KR20100131902A - Flood prediction information system of high-resolution rapid-updated-coupling and flood prediction information method of high- resolution rapid-updated-coupling - Google Patents

Abstract

PURPOSE: Flood prediction information system and method of high-resolution rapid-updated-cycle are provided to produce detail rainfall information of high-resolution(0.5 km) and high frequency(5 minutes). CONSTITUTION: A Flood prediction information system of high-resolution rapid-updated-cycle comprises: a lattice weather prediction module(100); a rainfall prediction module(200); a city basin flooding outflow forecast module(400) based on date generated from the lattice weather prediction module and the rainfall prediction module; and a flooding danger degree estimate module(600) based on city basin flooding outflow amount.

Description

FLOOD PREDICTION INFORMATION SYSTEM OF HIGH-RESOLUTION RAPID-UPDATED-COUPLING AND FLOOD PREDICTION INFORMATION METHOD OF HIGH- RESOLUTION RAPID-UPDATED-COUPLING}

The present invention relates to a high resolution and high frequency cycle flood information prediction system and method, in particular through a meteorological model and a hydrological model in communication with the grid of 0.5km interval of interest area every 5 minutes The present invention relates to a high resolution and high frequency cycle flood information prediction system and method that provides more accurate and detailed flood prediction information by producing flood prediction data at intervals.

River flood forecasting can be classified into weather, water level, rainfall-runoff, weather-rainfall-run, etc. Weather forecasting methods include Prototype Regional Observing and Forecasting Service (PROFS) in the United States, and Forecasting Rain Optimized (FRONTIER) in the UK. New rainfalls such as New Enhanced Techniques of Interactive Enhanced Radar and Satellite, and Sweden's Program for an Operation Meteorological Information System (PROMIS-90) can be used to determine the amount of rainfall that can be triggered by accurate quantitative precipitation prediction using ground rainfall stations, radars, and meteorological satellites. This is how you assess risk.

The water level method is a type of empirical formula that calculates the change in the downstream flow according to the change of the water level observation upstream. Rainfall-runoff is the most common method currently used in flood forecasting systems of the five major flood control stations.

However, for impending real-time flood risk forecasts, the accuracy and speed of forecasting can be ensured by directly linking with the rainfall forecasting model rather than the simple rainfall-runoff method. In addition, in order to provide accurate flood prediction information, detailed rainfall prediction information provided in real time is essential. In the past, it is common to produce rainfall prediction information about every three hours using a mesoscale weather model with a 30 km grid spacing. It was. However, considering the topographical conditions of Korea, where three sides consist of the sea and two thirds of the land consists of mountains, the three-hour rainfall information with 30 km grid spacing is difficult to apply as an input to the hydrological model. Particularly, in case of rainfall, it is often generated suddenly and the temporal and spatial variation is very large. Therefore, it is difficult to predict flood information according to local rainfall with the resolution information.

In addition, if the time-space resolution of the medium-scale weather model is increased for more detailed rainfall prediction information, it is almost impossible to operate it in conjunction with the actual hydrologic model because it takes 4 times more computation time every time the resolution is increased twice. It was. Therefore, there is a problem in that accurate flood information requires a linkage between a fine grid-scale quantitative precipitation calculation system and a hydrologic prediction model that can produce detailed local precipitation information in a short time.

In addition, there is a lack of a system capable of expressing them comprehensively through real-time communication between the quantitative precipitation calculation system of the fine grid scale and the hydrologic prediction model.

The object of the present invention is to develop a small geographic feature that is not represented in the medium-scale meteorological numerical model. To provide a high frequency cycle flood information prediction system and method.

In addition, another object of the present invention is a high-resolution high-frequency cycle flood information prediction system capable of generating detailed flood prediction information every 5 minutes by receiving detailed precipitation information from the quantitative precipitation calculation system and using it as an input field of the hydrologic model And a method.

In addition, another object of the present invention is to establish a system that can display the results of the interlocking results between the quantitative precipitation calculation system of the fine grid scale and the hydrologic prediction model on the web, so that the user can receive the flood prediction information in real time for the selected period and region, etc. To provide a high resolution, high frequency cycle flood information prediction system and method that can be identified.

