Increasing development in tropical regions provides new economic opportunities that can improve livelihoods, but it threatens the functional integrity and ecosystem services provided by terrestrial and aquatic ecosystems when conducted unsustainably. Given the small size of many islands, communities may have limited opportunities to replace loss and damage to the natural resources upon which they depend for ecosystem service provisioning, thus heightening the need for proactive, integrated management. This study quantifies the effectiveness of management strategies, stipulated in logging codes-of-practice, at minimizing soil erosion and sediment runoff as clearing extent increases, using Kolombangara Island, Solomon Islands as a case study. Further, we examine the ability of erosion reduction strategies to maintain sustainable soil erosion rates and reduce potential downstream impacts to drinking water and environmental water quality. We found that increasing land clearing-even with best management strategies in place-led to unsustainable levels of soil erosion and significant impacts to downstream water quality, compromising the integrity of the land for future agricultural uses, consistent access to clean drinking water, and important downstream ecosystems. Our results demonstrate that in order to facilitate sustainable development, logging codes of practice must explicitly link their soil erosion reduction strategies to soil erosion and downstream water quality thresholds, otherwise they will be ineffective at minimizing the impacts of logging activities. The approach taken here to explicitly examine soil erosion rates and downstream water quality in relation to best management practices and increasing land clearing should be applied more broadly across a range of ecosystems to inform decision-making about the socioeconomic and environmental trade-offs associated with logging, and other types of land use change.

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Abstract. 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.

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