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Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one. 

The underwater acoustic (UWA) channel is a complex and stochastic process with large spatial and temporal dynamics. This work studies the adaptation of the communication strategy to the channel dynamics. Specifically, a set of communication strategies are considered, including frequency shift keying (FSK), single-carrier communication, and multicarrier communication. Based on the channel condition, a reinforcement learning (RL) algorithm, the Depth Determined Strategy Gradient (DDPG) method along with a Gumbel-softmax scheme is employed for intelligent and adaptive switching among those communication strategies. The adaptive switching is performed on a transmission block-by-block basis, with the goal of maximizing a long-term system performance. The reward function is defined based on the energy efficiency and the spectral efficiency of the communication strategies. Simulation results reveal that the proposed method outperforms a random selection method in time-varying channels. 

An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract representation of the UWA channel impulse responses (CIRs) and can generate CIR samples with similar features. A customized training process is proposed to avoid the model collapse and being trapped in a gradient pit. The proposed deep generative model is validated using field experimental data sets. 

Existing analyses of salt marsh carbon budgets rarely quantify carbon loss as CO₂through the air–water interface in inundated marshes. This study estimates the variability of partial pressure of CO₂(pCO₂) and air–water CO₂fluxes over summer and fall of 2014 and 2015 using high‐frequency measurements of tidal waterpCO₂in a salt marsh of the U.S. northeast region. Monthly mean CO₂effluxes varied in the range of 5.4–25.6 mmol m⁻²marsh d⁻¹(monthly median: 4.8–24.7 mmol m⁻²marsh d⁻¹) during July to November from the tidal creek and tidally‐inundated vegetated platform. The source of CO₂effluxes was partitioned between the marsh and estuary using a mixing model. The monthly mean marsh‐contributed CO₂effluxes accounted for a dominant portion (69%) of total CO₂effluxes in the inundated marsh, which was 3–23% (mean 13%) of the corresponding lateral flux rate of dissolved inorganic carbon (DIC) from marsh to estuary. Photosynthesis in tidal water substantially reduced the CO₂evasion, accounting for 1–86% (mean 31%) of potential CO₂evasion and 2–26% (mean 11%) of corresponding lateral transport DIC fluxes, indicating the important role of photosynthesis in controlling the air–water CO₂evasion in the inundated salt marsh. This study demonstrates that CO₂evasion from inundated salt marshes is a significant loss term for carbon that is fixed within marshes.

 

Sources of dissolved and particulate carbon to the Fraser River system vary significantly in space and time. Tributaries in the northern interior of the basin consistently deliver higher concentrations of dissolved organic carbon (DOC) to the main stem than other tributaries. Based on samples collected near the Fraser River mouth throughout 2013, the radiocarbon age of DOC exported from the Fraser River does not change significantly across seasons despite a spike in DOC concentration during the freshet, suggesting modulation of heterogeneous upstream chemical and isotopic signals during transit through the river basin. Dissolved inorganic carbon (DIC) concentrations are highest in the Rocky Mountain headwater region where carbonate weathering is evident, but also in tributaries with high DOC concentrations, suggesting that DOC respiration may be responsible for a significant portion of DIC in this basin. Using an isotope and major ion mass balance approach to constrain the contributions of carbonate and silicate weathering and DOC respiration, we estimate that up to 33 ± 11% of DIC is derived from DOC respiration in some parts of the Fraser River basin. Overall, these results indicate close coupling between the cycling of DOC and DIC, and that carbon is actively processed and transformed during transport through the river network.

 

The propagation of acoustic waves under water is a highly complex and stochastic process. Such channel dynamics renders large performance variation in underwater acoustic (UWA) communications. Prediction of the UWA communication performance is critical for selection and adaptation of the communication strategies. This work explores the use of supervised learning for performance prediction in UWA communications. This work first quantifies the transmitter design, the UWA channel characteristics and the receiver design by numerical and categorical parameters. For a chosen performance metric (e.g., the bit error rate or the packet error rate), the performance prediction is cast individually into a numerical prediction problem and a classification problem. Using the data sets from two field experiments, the performance of typical supervised learning methods are examined. The data processing results reveal that some supervised learning methods can achieve fairly good numerical prediction or classification performance, and the discriminative models typically outperform the generative models. 

Underwater acoustic communications provide promising solutions for remote and real-time aquatic exploration and monitoring. However, the underwater environment is rich in various kinds of interferences. Those interferences could severely degrade the acoustic communication performance. This work tackles interference cancellation in a single-carrier modulated communication system. Based on the Nyqusit sampling theorem, the interference is parameterized by a finite number of unknown parameters. The Page test is applied to detect the presence of an interfering waveform in the received signal. An iterative receiver is developed, which iteratively performs the interference estimation/cancellation and traditional receiver processing. The proposed receiver is evaluated when the communication waveform is interfered by the ice-cracking impulsive noise and the sonar signal collected from the Arctic. The data processing results reveal that the proposed receiver achieves considerable decoding performance improvement through the iterative interference estimation and cancellation. 

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We firstdefineeach of the major C pools and fluxes and providerationalefor their importance to wetland C dynamics. For each approach, we clarifywhatcomponent of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such aswhereandwhenan approach is typically used,whocan conduct the measurements (expertise, training requirements), andhowapproaches are conducted, including considerations on equipment complexity and costs. Finally, we reviewkey covariatesandancillary measurementsthat enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

 

Abstract. Landfast sea ice (fast ice) is an important though poorly understood component of the cryosphere on the Antarctic continental shelf, where it plays a key role in atmosphere–ocean–ice-sheet interaction and coupled ecological and biogeochemical processes. Here, we present a first in-depth baseline analysis of variability and change in circum-Antarctic fast-ice distribution (including its relationship to bathymetry), based on a new high-resolution satellite-derived time series for the period 2000 to 2018. This reveals (a) an overall trend of -882±824 km2 yr−1 (-0.19±0.18 % yr−1) and (b) eight distinct regions in terms of fast-ice coverage and modes of formation. Of these, four exhibit positive trends over the 18-year period and four negative. Positive trends are seen in East Antarctica and in the Bellingshausen Sea, with this region claiming the largest positive trend of +1198±359 km2 yr−1 (+1.10±0.35 % yr−1). The four negative trends predominantly occur in West Antarctica, with the largest negative trend of -1206±277 km2 yr−1 (-1.78±0.41 % yr−1) occurring in the Victoria and Oates Land region in the western Ross Sea. All trends are significant. This new baseline analysis represents a significant advance in our knowledge of the current state of both the global cryosphere and the complex Antarctic coastal system, which are vulnerable to climate variability and change. It will also inform a wide range of other studies.