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

High-frequency dynamics of CO2 emission flux and its influencing factors in a subtropical karst groundwater-fed reservoir, south China

Revealing the magnitude, dynamics, and influencing factors of CO2 emissions across the water-air interface in karst water with high frequency is crucial for accurately assessing the carbon budget in a karst environment. Due to the limitations of observation methods, the current research is still very insufficient. To solve the above problems and clarify the main influencing factors of CO2 emission in karst water, this study selected Dalongdong (DLD) Reservoir, located in the typical karst peak and valley area in southwest China, to carry out a multi-parameter high-frequency monitoring study from January to December 2021, and used the thin boundary model method to estimate the CO2 flux across the water-air interface (CF). The average annual flux of DLD reservoir is 84.48 mmol·(m2·h)-1, which represents a CO2 source overall. However, during the stratification period in August, there is a transient carbon sink due to negative CO2 emission. The alteration of thermal stratification in water is crucial in regulating the seasonal variation of CF. Meanwhile, the diurnal variation is significantly influenced by changes in hydrochemical parameters during the thermal stratification stage. Compared to low wind speeds (<3 m/s), high wind speeds (≥3 m/s) have a greater impact on the CO2 flux. Furthermore, high-frequency continuous data revealed that the reservoir triggered a CO2 pulse emission during the turnover process, primarily at night, leading to unusually high CO2 flux values. It is of great significance to monitor and reveal the process, flux, and control factors of CO2 flux in land water at a high-frequency strategy. They will help improve the accuracy of regional or watershed carbon budgets and clarify the role of global land water in the global carbon budget.

Inferring floodplain bathymetry using inundation frequency

This study proposes a new method to retrieve the bathymetry of turbid-water floodplains from the inundation frequency (IF) data derived from over 32 years of composite optical remote sensing data. The new method was tested and validated over the Curuai floodplain in the lower Amazon River, where the entire bathymetry was surveyed in 2004, and water level gauge data has been available since 1960. The depth was estimated based on the relationship derived from IF and surveyed depth data, and the results were compared to those retrieved from bare-Earth DEM. We further assessed the sensitivity of the approach by analyzing the deepest part of the lake (i.e., permanent water body ~ 8m) with high IF, as well as the effect of gradual sedimentation in the lake over time. The results showed that the model is highly accurate and sensitive to IF changes even in the permanent water body areas, suggesting that this model can be used in other seasonal lakes worldwide with turbid-waters, where large-scale bathymetry surveys are not feasible due to high operation costs.

Using hydrodynamic and water quality variables to assess eutrophication in a tropical hydroelectric reservoir

Few studies have examined the influence of reservoir hydrodynamics on the water quality of its limnological zones. In this study, the relationships between the operational phases and the water quality of the limnological zones were assessed for the Amazonian reservoir Tucuruí. Limnological zones were clustered by means of an artificial neural network technique, and inputs used were water quality variables, measured at twelve stations between 2006 and 2016. Generalized Linear Models (GLMs) were then used to identify the influence of the operational phases of the reservoir on the water quality of its limnological zones. The GLM with a gamma-distributed response variable indicated that Chlorophyll-a concentrations in the riverine and transitional zones differed notably from those observed in the lacustrine zone. Chlorophyll-a concentrations were significantly lower during the operational falling water phase than in the low water phase (p < 0.05). The GLM with an inverse Gaussian-distributed response variable indicated that Secchi depth was significantly lower in the riverine than in the lacustrine limnological zone (p < 0.05). Our results suggest that more eutrophic conditions occur during the operational rising water phase, and that the area most vulnerable to eutrophication is the transitional zone. We demonstrate that the use of GLMs is suitable for determining areas and operational phases most vulnerable to eutrophication. We envisage that this information will be useful to decision-makers when monitoring the water quality of hydroelectric reservoirs with dendritic patterns and dynamic operational phases.

Interaction between carbon dioxide emissions and eutrophication in a drinking water reservoir: A three-dimensional ecological modeling approach

We developed a three-dimensional model to study the dynamics of carbon dioxide (CO₂) emission from a subtropical drinking water reservoir. The quantitative effects of dissolved CO₂ concentration on phytoplankton growth were coupled in an inorganic carbon module. Water quality monitoring was carried out to calibrate and validate the model. The simulated surface CO₂ concentrations showed no significant difference between seasons (p>0.05). Regarding the spatial distribution, high CO₂ concentrations were observed in the inflow and dam regions (p<0.05). Four scenarios of different atmospheric CO₂ pressures and eutrophic levels were simulated to test the following hypotheses: (1) eutrophication will reverse the carbon budgets in reservoir systems and (2) rising CO₂ levels will increase phytoplankton biomass. The results showed that water quality improvements will promote the emission of CO₂ into the atmosphere. Simultaneously, the elevated CO₂ in the air will stimulate algal biomass, especially in nutrient-rich systems. The systematic analysis of carbon cycling revealed the different internal transformation rates under different scenarios and showed that 32% of carbon was removed via CO₂ emission and carbon burial. The interaction provides a novel direction to understand the feedback loops between aquatic ecosystems and increasing CO₂ pressure in the future.

