Modelling approaches
River DOM processing is influenced by multiple dynamic drivers that often respond non-linearly to hydro-climatological events across catchments, such as floods, drought, and warming (Battin et al., 2023). In the past, modelling of DOM, carbon and nutrient reaction and transport through river networks was hindered by the lack of high-resolution hydrology and hydrography data products at watershed, national, and global scales. As a result, models were typically limited to specific water body types (e.g. lakes only, river reaches/segments only) or grouped catchments where output could not be discretized in such a way to allow for spatiotemporal trends to be identified. The intersection of sensor technology, river models and ML advances presents new opportunities for aquatic scientists and managers to develop digital representations of river systems (aka digital twins) to enhance aquatic science and management.
Increasing volumes of sensor data have enabled the expansion of metabolism estimation from the river reach scale to the network scale (Figure 4) using a range of model methods, including process-based (Segatto et al., 2020), empirical (Rodríguez-Castillo et al., 2019), ML (Segatto et al., 2021), or a combination (Pathak et al., 2022, Maavara et al., 2023). As sensor networks can gather data on the physical and chemical properties of rivers, such as temperature, light intensity, dissolved oxygen and nutrient concentrations, these data are usually used as input in process-based metabolism models to estimate reach-scale processes (Demars et al., 2015, Appling et al., 2018). Local metabolism rates can then be combined with information about the catchment environment to upscale to the river network scale, and as inputs for ML algorithms such as decision trees or neural networks.