CN113723024A - Method for simulating 'stream' -river channel '-river mouth' distributed flood process suitable for coastal areas - Google Patents
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Abstract
The invention discloses a 'stream' -river channel '-river mouth' distributed flood process simulation method suitable for coastal areas, which comprises the following steps: firstly, acquiring basic information data of a system, and generalizing a complex watershed system; then establishing a multi-source heterogeneous data three-dimensional monitoring system for the rainstorm flood of the drainage basin to perform multi-source data fusion; secondly, constructing a high-resolution 'stream' -river channel '-river mouth' distributed flood simulation model simultaneously considering water projects such as barrages, reservoirs and the like in the river basin; and finally, completing model calibration and verification based on multi-source fusion data, and dynamically simulating a key section and a waterlogging-prone area in real time. The method can realize the distributed flood simulation from stream to estuary, is expected to improve the effectiveness, the scientificity and the calculation precision of flood forecast in coastal areas, and perfects the flood simulation and prediction theory and method.
Description
Technical Field
The invention belongs to the field of flood forecasting, and particularly relates to a 'stream' -river channel '-river mouth' distributed flood process simulation method suitable for coastal areas.
Background
Under the influence of a plurality of factors such as climate change, bedding surface, human activities and the like, the frequency of extreme hydrological meteorological events such as local disastrous rainstorm, sudden flood, super typhoon and the like in coastal areas of China is increased in recent years, the strength is increased, the life and property safety of people is seriously threatened, and the development of economy and society is restricted. If the economic loss caused by flood is reduced and the utilization level of flood resources is improved, flood forecasting is the most important basic link in non-engineering measures, and effective forecasting results can guide the reservoir gate barrage lake to carry out peak staggering and peak clipping so as to reduce loss and play an important role in flood control and disaster reduction. Therefore, it is important to realize high-resolution and high-precision flood simulation. The current situation monitoring mainly focuses on single-point information acquisition, and the intelligent fusion application of multi-source data is lacked; and secondly, storm surge or astronomical flood is frequently encountered in the flood period of the coastal areas, and serious flood disasters are easily caused by flood superposition/combination, so that the flood simulation precision is seriously influenced. The traditional communication propagation mode is changed by an information technology taking big data and artificial intelligence as cores at the present stage, and the monitoring refinement and intelligentization degree can be improved by combining the artificial intelligence with a high-resolution satellite through multi-source three-dimensional monitoring. Therefore, the problem of inaccurate rainstorm and flood simulation is solved, on the one hand, the artificial intelligence technology is urgently needed to be fully utilized, the multi-source monitoring construction and fusion technology of the river basin rainstorm and flood is researched, and on the other hand, a high-resolution flood simulation model which simultaneously considers the water engineering such as the sluice dam in the river basin and the supporting effect of the river mouth storm surge is needed to be established.
Disclosure of Invention
Aiming at the defects that flood information (points) of important cross sections in a river area is only given usually and the timeliness and effectiveness of flood prevention are affected due to a plurality of factors such as data shortage, high numerical model coupling difficulty, unclear inter-feed mechanism between models, low calculation efficiency and the like in the traditional coastal area flood simulation, the invention provides a ' stream ' -river mouth ' distributed flood process simulation method suitable for the coastal area, so that the full-flow-area refined real-time simulation from the stream to the river mouth is realized, and real-time refined technical support is provided for dynamic flood early warning, disaster defense, emergency transfer and the like.
The invention adopts the following technical scheme:
a 'stream' -river channel '-river mouth' distributed flood process simulation method suitable for coastal areas comprises the following steps:
step 3, dividing a drainage basin into streams, river channels and river mouths according to drainage basin characteristics, wherein the streams are mountain main branches, the river channels are plain river networks, the river mouths are storm tides, a high-resolution 'streams' -river channels '-river mouths' distributed flood simulation model is further constructed, and the influence of water engineering of a reservoir and a pump gate station in the drainage basin is considered at the same time, and the distributed flood simulation model comprises a mountain main branch flood simulation model, a storm tide simulation model and a plain river network flood simulation model;
and 4, completing calibration and verification of the distributed flood simulation model based on multi-source fusion data, and dynamically simulating the peak flow, the flood volume, the peak current time, the flood water level of the key section and the submerging depth and the submerging range of the waterlogging-prone area in real time.