In order to achieve the above object, the present invention provides a grid weather prediction module for predicting the weather of an area divided into a grid of a constant size, a precipitation prediction module for quantitatively predicting precipitation of an area divided into a grid of a constant size, and Flooding based on urban watershed overflow forecasting module that predicts flooding in urban watershed based on the data generated by grid meteorological prediction module and precipitation forecasting module, and flooding based on urban watershed flooding generated by urban watershed overflow forecasting module It provides a high-resolution high-frequency cycle flood information prediction system comprising a flood risk assessment module for assessing the risk. The grid meteorological prediction module is a global 20km grid meteorological prediction module that simulates weather changes by dividing the entire globe into a grid having a size of 20 km, and a medium scale that simulates weather changes by dividing the entire globe into a grid having a size of 8 km. 8km grid meteorological prediction module, and a medium-sized 2km grid meteorological prediction module that simulates weather changes by dividing the entire globe into a grid having a size of 2km. It includes a grid weather prediction module, urban watershed runoff model input data conversion module for converting the weather forecast data and precipitation prediction data generated by the precipitation prediction module into a watershed rainfall which is input data of the urban watershed overflow forecasting module. And a flood risk assessment model input data converting module for converting the overflow amount generated by the flood risk assessment model input data converting module into input data of the flood risk assessment module. And a 3D image display module displaying a flood risk map derived from the flood risk assessment module and a flood map displaying the flood risk on the map.

In addition, the present invention comprises the steps of collecting the raw weather information, generating weather field prediction data based on the collected raw weather information, and generating rainfall prediction data based on the collected raw weather information; Generating a flood flow forecast data based on the meteorological field prediction data and the rainfall prediction data; and generating a flood risk and flood map based on the flood flow forecast data. Provides a method for predicting high frequency cycle flood information. The collecting the raw weather information includes collecting raw weather information including temperature, precipitation, evaporation amount, wind speed, humidity, vapor pressure, combination, clouding amount, and barometric pressure. Generating weather field prediction data based on the collected raw weather information, 3D weather field prediction including wind, temperature, position altitude, and humidity at 20 km grid / 3 hour intervals using a global scale weather model. Generating data, generating 3D weather field prediction data including wind, temperature, position altitude, and humidity at 8 km grid / 3 hour intervals using the medium-scale weather model, and using the medium-scale weather model. Generating 3D meteorological field prediction data including wind, temperature, position altitude, and humidity at km grid intervals of 5 minutes. The step of generating rainfall prediction data based on the collected raw weather information includes generating prediction data of 0.5 km grid / 5 minute intervals using a quantitative rainfall prediction model based on the collected raw weather information. Include. Generating rainfall prediction data based on the collected raw weather information, the step of deriving rainwater ejection amount based on the collected raw weather information, and deriving a flood flow rate based on the collected raw weather information Steps. The step of deriving the storm water discharge based on the collected raw weather information, the first impermeable region without ground retention, the second impermeable region with ground retention and the pitcher with ground retention Dividing the area into regions, and assuming that the second impermeable region and the permeable region are nonlinear reservoirs, deriving a flow rate through the surface using a continuous equation and a manning equation, and calculating the water depth and flow rate of the subwatershed (Storm Water Management Model) includes the step of deriving. Deriving a flood outflow based on the collected raw weather information, the step of deriving a flood outflow of the subwatershed using a Storm Water Management Model (SWMM).

The present invention can provide a high resolution-high frequency cycle flood information prediction system and method capable of monitoring sudden and heavy rainfall by providing high-resolution detailed rainfall information every 0.5 minutes with 0.5 km intervals sufficiently reflecting minute regional characteristics.

In addition, the present invention provides a high-resolution high-frequency cycle flood information prediction system and method capable of producing accurate and rapid flood risk forecasting data by receiving detailed rainfall forecasting information received at five-minute intervals reflecting microscopic features. Can provide.

In addition, the present invention displays detailed rainfall and flood forecasting results every 5 minutes through the web presentation system to provide information necessary for disaster prevention activities of front-line organizations and local governments for intensive rainfall and urban flooding frequent areas. High frequency cycle flood information prediction systems and methods can be provided.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various forms, and only the embodiments are intended to complete the disclosure of the present invention, and to those skilled in the art to fully understand the scope of the invention. It is provided to inform you. Like reference numerals in the drawings refer to like elements.

1 is a conceptual diagram of a high resolution-high frequency cycle flood information prediction system according to the present invention, and FIG. 2 is a SWMM model used in the high resolution-high frequency cycle flood information prediction method according to the present invention.

