scholarly journals Sea-level prediction for early warning information of coastal inundation in Belawan coastal area using Delft3D model

2021 ◽  
Vol 893 (1) ◽  
pp. 012034
Author(s):  
A M N Jaya ◽  
F P Sari ◽  
I J A Saragih ◽  
I Dafitra
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Early Warning ◽  
Hydrodynamic Model ◽  
Coastal Area ◽  
East Coast ◽  
Coefficient Of Determination ◽  
Wind Effect ◽  
Coastal Inundation ◽  
Better Than

Abstract Coastal inundation has a great impact on the environment, such as damage to infrastructure and pollution of land and water. One of the efforts to prevent coastal inundation is to predict the water level. Delft3D is a hydrodynamic model that's able to simulate the water level. Coastal inundation research using the Delft3D model is still rarely done in Indonesia, especially on the east coast of Sumatra. This research is conducted in Belawan coastal area by simulating the water level that caused the coastal inundation using the Delft3D model. The best bathymetry for the prediction of water level and the magnitude of the wind effect was obtained from the simulation. The final step is to predict the water level in Belawan coastal area. The result of this research shows that the Delft3D model can simulate the water level which causes the coastal inundation in the Belawan coastal area. The correlation of the Delft3D model is 0.9, and the RMSE of GEBCO bathymetry is 0.39 meters and the RMSE of NOAA bathymetry is 0.46 meters. The GEBCO bathymetry is better than NOAA bathymetry in describing the water level in the Belawan coastal area. The wind effect on the water level simulations is not significant because the coefficient of determination is 0.47%. Besides, the Delft3D model with GEBCO bathymetry input can predict the water level which causes the coastal inundation with correlation reaches 0.92 and RMSE is 0.39 meters.

scholarly journals Implementation of a Hydrodynamic Model for Salinity in Nueces and Corpus Christi Bays

2017 ◽  
Author(s):  
James C Davis
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Hydrodynamic Model ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Ocean Model ◽  
Corpus Christi ◽  
Corpus Christi Bay ◽  
Nueces River ◽  
Nueces Bay

The purpose of this research was to implement and calibrate a hydrodynamic model for Nueces Bay, Texas and use said model to predict how salinity and circulation in the bay could be affected by sea level rise and changes in freshwater input. The Nueces Bay estuary is fed fresh water from the Nueces River and connects to Corpus Christi Bay, which connects to the Gulf of Mexico. Salinities in Nueces Bay range from near 0 PSU at times of high riverine flow to over 50 PSU during drought periods. The model selected is the Finite Volume Coastal Ocean Model (FVCOM), an unstructured grid, finite-volume, three-dimensional primitive equation ocean model developed for the study of coastal oceanic and estuarine circulation.An unstructured triangular mesh was created for the bay and bathymetry was interpolated to the mesh. Forcing inputs included water level, water temperature, salinity, wind speed and direction, and the flow rate of the Nueces River. Several software utilities were created to facilitate set-up and testing of the model. Predictions were compared with measured water levels and salinity at several locations in the study area for years 2008 & 2010.The model produced good results for water level predictions with a mean absolute error of 6.5 cm over the test period. The model also produced overall realistic currents and salinity variations in Corpus Christi Bay with a mean absolute error of 1.7 psu at Ingleside. However, the model predicted salinity poorly in Nueces Bay with a mean absolute error greater than 6 psu at all stations and a maximum absolute error of greater than 20 psu. While its initial goal was to investigate the impact of sea level rise on salinity levels, the study focused instead on model performance for salinity predictions in Nueces Bay. The investigation revealed that while freshwater from the Nueces River was entering the system at the correct rate, the model was not accurately reflecting salinity response in cells further down river and ultimately in Nueces Bay.

scholarly journals Coastal Inundation Model in the Coastal Area of Palopo City, South Sulawesi Province

2021 ◽  
Vol 5 (1) ◽  
pp. 451-456
Author(s):  
Riza Aitiando Pasaribu ◽  
Pandu Setya Budi ◽  
Muhamad Abdul Ghofur Al Hakim ◽  
Farel Ahadyatulakbar Aditama ◽  
Nurina Hanum Ayuningtyas
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Spatial Data ◽  
Coastal Area ◽  
City Development ◽  
Conservation Areas ◽  
Coastal Inundation ◽  
South Sulawesi ◽  
The Impact ◽  
Mangrove Conservation

The impact of sea-level rise is perceived by many archipelagic countries such as Indonesia. The higher the sea level rises every year, the larger the disaster threat in the coastal area. The current condition of most coastal areas indicates various pressures caused by city development, including the coastal area of Palopo City in South Sulawesi Province. The sea-level rise is suspected to be the cause of coastal inundation in Palopo City which, so far has not been identified. Therefore, this study aims to draw a coastal vulnerability map of sub-districts in Palopo caused by coastal inundation using GIS technology. Analysis of the areas affected by coastal inundation is carried out by processing spatial data. The sub-districts areas affected by coastal inundation are only those located in the coastal zones. The affected area in Bara, Wara Selatan, Wara Utara, Wara Timur, and Telluwana sub-districts are 160.64 ha, 21.41 ha, 73.55 ha, 87.56 ha, and 56.65 ha, respectively. In Bara Sub-district, the areas affected by coastal inundation are residential and mangrove conservation areas. The affected areas in Telluwana Sub-district are residential, production forest, coastal conservation, and mangrove conservation areas. The affected areas in Wara Selatan, Wara Timur, and Wara Utara Sub-districts are all residential areas. By using sea-level rise data of 27 years with its highest tide model, the coastal inundation in 2040 which is predicted to occur in Palopo City can be modeled properly.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

SEA LEVEL BUDGET ALONG THE EAST COAST OF NORTH AMERICA

2017 ◽  
Author(s):  
Allison Pease ◽  
◽  
James Davis
Keyword(s):  
North America ◽  
Sea Level ◽  
East Coast

scholarly journals Spatial distribution of water level impact to back-barrier bays

2018 ◽  
Author(s):  
Alfredo L. Aretxabaleta ◽  
Neil K. Ganju ◽  
Zafer Defne ◽  
Richard P. Signell
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Water Levels ◽  
Analytical Models ◽  
Barnegat Bay ◽  
Sea Level Fluctuations ◽  
Flooding Hazard ◽  
Short Period ◽  
Inlet Geometry ◽  
Spatial Estimates

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70–100 %) and tidal frequencies (10–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

scholarly journals Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Benjamin H. Strauss ◽  
Philip M. Orton ◽  
Klaus Bittermann ◽  
Maya K. Buchanan ◽  
Daniel M. Gilford ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
East Coast ◽  
The United States ◽  
Hurricane Sandy ◽  
Water Levels ◽  
Anthropogenic Climate Change ◽  
Economic Damage ◽  
Sea Levels

AbstractIn 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over $60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately $8.1B ($4.7B–$14.0B, 5th–95th percentiles) of Sandy’s damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40–131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms.

scholarly journals A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

2021 ◽  
Vol 13 (10) ◽  
pp. 2003
Author(s):  
Daeyong Jin ◽  
Eojin Lee ◽  
Kyonghwan Kwon ◽  
Taeyun Kim
Keyword(s):  
Deep Learning ◽  
Chlorophyll A ◽  
Hydrodynamic Model ◽  
Ocean Color ◽  
Learning Model ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Temporal And Spatial Distribution ◽  
Deep Learning Model ◽  
Satellite Ocean Color

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

The high-resolution COASTAL CRETE ocean forecasting system 

2021 ◽  
Author(s):  
Katerina Spanoudaki ◽  
George Zodiatis ◽  
Nikos Kampanis ◽  
Maria Luisa Quarta ◽  
Marco Folegani ◽  
...  
Keyword(s):  
High Resolution ◽  
Oil Spill ◽  
Hydrodynamic Model ◽  
Coastal Area ◽  
Eastern Mediterranean ◽  
End Users ◽  
Marine Conservation ◽  
Tourism Industry ◽  
Maritime Safety ◽  
Forecasting System

<p>The coastal area of Crete is an area of increasing interest due to the recent hydrocarbon exploration and exploitation activities in the Eastern Mediterranean Sea and the increase of the maritime transport after the enlargement of the Suez Canal. National and local authorities, like ports and the coast guard, who are involved in maritime safety, such as oil spill prevention, the tourism industry and policy makers involved in coastal zone management, are key end users’ groups who can benefit from high spatial and temporal resolution forecasting products and information to support their maritime activities in the coastal sea area of the island. To support local end users and response agencies to strengthen their capacities in maritime safety and marine conservation, a high-resolution, operational forecasting system, has been developed for the coastal area of Crete. The COASTAL CRETE forecasting system implements advanced numerical hydrodynamic and sea state models, nested in CMEMS Med MFC products and produces, on a daily basis, 5-day hourly and 6-hourly averaged high-resolution forecasts of important marine parameters, such as sea currents, temperature, salinity and waves. The COASTAL CRETE high-resolution (~ 1km) hydrodynamic model is based on a modified POM parallel code implemented by CYCOFOS in the Eastern Mediterranean and the Levantine Basin, while for wave forecasts, the latest ECMWF CY46R1 parallel version including a number of new features, a state-of-the-art wave analysis and prediction model, with high accuracy in both shallow and deep waters has been implemented with a resolution of ~1.8 km. The COASTAL CRETE hydrodynamic model has been evaluated against the CMEMS Med MFC model and with satellite Sea Surface Temperature data with good statistical estimates. The COASTAL CRETE wave model is calibrated with in-situ data provided from the HCMR buoy network operating in the area. Both the CMEMS Med MFC products and COASTAL CRETE forecasts are made available through a customized instance of ADAM (Advanced geospatial Data Management platform) developed by MEEO S.r.l. (https://explorer-coastal-crete.adamplatform.eu/). This application provides automatic data exchange management capabilities between the CMEMS Med MFC and the COASTAL CRETE models, enabling data visualization, combination, processing and download through the implementation of the Digital Earth concept. Among the numerous functionalities of the platform, a depth slider allows to explore the COASTAL CRETE products through the depth dimension, and a sea current magnitude feature enables the visualization of the currents vectors by overlaying them to any available product/parameter, thus allowing comparisons and correlations. The downscaled high-resolution COASTAL CRETE forecasts will be used to deliver on demand information and services in the broader objectives of the maritime safety, particularly for oil spill and floating objects/marine litters predictions. Such a use case is presented for the port area of Heraklion, implementing nested fine grid hydrodynamic and oil spill models (MEDSLIK-II).</p><p>Acknowledgement: Copernicus Marine Environment Monitoring Service (CMEMS) DEMONSTRATION COASTAL-MED SEA. COASTAL-CRETE, Contract: 110-DEM5-L3.</p>

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