Frederico Terra de Almeida

This study presents an integrative review of the methods used to estimate and monitor suspended sediment concentration (SSC), an important parameter for understanding erosive processes, river dynamics, reservoir lifespan, and water quality. The search in the Scopus, Web of Science, and SciELO databases covered studies published between 1992 and 2025. Of the 452 records identified, 269 were considered eligible after removing duplicates. The studies were grouped into traditional methods, optical, acoustic, remote sensing, optical–acoustic integrations, uncommon methods, and parallel measurements. Traditional methods predominated (95 studies), followed by remote sensing (84), optical (40), and acoustic (17). Traditional methods are reliable but require high logistical effort and have low temporal resolution. Optical sensors allow continuous monitoring, although they require local calibration. Acoustic methods better represent the vertical and transversal distribution of SSC but are sensitive to particle characteristics. Remote sensing expands spatial and temporal coverage but depends on image resolution and atmospheric conditions. Therefore, it is concluded that no method is universally superior, and the choice should consider the objective, environment, scale, and acceptable uncertainty. Traditional methods remain relevant as they serve as a reference for the others.

The SWAT model is a fundamental tool for water resource management and is widely used in hydrological monitoring. In this context, a scientometric analysis was conducted to characterize the use of the SWAT model in Brazil, a country with significant water availability. A total of 119 scientific articles published between 2011 and 2024 in journals indexed in the Scopus and Web of Science databases were analyzed. These publications were distributed across 62 journals and involved 396 authors affiliated with 128 institutions from 19 countries. The study revealed a concentration of research in specific hydrographic regions and thematic areas, with 40% of the studies focusing on the impacts of land-use changes on hydrology and sediment yield. The main gaps identified relate to water quality modeling and the application of SWAT in urban basins. Furthermore, limitations regarding the transparency and availability of input data were observed, hindering study replicability and knowledge dissemination. The unequal geographic distribution of these applications represents a challenge for Brazilian science. Therefore, for SWAT research to mature and effectively integrate science with water sustainability, it is imperative to promote transparent data sharing and foster inter-regional collaborations. By overcoming these barriers, it will be possible to ensure study reproducibility and support the formulation of equitable public policies across the entire national territory.

Soil erosion is a critical global challenge and presents particularly alarming characteristics in the Amazon–Cerrado transition in Mato Grosso, a leading agricultural state in Brazil. Therefore, soil and water losses were evaluated under three soil cover conditions for corn and pasture cultivation (with vegetation cover, without vegetation cover, and without vegetation cover with soil scarified to a depth of 0.10 m) and four precipitation patterns (Advanced, Intermediate, Delayed, and Constant). The results showed that both soil cover and rainfall patterns directly influence the erosion processes. Soil loss increased up to sixfold under the Intermediate compared to the Constant rainfall, highlighting the strong influence of rainfall temporal distribution on erosion dynamics. The highest maximum runoff rates (MRR) and soil losses (SL) were recorded in tilled plots under maize cultivation, reaching 98.57 mm h⁻¹ and 5.90 g m⁻², respectively, under the intermediate pattern. In pasture areas, SL followed a similar pattern to the maize area, with maximum values of 6.96 g m⁻², but the MRR was recorded under the advanced pattern and in plots with cover (89.71 mm h⁻¹). This may be attributed to soil management conditions in pasture areas. Advanced and Intermediate patterns resulted in greater soil losses (3.78 and 2.04 g m², respectively), highlighting the impact of peak intensity timing on soil erosion. Greater soil losses were observed at the pasture experimental site than at the maize experimental site. Because the experiments were conducted at different locations with contrasting soil and management conditions, these differences should not be interpreted as being caused exclusively by crop type. The current study reinforces the need for erosion control and management strategies that account for natural variations in rainfall and soil cover to mitigate the negative impacts of land degradation on agricultural production and environmental sustainability.

As mudanças no uso e cobertura do solo e a dinâmica hidrometeorológica é fundamental para avaliar a gestão e a disponibilidade hídrica, e o objetivo deste trabalho na bacia do rio Celeste, entre 2013 e 2023. Com dados do MapBiomas e séries hidrometeorológicas, foram avaliadas transformações territoriais e seus efeitos sobre a vazão, por meio da avaliação de balanços hídricos e tendências. Verificou-se que, apesar da expansão das áreas agrícolas e da redução de pastagens e vegetação nativa, não houve significância na modificação do uso do solo. A série de vazão observada (Qm) apresentou variações ao longo do período, com maior disponibilidade hídrica até 2018 e declínio nos anos seguintes, e o teste de Mann-Kendall apontou tendência negativa fraca significativa (p = 0,05). A comparação entre a vazão observada e a vazão estimada pelo balanço hídrico (Qcalc = P – ETR) mostrou discrepâncias, sugerindo que a atuação de processos não captados pelo modelo simplificado deve ser reavaliada, como a análise de uma série de dados maior ou a inserção de informações sobre o uso dos recursos hídricos, o que reforça que a integração das informações hidrometeorológicas e de uso do solo é fundamental para a gestão dos recursos hídricos.

