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Coastal river deltas are centers of surface water nitrate processing, yet the mechanisms controlling spatio‐temporal patterns in nutrient variability are still little understood. Nitrate fluctuations in these systems are controlled by complex interactions between hydrological and biogeochemical drivers, which act together to transport and transform inorganic nutrients. Distinguishing the contributions of these drivers and identifying wetland zones where nitrate processing is occurring can be difficult, yet is critical to make assessments of nutrient removal capacity in deltaic wetlands. To address these issues, we analyze relationships among regional “external” (river discharge, tides, wind) and local “internal” (water level, temperature, turbidity, and nitrate) variables in a deltaic wetland in coastal Louisiana by coupling a process connectivity framework with information theory measures. We classify variable interactions according to whether they work uniquely, redundantly, or synergistically to influence nitrate dynamics and identify timescales of interaction. We find that external drivers work together to influence nitrate transport. Patterns of hydrological and sediment connectivity change over time due to tidal flushing and discharge variation. This connectivity influences the emergence of functional zones where local nitrate fluctuations and temperature and water level process couplings are strong controls on nitrate variability. High vegetation density decreases hydrological process connectivity, even during periods of high river discharge, but it also increases biogeochemical process connections, due to the lengthening of the hydraulic residence time. Based on these results we make recommendations for monitoring nitrate in a wetland.

River deltas are dynamic systems whose channels can widen, narrow, migrate, avulse, and bifurcate to form new channel networks through time. With hundreds of millions of people living on these globally ubiquitous systems, it is critically important to understand and predict how delta channel networks will evolve over time. Although much work has been done to understand drivers of channel migration on the individual channel scale, a global-scale analysis of the current state of delta morphological change has not been attempted. In this study, we present a methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry (PIV). This methodology is implemented on 48 river delta systems to create a global dataset of decadal-scale delta channel migration. By comparing delta channel migration distributions with a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, sediment flux magnitude, and frequency of flood events. An understanding of modern rates and patterns of channel migration in river deltas is critical for successfully predicting future changes to delta systems and for informing decision makers striving for deltaic resilience.

At a global scale, delta morphologies are subject to rapid change as a result of direct and indirect effects of human activity. This jeopardizes the ecosystem services of deltas, including protection against flood hazards, facilitation of navigation, and biodiversity. Direct manifestations of delta morphological instability include river bank failure, which may lead to avulsion, persistent channel incision or aggregation, and a change of the sedimentary regime to hyperturbid conditions. Notwithstanding the in‐depth knowledge developed over the past decades about those topics, existing understanding is fragmented, and the predictive capacity of morphodynamic models is limited. The advancement of potential resilience analysis tools may proceed from improved models, continuous observations, and the application of novel analysis techniques. Progress will benefit from synergy between approaches. Empirical and numerical models are built using field observations, and, in turn, model simulations can inform observationists about where to measure. Information theory offers a systematic approach to test the realism of alternative model concepts. Once the key mechanism responsible for a morphodynamic instability phenomenon is understood, concepts from dynamic system theory can be employed to develop early warning indicators. In the development of reliable tools to design resilient deltas, one of the first challenges is to close the sediment balance at multiple scales, such that morphodynamic model predictions match with fully independent measurements. Such a high ambition level is rarely adopted and is urgently needed to address the ongoing global changes causing sea level rise and reduced sediment input by reservoir building.

River deltas are complex, dynamic systems whose channel networks evolve in response to internal and external forcings. To capture these changes, methods to extract and analyze deltaic morphodynamics automatically using available remotely sensed imagery and experimental observations are needed. Here, we apply a promising method for the automatic extraction of channel presence called RivaMap, on both synthetic and experimental data sets, to investigate the changes experienced by the system in response to five changes in forcings. RivaMap is an automated method to extract nonbinarized channel locations from imagery based on a singularity index that combines the multiscale first and second derivatives of the image intensity to favor the identification of curvilinear features and suppress edges. We quantify how the channelization varies by computing the channelized response variance (CRV), which we define as the variance of each pixel's singularity index response through time. We find that increasing magnitudes of sediment inflow (Q_s) and water inflow (Q_w) result in corresponding increases in the maximum CRV. We find that increasing the ratio ofQ_stoQ_wresults in increased number of channelized areas. We see that adding cohesion to the exposed sediment surface of the experimental delta results in decreased magnitude and decreased number of channelized areas in the CRV. Finally, by observing changes to the CRV over time, we are able to quantify the timescale of internal channel reorganization events as the experimental delta evolves under constant forcings.