Nowcast flood predictions in the Amazon watershed based on the remotely sensed rainfall product PDIRnow and artificial neural networks.

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.

Environmental vulnerability assessment of the Doce River basin, southeastern Brazil.

Environmental vulnerability is an important tool to understand the natural and anthropogenic impacts associated with the susceptibility to environmental damage. This study aims to assess the environmental vulnerability of the Doce River basin in Brazil through Multicriteria Decision Analysis based on Geographic Information Systems (GIS-MCDA). Natural factors (slope, elevation, relief dissection, rainfall, pedology, and geology) and anthropogenic factors (distance from urban centers, roads, mining dams, and land use) were used to determine the environmental vulnerability index (EVI).

Modeling of surface sediment concentration in the Doce River basin using satellite remote sensing.

Surface sediment concentration (SSC) is linked to several problems related to water quality and its monitoring is costly because of the required fieldwork and laboratory analyses. Thus, sediment measurements are often sporadic, punctual, and performed during a short period. Orbital remote sensing allows the monitoring of SSC along the river channel permitting continuous and spatial information.

Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow.

There are different methods for predicting streamflow, and, recently machine learning has been widely used for this purpose. This technique uses a wide set of covariables in the prediction process that must undergo a selection to increase the precision and stability of the models. Thus, this work aimed to analyze the effect of covariable selection with Recursive Feature Elimination (RFE) and Forward Feature Selection (FFS) in the performance of machine learning models to predict daily streamflow.

Multivariate statistics for spatial and seasonal quality assessment of water in the Doce River basin, Southeastern Brazil.

This study employed multivariate statistical techniques in one of the main river basins in Brazil, the Doce River basin, to select and evaluate the most representative parameters of the current water quality aspects, and to group the stations according to the similarity of the selected parameters, for both dry and rainy seasons. Data from 63 qualitative monitoring stations, belonging to the Minas Gerais Water Management Institute network were used, considering 38 parameters for the hydrological year 2017/2018. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to reduce the total number of variables and to group stations with similar characteristics, respectively.

Influence of changes in land use and land cover and rainfall on the streamflow regime of a watershed located in the transitioning region of the Brazilian Biomes Atlantic Forest and Cerrado.

Climate change and the intensification of anthropogenic activities in watersheds have been substantially changing the streamflow regime, which is a problem for water resource managers. This study assesses the influence of the changes in land use and land cover and rainfall on the streamflow regime. This study also models the pattern of these streamflows according to the rainfall and land use and land cover in the Santo Antônio River watershed, located in the transitioning region of the Brazilian Biomes Atlantic Forest and Cerrado.

Machine learning models for streamflow regionalization in a tropical watershed.

This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q, Q and Q), besides the long-term average streamflow (Q).

Pluviometric and fluviometric trends in association with future projections in areas of conflict for water use.

Rapid population growth coupled with climate change has been putting pressure on natural resources worldwide, especially on water resources. The Paracatu basin located in Brazil is a basin which has been showing a reduction in its water availability for many years due to the growing demand for irrigation in the region. Therefore, the objective of the present work was to analyze the trends in the flow and precipitation data for the Paracatu basin and correlate them with land use between the years 1980 and 2019, and thus make a projection of flows through the year 2030 based on these results.

Use of multivariate statistical methods to analyze the monitoring of surface water quality in the Doce River basin, Minas Gerais, Brazil.

The objective of the present study was to evaluate the water quality data in the Minas Gerais portion of the Doce River basin in order to analyze the current monitoring network by identifying the main variables to be maintained in the network, their possible sources of pollution, and the best sampling frequency. Multivariate statistical techniques (factor analysis/principal components analysis, FA/PCA and cluster analysis, CA) complemented by the analysis of violation of the framing classes were used for this purpose. Water quality variables common to 64 monitoring sites were analyzed for the base period from 2010 to 2017.

Hydrological modelling in small ungauged catchments.

The knowledge of the frequency and magnitude of low flow events is necessary to mitigate social, economic and ecological impacts inside the basin. However, the measurement network in Brazil is still restricted to large drainage areas, while basins with less than 300 km2 remain ungauged. Among different flow estimation methods, we used a rainfall-runoff model designed specifically to estimate flow rates during the dry season in small ungauged basins: the Silveira Method (SM).

Methodological proposal for the allocation of water quality monitoring stations using strategic decision analysis.

In order to fill a gap in the monitoring of water quality in Brazil, the objective of this study was to propose a methodology to support the allocation of water quality monitoring stations in river basins. To achieve this goal, eight criteria were selected and weighted according to their degree of importance. It was taken into account the opinion of water resources management experts.

Film based on magnesium impregnated biochar/cellulose acetate for phosphorus adsorption from aqueous solution.

Phosphorus (P) is a nutrient necessary for agricultural production and a potential originator for eutrophication in water bodies, resulting in qualitative changes; it may also affect the aquatic ecosystem and human health. In addition, as a finite resource, the importance of studying strategies to remove it from water is evident, thus making possible its recycling. Many studies have used powdered materials, including biochars, for P water decontamination; however, the difficulty of separating and collecting these materials from water after adsorption may be difficult.

Environmental flow in the River Ondas basin in Bahia, Brazilian Cerrado.

This paper aimed to estimate the environmental flow of a water basin located in the Brazilian Cerrado using the bidimensional model River2D. The study was carried out in a stretch of the lower portion of the River Ondas in the western part of the state of Bahia, Brazil. To carry out the ecohydrological modeling, the following were used: topobathymetry, hydraulic characterization, the streamflows with the probability of non-exceedances (Q50, Q60, Q70, Q80, Q90, and Q95), and the Habitat Suitability Index for species of the genus Hypostomus.