This study aimed to identify areas at risk of deforestation in the Cerrado biome of the Brazilian Midwest (states of Mato Grosso, Mato Grosso do Sul, and Goiás) and to estimate potential soil losses due to water erosion under land-use change scenarios. The methodology integrated the Universal Soil Loss Equation (USLE), spatializing rainfall erosivity (R), soil erodibility (K), topographic factor (LS), and cover-management factor (CP), with the ACEU (Accessibility, Cultivability, Extractability and Unprotected/protection status) model to assess deforestation risk based on accessibility, agricultural suitability, extractive activities, and legal protection status. Results indicated an average soil loss of 0.11 t ha⁻¹ year⁻¹ under natural vegetation cover, with 90% of the area presenting losses below 0.25 t ha⁻¹ year⁻¹. However, 27.5% of the remaining natural cover is located in areas classified as high or very high deforestation risk, indicating significant environmental vulnerability. Simulated scenarios of land-use conversion to pasture and annual crops revealed substantial increases in soil loss, particularly under annual cropping systems, potentially exceeding soil loss tolerance thresholds across millions of hectares. The findings demonstrate that integrating deforestation risk assessment with erosion modeling is a strategic tool for environmental planning, reinforcing the importance of preserving native vegetation to maintain ecosystem services and ensure long-term environmental sustainability.

The conversion of native forests into agricultural areas without conservation practices can expose tons of soil organic carbon (SOC) to the atmosphere. This study aimed to evaluate the effect of land use and cover (LULC) on C in regions of the Caiabi (SBC) and Renato (SBR) River sub-basins, located in the Brazilian Cerrado–Amazon transition. Data on physical attributes and SOC were obtained by region (upper, middle, and lower), LULC (cropland, pasture, and native forest), and depth (0–10, 10–20, and 20–40 cm), with five replicates for each variable. The highest SOC values were found in areas with higher clay contents or in areas of native forest or crop residues. In the SBC, there was a negative correlation of SOC with sand and particle density (PD) and a positive correlation with silt. In the SBR, there was a positive correlation between SOC and microporosity and total porosity, and a negative correlation with sand, soil bulk density, and PD. The highest SOC values were found in the SBC upper region, in native forest (107 Mg ha⁻¹), cropland (69 Mg ha⁻¹), and pasture (49 Mg ha⁻¹). In the SBR upper region, the values were highest in pasture and cropland (93 and 58 Mg ha⁻¹), and in the lower region, the values were highest in native forest (48 Mg ha⁻¹). SOC varied in relation to the SBC and SBR regions, the LULC, depth, and physical attributes, especially soil texture.

The variability of rainfall, mainly convective, in the southern Amazon remains poorly understood due to the limited number of studies examining the relationships between the intensities and durations of rainfall events in this region. This study aimed to characterize the intensity patterns—hyetograms (advanced, intermediate, delayed, and constant, as well as observations of new patterns)—in the northern state of Mato Grosso (southern Amazon). Generally, most research in Brazil on this topic has focused on other regions of the country or used simulations or data disaggregation processes, limiting the representation of the regional reality. Historical data series from five conventional stations (with pluviograms) and ten automatic stations with data obtained by tipping rain gauges were analyzed. The analysis involved classifying 6187 events into four main patterns: Advanced (53.52%), Intermediate (31.74%), Delayed (14.58%), and Constant (less than 1%), with 93 events unclassified. The hourly distribution of rainfall revealed greater occurrence in the afternoon and evening periods, suggesting a predominance of thermal convection in regional dynamics. The results offer valuable insights for water planning, agricultural security, and adaptive infrastructure, in addition to promoting integration between science, engineering, and public policies aimed at environmental management and risk prevention.

Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning (ML) techniques, such as multi-layer perceptrons (MLPs) and support vector machines (SVMs), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod, and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature, and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon.

Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two Machine Learning (ML) techniques, such as Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is given by: SVM, MLP and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon.

As we delve deeper into the complexities of soil properties and processes, we must consider traditional methodologies and innovative approaches that take advantage of advances in soil management and conservation processes