Global evapotranspiration datasets assessment using water balance in South America

Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month⁻¹(MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month⁻¹. Tgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management

Evaluation of runoff estimation from GRACE coupled with different meteorological gridded products over the Upper Blue Nile Basin

Study Region: The Upper Blue Nile (UBN) basin, Ethiopia. Study Focus: In efforts to accurately close the water balance equation for the UBN basin using remote sensing products, river runoff is calculated as a residual from the water balance equation by incorporating Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and remote sensing products for precipitation (P) and evapotranspiration (ET). The calculated river runoff is then compared to the gauge records located at the basin's outlet. The best performing combination among the various combinations is chosen by aggregating rankings attributed to both error and linear fit metrics. The errors associated with each satellite product were assessed by forcing the In-Situ runoff to estimate the P, ET, and TWS. This methodology helps in addressing the uncertainty linked with each hydrological component. New Hydrological Insights for the Region: The best P, ET, and TWS combination performance products to estimate runoff are SM2RAIN-CCI, GLEAM, and GRACE Spherical Harmonic products, respectively. The statistical results for the six metrics are R2 = 0.7, slope = 1.6, y-intercept = - 0.5 cm, RMSE = 3 cm, MAE = 2.8 cm, and PBIAS = 36%. The uncertainty from each hydrological component was quantified and showed that improving the accuracy of P and ET estimation is a crucial step to successfully close the water balance

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Surface water and energy fluxes in South America : an integrated approach based on remote sensing and flux measurements

