José Stech

The response of the South Brazil Bight to the passage of wintertime cold fronts

On the southeastern continental shelf of Brazil the wintertime subtidal variability of the circulation is highly dominated by the passage of cold fronts. Hydrographic data for the region reveal that during this (July, August, September) season only a weak vertical stratification is observed. In this paper the response of the region to cold fronts is studied by using a barotropic finite element model, forced by a conceptual cold front wind field derived from the analysis of coastal winds and satellite imagery.

How do cold water biota arrive in a tropical bay near Rio de Janeiro, Brazil?

Abstract Previous ecological studies on foraminifera and ostracoda from the tropical Sepetiba Bay, located in the southern part of the State of Rio de Janeiro, Brazil, have noted the presence in this area of some cool water taxons typical of the Argentine continental platform. These studies have proposed that parcels of these temperate waters with their associated biological indicators are advected northward along the platform, penetrating into some tropical entrances of the southeast coast of Brazil. In the present study, ecological data (foraminifera, ostracoda and microbivalves), are used together with information obtained by satellite tracked drifting buoys and digital thermal imagery obtained from NOAA satellites to indicate the path taken by these high latitude species along the southern Brazilian coast to arrive in the Sepetiba Bay, near the city of Rio de Janeiro. Our general conclusion is that biotic elements native to the colder, less saline marine waters seen to the south of Brazil have been and are being advected northward along the inner part of the continental platform to about 22°S. Water parcels containing this biota may, in a sporadic fashion, enter into the tropical Sepetiba Bay. The passage of meteorological fronts through the region is considered to be an important if not the principal mechanism for the sporadic entry of the cool water into the Southern Coastal Entrance of the State of Rio de Janeiro.

Telemetric monitoring system for meteorological and limnological data acquisition

(2006). Telemetric monitoring system for meteorological and limnological data acquisition. SIL Proceedings, 1922-2010: Vol. 29, No. 4, pp. 1747-1750.

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

Improving the spectral unmixing algorithm to map water turbidity Distributions

In this paper we evaluate the suitability of the spectral unmixing algorithm to map the turbidity in the Curuai floodplain lake and enhance its applicability using autocorrelation modelling. The Spectral Unmixing Model (SMM) was applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance (MOD09) image, taking in-situ measurements close to the acquisition date. Fraction images of inorganic matter-laden water, dissolved organic matter-laden water, and phytoplankton-laden water were generated by SMM, using 4 MODIS spectral bands (blue, green, red, and near infrared). These endmembers were selected based on the dominance of these components, which affect water turbidity. These fraction images allowed assessing the turbidity distribution in the study area but showing only places with high or low turbidity. The kernel estimation algorithm was then used to verify the spatial correlation among the in-situ measurement data. The occurrence of clusters suggests that there are different spatial water regimes. One spatial regression model was then compiled for each water regime, each of which presented a better turbidity estimation as opposed to the one derived from the Ordinary Least Square (OLS). The methodology applied was hence useful to analyze the spatial distribution of turbidity in the Curuai floodplain lake.

Assessment of Spatial Interpolation Methods to Map the Bathymetry of an Amazonian Hydroelectric Reservoir to Aid in Decision Making for Water Management

The generation of reliable information for improving the understanding of hydroelectric reservoir dynamics is fundamental for guiding decision-makers to implement best management practices. In this way, we assessed the performance of different interpolation algorithms to map the bathymetry of the Tucurui hydroelectric reservoir, located in the Brazilian Amazon, as an aid to manage and operate Amazonian reservoirs. We evaluated three different deterministic and one geostatistical algorithms. The performance of the algorithms was assessed through cross-validation and Monte Carlo Simulation. Finally, operational information was derived from the bathymetric grid with the best performance. The results showed that all interpolation methods were able to map important bathymetric features. The best performance was obtained with the geostatistical method (RMSE = 0.92 m). The information derived from the bathymetric map (e.g., the level-area and level-volume diagram and the three-dimensional grid) will allow for optimization of operational monitoring of the Tucurui hydroelectric reservoir as well as the development of three-dimensional modeling studies.

Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms.

Turbidity in the amazon floodplain assessed through a spatial regression model applied to fraction images derived from MODIS/Terra

The objective of this paper was to estimate turbidity in the Curuai floodplain during the high water level period. Spatial regression models were developed by using fraction images derived from a linear spectral mixture model applied to a Moderate Resolution Imaging Spectroradiometer/Terra image and turbidity in situ data. As the turbidity in situ data showed spatial autocorrelation, they were divided into four spatial regimes (clusters). Thus, a spatial regression model was developed for each spatial regime. Through the Akaike information criterion, it was verified which spatial regime showed the best fit in the spatial regression model. The best fit was presented by the spatial regime 4 (R 2 = 0.80,p < 0.05). Then, the spatial regression model developed for the spatial regime 4 was applied to all floodplain lakes. The spatial regression models show potential for assessing the water turbidity in aquatic systems by considering a spatial dependence between samples.

Performance analysis of MODIS 500-m spatial resolution products for estimating chlorophyll-a concentrations in oligo- to meso-trophic waters case study: Itumbiara reservoir, Brazil

Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns. Algorithm O14a produced the best validation with a root mean square error (RMSE) of 30.4%, whereas O14b produced an RMSE of 32.41% using the mixed dataset calibration. O14a was applied to MOD09GA to build a time series for the reservoir for the year of 2009. The time-series analysis revealed that there were occurrences of algal blooms in the summer that were likely related to the additional input of nutrients caused by rainfall runoff. During the winter, however, the few observed algal blooms events were related to periods of atmospheric meteorological variations that represented an enhanced external influence on the processes of mixing and stratification of the water column. Finally, the use of remote sensing techniques can be an important tool for policy makers, environmental managers and the scientific community with which to monitor water quality.

Interactive Correlation Environment (ICE) — A Statistical Web Tool for Data Collinearity Analysis

Web tools for statistical investigation with an interactive and friendly interface enable users without programming knowledge to conduct their analyses. We develop an Interactive Correlation Environment (ICE) in an open access platform to perform spectral collinearity analysis for biogeochemical activity retrieval. We evaluate its performance on different browsers and applied it to retrieve chlorophyll-a (chl-a) concentration in a tropical reservoir. The use of ICE to retrieve water chl-a concentration got a Root Mean Square Error (RMSE) lower than 7% for seasonal datasets, enhancing ICEs ability to adapt it within season. An RMSE of 17% was found for the mixed dataset with a large range of chl-a concentrations. We conclude that the use of ICE is recommended, due to its quick response, easily manipulation, high accuracy, and empirical adaptation to seasonal variability. Its use is enhanced by the development of hyperspectral sensors, which allow the identification of several biogeochemical components, such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf nitrogen, and leaf chl-a concentration.