CN112132341A - Flood risk prediction method based on rainstorm process - Google Patents

Flood risk prediction method based on rainstorm process Download PDF

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CN112132341A
CN112132341A CN202011004876.5A CN202011004876A CN112132341A CN 112132341 A CN112132341 A CN 112132341A CN 202011004876 A CN202011004876 A CN 202011004876A CN 112132341 A CN112132341 A CN 112132341A
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flood
information
flood risk
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characteristic information
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CN112132341B (en
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权全
杨思敏
许美娇
梁伟佳
李平治
雷景春
高少泽
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Xian University of Technology
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/906—Clustering; Classification
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Abstract

The invention discloses a flood risk prediction method based on a rainstorm process, which relates to the technical field of flood risk prediction and comprises the following steps: acquiring meteorological data of a disaster-stricken flood occurrence period of a to-be-detected area in advance, and acquiring airflow track information of a corresponding period based on a HYSPLIT model; collecting GFS information and using the GFS information as data input information of an initial data field in a WRF mode, and carrying out scale reduction numerical simulation on each counted flood causing and rainstorm process of different types; evaluating flood risks and determining flood risk levels based on the acquired flood risk characteristic information, the flood exposure characteristic information and the social vulnerability characteristic information; and predicting the flood caused by the disaster based on the rainfall rainstorm center and time distribution of the area to be detected, the flood risk level and the occurrence rule of the flood process line. The flood risk evaluation method and the flood risk evaluation device realize flood risk evaluation from multiple angles, not only have high flood prediction precision, but also can carry out observation from the aspect of water vapor, and have high prediction precision and wide adaptability.

Description

Flood risk prediction method based on rainstorm process
Technical Field
The invention relates to the technical field of flood risk prediction, in particular to a flood risk prediction method based on a rainstorm process.
Background
Precipitation is an important link in the cycle process of a hydrological system, and the destructive power of flood is continuously enhanced along with the expansion of precipitation range, the increase of precipitation amount and the increase of precipitation intensity. Therefore, the method can cause serious harm to the economic development of the whole area, and how to accurately predict flood disasters in a shorter time is one of the key problems to be solved urgently in the field of water information.
At present, most of torrential flood disasters are mainly caused by rainstorms. Rainstorms that cause flood damage are referred to as "flood storms". High-quality and high-resolution rainfall forecast is one of the important prerequisites for mountain torrent disaster early warning. A rainstorm event can be simulated by atmospheric numerical models, and flooding is generally simulated by hydrodynamic models. The two dimensions are different, the former generally has larger dimension, and the latter has finer grid. The research on flood disasters is mainly based on a large-scale land-air coupling mode. The numerical mode is coupled with the hydrodynamic model, but the hydrodynamic model is not developed completely, and the forecasting precision of the refined forecasting information provided by the numerical mode needs to be improved. Coupling mechanism can be carried out around the atmospheric-land-hydrological process, the occurrence rule of precipitation and flood can be searched, and a powerful tool is provided for simulating and forecasting the torrential rain and flood.
The invention patent CN110020792A discloses a flood peak-flood volume combination-based high-rockfill dam construction flood risk prediction method. Step 1: respectively constructing a flood peak and a flood volume edge density function based on the historical flood data of the dam site; step 2: preferably, constructing flood peak-volume combined distribution by using a Copula function; and step 3: simulating a flood process and a discharge process according to a water power parameter distribution function of the discharge building and a reservoir capacity-flow relation curve, and obtaining a distribution sequence Hn of the monthly pre-dam highest water level through flood regulation calculation; and 4, step 4: according to the flood risk mathematical model of the high rock-fill dam, counting the times that the highest water level exceeds the elevation of the water retaining section before the monthly-pass dam, and obtaining the exceeding flood probability, namely the flood risk rate. By the flood peak-flood volume combination-based flood risk prediction method for high rock-fill dam construction, the problem that actual flood distribution cannot be accurately restored only by considering peak volume single variable or two independent same distribution variables in the conventional method can be solved, and the flood risk prediction precision is improved. But the forecasting precision of the refined forecasting information is lower, and certain limitation exists.
The patent CN104851360B of the invention of retrieval China discloses a method and a system for generating a flood risk graph, wherein the method comprises the following steps: generating a basic map layer of a flood risk monitoring area; dividing a computational grid on the basic map layer; flood risk information of each computational grid is respectively calculated; and rendering the corresponding calculation grids by respectively adopting the flood risk information to obtain a flood risk graph. The method and the device simulate and predict historical flood frequency and/or flood risk maps formed under future flood outbreaks, so that the real-time performance and accuracy of the flood risk map drawing are improved, the updating frequency and the updating speed of the flood risk maps are improved, the difficulty of generating the flood risk maps is reduced, and the sharing performance of the flood risk maps among different industries is improved. But the forecast precision of the refined forecast information is lower, and the prediction reference significance is not provided.
