CN115935732A - Multi-source composite disaster inundation risk simulation method for complex tidal river network - Google Patents
Multi-source composite disaster inundation risk simulation method for complex tidal river network Download PDFInfo
- Publication number
- CN115935732A CN115935732A CN202211463954.7A CN202211463954A CN115935732A CN 115935732 A CN115935732 A CN 115935732A CN 202211463954 A CN202211463954 A CN 202211463954A CN 115935732 A CN115935732 A CN 115935732A
- Authority
- CN
- China
- Prior art keywords
- data
- disaster
- tide
- river network
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for simulating multi-source composite disaster submerging risks of a complex tidal river network, which comprises a construction method of an astronomical tide-storm tide-flood-salt tide-rainfall multi-scale dynamic process full-coupling model, and can analyze submerging and salt tide upward risks of extreme composite disasters such as torrential flood, storm tide, salt tide, storm and the like of the complex river network under the climate change scene. By means of technical means such as potential inundation area determination, model grid optimization, numerical format setting, parallel acceleration and the like, on the basis of guaranteeing momentum and material flux conservation in a river network, the calculation efficiency is optimized to the greatest extent, and the purpose of considering both multi-source disaster inundation and brine layering high-precision simulation in a estuary-offshore trans-scale space area can be achieved. The invention can solve the problem that the hydrodynamic force, the salinity and the near-shore complex terrain are relatively isolated and can not interact in the traditional simulation, provides technical support for scientifically evaluating the risk and the recurrence period of multi-source disasters in estuary areas, and has outstanding social benefits and disaster prevention and reduction economic benefits.
Description
Technical Field
The invention belongs to the technical field of coastal disaster risk simulation and evaluation, and particularly relates to a multi-source composite disaster inundation risk simulation method for a complex tidal river network.
Background
Coastal low-lying areas are susceptible areas to disasters such as ocean storm surge and the like, generally have high fragility, and river estuary areas are easy to be attacked by river basin flood. Under the influence of climate change, the risk of composite chain disasters formed by extreme events such as extreme flood, high-strength tropical cyclone, storm tide, storm and salt tide is increased, and sea level rise, ground subsidence and high-strength tropical cyclone are increased [1-6] Further exacerbating the risk of flooding at estuaries, coastal areas and the probability of secondary geological disasters. Particularly, in a complex tidal river network area at the river mouth, under the dual actions of climate change and human activities, river beds in the river network area are unevenly cut, and rainstorm flood and urban waterlogging in a river basin tend to be more concentrated. Therefore, the urgent needs for the deep knowledge of the interaction mechanism of the multi-source composite disaster and the disaster risk assessment of the affected area, the affected degree, the corresponding recurrence period and the like need to disclose the interaction mechanism of the driving elements, the staggered characteristic of the multiple disaster-causing processes and the response rule of the staggered characteristic to the climate change in the composite catastrophe process by means of a fine numerical model for realizing the full coupling of the multi-scale dynamic process.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the technology, the invention aims to provide a simulation method of multi-source composite disaster inundation risks of a complex tidal river network. By establishing a multi-scale dynamic process coupling model of astronomical tide-storm tide-flood-salt tide-precipitation 'multi-head collision' in consideration of climate change, the problem that the relative isolation of water power, salinity and near-shore complex terrain in the traditional simulation can not interact is solved. The method can deepen the understanding of the combination, risk and evolution law of the composite disasters in complex environments such as estuary complex tidal river network and the like.
The technical scheme is as follows: in order to achieve the purpose, the technical scheme adopted by the invention is a method for simulating the multi-source composite disaster inundation risk of a complex tidal river network, which comprises the following steps:
step 1, building a grid model for a complex tidal river network;
and 4, extracting the flood and salt tide disaster information, and completing the analysis and simulation of the multi-source composite disaster flood risk.
Further, the step 1 includes, for the mesh model of the complex tidal river network, calculation area determination, mesh generation, data collection and mesh elevation value determination, and the step 1 includes:
step 1.1, determining a grid model calculation region according to the characteristics of a research region;
step 1.2, mesh generation is carried out on the calculation area;
and 1.3, calculating the water depth and the terrain value of the grid.
Further, the calculation areas in the step 1.1 include a tidal river network, a potential flooding area and a near continental shelf sea area outside the estuary. Step 1.1 comprises:
collecting topographic data, meteorological data, hydrological data and the like of a research area;
and determining the tidal zone boundary position of the estuary river network area according to the years of water level data of the hydrological and tide-detecting measuring stations, wherein the tidal river reach range needs to be included in the modeling range.
And determining potential inundation areas in the tidal river network and the river mouth area by using contour line threshold according to DEM (Digital Elevation Model) Elevation data, historical flood peak data and historical storm tide data of the research area. The specific selection of the contour line threshold value is greater than the linear superposition of the historical maximum water level height of the river network and the estuary area and the sea level ascending value, and the margin is considered to cover the nonlinear effect when the multi-source disaster encounters, and the margin can be 5m. The potential flooding area thus determined can effectively cover the coastal depression area, and can not increase the number of invalid computing units because of the inclusion of the area which can not be flooded. The calculation area comprises a tidal river network and a potential flooding area, and also comprises a near continental shelf sea area outside a river mouth, so as to ensure that the influence of the tropical cyclone on the far-field effect of storm surge water increase in the process of moving to the coast is considered.
Further, the mesh generation in step 1.2 includes horizontal mesh generation and/or vertical mesh generation, and step 1.2 includes:
and adopting non-structural meshes of triangular and/or quadrangular meshes for horizontal mesh generation. In order to better simulate the exchange of momentum flux and material flux between adjacent grids in a river channel, the horizontal grids are divided into zones according to the river channel, a flow dividing and converging region, an open sea, a potential flooding region and the like, the zones are separated by control lines, and the grid density of different zones is controlled by adjusting the node spacing on the control lines. Specifically, the river in the river network area adopts triangular and/or quadrilateral meshes along the trend of the river, and the areas of the flow distribution and confluence area, the open sea, the potential flooding and the like adopt triangular meshes. Meanwhile, the grid resolution of each area needs to be gradually changed and connected so as to ensure the stability of calculation.
