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Abstract Concentrations of total dissolved inorganic carbon (DIC) in freshwater ecosystems are controlled by terrestrial inputs and a myriad of in situ processes, such as aquatic metabolism. Dissolved CO₂is one of the components of DIC, and its dynamics are also regulated by chemical equilibrium with the DIC pool, so‐called carbonate buffering. Although its importance is generally recognized, carbonate buffering is still not consistently accounted for in freshwater studies. Here, we review key concepts in freshwater carbonate buffering, perform simulation experiments, and provide a case study of an alkaline river to illustrate calculations of DIC from CO₂. These analyses demonstrate that carbonate buffering can alter common interpretations of CO₂data, including carbon–oxygen coupling through production and respiration. As direct measurements of dissolved CO₂are increasingly common, accounting for CO₂equilibria with DIC is critical to understanding its role in carbon cycling within most freshwater systems. 

This dataset was collected in the Upper Clark Fork River, MT from July to August in 2021. The dataset contains high-frequency measurements of dissolved oxygen, alkalinity, partial pressure of carbon dioxide and other environmental variables. 

Abstract Rivers efficiently collect, process, and transport terrestrial‐derived carbon. River ecosystem metabolism is the primary mechanism for processing carbon. Diel cycles of dissolved oxygen (DO) have been used for decades to infer river ecosystem metabolic rates, which are routinely used to predict metabolism of carbon dioxide (CO₂) with uncertainties of the assumed stoichiometry ranging by a factor of 4. Dissolved inorganic carbon (DIC) has been less used to directly infer metabolism because it is more difficult to quantify, involves the complexity of inorganic carbon speciation, and as shown in this study, likely requires a two‐station approach. Here, we developed DIC metabolism models using single‐ and two‐station approaches. We compared metabolism estimates based on simultaneous DO and DIC monitoring in the Upper Clark Fork River (USA), which also allowed us to estimate ecosystem‐level photosynthetic and respiratory quotients (PQ_Eand RQ_E). We observed that metabolism estimates from DIC varied more between single‐ and two‐station approaches than estimates from DO. Due to carbonate buffering, CO₂is slower to equilibrate with the atmosphere compared to DO, likely incorporating a longer distance of upstream heterogeneity. Reach‐averaged PQ_Eranged from 1.5 to 2.0, while RQ_Eranged from 0.8 to 1.5. Gross primary production from DO was larger than that from DIC, as was net ecosystem production by . The river was autotrophic based on DO but heterotrophic based on DIC, complicating our understanding of how metabolism regulated CO₂production. We suggest future studies simultaneously model metabolism from DO and DIC to understand carbon processing in rivers. 

This package provides necessary supporting data and models for the manuscript titled "Divergent metabolism estimates from dissolved oxygen and inorganic carbon: implications for river carbon cycling". The entire dataset consists of sensor data collected at three reaches and metabolism estimates from different models. The sensor data include partial pressure of carbon dioxide in water, dissolved oxygen and temperature. At each reach, we established a two station approach, meaning at least one pair of sensor suits were distributed upstream and downstream. Results for metabolism estimates differ by solutes (i.e., oxygen or carbon based) and modelling approaches (i.e., single station or two station approach). In addition to data products, we also provide R packages for metabolism models. 

Abstract Measurements of riverine dissolved inorganic carbon, total alkalinity (A_T), pH, and the partial pressure of carbon dioxide (pCO₂) can provide insights into the biogeochemical function of rivers, including the processes that control biological production, chemical speciation, and air‐water CO₂fluxes. The complexity created by these combined processes dictates that studies of inorganic carbon be made over broad spatial and temporal scales. Time‐series data like these are relatively rare, however, because sampling and measurements are labor intensive and, for some variables, good measurement quality is difficult to achieve (e.g., pH). In this study, spectrophotometric pH and A_Twere quantified with high precision and accuracy at biweekly to monthly intervals over a four‐year period (2018–2021) along 216 km of the Upper Clark Fork River (UCFR) in the northern Rocky Mountains, USA. We use these and other time‐series data to provide insights into the processes that control river inorganic carbon, with a focus onpCO₂and air‐water CO₂fluxes. We found that seasonal snowmelt runoff increasedpCO₂and that expected increase and decrease ofpCO₂due to seasonal heating and cooling were likely offset by an increase and loss of algal biomass, respectively. Overall, the UCFR was a small net source (0.08 ± 0.14 mol m⁻² d⁻¹) of CO₂to the atmosphere over the four‐year study period with highly variable annual averages (0.0–0.10 mol m⁻² d⁻¹). The seasonally correlated, offsetting mechanisms highlight the challenges in predictingpCO₂and air‐water CO₂fluxes in rivers. 

Abstract Total alkalinity (A_T) is an important parameter in the study of aquatic biogeochemical cycles, chemical speciation modeling, and many other important fundamental and anthropogenic (e.g., industrial) processes. We know little about its short‐term variability, however, because studies are based on traditional bottle sampling typically with coarse temporal resolution. In this work, an autonomous A_Tsensor, named the Submersible Autonomous Moored Instrument for Alkalinity (SAMI‐alk), was tested for freshwater applications. A comprehensive evaluation was conducted in the laboratory using freshwater standards. The results demonstrated excellent precision and accuracy (± 0.1%–0.4%) over the A_Trange from 800 to 3000 μmol L⁻¹. The system had no drift over an 8 d test and also demonstrated limited sensitivity to variations in temperature and ionic strength. Three SAMI‐alks were deployed for 23 d in the Clark Fork River, Montana, with a suite of other sensors. Compared to discrete samples, in situ accuracy for the three instruments were within 10–20 μmol L⁻¹(0.3–0.6%), indicating good performance considering the challenges of in situ measurements in a high sediment, high biofouling riverine environment with large and rapid changes in temperature. These data reveal the complex A_Tdynamics that are typically missed by coarse sampling. We observed A_Tdiel cycles as large as 60–80 μmol L⁻¹, as well as a rapid change caused by a runoff event. Significant errors in inorganic carbon system modeling result if these short‐term variations are not considered. This study demonstrates both the feasibility of the technology and importance of high‐resolution A_Tmeasurements.