CN113821939A - Urban rainstorm intensity calculation method, system, equipment and storage medium based on space-time distribution characteristics - Google Patents
Urban rainstorm intensity calculation method, system, equipment and storage medium based on space-time distribution characteristics Download PDFInfo
- Publication number
- CN113821939A CN113821939A CN202111381892.0A CN202111381892A CN113821939A CN 113821939 A CN113821939 A CN 113821939A CN 202111381892 A CN202111381892 A CN 202111381892A CN 113821939 A CN113821939 A CN 113821939A
- Authority
- CN
- China
- Prior art keywords
- rainfall
- simulation
- historical
- grid unit
- rainstorm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Abstract
The invention discloses a city rainstorm intensity calculating method based on space-time distribution characteristics, a system, equipment and a storage medium, wherein the method can simulate the gridded rainfall intensity space-time distribution of any rainfall event process and city scale by constructing a regional meteorological model, then utilizes the regional meteorological model to execute rainfall simulation to obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit, finally respectively calculates the historical rainstorm intensity and/or future rainstorm intensity, carries out rainstorm intensity calculation by the simulation data based on the regional meteorological model, not only can be well suitable for areas with insufficient rainfall record to support rainstorm intensity analysis, but also can be applied to predicting the change process of designing the rainstorm intensity of cities in different periods in the future under the climate change background, and is used for designing, distributing and storing urban wading systems, Urban flood control and drainage, sponge urban construction and the like provide tools for fine design and evaluation.
Description
Technical Field
The invention relates to the technical field of urban drainage waterlogging prevention, in particular to a method, a system and equipment for calculating urban rainstorm intensity based on space-time distribution characteristics and a computer-readable storage medium.
Background
The design rainstorm intensity is an important basis and premise for planning, designing, evaluating and other works of a wading system in urban construction in China, whether an urban rainstorm intensity formula is scientific and reasonable and whether urban rainfall characteristics and rules are objectively reflected directly influences the urban drainage waterlogging prevention infrastructure construction, the sponge urban construction planning and the engineering project design construction, and further influences the whole drainage engineering construction investment scale, and the efficiency performance and the maintenance cost of the system. The precision of the rainstorm intensity formula in China is greatly improved after decades of practical development in the compilation of the rainstorm intensity formula, and the compilation system of the rainstorm intensity formula is still perfected along with the proposal and application of new theories, new technologies and new methods.
At present, the urban rainstorm intensity formula compilation in China is developed on the basis of long-term rainfall records of urban rainfall monitoring sites, and in order to meet the precision requirement, the rainfall records are usually required to be continuous for more than 20 years. In addition, urban rainfall is affected by global atmospheric circulation, sea and land factors, climate change and other large-scale environments, and is also affected by the combined action of various regional factors such as terrain and landform, "heat island" effect, "rain island" effect and the like caused by urbanization, and the spatial and temporal distribution of rainfall is often different on the urban scale. However, the weather monitoring stations in most cities in China are few in number and short in construction time, and the rainfall data records have the problems of long time interval, uneven quality, data loss and the like, so that the applicability of the traditional rainstorm intensity formula editing method is greatly influenced. At present, most cities only compile city rainstorm intensity formulas based on a single rainfall monitoring site, the difference of rainfall spatial distribution on the scale of the city is not considered in the work of compiling the city rainstorm intensity formulas, only Beijing, mansion and the like carry out rainstorm partition according to the characteristics of the city rainstorm, but the rainstorm is still based on rainfall records of a plurality of meteorological sites in the city, the number and the distribution of the rainfall monitoring sites, the influence of factors such as the completeness, the reasonability and the representativeness of rainfall data historical records on the result is large; meanwhile, in the division of the rainstorm subareas, the influence of factors such as terrain, underlying surfaces and the like is not considered, and the local area distortion is easily caused. In addition, the urban drainage and waterlogging prevention system and the sponge urban system which are designed based on the rainstorm intensity formula usually need long-term service after being built, so that the urban drainage and waterlogging prevention system and the sponge urban system are planned and designed to include future urban rainfall characteristics, however, the traditional rainstorm intensity formula is compiled based on historical rainfall records, only can reflect the current urban rainfall characteristics, and lacks prediction on urban future rainfall characteristic changes under the climate change background.
Disclosure of Invention
The invention provides a method, a system and equipment for calculating urban rainstorm intensity based on space-time distribution characteristics and a computer readable storage medium, which are used for solving the defects in the prior art.
According to one aspect of the invention, a city rainstorm intensity calculation method based on space-time distribution characteristics is provided, and comprises the following steps:
constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
configuring a regional meteorological model by using the obtained optimal model configuration scheme, and executing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit, and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit.
Further, the process of constructing the regional meteorological model, setting the simulation region and the simulation grid unit size of the target city, and obtaining the optimal model configuration scheme suitable for the rainfall event simulation of the target city specifically includes the following contents:
simulating urban rainfall dynamic evolution and gridding rainfall space-time distribution by adopting a regional meteorological model WRF, and setting the sizes of a simulation region and a simulation grid unit according to actual needs;
acquiring historical rainstorm records of a target city, and screening out multiple rainstorms and heavy rainfall events of rainfall levels above;
designing different model configuration schemes to simulate the evolution process of each field intensity rainfall event one by one, and obtaining simulated rainfall data of the field intensity rainfall events one by one;
comparing the historical rainfall data of each field rainfall event with the corresponding simulated rainfall data, statistically analyzing the correlation and the simulation deviation between the historical rainfall data and the corresponding simulated rainfall data, comprehensively evaluating the simulation effect of different model configuration schemes according to the analysis result, and screening out the model configuration scheme with the optimal simulation effect.