Conjunctive use of in situ gas sampling and chromatography with geospatial analysis to estimate greenhouse gas emissions of a large Amazonian hydroelectric reservoir

Hydroelectric power reservoirs are considered potential contributors to the greenhouse effect in the atmosphere through the emittance of methane and carbon dioxide. We combined in situ sampling and gas chromatography with geostatistical and remote sensing approaches to estimate greenhouse gas (GHG) emissions of a large hydropower reservoir. We used remote sensing data to estimate the water surface and geospatial interpolation to calculate total emissions as a function of reservoir surface area. The CH₄ and CO₂ gas concentrations were linearly correlated to sampling time, confirming the adequacy of the in situ sampling method to measure GHG diffusive fluxes from reservoir water surfaces. The combination of high purity (99.99%) ISO-norm gas standards with a gas chromatograph, enabled us to achieve low measurement detection limits of 0.16 and 0.60 μmol mol^-1, respectively, for CH₄ (using a flame ionization or FID detector) and CO₂ (using a thermal conductivity or TCD detector). Our results show that CO₂ emissions are significantly (an order of 5.10²-10³) higher than those of CH₄ in both the spatial and temporal domain for this reservoir. The total diffusive GHG emissions over a year (June 2011 to May 2012) of the Tucuruí hydropower reservoir being in operation, in units of tons of carbon, added up to 6.82 × 10³ for CH₄ and 1.19 × 10⁶ for CO₂. We show that in situ GHG sampling using small floating gas chambers and high precision gas chromatography can be combined with geospatial interpolation techniques and remote sensing data to obtain estimates of diffusive GHG emissions from large water bodies with fluctuating water surfaces such as hydropower reservoirs. We recommend that more measurements and observations on these emissions are pursued in order to support and better quantify the ongoing discussions on estimates and mitigation of GHG emissions from reservoirs in the Amazon region and elsewhere in the world.

Insights Into Limnothrix sp. Metabolism Based on Comparative Genomics

Currently only four genome sequences for Limnothrix spp. are publicly available, and information on the genetic properties of cyanobacteria belonging to this genus is limited. In this study, we report the draft genome of Limnothrix sp. CACIAM 69d, isolated from the reservoir of a hydroelectric dam located in the Amazon ecosystem, from where cyanobacterial genomic data are still scarce. Comparative genomic analysis of Limnothrix revealed the presence of key enzymes in the cyanobacterial central carbon metabolism and how it is well equipped for environmental sulfur and nitrogen acquisition. Additionally, this work covered the analysis of Limnothrix CRISPR-Cas systems, pathways related to biosynthesis of secondary metabolites and assembly of extracellular polymeric substances and their exportation. A trans-AT PKS gene cluster was identified in two strains, possibly related to the novel toxin Limnothrixin biosynthesis. Overall, the draft genome of Limnothrix sp. CACIAM 69d adds new data to the small Limnothrix genome library and contributes to a growing representativeness of cyanobacterial genomes from the Amazon region. The comparative genomic analysis of Limnothrix made it possible to highlight unique genes for each strain and understand the overall features of their metabolism.

Using CDOM optical properties for estimating DOC concentrations and pCO2 in the Lower Amazon River

Coloured dissolved organic matter (CDOM) is one of the major contributors to the absorption budget of most freshwaters and can be used as a proxy to assess non-optical carbon fractions such as dissolved organic carbon (DOC) and the partial pressure of carbon dioxide (pCO₂). Nevertheless, riverine studies that explore the former relationships are still relatively scarce, especially within tropical regions. Here we document the spatial-seasonal variability of CDOM, DOC and pCO₂, and assess the potential of CDOM absorption coefficient (a_CDOM(412)) for estimating DOC concentration and pCO₂ along the Lower Amazon River. Our results revealed differences in the dissolved organic matter (DOM) quality between clearwater (CW) tributaries and the Amazon River mainstream. A linear relationship between DOC and CDOM was observed when tributaries and mainstream are evaluated separately (Amazon River: N = 42, R² = 0.74, p<0.05; CW: N = 13, R² = 0.57, p<0.05). However, this linear relationship was not observed during periods of higher rainfall and river discharge, requiring a specific model for these time periods to be developed (N = 25, R² = 0.58, p<0.05). A strong linear positive relation was found between a_CDOM(412) and pCO₂(N = 69, R² = 0.65, p<0.05) along the lower river. pCO₂ was less affected by the optical difference between tributaries and mainstream waters or by the discharge conditions when compared to CDOM to DOC relationships. Including the river water temperature in the model improves our ability to estimate pCO₂ (N = 69; R² = 0.80, p<0.05). The ability to assess both DOC and pCO₂ from CDOM optical properties opens further perspectives on the use of ocean colour remote sensing data for monitoring carbon dynamics in large running water systems worldwide.