Search Results (255)

Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article

The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R² of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region. Full article

Mercury contamination from gold mining in the Amazon poses significant environmental and health threats to the biome and its local populations. The recent expansion of non-industrial mining areas has severely impacted territories occupied by traditional communities. To address the lack of sampling data in the region and better understand mercury dynamics, this study used the probabilistic model SERAFM to estimate the mercury distribution and bioaccumulation in fish. The analysis covered 8,259 sub-basins across three major Amazonian basins: the Branco, Tapajós and Xingu rivers. The findings revealed increasing downstream mercury levels, with notable accumulations in the main watercourses influenced by methylation processes and mining releases. The projected concentrations showed that an average of 27.47% of the sub-basins might not comply with Brazilian regulations, rising to 52.38% in the Branco and Tapajós river basins separately. The risk assessment of fish consumption based on the projections highlighted high mercury exposure levels among traditional communities, particularly indigenous populations, with an average of 49.79% facing an extremely high risk in the Branco and Tapajós river basins. This study demonstrated SERAFM’s capacity to fill information gaps in the Amazon while underscoring the need for enhanced data collection, culturally sensitive interventions and regulatory updates to mitigate mercury contamination in gold mining-affected areas. Full article

In 2018, an outbreak of human rabies caused by the hematophagous bat Desmodus rotundus hit the Brazilian Amazon Basin community of Melgaço, Brazil, resulting in the death of 10 people, 9 of them children. The incidence of rabies has been on the rise among populations in conditions of vulnerability in this ecosystem due to human expansion into sylvatic environments and limited access to public health services. To address this issue, in September 2019, a collaborative effort from national, local, and international institutions promoted and executed a pilot for pre-exposure prophylaxis of a population in high-risk areas for hematophagous bat-mediated rabies. This measure is usually only implemented in response to outbreaks. The pilot was conducted in Portel, in a nearby location to the previous outbreak, with the use of fluvial transportation, and 2987 individuals in 411 dwellings were successfully vaccinated. It established a methodology for pre-exposure prophylaxis for populations in conditions of vulnerability, identifying logistics and costs, as well as characterizing the target riverine population regarding risk factors associated with bites by hematophagous bats. This approach offers a proactive measure to prevent future outbreaks and provides valuable insights into how to address the issue of rabies in remote and difficult-to-reach areas. Full article

The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions. Full article

The pink river dolphin, or bufeo, is one of the dolphins which lives in the rivers of the Orinoco and Amazon basins in South America. The Bolivian bufeo population is considered a differentiated species (Inia boliviensis) from the Amazon and Orinoco species (Inia geoffrensis). Until now, no study has completed an extensive population genetics analysis of the bufeo in Bolivian rivers. We analyzed 82 bufeos from different rivers from the Mamoré and Iténez (Guaporé) river basins for the mt control region (CR), nuclear microsatellites, and DQB-1 gene sequences to determine if the inner rapids of these Bolivian river basins have some influence on the genetic structure of this species. The first relevant result was that the genetic diversity for CR, and the microsatellites were substantially lower in the Bolivian bufeos than in the dolphins studied in other areas of the Amazon and Orinoco. However, the DQB-1 gene sequences yielded similar genetic diversity to those found in other areas. The second relevant result is the existence of some significant genetic heterogeneity among the bufeo populations within Bolivia, although in a small degree, but this differentiation is independent of the inner rapids of the Bolivian rivers we sampled. The third relevant result was the existence of significant isolation by distance for the CR, but not for microsatellites and DQB-1 gene sequences. This was related to differential gene flow capacity of females (philopatric) and males (less philopatric and more migrants) and, possibly, to different selective patterns affecting the molecular markers studied. The fourth relevant result was related to diverse demographic changes of these bufeos. At least two or three bottleneck events and one or two population expansions have occurred in the Bolivian bufeo population. The major part of these events occurred during the Pleistocene. Full article

Aquatic environments, such as fluvial environments, play an important role in the transport of material from throughout the basin, and this material partially sediments along the way. The objective of this study was to analyze, from an ecotoxicological point of view, the concentrations of arsenic and heavy metals in sediment and the muscle of native fish, to correlate their interaction and to evaluate the potential risk to public health using carcinogenic risk indices in four rivers of the Peruvian Amazon. There were 27 sampling sites where sediment and fish (except for five points) samples were collected. A sampling pool was created with fish muscles from all species collected at each sampling site. Concentrations of As, Cd, Cr, Cu, Ni, Pb, Zn, and Hg were analyzed in both sediment and fish muscle, in duplicate. The results indicate the presence of concentrations higher than those recommended by international guidelines for sediment and food. Mercury (Hg) concentrations in the Tigre, Morona, and Pastaza rivers are up to six times higher than the recommended value for daily consumption. The carcinogenic risk due to the regular consumption of native species in the indigenous communities living on the banks of the four studied rivers is high. Full article