In the above technical solution, further, in step 2, the set of remote sensing satellites includes, but is not limited to, three-level products IMERGE and PERSIANN-CCS of TRMM, CMORPH, and GPM; a stereoscopic (inundation information) monitoring system based on a high-resolution satellite remote sensing image comprises but is not limited to MODIS surface reflectivity products, ETM +, Lansat-8, OLI, GF-1, WFV4, Sentinel-1 and SAR high-resolution remote sensing images.
Further, the multi-source rainfall data fusion in the step 2 comprises the following steps:
step 2-1-1, multi-source rainfall data deviation correction is carried out by adopting a quantile mapping method;
and 2-1-2, simultaneously taking the multi-source data after deviation correction as the input of a deep learning model, taking ground measured data obtained based on a rainfall station as the output of the model, taking the minimum damage function as the training criterion of a data fusion model, training and verifying the model, determining the structure and parameters of the model, and realizing data fusion.
Further, the multi-source remote sensing image data fusion in the step 2 comprises the following steps:
2-2-1, constructing a corresponding graph convolution network model according to different data modalities and water body remote sensing image information before and after disaster to perform intra-modality feature learning;
and 2-2-2, correlating the multi-mode features based on a Gauss-Bernoulli limited Boltzmann machine to generate correlated shared features, and fusing to realize high-resolution submerged water depth and submerged range information extraction.
Further, the step 3 of constructing a high-resolution 'stream' -river channel '-estuary' distributed flood simulation model includes the following steps:
step 3-1, constructing a mountainous area trunk and branch flood simulation model, and specifically comprising the following steps:
3-1-1, based on a production convergence mechanism, performing production convergence simulation on each reservoir by adopting a DHSVM distributed hydrological model;
3-1-2, constructing and solving a reservoir group multi-target combined optimization scheduling model, and calculating reservoir discharge;
and 3-1-3, calculating the flow of the reservoir through the Masjing root method by using the tributaries and converging the calculated flow to a main flow river channel, and then performing flood calculation according to a one-dimensional hydrodynamic model to simulate the flow and the water level of the key section.
And 3-2, constructing a storm surge simulation model. The method comprises the following specific steps:
step 3-2-1, classifying typhoons based on a self-organizing mapping neural network according to typhoon paths;
step 3-2-2 is based on historical actual measurement "typhoon-meteorological-tide level" observation data for the same type of typhoon: the typhoon observation data are characteristics of the typhoon such as central air pressure, central wind speed, moving speed, large wind radius, advancing direction and distance between a measuring station and the center of the typhoon; the meteorological observation data are local meteorological characteristics of the observation station such as air pressure, wind direction and wind speed and rainfall during the typhoon influence period; the tide observation data is astronomical tide level;
and 3-2-3, taking typhoon-meteorological-tide level observation data as an input factor, considering the influence of the trend of storm surge water increase, taking the water increase value as the input factor, and establishing a mathematical relation with the water increase value based on a deep learning model such as a traditional cyclic neural network model, a long-time memory network model and a gated cyclic unit neural network model to realize storm surge simulation.
And 3-3, constructing a plain river network flood simulation model. And taking the upstream mountain area effluent as an upstream water inlet boundary and taking the storm surge simulation tide level as a downstream effluent boundary, dividing a row flood accumulation area, and carrying out flood simulation on the plain area based on a hydromechanical coupling model. Particularly, carrying out runoff yield calculation based on a distributed hydrological model; the river course flood routing adopts hydrodynamic models, including a zero-dimensional model, a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model, wherein the one-dimensional hydrodynamic model simulates the course of river course flood routing; the two-dimensional hydrodynamic model is used for constructing and simulating the flooding conditions of the easily flooding area, including the flooding water depth and the flooding range. And meanwhile, considering the influence of the gate pump station engineering on the flood process, and constructing and solving a gate pump station control engineering group multi-objective joint optimization scheduling model.