The vertical mesh subdivision adopts LSC 2 According to the (Localized Sigma Coordinates with shaped Cells) mixed layering scheme, a two-dimensional grid is adopted for an upstream river reach with shallow water depth, and a three-dimensional grid is adopted for other areas, particularly areas with remarkable vertical layering effect.
When the power process of coupling the salt tide upward tracing is not needed in the multi-source disaster simulation, the horizontal mesh subdivision is carried out, and the planar two-dimensional mode of a mathematical model is adopted for calculation in the degradable dimension.
Further, the step 1.3 includes:
the requirement of the submergence simulation on the DEM elevation data resolution ratio is high, all data including local high-precision aerial survey water depth data, radar and/or laser ground elevation data and global DEM elevation data capable of being downloaded in a public mode are collected as far as possible, the data from different sources and with different resolutions are fused, and fused water depth or elevation data are obtained. The integration comprises the steps that after all data are unified through an elevation standard, data cutting, splicing and other processing can be carried out through tools such as Python software and GIS software, and the data are integrated into a single rasterized water depth topographic data file through data resampling.
And performing interpolation on nodes of the grid model by using the fused water depth or elevation data through methods such as bilinear interpolation, nearest neighbor interpolation and the like to obtain water depth or terrain values of each node of the model grid.
Further, in the step 2, the multi-scale dynamic process includes astronomical tide, storm tide, flood, salt tide and precipitation, and the multi-scale dynamic process is a fully coupled mathematical model, including a control equation, a discrete mode, initial and boundary conditions, calculation parameters, related data and the like solved by the multi-scale dynamic process, the established mathematical model can be used for calculating and solving the complex tidal river network grid model created in the step 1, and the step 2 includes:
and (3) solving the surface water flow under the combined action of a plurality of physical processes such as storm surge, flood and the like by adopting a SCHISM (Semi-empirical Cross-scale Hydroscience Integrated System Model) mode based on a finite element and finite volume discrete method of an unstructured grid. In the mode, a Navier-Stokes equation adopting Boussinesq hypothesis and static pressure hypothesis is solved by adopting an Eulerian-Lagrangian algorithm, and a substance transport equation taking salinity and temperature as state variables is solved simultaneously by simulating the salt tide tracing process.
Further, the step 2 further comprises: the open sea boundary outside the model adopts tide level process data extracted by a TOPEX/POSEIDON satellite altimeter inversion model TPXO, the upstream river boundary adopts actually measured or generalized flow process data, and meteorological fields such as wind, air pressure, precipitation and the like are applied to a mathematical model through sea surface momentum and flux exchange. The wind field and the air pressure field during the tropical cyclone influence research area can be constructed by means of a tropical cyclone parametric model, atmospheric power mode simulation or data reanalysis and the like. The salinity, initial field of temperature, and the given profile distribution over time at open sea boundaries may take the form of observation data or a global warm salt model numerical product.
Further, in order to better simulate the flooding process, the scusm mode in step 2 adopts a dry-wet algorithm for tracking the interface, and the calculation unit is distinguished by setting a very small water depth threshold value: when all nodes and side edges of a certain computing unit are wet, the computing unit is wet, and when any node or side edge is dry, the computing unit is judged to be dry; the calculation unit is the grid unit in step 1.2. The discrete format of the transport equation adopts a TVD (Total Variation cancellation) format with second-order precision, and the conservation of the material flux is ensured.
Before analog calculation research, model parameters (such as B parameter in rough Manning coefficient and Holland parameterized model, and maximum wind speed radius R) need to be measured by wind speed, air pressure and the like acquired by a meteorological station and water level, flow velocity, salinity and the like acquired by the hydrological station max Etc.) to carry out adjustment and calibration, thus achieving the full verification of the model.
Further, step 3 comprises:
the method is characterized in that the flooding process, the precipitation process, the tropical cyclone meteorological field and the like during historical multi-source composite disaster events are set, or the combination of the flooding process with different flood peak intensities and durations, the precipitation process with different intensities and durations and the tropical cyclone process with different intensities and traveling speeds in different reproduction periods is set, so that the flooding process formed by the respective action or the combined action of disaster sources such as mountain floods, storm tides, strong precipitation and the like in a tidal river network and river estuary area is simulated.
When the multi-source disaster multi-scale dynamic process is used for researching the response of the future climate change, the sea level change value needs to be reasonably predicted. The relative sea level variation for different emission scenarios can be predicted based on: estimating by adopting observation data of Sea Surface altitude (SSH) including a satellite altimeter, a GPS and a tide station, predicting based on an integration result of a CMIP5/6 climate system mode, or predicting by considering data of the two parties; when the prediction method based on the CMIP5/6 climate system mode is adopted, the ground settlement effect needs to be corrected, and the correction value can be determined through a satellite altimeter and data estimation of a long-term tide station or through a Glacial Interactive Adjustment (GIA) result.
The simulation employs parallel computing based on CPU acceleration.
Further, step 4 comprises:
the mathematical model outputs the results of water level, flow rate, salinity and the like moment by moment according to the set output time step length.
According to the water level result at each moment and the terrain elevation information at the grid nodes, the submerging depth and the submerging range distribution at each moment in the calculated domain can be calculated, and further the submerging hydrological process line at each node position can be obtained;
on the basis, the inundation duration, the maximum inundation depth (or the height of the flood peak), the corresponding peak arrival time and other information at each position can be further calculated and analyzed, namely the inundation analysis result. Based on the flooding analysis results, a flooding risk map is further plotted.
According to the salinity result, the plane salinity distribution at each time and the vertical salinity distribution at different section positions can be analyzed, and the saline water uptracking position can be further analyzed and extracted according to the 0.5 salinity contour line.
