A avaliação do gerenciamento e destinação, bem como a qualificação e quantificação dos resíduos radioativos gerados nas unidades de medicina e de pesqu...

The lack of observed data and calibration strategies, scale variability, and difficulties in representing heterogeneity of sediment-processes contribute to the usual challenges in achieving satisfactory results in hydro-sedimentological modeling, particularly when using the MUSLE equation for large-scale applications. As a consequence, we investigated five major topics: (1) a sediment-process-based parameterization technique (Hydro-sedimentological Response Unit map - HRUSed); (2) the quality of hydrological modeling with different process-focused parameterizations; (3) a calibration strategy based on the sediment discretization approach for hydro-sedimentological modeling; (4) the use of suspended sediment concentration (SSC) versus suspended sediment discharge (SSD) data for calibration; and (5) trade-offs between increasing the spatial resolution of a large-scale model and using the proposed HRUSed discretization. The current study demonstrated (1) the HRUSed map for South America and (2) a similar performance of large-scale hydrological modeling using a hydrological or hydro-sedimentological discretization approach. (3) The HRUSed discretization approach produced better hydro-sedimentological modeling results. (4) We improved the model’s performance for HRUSed (SSC and SSD results) and for HRU (Hydrological Response Unit map) only for SSD results. (5) Only more detailed spatial discretization has failed to improve process representation. However, increased spatial discretization with a process-parameterization approach focused on hydro-sedimentological dynamics improved model performance.

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.

In hydrosedimentological modelling, the lack of high temporal resolution field data is a limiting factor for the assessment of the performance of models. This way, the remote sensing images have been studied to correlate imagery information with suspended sediment concentration (SSC) in the last decades, aiming to complement field data, by improving the SSC information temporal and spatial resolution. Thus, the present work used the Google Earth Engine (GEE), a cloud-based platform, to systematically obtain red band reflectance information from Landsat 5 imagery to support large-scale hydrosedimentological modelling. The test case was to the Rio Grande do Sul state hydrological region in Brazil, a South American region with scarce SSC data. The methodology applied consisted in nine steps using GEE code: (1) river width analysis using remote sensing imagery to localize the virtual gauge stations (VGS) from the intersection between the discretization of hydrosedimentological model and the chosen rivers, (2) TM sensor definition, onboard of Landsat 5 satellite, (3) collection of red reflectance information between 1990 and 2010, based on previous works that presented better correlation between red reflectance and SSC, (4) in each VGS, we created a circle of radius equal to 1000 m, (5) to each image, we removed clouded-pixels, using the Landsat 5 quality bands, (6) we generated a dynamic water mask to each image to ensure that only pixels with water would be used to collected reflectance information, (7) finally, we calculated the mean of red band reflectance inside the intersection of water mask and circle buffer, removing the clouded-pixels, (8) we calculated a filter to remove remnants clouded-pixels and random errors from imagery, (9) we used the MGB-SED model to simulate long-term SSC in the region and we calibrated the model with the GEE data based on a correlation approach. The results found were: (i) 1267 virtual gauge stations, approximately 20 times the number of in situ SSC gauging stations available in the region, (ii) a larger area of data and greater temporal resolution, (iii) improvement in the correlation between model results and red reflectance, when we assess the model with SSC observed data. In conclusion, the work shows the potential of GEE to simply obtain large-scale reflectance data that could be used to improve the calibration processes of large-scale hydrosedimentological modelling.