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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. 

Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates.