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Item type:Publication, Vegetation warming and greenness decline across Amazonia during the extreme drought of 2023(Multidisciplinary Digital Publishing Institute (MDPI), 2024-07-01)In 2023, most parts of the world experienced exceptional heat. In particular, anomalous warm temperatures and heatwave events were evidenced across South America during the second half of the year. The situation was particularly critical in the Amazon region in terms of not only hydrometeorological drought but also ecological and socioeconomic impacts. In this study, remote-sensing data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to observe the changes in temperature and vegetation across Amazonia during the exceptional drought of 2023. This analysis was based on anomalies in the land surface temperature (LST) and vegetation indices: the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The amplitude of the LST (AMP-LST), an indicator of the energy partitioning between the latent and sensible heat flux, and fire counts were also considered. The results show widespread and extreme warming across Amazonia during the austral spring in 2023, accompanied by a decline in vegetation greenness, water stress conditions across northern Amazonia, and an enhanced fire occurrence across central and northern Amazonia. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, geeSEBAL-MODIS: Continental-Scale Evapotranspiration Based on the Surface Energy Balance for South America(Elsevier BV, 2023-12-07)Monitoring actual evapotranspiration (ET) is critical for the accurate assessment of water availability and water resources management, especially in areas with dry climates and frequent droughts. The Surface Energy Balance Algorithm for Land (SEBAL) has been used over several land and climate conditions, and is able to estimate ET at field scale with high accuracy. However, model complexity and subjective parameterization have hindered its operationalization, until the recent development of the geeSEBAL model, which implements the SEBAL model on the Google Earth Engine platform. Here, we present a unique methodology for a continental-scale application of SEBAL, called geeSEBAL-MODIS, that employs novel land surface temperature normalization techniques, enabling the application of contextual ET models to very large scales. We introduce a dynamic ET dataset for the entire South American continent, between 2002 and 2021, at 500 m spatial and 8 days temporal resolution. The satellite-based data were compared against daily ET measured at 27 flux towers as well as water balance-based annual ET from 29 large river basins. geeSEBAL-MODIS data were also compared to eight state-of-the-art global ET datasets. At local scale, geeSEBAL-MODIS demonstrated a satisfactory performance (correlation (r) = 0.65, Kling-Gupta Efficiency (KGE) = 0.64, Mean Absolute Error (MAE) = 0.83 mm day−1 (24.7 %) and Root Mean Squared Error (RMSE) = 1.07 mm day−1 (31.8 %)), with negligible bias. At basin scale, geeSEBAL-MODIS generally underestimated ET (bias = -85 mm year−1, r = 0.65, KGE = 0.47, MAE = 107 mm year−1 (10.1 %) and RMSE = 137 mm year−1 (12.9 %)). Compared to other global datasets, geeSEBAL-MODIS demonstrated better performance over multiple South American biomes, climates and land cover types. The developed dataset also provides lower errors (local monthly RMSE = 23.0 mm month−1 and basin annual RMSE = 138 mm year−1) and when compared to the performance of the global datasets (local monthly RMSE between 23.9 and 30.1 mm month−1 and basin annual RMSE between 161 and 308 mm year−1). The analyses demonstrate that geeSEBAL-MODIS can be used as a tool for monitoring climate change and human-related impacts on ET. The geeSEBAL-MODIS model opens the path for high accuracy global ET monitoring at moderate to high resolution, supporting advances in water resources management around the globe.