by the following equation (Monteith and Unsworth 1990): 1 u ¼ 2 þ 6:2u*−0:67 ga u*
ð6Þ
where u* is the friction velocity and u is the wind speed at 2 m above the ground.
Results and discussion Meteorological conditions and NDVI During the three wet seasons, only 28 days (19 days in October) were clear days (defined by CI>0.7) and the days with CI 0.5) and during October 1–15, 2011. The NEP and PAR were averaged with PAR bins of 200 μmol m−2 s−1. b Relationship between half-hourly NEP and Ta from June to October 2011 and c between daily NEP and daily average Ta. The data were averaged with Ta bins of 1 °C. d Relationship between NEP and NDVI, and NEP was 7day average before and after the NDVI sampling day from June to October in 2011 and 2012
Moreover, the increased cloudiness strengthened NEP significantly, which was also observed over grasslands on the northern Qinghai-Tibetan Plateau and the Heihe River Basin in northwestern China (Gu et al. 2003; Bai et al. 2012). The enhanced NEP under cloudy skies can be attributed to the increase in diffuse PAR (Bai et al. 2012). Under different irradiance conditions, half-hourly NEP increased with the increasing Ta below 12 °C, but
decreased when Ta was above 12 °C (Fig. 8b). This indicates that 12 °C of Ta was the optimal value for CO2 uptake at the half-hourly scale, which is the same as that for another alpine meadow (Fu et al. 2006). On the daily scale, Ta affected NEP across the entire year. The canopy released CO2 when Ta 5 °C (Fig. 8c). Moreover, NEP increased linearly with NDVI and NEP became positive when NDVI increased above a critical value (Fig. 8d). Therefore, the CO2 uptake period length in 2012 was shortened (129 days in 2012 and 164 days in 2013) due to the spring drought, and the ecosystem absorbed 31.2 and 24.3 % less CO2 in the 2012 wet season compared with 2011 and 2013. On the annual scale, the annual total NEP for 2013 (158.5 g C m−2 year−1) was also more than that for 2012 (114.2 g C m−2 year−1) (Table 1). The negative effect of seasonal drought on NEP was similar to other grassland ecosystems (Meyers 2001; Hunt et al. 2002; Kwon et al. 2008). On the contrary, the response of the wet season ET to the spring drought was different. The drought directly produced a significant reduction in ET/ETeq (0.43) for May 2012 (ET/ ETeq =0.88 for May 2013), but had little limitation to ET/ETeq for the wet season in 2012 because vegetation development affected ET/ETeq little. NDVI had a small effect on the monthly ET (R2 =0.27), leading to no inter-annual variability in ET/ ETeq for the wet seasons. The monthly ET showed a strong response to Rn (R2 =0.89), and the total ET during the wet seasons differed from 2.8 to 9.0 % (Table 1). Carbon sequestration ability The maximum uptake and release for the monthly average diurnal cycles at the observation site were −8.8 and 2.7 μmol m−2 s−1, respectively. These maximum values were similar to those for the Dangxiong alpine meadow (Shi et al. 2006), but a little lower than those for the Haibei alpine meadow (Kato et al. 2004b). Compared with the low-lying grasslands, these three alpine meadow ecosystems have much lower potential for CO2 uptake at the half-hourly scale. For example, the maximum CO2 uptake at our site was 45 and 28 % of those for a C3 grassland in California and a C4 tallgrass prairie in Oklahoma, respectively (Table 2). The low temperature may be responsible for the difference in half-hourly CO2 flux between alpine ecosystems and low-lying grasslands. On the annual scale, the CO2 uptake for this site (mean value=135.4 g C m−2 year−1) was larger than the Haibei
alpine meadow (mean value=120.9 g C m−2 year−1) located at a comparable elevation (Kato et al. 2004b). The annual CO2 uptake for the Dangxiong alpine meadow was much lower (potentially a CO2 source) due to the higher elevation and the shorter CO2 uptake period. In general, annual CO2 uptake for different C3 grasslands was similar but much less than that for a C4 tallgrass prairie in Oklahoma (Suyker and Verma 2001). Under a semi-humid climate, C3 grasslands can act as a CO2 sink or source due to the inter-annual variation in precipitation (Table 2). Kato et al. (2006) found that air temperature was the dominant factor in determining the annual CO2 uptake at the Haibei alpine meadow. At this alpine meadow, the response of annual total NEP to air temperature for this wet alpine meadow requires a longer period of measurements.
Conclusion The 2.5-year eddy covariance measurements represent clear differences in the response of ET and NEP to the spring drought effects. ET over this alpine meadow was energy limited. In the wet seasons, the seasonal and inter-annual variations in the ET/ETeq were not significant. This enables to accurately describe ET using ETeq to this study site and likely to the similar regions with high SWC and small VPD. The spring drought effect was larger for NEP than for ET. The seasonal trends of NEP were mainly modified by NDVI. Therefore, later leaf emergence in 2012 depressed NDVI and led to a 28 % reduction in NEP. On the half-hourly and daily scales, air temperature had a major influence on NEP. The response of NEP to air temperature on annual scales should be investigated based on the longer measurements. Acknowledgments This study was supported by the National Natural Science Foundation of China (grant No.: 41030106 and 41305012) and the Third Tibetan Plateau Scientific Experiment: Observations for Boundary Layer and Troposphere (GYHY201406001). We also greatly appreciate the staffs from Lijiang Meteorological Administration for their help in the maintenance of the measurements.
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