Seasonal and Diurnal Variations in the Priestley–Taylor Coefficient for a Large Ephemeral Lake

Abstract

The Priestley–Taylor equation (PTE) is widely used with its sole parameter (α) set as 1.26 for estimating the evapotranspiration (ET) of water bodies. However, variations in α may be large for ephemeral lakes. Poyang Lake, which is the largest freshwater lake in China, is water-covered and wetland-covered during its high-water and low-water periods, respectively, over a year. This paper examines the seasonal and diurnal variations in α using eddy covariance observation data for Poyang Lake. The results show that α = 1.26 is overall feasible for both periods at daily and subdaily scales. No obvious seasonal trend was observed, although the standard deviation in α for the wetland was larger than that for the water surface. The mean bias in evaporation estimations using the PTE was less than 5 W·m−2 during both periods, and the root mean square errors were much smaller than the average evaporation measurements at daily scale. U-shaped diurnal patterns of α were found during both periods, due partly to the negative correlation between α and the available energy (A). Compared to the vapor pressure deficit (VPD), wind speed (u) exerts a larger contribution to these variations. In addition, u is positively correlated with α during both periods, however, VPD was positively and negatively correlated with α during the high-water and low-water periods, respectively. Subdaily α exhibited contrasting clusters in the (u, VPD) plane under the same available energy ranges. Our study highlights the seasonal and diurnal course of α and suggests the careful use of PTE at subdaily scales.

Radiative and Aerodynamic Contribution to Evaporation: Eddy‐Covariance Comparison Between a Plain and a Plateau Lake

“…The lake‐atmosphere interaction can change the surrounding atmospheric circulation, and influence the climate and weather conditions on local or even global scale (Duan & Bastiaanssen, 2015; Dutra et al., 2010; Ljungemyr et al., 1996; Long et al., 2007; Rouse et al., 2005; Samuelsson & Tjernström, 2001). However, until recent decades, actual lake evaporation has become measurable in high‐frequency with the development of the eddy covariance (EC) system (Blanken et al., 2011; Bouin et al., 2012; Gan et al., 2020; Liu, Hiyama, et al., 2012; Liu, Zhang, et al., 2012; McGloin et al., 2014; Metzger et al., 2018; Zhao & Liu, 2018). Surface water fluxes have been reported in lakes worldwide (Cui et al., 2020; Gan & Liu, 2020; Wang et al., 2018, 2020).…”

Evaluation of the Performance of Different Methods for Estimating Evaporation over a Highland Open Freshwater Lake in Mountainous Area

“…The P-T coefficient taken as a constant of 1.26 without consideration its seasonal variability (larger than 1.26 in winter and smaller than 1.26 in summer, not shown) might introduce uncertainties and cause larger RMSE and MAE of the PT method in comparison with the PM method. However, in Poyang lake, a large shallow subtropical lake in China, no obvious seasonal trend in α was observed [68], and the PT method performed slightly better than the PM method [64]. The BS method displayed the largest bias for lake evaporation estimation among the combination methods from daily to monthly timescales, where its MAE value was almost twice as much as the value from the PM method.…”

Intra-Seasonal and Intra-Annual Variation of the Latent Heat Flux Transfer Coefficient for a Freshwater Lake

“…In this study, it was set to the widely accepted α = 1.26. Its value over water surfaces may vary between 1.1 and 1.4 [14,34,38], especially in a diurnal scale [11,[39][40][41]; however, it was confirmed by many theoretical studies and observations that 1.26 is feasible in most of the cases [13,42]. The PT equation is written as:…”