Precipitation is the primary source of water in a river basin and plays a key role in water resource management by affecting water availability. However, large river basins like the São Francisco River Basin in Brazil show significant variability in rainfall patterns, leading to different water supply levels across sub-regions. To identify areas with similar rainfall patterns, the k-means clustering method was used on rainfall stations throughout the basin. The clusters were validated by analyzing their means, medians, and interquartile ranges. This analysis was supported by a multi-criteria assessment using two methods: one with a correlation matrix and the other with a covariance matrix. As a result, the stations were grouped into five homogeneous clusters based on precipitation. The two clusters in the southern basin were heavily influenced by the South Atlantic Convergence Zone (SACZ). In contrast, the northern clusters were mainly affected by the Intertropical Convergence Zone (ITCZ) and orographic features such as the Borborema Plateau. The coastal cluster was influenced by the sea and trade winds blowing from east to west. Multivariate analysis showed high correlation and seasonal similarity among stations within the same cluster. Clusters 4 and 5 experienced the highest average precipitation, while clusters 1 and 2 recorded the lowest, with cluster 2 being particularly notable.

Rainfall time series are essential for hydrological and climate studies; however, data scarcity remains a persistent challenge that compromises the reliability of analyses and modeling. This study compares the performance of regression based models and machine learning (ML) methods for gap filling in monthly rainfall data from Northern Minas Gerais, Brazil, a region characterized by high climatic variability and limited monitoring infrastructure. Ten missing data levels, ranging from 5 The graphical abstract presents the workflow used to evaluate the performance of gap-filling techniques in monthly rainfall time series from Northern Minas Gerais, Brazil. The process begins with historical rainfall data, in which artificial gaps ranging from 5

Reference evapotranspiration (ET0) is a fundamental hydrological variable for quantifying crop water requirements, conducting watershed water balance assessments, and optimizing irrigation management. However, the limited availability of high-quality meteorological data hinders the accurate determination of ET0 across most regions of Brazil. Therefore, this study aimed to estimate ET0 in areas lacking meteorological observations by employing ERA5 reanalysis climate data and artificial neural network (ANN) models, as well as to evaluate its spatial and temporal variability. Observational data from 32 automated weather stations in the State of Mato Grosso, Brazil, were utilized to estimate ET0 using the Penman-Monteith FAO56 method. These estimates were linked to ERA5 meteorological data, including global solar radiation and top-of-atmosphere radiation, using ANN models. The Mann-Kendall test was applied to detect trends in the ET0 time series for the period 1980–2019. In Mato Grosso, the Pantanal biome exhibited the highest ET0 values from October to March, followed by the Cerrado (Brazilian Savanna) and Amazon biomes. Statewide, the peak evapotranspirative demand occurred in August, September, and October, whereas April, May, and June recorded the lowest values. All biomes in Mato Grosso contained areas with statistically significant increasing trends in ET0. The proposed models demonstrated satisfactory error metrics for ET0 estimation, and the methodological approach enabled a robust assessment of the spatiotemporal dynamics of this hydrological variable, facilitating ET0 estimation even in data-scarce regions.

Historical precipitation series are essential for conducting hydrological studies; however, their quality may be compromised by missing data in the records. In such cases, the use of gap-filling methods is crucial to ensure data consistency and continuity. Although different methods are used to fill gaps in rainfall time series, studies that systematically evaluate the influence of the selection of supporting stations on the accuracy of the estimates are still limited. Therefore, this study aimed to assess the influence of the selection of supporting rain gauge stations on gap filling in historical precipitation series. Nine rainfall stations were selected in the northern region of Minas Gerais, Brazil, eight of which were used as supporting stations for filling gaps in the central station. Missing data rates of 10% and 40% were simulated, and the missing values were filled using the Linear Regression method. The performance of the fillings was evaluated through the RMSE, SMAPE, and NSE indices. The results indicated good performance of the Linear Regression method for gap filling and highlighted the importance of conducting a detailed spatial analysis of the region, considering topographic features, to select nearby stations free from orographic barriers, ensuring greater reliability in the completion of rainfall series.