The Madeira River, a major tributary of the Amazon River, often undergoes severe flood and drought conditions. This study seeks to investigate the climate processes associated with the opposing extreme precipitation events in the Madeira River basin and to relate them to river discharge variability based on a flood awareness dataset. Despite the uncertainty in the observational datasets, the annual precipitation cycle exhibits a rainy season from November to March. A significant result is the high correlation between the rainy season variability in the Madeira River basin and the sea surface temperature (SST) anomalies in the tropical North Atlantic Ocean and the southwestern South Atlantic Ocean. This result indicates that improving the Atlantic SST representation in climate modeling allows for capturing extreme precipitation events in the region. In addition to this impact, certain Madeira River tributaries present significant climate trends. The river discharge variability reveals an increase in hydrological extremes in recent years in the upper sector, but more significantly, in the lower basin, where it has reduced by more than 400 m³/s per decade. These findings highlight the need to improve in situ data and climate and hydrological modeling, with a focus on describing the intense climate variability and trends in river discharges. Full article

In this first field survey of an entire bee fauna for any part of Peru, we report a total of 1796 bees belonging to 181 species or morphospecies in four families. The taxonomic impediment was pronounced with only 80 species of 181 that could be named. With such a high proportion of undetermined species, it is not possible to adequately compare pollinator communities across different studies, assess historical changes or analyze endemism patterns to document ecology, behavior and evolution of the species and genera. This information is required to provide a sound basis for policymakers to protect habitats for the conservation of native pollinators. Full article

Several countries are reporting natural populations of P. falciparum with deletions in the pfhrp2/3 genes that can lead to false-negative results in rapid diagnostic tests. To investigate the prevalence of deletion in the pfhrp2/3 genes in the Rio Negro basin in the Brazilian Amazon and identify whether there is clinical differentiation between individuals infected by these parasites, clinical samples collected from 2003 to 2016 were analyzed from symptomatic and asymptomatic P. falciparum-infected individuals. The molecular deletion of pfhrp2 and pfhrp3 genes was evaluated using the protocols recommended by the WHO. From 82 samples used, 28 (34.2%) had a single deletion in pfhrp2, 19 (23.2%) had a single deletion in pfhrp3, 15 (18.3%) had a double deletion (pfhrp2/3), and 20 (24.4%) did not have a deletion in either gene. In total, 29.3% of individuals had an asymptomatic plasmodial infection and were 3.64 times more likely to have parasites with a double deletion (pfhrp2/3) than patients with clinical malaria (p = 0.02). The high prevalence of parasites with pfhrp2/3 deletions shows the need to implement a surveillance program in this area. Deletions in parasites may be associated with the clinical pattern of the disease in this area. More studies must be carried out to elucidate these findings. Full article

Fish migrate for varied reasons, including to avoid predators and to access feeding, spawning, and nursery habitats, behaviors that enhance their survival and reproductive rates. However, the migratory ecology of many important fishes, especially those in river–floodplain ecosystems, remains poorly understood. One fish of the Amazon Basin whose migratory behavior is poorly understood is the catfish Pseudoplatystoma fasciatum. Here, we used otolith elemental microchemistry to characterize the migration ecology of P. fasciatum in the Amazon Basin. The main research questions of this study were: (1) does P. fasciatum move between waters with different Sr isotopic signatures (⁸⁷Sr/⁸⁶Sr) and chemical compositions? (2) What distance do they migrate? (3) Is the migration of P. fasciatum related to age? And (4) does P. fasciatum migrate mainly upstream, downstream, or in both directions? We assessed whether P. fasciatum migrates between waters with different ⁸⁷Sr/⁸⁶Sr values, comparing the Sr isotopic signature of otolith transects of each individual with the range of Sr isotopic signatures within the respective rivers. We found that 34% of the 71 fish analyzed migrated between rivers with different Sr isotopic signatures and 66% did not. The mean migration distance migrated was 126 km, with most specimens migrating between 72 and 237 km. Apparently, no fish of age one or age six or older migrated. All fish that migrated were between two and five years of age, with 20% of the specimens that migrated being two years old, 40% three years old, 30% four years old, and 20% five years old. Sixty-six percent of all individuals that migrated between rivers with different Sr signatures did so bidirectionally, while 33% moved unidirectionally. According to our definition of homing behavior in which fish migrated back to the same river where they were born, 41% of all fish that migrated displayed apparent homing behavior. Our findings provide insights into the migratory ecology of P. fasciatum, corroborating and refining knowledge reported in the literature. Our results on the migratory ecology of P. fasciatum have implications for sustainable fisheries conservation and management: conserving P. fasciatum requires habitat maintenance and suitable fishing practices in spawning and nursery habitats, and managers must consider large geographic areas for effective fishery management and conservation. Full article

The Tocantins–Araguaia basin is one of South America’s largest river systems, across three Brazilian states (Maranhão, Tocantins, and Pará), within the Legal Amazon region. Despite draining extensive Cerrado savanna and rainforest ecosystems, it has suffered significant degradation, notably in the past 40 years. Human activities, including agricultural expansion, deforestation, and the introduction of non-native species, have worsened the environmental damage, which is alarming since many residents and villages along the middle Tocantins River rely on it for water supply, recreation, and fishing. This study assessed the concentration of potentially toxic and essential elements in water samples from four sampling sites distributed along the middle Tocantins River. The monitoring occurred throughout 2023, involving the measurement of parameters both on-site and in the laboratory. Water quality and its health implications were evaluated using the Weighted Arithmetic Water Quality Index (WAWQI), the Water Quality Index (WQI), and the health risk assessment index. The levels of aluminum, copper, iron, magnesium, and selenium exceeded legal standards. Seasonal fluctuations indicate a complex dynamic influenced by climatic or seasonal factors, with February showing the highest values. Site P1, located in urban areas, exhibited elevated mean concentrations for conductivity, total dissolved solids (TDS), and chlorophyll, indicating the need for continuous monitoring. The nitrogen concentrations at P1 raise concerns regarding drinking water quality, which is a concern for the region’s residents who use untreated river water. Despite seasonal variations in element concentrations, the overall WAWQI categorized all sections as “Excellent,” and the WQI rated as “Good.” Human health risk assessments detected no risks, but continuous monitoring and interventions are crucial for sustained water quality improvement. Full article

This research aimed to analyze the relationship between deforestation (DFT) and climatic variables during the rainy (CHU+) and less-rainy (CHU−) seasons in the Tapajós River basin. Data were sourced from multiple institutions, including the Climatic Research Unit (CRU), Center for Weather Forecasts and Climate Studies (CPTEC), PRODES Program (Monitoring of Brazilian Amazon Deforestation Project), National Water Agency (ANA) and National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration (NCEP/NOAA). The study assessed anomalies (ANOM) in maximum temperature (TMAX), minimum temperature (TMIN) and precipitation (PREC) over three years without the occurrence of the El Niño–Southern Oscillation (ENSO) atmospheric–oceanic phenomenon. It also examined areas with higher DFT density using the Kernel methodology and analyzed the correlation between DFT and climatic variables. Additionally, it assessed trends using the Mann–Kendall technique for both climatic and environmental data. The results revealed significant ANOM in TEMP and PREC. In PREC, the highest values of ANOM were negative in CHU+. Regarding temperature, the most significant values were positive ANOM in the south, southwest and northwestern regions of the basin. Concerning DFT density, data showed that the highest concentration was of medium density, primarily along the highways. The most significant correlations were found between DFT and TEMP during the CHU− season in the Middle and Lower Tapajós sub-basins, regions where the forest still exhibits more preserved characteristics. Furthermore, the study identified a positive trend in TEMP and a negative trend in PREC. Full article

Fish growth is a fundamental biological process driven by a multitude of intrinsic (within-individual) and extrinsic (environmental) factors that underpin individual fitness and population dynamics. Interannual variability in river hydrology regarding the intensity and duration of floods and droughts can induce interannual variations in the biotic and abiotic variables that regulate fish growth. However, the understanding of how interannual variability in river hydrology affects fish growth remains limited for most species and ecosystems. We evaluated how inter-annual hydrological variations within the Amazon River basin influence the growth of the catfish Pseudoplaystoma fasciatum. Our research questions were as follows: Do floods lead to the faster growth of P. fasciatum and droughts lead to the slower growth? And do floods and droughts affect all age classes in the same manner? We sampled 364 specimens of P. fasciatum from five sites in the Amazon basin, estimated their growth rates, and related the growth rates to indices of the intensity of floods and droughts. We fitted linear mixed-effects models to test the relationship between growth increments and hydrological indices (with F and D quantifying the intensities of floods and droughts, respectively), age as fixed effects, and basins and Fish ID as random effects. We found an inverse relationship between the increment width in the fish hard parts and hydrological indices. That is, intense floods and droughts negatively affected the growth rates. We also found that the growth of P. fasciatum was no different in years with intense and mild floods across age classes 1–5, although was different for age class 6. However, the growth of P. fasciatum was faster in years of mild droughts for all age classes. Our results showing that the growth of P. fasciatum was slower in years of intense droughts are supported by those of previous studies in the Amazon basin and elsewhere. However, our results showing for the first time that the growth of P. fasciatum is slower in years of intense flooding is the opposite of patterns found in other studies. These results thus suggest that the growth of P. fasciatum is maximized within an optimum range of hydrological conditions, where neither floods nor droughts are intense. Full article

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area. Full article