Assessment of Different Complementary-Relationship-Based Models for Estimating Actual Terrestrial Evapotranspiration in the Frozen Ground Regions of the Qinghai-Tibet Plateau - Chengpeng Shang; Tonghua Wu; Ning Ma; Jiemin Wang; Xiangfei Li; Xiaofan Zhu; Tianye Wang; Guojie Hu; Ren Li; Sizhong Yang; Jie Chen; Jimin Yao; et al. (Remote Sensing, 2022)
Actual evapotranspiration (ETa) is important since it is an important link to water, energy, and carbon cycles. Approximately 96% of the Qinghai-Tibet Plateau (QTP) is underlain by frozen ground, however, the ground observations of ETa are particularly sparse–which is especially true in the permafrost regions–leading to great challenge for the accurate estimation of ETa. Due to the impacts of freeze-thaw cycles and permafrost degradation on the regional ET process, it is therefore urgent and important to find a reasonable approach for ETa estimation in the regions. The complementary relationship (CR) approach is a potential method since it needs only routine meteorological variables to estimate ETa. The CR approach, including the modified advection-aridity model by Kahler (K2006), polynomial generalized complementary function by Brutsaert (B2015) and its improved versions by Szilagyi (S2017) and Crago (C2018), and sigmoid generalized complementary function by Han (H2018) in the present study, were assessed against in situ measured ETa at four observation sites in the frozen ground regions. The results indicate that five CR-based models are generally capable of simulating variations in ETa, whether default and calibrated parameter values are employed during the warm season compared with those of the cold season. On a daily basis, the C2018 model performed better than other CR-based models, as indicated by the highest Nash-Sutcliffe efficiency (NSE) and lowest root mean square error (RMSE) values at each site. On a monthly basis, no model uniformly performed best in a specific month. On an annual basis, CR-based models estimating ETa with biases ranging from −94.2 to 28.3 mm year−1, and the H2018 model overall performed best with the smallest bias within 15 mm year−1. Parameter sensitivity analysis demonstrated the relatively small influence of each parameter varying within regular fluctuation magnitude on the accuracy of the corresponding model.