(searched for: doi:10.30564/jasr.v3i3.2163)
Scientific Reports, Volume 12, pp 1-10; https://doi.org/10.1038/s41598-022-23606-x
Our oceans are critical to the health of our planet and its inhabitants. Increasing pressures on our marine environment are triggering an urgent need for continuous and comprehensive monitoring of the oceans and stressors, including anthropogenic activity. Current ocean observational systems are expensive and have limited temporal and spatial coverage. However, there exists a dense network of fibre-optic (FO) telecommunication cables, covering both deep ocean and coastal areas around the globe. FO cables have an untapped potential for advanced acoustic sensing that, with recent technological break-throughs, can now fill many gaps in quantitative ocean monitoring. Here we show for the first time that an advanced distributed acoustic sensing (DAS) interrogator can be used to capture a broad range of acoustic phenomena with unprecedented signal-to-noise ratios and distances. We have detected, tracked, and identified whales, storms, ships, and earthquakes. We live-streamed 250 TB of DAS data from Svalbard to mid-Norway via Uninett’s research network over 44 days; a first step towards real-time processing and distribution. Our findings demonstrate the potential for a global Earth-Ocean-Atmosphere-Space DAS monitoring network with multiple applications, e.g. marine mammal forecasting combined with ship tracking, to avoid ship strikes. By including automated processing and fusion with other remote-sensing data (automated identification systems, satellites, etc.), a low-cost ubiquitous real-time monitoring network with vastly improved coverage and resolution is within reach. We anticipate that this is a game-changer in establishing a global observatory for Ocean-Earth sciences that will mitigate current spatial sampling gaps. Our pilot test confirms the viability of this ‘cloud-observatory’ concept.
Water, Volume 12; https://doi.org/10.3390/w12123397
Coastal flooding is a global phenomenon that results in severe economic losses, threatens lives, and impacts coastal communities worldwide. While recent developments in real-time flood forecasting systems provide crucial information to support coastal communities during coastal disasters, there remains a challenge to implement such systems in data-poor regions. This study demonstrates an operational real-time coupled surge wave guidance system for the coastal areas of Southern Brazil. This system is based on the recently developed integrated flood (iFLOOD) model, which utilizes the coupled hydrodynamic and phase-averaged ADCIRC–SWAN wave numerical model, driven by astronomical tides and atmospheric forcing from the Global Forecast System (GFS). This numerical modeling framework can simulate water levels and waves with a lead time of 84 h. A version of the coupled ADCIRC–SWAN model calibrated for Brazil, i.e., iFLOOD-Brazil, was operationally implemented (i.e., twice a day) over a period of 4 months (April to September 2020) for normal daily weather validation, as well as during a recent “bomb” cyclone that strongly impacted the southern coast of the country in June 2020. The real-time water levels and waves forecasted by iFLOOD-Brazil showed promising results against observations, with root mean square error (RMSE) values of 0.32 m and 0.68 m, respectively, for normal daily weather. Additionally, the RMSE values were 0.23 m for water levels and 1.55 m for waves during extreme weather, averaged over eight water level and two wave recording stations. In order to improve real-time predictions, a bias correction scheme was introduced and was shown to improve the water level and wave forecasts by removing the known systematic errors resulting from underestimation of astronomical tides and inadequate initial boundary conditions. The bias-corrected forecasts showed significant improvements in forecasted wave heights (0.47 m, 0.35 m) and water levels (0.17 m, 0.28 m) during daily and extreme weather conditions. The real-time iFLOOD-Brazil forecast system is the first step toward developing an accurate prediction model to support effective emergency management actions, storm mitigation, and planning in order to protect these economically valuable and socially vulnerable coastal areas.