Further, the process of performing historical rainfall simulation by using the configured regional meteorological model to obtain the historical rainfall time series data of each simulation grid unit specifically includes the following steps:
collecting the occurrence date of the annual maximum daily rainfall of the target city for continuous years, and sequencing according to a preset sequence;
adopting a configured regional meteorological model to carry out gridding simulation of annual maximum daily rainfall events one by one according to a sequence, recording the time interval of a simulation result to be 1 hour, extracting historical simulated rainfall time sequence data of each simulated grid unit, repeating the above contents, and completing gridding simulation of annual maximum daily rainfall;
and counting accumulated rainfall of different rainfall durations based on historical simulated rainfall time sequence data of each simulated grid unit aiming at the maximum daily rainfall event of each year, and calculating to obtain the historical maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct the historical maximum rainfall spatial matrix of each rainfall duration.
Further, the process of performing future rainfall simulation by using the configured regional meteorological model to obtain future rainfall time series data of each simulation grid unit specifically includes the following contents:
acquiring climate prediction result data of a global climate model;
inputting climate prediction result data of the global climate model as boundary conditions and initial conditions of the regional climate model, and performing continuous future rainfall simulation;
and extracting future simulated rainfall time sequence data of each simulated grid unit in the target period, counting accumulated rainfall of different rainfall durations, and calculating to obtain future maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct a future maximum rainfall spatial matrix of each rainfall duration.
Further, the calculating the historical rainstorm intensity of gridding the target city based on the historical rainfall time series data of each simulation grid unit specifically includes the following steps:
based on the historical annual maximum rainfall spatial matrix of each rainfall duration, adopting a generalized extreme value distribution function to perform distribution fitting on rainfall extreme values corresponding to each rainfall duration of the simulation grid unit;
fitting to obtain a position parameter space matrix, a scale parameter space matrix and a shape parameter space matrix of the generalized extreme value distribution function corresponding to each rainfall duration;
calculating the design rainstorm intensity corresponding to each recurrence period and each rainfall duration of each simulation grid unit by adopting a rainstorm intensity formula based on generalized extreme value distribution function calculation so as to construct a design rainstorm intensity spatial matrix corresponding to each recurrence period and each rainfall duration;
and importing the designed rainstorm intensity space matrix corresponding to each recurrence period and each rainfall duration into an ArcGIS system, and extracting the rainstorm intensity space data of the target city domain according to the shp file of the target city domain.
Furthermore, the simulation area is set by adopting a single-layer simulation grid unit or a multi-layer nested simulation grid unit, and the horizontal resolution of the target layer simulation grid unit is set to be 1 km-4 km.
Further, the following contents are also included:
and visually displaying the calculation results of the historical rainstorm intensity and/or the future rainstorm intensity.
In addition, the invention also provides a city rainstorm intensity calculation system based on the space-time distribution characteristics, which adopts the method as described above and comprises the following steps:
the model construction module is used for constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
the gridding rainfall simulation module is used for configuring a regional meteorological model by using the obtained optimal model configuration scheme, and performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
and the rainstorm intensity calculating module is used for calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit.
In addition, the present invention also provides an apparatus comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used for executing the steps of the method by calling the computer program stored in the memory.
In addition, the present invention also provides a computer-readable storage medium for storing a computer program for calculating the intensity of urban rainstorm based on spatiotemporal distribution characteristics, the computer program, when executed on a computer, performing the steps of the method as described above.
The invention has the following effects:
according to the urban rainstorm intensity calculation method based on the space-time distribution characteristics, the regional meteorological model is constructed, not only can any rainfall event process and grid rainfall intensity space-time distribution of urban scales be simulated, but also the simulation area and the simulation grid unit size of a target city can be designed according to actual requirements, meanwhile, the regional meteorological model is coupled with the combined action of various environmental factors such as urban terrain, underlying surfaces and vegetation, the historical rainfall process of the city can be well simulated, the method is particularly suitable for calculating the rainstorm intensity of cities in complicated terrain areas such as hills and plateaus, and the influence of various factors such as terrain, underlying surface distribution and urban heat island effect cannot be considered when a pluviometer is adopted for urban scale space interpolation in the prior art. And moreover, an optimal model configuration scheme suitable for simulating the rainfall event of the target city is obtained, and the accuracy of the rainfall simulation result is ensured. Then, performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit, finally respectively calculating historical rainstorm intensity and/or future rainstorm intensity based on the historical rainfall time sequence data and/or the future rainfall time sequence data obtained by simulation, and calculating rainstorm intensity based on the simulation data of the regional meteorological model, so that the method can be well suitable for regions with few urban meteorological monitoring station data, short station building time, interrupted/missing rainfall records and insufficient rainfall record to support analysis of the rainstorm intensity, can be applied to predicting the change process of designing the rainstorm intensity of cities in different periods in the future under the climate change background, and can be used for designing urban wading systems, preventing and draining floods, and draining floods in different periods, Sponge city construction and the like provide tools for fine design and evaluation.
In addition, the urban rainstorm intensity calculation system, the urban rainstorm intensity calculation device and the urban rainstorm intensity calculation computer-readable storage medium based on the space-time distribution characteristics have the advantages.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a city rainstorm intensity calculation method based on space-time distribution characteristics according to a preferred embodiment of the invention.
Fig. 2 is a schematic view of a sub-flow of step S1 in fig. 1.
Fig. 3 is a sub-flowchart of step S2 in fig. 1.
Fig. 4 is another sub-flowchart of step S2 in fig. 1.
Fig. 5 is a sub-flowchart of step S3 in fig. 1.
FIG. 6 is a flow chart of a city rainstorm intensity calculation method based on space-time distribution characteristics according to another embodiment of the present invention.
Fig. 7 is a schematic diagram of WRF model configuration using the urban area of the long sandy city of the south of the Hunan province as a model simulation center by using the method for calculating the intensity of urban rainstorm based on the space-time distribution characteristics according to the preferred embodiment of the present invention.
Fig. 8 is a schematic diagram of the distribution of the rainstorm intensity in minutes of 50 years for 24-hour rainfall duration in the urban area of the Changsha city in 2019, which is calculated by the method for calculating the urban rainstorm intensity based on the space-time distribution characteristics according to the preferred embodiment of the present invention.