Further, the multi-objective combined optimal scheduling model for the reservoir group in the step 3-1-2 specifically constructs a reservoir combined optimal flood control scheduling model taking the minimum and maximum peak clipping criteria of the highest water level before each reservoir in the scheduling period as the target and taking water balance, reservoir water level capacity limit, reservoir discharge capacity limit, reservoir initial and final reservoir capacity limit and downstream flood control section flow limit as constraint conditions. The solving method can adopt multi-target optimization algorithms including but not limited to multi-target genetic algorithm (NSGA-II), multi-target particle swarm algorithm (MOPSO), multi-target Grey wolf optimization algorithm (MOGWOO) and the like.
Further, the gate pump station control project group multi-objective joint optimization scheduling model in the step 3-3 is specifically constructed by taking minimization of start loss of a flood storage area in a plain river network row and minimization of the highest water level of a main control station of main control station as targets and taking water balance of a flood control section, safety flood control limit of river levee, hydraulic limit of a pump gate boundary, limit of the number of gate opening holes and limit of a gate opening mode as constraint conditions. The solving method can adopt a multi-objective optimization algorithm including but not limited to NSGA-II, MOPSO, MOGWO and the like.
Further, the hydrokinetic coupling model in the step 3-3 is coupled with the main flow one-dimensional hydrodynamic model through the interval production confluence model, the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model, and the like, so that the hydrokinetic model is coupled. The coupling between the interval production confluence model and the main flow one-dimensional hydrodynamic model is realized by connecting the production flow of the hydrographic model to the specified river channel section of the one-dimensional hydrodynamic model, the coupling between the one-dimensional hydrodynamic models is realized by adopting a node generalization mode, and the coupling between the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model is realized by adopting a mode of constructing a virtual weir flow communication model.
Further, the model parameters to be calibrated of the distributed flood simulation model from "stream" to "river channel" to "estuary" in step 4 include: one is distributed hydrological model parameters, which mainly comprise constant parameters, vegetation parameters and soil parameters; one is a hydraulic model parameter which mainly comprises a water level volume relation of a zero-dimensional model, river course roughness of one-dimensional and two-dimensional models and the like; meanwhile, storm surge model parameters based on deep learning are considered, and the storm surge model parameters mainly comprise the number of neural network layers, the number of neurons, the number of hidden layer nodes and the learning rate.
The rating and verification of the 'stream' -river channel '-river mouth' distributed flood simulation model are as follows: selecting representative regional water conditions of the flood year, the open water year and the dry water year, adopting actual measurement flood data of multiple regions to test the model, utilizing the fused rainfall data and the actual measurement site data of corresponding time periods as the input of the model, and taking the peak flow, the flood volume, the peak current time, the flood water level and the submerging range as evaluation indexes to evaluate the model.
Compared with the prior art, the invention has the following beneficial effects:
(1) the forecasting precision can be obviously improved by fusing multi-source data, the forecasting period can be effectively prolonged, the forecasting uncertainty information can be provided, and a decision maker can be helped to consider the risk information quantitatively;
(2) aiming at the problem that a land observation system based on a rainfall station and a radar has a limited coverage range, rainstorm flood information of a high-resolution satellite is rapidly mined through artificial intelligence, and information such as rainfall, water level, submerging position and submerging range is input into a distributed flood model as supplementary data to perform model calibration verification, so that the problem of insufficient measured data can be effectively solved;
(3) a distributed flood simulation model which is coupled with the distributed hydrology model, the two-dimensional hydrodynamics numerical model and the estuary storm surge model is constructed, so that full-basin refined real-time simulation from stream to estuary can be realized;
(4) the influence of water projects such as a pond, a weir dam, a reservoir and the like on the flood process is considered, and the refinement degree and precision of simulation can be improved;
(5) the storm surge simulation model based on deep learning is established, the defects that the traditional storm surge forecasting operation is complex and the model precision is insufficient can be overcome, and the operation efficiency is improved.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a diagram of a distributed simulation model construction of the present invention;
FIG. 3 is a storm surge model building diagram (taking a long-term and short-term memory network model as an example) in the present invention;
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, a method for simulating a "stream" - "river" - "estuary" distributed flood process suitable for coastal areas specifically includes the following steps:
and 2, establishing a multi-source heterogeneous data three-dimensional monitoring system for the rainstorm flood of the drainage basin, and fusing the multi-source heterogeneous data.
