scholarly journals Coastal Flood Modeling Challenges in Defended Urban Backshores

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 450 ◽  
Author(s):  
Timu Gallien ◽  
Nikos Kalligeris ◽  
Marie-Pierre Delisle ◽  
Bo-Xiang Tang ◽  
Joseph Lucey ◽  
...  
Keyword(s):  
Water Levels ◽  
Flood Modeling ◽  
Flood Prediction ◽  
Integrated Framework ◽  
Hydrologic Processes ◽  
Coastal Flood ◽  
Event Prediction ◽  
Overland Flows ◽  
Hydraulic Infrastructure ◽  
Hydrologic Inputs

Coastal flooding is a significant and increasing hazard. There are multiple drivers including rising coastal water levels, more intense hydrologic inputs, shoaling groundwater and urbanization. Accurate coastal flood event prediction poses numerous challenges: representing boundary conditions, depicting terrain and hydraulic infrastructure, integrating spatially and temporally variable overtopping flows, routing overland flows and incorporating hydrologic signals. Tremendous advances in geospatial data quality, numerical modeling and overtopping estimation have significantly improved flood prediction; however, risk assessments do not typically consider the co-occurrence of multiple flooding pathways. Compound flooding refers to the combined effects of marine and hydrologic processes. Alternatively, multiple flooding source–receptor pathways (e.g., groundwater–surface water, overtopping–overflow, surface–sewer flow) may simultaneously amplify coastal hazard and vulnerability. Currently, there is no integrated framework considering compound and multi-pathway flooding processes in a unified approach. State-of-the-art urban coastal flood modeling methods and research directions critical to developing an integrated framework for explicitly resolving multiple flooding pathways are presented.

Enhanced tide model: Improving tidal predictions with integration of wind data

Shore & Beach ◽  
2020 ◽  
pp. 40-45
Author(s):  
Thomas Huff ◽  
Rusty Feagin ◽  
Jens Figlus
Keyword(s):  
Water Level ◽  
Empirical Model ◽  
Prediction Error ◽  
Tide Gauge ◽  
Water Levels ◽  
Wind Data ◽  
Flood Prediction ◽  
Tidal Model ◽  
The Public ◽  
Coastal Flood

Publicly available tidal predictions for coastlines are predominantly based on astronomical predictions. In shallow water basins, however, water levels can deviate from these predictions by a factor of two or more due to wind-induced fluctuations from non-regional storms. To model and correct these wind-induced tidal deviations, a two-stage empirical model was created: the Enhanced Tidal Model (ETM). For any given NOAA tide gauge location, this model first measured the wind-induced deviation based on a compiled dataset, and then adjusted the astronomical predictions into the future to create a 144-hour forecast. The ETM, when incorporating wind data, had only 76% of the error of NOAA astronomical tidal predictions (e.g. if NOAA had 1.0 ft. of error, ETM had only 0.76 ft. error from the observed water level). Certain ETM locations had approximately half (49%) as much prediction error as NOAA. With the improvement in tidal accuracy prediction, the ETM has the ability to significantly aid in navigation along with coastal flood prediction. We envision the ETM as a resource for industry and the public to make informed decisions that impact their livelihood.

Mercury trends and cycling in northern Wisconsin related to atmospheric and hydrologic processes

2019 ◽  
Vol 76 (5) ◽  
pp. 831-846 ◽  
Author(s):  
C.J. Watras ◽  
D. Grande ◽  
A.W. Latzka ◽  
L.S. Tate
Keyword(s):  
Time Series ◽  
Water Cycle ◽  
Water Levels ◽  
Hydrologic Processes ◽  
Littoral Sediments ◽  
Long Term Trends ◽  
Acid Base Status ◽  
Explanatory Mechanism ◽  
Regional Water

Atmospheric deposition is the principal source of mercury (Hg) to remote northern landscapes, but its fate depends on multiple factors and internal feedbacks. Here we document long-term trends and cycles of Hg in the air, precipitation, surface water, and fish of northern Wisconsin that span the past three decades, and we investigate relationships to atmospheric processes and other variables, especially the regional water cycle. Consistent with declining emission inventories, there was evidence of declining trends in these time series, but the time series for Hg in some lakes and most fish were dominated by a near-decadal oscillation that tracked the regional oscillation of water levels. Concentrations of important solutes (SO4, dissolved organic carbon) and the acid–base status of lake water also tracked water levels in ways that cannot be attributed to simple dilution or concentration. The explanatory mechanism is analogous to the “reservoir effect” wherein littoral sediments are periodically exposed and reflooded, altering the internal cycles of sulfur, carbon, and mercury. These climatically driven, near-decadal oscillations confound short or sparse time series and complicate relationships among Hg emissions, deposition, and bioaccumulation.

