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

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil : a case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ETa), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ETa, and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was per-formed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ETa yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ETa estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and 7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (rah) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agri-culture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period

Patterns and drivers of evapotranspiration in South American wetlands

Evapotranspiration (ET) is a key process linking surface and atmospheric energy budgets, yet its drivers and patterns across wetlandscapes are poorly understood worldwide. Here we assess the ET dynamics in 12 wetland complexes across South America, revealing major differences under temperate, tropical, and equatorial climates. While net radiation is a dominant driver of ET seasonality in most environments, flooding also contributes strongly to ET in tropical and equatorial wetlands, especially in meeting the evaporative demand. Moreover, significant water losses through wetlands and ET differences between wetlands and uplands occur in temperate, more water-limited environments and in highly flooded areas such as the Pantanal, where slow river flood propagation drives the ET dynamics. Finally, floodplain forests produce the greatest ET in all environments except the Amazon River floodplains, where upland forests sustain high rates year round. Our findings highlight the unique hydrological functioning and ecosystem services provided by wetlands on a continental scale

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

Artificial neural network model of soil heat flux over multiple land covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America

Artificial neural network model of soil heat flux over multiple land covers in South America

Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the artificial neural network (ANN) to predict mid-morning G using extensive remote sensing and meteorological reanalysis data over a broad range of climates and land covers in South America. Surface temperature (Ts), albedo (α), and enhanced vegetation index (EVI), obtained from a moderate resolution imaging spectroradiometer (MODIS), and net radiation (Rn) from the global land data assimilation system 2.1 (GLDAS 2.1) product, were used as inputs. The ANN’s predictions were validated against measurements obtained by 23 flux towers over multiple land cover types in South America, and their performance was compared to that of existing and commonly used models. The Jackson et al. (1987) and Bastiaanssen (1995) G prediction models were calibrated using the flux tower data for quadratic errors minimization. The ANN outperformed existing models, with mean absolute error (MAE) reductions of 43% and 36%, respectively. Additionally, the inclusion of land cover information as an input in the ANN reduced MAE by 22%. This study indicates that the ANN’s structure is more suited for large-scale G prediction than existing models, which can potentially refine SEB fluxes and ET estimates in South America