As shown in FIG. 1, the high resolution-high frequency cycle flood information prediction system according to the present invention includes a grid weather prediction module 100, a precipitation prediction module 200, and an urban watershed outflow model input data conversion module 300. And, urban watershed overflow amount prediction module 400, flood risk assessment model input data conversion module 500, flood risk assessment module 600, and 3D image display module 700.

The grid meteorological prediction module 100 is for simulating weather changes at regular grid intervals, and includes a global 20 km grid weather prediction module 120, a medium 8 km grid weather prediction module 140, and a medium scale 2 km grid weather prediction. Module 160. The grid modules of various sizes divide the entire globe into grids of 20 km, 8 km and 2 km, and simulate the weather changes. Of course, the grid meteorological prediction module 100 generates 3D meteorological field prediction data including wind, temperature, position altitude, and humidity using a meteorological model based on raw meteorological information collected from related agencies such as the Korea Meteorological Agency. In addition, the microscale 0.5km grid quantitative precipitation prediction module 200 predicts the precipitation of the area divided into a 0.5km grid.

Urban watershed outflow model input data conversion module 300 converts the input data for input to the urban watershed overflow forecast module (400). In this case, the input data input to the urban watershed overflow prediction module 400 is prediction data generated by the grid meteorological prediction module 100 and the precipitation prediction module 200, and the urban watershed outflow model input data conversion module 300 The prediction data is converted into watershed rainfall for input into the urban watershed overflow forecasting module 400.

The urban watershed overflow forecasting module 400 inputs the watershed rainfall converted from the predicted data by the urban watershed outflow model input data conversion module 300 to predict the floodwater spillage of the urban watershed.

The flood risk assessment model input data conversion module 500 converts the flood runoff quantity predicted by the urban watershed overflow forecast module 400 into the input data of the flood risk assessment module.

The flood risk assessment module 600 evaluates the flood risk for the region during rainfall based on the input data converted from the flood risk assessment model input data. The present invention uses the SWMM (Storm Water Management Model) for this purpose. SWMM is a comprehensive model that can simulate the surface and subsurface flows caused by rainfall events in urban watersheds, traces of runoff to drainage networks, simulation of reservoirs, treatment of pollutants and cost calculations. It is configured to simulate the flow rate and water quality in the system. In addition, SWMM simulates real rainfall events by considering the characteristics of the system, such as rainfall column topography, meteorological inputs, subwatersheds and sewer lines, to predict runoff flow and water quality. As shown in FIG. 2, the SWMM model includes four execution blocks and a rainfall (RUNOFF) block, a TRANSPORT block, an EXTRAN block, and a storage / treatment block. It consists of five sub-blocks, such as a RAIN block, a TEMPERATURE block, a COMBIN block, and a STATISTICS block, and includes 126 sub-programs. In this case, the present invention uses a runoff block and a transport block of the SWMM.

The 3D image display module 700 displays the flood risk assessed by the flood risk assessment module 600 as a 3D image. In this case, the 3D image may be implemented by displaying the depths of rivers, rivers, and the like in stages in the color based on the map of the area where the flood risk is evaluated.

As described above, the present invention communicates the meteorological model and the hydrological model to grid the grid at 0.5 km intervals of the region of interest so that high-resolution detailed rainfall information is sufficiently reflected every 5 minutes. To provide high-resolution, high-frequency cycle flood information forecasting systems that can monitor sudden and intensive rainfall. In addition, the present invention is to provide a high-resolution high-frequency cycle flood information prediction system capable of producing accurate and rapid flood risk forecasting data by receiving detailed rainfall forecasting information received at five-minute intervals reflecting fine geographic characteristics Can be. In addition, the present invention displays detailed rainfall and flood prediction results every 5 minutes through the web presentation system to provide information necessary for disaster prevention activities of front-line organizations and local governments for intensive rainfall and urban flooding frequent areas. High frequency cycle flood information prediction system can be provided.

Next, a high resolution-high frequency cycle flood information prediction method using the high resolution-high frequency cycle flood information prediction system according to the present invention will be described with reference to the accompanying drawings. In the following description, the overlapping description of the high resolution-high frequency cycle flood information prediction system according to the present invention described above will be omitted or briefly described.

