Energy balance closures in diverse ecosystems of an endorheic river basin
Introduction
The eddy covariance method (EC), measuring the vertical turbulent fluxes by the covariance between turbulent fluctuations of the vertical wind and quantity of measured scalars, has been one of the most direct approaches for measuring water and heat flux exchanges between the biosphere and atmosphere (Wilson et al., 2002; Jung et al., 2010; Aubinet et al., 2012; Stoy et al., 2013). Over the past decades, this method has been widely used throughout the world. Up to now, a global network of EC sites called FLUXNET (https://fluxnet.fluxdata.org) was established across the Americas, Europe, Asia, Africa, and Australia. However, one common problem across these EC sites is that the turbulent fluxes (the sum of sensible and latent heat fluxes) are smaller than the available energy (the difference between net radiation, ground heat flux and storage term). This underestimation can be 10%–30% of the available energy (Twine et al., 2000; Wilson et al., 2002; Li et al., 2005; Oncley et al., 2007; Foken, 2008; Foken et al., 2010; Franssen et al., 2010; Mauder et al., 2010; Leuning et al., 2012; Stoy et al., 2013; Xu et al., 2017; McGloin et al., 2018). This systematic bias is called the surface energy imbalance or non-closure problem and has been the subject of active research in recent years. Potential reasons for this energy imbalance problem have been proposed by many researchers (Mahrt, 1998; Twine et al., 2000; Foken et al., 2006; Foken, 2008; Wang et al., 2009; Foken et al., 2011; Leuning et al., 2012; Wohlfahrt and Widmoser, 2013; Zhou and Li, 2018), including mismatch in the footprints of radiation and turbulent flux measurements, measurement/computation errors, significant advective fluxes, and inadequate sampling of large-scale, low-frequency turbulent eddies. A recent panel discussion concluded that the inadequate sampling of large-scale turbulent eddies is likely to be one of the leading contributors to the surface energy imbalance (Foken et al., 2011), which has been confirmed by many observational studies (Panin et al., 1998; Stoy et al., 2013; Eder et al., 2015a; Gao et al., 2016, 2017; Xu et al., 2017) and large-eddy simulation (LES) studies (Kanda et al., 2004; Inagaki et al., 2006; Eder et al., 2015b; De Roo and Mauder, 2018; Zhou et al., 2018).
Over the past decade, many researchers have focused on this surface energy imbalance problem and hence much knowledge on this problem has been obtained. For example, some studies explored the relationships between surface heterogeneity and flux imbalance (Panin et al., 1998; Inagaki et al., 2006; Stoy et al., 2013; Eder et al., 2015a, b; Xu et al., 2017; Zhou et al., 2018). These studies found that the larger the surface heterogeneity, the larger the flux imbalance problem occurs. Additionally, other studies explored the effects of meteorological variables such as friction velocity (u*) and atmospheric stability on the flux imbalance (Wilson et al., 2002; Li et al., 2005; Franssen et al., 2010; Stoy et al., 2013; McGloin et al., 2018). Recently, Gao et al. (2017) discovered that the enlarged phase difference between the vertical velocity and water vapor associated with large eddies leads to an increased energy imbalance at a cotton field in California. Based on the hypothesis that the correlation coefficient of two variables is an indication that these two variables are out of phase (Gao et al., 2017), McGloin et al. (2018), who calculated the correlation coefficients for vertical wind velocity and water vapor (Rwq) and vertical wind velocity and sonic temperature (RwT) at five EC towers in the Czech Republic, found that the larger the phase differences between the vertical velocity and scalars (i.e., water vapor and sonic temperature), the smaller the surface energy imbalance. The systematic conclusions on the relationships between the surface energy imbalance and related variables can be found in the review paper by Zhou and Li (2018). Further similar studies at sites covering a wide variety of ecosystems, which have received little focus, such as alpine meadow, desert, shrub, and crops in endorheic river basins, will contribute to a more comprehensive understanding of the surface energy imbalance problem.