Therefore, a flood risk prediction method based on a rainstorm process is needed.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a torrential rain process-based flood risk prediction method, which comprises the steps of acquiring meteorological data of a disaster flood occurrence period of a region to be detected and acquiring airflow track information of a corresponding period based on a HYSPLIT model; collecting GFS information and using the GFS information as initial data field data input information in a WRF mode, carrying out downscaling numerical simulation on various flood causing and rainstorm processes of different types, estimating flood risks and determining flood risk levels based on the acquired flood risk characteristic information, flood exposure characteristic information and social vulnerability characteristic information, predicting flood causing floods based on the rainfall and rainstorm center and time distribution of a region to be detected, the flood risk levels and the occurrence rules of flood process lines, and realizing flood risk evaluation from multiple angles.
The technical scheme of the invention is realized as follows:
a flood risk prediction method based on a rainstorm process comprises the following steps:
step S1, acquiring meteorological data of a disaster-stricken flood occurrence period of a to-be-detected area in advance, and acquiring airflow track information of a corresponding period based on a HYSPLIT model;
step S2, collecting GFS information and using the GFS information as WRF mode initial data field input information, and carrying out scale reduction numerical simulation on each counted flood causing and rainstorm process of different types, wherein the scale reduction numerical simulation comprises flood danger characteristic information for calibrating regional flood risks, flood exposure characteristic information and social vulnerability characteristic information;
step S3, based on the acquired flood hazard characteristic information, flood exposure characteristic information and social vulnerability characteristic information, evaluating flood risks and determining flood risk levels;
and step S4, forecasting the flood based on the precipitation rainstorm center and time distribution of the area to be tested, the flood risk level and the occurrence rule of the flood process line.
Further, the step of obtaining the airflow track information of the corresponding time period by the HYSPLIT model includes the following steps:
acquiring strong precipitation time of input disaster-causing flood and longitude and latitude coordinates of a disaster center in a HYSPLIT mode;
determining a plurality of daily trajectories for a fixed period of pushback, represented as:
P'(t+Δt)＝P(t)+V(P,t)Δt
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P',t+Δt)]Δt
the position of the mass point at the next moment is determined by the product of the average speed of the last moment and the speed average of the point at which the first guess value is located and the time step;
wherein, the integration time step needs to satisfy the following conditions:
Umax(grid-unitsmin-1)Δt(min)<0.75(grid-units)，
track clustering is carried out on the daily track, similar cluster pairing is carried out, after each iteration, the total space variance and the TSV change percentage of the previous iteration are determined, and a TSV change graph is drawn;
determining the number of TSV change rate determination clusters and generating a final typing result;
and determining the precipitation source of the disaster-causing flood and the occupied weight of different paths.
Further, the step of performing downscaling numerical simulation on the counted flood inducing and rainstorm process of each different type includes the following steps:
determining the central longitude and latitude of the simulation area and the number of horizontal grid points of the triple area by adopting a triple nesting and vertical 30-layer WRF mode;
simulating the trend, the falling area, the intensity, the central position of heavy rainfall and the time period of heavy rainfall of the forecasted rainstorm rain zone.
Further, the step of calibrating the flood risk characteristic information of the regional flood risk includes the following steps:
dividing an area to be measured into 200 x 200m grids, and determining the flood depth of each grid;
classifying the flood depth of each grid by the determined specified depth range to obtain flood depth index L data;
determining 3 flooding maps as flood duration classification references based on the Mike flood model, and classifying the flood durations to obtain flood duration index T data;
flood risk for each grid, H, is expressed as:
H＝μL+(1-μ)T
wherein, L is flood depth index, T is flood duration index, mu is a weighting factor between 0 and 1, and mu is 0.5.
Further, the step of calibrating flood exposure characteristic information of the regional flood risk includes the following steps:
pre-characterizing human activity, population density and spatial pattern information;
flood exposure E is expressed as:
E＝λLC+(1-λ)NC
where LC is land use/coverage data, NC is night light conditions, λ is a weighting factor between 0 and 1, and λ takes 0.5.
Further, the step of calibrating the social vulnerability characteristic information includes the following steps:
social vulnerability V, expressed as:
V＝ωM+(1-ω)S
where M is the socioeconomic case, S is the employment and education case, ω is a weighting factor between 0 and 1, ω is 0.5.