Fig. 9 is a schematic diagram of the spatial distribution difference of the rainstorm intensity of 50 years and one minute in each city area of the Changsha city in 2019, which is calculated by the urban rainstorm intensity calculation method based on the space-time distribution characteristics according to the preferred embodiment of the present invention, and corresponds to the 24-hour rainfall duration.
Fig. 10 is a schematic diagram of the spatial distribution of the rainstorm intensity designed by meshing the 24-hour rainfall duration in each recurrence period in the urban area of the long-sand city of 2040 years, which is calculated by the method for calculating the urban rainstorm intensity based on the space-time distribution characteristics according to the preferred embodiment of the present invention.
Fig. 11 is a schematic diagram of the spatial distribution of the rainstorm intensity designed by using the urban area of the long sand city in 2060 years to grid the duration of rainfall of 24 hours in each recurrence period, which is calculated by the method for calculating the urban rainstorm intensity based on the space-time distribution characteristics according to the preferred embodiment of the invention.
FIG. 12 is a block diagram of a city rainstorm intensity calculation system based on space-time distribution characteristics according to another embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the accompanying drawings, but the invention can be embodied in many different forms, which are defined and covered by the following description.
As shown in fig. 1, a preferred embodiment of the present invention provides a method for calculating urban rainstorm intensity based on spatiotemporal distribution characteristics, which comprises the following steps:
step S1: constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
step S2: configuring a regional meteorological model by using the obtained optimal model configuration scheme, and executing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
step S3: calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit, and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit.
It can be understood that, the urban rainstorm intensity calculation method based on the space-time distribution characteristics of the embodiment can simulate not only any rainfall event process and the gridding rainfall intensity space-time distribution of the urban scale by constructing the regional meteorological model, but also the simulation area and the simulation grid unit size of the target city can be designed according to the actual requirements, and meanwhile, the regional meteorological model couples the combined action of various environmental factors such as urban terrain, underlying surface and vegetation, and can well simulate the historical rainfall process of the city. And moreover, an optimal model configuration scheme suitable for simulating the rainfall event of the target city is obtained, and the accuracy of the rainfall simulation result is ensured. Then, performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit, finally respectively calculating historical rainstorm intensity and/or future rainstorm intensity based on the historical rainfall time sequence data and/or the future rainfall time sequence data obtained by simulation, and calculating rainstorm intensity based on the simulation data of the regional meteorological model, so that the method can be well suitable for regions with few urban meteorological monitoring station data, short station building time, interrupted/missing rainfall records and insufficient rainfall record to support analysis of the rainstorm intensity, can be applied to predicting the change process of designing the rainstorm intensity of cities in different periods in the future under the climate change background, and can be used for designing urban wading systems, preventing and draining floods, and draining floods in different periods, Sponge city construction and the like provide tools for fine design and evaluation.
It can be understood that, as shown in fig. 2, the step S1 specifically includes the following steps:
step S11: simulating urban rainfall dynamic evolution and gridding rainfall space-time distribution by adopting a regional meteorological model WRF, and setting the sizes of a simulation region and a simulation grid unit according to actual needs;
step S12: acquiring historical rainstorm records of a target city, and screening out multiple rainstorms and heavy rainfall events of rainfall levels above;
step S13: designing different model configuration schemes to simulate the evolution process of each field intensity rainfall event one by one, and obtaining simulated rainfall data of the field intensity rainfall events one by one;
step S14: comparing the historical rainfall data of each field rainfall event with the corresponding simulated rainfall data, statistically analyzing the correlation and the simulation deviation between the historical rainfall data and the corresponding simulated rainfall data, comprehensively evaluating the simulation effect of different model configuration schemes according to the analysis result, and screening out the model configuration scheme with the optimal simulation effect.
Specifically, a regional meteorological model WRF (weather Research and forecasting) is used as a latest generation of regional scale meteorological model, Euler coordinates and regional range orthogonal grid division are adopted, atmospheric scientific Research and numerical prediction with horizontal resolution of 1 km-10 km can be realized, and the method is widely applied to important weather characteristic simulation and prediction of different scales from cloud scale to weather scale. Optionally, simulatingThe area is set by adopting a single-layer simulation grid unit or a multi-layer nested simulation grid unit, the horizontal resolution of a target layer simulation grid unit is set to be 1 km-4 km, wherein the target layer simulation grid unit refers to the single-layer simulation grid unit or the simulation grid unit at the bottommost layer in the multi-layer nested simulation grid unit, and the simulation grid units in the subsequent description refer to the target layer simulation grid unit. Then, screening out multiple rainstorms and heavy rainfall events with rainfall levels above according to the historical rainstorm records of the target city, and acquiring corresponding historical rainfall records. And then referring to a WRF model user manual and a recommended combination scheme of a regional rainstorm event simulation model physical mechanism parameterization scheme reported in documents, establishing a series of model physical mechanism parameterization schemes for regional rainstorm event simulation, wherein the model physical mechanism parameterization schemes comprise a microclimate scheme, a cloud accumulation convection scheme, a long-wave radiation scheme, a short-wave radiation scheme, a boundary layer scheme and a land process scheme, constructing a scheme library, selecting different combination configuration schemes, and simulating the screened multiple field strong rainfall events one by one to obtain simulated rainfall data corresponding to each field strong rainfall event. Finally, the historical rainfall data of each rainfall event is compared and analyzed with the corresponding rainfall simulation data, for example, the correlation (R) between the historical rainfall data and the rainfall simulation data is analyzed2) And Root Mean Square Error (RMSE), which means that the simulation effect is optimal when the correlation is highest and the RMSE is smallest, wherein the correlation (R)2) And the Root Mean Square Error (RMSE) are well known in the art and will not be described further herein. And comprehensively evaluating the simulation effect of the regional meteorological model under each combined configuration scheme based on the comparative analysis result of the historical rainfall data and the simulated rainfall data, screening out the combined configuration scheme with the best simulation effect, and taking the combined configuration scheme as the optimal model configuration scheme.
