Hugo de Oliveira Fagundes

The formation of pit lakes in decommissioned open-pit mines has raised concerns regarding long-term water quality. Turbidity, a key indicator of suspended particulate matter, influences water clarity and aquatic ecological processes. This study estimates surface turbidity in the Águas Claras Mine (MAC) pit lake in Nova Lima, Brazil, using satellite imagery and in situ data to generate a continuous time series and assess compliance with thresholds established by current Brazilian environmental legislation. Landsat 5 and 8 imagery were used to derive a spectral turbidity index. Based on the temporal overlap between satellite and field data, a linear regression model (R² = 0.77) was developed and applied to extend the turbidity time series. The results indicate that turbidity values remained below the legal limits for Class 1 freshwater. Higher turbidity levels were observed during the initial filling phase, associated with exposed slopes, as well as episodic increases during the rainy season due to sediment runoff. Over time, progressive revegetation and minimal anthropogenic disturbance contributed to the stabilization of water quality conditions. The integration of in situ measurements and remote sensing proved to be an effective approach for monitoring water quality in post-mining environments, supporting both environmental liability assessment and closure management.

The biggest Brazilian rainfall event occurred in May 2024 affecting millions of people. The damage caused includes several deaths and other social issues, billions in economic losses, and massive environmental devastation with more than 10,000 landslides and sediment settling in flat areas. In this context of extreme events, we aimed to estimate the suspended sediment transport in the Guaíba basin during this unprecedented flood. We used the model for large basins MGB-SED and daily precipitation to compute sediment erosion, transport and deposition. The model was calibrated considering the historical period, prioritizing the adequate representation of extreme events, resulting in KGE values higher than 0.4 in the main sediment stations. For the first time, our results provided sediment yield estimates for this event: 5 million tons of suspended sediment were delivered to Guaíba from April 27 to June 17, 2024. The Taquari River was the tributary that transported the most suspended sediment, reaching a peak of 554,500 tons on May 2, which is five times greater than the highest simulated peak in the historical period. After the event, a large deposition of coarse sediments in the lowland areas, silting up the rivers and islands was widely reported. Despite this, we observed from satellite images that the morphological changes (e.g. bank erosion and the appearance/ changes in sand banks) along the main channels were insignificant compared to the event scale. We conclude that, even in the face of the unprecedented sediment and water flow, these rivers demonstrate a bank stability condition and high suspended sediment transport capacity, even suggesting an equilibrium condition.

Deriving sediment yield (SY) from discharge (Q) and suspended sediment concentration (SSC) measurements is subject to multiple sources of error, including the sampling method, the sampling scheme and frequency, the load calculation method, and the measuring period. While the uncertainty from these individual sources of error has been studied in various contexts, their combined effect on SY calculations remains largely unquantified. This is mainly due to their complex and counteracting influences, the absence of detailed sampling protocol information, and the lack of true reference data. Still, estimating the total uncertainty on current and historical SY measurements is crucial to understand how these observational errors propagate in SY modelling and can impact subsequent model interpretation and decision-making.Here, we aim to develop a tool that can provide realistic ranges of total uncertainty in SY observations worldwide for which limited metadata is reported. We do this by means of Monte Carlo simulations and machine learning. Using available long-term daily Q and SSC series, we quantify the effect of measurement-related sources of error, as well as their relative importance, on SY calculations. We apply this method on a (spatially) diverse selection of ∼180 gauging stations and further explore the relationship between SY uncertainty and catchment characteristics, including upstream area, land cover, and climate. Preliminary findings indicate that the range of uncertainty in SY calculations is mainly influenced by the sampling frequency, whereas the load calculation method and the sampling scheme can introduce important biases. Measuring errors on individual Q and SSC observations have relatively little impact on total SY uncertainty, provided that these measurements are unbiased. When considering long-term average SY, the length of the measuring period then becomes the most important source of uncertainty. Overall, the combined effect of these sources of error can lead to deviations up to three orders of magnitude from the true SY. Using these sampling-related variables and catchment characteristics derived from global hydro-environmental datasets, we further apply a gradient boosting algorithm to predict total uncertainty in annual SY and achieve a Nash-Sutcliffe model efficiency of ∼0.76. The model resulting from this work can thus provide scientists with realistic uncertainty estimates on existing SY observations with only basic metadata information available.

The increase in atmospheric CO2 concentration elevates atmospheric temperature and enhances water storage capacity. This could lead to more extreme precipitation events, while simultaneously, higher temperatures may cause land and soil to dry out. Such alterations create ambiguous expectations regarding the direction of hydrological changes in the following decades. This work quantifies streamflow changes on flood discharges in South America based on the MGB‐SA hydrodynamic‐hydrological model forced with the Climate Model Intercomparison Project (CMIP6) ensemble of climate projections. Future projections indicate that floods are expected to increase in magnitude and become up to five times more frequent in Southern Brazil, a region recently impacted by unprecedented flooding. In contrast, ecosystems such as the Amazon and the Pantanal will likely experience less frequent floods in the coming decades. These projections have relevant implications not only for flood risk in populated areas but also for ecological dynamics.

River sediment fluxes have been impacted in South America (SA), one of the continents with the highest erosion and sediment transport rates globally. However, the magnitude and spatio‐temporal distribution of the main drivers of changes have been poorly identified and explored. Here, we performed simulations using a hydrological‐hydrodynamic‐sediment model to comprehensively estimate the spatial and temporal sediment changes and trends in SA from 1984 to 2019. We found that 51% of the main SA rivers experienced significant changes in simulated sediment transport (QST) over this period, with 36% due to Amazon deforestation and river damming and 15% due to precipitation changes. We also estimated a 10% reduction in the average sediment delivery to the oceans. Deforestation was responsible for QST changes higher than 80% in some Amazon sites, and hydropower expansion led to a greater reduction of sediment flows (80%–100%) in the Tocantins, Uruguay, Upper Paraná, lower São Francisco, Desaguadero, and Negro rivers. In addition, our results suggest that reservoirs built in the Amazon region in the 2011–2019 period are also affecting sediment transport. Our modeling outputs provide unprecedented information about the status of river sediment dynamics in SA, and a template to develop evidence‐based strategies and transboundary policies related to continental‐wide sediment dynamics and the conservation and restoration of ecosystems.

Sediment flows dynamics (erosion, transport and deposition) have been disrupted in South America (SA), a continent with the highest erosion and sediment transport rates globally. However, the magnitude and spatial distribution of the main drivers of changes have been poorly identified and explored. Here, we performed simulations using a hydrological-hydrodynamic-sediment model to comprehensively estimate the spatial and temporal sediment changes and trends in SA from 1984 to 2019. We found that 51% of the main SA rivers experienced significant changes in simulated sediment transport (QST) over this period, with 36% due to Amazon deforestation and river damming and 15% due to precipitation changes. We also estimated a 10% reduction in the average sediment delivery to the oceans. Deforestation was responsible for QST changes above 80% in some Amazon sites, and hydropower expansion led to a greater reduction of sediment flows (as high as 80-100%) in the Tocantins, Uruguay, Upper Paraná, lower São Francisco, Desaguadero, and Negro rivers. In addition, our results suggest that reservoirs built in the Amazon region in the last decade are also affecting sediment transport. Our modeling outputs provide unprecedented information about the status of sediment dynamics in SA, and a means to develop evidence-based strategies and transboundary policies related to continental-wide sediment dynamics and the conservation and restoration of ecosystems.