Bio-Optical Modeling in a Tropical Hypersaline Lagoon Environment

In this chapter, we attempted to present an overview of the use of remote sensing to monitor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation developed for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an extreme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-optical algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field

Influence of summertime mesoscale convective systems on the heat balance and surface mixed layer dynamics of a large Amazonian hydroelectric reservoir

We evaluated the impacts of summertime mesoscale convective systems (MCS) on the heat balance and diel surface mixed layer (SML) dynamics of the Brazilian Amazon's Tucuruí Hydroelectric Reservoir (THR). We used a synergistic approach that combines in situ data, remote sensing data, and three-dimensional (3-D) modeling to investigate the typical behavior of the components of the heat balance and the SML dynamics. During the study period (the austral summer of 2012–2013), 22 days with MCS activity were identified. These events occurred approximately every 4 days, and they were most frequent during January (50% of the observations). An analysis of local meteorological data showed that when MCS occur, the environmental conditions at THR change significantly (p-value < 0.01). The net longwave flux, which was the heat balance component most strongly impacted by MCS, increased more than 32% on days with MCS activity. The daily integrated heat balance became negative (−54 W m−2) on MCS days, while the balance was positive (19 W m−2) on non-MCS days. In response to the changes in the heat balance, the SML dynamics changed when a MCS was over the THR. The SML depth was typically 28% higher on the days with MCS (∼1.6 m) compared with the days without MCS (∼1.3 m). The results indicate that MCS are one of the main meteorological disturbances driving the heat balance and the mixing dynamics of Amazonian hydroelectric reservoirs during the summer. These events may have implications for the water quality and greenhouse gas emissions of Amazonian reservoirs

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 ICE's 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