Recently, additional knowledge is obtained based on the LESs. For example, in our previous study based on LESs, we found that the decay of the turbulent kinetic energy (TKE) during the afternoon period is responsible for the rapid increase of the flux imbalance during the afternoon and there is a linear relationship between the flux imbalance and TKE (Zhou et al., 2018). However, whether the above conclusions are valid in the field requires verification using the EC data over various ecosystems. In addition, based on LESs, it was found that during daytime, the energy balance closure ratio (EBR) values increase in the morning and decrease in the afternoon (Zhou et al., 2018), which is consistent with the field observations of Stoy et al. (2013), who analyzed data from FLUXNET sites across 173 ecosystems. However, it is difficult to explain the other diurnal variations of EBR (i.e., EBR values increase throughout the daytime period) found by Wilson et al. (2002), who also analyzed data from 22 FLUXNET sites, and by Xu et al. (2017) at some of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER; Li et al., 2013, 2017) sites. The variables that are responsible for these different EBR diurnal variations remain unclear. Therefore, in this study, we use the EC data obtained in HiWATER to analyze the EBR values over a wide variety of ecosystems such as alpine meadow, desert, shrub, and crops in the Heihe River Basin, which is representative of endorheic river basins (Cheng et al., 2015; Li et al., 2016). We aim to analyze the relationships between the EBR values and related variables over various ecosystems in the Heihe River Basin, and examine whether the knowledge obtained from previous studies is valid over these ecosystems, especially the recent knowledge obtained from LESs. We also aim to explore the reasons for different EBR diurnal variations existing in previous studies.
Section snippets
Study area
The Heihe River Basin (HRB) is the second largest endorheic river basin in China (Fig. 1), covering an area of approximately 1,432,000 km2 (Li et al., 2013). This basin is characterized by distinct cold and arid landscapes distributed from upstream to downstream: snow/glaciers, frozen soil, alpine meadow, forest, grass, river, irrigated crops, riparian ecosystem, Gobi and desert (Li et al., 2013), and this basin is representative of endorheic river basins (Cheng et al., 2015). Therefore, the
Surface energy balance
The surface energy balance can be expressed as follows:where Rn is net radiation; G is the ground heat flux; H and LE are the sensible and latent heat fluxes, respectively; and S is the storage term including the sensible and latent heat storages in the air layer from the surface to the sensor (Sa), canopy heat storage (Sc) and photosynthesis storage (Sp). The Rn, H and LE are measured by the stations. G is calculated by combining the soil heat flux measured using the
Energy balance closure ratios over different landscapes
Fig. 3 shows the half-hour sums of the LE and H values against the Rn−G−S values, respectively. The results are summarized in Table 2. The half-hour EBR values range from 0.53 to 0.99, with a mean value of 0.84. The corresponding coefficients of determination (R2) range from 0.87 to 0.94, with a mean value of 0.90. These results are similar to those obtained in other regions (Wilson et al., 2002; Li et al., 2005; Mauder et al., 2006, 2007; Oncley et al., 2007; Zuo et al., 2012; Liu et al., 2013
The variables responsible for diurnal variations of EBR values
In Sect. 4.1, Fig. 4 shows two different diurnal variations of EBR values during the daytime (9:00 ˜ 19:00) at different sites. Similarly, we also found correlations between EBR values and related variables in Sect. 4.2–Sect. 4.6. Here, we examine the variables (i.e., u*, TT, z/L, TKE, RI, Rwθ, and Rwq) responsible for the diurnal variations of EBR during the daytime (9:00 ˜ 19:00). Note that due to a lack of consecutive remote sensing images, it is impossible to analyze the correlation between
Acknowledgments
This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20100104), the National Natural Science Foundation of China (Grant No. 41630856 and 91425303), and the 13th Five-year Informatization Plan of Chinese Academy of Sciences (Grant No. XXH13505-06). We express our sincere appreciation to the professor Shaomin Liu and Dr. Ziwei Xu, State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, for their
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