Abstract: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 OPEN ACCESS Remote Sens. 2013, 5 4775 based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

Abstract. Abundant research has been devoted to understanding the complexity of the biogeochemical and physical processes that are responsible for greenhouse gas (GHG) emissions from hydropower reservoirs. These systems may have spatially complex and heterogeneous GHG emissions due to flooded biomass, river inflows, primary production and dam operation. In this study, we investigated the relationships between the water-air CO 2 fluxes and the phytoplanktonic biomass in the Funil Reservoir, which is an old, stratified tropical reservoir that exhibits intense phytoplankton blooms and a low partial pressure of CO 2 (pCO 2 ). Our results indicated that the seasonal and spatial variability of chlorophyll concentrations (Chl) and pCO 2 in the Funil Reservoir are related more to changes in the river inflow over the year than to environmental factors such as air temperature and solar radiation. Field data and hydrodynamic simulations revealed that river inflow contributes to increased heterogeneity during the dry season due to variations in the reservoir retention time and river temperature. Contradictory conclusions could be drawn if only temporal data collected near the dam were considered without spatial data to represent CO 2 fluxes throughout the reservoir. During periods of high retention, the average CO 2 fluxes were 10.3 mmol m −2 d −1 based on temporal data near the dam versus −7.2 mmol m −2 d −1 with spatial data from along the reservoir surface. In this case, the use of solely temporal data to calculate CO 2 fluxes results in the reservoir acting as a CO 2 source rather than a sink. This finding suggests that the lack of spatial data in reservoir C budget calculations can affect regional and global estimates. Our results support the idea that the Funil Reservoir is a dynamic system where the hydrodynamics represented by changes in the river inflow and retention time are potentially a more important force driving both the Chl and pCO 2 spatial variability than the in-system ecological factors.

Abstract: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 Tucuruí 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 Tucuruí hydroelectric reservoir as well as the development of three-dimensional modeling studies.

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. OPEN ACCESSRemote Sens. 2014, 6 1635 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.

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.