The multi-source heterogeneous data stereoscopic monitoring system comprises a rainfall and remote sensing image stereoscopic monitoring system based on a rainfall station and a high-resolution remote sensing satellite, wherein a remote sensing satellite set comprises three-level products IMERGE and PERSIANN-CCS of TRMM, CMORPH and GPM, but is not limited to TRMM, CMORPH and GPM; a stereoscopic (inundation information) monitoring system based on a high-resolution satellite remote sensing image comprises but is not limited to MODIS surface reflectivity products, ETM +, Lansat-8, OLI, GF-1, WFV4, Sentinel-1 and SAR high-resolution remote sensing images.
The multi-source rainfall data fusion comprises the following steps:
step 2-1-1, multi-source rainfall data deviation correction is carried out by adopting a quantile mapping method;
and 2-1-2, simultaneously taking the multi-source data after deviation correction as the input of a deep learning model such as a traditional cyclic neural network model, a long-time memory network model and a gated cyclic unit neural network, taking ground measured data obtained based on a rainfall station as model output, taking the minimum damage function as a training criterion of a data fusion model, performing model training and verification, determining a model structure and parameters, and realizing data fusion.
The multi-source remote sensing image data fusion comprises the following steps:
2-2-1, constructing a corresponding graph convolution network model according to different data modalities and water body remote sensing image information before and after disaster to perform intra-modality feature learning;
and 2-2-2, correlating the multi-mode features based on a Gauss-Bernoulli limited Boltzmann machine to generate correlated shared features, and fusing to realize high-resolution submerged water depth and submerged range information extraction.
And 3, constructing a high-resolution 'stream' -river channel '-river mouth' distributed flood simulation model simultaneously considering the influence of reservoir and pump gate station water engineering in the watershed.
The method specifically comprises the following steps:
step 3-1, dividing according to the characteristics of the drainage basin to construct a model, wherein the model is mainly divided into dry branches (brooks) in mountainous areas, plain river networks (river channels) and storm tides (river mouths);
step 3-2, constructing a mountainous area trunk and branch flood simulation model, and specifically comprising the following steps:
3-2-1, based on a production convergence mechanism, performing production convergence simulation on each reservoir by adopting a DHSVM distributed hydrological model;
3-2-2, constructing and solving a reservoir group multi-target combined optimization scheduling model, and calculating reservoir discharge; specifically, a reservoir combined flood control optimal scheduling model taking the minimum and maximum peak clipping criteria of the highest water level before each reservoir in the scheduling period as the target and taking water balance, reservoir water level and reservoir capacity limit, reservoir discharge capacity limit, reservoir initial and final reservoir capacity limit and downstream flood control section flow limit as constraint conditions is constructed. The solving method can adopt multi-target optimization algorithms including but not limited to multi-target genetic algorithm (NSGA-II), multi-target particle swarm algorithm (MOPSO), multi-target Grey wolf optimization algorithm (MOGWOO) and the like.
And 3-2-3, calculating the flow of the reservoir through the Masjing root method by using the tributaries and converging the calculated flow to a main flow river channel, and then performing flood calculation according to a one-dimensional hydrodynamic model to simulate the flow and the water level of the key section.
Step 3-3 as shown in fig. 3, a storm surge simulation model is constructed. The method comprises the following specific steps:
3-3-1, classifying typhoons based on a self-organizing mapping neural network according to typhoon paths;
step 3-3-2 is based on historical actual measurement "typhoon-meteorological-tidal level" observation data for the same type of typhoon: the typhoon observation data are characteristics of the typhoon such as central air pressure, central wind speed, moving speed, large wind radius, advancing direction and distance between a measuring station and the center of the typhoon; the meteorological observation data are local meteorological characteristics of the observation station such as air pressure, wind direction and wind speed and rainfall during the typhoon influence period; the tide observation data is astronomical tide level;
and 3-3-3, taking typhoon-meteorological-tide level observation data as an input factor, considering the influence of the trend of storm surge water increase, taking the water increase value as the input factor, and establishing a mathematical relation with the water increase value based on a deep learning model such as a traditional cyclic neural network model, a long-time memory network model and a gated cyclic unit neural network model to realize storm surge simulation.
And 3-4, constructing a plain river network flood simulation model. And taking the upstream mountain area effluent as an upstream water inlet boundary and taking the storm surge simulation tide level as a downstream effluent boundary, dividing a row flood accumulation area, and carrying out flood simulation on the plain area based on a hydromechanical coupling model. Particularly, carrying out runoff yield calculation based on a distributed hydrological model; the river course flood routing adopts hydrodynamic force models, including a zero-dimensional model, a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model, wherein the one-dimensional river course hydrodynamic force model simulates the river course flood routing process; the two-dimensional model is constructed to simulate the flooding conditions of the waterlogging-prone area, including the flooding water depth and the flooding range. And meanwhile, considering the influence of the gate pump station engineering on the flood process, and constructing and solving a gate pump station control engineering group multi-objective joint optimization scheduling model. Wherein. The construction of the hydrodynamic model with different dimensions specifically comprises the following steps:
a zero-dimensional model. For ponds and small lake zero-dimensional areas, the influence on the flood behavior is mainly expressed in the exchange of water volume, the momentum exchange can be ignored, the index reflecting the flood behavior is the water level, the change rule of the water level must follow the water conservation principle, the pure water volume flowing into the area is equal to the storage increment in the area, and the water volume balance principle is utilized to obtain:
wherein Q is the flow rate, m3S; a (z) is the water surface area in the flood storage area, m2(ii) a Z is water level, m; t is time, s.
② one-dimensional hydrodynamic model. The one-dimensional water flow model can conveniently and rapidly forecast the flood evolution of the long river reach, can simulate the water surface line of the river channel, and is very flexible when some buildings on the river channel are processed. The method is based on a water quantity and momentum conservation equation of vertical integral, namely a one-dimensional open channel non-constant gradual change flow Sawny Vietnam equation set is used as a one-dimensional hydrodynamic model, the full-flow-domain coupling solving requirement is considered, and a Preissmann implicit difference format is adopted for equation solving.
In the formula: x is the distance, m; t is time, s; a is the cross-sectional area of water passing, m2(ii) a B is the width of the water surface of the river channel, m; q is the flow, m3S; z is water level, m; k is a flow modulus and reflects the actual flow capacity of the river channel; q is the side inflow flow, the branch flow per unit length, m3/s；VxThe velocity (m/s) of the side inflow along the length direction of the river; α is a momentum correction coefficient.
And the two-dimensional hydrodynamic model. The two-dimensional water flow model can simulate large-scale water flow movement and provide richer calculation information, such as flow field distribution, submerging area, submerging range and the like. A two-dimensional hydrodynamic model is adopted to simulate the flow process of the water surface under different terrain conditions such as roads, riverways and the like, and the basic equation set for describing plane two-dimensional water flow motion is as follows:
in the formula, x and y are independent variable time and plane coordinates respectively; h is Z-ZdDepth of water, Z water level, ZdIs the riverbed elevation; u, v are flow rates in the x and y directions; n is a roughness coefficient, and f is a Coriolis coefficient; ex、EyDiscrete coefficients in the X and Y directions, respectively; q is a source item including water taking and draining; f is the Coriolis coefficient; g is the acceleration of gravity.
The gate pump station control project group multi-objective combined optimization scheduling model specifically constructs a gate pump station control project combined flood control optimization scheduling model which aims at minimizing the starting loss of a plain river network row flood accumulation area and minimizing the highest water level of a main control station of main flow and takes the flood control section water balance, river embankment safety flood control limit, pump gate boundary hydraulic limit, gate opening hole number limit and gate opening mode limit as constraint conditions. The solving method can adopt a multi-objective optimization algorithm including but not limited to NSGA-II, MOPSO, MOGWO and the like.
The hydrokinetic coupling model is coupled with the main flow one-dimensional river channel model through the interval production confluence model, the coupling between the one-dimensional river channels, the coupling between the one-dimensional hydrodynamics model and the two-dimensional hydrodynamics model and the like, so that the hydrokinetic model is coupled. The coupling between the interval production confluence model and the main flow one-dimensional river channel model is realized by connecting the production flow of the hydrological model to a specified section of the one-dimensional river channel, the coupling between the one-dimensional river channels is realized by adopting a node generalization mode, and the coupling between the one-dimensional hydrodynamics model and the two-dimensional hydrodynamics model is realized by adopting a mode of constructing a virtual weir flow communication model.
And 4, completing calibration and verification of the distributed flood simulation model based on multi-source fusion data, and dynamically simulating the peak flow, the flood volume, the peak current time, the flood water level of the key section and the submerging depth and the submerging range of the waterlogging-prone area in real time.
The model parameters needed to be calibrated of the 'stream' -river channel '-estuary' distributed flood simulation model comprise: one is distributed hydrology model parameters, which mainly comprise constant parameters, vegetation parameters and soil parameters; one is a hydraulic model parameter which mainly comprises a water level volume relation of a zero-dimensional model, river course roughness of a one-dimensional model and a two-dimensional model and the like; meanwhile, storm surge model parameters based on deep learning are considered, and the storm surge model parameters mainly comprise the number of neural network layers, the number of neurons, the number of hidden layer nodes and the learning rate; the calibration and verification of the stream-river channel-river mouth distributed flood simulation model can select representative regional water conditions of the full-blown, open-water and dry-water years, the model is tested by adopting actual flood data of multiple regions, rainfall data and actual station data are fused in corresponding time intervals as the input of the model, and the flood peak flow, the flood volume, the peak current time, the flood water level and the submerging range are used as evaluation indexes to evaluate the model.
Claims (8)
1. A 'stream' -river channel '-river mouth' distributed flood process simulation method suitable for coastal areas is characterized by comprising the following steps:
step 1, acquiring system basic information data, and generalizing a complex watershed system, wherein the complex watershed system comprises a river system, a reservoir, a pump gate station, a hydrological station, a rainfall station and a tide level station;
step 2, establishing a multi-source heterogeneous data stereoscopic monitoring system for storm flood of the drainage basin, wherein the multi-source heterogeneous data stereoscopic monitoring system comprises a rainfall and remote sensing image stereoscopic monitoring system based on a rainfall station and a high-resolution remote sensing satellite; further fusing multi-source rainfall and remote sensing image data of the high-resolution remote sensing satellite respectively;
step 3, dividing a drainage basin into streams, river channels and river mouths according to drainage basin characteristics, wherein the streams are mountain main branches, the river channels are plain river networks, the river mouths are storm tides, a high-resolution 'streams' -river channels '-river mouths' distributed flood simulation model is further constructed, and the influence of water engineering of a reservoir and a pump gate station in the drainage basin is considered at the same time, and the distributed flood simulation model comprises a mountain main branch flood simulation model, a storm tide simulation model and a plain river network flood simulation model;
and 4, completing calibration and verification of the distributed flood simulation model based on multi-source fusion data, and dynamically simulating the peak flow, the flood volume, the peak current time, the flood water level of the key section and the submerging depth and the submerging range of the waterlogging-prone area in real time.
2. The method for simulating a distributed flood process from "stream" - "river" - "estuary" suitable for coastal areas of claim 1, wherein the multi-source rainfall data fusion in the step 2 comprises the following steps:
step 2-1-1, multi-source rainfall data deviation correction is carried out by adopting a quantile mapping method;
and 2-1-2, simultaneously taking the multi-source data after deviation correction as the input of a deep learning model, taking ground measured data obtained based on a rainfall station as the output of the model, taking the minimum damage function as the training criterion of a data fusion model, training and verifying the model, determining the structure and parameters of the model, and realizing data fusion.
3. The method for simulating the distributed flood process from "stream" to "river" to "estuary" suitable for coastal areas of claim 1, wherein the multi-source remote sensing image data fusion in the step 2 comprises the following steps:
2-2-1, constructing a corresponding graph convolution network model according to different data modalities and water body remote sensing image information before and after disaster to perform intra-modality feature learning;
and 2-2-2, correlating the multi-mode features based on a Gauss-Bernoulli limited Boltzmann machine to generate correlated shared features, and fusing to realize high-resolution submerged water depth and submerged range information extraction.