3 is a flowchart of a high resolution-high frequency cycle flood information prediction method according to the present invention, and FIG. 4 is a storm water curve graph derived according to the high resolution-high frequency cycle flood information prediction method according to the present invention. FIG. 5 is a flood map in which a flood simulation is performed according to the high resolution-high frequency cycle flood information prediction method according to the present invention.

High resolution-high frequency cycle flood information prediction method according to the present invention, as shown in Figure 2, collecting the raw weather information (S ₁ ), generating a 3D weather field prediction data (S ₂ ), Generating a rainfall forecast data (S ₃ ), generating a flood runoff prediction data (S ₄ ), and creating a flood risk and flood map (S ₅ ).

In collecting raw weather information (S ₁ ), raw weather information is collected from a related agency such as a meteorological office. At this time, the raw weather information may include air temperature, precipitation, evaporation amount, wind speed, humidity, steam pressure, day combination, cloud cloud amount, sea level air pressure.

In generating the 3D weather field prediction data (S ₂ ), the 3D weather field prediction data is generated by using the global scale, the medium scale, and the small scale weather model. Generating 3D weather field prediction data includes generating 3D weather field prediction data using a global scale weather model (S _2-1 ), and generating 3D weather field prediction data using a medium-scale weather model. (S _2-2 ), and generating 3D weather field prediction data using the small scale weather model (S _2-3 ).

In the step of generating 3D weather field prediction data using the global scale weather model (S _2-1 ), the global scale includes wind, temperature, position altitude, and humidity at 20 km grid / 3 hours. Generate 3D weather field forecast data. At this time, the weather station

In the step of generating 3D weather field prediction data using the medium-scale weather model (S _2-2 ), the 3D weather field including the wind, temperature, position altitude, and humidity of 8 km grid / 3 hours using the medium-scale weather model is used. Generate forecast data.

In generating the 3D weather field prediction data using the small-scale weather model (S _2-3 ), the 3D weather field including the wind, temperature, position altitude, and humidity at 2 km grid / 5 minutes interval using the medium-scale weather model is used. Generate forecast data.

Generating prediction data of rainfall using the quantitative rainfall prediction model (S ₃ ) generates prediction data of 0.5 km grid / 5 minute intervals using the quantitative rainfall prediction model. At this time, the area rainfall is calculated from the sum of the weights of the gratings and the rainfall of the gratings. This is expressed as Equation 1 below.

[Equation 1]

는 면적 강우량이고,

는

번째 격자에 포함되는 대상유역의 면적이다. 또한,

는 대상유역의 총 면적을 의미한다.here,

Is the area rainfall,

Is

The area of the target basin included in the first grid. Also,

Is the total area of the watershed.

)과 지면저류가 있는 불투수영역(

), 그리고 지면저류가 있는 투수영역(

)의 세 부분으로 구분한다. 그리고 이들 지표면을 통한 유출량은 유역을 비선형저수지(nonlinear reservoir)로 가정한 후 연속방정식과 Manning 식을 사용하여 계산할 수 있다. 이때, 지표면 유출이 발생하는 유역에서 초기(

)조건으로 지표면 저류량을 0으로 둔다. 또한, 경계조건은 유역의 상류 끝에서 외부유입이 없는 것으로 가정한다. 즉, 상류 유역에서 강우로 인해 발생하는 지표면유출은 수리학적으로 연결된 하류 유역에 유입되며, 다른 유역으로 유출되지 않는다. 지표면 유출에 대한 기본방정식은 마찰경사를 유역경사와 같다고 가정하는 운동파 근사법의 비선형 저류방정식이 사용된다.In the step of generating rainfall prediction data (S ₃ ), in order to simulate the rainfall-flow phenomenon, an impermeable area without ground storage is used for the subwatershed where the surface runoff occurs.

) And impervious areas with ground retention (

) And permeable area with ground retention (

) Into three parts. The runoff through these land surfaces can be calculated using a continuous equation and a manning equation, assuming that the watershed is a nonlinear reservoir. At this time, the initial stage in the watershed

Leave the surface reservoir at zero. In addition, boundary conditions assume no external inflow at the upstream end of the watershed. That is, surface runoff due to rainfall in upstream basins flows into hydraulically linked downstream basins and does not flow into other basins. The basic equations for surface runoff are kinematic wave nonlinear storage equations that assume frictional slopes are equivalent to watershed slopes.

