Abstract
Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.
Highlights
Evapotranspiration (ET) has a critical influence on the cycling of water and energy and represents a crucial relationship for feedbacks and interactions between those cycles [1,2]
Global Land Evaporation Amsterdam Model (GLEAM) interception is exclusively sensitive to precipitation, while the remainder of the GLEAM components are largely sensitive to net radiation, soil evaporation especially
The results show that perturbing precipitation shifts the partitioning of GLEAM from transpiration to soil evaporation at our study sites
Summary
Evapotranspiration (ET) has a critical influence on the cycling of water and energy and represents a crucial relationship for feedbacks and interactions between those cycles [1,2]. Future changes to regional and global climate are expected to significantly alter both the supply (precipitation, snow and groundwater) and demand (ET) side of the hydrological cycle [3,4]. These hydrological impacts are projected to involve increased drought severity, frequency and duration [5,6,7,8]. Due to the large spatial scale at which estimates of ET are meaningful for climate research, remote sensing-based models have become a dominant means to derive ET fluxes [2,15,16]. Accurate satellite-based estimates of ET exist with global coverage at several spatial and temporal scales [16,17]
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