South America is a water-abundant continent, home to the world's largest river basins and rainforest, which plays a crucial role in providing moisture to other regions of the continent through evapotranspiration (). is a crucial indicator of the earth's ecosystem functioning, linking the water, energy, and carbon cycles. Due to the great challenge of obtaining information based on in situ measurements, remote sensing data has become a great opportunity to obtain estimations. Based on measurements and estimations based on remote sensing data, this study aimed to evaluate the dynamics, patterns and controls of water and energy fluxes in South America, seeking to answer three main questions: i) can remote sensing data provide accurate information on the water balance?; ii) how do the factors controlling vary across different biomes and land use and land cover (LULC) conditions? and iii) can remote sensing models represent accurately patterns and its components under different LULC conditions? To answer the first question, we performed a water balance analysis, evaluating the uncertainties of precipitation and estimations using in situ measurements, and conducting an analysis to understand how much these uncertainties can be affected due to the basin’s scales. The results showed that due to the uncertainties related to each of the variable from remote sensing it is not yet possible to achieve the water balance closing. However, the approach proved to be a great alternative to evaluate the dynamics of water fluxes from small to large basins, especially in those where in situ measurement is still scarce. To seek to answer the second question, we evaluated the influence of biotic and abiotic factors on control processes, based on surface and aerodynamic conductances and the decoupling factor, at 20 flux measurement sites in South America. Through this analysis, different patterns of latent () and sensible () heat fluxes were verified, and different degrees of importance of biotic and abiotic controls on the process according to different LULC conditions. Finally, based on 11 flux measurement sites and four models (MOD16, GLEAM, PML and SSEBOP), we assessed the accuracy of estimates in the Amazon basin, and the representation of fluxes in forest, pasture, and soybean areas, in the Tapajós basin. The results showed that obtaining accurate estimates is still a major challenge in the Amazon basin, especially in humid and seasonally flooded sites. Significant discrepancies between the models and between measurements were found, and these discrepancies were even more significant when evaluated the individual components. However, even though each model did not perform significantly under all climatic and vegetation conditions, they present together a great opportunity to improve the accuracy of estimates, leading to an improved understanding of the impacts on water and energy fluxes due to human activities. Thus, these results demonstrate the potential and limitations of hydrological components obtained by remote sensing, especially for , and how LULC changes may modify this flux in South America.A América do Sul é um continente abundante em água, abrigando as maiores bacias hidrográficas e a maior floresta tropical do mundo, a floresta Amazônica. A Amazônia desempenha um papel crucial no fornecimento de umidade para outras regiões do continente por meio da evapotranspiração (). A é um indicador crucial do funcionamento do ecossistema terrestre, interligando os ciclos da água, energia e carbono. Devido ao grande desafio de obtenção de informações de por medições in situ, o uso de dados de sensoriamento remoto tem se mostrado uma grande alternativa para obter estimativas desta variável. Com base em dados medidos e estimados por sensoriamento remoto foi conduzido um estudo que visou analisar a dinâmica, os padrões e os controles dos fluxos de água e energia na América do Sul, buscando responder a três questões principais: i) os dados de sensoriamento remoto podem fornecer informações precisas sobre o balanço hídrico?; ii) como os fatores que controlam a variam em diferentes biomas e condições de uso e cobertura do solo (LULC)?; e iii) os modelos de sensoriamento remoto conseguem representar com acurácia os padrões de e das suas componentes em diferentes condições de LULC? Para responder a primeira pergunta realizou-se uma análise de balanço hídrico, na qual foi avaliada as incertezas das estimativas de precipitação e usando medições in situ, e uma análise do quanto essas incertezas podem ser afetadas devido ao efeito de escala das bacias analisadas. Os resultados mostraram que devido às incertezas relacionadas com cada uma das componentes estimadas por sensoriamento remoto ainda não é possível alcançar o fechamento do balanço hídrico. No entanto, a abordagem demonstrou ser uma grande alternativa para avaliar a dinâmica dos fluxos de água, de pequenas a grandes bacias, especialmente naquelas onde a medição in situ ainda é escassa. Para buscar responder a segunda pergunta analisou-se a influência dos fatores bióticos e abióticos no controle dos processos de , por meio da análise das condutâncias de superfície e aerodinâmica e do fator de desacoplamento em 20 locais de monitoramento de fluxo na América do Sul. Por meio desta análise verificou-se diferentes padrões dos fluxos de calor latente () e sensível (), além de diferentes graus de importância dos controles bióticos e abióticos sobre o processo de e de acordo com as diferentes condições de LULC. Por fim, com base em 11 locais de monitoramento de fluxo e quatro modelos de (MOD16, GLEAM, PML e SSEBOP), analisou-se a acurácia destas estimativas na bacia amazônica, e a representação dos fluxos de em áreas de floresta, pastagem e soja, na bacia do Tapajós. Os resultados mostraram que a obtenção de estimativas acuradas de ainda é um grande desafio na bacia Amazônica, principalmente em locais úmidos e sazonalmente inundados. Discrepâncias significativas entre os modelos e entre as medições foram encontradas, sendo estas discrepâncias ainda mais expressivas quando se analisou as componentes individuais de . No entanto, os resultados deste estudo demonstraram que apesar de cada modelo não apresentar um desempenho significativo em todas as condições climáticas e de vegetação, estes apresentam em conjunto, uma grande oportunidade para melhorar a acurácia das estimativas de , propiciando um aprimoramento na compreensão dos impactos nos fluxos de água e energia devido a atividades antrópicas. Deste modo, estes resultados enfatizam os potenciais e limitações das variáveis hidrológicos obtidas por sensoriamento remoto, especialmente para a , e como as mudanças LULC podem modificar este fluxo na América do Sul

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

Amazon hydrology from space : scientific advances and future challenges

As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure

Global pattern and mechanism of terrestrial evapotranspiration change indicated by weather stations

Accurate estimation of global terrestrial evapotranspiration (ET) is essential to understanding changes in the water cycle, which are expected to intensify in the context of climate change. Current global ET products are derived from physics-based, yet empirical, models, water balance methods, or upscaling from sparse in situ observations. However, these products contain substantial limitations such as the coarse resolution due to the coarse climate reanalysis forcing data, the assumptions on the parameterization of the process, the sparsity of the observations, and the lack of global accuracy validation. Using estimates of ET based on the global weather station network and machine learning, we show that global ET ranged from 493 to 522 mm yr-1 and increased at the rate of 0.60 mm yr-2 from 2003 to 2019. Between the two periods of 2003-2010 and 2011-2019, 61.7% of stations showed an increase in ET. At the large river basin scale, the reliability of the produced ET in this study is comparable to gridded ET data and even higher in regions where weather stations are relatively dense and more representative. Correlation analysis and causal network analysis showed that the main drivers of ET long-term changes are changes in air temperature, radiation, vegetation conditions, and vapor pressure deficit. There is great variability in the causal mechanisms of ET change across vegetation cover and across seasons. This study highlights the promise of using weather stations to complement global ET and water cycle studies at the station scale