It can be understood that, as shown in fig. 3, the process of performing the historical rainfall simulation by using the configured regional meteorological model in step S2 to obtain the historical rainfall time series data of each simulation grid unit specifically includes the following steps:
step S21 a: collecting the occurrence date of the annual maximum daily rainfall of the target city for continuous years, and sequencing according to a preset sequence;
step S22 a: adopting a configured regional meteorological model to carry out gridding simulation of annual maximum daily rainfall events one by one according to a sequence, recording the time interval of a simulation result to be 1 hour, extracting historical simulated rainfall time sequence data of each simulated grid unit, repeating the above contents, and completing gridding simulation of annual maximum daily rainfall;
step S23 a: and counting accumulated rainfall of different rainfall durations based on historical simulated rainfall time sequence data of each simulated grid unit aiming at the maximum daily rainfall event of each year, and calculating to obtain the historical maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct the historical maximum rainfall spatial matrix of each rainfall duration.
Specifically, rainfall time sequence recording data of the target city is collected, preferably, the rainfall time sequence recording data of the target city in the long-term national meteorological site hour is selected, if the rainfall time sequence recording data does not exist, the same-precision recording data of adjacent cities are adopted, the data are sorted, after the verification is correct, the occurrence date of the annual maximum daily rainfall is screened out, and the data are sorted according to the time sequence and numbered. Then, selecting the annual maximum daily rainfall event with the number of 1, carrying out gridding simulation by using a configured regional meteorological model, recording the time interval of the simulation result to be 1 hour, and then extracting historical simulated rainfall time sequence data of each simulated grid unit; and selecting the annual maximum daily rainfall event with the number 2, repeating the above contents continuously until the gridding simulation of all annual maximum daily rainfall is completed, and extracting the historical simulated rainfall time sequence data of each corresponding simulation grid unit respectively. Then, for each annual maximum daily rainfall event, the accumulated rainfall for different rainfall durations can be obtained through statistics based on historical simulated rainfall time sequence data of each simulated grid unit, the historical annual maximum rainfall for each simulated grid unit corresponding to different rainfall durations is obtained through calculation, for example, the annual maximum rainfall event occurrence date of 2019 is 5, 13 days, the rainfall for 3 hours is accumulated by 3 hours when the maximum rainfall corresponds to the day 00-24, and the value with the maximum accumulated value is screened. Thereby, constructHistorical annual maximum rainfall spatial matrix for each rainfall duration
Wherein, in the step (A),
represents the historical maximum rainfall at grid coordinates (m, n) for a period of rainfall t, m and n being the maximum values of the grid coordinates, t representing the period of rainfall.
Optionally, in step S21a, the maximum rainfall on different rainfall duration of each annual maximum daily rainfall day is also obtained and filled in table 1.
TABLE 1 annual maximum daily rainfall event record for a city weather site
For each annual maximum daily rainfall event, after obtaining the historical maximum rainfall of each simulation grid unit corresponding to different rainfall histories in the step S23a, comparing and analyzing the actually recorded maximum rainfall of different rainfall durations with the corresponding historical maximum rainfall obtained through simulation, and respectively calculating a simulated rainfall correction coefficient corresponding to the annual maximum daily rainfall event, specifically, obtaining the correction coefficient by adopting a linear fitting manner, or directly adopting the correlation coefficient obtained through calculation in the step S1 as the correction coefficient. And correcting the maximum rainfall in the historical annual maximum rainfall spatial matrix of each rainfall duration obtained in the step S23a by adopting a simulated rainfall correction coefficient corresponding to the annual maximum rainfall event, so that the accuracy of the model simulation result is further ensured.
It can be understood that, as shown in fig. 4, the process of performing future rainfall simulation by using the configured regional meteorological model in step S2 to obtain future rainfall time series data of each simulation grid unit specifically includes the following steps:
step S21 b: acquiring climate prediction result data of a global climate model;
step S22 b: inputting climate prediction result data of the global climate model as boundary conditions and initial conditions of the regional climate model, and performing continuous future rainfall simulation;
step S23 b: and extracting future simulated rainfall time sequence data of each simulated grid unit in the target period, counting accumulated rainfall of different rainfall durations, and calculating to obtain future maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct a future maximum rainfall spatial matrix of each rainfall duration.
Specifically, the latest Climate change prediction result data of the global Climate model issued by IPCC (international weather on weather change) is acquired, and then the acquired Climate change prediction result data is used as the boundary condition and initial condition input of the regional Climate model WRF to perform continuous future rainfall simulation. Then, extracting future simulated rainfall time sequence data of each simulated grid unit in a target period according to actual needs, counting accumulated rainfall of different rainfall durations with the same historical simulated rainfall time sequence data, calculating to obtain future maximum rainfall of each simulated grid unit corresponding to different rainfall durations, and constructing a future maximum rainfall spatial matrix of each rainfall duration
Wherein, in the step (A),
represents the future maximum rainfall for grid (m, n) at a rainfall duration of t, with m and n being the maximum values of the grid coordinates, and t representing the rainfall duration.
It can be understood that, as shown in fig. 5, the step S3 of calculating the historical rainstorm intensity of the grid of the target city based on the historical rainfall time series data of each simulation grid cell specifically includes the following steps:
step S31: based on the historical annual maximum rainfall spatial matrix of each rainfall duration, adopting a generalized extreme value distribution function to perform distribution fitting on rainfall extreme values corresponding to each rainfall duration of the simulation grid unit;
step S32: fitting to obtain a position parameter space matrix, a scale parameter space matrix and a shape parameter space matrix of the generalized extreme value distribution function corresponding to each rainfall duration;
step S33: calculating the design rainstorm intensity corresponding to each recurrence period and each rainfall duration of each simulation grid unit by adopting a rainstorm intensity formula based on generalized extreme value distribution function calculation so as to construct a design rainstorm intensity spatial matrix corresponding to each recurrence period and each rainfall duration;
step S34: and importing the designed rainstorm intensity space matrix corresponding to each recurrence period and each rainfall duration into an ArcGIS system, and extracting the rainstorm intensity space data of the target city domain according to the shp file of the target city domain.
Specifically, for the historical annual maximum rainfall spatial matrix of each rainfall duration, a generalized extremum distribution function (GEV) is firstly adopted to perform distribution fitting on rainfall extrema corresponding to each rainfall duration of the simulation grid unit. For example, for the same rainfall duration, a historical annual maximum rainfall spatial matrix corresponding to N annual maximum rainfall events may be obtained, N matrices are total, a GEV function is adopted to perform distribution fitting on rainfall extrema of each simulation grid unit, and a specific probability density distribution function is expressed as:
wherein the content of the first and second substances,
representing a simulation grid cell (x，y) Probability density distribution function of rainfall corresponding to the rainfall when the rainfall duration is t,
respectively represent the simulation grid cells (x，y) And when the rainfall duration is t, the position parameter, the scale parameter and the shape parameter of the corresponding GEV function.
Then, a position parameter space matrix, a scale parameter space matrix and a shape parameter space matrix of the GEV function corresponding to each rainfall duration are obtained by utilizing GEV function fitting, and the position parameter space matrix, the scale parameter space matrix and the shape parameter space matrix are respectively as follows:
and calculating the designed rainstorm intensity corresponding to each reappearance period of each simulation grid unit of the target city based on a rainstorm intensity formula obtained by the GEV function calculation, wherein the specific calculation formula is as follows:
wherein the content of the first and second substances,Tin order to achieve the end of the recovery period,
(ii) a simulation grid cell obtained by fitting a GEV functionx，y) In the process of recurrenceTDuration of rainfalltDesigned for storm intensity. Constructing a design rainstorm intensity space matrix corresponding to each recurrence period and each rainfall duration, specifically comprising the following steps:
。
and finally, importing the designed rainstorm intensity space matrix corresponding to each recurrence period and each rainfall duration into an ArcGIS system, and extracting the rainstorm intensity space data of the target city domain by using an extract by mask command according to the shp file of the target city domain.
It can be understood that the specific process of calculating the future rainstorm intensity of the gridding of the target city based on the future rainfall time series data of each simulation grid unit in step S3 is the same as the above process of calculating the historical rainstorm intensity, and the difference is only that the historical annual maximum rainfall amount spatial matrix of each rainfall duration is replaced by the future annual maximum rainfall amount spatial matrix of each rainfall duration, so the specific process is not repeated herein, and the above contents are referred to.
Optionally, as shown in fig. 6, in another embodiment of the present invention, the method for calculating urban rainstorm intensity based on spatio-temporal distribution characteristics further includes the following steps:
step S4: and visually displaying the calculation results of the historical rainstorm intensity and/or the future rainstorm intensity.
Through carrying out visual show with historical rainstorm intensity calculated result and/or future rainstorm intensity calculated result, be convenient for the user to observe in real time and know city grid rainstorm intensity.
It is understood that, as an example, taking the long sand city in Hunan province as an example, the urban rainstorm intensity calculation method of the invention is adopted to calculate the design rainstorm intensity spatial distribution of the 24-hour rainfall duration in 50 years in the urban grid of the long sand city in 2019. As shown in fig. 7, with the urban area of the long shakecity as a model simulation center, configuring a model frame in a WRF model, setting a model mode to be "ARW", nesting layers to be 3 layers, D01, D02 and D03 respectively, and a planar mesh resolution ratio to be 1:3:3, wherein the mesh numbers of D01, D02 and D03 are 6708, 4752 and 3240 respectively, the geographic data resolution is set to be gtopo _5m + modis _30s _ keys +5m + default, gtopo _2m + modis _30s _ keys +2m + default and gmted2010_30s + modis _30s _ keys +30s + default respectively, the model map projection mode is lambert, the center longitude and latitude are 28.2 and 113.0, truelat1=30, turelat2=60, and round _ lon = 113.
Referring to a WRF model user manual and a recommended combination scheme of a regional rainstorm event simulation model physical mechanism parameterization scheme reported in literature, a series of regional rainstorm event simulation model physical mechanism parameterization schemes are established, wherein the schemes comprise a microclimate scheme, a cumulus convection scheme, a long-wave radiation scheme, a short-wave radiation scheme, a boundary layer scheme and a land process scheme, and a scheme library is established, and is specifically shown in a table 2:
TABLE 2 various combinatorial schemes of the WRF model scheme library
Wherein, the D03 grid can not set the cumulus convection scheme.
Then, various different combination schemes are set in the WRF model, grid simulation is carried out on the following rainstorm events, the simulation starting and stopping time of a plurality of rainstorm events is set as shown in table 3, the preset simulation period comprises a model assimilation period (about 5-7 days), a rainstorm event simulation period (about 7-10 days) and a simulation period after the rainstorm event (about 1-3 days), the total time is about 13-20 days, the annual maximum rainfall day corresponds to the last day of the rainstorm event simulation period, and if the duration of the rainstorm event exceeds the preset duration, the simulation period needs to be adjusted according to the actual situation.
TABLE 3 multiple rainstorm event simulation Start and stop date settings
Extracting 1-hour rainfall event sequence corresponding to geographical position near the weather station in the simulation result, analyzing correlation between simulation rainfall and actual measurement record rainfall (R 2 ) And root mean square error (RMSE) The simulation ability of different combination schemes to different rainstorm incident rainfall amounts of a target city is evaluated by taking the combination as an index, and a scheme combination most suitable for simulating the long sand rainstorm incident is screened out, wherein the scheme combination is specifically as shown in a table 4:
table 4, WRF model for simulation of sand storm event
Then, a daily rainfall data set of national weather stations (number: 57687) in the Changsha city in 1980-2019 is obtained, the annual maximum rainfall days of each year are screened out, rainfall events are sorted according to the daily rainfall from large to small, and the corresponding daily rainfall is recorded, and the method is specifically shown in table 5.
