The 2019 northeast Brazil oil spill: scenarios.

During the last quarter of 2019, the beaches, mangroves, and estuaries of Northeast Brazil received an unprecedented volume of crude oil from the sea, which became the worst environmental disaster ever to reach the Brazilian coast. The oil, having reached the shores completely unnoticed, left both society and government agents completely clueless on (i) where the oil was coming from; (ii) how much oil was still in the ocean to reach the shorelines; and (iii) which beaches were going to be affected next! By exploring remote sensing data and ocean numerical modeling, along with oil dispersion chemistry on sea water, this study investigates the possible origin and path of the spill and whether it could have been detected from space. The oil dispersion modeling simulations performed for this investigation revealed a possible region and timing of the oil spill, also indicating the likelihood of it being advected toward the shoreline under the ocean surface.

Estimating Energy Dissipation Rate from Breaking Waves Using Polarimetric SAR Images.

The total energy dissipation rate on the ocean surface, ϵt (W m-2), provides a first-order estimation of the kinetic energy input rate at the ocean-atmosphere interface. Studies on the spatial and temporal distribution of the energy dissipation rate are important for the improvement of climate and wave models. Traditional oceanographic research normally uses remote measurements (airborne and platforms sensors) and in situ data acquisition to estimate ϵt; however, those methods cover small areas over time and are difficult to reproduce especially in the open oceans. Satellite remote sensing has proven the potential to estimate some parameters related to breaking waves on a synoptic scale, including the energy dissipation rate. In this paper, we use polarimetric Synthetic Aperture Radar (SAR) data to estimate ϵt under different wind and sea conditions. The used methodology consisted of decomposing the backscatter SAR return in terms of two contributions: a polarized contribution, associated with the fast response of the local wind (Bragg backscattering), and a non-polarized (NP) contribution, associated with wave breaking (Non-Bragg backscattering). Wind and wave parameters were estimated from the NP contribution and used to calculate ϵt from a parametric model dependent of these parameters. The results were analyzed using wave model outputs (WAVEWATCH III) and previous measurements documented in the literature. For the prevailing wind seas conditions, the ϵt estimated from pol-SAR data showed good agreement with dissipation associated with breaking waves when compared to numerical simulations. Under prevailing swell conditions, the total energy dissipation rate was higher than expected. The methodology adopted proved to be satisfactory to estimate the total energy dissipation rate for light to moderate wind conditions (winds below 10 m s-1), an environmental condition for which the current SAR polarimetric methods do not estimate ϵt properly.

Carbon dioxide emissions from Tucuruí reservoir (Amazon biome): New findings based on three-dimensional ecological model simulations.

We used a three-dimensional model to assess the dynamics of diffusive carbon dioxide flux (F(CO2)) from a hydroelectric reservoir located at Amazon rainforest. Our results showed that for the studied periods (2013 summer/wet and winter/dry seasons) the surface averaged F(CO2) presented similar behaviors, with regular emissions peaks. The mean daily surface averaged F(CO2) showed no significant difference between the seasons (p>0.01), with values around -1338mg Cm-2day-1 (summer/wet) and -1395mg Cm-2day-1 (winter/dry). At diel scale, the F(CO2) was large during the night and morning and low during the afternoon in both seasons. Regarding its spatial distribution, the F(CO2) showed to be more heterogeneous during the summer/wet than during the winter/dry season. The highest F(CO2) were observed at transition zone (-300mg Cm-2h-1) during summer and at littoral zone (-55mg Cm-2h-1) during the winter. The total CO2 emitted by the reservoir along 2013 year was estimated to be 1.1Tg C year-1. By extrapolating our results we found that the total carbon emitted by all Amazonian reservoirs can be around 7Tg C year-1, which is 22% lower than the previous published estimate. This significant difference should not be neglected in the carbon inventories since the carbon emission is a key factor when comparing the environmental impacts of different sources of electricity generation and can influences decision makers in the selection of the more appropriate source of electricity and, in case of hydroelectricity, the geographical position of the reservoirs.

Simultaneous Measurements of Chlorophyll Concentration by Lidar, Fluorometry, above-Water Radiometry, and Ocean Color MODIS Images in the Southwestern Atlantic.

Comparisons between in situ measurements of surface chlorophyll-a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic. In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3M(RAD)), Ocean Chlorophyll 4 bands (OC4v4(RAD)), and Ocean Chlorophyll 2 bands (OC2v4(RAD)). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3M(SAT), and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01(SAT)), and Carder(SAT). In the present work, MODIS, lidar and in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg/m(3). In general, the empirical algorithms applied to the in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m(3) (OC2v4(RAD)). The semi-analytical GSM01 algorithm applied to MODIS data performed better (rmse 0.28, rmse-L 0.08, mean diff. -0.01 mg/m(3)) than the Carder and the empirical OC3M algorithms (rmse 1.14 and 0.36, rmse-L 0.34 and 0.11, mean diff. 0.17 and 0.02 mg/m(3), respectively). We find that rmsd values between MODIS relative to the in situ radiometric measurements are < 26%, i.e., there is a trend towards overestimation of R(RS) by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.