Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

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 ($E{T}_{gl}$) and the average actual $ET$ ensemble ($E{T}_{ens}$) based on remote sensing, climate reanalysis, land-surface, and biophysical models to $ET$ computed from basin-scale water balance ($E{T}_{wb}$) 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 $E{T}_{gl}$ and $E{T}_{ens}$ with $E{T}_{wb}$. 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 $E{T}_{wb}$ as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 ($E{T}_{ens}$), and RMSE ranged from 35.6 ($E{T}_{ens}$) to 40.5 mm·month⁻¹ (MOD16). Overall, $E{T}_{gl}$ estimates ranged from 0 to 150 mm·month⁻¹ in most basins in South America, while $E{T}_{wb}$ estimates showed maximum rates up to 250 mm·month⁻¹. $E{T}_{gl}$ 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 $E{T}_{gl}$ and $E{T}_{wb}$, 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 $E{T}_{gl}$ and $E{T}_{wb}$ in the humid tropics (in the Amazon), with $E{T}_{gl}$ 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 $E{T}_{gl}$ and $E{T}_{wb}$ 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.

Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km²). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, which can compromise the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data.

The 10th Surface Water and Ocean Topography (SWOT) Applications Meeting, held one year after the satellite's launch, highlighted significant milestones in mission progress and showcased the innovative work of SWOT Early Adopters (EA) using mission data products. Over 100 participants from diverse sectors convened to discuss operational applications leveraging SWOT's unprecedented water surface measurements. The meeting emphasized applied science efforts to enhance hydrology and oceanographic models. This summary highlights the breadth of operational and private‐sector uses of SWOT data, emphasizing its potential to drive new innovations and deliver societal benefits, such as improved water resource management, flood prediction, and climate resilience.