TABLE 5 rainfall data of the maximum rainy days in 1980-2019 recorded by national weather station in Changsha
And (3) setting a WRF model by using an optimal parameterization scheme in a combined mode, sequentially simulating according to acquired annual maximum rainfall day event data sets of the Changsha in 1980-2019 and rainfall event numbers, and extracting a rainfall value corresponding to the annual maximum rainfall day of the D03 grid. And calculating the simulated rainfall correction coefficient of each rainfall event according to the rainfall data of the 40-field maximum daily rainfall events recorded by the sand station and the simulated rainfall of the corresponding grid unit. And obtaining the corrected rainfall of the simulation grid unit by multiplying the simulated rainfall of each simulation grid unit in the D03 grid by the correction coefficient.
And aiming at the D03 simulation grid unit, fitting a GEV function grid by grid according to the obtained corrected rainfall capacity of 40 rainfall events within 24-hour rainfall duration to obtain corresponding three parameter (sigma, mu and k) values of the GEV function, and then calculating the rainstorm intensity of the D03 grid within different reappearance periods within 24-hour rainfall duration according to a design rainstorm intensity formula obtained based on the GEV function. In the ArcGIS system, a shot file of the Changsha city domain is imported, and the rainstorm intensity distribution of each simulation grid unit in the Changsha city domain is extracted by using an extract by mask command. The calculation results are shown in fig. 8 and fig. 9, fig. 8 is a distribution diagram of the 50-year-one-time-minute rainstorm intensity corresponding to the 24-hour rainfall duration in the 2019 long-term Shac city area, and fig. 9 is a spatial distribution difference diagram of the 50-year-one-time rainstorm intensity corresponding to the 24-hour rainfall duration in the 2019 long-term Shac city area.
It is to be understood that, similarly, as another example, taking the Changsha city in Hunan province as an example, the urban rainstorm intensity calculation method of the present invention is used to calculate the spatial distribution map of the designed rainstorm intensity for meshing the 24-hour rainfall duration in each recurrence period in the 2040 year and 2060 year of CMIP 5-RCP 8.5 scenario climate prediction result data based on the MRI-CGCM global climate model, and the specific results are shown in FIG. 10 and FIG. 11.
In addition, as shown in fig. 12, another embodiment of the present invention further provides a city rainstorm intensity calculating system based on the spatiotemporal distribution characteristics, and preferably adopts the city rainstorm intensity calculating method of the above embodiment, where the calculating system includes:
the model construction module is used for constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
the gridding rainfall simulation module is used for configuring a regional meteorological model by using the obtained optimal model configuration scheme, and performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
and the rainstorm intensity calculating module is used for calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit.
It can be understood that, the urban rainstorm intensity calculation system based on the time-space distribution characteristic of this embodiment, through constructing regional meteorological model, not only can simulate the gridding rainfall intensity time-space distribution of arbitrary rainfall event process and urban dimension, and can design the simulation region and the simulation grid unit size of target city according to the actual demand, and simultaneously, regional meteorological model has coupled the combined action of multiple environmental factors such as urban topography, underlying surface, vegetation, can simulate the historical rainfall process in city well, be particularly useful for the rainstorm intensity calculation in complicated regional cities of topography such as hills, plateau, and the tradition adopts many rain gauges to carry out urban dimension spatial interpolation, can't consider the influence of multiple factors such as topography, underlying surface distribution, urban heat island effect. And moreover, an optimal model configuration scheme suitable for simulating the rainfall event of the target city is obtained, and the accuracy of the rainfall simulation result is ensured. Then, performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit, finally respectively calculating historical rainstorm intensity and/or future rainstorm intensity based on the historical rainfall time sequence data and/or the future rainfall time sequence data obtained by simulation, and calculating rainstorm intensity based on the simulation data of the regional meteorological model, so that the method can be well suitable for regions with few urban meteorological monitoring station data, short station building time, interrupted/missing rainfall records and insufficient rainfall record to support analysis of the rainstorm intensity, can be applied to predicting the change process of designing the rainstorm intensity of cities in different periods in the future under the climate change background, and can be used for designing urban wading systems, preventing and draining floods, and draining floods in different periods, Sponge city construction and the like provide tools for fine design and evaluation.
Optionally, the computing system further comprises:
and the visual display module is used for visually displaying the calculation results of the historical rainstorm intensity and/or the future rainstorm intensity.
It can be understood that each module in the system of this embodiment corresponds to each step of the method embodiment, and therefore, the specific working process of each module is not described herein again, and reference may be made to the method embodiment.
In addition, another embodiment of the present invention further provides an apparatus, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is used for executing the steps of the method described above by calling the computer program stored in the memory.
In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for calculating urban rainstorm intensity based on spatiotemporal distribution characteristics, the computer program, when executed on a computer, performing the steps of the method as described above.