Estimativa da evapotranspiração real via sensoriamento remoto

Submitted by Janaina Pereira ([email protected]) on 2019-11-27T18:23:00Z No. of bitstreams: 1 Dissertação - FINAL - 01 Bruno Cesar Comini.pdf: 11508873 bytes, checksum: 0db0e5a33b294a532827c8687c88c97d (MD5)Approved for entry into archive by Eliane Araujo ([email protected]) on 2019-11-29T13:12:31Z (GMT) No. of bitstreams: 1 Dissertação - FINAL - 01 Bruno Cesar Comini.pdf: 11508873 bytes, checksum: 0db0e5a33b294a532827c8687c88c97d (MD5)Made available in DSpace on 2019-12-10T16:50:27Z (GMT). No. of bitstreams: 1 Dissertação - FINAL - 01 Bruno Cesar Comini.pdf: 11508873 bytes, checksum: 0db0e5a33b294a532827c8687c88c97d (MD5) Previous issue date: 2018-09-28CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorA evapotranspiração real (ETa) é um dos mais importantes processos do ciclo hidrológico e principal responsável pelas perdas de água na superfície. O conhecimento preciso da taxa de ETa no tempo e no espaço é necessário para a modelagem do balanço hídrico em bacias hidrográficas e identificação da produtividade agrícola, entre outras aplicações. A ETa é determinada in situ por torres de fluxo e por lisímetros, cuja rede de monitoramento é incapaz de representar a sua grande variabilidade espacial. O uso do sensoriamento remoto não permite uma medida direta de ETa, mas possibilita a estimativa da fração evaporativa que, junto a dados meteorológicos, é utilizada para derivar ETa e sua distribuição espacial. Vários modelos utilizam informações de temperatura da superfície e índice de vegetação, captadas por diferentes sensores remotos, como o MODIS e o Landsat, para derivar o valor de ETa. Neste estudo, foi avaliado o modelo de balanço de energia de superfície SSEBop. O modelo foi aplicado com dados MODIS, sendo realizadas 8 parametrizações diferentes, na região do Sistema Aquífero Urucuia (SAU) e foi confrontado com dados de balanço hídrico mensal, estimado pelo modelo SMAP, e balanço hídrico anual, ambos calculados em 4 bacias. O modelo também foi implementado com imagens Landsat 7 e Landsat 8 no Estado do Rio Grande do Sul e validado com dados de ETa medida em duas torres de fluxo instaladas em culturas de arroz irrigado, nos municípios de Paraíso de Sul e Cachoeira do Sul. Finalmente, o modelo foi utilizado para estimar ETa, com imagens Landsat 7 e Landsat 8, em uma plantação de tomates irrigada por pivô central, e comparado a dados de consumo da irrigação. A comparação das 8 parametrizações do modelo revelou que a última versão do modelo apresenta resultados mais próximos aos do balanço hídrico, mas com diferenças pouco expressivas entre a utilização de dados meteorológicos ou climatológicos, e de uma ou mais áreas para o cálculo do fator c. A ETa anual estimada pelo SSEBop mostrou-se próxima à calculada pelo balanço hídrico anual, com erros entre 10 e 20%, porém com baixa correlação linear. A ETa foi superestimada pelo SSEBop na estação seca e subestimada na estação chuvosa, em relação ao modelo SMAP mensal. Comparada à ETa medida nas torres de fluxo, o modelo SSEBop apresentou erros entre 0,8 e 1,6 mm/dia (17% e 34%), com superestimativa dos valores intermediários de ETa. A aplicação do SSEBop na plantação de tomates mostrou boa aproximação entre os valores de ETa e de lâmina de água aplicada. Este estudo demonstrou o potencial do sensoriamento remoto, especialmente do modelo SSEBop, na estimativa e monitoramento de ETa em escala regional e local, bem como de sua aplicação na estimativa do consumo de água para irrigação.Actual evapotranspiration (ETa) is one of the main hydrological cycle processes and the main cause of surface water loss. The precise knowledge of ETa rates along time and space is necessary for modeling water balance in watersheds and identifying agricultural net production, among other applications. ETa is determined in situ by flux towers and by lysimeters, which monitoring network is unable to represent its large spatial variability. Remote sensing is unable to directly measure ETa, but it makes possible the estimation of the evaporative fraction that, combined with meteorological data, is used to derive ETa. Several models use information on surface temperature and vegetation index, estimated by different remote sensors, such as MODIS and Landsat, to derive ETa. In this study, the SSEBop surface energy balance model was evaluated. The model was applied with MODIS data, via 8 different parameterizations, in the region of the Urucuia Aquifer System (SAU) and compared with monthly water balance data, estimated by the SMAP model, and annual water balance, both calculated in 4 basins. The model was also implemented with Landsat 7 and Landsat 8 images in Rio Grande do Sul State and validated with ETa data measured in two flux towers installed in irrigated rice fields in Paraíso de Sul and Cachoeira do Sul towns. Finally, the model was used to derive ETa with Landsat 7 and Landsat 8 images in a tomato plantation irrigated by a central pivot and compared to irrigation consumption data. Comparison of the model 8 parameterizations revealed that the last version of SSEBop results are closer to those of the water balance, however with unexpressive differences between the use of meteorological or climatological data, or one or more areas for computing c factor. SSEBop annual ETa was close to that calculated by annual water balance, with errors ranging from 10 to 20%, but with a low linear correlation. ETa was overestimated by the SSEBop in dry season and underestimated in rainy season, when compared to the SMAP model. Compared to flux tower ETa, the SSEBop model presented errors between 0.8 and 1.6 mm/day (17% and 34%), with an overestimation of intermediate ETa values. The application of SSEBop in the tomato plantation showed a good approximation between the values of ETa and irrigation depth. This study demonstrated the potential of remote sensing, especially the SSEBop model, for regional and local ETa estimation, as well as its use for estimating irrigation water consumption