Emissions from flooded land represent a direct source of anthropogenic greenhouse gas (GHG) emissions. Methane emissions from large, artificial water bodies have previously been considered, with numerous studies assessing emission rates and relatively simple procedures available to determine their surface area and generate upscaled emissions estimates. In contrast, the role of small artificial water bodies (ponds) is very poorly quantified, and estimation of emissions is constrained both by a lack of data on their spatial extent and a scarcity of direct flux measurements. In this study, we quantified the total surface area of water bodies < 10⁵ m² across Queensland, Australia, and emission rates from a variety of water body types and size classes. We found that the omission of small ponds from current official land use data has led to an underestimate of total flooded land area by 24 %, of small artificial water body surface area by 57 % and of the total number of artificial water bodies by 1 order of magnitude. All studied ponds were significant hotspots of methane production, dominated by ebullition (bubble) emissions. Two scaling approaches were developed with one based on pond primary use (stock watering, irrigation and urban lakes) and the other using size class. Both approaches indicated that ponds in Queensland alone emit over 1.6 Mt CO₂ eq. yr⁻¹, equivalent to 10 % of the state's entire land use, land use change and forestry sector emissions. With limited data from other regions suggesting similarly large numbers of ponds, high emissions per unit area and under-reporting of spatial extent, we conclude that small artificial water bodies may be a globally important missing source of anthropogenic greenhouse gas emissions.

The gauging of free surface flows in waterways provides the foundation for monitoring and managing the water resources of built and natural environments. A significant body of literature exists around the techniques and benefits of optical surface velocimetry methods to estimate flows in waterways without intrusive instruments or structures. However, to date, the operational application of these surface velocimetry methods has been limited by site configuration and inherent challenging optical variability across different natural and constructed waterway environments. This work demonstrates a significant advancement in the operationalisation of non-contact stream discharge gauging applied in the computer vision stream gauging (CVSG) system through the use of methods for remotely estimating water levels and adaptively learning discharge ratings over time. A cost-effective stereo camera-based stream gauging device (CVSG device) has been developed for streamlined site deployments and automated data collection. Evaluations between reference state-of-the-art discharge measurement technologies using DischargeLab (using surface structure image velocimetry), Hydro-STIV (using space–time image velocimetry), acoustic Doppler current profilers (ADCPs), and gauging station discharge ratings demonstrated that the optical surface velocimetry methods were capable of estimating discharge within a 5 %–15 % range between these best available measurement approaches. Furthermore, results indicated model machine learning approaches leveraging data to improve performance over a period of months at the study sites produced a marked 5 %–10 % improvement in discharge estimates, despite underlying noise in stereophotogrammetry water level or optical flow measurements. The operationalisation of optical surface velocimetry technology, such as CVSG, offers substantial advantages towards not only improving the overall density and availability of data used in stream gauging, but also providing a safe and non-contact approach for effectively measuring high-flow rates while providing an adaptive solution for gauging streams with non-stationary characteristics.

Emissions from flooded land represent a direct source of anthropogenic greenhouse gas emissions. Methane emissions from large, artificial water bodies have previously been considered, with numerous studies assessing emission rates and relatively simple procedures available to determine their surface area and generate upscaled emissions estimates. In contrast, the role of small artificial water bodies (ponds) is very poorly quantified, and estimation of emissions is constrained both by a lack of data on their spatial extent, and a scarcity of direct flux measurements. In this study, we quantified the total surface area of water bodies < 10⁵ m² across Queensland, Australia, and emission rates from a variety of water body types and size classes. We found that the omission of small ponds from current official land-use data has led to an under-estimate of total flooded land area by 24 %, of small artificial water body surface area by 57 %, and of the total number of artificial water bodies by an order of magnitude. All studied ponds were significant hotspots of methane production, dominated by ebullition (bubble) emissions. Two scaling approaches were developed with one based on pond primary use (stock watering, irrigation and urban lakes) and the other using size class. Both approaches indicated that ponds in Queensland alone emit over 1.6 Mt CO₂-eq yr⁻¹, equivalent to 10 % of the state's entire Land Use, Land Use Change and Forestry sector emissions. With limited data from other regions suggesting similarly large numbers of ponds, high emissions per unit area, and under-reporting of spatial extent, we conclude that small artificial water bodies may be a globally important missing source of anthropogenic greenhouse gas emissions.

The gauging of free surface flows in waterways provides the foundation for monitoring and managing the water resources of built and natural environments. A significant body of literature exists around the techniques and benefits of optical surface velocimetry methods to estimate flows in waterways without intrusive instruments or structures. However, to date the operational application of these surface velocimetry methods has been limited by site configuration and inherent challenging optical variability across different natural and constructed waterway environments. This work demonstrates a significant advancement in the operationalisation of non-contact stream discharge gauging applied in the computer vision stream gauging (CVSG) system through the use of methods for remotely estimating water levels and adaptively learning discharge ratings over time. A cost-effective stereo camera-based stream gauging device (CVSG device) has been developed for streamlined site deployments and automated data collection. Evaluations between reference state-of-the-art discharge measurement technologies using DischargeLab (using surface structure image velocimetry), Hydro-STIV (using space-time image velocimetry), ADCPs (acoustic doppler current profilers), and gauging station discharge ratings demonstrated that the optical surface velocimetry methods were capable of estimating discharge within best available measurement error margins of 5–15 %. Furthermore, results indicated model machine learning approaches leveraging data to improve performance over a period of months at the study sites produced a marked 5–10 % improvement in discharge estimates, despite underlying noise in stereophotogrammetry water level or optical flow measurements. The operationalisation of optical surface velocimetry technology, such as CVSG, offers substantial advantages towards not only improving the overall density and availability of data used in stream gauging, but also providing a safe and non-contact approach for effectively measuring high flow rates while providing an adaptive solution for gauging streams with non-stationary characteristics.