Further, the step of assessing flood risk and determining a flood risk level, wherein the flood risk FRF is expressed as:
FRF＝H×E×V。
the invention has the beneficial effects that:
the invention discloses a flood risk prediction method based on a rainstorm process, which comprises the steps of acquiring meteorological data of a disaster-stricken flood occurrence period of a region to be detected, and acquiring airflow track information of a corresponding period based on a HYSPLIT model; collecting GFS information and using the GFS information as initial data field data input information in a WRF mode, carrying out downscaling numerical simulation on various flood causing and rainstorm processes of different types, estimating flood risks and determining flood risk levels based on the acquired flood risk characteristic information, flood exposure characteristic information and social vulnerability characteristic information, predicting flood causing floods based on the rainfall and rainstorm center and time distribution of a region to be detected, the flood risk levels and the occurrence rule of flood process lines, and realizing flood risk evaluation from multiple angles.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a flood risk prediction method based on a rainstorm process according to an embodiment of the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
According to an embodiment of the invention, a flood risk prediction method based on a rainstorm process is provided.
As shown in fig. 1, a flood risk prediction method based on a rainstorm process according to an embodiment of the present invention includes the following steps:
step S1, acquiring meteorological data of a disaster-stricken flood occurrence period of a to-be-detected area in advance, and acquiring airflow track information of a corresponding period based on a HYSPLIT model;
step S2, collecting GFS (global Forecast System) information and using the information as WRF mode initial data field input information, and carrying out scale reduction numerical simulation on the counted flood causing rainstorm processes of different types, wherein the scale reduction numerical simulation comprises flood danger characteristic information, flood exposure characteristic information and social vulnerability characteristic information for calibrating regional flood risks, and the scale reduction numerical simulation comprises the following steps:
acquiring Flood disaster data, inputting the cross section of a river channel, the water depth of a Flood area and the like into a Mike Flood model, and calculating Flood danger;
acquiring data of night lamp lighting conditions and land utilization/land coverage, and performing flood exposure calculation;
social economy, population, employment and education data are obtained, and social vulnerability is calculated;
step S3, based on the acquired flood hazard characteristic information, flood exposure characteristic information and social vulnerability characteristic information, evaluating flood risks and determining flood risk levels;
and step S4, forecasting the flood based on the precipitation rainstorm center and time distribution of the area to be tested, the flood risk level and the occurrence rule of the flood process line.
By means of the scheme, meteorological data of disaster-stricken flood occurrence periods in a research area are obtained in advance, and airflow tracks of corresponding periods are obtained by using a HYSPLIT model; acquiring GFS data for preprocessing, using the GFS data as initial data field data in a WRF mode, and carrying out scale reduction numerical simulation on each counted flood causing and rainstorm process of different types; the flood risk of a certain area is the result of considering the comprehensive action of flood hazard, flood exposure and vulnerability factors. Acquiring Flood disaster data, inputting the cross section of a river channel, the water depth of a Flood area and the like into a Mike Flood model, and calculating Flood danger; acquiring data of night lamp lighting conditions and land utilization/land coverage, and performing flood exposure calculation; social economy, population, employment and education data are obtained, and social vulnerability is calculated; multiplying flood danger, flood exposure and vulnerability, evaluating and calculating flood risk and classifying risk levels; and analyzing the rainstorm center and time distribution of rainfall, the flood risk level and the occurrence rule of the flood process line, and predicting the flood caused by the disaster. The flood prediction precision is high, and the application range is wide.
The airflow track of the HYSPLIT model disaster-causing rainfall comprises the following steps:
and converting the corresponding time of the disaster flood and the occurrence of strong rainfall into UTC time used by GDAS data. Inputting the strong precipitation time of disaster-causing flood and longitude and latitude coordinates of a disaster center in a HYSPLIT mode, setting the total running time of the model and importing downloaded meteorological data. And operating a HYSPLIT mode, and calculating a plurality of daily tracks of the back-push fixed time period.
P'(t+Δt)＝P(t)+V(P,t)Δt
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P',t+Δt)]Δt
The position of the particle at the next moment is obtained by the product of the average speed of the last moment and the speed average of the point where the first guess value is located and the time step.
Where the integration time step is varied (1 minute-1 hour), the following equation is satisfied:
Umax(grid-unitsmin-1)Δt(min)<0.75(grid-units),
and (3) carrying out track clustering on the daily track, carrying out pairing of similar clusters, calculating the Total Space Variance (TSV) and the TSV change percentage of the previous iteration after each iteration, and drawing a TSV change graph. The number of clusters should be determined by comprehensively considering the TSV change map and the TSV change rate. After selecting the proper cluster number, generating the final typing result, including two types of the average graph and the typing graph. The average graph shows a plurality of moisture traces and their fractions, while the profile shows the trace composition of each moisture path. The precipitation source of the current disaster-causing flood and the occupied weight of different paths can be reflected.
Wherein, the step of simulating the flood and storm inducing processes of different types comprises the following steps:
and determining the central longitude and latitude of the simulation area and the number of horizontal grid points of the triple area by adopting a triple nesting and vertical 30-layer WRF mode. Simulating the trend, the falling area, the intensity, the central position of heavy rainfall and the time period of heavy rainfall.
Wherein, the flood risk calculation comprises the following steps:
flood hazards reflect meteorological phenomena, topography and hydrologic features that cause flood disasters.
Dividing the downstream of the Weighe into 200 × 200m grids, determining the flood depth of each grid, and classifying the flood depth of each grid through a specified depth range determined by questionnaire survey. Obtaining flood depth index L data;
and then driving the Mike model by using forecast data such as rainfall in the WRF mode, realizing unidirectional coupling between the WRF mode and the Mike model, and constructing a rainstorm flood forecasting system in a research area. And 3 flooding maps calculated according to the Mike flood model are used as flood duration classification references to classify the flood durations. Obtaining flood duration index T data;
the flood risk H of each grid is calculated by the following formula:
H＝μL+(1-μ)T
wherein, L is flood depth index, T is flood duration index; μ is a weighting factor between 0 and 1, and μ is taken to be 0.5.
The flood exposure calculation comprises the following steps:
flood exposure reflects the economic value and damage level of exposure to the disaster; the land utilization/coverage data can represent the land surface coverage situation, and the more the land utilization/coverage situation is, the greater the economic loss is when a disaster occurs; night light satellite imagery may characterize human activities, population density, and spatial patterns.
The flood exposure E is determined by the following formula:
E＝λLC+(1-λ)NC
where LC is land use/coverage data and NC is night light conditions. λ is a weighting factor between 0 and 1, λ being 0.5.
Wherein the step of calculating the social vulnerability comprises the steps of:
social vulnerability reflects the ability of people to cope and resist disaster events and post-disaster reconstruction. The social economic level represents the developed economic degree of the area, and when a disaster is encountered, the higher the economic level is, the stronger the resistance is, the more convenient the rescue is, and the disaster can be timely rebuilt. The high education level people can acquire disaster-resistant information through various channels such as networks and news, judge disaster risks in advance, avoid disasters and escape.
The social vulnerability V is determined by the following formula:
V＝ωM+(1-ω)S
where M is the socioeconomic case and S is the employment and education case. ω is a weighting factor between 0 and 1, ω being 0.5.
Wherein, step said flood risk and divide the risk classification, including the following steps:
the flood risk FRF is determined by the following formula:
flood risk ═ f (flood hazard, flood exposure, social vulnerability)
FRF＝H×E×V
The method is characterized in that weather, society and economy are considered in a multi-angle mode, the evaluation results of flood danger, flood exposure and social vulnerability are subjected to standardized processing, the FRF value is calculated according to a percentile threshold value method, and rainstorm flood disaster risks are divided into five types: 0-20 is low flood risk, 20-40 is low flood risk, 40-60 is medium flood risk, 60-80 is high flood risk, 80-100 is high flood risk.
Analyzing precipitation and flood occurrence rules, wherein the method comprises the following steps:
for the same underlying surface, the types of floods caused by different rain types and precipitation densities are different. Simulating and forecasting the central position of strong rainfall and the time period of the strong rainfall by using a WRF mode, and analyzing the change conditions of the water level and the flow of the flood under the condition; summarizing the precipitation characteristics of the extra-large flood disaster from the time distribution, the falling area and the classification of the precipitation, and establishing a rule between the two characteristics; and carrying out comparative analysis on the water level, the flow rate and the process line of the flood and the historical flood process.
In summary, by means of the technical scheme of the invention, meteorological data of disaster-stricken flood occurrence periods in a research area are obtained in advance, and an airflow track of a corresponding period is obtained by using a HYSPLIT model; acquiring GFS data for preprocessing, using the GFS data as initial data field data in a WRF mode, and carrying out scale reduction numerical simulation on each counted flood causing and rainstorm process of different types; the flood risk is the result of considering the comprehensive action of flood hazard, flood exposure and vulnerability factors. Acquiring Flood disaster data, inputting the cross section of a river channel, the water depth of a Flood area and the like into a Mike Flood model, and calculating Flood danger; acquiring data of night lamp lighting conditions and land utilization/land coverage, and performing flood exposure calculation; social economy, population, employment and education data are obtained, and social vulnerability is calculated; multiplying flood danger, flood exposure and vulnerability, evaluating and calculating flood risk and classifying risk levels; and analyzing the rainstorm center and time distribution of rainfall, the flood risk level and the occurrence rule of the flood process line, and predicting the flood caused by the disaster. The flood prediction precision is high, and the application range is wide.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A flood risk prediction method based on a rainstorm process is characterized by comprising the following steps:
acquiring meteorological data of a disaster-stricken flood occurrence period of a to-be-detected area in advance, and acquiring airflow track information of a corresponding period based on a HYSPLIT model;
collecting GFS information and using the GFS information as WRF mode initial data field input information, and carrying out scale reduction numerical simulation on the counted flood causing and rainstorm processes of different types, wherein the scale reduction numerical simulation comprises flood danger characteristic information for calibrating regional flood risks, flood exposure characteristic information and social vulnerability characteristic information;
evaluating flood risks and determining flood risk levels based on the acquired flood risk characteristic information, the flood exposure characteristic information and the social vulnerability characteristic information;
and predicting the flood caused by the disaster based on the rainfall rainstorm center and time distribution of the area to be detected, the flood risk level and the occurrence rule of the flood process line.
2. The torrential rain process-based flood risk prediction method according to claim 1, wherein the HYSPLIT model obtains airflow trajectory information for a corresponding time period, comprising the steps of:
acquiring strong precipitation time of input disaster-causing flood and longitude and latitude coordinates of a disaster center in a HYSPLIT mode;
determining a plurality of daily trajectories for a fixed period of pushback, represented as:
P'(t+Δt)＝P(t)+V(P,t)Δt
P(t+Δt)＝P(t)+0.5[V(P,t)+V(P',t+Δt)]Δt
the position of the mass point at the next moment is determined by the product of the average speed of the last moment and the speed average of the point at which the first guess value is located and the time step;
wherein, the integration time step needs to satisfy the following conditions:
Umax(grid-unitsmin-1)Δt(min)<0.75(grid-units)，
track clustering is carried out on the daily track, similar cluster pairing is carried out, after each iteration, the total space variance and the TSV change percentage of the previous iteration are determined, and a TSV change graph is drawn;
determining the number of TSV change rate determination clusters and generating a final typing result;
and determining the precipitation source of the disaster-causing flood and the occupied weight of different paths.
3. The torrential rain process-based flood risk prediction method of claim 1, wherein said down-scaling numerical simulation of each of said statistically different types of flood inducing torrential rain processes comprises the steps of:
determining the central longitude and latitude of the simulation area and the number of horizontal grid points of the triple area by adopting a triple nesting and vertical 30-layer WRF mode;
simulating the trend, the falling area, the intensity, the central position of heavy rainfall and the time period of heavy rainfall of the forecasted rainstorm rain zone.
4. The torrential rain process-based flood risk prediction method according to claim 1, wherein said step of calibrating flood risk characteristic information of regional flood risk comprises the steps of:
dividing an area to be measured into 200 x 200m grids, and determining the flood depth of each grid;
classifying the flood depth of each grid by the determined specified depth range to obtain flood depth index L data;
determining 3 flooding maps as flood duration classification references based on the Mike flood model, and classifying the flood durations to obtain flood duration index T data;
flood risk for each grid, H, is expressed as:
H＝μL+(1-μ)T
wherein, L is flood depth index, T is flood duration index, mu is a weighting factor between 0 and 1, and mu is 0.5.
5. The torrential rain process-based flood risk prediction method of claim 4, wherein said step of calibrating flood exposure characteristic information of regional flood risks comprises the steps of:
pre-characterizing human activity, population density and spatial pattern information;
flood exposure E is expressed as:
E＝λLC+(1-λ)NC
where LC is land use/coverage data, NC is night light conditions, λ is a weighting factor between 0 and 1, and λ takes 0.5.
6. The torrential rain process-based flood risk prediction method of claim 5, wherein said step of calibrating social vulnerability profile information comprises the steps of:
social vulnerability V, expressed as:
V＝ωM+(1-ω)S
where M is the socioeconomic case, S is the employment and education case, ω is a weighting factor between 0 and 1, ω is 0.5.
7. The torrential rain process based flood risk prediction method of claim 6, wherein said step of assessing flood risk and determining flood risk level, whose flood risk FRF is expressed as:
FRF＝H×E×V。
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Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN113468482A (en) * 2021-07-06 2021-10-01 中国人民解放军31440部队 Rainstorm weather simulation forecasting method based on WRF mode
CN113723824A (en) * 2021-09-01 2021-11-30 廊坊市气象局 Rainstorm disaster risk assessment method

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