Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The aquaculture activity in this area began in the early 1990s and is currently one of the largest shrimp producers in Brazil. This study generated a spatial-temporal analysis of vegetation index and land use and land cover (LULC) using remote sensing images from Landsat satellites processed using geographic information systems (GIS). The findings showed an increase in the water bodies class where shrimp farms are found. In addition, to help us discuss the results, data from the Global Surface Water Explorer was also used to understand this change throughout intra and interannual water variability. Besides shrimp farms’ intensification, agricultural areas in the LJRSB also increased, mainly in the irrigated perimeter lands (IPLs), causing a loss in the Caatinga native vegetation. In summary, over recent years, significant changes have been noticeable in the LJRSB coastal region, caused by an increase in shrimp farms mainly located on the Jaguaribe River margins, destroying the native riparian forest.

This study investigates a summer heat wave (HW) associated with downslope winds (DWs) affecting the central region of the state of Rio Grande do Sul (RS), Brazil. The temporal evolution of both phenomena is analyzed in the atmospheric boundary layer (ABL) using a combination of micrometeorological and rawinsonde data. For spatial characterization, ERA-5 reanalysis data are used. The HW covered a large area in southern Brazil, Argentina, and Paraguay. The main features of the HW were locally enhanced in the central region of RS by the development of DWs. The establishment of DWs near the surface depends on the dynamics of the ABL and local topographic features. The results showed that DWs that occurred during the HW contributed to the extreme temperatures and were associated with strong northerly winds, low relative humidity, and a drop in the dew points. Together, these extreme events influenced the turbulent and mean flow patterns of the ABL. The increase in turbulent activity associated with the warming of the ABL favored enhanced growth of morning ABL, while at night the simultaneous effects of radiative and turbulence cooling inhibited the formation of a strong stable ABL. The analysis highlights the complex interplay of synoptic and local factors associated with DWs and HW.

Water quality and the useful life of reservoirs and dams are influenced by the entry of suspended solids, in addition to reducing theirtransparency and storage capacity. It is primary to monitor and analyses its space-time dynamics. Thus, the objective of this work isto characterize the dynamics of the Itaipu Reservoir waters from turbidity, rainfall and spectral reflectance data. To characterize thedynamics, the reservoir was divided into 18 aquatic compartments between upstream and downstream, using precipitation data fromthe TRMM sensor and Landsat 8 images in different precipitation situations. NDWI, MNDWI and NDTI water spectral indexes werecalculated from Landsat 8 images. The results showed high correlation between the NDTI index and the turbidity (R² = 0.91). Then theNDTI images were reclassified into low, medium and high turbidity. A strong correlation between turbidity and 4 Band correspondingto the spectral range of red (R² = 0.94) was also obtained. The precipitation has a determinant influence, being the Paraná River, in theperiods of greater precipitation, the main agent in sediment transport. The space-time dynamics showed that the lateral compartmentsof the reservoir have less influence on sediment transport. In this sense, our analysis brought new elements to understand the turbidityvariation in these Itaipu Reservoir compartments, as well as the spectral reflectance dynamics in the space-time characterization relatedto turbidity.

Considering the influence of the downslope windstorm called “Vento Norte” (VNOR; Portuguese for “North Wind”) in planetary boundary layer turbulent features, a new set of turbulent parameterizations, which are to be used in atmospheric dispersion models, has been derived. Taylor’s statistical diffusion theory, velocity spectra obtained at four levels (3, 6, 14, and 30 m) in a micrometeorological tower, and the energy-containing eddy scales are used to calculate neutral planetary boundary layer turbulent parameters. Vertical profile formulations of the wind velocity variances and Lagrangian decorrelation time scales are proposed, and to validate this new parameterization, it is applied in a Lagrangian Stochastic Particle Dispersion Model to simulate the Prairie Grass concentration experiments. The simulated concentration results were shown to agree with those observed.

Our goal is to compare the performance of Classification and Regression Tree, Naive Bayes and Random Forest algorithms, from supervised image classification, and approaches on Pixel-Based Image analysis (PBIA) and Geographic Object-Based Image Analysis (GEOBIA), to classify turbidity in reservoirs. Tod do so, we use Landsat 8 image and bands and spectral indices, as predictive parameters, as well as the classification algorithms based on PBIA and GEOBIA. The Brazilian Itaipu reservoir was adopted, as a case study. Our results show that the RF classifier obtained the highest accuracy in both classification approaches, followed by CART and NB. The KA and OA indices of the GEOBIA classifications were superior to the PBIA classifications in both algorithms. This study contributes with an approach to quickly and accurately delineating turbidity spectral limits in reservoirs.