4. The method for simulating the distributed flood process from "stream" - "river channel" - "river mouth" applicable to coastal areas as claimed in claim 1, wherein the step 3 of constructing the high-resolution distributed flood simulation model from "stream" - "river channel" - "river mouth" comprises the following steps:
step 3-1, constructing a mountainous area trunk and branch flood simulation model, and specifically comprising the following steps:
3-1-1, based on a production convergence mechanism, performing production convergence simulation on each reservoir by adopting a DHSVM distributed hydrological model;
3-1-2, constructing and solving a reservoir group multi-target combined optimization scheduling model, and calculating reservoir discharge;
3-1-3, calculating and converging the reservoir discharge flow to a main flow river channel by using a tributary through an MaskIn method, and then performing flood calculation according to a one-dimensional hydrodynamic model to simulate the critical section overflow and water level;
step 3-2, constructing a storm surge simulation model, which comprises the following specific steps:
step 3-2-1, classifying typhoons based on a self-organizing mapping neural network according to typhoon paths;
step 3-2-2 is based on historical actual measurement "typhoon-meteorological-tide level" observation data for the same type of typhoon: the typhoon observation data comprise central air pressure, central wind speed, moving speed, large wind radius, advancing direction and distance between a measuring station and the center of the typhoon; the meteorological observation data comprise air pressure, wind direction and wind speed and rainfall during the typhoon influence period; the tide observation data is astronomical tide level;
step 3-2-3, taking typhoon-weather-tide level observation data as an input factor, considering the influence on the trend of storm surge water increase, simultaneously taking an early-stage water increase value as an input factor, and establishing a mathematical relation with the water increase value based on a deep learning model to realize storm surge simulation;
3-3, constructing a plain river network flood simulation model, dividing a flood storage area by taking the upstream mountain area effluent as an upstream incoming water boundary and taking a storm surge simulation tide level as a downstream effluent boundary, and performing flood simulation on the plain area based on a hydromechanical coupling model; particularly, carrying out runoff yield calculation based on a distributed hydrological model; the river course flood routing adopts hydrodynamic models, including a zero-dimensional model, a one-dimensional hydrodynamic model and a two-dimensional hydrodynamic model, wherein the one-dimensional hydrodynamic model simulates the course of river course flood routing; constructing and simulating the flooding conditions of the easily flooding area including the flooding water depth and the flooding range by using a two-dimensional hydrodynamic model; and meanwhile, considering the influence of the gate pump station engineering on the flood process, and constructing and solving a gate pump station control engineering group multi-objective joint optimization scheduling model.
5. The method according to claim 4, wherein the reservoir group multi-objective combined optimal scheduling model in step 3-1-2 is a reservoir combined flood control optimal scheduling model with constraints of water balance, reservoir level capacity limit, reservoir discharge capacity limit, reservoir initial and final reservoir capacity limit, and downstream flood control section flow limit, and with the objective of the highest water level minimum and maximum peak clipping criteria before each reservoir during scheduling.
6. The method according to claim 4, wherein the gate pump station control project group multi-objective combined optimization scheduling model in the step 3-3 is a gate pump station control project combined flood control optimization scheduling model with the constraint conditions of flood control section water balance, river bank safety flood control limit, pump gate boundary hydraulic power limit, gate open hole number limit, and gate open mode limit, aiming at minimizing the startup loss of flood storage area in plain river network and the highest water level of main control station of main stream.
7. The method according to claim 4, wherein the hydrokinetic coupling model in step 3-3 is coupled with the one-dimensional hydrokinetic model of main current through an interval production confluence model, and the one-dimensional hydrokinetic model is coupled with the one-dimensional hydrokinetic model of main current, and the one-dimensional hydrokinetic model is coupled with the two-dimensional hydrokinetic model to realize the coupling of the hydrokinetic model; the coupling between the interval production confluence model and the main flow one-dimensional hydrodynamic model is realized by connecting the production flow of the hydrographic model to a river section appointed by the one-dimensional hydrodynamic model, the coupling between the one-dimensional hydrodynamic models is realized by adopting a node generalization mode, and the coupling between the one-dimensional hydrodynamic model and the two-dimensional hydrodynamic model is realized by adopting a mode of constructing a virtual weir flow communication model.
8. The method according to claim 1, wherein the calibration and verification of the "stream" - "river course" - "river mouth" distributed flood simulation model in step 4 can select representative regional water conditions of the year with rich water, the year with open water and the year with dry water, the model is verified by using actual flood data of multiple regions, rainfall data and actual site data are fused at corresponding time intervals as input of the model, and the model evaluation is performed by using peak flow, flood volume, peak present time, flood level and flood submergence range as evaluation indexes.
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