Has the advantages that: aiming at a estuary complex tidal river network, the invention optimizes the model calculation efficiency to the greatest extent on the basis of ensuring the conservation of momentum flux and material flux in the river network by technical means of determination of a potential flooding area, partition setting of model horizontal and vertical grids, calculation setting of numerical formats and parameters, parallel acceleration and the like, and can realize the purpose of giving consideration to both composite disaster flooding and brine layering high-precision simulation in estuary-offshore cross-scale space areas.
The invention can solve the problem that the traditional simulation is relatively isolated and can not interact with each other in the medium water power, salinity and near-shore complex terrain, provides technical support for scientifically evaluating the occurrence and recurrence period of multi-source composite disasters in estuary areas, and has outstanding social benefits and disaster prevention and reduction economic benefits. Through researching the interaction mechanism of the multi-source disaster and the salt tide upward, the damage of the corresponding composite disaster to the ecological system is reduced, and the ecological benefit is remarkable.
Drawings
The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a relationship diagram of a multi-dynamic process solved by the simulation method of the multi-source composite disaster inundation risk of the complex tidal river network.
Fig. 2 is a calculation region and a partial enlarged view of a multi-source composite disaster inundation model of a complex tidal river network for a pearl river mouth.
FIG. 3 is a schematic diagram of horizontal and vertical subdivision of a local grid of a model.
FIG. 4 is a water depth topographic condition of the model tidal river network area after interpolation.
FIG. 5 shows the tropical cyclone instantaneous wind field calculated by Holland model and its verification.
FIG. 6 is a simulated vertical distribution of salt tide up-going instantaneous salinity in the watercourse of the sharpening machine during the 2018 super typhoon "mangosteen".
Fig. 7 is a graph of simulated maximum flooding depth profiles for different disaster scenario scenarios. Wherein, the left graph is a simulation result of flood in 6 months in 2005; the right diagram is the situation that under the circumstance that the sea level rises by 1m, the super typhoon which travels along the WNW direction encounters astronomical climax and is submerged by storm surge formed in hong Kong of China when landing along the bank at the entrance of the Zhujiang river at an unfavorable landing position.
Detailed Description
The following further illustrates an embodiment of the simulation method for the multi-source composite disaster inundation risk of the complex tidal river network according to the present invention with reference to the accompanying drawings and the specific examples. It is to be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
As shown in fig. 1, river basin flood, astronomical tide, storm tide, strong precipitation and sea level rise are all driving elements causing the risk of flooding of a complex river network, and they have differences in time and space scales, spatially cover the complex surface flow from the stream to the estuary and then to the ocean, and span the time course of several hours to hundreds of years, and the multi-scale dynamic process coupling for simultaneously realizing these multi-source disasters in a mathematical model puts high demands on the simulation technology. Meanwhile, the further coupling of the salt tide upward process requires that the model can well solve the diffusion process of the substance flux (such as salt flux and heat flux), and has higher requirements on the conservation of the substance flux. For simulating the salt tide upward tracing process and salinity stratification (or density stratification) along the vertical direction, a plane two-dimensional model based on a shallow water equation integrated along the depth cannot be compared with a full three-dimensional model in simulation effect and precision.
Although a disaster chain concept model and a large number of single disaster research results such as storm surge flood and geological disaster exist at present, the research on the multi-head composite disaster which considers multiple processes such as astronomical tide, storm surge, flood, salt tide and precipitation is rarely seen. At present, submerging simulation mainly has two types of modes, one is a surface water flow model based on rasterized DEM elevation data. By LISFLOOD-FP model [7,8] For example, the equation it solves is typically a one-dimensional form of momentum equation based on the Saint-Venant equation quasi-linearization:
wherein q is single width flow, h is water depth, z is bed surface elevation, g is gravity acceleration, n is Manning coefficient, and R is hydraulic radius. The model has the advantages that the model is conveniently coupled with a distributed hydrological model and fused with a GIS (Geographic Information System), can efficiently simulate the flood routing in a large-scale watershed and is convenient to visually display in a GIS platform; one of the main reasons for the high efficiency of the simulation mode is that the solution of the equation is based on the water surface height difference of adjacent units, convection and diffusion terms are not considered, and the simplified equation is not suitable for solving the problems of steep terrain gradient, discontinuous water flow and the like; while the effective simulation of such models is very dependent on the accuracy of the DEM elevation data.
For a complex tidal river network, the connectivity among river potential control nodes in the river network is complex, and due to the influence of the fluctuation tide of the open sea, the water flow in the river network is in periodic reciprocating flow under the dual actions of runoff and tide, and the fine simulation of the power process requires the flow dividing ratio characteristics of different key water dividing nodes in the river network to be replayed. The second type of model is a model based on hydrodynamic simulation, and is also the mode adopted by the invention, but the existing simulation method has little research on multi-source and multi-power process simulation. Generally, the hydrodynamic simulation model solves the Navier-Stokes equation based on the Boussinesq assumption and the static pressure assumption, so that the interaction between different dynamic factors can be completely reflected, but the calculation resource requirement is generally higher than that of the former method. Because of this, flooding and flooding process simulation studies typically employ planar two-dimensional models. Even so, when multi-source composite disasters in the processes of watershed flood, astronomical tide, storm tide, strong precipitation, salt tide, sea level ascending and the like and the interaction thereof need to be considered simultaneously in simulation, particularly when high-precision simulation of flooding simulation and saline water ascending process is considered simultaneously in a large space range, the three-dimensional mode is superior to the two-dimensional mode, so that the relation between physical process solving and calculating efficiency needs to be balanced, and multi-power process coupling simulation of the complex river network multi-source composite disasters is realized through the technical layers of model construction, optimization and the like.
Therefore, a simulation method of multi-source composite disaster inundation risk of a complex tidal river network is needed to solve the above problems.
Reference documents:
[1]Beckley BD,Callahan PS,Hancock III DW,et al.On the“Cal-Mode”Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series.J Geophys Res Oceans.2017；122:8371–8384.
[2]Frederikse T,Landerer F,Caron L,et al.The causes of sea-level rise since 1900. Nature.2020；584:393–397.