Evaluation of a synthetic rainfall model, P-CLIPER, for use in coastal flood modeling

2018 ◽  
Vol 92 (2) ◽  
pp. 699-726 ◽  
Author(s):  
Kevin M. Geoghegan ◽  
Patrick Fitzpatrick ◽  
Randall L. Kolar ◽  
Kendra M. Dresback
Keyword(s):  
Flood Modeling ◽  
Coastal Flood ◽  
Rainfall Model

scholarly journals Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Geofizika ◽  
2020 ◽  
Vol 37 (2) ◽  
pp. 131-156
Author(s):  
Vahdettin Demir ◽  
Asli Ülke Keskin
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Two Dimensions ◽  
Water Levels ◽  
Likelihood Method ◽  
Roughness Coefficient ◽  
Sieve Analysis ◽  
Flood Modeling ◽  
Manning Roughness

Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

scholarly journals Modelling Coastal Flood Propagation under Sea Level Rise: A Case Study in Maria, Eastern Canada

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 76 ◽  
Author(s):  
David Didier ◽  
Marion Bandet ◽  
Pascal Bernatchez ◽  
Dany Dumont
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Overland Flow ◽  
Water Levels ◽  
Model Complexity ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Coastal Flood ◽  
The Impact ◽  
Flow Velocities

Coastal management often relies on large-scale flood mapping to produce sea level rise assessments where the storm-related surge is considered as the most important hazard. Nearshore dynamics and overland flow are also key parameters in coastal flood mapping, but increase the model complexity. Avoiding flood propagation processes using a static flood mapping is less computer-intensive, but generally leads to overestimation of the flood zone, especially in defended urban backshore. For low-lying communities, sea level rise poses a certain threat, but its consequences are not only due to a static water level. In this paper, the numerical process-based model XBeach is used in 2D hydrodynamic mode (surfbeat) to reproduce an observed historical flood in Maria (eastern Canada). The main goal is to assess the impacts of a future storm of the same magnitude in the horizon 2100 according to an increase in sea level rise. The model is first validated from in situ observations of waves and water levels observed on the lower foreshore. Based on field observations of a flood extent in 2010, the simulated flooded area was also validated given a good fit (59%) with the actual observed flood. Results indicate that the 2010 storm-induced surge generated overwash processes on multiple areas and net landward sediment transport and accumulation (washover lobes). The flood was caused by relatively small nearshore waves (Hs < 1 m), but despite small water depth (>1.2 m), high flow velocities occurred in the main street (U > 2 m/s) prior to draining in the salt marsh. The impact of sea level rise on the low-lying coastal community of Maria could induce a larger flood area in 2100, deeper floodwater, and higher flow velocities, resulting in higher hazard for the population.

scholarly journals New York City Panel on Climate Change 2015 Report Chapter 4: Dynamic Coastal Flood Modeling

2015 ◽  
Vol 1336 (1) ◽  
pp. 56-66 ◽  
Author(s):  
Philip Orton ◽  
Sergey Vinogradov ◽  
Nickitas Georgas ◽  
Alan Blumberg ◽  
Ning Lin ◽  
...  
Keyword(s):  
Climate Change ◽  
New York ◽  
New York City ◽  
York City ◽  
Flood Modeling ◽  
Coastal Flood

scholarly journals Correction to: An integrated framework of coastal flood modelling under the failures of sea dikes: a case study in Shanghai

2021 ◽  
Author(s):  
Qian Ke ◽  
Jiangshan Yin ◽  
Jeremy D. Bricker ◽  
Nicholas Savage ◽  
Erasmo Buonomo ◽  
...  
Keyword(s):  
Flood Modelling ◽  
Integrated Framework ◽  
Coastal Flood ◽  
Sea Dikes

scholarly journals Evaluation of the Uncertainty of Flash Flood Prediction Using the RRI Model in Mountainous Rivers

10.29007/n72w ◽  
2018 ◽  
Author(s):  
Yosuke Nakamura ◽  
Koji Ikeuchi ◽  
Shiori Abe ◽  
Toshio Koike ◽  
Shinji Egashira
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Hydrological Model ◽  
Flash Flood ◽  
Flood Damage ◽  
Water Levels ◽  
Uncertainty Distribution ◽  
Flood Prediction ◽  
The Mean ◽  
Level 2

