Evaluating the effectiveness of different surface resistance schemes coupled with Penman-Monteith model for estimating actual evapotranspiration − a global comparative study - Shu, Zhangkang; Jin, Junliang; Menzel, Lucas; Zhang, Jianyun; Luo, Jianfeng; Wang, Guoqing; Cui, Ningbo; Guan, Tiesheng; Liu, Yanli (Journal of hydrology, 2025)
Accurate estimation of terrestrial ecosystem water-heat fluxes is crucial for agricultural production, ecosystem monitoring, and eco-hydrological model development. The selection and optimization of parameterization schemes for surface resistance (rs), a pivotal parameter in the water-carbon cycle, significantly impact the uncertainty of actual evapotranspiration (ET) estimation in Penman-Monteith (PM) models. This study investigates the effects of rs scheme selection and soil water function optimization on the PM modeling. Three types of rs parameterization schemes—Katerji-Perrier (KP) based on atmosphere, Kelliher-Leuning (KL) based on vegetation-atmosphere, and Jarvis based on soil-vegetation-atmosphere—were evaluated alongside three soil moisture constraint functions (Jarvis1, Jarvis2, Jarvis3). The sensitivity of rs and ET to various environmental factors was also analyzed. Model evaluations were conducted at 100 FLUXNET2015 flux towers worldwide, covering ten ecosystems: croplands (CRO), closed shrublands (CSH), deciduous broadleaf forests (DBF), evergreen broadleaf forests (EBF), evergreen needleleaf forests (ENF), grasslands (GRA), mixed forests (MF), open shrublands (OSH), savannas (SAV), and woody savannas (WSA). Results indicated that the PM-Jarvis3 model, which includes wilting point constraint weights and a nonlinear soil moisture response, significantly improved the simulation performance of rs and ET. The effectiveness of rs models varied considerably across ecosystems, with no single model consistently providing optimal ET simulation at all sites. Specifically, the PM-Jarvis3 model demonstrated clear advantages in ENF, GRA, and compound ecosystems (e.g., CSH, OSH, SAV, WSA), while the PM-KL model excelled at some agricultural and forested sites (e.g., DBF, EBF, MF). The PM-KP model showed consistent performance with PM-Jarvis3 only in agro-ecosystems. Overall, PM-Jarvis3 emerged as the most robust model across all biomes. Vapor pressure deficit and net radiation were identified as critical sensitivity factors in all three ET models, with leaf area index being crucial in the PM-KL and PM-Jarvis3 models. According to the PM-Jarvis3 model, soil moisture and CO2 were significant factors for rs and ET simulation in most regions. Despite these findings, the sensitivity of rs and ET to environmental factors remains highly variable among ecosystems. Our improved PM-Jarvis3 model enhances the understanding of the influence of environmental factors on rs and ET simulations globally, providing valuable insights for terrestrial ecohydrological modeling.
Online verfügbar: 9. März 2025, Artikelversion: 11. März 2025
Gesehen am 13.08.2025