Monitoring surface water quality is essential for assessing water resources and identifying their quality patterns. Traditional monitoring methods, based on conventional point-sampling stations, are reliable but costly and limited in frequency and spatial coverage. These constraints hinder the ability to evaluate water quality parameters at the temporal and spatial scales required to detect the effects of extreme events on aquatic systems. Satellite imagery offers a viable complementary alternative to enhance the temporal and spatial monitoring scales of traditional assessment methods. However, limitations related to spectral, spatial, temporal, and/or radiometric resolution still pose significant challenges to prediction accuracy. This study aimed to propose a methodology for predicting optically active and inactive water quality parameters in lotic and lentic environments using remote-sensing data and machine-learning techniques. Three remote-sensing datasets were organized and evaluated: (i) data extracted from Sentinel-2 imagery; (ii) data obtained from raw PlanetScope (PS) imagery; and (iii) data from PS imagery normalized using the methodology developed by Dias. Data on water quality parameters were collected from 24 monitoring stations located along the Paraopeba River channel and the Três Marias Reservoir, covering the period from 2016 to 2023. Four machine-learning algorithms were applied to predict water quality parameters: Random Forest, k-Nearest Neighbors, Support Vector Machines with Radial Basis Function Kernel, and Cubist. Model performance was evaluated using four statistical metrics: root-mean-square error, mean absolute error, Lin′s concordance correlation coefficient, and the coefficient of determination. Models based on normalized PS data achieved the best performance in parameter estimation. Additionally, decision-tree-based algorithms showed superior generalization capability, outperforming the other models tested. The proposed methodology proved suitable for this type of analysis, confirming not only the applicability of PS data but also providing relevant insights for its use in diverse environmental-monitoring applications.

Soil Organic Carbon (SOC) is a paramount soil attribute for climate regulation, soil fertility, and agricultural productivity. The global demand for SOC testing came in response to expanding soil management practices aimed at ensuring soil health. This study explores enhanced accuracy in predicting SOC using soil spectroscopy (proximal sensing). A Soil Spectral Library (SSL), made from 127 soil profiles in Northeast Brazil, mainly by using soils from a semi-arid region, was used. Four modeling scenarios were employed, incorporating distinct covariable sets: 1) diffuse reflectance from laboratory spectroscopy (SSL); 2) diffuse reflectance and radar vegetation indices from all-weather and globally available Sentinel-1 satellite data; 3) diffuse reflectance and environmental factors; 4) all covariables. Integration of radar vegetation indices and environmental factors significantly improved SOC estimates by soil spectroscopy. Predicting SOC solely from SSL reflectance data yielded an average RMSE of 4.54 g kg- 1 and R2 of 0.62. However, by using all covariables significantly reduced RMSE by approximately 13 % (to 3.94 g kg- 1) and increased R2 by 14 % (to 0.71). This comprehensive approach, combining SSL, satellite radar vegetation indices, and environmental variables, substantially advances SOC spectroscopic prediction accuracy, offering valuable insights for applications in agriculture and environmental monitoring. These findings contribute to the reliability of proximal and remote sensing methodologies in soil testing.

This is a database containing rainfall intensity–duration–frequency equations (IDF equations) for 6550 pluviographic and pluviometric stations in Brazil. The database was compiled from 370 different publications and contains the following information: station identification, geographic position, size and period of the rainfall series used, parameters of the IDF equations, and literature references. The database is available on Mendeley Data (DOI: 10.17632/378bdcmnc8.1) in the form of spreadsheets and vector files. Since the launch of the Pluvio 2.1 software in 2006, which included 549 IDF equations obtained in the country, this is the largest and most accessible database of IDF equations in Brazil. The data provided may be useful, among other purposes, for designing hydraulic structures, controlling water erosion, planning land use, and water resource planning and management.