[3]DeConto RM,Pollard D.Contribution of Antarctica to past and future sea-level rise. Nature.2016；531:591–597.
[4]Webster PJ,Holland GJ,Curry JA,et al.Changes in Tropical Cyclone Number, Duration,and Intensity in a Warming Environment.Science.2005；309:1844–1846.
[5]Elsner JB,Kossin JP,Jagger TH.The increasing intensity of the strongest tropical cyclones.Nature.2008；455:92–95.
[6]Song J,Klotzbach PJ,Tang J,et al.The increasing variability of tropical cyclone lifetime maximum intensity.Scientific Reports.2018；8:16641.
[7]Bates PD,De Roo APJ.A simple raster-based model for flood inundation simulation. Journal of Hydrology.2000；236:54–77.
[8]Bates PD,Horritt MS,Fewtrell TJ.A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling.Journal of hydrology(Amsterdam).2010；387:33–45.
example (b):
in this embodiment, the estuary of the pearl river delta is taken as an example, and simulation research of a single or composite disaster source is performed in different situations.
The embodiment of the application discloses a method for simulating multi-source composite disaster inundation risks of a complex tidal river network, which comprises the following steps:
step 1, building a grid model aiming at a pearl estuary according to the characteristics of a research area;
for the mesh model of the Zhujiang river mouth in the step 1, the step 1 comprises the following steps:
step 1.1, determining a grid model calculation region according to the characteristics of a research region;
the model calculation area comprises a tidal river network river reach, an adjacent potential flooding area and a near continental shelf sea area outside a river mouth.
Collecting topographic data, meteorological data, hydrological data and the like of a research area;
and determining the tidal zone boundary position of the estuary river network area according to the years of water level data of the hydrological and tide-detecting measuring stations, wherein the tidal river reach range needs to be included in the modeling range.
And determining a potential flooding area at the tidal river reach by using a certain contour threshold according to DEM elevation data, historical flood peak data and historical storm surge data of the research area. The specific selection of the contour line threshold value is larger than the linear superposition of the historical maximum water level height of the river network and the estuary area and the sea level ascending value, the allowance is considered to cover the nonlinear effect when the multi-source disaster encounters, the allowance can be 5m, and the selection suggestion of the contour line threshold value is generally not smaller than 15-20 m. The potential flooding area thus determined can effectively cover the coastal depression area, and can not increase the number of invalid computing units because of the inclusion of the area which can not be flooded. The model calculation range comprises the tidal river network river reach and the adjacent potential submerged area, and also comprises the near continental shelf sea area outside the estuary, so as to ensure that the influence of the tropical cyclone on the far-field effect of storm surge water increase in the process of moving to the coast is considered. And establishing a multi-source disaster multi-scale dynamic process fully-coupled model aiming at the calculation area.
Fig. 2 shows the calculated area determined for the seine of the sequin-bowden ostium. Covering the south China sea area (105.2-120.4 degrees E, 14.8-23.6 degrees N) with the pearl estuary as the center, the upstream boundary of the tidal river network mainly comprises a Broussonetia papyrifera, a Gaoyou, a stone horn, a duck hillock and a stone mouth. The potential inundated area is determined by a 20m contour envelope.
Step 1.2, mesh generation is carried out on the calculation area;
the mesh division comprises horizontal mesh division and/or vertical mesh division, and the horizontal division of the calculation area adopts non-structural meshes of triangular and/or quadrilateral meshes. In order to better simulate the exchange of momentum flux and material flux between adjacent grids in a river channel, the horizontal grid division is arranged in a subarea mode according to the river channel, a flow dividing and converging area, an open sea area, a potential flooding area and the like, all areas are separated by control lines, and the grid density of different local areas is controlled by adjusting the node distance on the control lines. Specifically, the river in the river network area adopts triangular and/or quadrilateral meshes along the trend of the river, and the areas of the flow distribution and confluence area, the open sea, the potential flooding and the like adopt triangular meshes. In this embodiment, a mesh is generated by using SMS (Surface-water Modeling System) software, and the mesh form of different polygonal areas is realized by setting patch or paging.
The vertical mesh subdivision adopts LSC 2 Mixed layering scheme on waterThe 2D grid is adopted for the shallow upstream river reach, the 3D grid is adopted for other areas, particularly the areas with obvious vertical layering effect, the grid subdivision scheme can improve the calculation efficiency to the maximum extent, and the stability of model calculation can also be improved. Fig. 3 shows the local arrangement of horizontal grids and the vertical layered arrangement of knife sharpening gate water channels at different water depth positions along the line.
When the power process of coupling the salt tide upward tracing is not needed in the multi-source disaster simulation, the horizontal mesh subdivision is carried out, and the planar 2D mode of the model is adopted for calculation in the degradable dimension.
And 1.3, calculating the water depth and the terrain value of the grid.
The requirement of the submerging simulation on DEM elevation data resolution is high, all data including local high-precision aerial survey water depth data, radar and/or laser ground elevation data and publicly downloaded global DEM elevation data are collected as much as possible, the data from different sources and different resolutions are fused, and fused water depth or elevation data are obtained. The fusion comprises the steps that after all data are unified through elevation datum, data cutting, splicing and other processing can be carried out through tools such as Python software, GIS software and the like, and the data are merged into a single rasterized water depth topographic data file through data resampling; and performing interpolation on nodes of the grid model by using the fused water depth or elevation data through methods such as bilinear interpolation, nearest neighbor interpolation and the like to obtain water depth or terrain values of all nodes of the model grid. The fused data in the embodiment comprises ETOPO1 data (https:// ngdc.noaa.gov/mgg/global. Html), sailing guarantee part chart of army commander of China civil liberation army and naval commander, local sailing water depth and global ASTER GDEM data (https:// lpdaac.usgs.gov/products/astgtmv002 /). Fig. 4 shows the water depth and the terrain distribution of each grid node after interpolation in the river network area.
the multi-scale power process comprises astronomical tide, storm tide, flood, salt tide and precipitation, and a fully coupled mathematical model of the multi-scale power process of astronomical tide-storm tide-flood-salt tide-precipitation, and comprises a control equation, a discrete mode, initial and boundary conditions, calculation parameters, relevant data and the like which are solved by the multi-power process, the established mathematical model can be used for calculating and solving a grid model established by the complex tidal river network of the river mouths of the pearl river established in the step 1, and the step 2 comprises the following steps:
step 2.1 control equation
The SCHISM mode of the finite element/finite volume discrete method based on the non-structural grid solves the surface water flow under the combined action of a plurality of physical processes such as storm surge, flood and the like. The model adopts Eulerian-Lagrangian algorithm to solve Navier-Stokes equation adopting Boussinesq hypothesis and static pressure hypothesis, wherein the continuous equation is as follows:
the momentum equation is:
the mass transport equation:
seawater state equation:
ρ＝ρS，T，p (4)
wherein x, y, z represent three axes of spatial coordinates; t is time; u = (u, v) horizontal flow velocity vector; w is a vertical velocity component; eta is the water surface height; h is the water depth; g is the acceleration of gravity;
is Laplace operator; c is the concentration of matter (salinity, temperature, etc.); s and T are respectively salinity and temperature; f h Are substance diffusion terms and source sink terms; p is hydrostatic pressure; rho is water density; ν is a molecular dynamic viscosity coefficient; kappa is the diffusion coefficient; f is an additional volume force term including Coriolis force, earth potential and the likePhysical process and actions of sea surface, bottom and the like, and the form is as follows:
wherein k is a unit vector, and the direction is vertical and upward; f is the Coriolis coefficient; rho 0 Is a seawater reference density; p is a radical of formula A Is atmospheric pressure;
is the earth potential, and alpha is the effective earth elastic coefficient; zeta is water level elevation; μ is the horizontal vortex viscosity coefficient.
Step 2.2 construction of the tropical cyclonic meteorological field
The tropical cyclone meteorological field comprises a wind field and an air pressure field, and the construction of the tropical cyclone meteorological field can be realized through a tropical cyclone parameterized model, an atmospheric dynamic mode simulation or data reanalysis. One of the purposes of the research in this case is to identify the interaction mechanism between astronomical tide-storm tide-flood-salt tide-precipitation multisource risks and the dynamic process thereof, and to conveniently construct a composite scenario of 'multi-head-collision' encounters, in this embodiment, a parameterized model is used to construct a meteorological field accompanying tropical cyclone. Since Holland model was proposed in 1980, various models such as Jelesninanski model, emanuel & Rotunno model, fujita & Takahashi model, and Ueno model were developed and widely used. In view of the good performance of the Holland model in reproducing the tropical cyclone wind field and the air pressure field, the embodiment adopts the model, and the specific formula form is as follows:
wherein r is the distance to the center of the tropical cyclone, and P is the air pressure at the position of r; p c Is the central air pressure of cyclone; p n As background air pressure; v. of g Is the gradient wind speed; v. of m Is the maximum wind speed; maximum wind speed radius R max From Willoughby&Calculating a Rahn formula; b is Holland B parameter; v. of mc Is the speed of cyclonic movement; gamma is the boundary layer adjustment coefficient, rho a Is the air density; f is the Coriolis coefficient.
Step 2.3 calculation settings and rating of model parameters
The open sea boundary of the model adopts tide level process data extracted by a TOPEX/POSEIDON satellite altimeter inversion model TPXO, the upstream river boundary adopts actually measured flow process data, and the meteorological fields such as wind, air pressure, precipitation and the like are applied to the mathematical model through sea surface momentum and flux exchange. Salinity, temperature initiation field, and profile distribution over time at open sea boundaries given the global temperature salinity value product provided with HYCOM. The sea surface boundary conditions take the form of:
in which wind stress tau w Is calculated by using τ w ＝ρ a ·C d ·|u 10 |u 10 ，C d Is the sea surface friction coefficient, u 10 The wind speed is 10 meters at the sea surface. The seafloor boundary conditions take the form of:
wherein tau is b Is the sea floor friction drag stress in the form of tau b ＝C D ·|u b |u b ，C D Is the coefficient of friction resistance of the sea floor, u b Is the boundary layer top flow velocity.
In order to better simulate the submerging process, a dry-wet algorithm for tracking an interface is adopted, and a very small water depth threshold value is set to distinguish a calculation unit: when all nodes and sides of a unit are wet, the unit is wet, and when any node or side is dry, the unit is judged to be dry. The discrete format of the transport equation adopts a TVD format with second-order precision, so that the conservation of the material flux is ensured. The water depth threshold in this example was taken to be 0.01m.
Before analog calculation research is carried out on the numerical model, model parameters (such as B parameter in a rough Manning coefficient and Holland parameterized model and maximum wind speed radius R) need to be subjected to actual measurement data of wind speed, air pressure and the like acquired by a meteorological station and water level, flow speed, salinity and the like acquired by a hydrological station max Etc.) to carry out adjustment and calibration, thus achieving the full verification of the model. Because the river network area coverage is wide, influenced by different factors such as topography, slope, substrate, the roughness has the difference in the magnitude of each river section, on the basis of the experience of the previous research and calibration, the value situation of the Mannich coefficient in the research is as follows: the Manning coefficient of the whole research area is 0.012-0.03, and gradually increases from open sea to estuary and then to upstream of river network; the average is 0.016 in the smart ocean; the upstream of the west river in the river network area is 0.028, the upstream of the north river is 0.03, and the average of the middle section is 0.025; the land area is 0.03. Fig. 5 shows the verification of the transient wind field and wind speed process of the typhoon "mangosteen" in 2018 by using the calibrated Holland parameterized model.
by setting flood processes, precipitation processes, tropical cyclone meteorological fields and the like in the historical multi-source composite disaster event, or encountering combinations of flood processes with different flood peak intensities and durations, precipitation processes with different intensities and durations, tropical cyclone processes with different intensities, traveling speeds and the like in different reappearance periods, the flooding processes formed in the tidal river network and river estuary regions due to the respective effects or the combined effects of disaster sources such as mountain floods, storm tides, strong rainfall and the like are calculated.
When the multi-source disaster multi-scale dynamic process is used for researching the response of the future climate change, the sea level change value needs to be reasonably predicted. The relative Sea level changes under different emission scenes can be estimated based on Sea Surface Height (SSH) observation data including a multi-satellite fusion altimeter, a GPS (Global Positioning System), a tide station and the like, and can also be reasonably estimated based on an integration result of a CMIP (Coupled Model interference Project) 5/6 climate System mode or by considering multi-party data; when the prediction method based on the CMIP5/6 climate system mode is adopted, the ground settlement effect needs to be corrected, and the correction value can be determined through a satellite altimeter and data estimation of a long-term tide station, or through a glacier equalization Adjustment (GIA) result. The reasonable prediction of sea level rise under the climate change situation is an important component for researching the response of the multiscale power process of the multisource disaster to future climate change. The assumption of the sea level rising scenario in this embodiment is based on the integration result of the observation data of the satellite altimeter and the tide station and the CMIP5 climate system mode. Specifically, based on 1993-2021 AVISO (engineering, validation and Interpretation of Satellite environmental geographic data) sea level height data of a multi-Satellite fusion altimeter and a tide station, statistical methods such as EOF (electronic Orthogonal function) decomposition are adopted to evaluate and screen out an ACCESS1-0 (Acoustic communication simulation and Earth System Satellite coupled Model) which is consistent with sea level height measured characteristics from the aspects of average dynamic terrain, main modes of change and return evaluation of time sequence thereof of sea level height, FGLS-G2 (Flexible Global environmental-atmospheric-fluidic-Satellite grid Model, version 2, terrestrial institute of science and institute of atmospheric physics, and Global GFAL DL 2 (geographic Global environmental Laboratory simulation System G2) and a marine Fluid Laboratory 15-like marine Fluid dynamic Model. And estimating sea level height change results under the situations of RCP4.5, RCP8.5 and the like by adopting the mode integration results. On the other hand, by using the satellite altimeter and the glacier balance adjustment (GIA) result and combining the long-term tide test data of the representative station on the coast of China, the change characteristics of the relative sea level of each coastal region and the rising trend of the last 30 years are analyzed, and the ground settlement rate distribution on the near shore of China is estimated. By combining the CMIP5 mode integration result and the estimated ground settlement rate, the distribution situation of the relative sea level rise of the coastal areas of China in 2100 years can be predicted. According to the estimation result under the RCP8.5 scene, the relative sea level changes of the sea area near the Zhujiang estuary in 2050 and 2100 years are 0.4 m and 0.9m respectively. The scenario of sea level elevation assumes that it is applied to the mathematical model by the terrain lifting method.
The simulation of different specific cases adopts parallel computation based on CPU acceleration, and corresponding computation tasks are submitted on a cluster.
And 4, extracting the flood and salt tide disaster information, and completing the analysis and simulation of the multi-source composite disaster flood risk.
The model outputs results of water level, flow rate, salinity and the like moment by moment according to the set output time step length (1 h). According to the water level result at each moment and the terrain elevation information at the grid nodes, the submerging depth and the submerging range distribution at each moment in the calculated domain can be calculated, and further the submerging hydrological process line at each node position can be obtained; on the basis, information such as submerging duration, maximum submerging depth (or flood peak height) and corresponding peak arrival time at each position can be further calculated. According to the salinity result, the plane salinity distribution at each time and the vertical salinity distribution at different section positions can be analyzed, and the saline water uptracking position can be further analyzed and extracted according to the 0.5 salinity contour line.
For the specific research of the embodiment, fig. 6 is a simulated vertical distribution situation of the upward instantaneous salinity of the salt tide in the watercourse of the sharpening unit during the period of 2018 super strong typhoon "mangosteen". Fig. 7 shows a simulated maximum flooding depth distribution diagram for different disaster combination scenarios. The left graph is maximum flooding depth distribution information which is obtained by performing simulation extraction on the flooding of the drainage basin in month 6 in 2005; the right diagram is a situation that under the circumstance that the sea level rises by 1m, the super typhoon which travels along the WNW (West-Northwest) direction encounters astronomical climax and is submerged by storm surge formed in hong Kong of China when landing along the shore at the Zhujiang river mouth at an unfavorable landing position.
In specific implementation, the application provides a computer storage medium and a corresponding data processing unit, wherein the computer storage medium can store a computer program, and the computer program can run the inventive content of the multi-source composite disaster inundation risk simulation method for the complex tidal river network and part or all of the steps in each embodiment provided by the invention when being executed by the data processing unit. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
It is clear to those skilled in the art that the technical solutions in the embodiments of the present invention can be implemented by means of a computer program and its corresponding general-purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a computer program, that is, a software product, where the computer program software product may be stored in a storage medium, and includes several instructions to enable a device (which may be a personal computer, a server, a single chip microcomputer, MUU, or a network device, etc.) including a data processing unit to execute the method in each embodiment or some parts of the embodiments of the present invention.
The invention provides a simulation method of multi-source composite disaster inundation risk of a complex tidal river network, and a plurality of methods and ways for realizing the technical scheme are provided. All the components not specified in the present embodiment can be realized by the prior art.
Claims (10)
1. A multi-source composite disaster inundation risk simulation method for a complex tidal river network comprises the following steps:
step 1, building a grid model for a complex tidal river network;
step 2, establishing and setting a multi-scale power process full-coupling mathematical model;
step 3, simulating and calculating a dynamic coupling process of the multi-source disaster;
and 4, extracting the flood and salt tide disaster information, and completing the analysis and simulation of the multi-source composite disaster flood risk.
2. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 1, wherein: the grid model for the complex tidal river network in the step 1 comprises calculation area determination, grid subdivision, data collection and grid elevation value determination, and the step 1 comprises the following steps:
step 1.1, determining a grid model calculation region according to the characteristics of a research region;
step 1.2, mesh generation is carried out on the calculation area;
and 1.3, calculating the water depth and the terrain value of the grid.
3. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 2, wherein: the calculation areas in the step 1.1 comprise a tidal river network, a potential flooding area and a near continental shelf sea area outside a river mouth; step 1.1 comprises: collecting topographic data, meteorological data and hydrological data of a research area; determining the position of a tidal zone boundary of a estuary river network area according to the water level data of the hydrology and tide-checking measuring station for many years, wherein the tidal river reach range needs to be included in a modeling range; determining a potential flooding area in the tidal river network and the river mouth area by using a contour line threshold according to DEM elevation data, historical flood peak data and historical storm surge data of a research area; the contour line threshold value is selected to be larger than the linear superposition of the historical maximum water level height of the river network and the estuary area and the sea level ascending value, and the margin is considered to cover the nonlinear effect when the multi-source disaster encounters.
4. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 3, wherein: the mesh generation in the step 1.2 includes horizontal mesh generation and/or vertical mesh generation, and the step 1.2 includes: the horizontal mesh subdivision adopts non-structural meshes of triangular and/or quadrilateral meshes, the subdivision of the horizontal mesh is arranged in a partition mode according to a river channel, a flow distribution convergence area, an open sea and a potential flooding area, all the areas are separated by control lines, and the mesh density of different areas is controlled by adjusting the node distance on the control lines; the river network area adopts triangular and/or quadrangular meshes along the trend of the river channel, and the flow distribution and convergence area, the open sea and the potential flooding area adopt triangular meshes; meanwhile, the grid resolutions of all the areas are gradually changed and connected;
the vertical mesh subdivision adopts LSC 2 The mixed layering scheme adopts two-dimensional grids for an upstream river reach with shallow water depth and adopts three-dimensional grids for other areas of the area with obvious vertical layering effect;
when the power process of coupling the salt tide upward tracing is not needed in the multi-source disaster simulation, the horizontal grid subdivision is carried out, and the dimension reduction is carried out by adopting a plane two-dimensional mode of a mathematical model.
5. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 4, wherein: the step 1.3 comprises the following steps:
the method comprises the steps that flooding simulation has high requirements on DEM elevation data resolution, all data including local high-precision aerial survey water depth data, radar and/or laser ground elevation data and global DEM elevation data capable of being downloaded in a public mode are collected, data of different sources and different resolutions are fused, and fused water depth or elevation data are obtained; the fusion comprises the steps that after all data are unified through elevation datum, data cutting and splicing processing are carried out, and the data are merged into a single rasterized water depth terrain data file through data resampling; and performing interpolation on the nodes of the grid model by adopting the fused water depth or elevation data to obtain the water depth or terrain value of each node of the grid.
6. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 5, wherein: in the step 2, the multi-scale dynamic process comprises astronomical tide, storm tide, flood, salt tide and precipitation, the multi-scale dynamic process is a fully coupled mathematical model which comprises a control equation, a discrete mode, initial and boundary conditions, calculation parameters and related data solved by the multi-scale dynamic process, the established mathematical model can be used for calculating and solving the complex tidal river network grid model established in the step 1, and the step 2 comprises the following steps:
adopting a SCHISM mode based on finite element of a non-structural grid and a finite volume discrete method to solve the surface water flow under the combined action of multiple physical processes including storm surge and flood; the finite element and finite volume discrete method adopts Eulerian-Lagrangian algorithm to solve Navier-Stokes equation adopting Boussinesq hypothesis and static pressure hypothesis, and the simulation of the salt tide tracing process solves the material transportation equation with salinity and temperature as state variables at the same time.
7. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 6, wherein: the step 2 further comprises: the open ocean boundary of the grid model adopts tide level process data extracted by a TOPEX/POSEIDON satellite altimeter inversion model TPXO, the upstream river boundary adopts actually measured or generalized flow process data, and a meteorological field comprising wind, air pressure and precipitation is applied to a mathematical model through sea surface momentum and flux exchange; the wind field and the air pressure field in the tropical cyclone influence research area are constructed in a tropical cyclone parameterized model, atmospheric power mode simulation or data reanalysis mode; salinity, initial field of temperature, and profile distribution over time at open sea boundaries are given using either observation data or global warm salt model numerical products.
8. The method for simulating multi-source composite disaster inundation risk of complex tidal river network as claimed in claim 7, wherein: in the SCHISM mode in the step 2, a dry-wet algorithm of a tracking interface is adopted, and a water depth threshold value is set to distinguish a calculation unit: when all nodes and side edges of a certain computing unit are wet, the unit is wet, and when any node or side edge is dry, the computing unit is judged to be dry; the calculation unit is the grid unit in step 1.2; the discrete format of the transport equation adopts a TVD format with second-order precision;
before analog calculation research is carried out on the mathematical model, model parameters are adjusted and rated according to actual measurement data such as wind speed, air pressure and the like acquired by a meteorological station and water level, flow speed, salinity and the like acquired by a hydrological station, wherein the model parameters comprise a rough-rate Manning coefficient, a parameter B in a Holland parameterized model and a maximum wind speed radius R max 。
9. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 8, wherein: the step 3 comprises the following steps:
by setting flood processes, precipitation processes and tropical cyclone meteorological fields in historical multi-source composite disaster events, or encounter combinations of flood processes with different flood peak intensities and durations, precipitation processes with different intensities and durations and tropical cyclone processes with different intensities and traveling speeds in different reappearance periods, the flooding processes formed in flood sensing river networks and/or river mouth areas due to the fact that mountain floods, storm tides and strong precipitation disaster sources act or cooperate with each other are simulated;
when the multi-source disaster multi-scale dynamic process is used for researching the response of the future climate change, the sea level change value needs to be predicted: the relative sea level variation in different emission scenarios can be predicted based on: calculating by adopting observation data of sea surface height including a satellite altimeter, a GPS (global positioning system) and a tide station, predicting based on an integration result of a CMIP (China Mobile Internet protocol) 5/6 climate system mode, or predicting by considering data of the two parties; when a prediction method based on a CMIP5/6 climate system mode is adopted, the ground settlement effect needs to be corrected, and the correction value can be determined through a satellite altimeter and data estimation of a long-term tide station or through a glacier balance adjustment result;
the simulation employs parallel computing based on CPU acceleration.
10. The method for simulating multi-source composite disaster inundation risk of complex tidal river network according to claim 9, wherein: the step 4 comprises the following steps:
the mathematical model outputs numerical results including water level, flow rate and salinity moment by moment according to the set output time step; calculating submerging depth and distribution of submerging range at each moment in the calculated area according to the water level result at each moment and topographic elevation information at the grid nodes, and further obtaining a submerging hydrological process line at each node position;
on the basis, calculating and analyzing to obtain submerging duration, maximum submerging depth or flood peak height and corresponding peak arrival time information at each position; drawing an inundation risk graph;
and analyzing the plane salinity distribution and the vertical salinity distribution conditions at different section positions at each moment according to the salinity result, and analyzing and extracting the saline water upstream position according to a 0.5 salinity contour line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211463954.7A CN115935732A (en) | 2022-11-22 | 2022-11-22 | Multi-source composite disaster inundation risk simulation method for complex tidal river network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211463954.7A CN115935732A (en) | 2022-11-22 | 2022-11-22 | Multi-source composite disaster inundation risk simulation method for complex tidal river network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115935732A true CN115935732A (en) | 2023-04-07 |
Family
ID=86648332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211463954.7A Pending CN115935732A (en) | 2022-11-22 | 2022-11-22 | Multi-source composite disaster inundation risk simulation method for complex tidal river network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115935732A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767549A (en) * | 2020-12-29 | 2021-05-07 | 湖北中南鹏力海洋探测系统工程有限公司 | Equal-height surface generation method for sea state data of high-frequency ground wave radar |
| CN117520718A (en) * | 2024-01-05 | 2024-02-06 | 长江水利委员会水文局长江中游水文水资源勘测局 | Tidal river hydrologic data processing method and system |
-
2022
- 2022-11-22 CN CN202211463954.7A patent/CN115935732A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112767549A (en) * | 2020-12-29 | 2021-05-07 | 湖北中南鹏力海洋探测系统工程有限公司 | Equal-height surface generation method for sea state data of high-frequency ground wave radar |
| CN112767549B (en) * | 2020-12-29 | 2023-09-01 | 湖北中南鹏力海洋探测系统工程有限公司 | Contour surface generation method of high-frequency ground wave radar sea state data |
| CN117520718A (en) * | 2024-01-05 | 2024-02-06 | 长江水利委员会水文局长江中游水文水资源勘测局 | Tidal river hydrologic data processing method and system |
| CN117520718B (en) * | 2024-01-05 | 2024-03-19 | 长江水利委员会水文局长江中游水文水资源勘测局 | Tidal river hydrologic data processing method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yamazaki et al. | A physically based description of floodplain inundation dynamics in a global river routing model | |
| Elias et al. | Understanding processes controlling sediment transports at the mouth of a highly energetic inlet system (San Francisco Bay, CA) | |
| Sánchez et al. | A non-equilibrium sediment transport model for coastal inlets and navigation channels | |
| Lopes et al. | Local sea level change scenarios for the end of the 21st century and potential physical impacts in the lower Ria de Aveiro (Portugal) | |
| KR101613186B1 (en) | Method and system for predicting ocean circulation and wave in coastal areas | |
| CN115935732A (en) | Multi-source composite disaster inundation risk simulation method for complex tidal river network | |
| Luz Clara et al. | Future sea level rise and changes on tides in the Patagonian Continental Shelf | |
| Su et al. | Exploratory morphodynamic hindcast of the evolution of the abandoned Yellow River delta, 1578–1855 CE | |
| Deng et al. | The dynamic equilibrium shore model for the reconstruction and future projection of coastal morphodynamics | |
| Wang et al. | Modeling regional-scale sediment transport and medium-term morphology change at a dual-inlet system examined with the Coastal Modeling System (CMS): A case study at Johns Pass and Blind Pass, West-central Florida | |
| Wei et al. | Long-term process-based morphodynamic modeling of the Pearl River Delta | |
| Zou et al. | Tide-surge and wave interaction in the Gulf of Maine during an extratropical storm | |
| Wang et al. | The nonlinear response of storm surge to sea-level rise: a modeling approach | |
| Mandal et al. | An early warning system for inundation forecast due to a tropical cyclone along the east coast of India | |
| KR20160073604A (en) | Method and system for analyzing ocean circulation in coastal areas | |
| Nakamura et al. | Street-scale storm surge load impact assessment using fine-resolution numerical modelling: a case study from Nemuro, Japan | |
| Duong | Climate change induced coastline change adjacent to small tidal inlets | |
| Li et al. | A modeling study of the impact of major storms on seabed shear stress and sediment transport on the G rand B anks of N ewfoundland | |
| Deng et al. | A method for assessing the coastline recession due to the sea level rise by assuming stationary wind-wave climate | |
| Bacopoulos et al. | Sea-level rise impact on salt marsh sustainability and migration for a subtropical estuary: GTMNERR (Guana Tolomato Matanzas National Estuarine Research Reserve) | |
| Nunez et al. | A multi-scale approach for simulating tidal marsh evolution | |
| Deb et al. | Hydrodynamic modeling of a complex salt marsh system: Importance of channel shoreline and bathymetric resolution | |
| Tanaka et al. | DEM-based river cross-section extraction and 1-D streamflow simulation for eco-hydrological modeling: a case study in upstream Hiikawa River, Japan | |
| Hartog et al. | Mechanisms that influence the performance of beach nourishment: a case study in Delray Beach, Florida, USA | |
| Muñoz-Pérez et al. | Reversal in longshore sediment transport without variations in wave power direction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |