Tide Forecast Research Articles

Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper.

Short-term forecast of Karenia brevis trajectory on the West Florida Shelf

Blooms of the toxic dinoflagellate Karenia brevis, also known as harmful algal blooms (HABs) or red tides, occur almost annually on the west coast of Florida, killing fish and other marine life, threatening public health and adversely impacting local economies. Mitigating such effects requires improved red tide forecast capabilities on the West Florida Shelf. A short-term Lagrangian trajectory forecast tool is developed to help federal, state, and local end users monitor and manage red tides on the west coast of Florida. The forecast products are based on the West Florida Coastal Ocean Model (WFCOM) and the Tampa Bay Coastal Ocean Model (TBCOM) nowcast/forecast systems. Observed K. brevis cell count data are uploaded daily into the models to generate 3.5-day forecasts of the bloom trajectories both on the shelf and in the estuaries. The tracking tool displays modeled bloom trajectories at the surface and near-bottom with five categories of cell concentrations (each approximately representing an order of magnitude difference in concentration). More general and user-friendly maps are also produced to provide red tide advisories along the coast, including those integrated with satellite imagery.

COASTAL MAREJADAS FORECAST SYSTEM, VALIDATION AND INSTITUTIONAL LINK IN CHILE

Marejadas, defined as the combination of waves and tides that affect the usual socio-economic activities on the coast and that can cause damage to its infrastructure (Molina, 2020), becomes relevant since the beginning of the past decade on the Chilean coast due to several events that occurred between 2013 and 2017. Considering that, a local wave forecast (MarejadasUV) was developed by Universidad de Valparaíso in Valparaíso bay, Chile (marejadas.uv.cl), to distinguish wave magnitude along the coast. Combined whit tide forecasting and coastal geometry, the forecast system was capable to evaluate hourly run-up and overtopping for the next seven days, which allows implementation of an impact scale for communication purposes (Molina, 2020).

Tuning the Model Winds in Perspective of Operational Storm Surge Prediction in the Adriatic Sea

In the Adriatic Sea, the sea surface wind forecasts are often underestimated, with detrimental effects on the accuracy of sea level and storm surge predictions. Among the various causes, this mainly depends on the meteorological forcing of the wind. In this paper, we try to improve an existing numerical method, called “wind bias mitigation”, which relies on scatterometer wind observations to determine a multiplicative factor Δw, whose application to the model wind reduces its inaccuracy with respect to the scatterometer wind. Following four different mathematical approaches, we formulate and discuss seven new expressions of the multiplicative factor. The eight different expressions of the bias mitigation factor, the original one and the seven formulated in this study, are assessed with the aid of four datasets of real sea surface wind events in a variety of sea level conditions in the northern Adriatic Sea, several of which gave rise to high water events in the Venice Lagoon. The statistical analysis shows that some of the seven new formulations of the wind bias mitigation factor are able to lower the model-scatterometer bias with respect to the original formulation. For some other of the seven new formulations, the absolute bias, with respect to scatterometer, of the mitigated model wind field, results lower than that supplied by the unmodified model wind field in 81% of the considered storm surge events in the area of interest, against the 73% of the original formulation of the wind bias mitigation. This represents an 11% improvement in the bias mitigation process, with respect to the original formulation. The best performing of the seven new wind bias mitigation factors, that based on the linear least square regression of the squared wind speed (LLSRE), has been implemented in the operational sea level forecast chain of the Tide Forecast and Early Warning Centre of the Venice Municipality (CPSM), to provide support to the operation of the MO.SE. barriers in Venice.

PIRT: A Physics-Informed Red Tide Deep Learning Forecast Model Considering Causal-Inferred Predictors Selection

In this paper, a Physics-Informed Red Tide forecast model (PIRT) considering causal-inferred predictors selection is proposed. Specifically, the directed acyclic graph-graph neural network (DAG-GNN) method is first applied to quantify the causality among multiple ocean-atmosphere-biology variables for selecting the most significant predictors of the red tides (or other chlorophyll variations). Then, the encoder-decoder model consisting of an Energy Attention Module (EAM) is built for daily red tide forecasting. The multi-sourced multivariate dataset during 2010-2020 covering the East China Sea serves to train and evaluate PIRT. The experimental results demonstrate that the predictors in the learned causal graph are closely related to the occurrence and decay of red tides, which exhibits high physical interpretability. PIRT has a superior forecasting skill, the predictions of which are with highly consistent spatial patterns especially in extreme events. The 7-lead-day forecast errors for chlorophyll are within 0.9 mg∙m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which is much better than the other models. This also indicates that PIRT can be used as a reliable tool to study the ecology of the East China Sea.

풍수해 생활권 종합정비계획 수립 시 하류경계조건의 고려

The ‘Comprehensive Maintenance Plan for Neighborhood Unit in storm and flood disaster’ is a meaningful project in that it integrates and implements all storm and flood-related maintenance plans that have been dispersed by ministries. In this paper, we examined whether the 'Comprehensive Maintenance Plan for Neighborhood Unit' can be included in the 'intensive heavy rain' in August 2022 and 'Hinnamnor' in September, and whether it can be applied appropriately in inland areas without unreasonableness. The ‘flooding by Hinnamnor in Pohang’, ‘the claim of flood damage caused by the operation of Lake Sihwa’, and ‘flooding of the Moraenae stream’ had a common cause. In order to solve this problem, 'Pohang flooding by Hinnamnor' requires a separate design that considers sea level rise due to typhoon low pressure rather than dealing with tide forecast alone. There is a need for institutional arrangements (guidelines, etc.) In addition, in the case of the 'Moraenae District', cases caused by the rise of the 'downstream boundary condition' of nearby rivers must be reflected in the design.

Modelling of Sea Water Level During High Tide Using Statistical Method and Neural Network

Recently, the rise of sea level has caused an increase in rising tides that affected about three million locations around the world. The tide rising phenomenon has been occurring in Peninsular Malaysia since the 20th century. The purpose of this study is to determine the most critical station and forecast three stations located on the West Coast of Peninsular Malaysia. The Box Plot analysis method has been used in this study; the results shown that Bagan Datuk Station is the most critical station. This is due to the maximum tide’s value of Bagan Datuk Station experienced the highest increment of 0.45 m, compared to Port Klang station and Permatang Sedepa Station with only 0.2 m increment in 10 years. However, these three stations are also experiencing rising tides. Thus, the focus of managing coastal structures should be given to all these three stations as well. In addition, for forecasting, the Artificial Neural Network (ANN) forecasting model provides better forecasting results compared to the Autoregressive Integrated Moving Average (ARIMA) model for long-term forecast. In this study, the artificial Neural Network (ANN) forecasting model obtained value of RMSE 0.05642 at Bagan Datuk Station compared to the RMSE value of 0.0928 obtained from the ARIMA model at the same station. Besides, MAE value of ANN method, 0.04387 compared to the MAE value of ARIMA which is worth 0.06391 at Bagan Datuk Station. This study can conclude that the Artificial Neural Network (ANN) forecasting model is better in high tide forecasting.

Application of BP Neural Networks in Tide Forecasting

Tidal phenomenon is a significant dynamical phenomenon in the ocean, and the accurate prediction of tide is an important task for various maritime activities. This paper proposes analysis method considering tidal periodicity and apply it to the actual tide prediction. The results prove that this method can solve the delay problem in tide prediction, improve the accuracy of prediction. Compared with the tidal harmonic analysis method, the prediction result of this method is more accurate and requires less data for short-term tidal forecast. Although this study can only provide an accurate forecast for 3 days, it is enough to deal with risks. How to improve the accuracy of long-term prediction is one of the future research directions.

Effects of green tide on microbial communities in waters of the Jiangsu coastal area, China.

Recently, green tide outbreaks have resulted in severe coastal ecology and economic effects in China. Jiangsu coastal areas are usually the site of early green tide outbreaks. To clarify the effects of green tide outbreaks in Jiangsu coastal areas, this study analyzed microbial communities during green tide-free and green tide outbreak periods (May and July, respectively) through 16S rDNA sequencing. Sequences were clustered into 4117 operational taxonomic units (OTUs), 1044 and 3834 of which were obtained from the May and July groups, respectively. Redundancy analysis indicated that green tide occurrence was closely associated with the temperature, pH, and concentrations of various nutrients. Diversity analysis revealed that the July group had a richer microbial community than the May group, in agreement with the results of propagule culture. Moreover, comparative analysis revealed that samples in the May and July groups clustered together. According to Megan analysis, the May group had much more Psychrobacter, Sulfitobacter, and Marinomonas than the July group, whereas the other genera were predominantly found in July, such as Ascidiacerhabitans, Synechococcus Hydrotalea, and Burkholderia-Paraburkholderia. These findings suggest that green tide outbreaks affect marine microbial communities, and detecting the changes in the identified genera during green tide outbreaks may contribute to green tide forecasting. PRACTITIONER POINTS: Jiangsu coastal areas are usually the site of early green tide outbreaks. Green tide occurrence was related to the concentrations of various nutrients. Microbial species and community structure significantly changed after green tide outbreak.

A Coastal Flood Event Database for the Southeastern Georgia and Southeastern South Carolina Coast and the Operational Implementation of a Tide Forecast Tool

Coastal flooding occurs when saltwater inundates normally dry land and the resulting impacts can range from minor flooding of low-lying areas along the coast, to significant damage to property and structures. Previous research consistently suggests that if sea-level rise continues to increase along the East Coast of the United States, coastal flooding will occur more frequently. In order to document the history of coastal flooding along the southeastern Georgia and southeastern South Carolina coast, a coastal flood event database was created for National Ocean Service tide gauges located in Charleston Harbor, South Carolina and Fort Pulaski, Georgia. Trends from the data show that coastal flooding is occurring more frequently with time at both tide gauges, particularly over the last five to ten years. Because of the increased frequency and worsening impacts of tidal flooding, a tide forecast tool is implemented operationally in an effort to improve deterministic tide forecasts. This study extends the dataset used in the Charleston Harbor forecast tool, expands the tool to Fort Pulaski, and compares the synoptic category forecast equations to an all-inclusive equation that does not differentiate by synoptic category. Results show that there is virtually no difference in the forecast accuracy between the all-inclusive forecast equation and the specific forecast equations based on synoptic category. Furthermore, the all-inclusive forecast equation can be implemented operationally, will help improve deterministic tide forecasts, and will likely aid in the decision-making process for Coastal Flood Watches, Warnings, and Advisories issued by the National Weather Service office in Charleston, South Carolina.

VALIDACIÓN E IMPLEMENTACIÓN DE UNA HERRAMIENTA OPERACIONAL PARA EL PRONÓSTICO DE MAREAS EN EL PACÍFICO COLOMBIANO (CASO DE ESTUDIO ISLA GORGONA)

Las mareas son los procesos más regulares que se presentan en los océanos, producto del movimiento periódico de ascenso y descenso del nivel del mar. Esta clase de proceso hidrodinámico a escala local en zonas costeras e insulares es de gran importancia, debido a que las islas pequeñas juegan un papel muy importante para la pesca, el turismo y actividades socioeconómicas en general. En La isla Gorgona situada en el Pacífico Colombiano, es de gran importancia comprender la dinámica mareal, ya que sus alteraciones a nivel regional y local controlan en gran medida los atributos particulares de la biota de Gorgona, la cual tiene un impacto en los procesos ecológicos que moldean la diversidad marina del parque, afectando las actividades socioeconómicas de la isla. Es por esto, que el presente trabajo tiene como objetivo determinar el régimen mareal en la Isla Gorgona (Cauca), identificando las principales componentes armónicas que ejercen mayor influencia en la zona; y el régimen medio del nivel del mar, a partir de modelación numérica y datos medidos in-situ.

Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA

Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM’s storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.

Ramping up the fight against Florida’s red tides

The words “red tide” routinely generate red alerts among Florida’s fishermen, coastal property owners, tourism operators, and health officials. This past summer and fall were no exception. These toxic algal blooms—which often tinge the water red but sometimes turn it brown, orange, or even slightly greenish—close beaches, kill innumerable fish, and taint filter-feeding shellfish such as oysters, rendering them inedible. The neurotoxins generated by the phytoplankton that create red tides also affect birds, marine mammals, and sea turtles and can harm human health. Breaking surf lofts toxin-filled droplets of sea spray that can irritate lungs and severely aggravate lung conditions, such as emphysema or asthma. Adding to their devastation, red tides can seem random. Some years, as in 2017 and 2018, they persist for months; in others, they may hardly make a showing or bloom and then fade only to return with a vengeance. Toxic algal blooms known as red tide, which often tinge the water red but sometimes turn it brown, orange, or even slightly greenish, close beaches, kill fish, and taint shellfish. The bloom pictured took place in September of 2016 in the Gulf of Mexico in the coastal waters of southwestern Florida. Image credit: Mote Marine Laboratory & Aquarium. Scientists now have a decent sense of when and where red tides will appear, but new data-gathering efforts could help monitor the blooms as they develop, providing more accurate red tide forecasts for coastal areas. And researchers are starting to devise and test ways to mitigate or control Florida’s red tides to improve the quality of life in the state's coastal communities. But the age-old phenomenon of red tides continues to present scientists and policymakers with complex research and management challenges. Although the most distressing effects of red tides manifest themselves in coastal waters, these harmful algal blooms …

Total Suspended Solid (TSS) Distributed by Tidal Currents during Low to High Tide Phase in the Waters of Sayung, Demak: Its Relations to Water Quality Parameters

Sayung waters is a region highly vulnerable to catastrophic erosion along the coast, which is directly followed by an increase suspended sediments and particles from the bottom of the waters that was stirred by oceanography factors. The purpose of this study was to determine the concentration and distribution of the latest TSS condition and its effect on water quality parameters in the waters of Sayung. The sampling method is using purposive sampling, with the stations spread out along the coastal area of Sayung, the main data consist of current, tide, bathymetry, coastline and water quality, and the secondary data consist of RBI map and tide forecasting, those data is analyzed numerically and statistically. TSS value ranged between 23,1-199,6 mg.L-1, the distribution of TSS is simulated in the condition of ebb to tide with current speed ranged between 0-0.41 ms-1, that distribution also influenced by physical water factors such as salinity, temperature, and density and has impacts to enhancing the turbidity and indirectly decrease the photosynthesis activity and inhibit the oxygen cycle in the Sayung waters.

Red tide time series forecasting by combining ARIMA and deep belief network

The red tide occurs frequently in recent years. The process of the growth, reproduction, extinction of the red tide algal has a complex nonlinear relationship with the environmental factors. The environmental factors have characteristics including time continuity and spatial heterogeneity. These characteristics make it arduous to forecast red tide. This paper mainly analyzes the related factors of the red tide disasters. Based on the strong forecasting ability of Autoregressive Integrated Moving Average (ARIMA) model and the powerful expression ability of Deep Belief Network (DBN) on nonlinear relationships, a hybrid model which combines ARIMA and DBN is proposed for red tide forecasting. The corresponding ARIMA model is built for each environmental factor in different coastal areas to describe the temporal correlation and spatial heterogeneity. The DBN serves to capture the complex nonlinear relationship between the environmental factors and the red tide biomass, and then realizes the warning of red tide in advance. Furthermore, Particle swarm optimization (PSO) is introduced to enhance the speed of model training. Finally, ship monitoring data collected in Zhoushan coastal area and Wenzhou coastal area during 2008–2014 is used as the experimental dataset. The proposed ARIMA-DBN model is applied to forecasting red tide. The experimental results demonstrate that the proposed method achieves a good forecast of red tide.

SPATIAL DISTRIBUTION OF SEA SURFACE TEMPERATURE IN WEST SUMATERA SEAWATERS ASSOCIATED WITH INDIAN OCEAN DIPOLE (IOD) EVENT IN TRANSITIONAL SEASONS (AUGUST-OCTOBER) (Case Study: Pasumpahan and Sibonta Island)

&lt;p class="katakunci"&gt;Along with the issue of climate change which is characterized by an increase in global temperature, the sea surface temperatures (SST) also allegedly helped change that has direct and consequential to the phenomenon of coral bleaching that occurred in the waters Pasumpahan, Sibonta and several other areas in West Sumatra. The purpose of this study was to identify anomalies in the sea surface temperature in the waters Pasumpahan and Sibonta due to the influence of the phenomenon of Indian Ocean Dipole. The method used is purposive quantitative with perform spatial analysis (Inverse Distance Weighted). Primary data are consisted of SST and tide data which measured using the HOBO U20 titanium water level data logger. The secondary data are consisted of RBI map, Google eye image and tide forecast. SST around Pasumpahan waters ranged from 30-30,6&lt;sup&gt;o&lt;/sup&gt;C and Sibonta waters ranged from 30,1-30,6°C. At a depth of 25 meters, Pasumpahan waters temperature ranged from 29,152-30,457°C and Sibonta waters temperature ranged from 26,28-28,5°C, the surface elevation ranged from 0,83 to 1,17 m from MSL point, rainfall average in August until October ranged from 5,64-7,71 mm. The rainfall, temperature and surface elevation changes are affecting each other. Temperature anomalies occurred in the Sibonta waters due to the closer location with Indian Ocean, directly influence to Sibonta waters temperature that is relatively volatile.&lt;/p&gt;

Development of an adaptive Kalman filter-based storm tide forecasting model

Extreme storm tide usually causes flooding of low-lying land in a coastal city. Hence, developing an efficient and accurate forecasting model for issuing a timely warning is important. In this study, an adaptive Kalman filter-based storm tide forecasting model was proposed and applied to the inner Harbor of Macau. The model is a dynamic linear regression model with the harmonic tidal prediction, wind speed, wind direction and atmospheric pressure as its input parameters. With persistence forecast of weather assumed during the prediction period, the model was tested with 40 cases of storm tide induced by tropical cyclones in Macau between 2005 and 2012. Success was found for forecasts with lead times up to 3 hours. The proposed adaptive model is considered a practical tool for storm tide forecast in small coastal cities.

Hydrodynamics of Bontang Seawaters: Its Effects on the Distribution of Water Quality Parameters

Bontang is a crowded with residential and industrial activities which produce heat waste into surrounding waters. This condition may cause environmental problems, and changes in water condition. Knowledge about dynamics of physic and chemical waters condition in Bontang city is very important as an effort to address environmental issues as part of coastal zone management and protection. The aim of this research is to understand hydrodynamic characteristics (tide and current) and the influence to distribution of physics and chemical in Bontang waters base on primary data (current and tide during one month, physics and chemicals waters data taken by in situ) and secondary data (bathymetry and tide forecasting). Hydrodynamic simulation is based on MIKE 21 and supported by CD-Oceanography and WR plot. The results show that the current is dominant move toward the Southwest and Northeast with current speed ranged from 0-0,22 m.s-1. The results of hydrodynamic simulation in the surface water show that the current move base on tide movement with current speed ranged from 0-0,24 m.s-1. The results of physic and chemical concentration is analysed by ArcGIS 10 to know spatial distribution of all parameters. Surface temperature ranged from 29-31,8oC, surface density ranged from 20-20,6 ppm, salinity ranged from 33,1-33,5 ppm, dissolve oxygen concentration ranged from 0,078-0,11 ppm. Distribution of all physic and chemical parameters is influenced by current and tide movement. Keywords: current, numerical model, water quality, Bontang waters

The integrated ocean dynamics and acoustics project

The goal of timely and accurate acoustics modeling in the ocean depends on accurate environmental input information. Acoustic propagation modeling has improved to the point of possibly being ahead of ocean dynamical modeling from the standpoint that some significant ocean features having strong acoustic effects are not faithfully reproduced in many models, particularly data-driven ocean models. This in part stems from the fact that ocean models have developed with other goals in mind, but computational limitations also play a role. The Integrated Ocean Dynamics and Acoustics (IODA) MURI project has as its goals improving ocean models, and also making continued improvements to acoustic models, for the purpose of advancing ocean acoustic modeling and prediction capabilities. Two major focuses are improved internal tide forecasting and improved nonlinear internal wave forecasting, which require pushing the state of the art in data-constrained mesoscale feature modeling as well as developing specialized high-...

The real-time needs of emergency managers for tropical cyclone storm tide forecasting: results of a participatory stakeholder engagement process

Natural hazard forecasting products and associated decision support systems provide emergency managers with key information in real-time situations for making life-critical decisions on evacuation orders, deployment of response resources and public information campaigns. A stakeholder engagement strategy was used to investigate potential improvements to storm tide forecasting and decision support systems during tropical cyclone events for the case study site of Queensland, Australia. The requirements of the end-users of the system were identified through a comprehensive survey of local government disaster management staff. The survey assessed their use of the current system, soliciting suggestions for improvements and identifying ‘lessons learnt’ from recent extreme events. Analysis of the survey responses highlights issues with and opportunities for improvement to storm tide forecasting products, including improving spatial resolution and visualisation of warning information and improving the complexity of communication of uncertainty. In particular, the key dilemma between forecast uncertainty and the long timelines required for effective evacuation was highlighted. The survey responses and analysis present a unique insight into stakeholder requirements and will enable user-driven development of a future warning system rather than a forecast-driven product. Broader recommendations for improvement to the storm tide emergency management process derived from the survey include continuous improvement in the accuracy of tropical cyclone forecasting and improving the capacity of both disaster managers and the community to reduce response times to storm tide threat through education and long-term planning decisions.