Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions

Multi-scale actual evapotranspiration mapping in South America with remote sensing data and the geeSEBAL model

O monitoramento preciso da evapotranspiração (ET) é crucial para gerenciar os recursos hídricos, garantir a segurança alimentar e avaliar os impactos das mudanças climáticas. Modelos de Balanço de Energia da Superfície (SEB) que usam dados de sensoriamento remoto são os mais confiáveis para estimar a ET, mas muitas vezes são difíceis de aplicar em grande escala devido ao longo tempo de processamento, necessidade de calibração local, entre outros obstáculos. Esta tese tem como foco a melhoria do geeSEBAL, uma implementação do modelo Surface Energy Balance Algorithm for Land (SEBAL) na plataforma Google Earth Engine (GEE), adaptando-o para modelagem em escala continental, usando imagens do Moderate Resolution Imaging Spectroradiometer (MODIS). O novo modelo, chamado geeSEBALMODIS, foi usado para gerar uma série temporal de ET a cada 8 dias para a América do Sul com pixels de 500 m. Estudos de validação mostram que o geeSEBAL-MODIS é mais preciso do que outros produtos globais de ET, com uma redução do erro de 13% na escala de campo e 30% na escala de bacia hidrográfica. O conjunto de dados está disponível para o público e pode ser usado para monitorar tanto mudanças climáticas em grande escala quanto as variações locais de ET relacionadas às atividades humanas. A análise de tendências mostra um aumento de 8,4% na ET sobre a América do Sul, associado ao aumento da demanda atmosférica, e à redução da precipitação e disponibilidade de água. Esses resultados destacam a importância de informações precisas sobre os processos do ciclo hidrológico para auxiliar no planejamento e gerenciamento dos recursos hídricos em um cenário de maior escassez. Nesse contexto, projetos como o OpenET, que fornece dados confiáveis e de alta resolução espacial de ET nos Estados Unidos, são cruciais para monitorar o consumo de água e auxiliar no desenvolvimento sustentável. Este trabalho também apresenta uma reprodução parcial do processo do OpenET para a intercomparação de modelos de sensoriamento remoto com dados de torres de fluxo, usando torres micrometeorológicas na América do Sul. Os resultados são promissores e abrem caminho para a expansão do OpenET além dos Estados Unidos e em direção a uma aplicação global.Accurately monitoring evapotranspiration (ET) is crucial for managing water resources, ensuring food security, and assessing the impacts of climate change. Surface Energy Balance (SEB) models that use remote sensing data are the most reliable for estimating ET, but they are often challenging to apply on a large scale due to long processing times, and local calibration requirements, among other obstacles. This dissertation focuses on improving geeSEBAL, an implementation of the Surface Energy Balance Algorithm for Land (SEBAL) model on the Google Earth Engine (GEE) platform, by adapting it for continental-scale modeling using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The new model, called geeSEBAL-MODIS, was used to generate a temporal series of ET every 8 days for South America with pixels of 500 m. Validation studies show that geeSEBAL-MODIS is more accurate than other global ET products, with a reduction in error of 13% at the field scale and 30% at the basin scale. The dataset is publicly available and can be used to monitor both largescale climate change and local ET variations related to human activities. Trend analysis shows an 8.4% increase in ET over South America, associated with increased atmospheric demand, and reductions in precipitation and water availability. These findings underscore the importance of accurate information on hydrological cycle processes to assist in planning and managing water resources in a scenario of greater scarcity. In this context, projects like OpenET, which provides reliable and high spatial-resolution ET data in the United States, are crucial for monitoring water consumption and aiding in sustainable development. This work also presents a partial reproduction of the OpenET process for intercomparing remote sensing models with flux tower data, using micrometeorological towers in South America. The results are promising and pave the way for expanding OpenET beyond the United States and toward global application