Typical forms of computer-readable storage media include: floppy disk (floppy disk), flexible disk (flexible disk), hard disk, magnetic tape, any of its magnetic media, CD-ROM, any of the other optical media, punch cards (punch cards), paper tape (paper tape), any of the other physical media with patterns of holes, Random Access Memory (RAM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), FLASH erasable programmable read only memory (FLASH-EPROM), any of the other memory chips or cartridges, or any of the other media from which a computer can read. The instructions may further be transmitted or received by a transmission medium. The term transmission medium may include any tangible or intangible medium that is operable to store, encode, or carry instructions for execution by the machine, and includes digital or analog communications signals or intangible medium that facilitates communication of the instructions. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A city rainstorm intensity calculation method based on space-time distribution characteristics is characterized by comprising the following steps:
constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
configuring a regional meteorological model by using the obtained optimal model configuration scheme, and executing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit, and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit;
the process of constructing the regional meteorological model, setting the simulation region and the simulation grid unit size of the target city, and obtaining the optimal model configuration scheme suitable for the rainfall event simulation of the target city specifically comprises the following contents:
simulating urban rainfall dynamic evolution and gridding rainfall space-time distribution by adopting a regional meteorological model WRF, and setting the sizes of a simulation region and a simulation grid unit according to actual needs;
acquiring historical rainstorm records of a target city, and screening out multiple rainstorms and heavy rainfall events of rainfall levels above;
designing different model configuration schemes to simulate the evolution process of each field intensity rainfall event one by one, and obtaining simulated rainfall data of the field intensity rainfall events one by one;
comparing the historical rainfall data of each field rainfall event with the corresponding simulated rainfall data, statistically analyzing the correlation and the simulation deviation between the historical rainfall data and the corresponding simulated rainfall data, comprehensively evaluating the simulation effect of different model configuration schemes according to the analysis result, and screening out the model configuration scheme with the optimal simulation effect.
2. The urban rainstorm intensity calculation method based on space-time distribution characteristics as claimed in claim 1, wherein the process of obtaining the historical rainfall time series data of each simulation grid unit by performing historical rainfall simulation by using the configured regional meteorological model specifically comprises the following steps:
collecting the occurrence date of the annual maximum daily rainfall of the target city for continuous years, and sequencing according to a preset sequence;
adopting a configured regional meteorological model to carry out gridding simulation of annual maximum daily rainfall events one by one according to a sequence, recording the time interval of a simulation result to be 1 hour, extracting historical simulated rainfall time sequence data of each simulated grid unit, repeating the above contents, and completing gridding simulation of annual maximum daily rainfall;
and counting accumulated rainfall of different rainfall durations based on historical simulated rainfall time sequence data of each simulated grid unit aiming at the maximum daily rainfall event of each year, and calculating to obtain the historical maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct the historical maximum rainfall spatial matrix of each rainfall duration.
3. The urban rainstorm intensity calculation method based on space-time distribution characteristics as claimed in claim 1, wherein the process of obtaining the future rainfall time series data of each simulation grid unit by using the configured regional meteorological model specifically comprises the following steps:
acquiring climate prediction result data of a global climate model;
inputting climate prediction result data of the global climate model as boundary conditions and initial conditions of the regional climate model, and performing continuous future rainfall simulation;
and extracting future simulated rainfall time sequence data of each simulated grid unit in the target period, counting accumulated rainfall of different rainfall durations, and calculating to obtain future maximum rainfall of each simulated grid unit corresponding to different rainfall durations so as to construct a future maximum rainfall spatial matrix of each rainfall duration.
4. The method for calculating urban rainstorm intensity based on spatio-temporal distribution characteristics according to claim 2, wherein the calculating of the grid-like historical rainstorm intensity of the target city based on the historical rainfall time series data of each simulation grid cell specifically comprises the following steps:
based on the historical annual maximum rainfall spatial matrix of each rainfall duration, adopting a generalized extreme value distribution function to perform distribution fitting on rainfall extreme values corresponding to each rainfall duration of the simulation grid unit;
fitting to obtain a position parameter space matrix, a scale parameter space matrix and a shape parameter space matrix of the generalized extreme value distribution function corresponding to each rainfall duration;
calculating the design rainstorm intensity corresponding to each recurrence period and each rainfall duration of each simulation grid unit by adopting a rainstorm intensity formula based on generalized extreme value distribution function calculation so as to construct a design rainstorm intensity spatial matrix corresponding to each recurrence period and each rainfall duration;
and importing the designed rainstorm intensity space matrix corresponding to each recurrence period and each rainfall duration into an ArcGIS system, and extracting the rainstorm intensity space data of the target city domain according to the shp file of the target city domain.
5. The urban rainstorm intensity calculation method based on the space-time distribution characteristics as claimed in claim 1, wherein the simulation area is set by using a single-layer simulation grid unit or a multi-layer nested simulation grid unit, and the horizontal resolution of the target-layer simulation grid unit is set to be 1 km-4 km.
6. The method for calculating the intensity of urban rainstorm based on spatio-temporal distribution characteristics according to claim 1, further comprising the following steps:
and visually displaying the calculation results of the historical rainstorm intensity and/or the future rainstorm intensity.
7. An urban rainstorm intensity calculation system based on space-time distribution characteristics, which adopts the method of any one of claims 1-6, and is characterized by comprising the following steps:
the model construction module is used for constructing a regional meteorological model, setting the sizes of a simulation region and a simulation grid unit of a target city, and obtaining an optimal model configuration scheme suitable for simulating rainfall events of the target city;
the gridding rainfall simulation module is used for configuring a regional meteorological model by using the obtained optimal model configuration scheme, and performing historical rainfall simulation and/or future rainfall simulation by using the configured regional meteorological model to respectively obtain historical rainfall time sequence data and/or future rainfall time sequence data of each simulation grid unit;
and the rainstorm intensity calculating module is used for calculating the gridded historical rainstorm intensity of the target city based on the historical rainfall time sequence data of each simulation grid unit and/or calculating the gridded future rainstorm intensity of the target city based on the future rainfall time sequence data of each simulation grid unit.
8. An apparatus comprising a processor and a memory, the memory having stored therein a computer program, the processor being configured to perform the steps of the method of any one of claims 1 to 6 by invoking the computer program stored in the memory.
9. A computer-readable storage medium for storing a computer program for calculating urban rainstorm intensity based on spatio-temporal distribution features, wherein the computer program when run on a computer performs the steps of the method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111381892.0A CN113821939B (en) | 2021-11-22 | 2021-11-22 | Urban rainstorm intensity calculation method, system, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111381892.0A CN113821939B (en) | 2021-11-22 | 2021-11-22 | Urban rainstorm intensity calculation method, system, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113821939A true CN113821939A (en) | 2021-12-21 |
| CN113821939B CN113821939B (en) | 2022-03-15 |
Family
ID=78917930
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111381892.0A Active CN113821939B (en) | 2021-11-22 | 2021-11-22 | Urban rainstorm intensity calculation method, system, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113821939B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114547991A (en) * | 2022-01-24 | 2022-05-27 | 中机国际工程设计研究院有限责任公司 | Urban drainage system planning design optimization system based on climate change |
| CN114791638A (en) * | 2022-06-29 | 2022-07-26 | 山东大学 | Super-heavy rainstorm monitoring method and system based on multi-source data |
| CN115034428A (en) * | 2022-04-20 | 2022-09-09 | 中国地质大学（武汉） | Urban rainstorm space-time aggregation and urban dynamic and thermodynamic action quantification method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5557040A (en) * | 1994-08-25 | 1996-09-17 | Aerometrics, Inc. | Method and apparatus for precipitation detection and differentiation |
| JP2009008440A (en) * | 2007-06-26 | 2009-01-15 | Mitsubishi Electric Corp | Weather radar device |
| CN103902828A (en) * | 2014-04-02 | 2014-07-02 | 北京工业大学 | Method for determining urban 24-hours long-duration rainstorm intensity |
| CN107977758A (en) * | 2018-01-04 | 2018-05-01 | 郑州云海信息技术有限公司 | A kind of rainfall intensity forecasting procedure and relevant apparatus |
| CN112785053A (en) * | 2021-01-15 | 2021-05-11 | 北京市水科学技术研究院 | Method and system for forecasting urban drainage basin flood |
-
2021
- 2021-11-22 CN CN202111381892.0A patent/CN113821939B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5557040A (en) * | 1994-08-25 | 1996-09-17 | Aerometrics, Inc. | Method and apparatus for precipitation detection and differentiation |
| JP2009008440A (en) * | 2007-06-26 | 2009-01-15 | Mitsubishi Electric Corp | Weather radar device |
| CN103902828A (en) * | 2014-04-02 | 2014-07-02 | 北京工业大学 | Method for determining urban 24-hours long-duration rainstorm intensity |
| CN107977758A (en) * | 2018-01-04 | 2018-05-01 | 郑州云海信息技术有限公司 | A kind of rainfall intensity forecasting procedure and relevant apparatus |
| CN112785053A (en) * | 2021-01-15 | 2021-05-11 | 北京市水科学技术研究院 | Method and system for forecasting urban drainage basin flood |
Non-Patent Citations (2)
| Title |
|---|
| SIVA PRIYA THIAGARAJAH 等: "The effect of rain attenuation on S-band terrestrial links", 《2013 IEEE SYMPOSIUM ON WIRELESS TECHNOLOGY & APPLICATIONS (ISWTA)》 * |
| 唐倩 等: "昆山市暴雨时空分布特征及设计暴雨强度公式推求", 《热带气象学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114547991A (en) * | 2022-01-24 | 2022-05-27 | 中机国际工程设计研究院有限责任公司 | Urban drainage system planning design optimization system based on climate change |
| CN114547991B (en) * | 2022-01-24 | 2023-09-29 | 中机国际工程设计研究院有限责任公司 | Urban drainage system planning, designing and optimizing system based on climate change |
| CN115034428A (en) * | 2022-04-20 | 2022-09-09 | 中国地质大学（武汉） | Urban rainstorm space-time aggregation and urban dynamic and thermodynamic action quantification method |
| CN114791638A (en) * | 2022-06-29 | 2022-07-26 | 山东大学 | Super-heavy rainstorm monitoring method and system based on multi-source data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113821939B (en) | 2022-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113821939B (en) | Urban rainstorm intensity calculation method, system, equipment and storage medium | |
| Gebre | Application of the HEC-HMS model for runoff simulation of Upper Blue Nile River Basin | |
| CN111651885A (en) | Intelligent sponge urban flood forecasting method | |
| CN110222911B (en) | Rainfall station network optimization layout method based on satellite remote sensing and ground data cooperation | |
| CN109522603A (en) | Vehicle-mounted Lagrangian Real-Time Atmospheric pollution traceability system and method based on cloud platform | |
| CN113344291B (en) | Urban inland inundation range forecasting method, device, medium and equipment | |
| Hassan | Climate change impact on groundwater recharge of Umm er Radhuma unconfined aquifer Western Desert, Iraq | |
| CN110826231B (en) | Multi-source data integration method for meteorological site | |
| CN113505521B (en) | Urban waterlogging rapid forecasting method based on neural network-numerical simulation | |
| CN112633544A (en) | Predicted wind speed correction method and device | |
| CN110008439A (en) | Rainfall data Spatio-temporal modeling interpolation algorithm based on matrix decomposition | |
| CN112528516A (en) | Watershed water environment management method coupling land utilization type and climate change | |
| CN110619433B (en) | Rapid selection method and system for power grid heavy rain numerical mode parameterization scheme | |
| CN113506371B (en) | Street scale climatic diagram drawing method and device, electronic equipment and storage medium | |
| CN110543660A (en) | low-impact development simulation method, system and related device | |
| CN114547991B (en) | Urban drainage system planning, designing and optimizing system based on climate change | |
| CN115965243A (en) | Model chain for dynamic evaluation of flood risk under land use change | |
| CN113935622A (en) | Ecological geological environment quality evaluation method and system and electronic equipment | |
| CN117195749B (en) | Regional highway network risk prediction method by climate change and readable storage medium | |
| Ahlawat | Space-time variation in rainfall and runoff: upper Betwa catchment | |
| CN113592664B (en) | Crop production space prediction simulation method, device, model and storage medium | |
| CN116108672B (en) | Outdoor sound propagation prediction model construction method based on geographic information system | |
| CN116482763B (en) | Probabilistic earthquake and tsunami disaster analysis method based on logic tree method | |
| Pegram et al. | A linear catchment model for real time flood forecasting | |
| Broadbent | Interactive comment on" The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1. 0): an efficient and user-friendly model of city cooling" by Ashley M |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |