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The overarching theme of the Center’s Aquatic application area continues to be the creation and application of a new genre of wireless sensing systems that will provide real-time monitoring capabilities of chemical, physical and biological parameters in freshwater and coastal marine ecosystems. High-resolution temporal and spatial measurements are essential for understanding the highly dynamic nature of aquatic ecosystems and the rapid response of microbial communities to environmental driving forces. Our unique approach to aquatic sensing and sampling, Networked Aquatic Microbial Observing Systems (NAMOS), employs coordinated measurements between stationary sensing nodes (buoys and pier-based sensors) and robotic vehicles (surface robotic boats and autonomous underwater vehicles) to provide in-situ, real-time presence for observing plankton dynamics (e.g. phytoplankton abundance, dissolved oxygen), and linking them to pertinent environmental variables (e.g. temperature, light, nutrients, etc.). Specific projects undertaken in this application area involve the development and deployment of sensor networks to examine harmful algal blooms within King Harbor, City of Redondo Beach, and the construction of mobile sensor networks in open coastal waters off southern California. The latter research involves deployments of autonomous surface and underwater vehicles, and the development of hardware and software for coordinated activities of these robotic vehicles.

The Bornean islands of Sipadan and Sangalakki offer examples of the positive and negative effects of ecotourism. The area is thought by many to be the most concentrated region of marine wildlife in the world and is home to several green turtle populations. The fish population in the area has increased since the arrival of tourism, as certain fishing practices have given way to the tourist dollar. The turtles, on the other hand, are being denied access to their breeding grounds by new hotels. Efforts at developing a sustainable ecotourism industry in the area are discussed.

The use of High Frequency Radar (HFR) data is increasing worldwide for operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian based empirical real-time, Short-Term Prediction (L-STP) system is presented in order to provide short term forecasts of up to 48 hours of ocean currents from HFR data. The method is based on the finding of historical gridded analogues of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as did the historical analogue, we obtain a short-term prediction of the surface currents. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. The L-STP improves on previous prediction systems implemented for the SE Bay of Biscay and provides good results for the Red Sea study area. A comparison of the L-STP methodology with predictions based on persistence and reference fields has been performed in order to quantify the error introduced by this Lagrangian approach. Furthermore, a temporal sensitivity analysis has been addressed to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill-score has been developed using the results of this analysis which allows to identify periods when the short-term prediction performance is more likely to be low and persistence can be used as a better predictor for the future currents. [ABSTRACT FROM AUTHOR]

The Red Sea holds one of the most diverse marine ecosystems in the world, although fragile and vulnerable to ocean warming. Several studies have analysed the spatiotemporal evolution of the temperature in the Red Sea using satellite data, thus focusing only on the surface layer and covering the last ~30 years. To better understand the long-term variability and trends of the temperature in the whole water column, we produce a 3D gridded temperature product (TEMPERSEA) for the period 1958-2017, based on a large number of in situ observations, covering the Red Sea and the Gulf of Aden. After a specific quality control, a mapping algorithm based on optimal interpolation has been applied to homogenize the data. Also, an estimate of the uncertainties of the product has been generated. The calibration of the algorithm and the uncertainty computation has been done through sensitivity experiments based on synthetic data from a realistic numerical simulation. TEMPERSEA has been compared to satellite observations of sea surface temperature for the period 1981-2017, showing good agreement specially in those periods with a reasonable number of observations were available. Also, very good agreement has been found between air temperatures and reconstructed sea temperatures in the upper 100 m for the whole period 1958-2017 enhancing the confidence on the quality of the product. The product has been used to characterize the spatio-temporal variability of the temperature field in the Red Sea and the Gulf of Aden at different time scales (seasonal, interannual and multidecadal). Clear differences have been found between the two regions suggesting that the Red Sea variability is mainly driven by air-sea interactions, while in the Gulf of Aden, the lateral advection of water also plays a relevant role. Regarding long term evolution, our results show only positive trends above 40 m depth, with maximum trends of 0.045 + 0.016 °C decade-1 at 15 m, and the largest negative trends at 125 m (-0.072 + 0.011 °C decade-1). Multidecadal variations have a strong impact on the trend computation, and restricting them to the last 30-40 years of data can bias high the trend estimates. [ABSTRACT FROM AUTHOR]

Oceanic time-series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset was dominated by four water column profile types: active upwelling, offshore influence, subsurface chlorophyll maximum, and surface bloom. On average, waters across the SPC were most similar to offshore profiles. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC, and SPOT was least similar to SPC locations that were closest to the Palos Verdes Peninsula. Subsurface chlorophyll maxima with co-located chlorophyll and particle maxima were common in 2013 and 2014 suggesting that these subsurface chlorophyll maxima might contribute significantly to the local primary production. These results indicate that high-resolution in situ glider deployments can be used to determine the spatial domain of time-series data, allowing for broader application of these datasets and greater integration into modeling efforts. [ABSTRACT FROM AUTHOR]