에 대한 유효우량

(= 강우강도-증발 및 침투율, mm/sec)와 유출률

, 흐름체적

, 소유역의 폭

, 수심

을 가지고 연속방정식을 구성하면 수식 2와 같다.Runoff blocks are used in the Storm Water Management Model (SWMM) to determine the depth and flow rate of each subwatershed. In this case, a continuous equation and a manning equation are used for this. Surface area of subwatershed

Effectiveness for

(= Rainfall intensity-evaporation and penetration rate, mm / sec) and runoff rate

, Flow volume

Width of subwatershed

, Depth

If we construct a continuous equation with,

[Equation 2]

라고 가정하여 Manning 식을 적용하면 수식 3과 같이 유출률을 구할 수 있다.here,

If we apply Manning's equation, we can get outflow rate like Equation 3.

[Equation 3]

은 Manning의 조도계수,

는 지면저류 손실수심,

는 소유역 경사이다.here,

Is Manning's roughness coefficient,

Is the depth of ground loss loss,

Is a subwatershed slope.

와 같이 근사되었다. 수식 2와 수식 3을 결합하면 수식 4와 같은 하나의 비선형 미분방정식을 얻을 수 있다.In Equation 3, the equal radius is

Approximated as Combining Equations 2 and 3 yields one nonlinear differential equation such as Equation 4.

[Equation 4]

In addition, the differential equation of Equation 4 can be expressed as shown in Equation 5 below.

[Equation 5]

은

시각의 수심,

은

시각의 수심,

는 시간간격(

),

는 시간간격당 평균 초과강수량

이다. 상기와 같은 비선형 방정식에서

는 Newton-Raphson 반복법을 이용하여 구하고 이 값으로부터 수식 3을 이용하여 각 구간의 순간 유출량을 구한다. 여기서 계산된 순간 유출값은 유입구나 측구 및 관로 등에 유입되며, 연속되는 SWMM 모형내 다른 블록에 전달되는 입력자료(유량자료)가 된다. 이 유출량은 각 소유역에서 구해지고 전체 유역의 유출량은 이의 합으로 표현된다. Newton-Raphson 계산과정은 수식 6으로 변환하여 수행한다.In Equation 5

silver

Depth of vision,

silver

Depth of vision,

Is the time interval (

),

Is the average amount of excess precipitation per time interval

to be. In nonlinear equations like this

Is obtained by using Newton-Raphson iteration method, and from this value, equation 3 is used to find the instantaneous runoff of each section. The calculated instantaneous runoff flows into inflows, sideways, pipelines, etc. and becomes input data (flow data) to other blocks in the continuous SWMM model. This runoff is obtained from each subwatershed, and the runoff for the entire watershed is expressed as the sum of these runoffs. The Newton-Raphson calculation process is performed by converting to Equation 6.

[Equation 6]

은 유역폭, 경사, 조도계수 등을 하나로 표현한 변수로서 유역의 특성에 따라 변화하는 유역특성 인자이다. Newton-Raphson 반복법을 사용하기 위해 Newton 함수를 구성한다.here,

Is a variable representing the watershed width, slope, and roughness coefficient, and is a watershed characteristic factor that changes according to the characteristics of the watershed. Construct a Newton function to use the Newton-Raphson iteration.

[Formula 7]

Differentiating Equation 7 gives Equation 8.

[Equation 8]

값을 계산하기 위해 다음과 같이 Newton-Raphson 반복법을 사용하며, 이는 수식 9와 같이 구할 수 있다.here,

The Newton-Raphson iteration method is used to calculate the value, which can be obtained as shown in Equation 9.

[Equation 9]

가 0에 수렴하는

값으로부터

단계의 수심

이 계산된다.At this time, iterative calculation process

Converges to zero

From value

Depth of steps

This is calculated.

In the SWMM (Storm Water Management Model), the flow governing equation of the transport block is composed of kinematic wave equations such as Equation 10 and Equation 11.

[Equation 10]

[Equation 11]

는 수로/관길이,

는 시간,

는 수로/관경사,

는 마찰경사,

는 유량,

는 흐름 단면적이다.here,

Channel / pipe length,

Time,

Channel / viewer,

The friction inclination,

Is the flow rate,

Is the flow cross section.

In this case, the friction slope in Equation 11 can be obtained from Equation 12 from the Manning equation.

Equation 12

은 Manning의 조도계수,

은 동수반경이다. 위의 두 식, 즉, 수식 11과 수식 12로부터 수식 13과 같이 유량을 구할 수 있다.here,

Is Manning's roughness coefficient,

Is the same radius. From the above two equations, that is, Equation 11 and Equation 12, the flow rate can be obtained as shown in Equation 13.

Equation 13

와 동수반경

은 수심

의 함수이다.Flow cross section in Equation 13

Radius of equality

Silver depth

Is a function of.

As described above, the amount of flooding and stormwater discharge generated in the urban basin can be calculated through the rainfall forecasted in units of 5 minutes by the aforementioned method. At this time, if the rainfall is more than a predetermined value will be a good ejection amount as shown in Figure 4, if the rainfall is generated can be used as an input data of the flood prediction model can be performed inundation analysis.

In the generation of overflow forecast data (S ₄ ), the flooding analysis is performed using the rainfall output generated when the rainfall is above a certain value as input data for the overflow prediction model. The model used at this time is FLUMEN (FLUvial Modeling ENgine), a program developed by Beffa, Switzerland, and has been used for flood flood analysis in Switzerland, Germany and Australia.

The governing equation of the FLUMEN model is the depth-integrated nonlinear shallow water equation, which is represented by Eq.

[Equation 14]

의 보존형 벡터로 수심 H와 비유량 q와 r의 항으로 표현된다. x 및 y축의 flux 벡터 E, G와 source 벡터 S는 각각 수식 15와 같다.Where x and y are horizontal and vertical directions, t is time,

Conserved vector of, expressed in terms of depth H and specific flow q and r. The flux vectors E, G, and the source vector S of the x and y axes are the same as Equation 15, respectively.

Equation 15

E = pmatrix {q # {q ^ 2 over h} + {g over 2} h ^ 2 # gr over h}; G = pmatrix {q # gr over h # {r ^ 2 over h} + {g over 2} h ^ 2}; S = pmatrix {0 # gh {PARTIAL_ {Z} _ {b} over PARTIAL_ {x} + {tau_ {bx} over rho}} # gh {PARTIAL_ {Z} _ {b} over PARTIAL_ {y} + {tau_ {by} over rho}}

는 유체의 밀도,

는 하상고도,

는 하상전단력이다. 비선형 천수방정식들은 정수압 분포가 가정된 Navier-Stokes 방정식으로부터 유도되었다.Where g is the acceleration of gravity,

Is the density of the fluid,

Is the altitude,

Is the bed shear force. Nonlinear shallow water equations are derived from the Navier-Stokes equation assuming hydrostatic pressure distribution.

Stormwater discharge is inputted in the form of Point-Source into the irregular triangular network of the target watershed, and the flow of stormwater discharged at predetermined time intervals is calculated.

In the step of preparing a flood risk and flood map (S ₅ ), a flood risk and flood map is prepared using an evaluation risk evaluation model. As shown in FIG. 5, the depth of water may be expressed in steps of various colors on the map of the area where the flood simulation is performed.

As described above, the present invention communicates the meteorological model and the hydrological model to grid the grid at 0.5 km intervals of the region of interest, thereby obtaining high-resolution detailed rainfall information sufficiently reflecting the fine regional characteristics. It can be provided every 5 minutes to provide a high-resolution, high-frequency cycle flood forecasting method that can monitor sudden and torrential downpours. In addition, the present invention provides a high-resolution high-frequency cycle flood information prediction method that can generate accurate and rapid flood risk forecasting data by receiving detailed rainfall forecasting information received at 5 minute intervals reflecting fine geographic characteristics. Can be. In addition, the present invention displays detailed rainfall and flood prediction results every 5 minutes through the web presentation system to provide information necessary for disaster prevention activities of front-line organizations and local governments for intensive rainfall and urban flooding frequent areas. High frequency cycle flood information prediction method can be provided.

1 is a conceptual diagram of a high-resolution high-frequency cycle flood information prediction system according to the present invention.

2 is a SWMM model used in the high-resolution high-frequency cycle flood information prediction method according to the present invention.

3 is a flowchart of a method for predicting high resolution-high frequency cycle flood information according to the present invention;

4 is a storm water flow curve graph derived according to the high-resolution high-frequency cycle flood information prediction method according to the present invention.

