Introduction
Despite the importance of river systems for water supply and other ecosystem services, such as regulation of nutrient cycles (e.g. nitrogen and phosphorus) and supporting fisheries, they are highly degraded ecosystems due to anthropogenic stressors such as modified flows, urbanisation, agriculture and wastewater (Vörösmarty et al., 2010). By altering physical, chemical and biological components of freshwaters, anthropogenic interventions play an important role influencing climate change through greenhouse gas (GHG) emissions. River systems globally contribute estimated annual CO2 emissions equivalent to 20-24% of fossil fuel emissions, 35-65% of the CH4emissions from all sources, and 4-5% of N2O total emissions (Rosentreter et al., 2021, Friedlingstein et al., 2022, Battin et al., 2023). However, global estimates of river GHG emissions remain highly uncertain, due to sparse data availability and inconsistent monitoring practices, perpetuating large gaps in international emissions inventories and preventing effective management (Rudee & Phillips, 2021). The Paris Agreement signed at the UN Climate Change Conference (COP21) in 2015 recognized the crucial need to quantify GHG emissions from sources and sinks that have not yet been adequately quantified. More effective river catchment monitoring and management are needed urgently for countries to respond effectively to global climate change threats by better managing carbon emissions.
Quantifying aquatic carbon cycle processes is challenging. Processes such as photo-oxidation, metabolism (production, respiration) and methanogenesis can be estimated from dissolved gas measurements, organic matter degradation assays, GHG emissions (e.g., floating chambers) or from dissolved gas concentrations relative to atmospheric concentrations (Duc et al., 2013, Appling et al., 2018, Aho et al., 2021). However, most studies have collected short-duration datasets in-situ, at small numbers of sites, with low temporal resolution. Estimates of photo-oxidation and decomposition with experimental manipulations are also typically resolved at weekly-to-monthly timescales. Even where daily-to-weekly sampling takes place, it often occurs at selected locations during daylight hours, or misses important events such as flow peaks (Bieroza et al., 2023). Thus, we lack a clear understanding of how river stressors and management activities influence emission ‘hotspots’ in space, and/or ‘hot moments’ in time (Zhang et al., 2021b), risking either over- or under-estimation of emissions. Recent reviews and opinion articles have broadly outlined a need for global river observation systems for river carbon monitoring (Battin et al., 2023, Dean & Battin, 2024) but lacked details on how these networks could be implemented. Here we evaluate how recent advances in autonomous (field deployable and wireless) sensor networks, and robotic mobile sensing platforms, can be harnessed to meet this requirement by combining high-frequency, continuous data at multiple locations, with machine learning (ML) models to improve carbon emission estimates and overall water management in river networks.
The emergence of sensor technologies for high-resolution space/time monitoring offers the potential to evaluate fundamental linkages between hydrological regime, physicochemical conditions and nutrient dynamics to fill knowledge gaps in understanding processes related to carbon emissions. Links between river physical properties, network structure and ecosystem carbon cycle parameters, including metabolism, have advanced notably with Cole et al.’s (2007) concept of “leaky pipes” for carbon loss along the land-ocean aquatic continuum (LOAC), and the Pulse-Shunt Concept, which added transport vs reaction timescales related to flow (Raymond et al., 2016). Wollheim et al. (2018) proposed a similar River Network Saturation (RNS) concept, describing how river networks become saturated with carbon at high flows, particularly in low-order streams, where terrestrial carbon is “pulsed” to river networks and “shunted” downstream because high flows restrict time for uptake reactions in quantifiable amounts. Thus, most annual downstream carbon export occurs during a small number of high flow events (Raymond et al., 2016). At low flows, particularly in high-order rivers, carbon uptake fluxes and subsequent emissions are much higher as transport timescales are long and reactions can occur by photomineralization and co-metabolism on bio-aggregates (Battin et al., 2008). Continuous measurements of dissolved oxygen have enabled many of these advances in understanding river carbon cycling processes of primary production and respiration, but the spatial distribution of monitoring systems remains limited and globally unbalanced. For example, across North America, the relatively widespread availability of sensor data (Figure 1), has promoted an understanding of key drivers of river carbon cycling and CO2 emissions, as demonstrated through the StreamPULSE project (Appling et al., 2018). A range of datasets are also collected in regional initiatives (e.g. Figure 1b, c) yet for large parts of the world, including much of the global south, we still have only patchy knowledge of the parameters needed to quantify carbon transformations and emissions, or data collected are not open access (Dean & Battin, 2024). Even in countries with advanced sensor networks, there are still large gaps spatially between sensor locations (Fig 1b), and high-order, poorly mixed rivers, which present challenges to developing representative datasets, unless multiple sensors are deployed across river cross sections.
Sensor network developments can improve our understanding of spatial and temporal carbon dynamics significantly (Segatto et al., 2023) but cost prevents monitoring all rivers. Coupling sensor developments with advances in fixed sensor technology and data analytics, as well as mobile robotics and ML, will be vital to achieve spatially continuous data and interpolate spatially explicit datasets to derive whole catchment understanding (O’Grady et al., 2021, Khandelwal et al., 2023). By automating sensors using computer science advances and telemetry systems, it is becoming possible to monitor, in near real-time, how aquatic ecosystems are functioning. Additionally, the Internet of Things (IoT) offers significant potential in delivering up-to-date water quality data with a high level of precision and accuracy, enabling the detection of even minor fluctuations in water quality. IoT facilitates the connection of various instruments, including electronic devices and sensors, utilising the communication infrastructure and cloud computing resources already in place (Amador-Castro et al., 2024). This offers the potential to validate existing carbon dynamic scientific models and develop the next generation of catchment-scale numerical predictive models. There is now a potential for a step-change in adaptive management, moving away from current low-resolution, relatively slow turnover data collection, with delayed analytics that impede effective decision-making, to faster and more accurate workflows, even at national scales. This will subsequently enable scientists to advance emission quantifications at national to global levels and develop intervention plans.