In recent years, flood damage caused by flash floods in mountainous rivers has been frequently reported in Japan. In order to ensure a sufficient lead time for safe evacuation, it is necessary to predict river water levels in real time utilizing a hydrological model. In this study, we conducted flood prediction using the RRI model and rainfall forecasted for the next 6 hours in the Kagetsu River basin (136.1 km2) in July 2017, evaluated the uncertainty regarding the prediction, and illustrated the results using a box-plot. The evaluation found that the mean error of the forecasted water level was approximately - 0.3 m in the prediction for the initial 3 hours and -0.97 m at the 6th hour. Also, the study investigated the possibility of correcting water levels forecasted by clarifying an uncertainty distribution. As a result, the water level forecasted was found to be underestimated because it was predicted to rise as high as Warning Level 2, while the water level forecasted with bias correction was predicted to reach Warning Level 4. Moreover, the lead time was estimated to prolong by 2 hours. Overall, the study suggested that flood forecasting can be improved by considering the uncertainty involved in prediction.

scholarly journals Enhancing Flood Hazard Assessments in Coastal Louisiana Through Coupled Hydrologic and Surge Processes

2021 ◽  
Vol 3 ◽  
Author(s):  
Matthew V. Bilskie ◽  
Haihong Zhao ◽  
Don Resio ◽  
John Atkinson ◽  
Zachary Cobell ◽  
...  
Keyword(s):  
Flood Hazard ◽  
Water Levels ◽  
Rainfall Runoff ◽  
Total Water ◽  
Antecedent Rainfall ◽  
Total Rainfall ◽  
Coastal Flood ◽  
Coastal Basins ◽  
Rainfall Model ◽  
Coastal Louisiana

Traditional coastal flood hazard studies do not typically account for rainfall-runoff processes in quantifying flood hazard and related cascading risks. This study addresses the potential impacts of antecedent rainfall-runoff, tropical cyclone (TC)-driven rainfall, and TC-driven surge on total water levels and its influence in delineating a coastal flood transition zone for two distinct coastal basins in southeastern Louisiana (Barataria and Lake Maurepas watersheds). Rainfall-runoff from antecedent and TC-driven rainfall along with storm surge was simulated using a new rain-on-mesh module incorporated into the ADCIRC code. Antecedent rainfall conditions were obtained for 21 landfalling TC events spanning 1948–2008 via rain stations. A parametric, TC-driven, rainfall model was used for precipitation associated with the TC. Twelve synthetic storms of varying meteorological intensity (low, medium, and high) and total rainfall were utilized for each watershed and provided model forcing for coastal inundation simulations. First, it was found that antecedent rainfall (pre-TC landfall) is influential up to 3 days pre-landfall. Second, results show that antecedent and TC-driven rainfall increase simulated peak water levels within each basin, with antecedent rainfall dominating inundation across the basin's upper portions. Third, the delineated flood zones of coastal, transition, and hydrologic show stark differences between the two basins.

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

2021 ◽  
Author(s):  
Louise Petersson ◽  
Marie-Claire ten Veldhuis ◽  
Govert Verhoeven ◽  
Zoran Kapelan ◽  
Innocent Maholi ◽  
...  
Keyword(s):  
Risk Management ◽  
Data Acquisition ◽  
Flood Risk ◽  
Drainage System ◽  
Flood Risk Management ◽  
Future Research ◽  
Flood Modeling ◽  
Flood Prediction ◽  
Urban Flood ◽  
Community Mapping

&lt;p&gt;We demonstrate a framework for urban flood modeling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We demonstrate that data acquisition based on community mapping can be affordable, comprehensible, quality assured and open source, making it applicable in resource-strained contexts. The framework was demonstrated and validated on a case study in Dar es Salaam, Tanzania. The results obtained show that the community mapped data supports flood modeling on a level of detail that is currently inaccessible in many parts of the world. The results obtained also show that the community mapping approach is appropriate for datasets that do not require extensive training, such as flood extent surveys where it is possible to cross-validate the quality of reports given a suitable number and density of data points. More technically advanced features such as dimensions of urban drainage system elements still require trained mappers to create data of sufficient quality. This type of mapping can, however, now be performed in new contexts thanks to the development of smartphones. Future research is suggested to explore how community mapping can become an institutionalized practice to fill in important gaps in data-scarce environments.&lt;/p&gt;

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