This paper aimed to perform an ecohydrological analysis of the Ondas river basin, located in the Brazilian Cerrado, using the Physical Habitat Simulation (PHABSIM) model, in order to determine the monthly ecological flows for its lower course. For calibration and simulation in PHABSIM, field experiments were conducted during dry and rainy periods, in a 300-m stretch located in the lower course of the Ondas river. The estimate of monthly ecological flows was obtained by analysing the Weighted Usable Area of the ichthyofauna species of the river stretch, as a function of the streamflows with a probability of nonexceedance of 50% (Q(50)), 60% (Q(60)), 70% (Q(70)), 80% (Q(80)), 90% (Q(90)) and 95% (Q(95)). In the dry period, the ecological flow varied between 31.72 and 40.14 m(3) s(-1), whereas in the rainy season, it presented values between 33.23 and 51.94 m(3) s(-1). Considering the water use rights criterion of the State of Bahia, Brazil, where it is permissible to use up to 80% of the Q(90), it was verified that the adoption of ecological flows would considerably reduce the quantity of water that could be used for human purposes but would maintain the habitat of the bioindicator species studied. The ecological flow regime obtained provides subsidies for discussions and negotiations on the water resources management in the Ondas river basin, considering the ecohydrological aspects affecting the region, in addition to the water quantitative and qualitative factors.

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R2). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R2 and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets.

The aim of this study was to develop artificial neural network (ANN) models to predict floods in the Branco River, Amazon basin. The input data for the models included the river levels and the average rainfall within the drainage area of the basin, which was estimated from the remotely sensed rainfall product PDIRnow. The hourly water level data used in the study were recorded by fluviometric telemetric stations belonging to the National Agency of Water. The multilayer perceptron was used as the neural framework of the ANNs, and the number of neurons in each layer of the model was determined via optimization with the SCE-UA algorithm. Most of the fitted ANN models showed Nash–Sutcliffe efficiency index values greater than 0.9. It is possible to conclude that the ANNs are effective for predicting the flood levels of the Branco River, with horizons of 6, 12 and 24 h; thus, constituting a viable option for use in river-flood warning systems in the Amazon basin. For the forecast with a 24-h horizon, it is essential to include the average rainfall of the basin that accumulated over the last 48 h as input data into the ANNs, along with the levels measured by the streamflow stations. The indirect rainfall estimates provided by PDIRnow are an excellent alternative as input data for ANN models used to predict floods and constitute a viable solution for regions where the density of rain gauge stations is low, as is the case in the Amazon basin.

Understanding climate change and land use impacts is crucial for mitigating environmental degradation. This study assesses the environmental vulnerability of the Doce River Basin for 2050, considering future climate change and land use and land cover (LULC) scenarios. Factors including slope, elevation, relief dissection, precipitation, temperature, pedology, geology, urban distance, road distance, and LULC were evaluated using multicriteria analysis. Regional climate models Eta-HadGEM2-ES and Eta-MIROC5 under RCP 4.5 and RCP 8.5 emission scenarios were employed. The Land Change Modeler tool simulated 2050 LULC changes and hypothetical reforestation of legal reserve (RL) areas. Combining two climate and two LULC scenarios resulted in four future vulnerability scenarios. Projections indicate an over 300 mm reduction in average annual precipitation and an up to 2 °C temperature increase from 2020 to 2050. Scenario 4 (RCP 8.5 and LULC for 2050 with reforested RLs) showed the greatest basin area in the lowest vulnerability classes, while scenario 3 (RCP 4.5 and LULC for 2050) exhibited more high-vulnerability areas. Despite the projected relative improvement in environmental vulnerability by 2050 due to reduced rainfall, the complexity of associated relationships must be considered. These results contribute to mitigating environmental damage and adapting to future climatic conditions in the Doce River Basin.

River cross-section characteristics are not available for many regions of the world, and they are generally expressed as hydraulic geometry (HG) relationships. These relationships usually represent average characteristics of a region, which can generate biased estimates in smaller basins. Therefore, to improve the representation of river channel geomorphological characteristics, the objective of this study was to use a large database of river cross-section geometries to refine HG relationships for five river basins in Brazil. HG relationships were developed for the entire basins and for hydraulically homogeneous regions, obtained through a geographical convenience method. The relationship between the coefficients of the geometric relationships and morphoclimatic characteristics was assessed, showing a trend between precipitation and slope and the coefficients of the depth relationships. The estimated bankfull width and depth values were also compared to observed data and global datasets, showing an improvement in estimates with the more refined HG relationships.

In studies of flow regionalization, the uncertainties associated with the precariousness or non-existence of flow and precipitation data are problems faced by researchers and managers of water resources, especially in emerging countries such as Brazil. Given this, the usage of precipitation databases obtained by satellites has become more prevalent, allowing accurate precipitation estimates in regions where punctual data obtained from rain gauges are precarious. Therefore, the objective of this study was to compare different precipitation databases, used as predictive variables, in flow regionalization studies. The study area considered was the hydrographic basin of the Paranaíba River. Flow data from streamflow gauges present in the study area and precipitation data obtained from rain gauges were used, which were interpolated by the simple kriging method, the TRMM satellite and WorldClim. The study area was divided into four homogeneous regions, and only for region 4 the generated regionalization equations have some use restriction, since some adjustments proved to be unsatisfactory. The best fits of the regionalization equations were obtained using the precipitation data from the rain gauges interpolated by simple kriging, however, the use of precipitation data from TRMM and WorldClim as predictive variables of the flow provided similar results. Therefore, it can be considered that the alternative databases (TRMM and WorldClim) used in the study are presented as an option to replace the data observed in the rain gauges, resulting in a gain of time by the researchers and management bodies in the studies of flow regionalization.

The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an environmental vulnerability index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The developed EVI was applied to the headwater region of the São Francisco River Basin (SFRB), Brazil. The proposed index was based on the AHP multicriteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility, and quantitative water balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land use above their capacity, generate the most vulnerable areas in the headwaters of the SFRB. The highest EVI values are primarily linked to regions with shallow, easily erodible soils like Leptosols and Cambisols, found in steep areas predominantly used for pasture. In the SFBR, the greatest vulnerability was observed within a 5 km buffer around conservation units, covering approximately 32.4% of the total area. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience to maintain ecosystem services.

The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an Environmental Vulnerability Index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The EVI developed was applied to the headwater’s region of the São Francisco River basin, Brazil. The proposed index was based on the AHP multi-criteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility and Quantitative Water Balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land uses above their capacity, generate the most vulnerable areas in the headwaters of the São Francisco River basin. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience in maintaining ecosystem services.

Evaporation, together with precipitation, is the most important component of the hydrological cycle, and knowledge of the local values of lake evaporation has applications in reservoir design and management. The objective of this study was to estimate lake evaporation at locations without meteorological monitoring using ERA5 reanalysis data and artificial neural networks (ANNs). Data from 32 automatic stations in the state of Mato Grosso were used to estimate evaporation using the method of Penman (1948). The evaporation values were related to ERA5 data and radiation data at the top of the atmosphere using multilayer perceptron ANN models. The Mann-Kendall test was used for trend analysis in the estimated monthly evaporation series. From the analysis of the results, it is concluded that it is possible to quantify the spatial and temporal distribution of evaporation from lakes with data from ERA5 reanalysis and the use of ANNs. The historical evaporation series for the period 1980 to 2019 showed a positive trend in certain parts of the Brazilian Savanna and Amazon biomes. Isolated areas of the Pantanal biome also showed a positive trend for monthly evaporation. The proposed methodology allows for the precise and accurate estimation of evaporation from liquid surfaces at locations without meteorological monitoring.

A significant portion of Brazilian cities has been developed along flat areas adjacent to rivers, where populations are vulnerable and susceptible to flood events, as seen in Ponte Nova- MG. To contribute to flood event prevention, it is essential to delineate flood-prone areas. This study aimed to perform hydrodynamic modeling to simulate flood zones in the municipality of Ponte Nova- MG. The Digital Terrain Model and flow data from the Ponta Nova Jusante fluviometric station, located in the Piranga river basin, were used. Maximum flow events associated with return periods (RP) of 5, 20, 50, and 100 years were considered. The HEC-RAS hydrodynamic model was used to model the flood zones. The obtained maximum flow results ranged from 647.97 to 1,158.29 m(3)/s for RPs of 5 and 100 years, respectively. The flood zones showed that the municipality has areas with very high flood susceptibility on both banks of the Piranga River and that the zones associated with RPs of 50 and 100 years reach the floodplains, which are currently occupied by buildings and urban roads.

A precipitação é crucial para a produção agrícola no estado do Mato Grosso. Contudo, a rede de monitoramento dos dados de chuvas é insuficiente e desuniformemente distribuída, afetando a determinação do balanço hídrico, a detecção de secas e a gestão de recursos hídricos. Considerando o potencial dos produtos de precipitação oriundos de sensoriamento remoto para estimar a precipitação em locais com monitoramento deficiente, este trabalho teve como objetivo validar os dados CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) para o estado de Mato Grosso, bem como analisar sua distribuição espacial e temporal na região. As estimativas do CHIRPS foram comparadas com séries históricas registradas em 154 estações pluviométricas da rede hidrometeorológica nacional, tendo sido quantificadas seis métricas de erro para validação do produto. A partir dos resultados obtidos confirmou-se que o bioma Amazônia apresenta a maior precipitação do estado, seguido pelo Cerrado. Em contraste, o bioma Pantanal possui a menor precipitação média mensal e anual em comparação aos outros biomas do estado. Comprovou-se, ainda, que o produto CHIRPS estima de forma adequada a precipitação total mensal e a média histórica mensal, revelando-se uma ferramenta promissora para a gestão de recursos hídricos, detecção de secas, estudos de balanço hídrico e modelagem hidrológica em bacias hidrográficas no estado de Mato Grosso. Contudo, para totais diários, o erro de estimativa é elevado, não sendo recomendada sua utilização direta sem correção dos dados ou calibração em modelos hidrológicos.

Background: Vegetation indices have recently been proposed for remote sensing SAR (synthetic aperture radar) sensor measurements to monitor vegetation. However, they still lack validation studies on different vegetation types for their correct application. Thus, the objective of this study was to test the applicability of the Dual-polarization SAR Vegetation Index (DPSVI) and the modified DPSVI (DPSVIm) as indicators of aboveground biomass (AGB) from dense forest fragments. Results: Three forest fragments, comprising 54 forest plots with AGB ranging from 12 up to 220 Mg ha-1 , were studied. These forest fragments belong to the Brazilian Atlantic Forest biome, and were located within the Doce river hydrographic basin in the state of Minas Gerais, Brazil. AGB was compared with the DPSVI and DPSVIm indices, computed from dual-polarization Sentinel-1 images, using Spearman's rank correlation test through two approaches. In the first approach (A1), correlation tests were performed using all forest plots; in the second approach (A2), samples were taken from plots on flat to undulating terrain slopes. The correlation of AGB with DPSVI presented no significant correlation (p-value >> 0.05). In contrast, for DPSVIm, the correlation with AGB was significant and positive, with coefficients ranging from 0.4 in approach A1 to 0.7 in approach A2. Conclusion: While the DPSVI index did not show a correlation with the AGB of the studied forests, even though it is a C-band index, the DPSVIm was found to be a good indicator of the amount of AGB in forests and therefore has potential for applications in future studies, particularly in areas with reasonable slope or flat terrain.

Recently, there has been an increase in the number of natural disasters caused by extreme events, which are enhanced by climate change and anthropogenic interference. Therefore, understanding the hydrological behavior in areas with high vulnerability to floods and water scarcity is essential to capably manage water resources. In this context the study aimed to analyze the streamflow trend in the Piranga river basin, as well as to evaluate the determining factors in the streamflow variation regime in the watershed. For this reason, historical series of seven stream gauging stations were analyzed, adopting the base period of studies from 1975 to 2018. In order to identify the trend in maximum, average and minimum streamflow data, the Mann-Kendall, Pettitt and Spearman correlation tests were used. To understand the possible causes of streamflow trends, precipitation data, land use and occupation, and water demand were analyzed. It was observed that all stations showed some significant trend of streamflow reduction, especially in the dry season, having reduced from 10 to 35% comparing to the historical series average. On an annual scale, significant trends of reduction in average and minimum streamflow were detected. The change in streamflow behavior was not related to the distribution of precipitation over the years in the watershed. The cause of streamflow reduction may be related to the increase in water demand and with changes in land use and occupation, mainly characterized by the increase in planted forest, forest formation and urban areas and the reduction of areas destined to agriculture. The methodology proposed in this study can be adapted to other watersheds in the world, aiming to assist in the planning of water resources.