5 is a flood map performing a flood simulation according to the high-resolution high-frequency cycle flood information prediction method according to the present invention.

<Explanation of symbols for the main parts of the drawings>

100: lattice weather prediction module 200: precipitation prediction module

300: city watershed outflow model input data conversion module

400: urban watershed overflow forecasting module

500: flood risk assessment model input data conversion module

600: flood risk assessment module

700: 3D image display module

Claims (12)

A grid weather prediction module for predicting the weather of an area divided into grids of a constant size; A precipitation prediction module for quantitatively predicting precipitation in regions divided into grids of a constant size; An urban watershed overflow forecasting module for predicting a floodfall of an urban basin based on data generated by the grid meteorological prediction module and the precipitation prediction module; High-frequency-high frequency cycle flood information prediction system comprising a flood risk assessment module for evaluating the flood risk based on the urban watershed overflow amount generated by the urban watershed overflow amount prediction module. The method according to claim 1, The grid meteorological prediction module is a global 20km grid meteorological prediction module that simulates weather changes by dividing the entire globe into a grid having a size of 20 km, and a medium scale that simulates weather changes by dividing the entire globe into a grid having a size of 8 km. A high-frequency-frequency cycle flood information prediction system comprising an 8 km grid weather forecast module and a medium 2 km grid weather forecast module that simulates weather changes by dividing the entire globe into a grid having a size of 2 km. The method according to claim 1, And a city watershed outflow model input data converting module for converting the weather forecast data and the rainfall prediction data generated by the precipitation forecasting module into a watershed rainfall which is input data of the urban watershed overflow forecasting module. A high resolution high frequency cycle flood information prediction system. The method according to claim 1, High resolution-high frequency cycle flood information prediction system comprising a flood risk assessment model input data conversion module for converting the overflow flow generated by the flood risk assessment model input data conversion module into input data of the flood risk assessment module . The method according to claim 1, And a 3D image display module for displaying a flood risk map derived from the flood risk assessment module and a flood map displaying the flood risk on the map. Collecting raw weather information; Generating weather field prediction data based on the collected raw weather information; Generating rainfall prediction data based on the collected raw weather information; Generating a flood overflow forecasting data based on the meteorological field prediction data and the rainfall prediction data; High-frequency-high frequency cycle flood information prediction method comprising the step of creating a flood risk and flood map based on the overflow runoff forecast data. The method according to claim 6, Collecting the raw weather information, A method for predicting high resolution-high frequency cycle flood information, comprising collecting raw weather information including temperature, precipitation, evaporation, wind speed, humidity, vapor pressure, union, cloud cover, and barometric pressure. The method according to claim 6, Generating weather field prediction data based on the collected raw weather information, Generating 3D meteorological field prediction data including wind, temperature, position altitude, and humidity at 20 km grid / 3 hours using a global scale weather model; Generating 3D weather field prediction data including wind, temperature, position altitude, and humidity at 8 km grid / 3 hour interval using medium scale weather model; Generating 3D meteorological field prediction data including wind, temperature, position altitude, and humidity at 2 km grid / 5 minute interval using medium-scale meteorological model. . The method according to claim 6, Generating rainfall prediction data based on the collected raw weather information, And generating prediction data of 0.5 km grid / 5 minute interval rainfall using the quantitative rainfall prediction model based on the collected raw weather information. The method according to claim 9, Generating rainfall prediction data based on the collected raw weather information, Deriving rainwater ejection based on the collected raw weather information; Deriving a flood flow rate based on the collected raw weather information, characterized in that it comprises a high-frequency cycle flood information prediction method. The method according to claim 10, Deriving rainwater ejection amount based on the collected raw weather information, Dividing the subwatershed in which the surface runoff occurs into a first impermeable region having no ground retention, a second impermeable region having ground retention and a permeable region having ground retention; Assuming that the second impermeable region and the permeable region are nonlinear reservoirs, deriving a flow rate through the surface using a continuous equation and a manning equation; High-frequency-high frequency cycle flood information prediction method comprising the step of deriving the water depth and flow rate of the subwatershed using a Storm Water Management Model (SWMM). The method of claim 11, Deriving a flood outflow based on the collected raw weather information, High-frequency-high frequency cycle flood information prediction method comprising the step of deriving the flood flow of the subwatershed using a Storm Water Management Model (SWMM).

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