High temporal and spatial resolution drought and flood conversion prediction management system based on WRF

Abstract

The present invention discloses a high spatiotemporal resolution drought-flood conversion prediction and management system based on WRF, which relates to the technical field of drought-flood conversion prediction. The present invention performs grouping processing on original data and calculates corresponding indexes of observation data and global model data in the grouped original data. Based on the corresponding indexes, the present invention can not only effectively screen out observation data combinations with high spatial and temporal resolution, but also effectively screen out global model data combinations with good spatial coverage and temporal coverage, and form a higher-quality data set after integration. Subsequently, in this way, the WRF model can make more full use of the characteristics of the high-quality data set during subsequent initialization and boundary condition setting, thereby achieving the effect of significantly improving spatial resolution and temporal resolution, that is, it can provide the effect of high spatiotemporal resolution in a true sense, and thus can effectively improve the accuracy of the WRF model for simulation results.

Description

Technical Field

[0001] The present invention relates to the technical field of drought-flood conversion prediction, and in particular to a drought-flood conversion prediction management system with high temporal and spatial resolution based on WRF. Background Art

[0002] In order to better respond to natural disasters such as droughts and floods, drought-flood conversion prediction and management methods based on high temporal and spatial resolution are particularly important. The Weather Research and Forecasting (WRF) model, as a widely used numerical weather forecasting tool, is widely used in meteorology, hydrology and other fields due to its flexibility and accuracy.

[0003] The existing method usually first determines the area to be simulated and collects data from multiple sources, including ground meteorological station observation data and global reanalysis data, namely global model data. After preprocessing, the collected data are integrated into a unified data set for subsequent WRF model initialization and boundary condition setting. Next, the integrated data set is used to configure the WRF model. This step includes: selecting appropriate microphysical processes, radiation transfer, boundary layer parameterization and other schemes according to the characteristics of the area to be simulated, and then defining the scope and resolution of the area to be simulated. Usually, a nested grid structure is used to improve the spatial resolution of key areas. Then, the WRF model is initialized and boundary conditions are set. The initialization process includes using the reanalysis data provided by the global model as the initial field and setting the atmospheric state at the start of the simulation in the area to be simulated. These data provide large-scale meteorological background information, such as the spatial distribution of variables such as temperature, humidity, wind speed, and air pressure. Observational data with higher spatiotemporal resolution than the global model data are then integrated into the initial field, thereby significantly improving the spatial and temporal resolution of the initial field. Specifically, observational data can fill in the blank areas in the global model data and provide a more detailed description of surface characteristics, while enabling the initial field to more accurately reflect the atmospheric state. The WRF model is used to analyze changes in atmospheric conditions over a short period of time, thereby improving temporal resolution. The boundary condition setting process includes using global model data to provide lateral boundary conditions to ensure that the large-scale circulation characteristics outside the simulated area evolve reasonably during the entire simulation period. Subsequently, the boundary conditions provided by the outer grid are improved by using the inner grid of the nested grid structure in the WRF model and combining it with observational data with high-resolution characteristics, thereby improving the resolution of the entire simulated area. Finally, after completing the above preparations, the WRF model is started for simulation. After the simulation is completed, the corresponding precipitation and soil water data are obtained, and the corresponding standardized precipitation index (SPEI) and standardized soil water index (SSMI) are calculated. A multi-factor standardized drought-flood conversion index (MSDFI) is developed based on the two-variable Copula function, and its drought-flood conversion trend is identified based on the calculation results. Finally, corresponding risk management strategies are formulated based on these trends.

[0004] Although existing methods have achieved certain success, they still face the following major problems in practical applications:

[0005] In the existing method, the original data is often used directly to initialize the WRF model and set the boundary conditions. This, in the absence of effective data screening, not only reduces the accuracy of initializing the WRF model and setting the boundary conditions using global model data, but also reduces the effectiveness of subsequent observation data in improving the spatial resolution and temporal resolution of the initial field and boundary conditions. As a result, after the WRF model is initialized and the boundary conditions are set using the original data, it is unable to effectively provide high spatiotemporal resolution in the actual application process. This not only affects the accuracy of the WRF model simulation results, but also reduces the scientific nature of the final risk management strategy formulation by reducing the accuracy of identifying drought-flood conversion trends.

[0006] Therefore, the existing technology urgently needs a technical solution for a high temporal and spatial resolution drought-flood conversion prediction and management system based on WRF. Summary of the Invention

[0007] In order to solve the above technical problems, the present invention provides a high temporal and spatial resolution drought-flood conversion prediction and management system based on WRF, which specifically includes the following modules:

[0008] Data collection module: used to determine the area to be simulated, collect observation data and global model data of the area to be simulated, and integrate them into a data set;

[0009] Data analysis module: connected to the data collection module, used to analyze the data set of the area to be simulated and obtain the final data set of the area to be simulated based on the analysis results;

[0010] Data grouping unit: used to divide the data set of the area to be simulated into at least two groups;

[0011] High temporal and spatial resolution adaptability index calculation unit: used to calculate the high temporal and spatial resolution adaptability index of each set of observation data of the area to be simulated;

[0012] Spatial resolution index calculation subunit: used to calculate the spatial resolution index of each set of observation data of the area to be simulated;

[0013] Ideal spatial resolution determination interface: used to determine the ideal spatial resolution of the area to be simulated;

[0014] Interface for determining the total area of ​​the area to be simulated: used to determine the total area of ​​the area to be simulated using GIS tools;

[0015] Observation point number statistics interface: used to count the number of observation points in the data set of each group of areas to be simulated;

[0016] Equivalent resolution calculation interface: used to calculate the equivalent resolution of each set of observation data in the area to be simulated based on the total area of ​​the area to be simulated and the number of observation points in each set of observation data in the area to be simulated;

[0017] Spatial resolution index calculation interface: used to calculate the spatial resolution index of each set of observation data in the area to be simulated based on the ideal spatial resolution of the area to be simulated and the equivalent resolution of each set of observation data in the area to be simulated;

[0018] Time resolution index calculation subunit: used to calculate the time resolution index of each group of areas to be simulated;

[0019] Ideal time resolution determination interface: used to determine the ideal time resolution of the area to be simulated;

[0020] Time interval acquisition interface: used to obtain the timestamp information of the observation data of each group of areas to be simulated at the time of observation, and record the time interval of each observation of the observation data of each group of areas to be simulated according to the timestamp information;

[0021] Actual time interval calculation interface: used to calculate the mean of all time intervals and obtain the actual time interval of each set of observation data in the area to be simulated;

[0022] Time resolution index calculation interface: used to calculate the time resolution index of each set of observation data in the area to be simulated based on the ideal time resolution of the area to be simulated and the actual time interval of each set of observation data in the area to be simulated;

[0023] High spatiotemporal resolution adaptability index calculation subunit: used to combine the spatial resolution index and the temporal resolution index to obtain the high spatiotemporal resolution adaptability index of each set of observation data in the area to be simulated;

[0024] Spatiotemporal coverage capability index calculation unit: used to calculate the spatiotemporal coverage capability index of each set of global model data for the area to be simulated;

[0025] Spatial coverage index calculation subunit: used to calculate the spatial coverage index of each set of global model data for the area to be simulated;

[0026] Coverage determination interface: used to determine the coverage of the global model data for each group of areas to be simulated through the coverage map of the global model data;

[0027] Coverage area calculation interface: used to determine the boundary of the area to be simulated, and use GIS tools to superimpose the boundary of the area to be simulated with the coverage of the global model data of each group of areas to be simulated, calculate the area of ​​the overlapping part after superposition, and obtain the coverage area of ​​the global model data of each group of areas to be simulated;

[0028] Spatial coverage index calculation interface: used to calculate the spatial coverage index of the global model data for each group of areas to be simulated based on the coverage area of ​​the global model data for each group of areas to be simulated and the total area of ​​the areas to be simulated;

[0029] Time coverage length index calculation subunit: used to calculate the time coverage length index of each group of global model data in the area to be simulated;

[0030] The total simulation time required determination interface is used to determine the total simulation time required for the area to be simulated;

[0031] Time series information acquisition interface: used to obtain time series information of global pattern data;

[0032] Start time and end time acquisition interface: used to record the start time and end time of each group of global model data for the area to be simulated based on the time series information of the global model data;

[0033] Coverage time length calculation interface: used to subtract the start time from the end time of the global model data of each group of areas to be simulated to obtain the coverage time length of the global model data of each group of areas to be simulated;

[0034] Time coverage length index calculation interface: used to calculate the time coverage length index of the global model data for each group of areas to be simulated based on the coverage time length of the global model data for each group of areas to be simulated and the total simulation time required for the areas to be simulated;

[0035] The spatiotemporal coverage capability index calculation subunit is used to combine the spatial coverage index and the temporal coverage length index to obtain the spatiotemporal coverage capability index of each group of global model data for the area to be simulated;

[0036] Data combination unit: used to obtain the optimal observation data combination of the area to be simulated and the optimal global model data combination of the area to be simulated based on the high temporal and spatial resolution adaptability index and the temporal and spatial coverage capability index;

[0037] Threshold calculation subunit: used for calculating the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index respectively according to the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index;

[0038] First mean and standard deviation calculation interface: used to calculate the first mean of the high spatiotemporal resolution adaptability index of each set of observation data in the area to be simulated, and obtain the first standard deviation of the high spatiotemporal resolution adaptability index of each set of observation data in the area to be simulated based on the first mean;

[0039] High spatiotemporal resolution adaptability index threshold calculation interface: used to calculate the threshold of the high spatiotemporal resolution adaptability index based on the first mean and the first standard deviation;

[0040] Second mean and standard deviation calculation interface: used to calculate the second mean of the spatiotemporal coverage capability index of each set of global model data for the simulated area, and obtain the second standard deviation of the spatiotemporal coverage capability index of each set of global model data for the simulated area based on the second mean;

[0041] Temporal and spatial coverage capability index threshold calculation interface: used to calculate the threshold of the temporal and spatial coverage capability index based on the second mean and the second standard deviation;

[0042] Data group screening subunit: used to screen the groups of observation data of the area to be simulated and the groups of global model data of the area to be simulated according to the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index;

[0043] Observation data screening interface: used to retain the observation data of the current group of areas to be simulated if the high spatiotemporal resolution adaptability index of the observation data is greater than or equal to the threshold of the high spatiotemporal resolution adaptability index; if the high spatiotemporal resolution adaptability index of the observation data of the current group of areas to be simulated is less than the threshold of the high spatiotemporal resolution adaptability index, then remove the observation data of the current group of areas to be simulated;

[0044] Global model data screening interface: used to retain the global model data of the current group of to-be-simulated areas if the spatiotemporal coverage capability index of the global model data is greater than or equal to the spatiotemporal coverage capability index threshold; if the spatiotemporal coverage capability index of the current group of to-be-simulated areas is less than the spatiotemporal coverage capability index threshold, then remove the global model data of the current group of to-be-simulated areas;

[0045] Data combination subunit: used to integrate all the groups of observation data of the area to be simulated to obtain the optimal combination of observation data of the area to be simulated, and to integrate all the groups of global model data of the area to be simulated to obtain the optimal combination of global model data of the area to be simulated;

[0046] Final data set acquisition unit: used to integrate the optimal observation data combination of the area to be simulated with the optimal global model data combination of the area to be simulated to obtain the final data set of the area to be simulated;

[0047] Model setting module: connected to the data analysis module, used to build the WRF model and use the final data set of the area to be simulated to initialize the WRF model and set the boundary conditions to obtain the completed WRF model;

[0048] Simulation result acquisition module: connected to the model setting module, used to simulate the area to be simulated using the set WRF model to obtain the simulation results of the area to be simulated;

[0049] Drought-flood conversion trend acquisition module: connected to the simulation result acquisition module, it is used to calculate the drought-flood index of the simulated area based on the simulation results of the simulated area, and identify the drought-flood conversion trend of the simulated area based on the calculation results; obtain precipitation and soil water data, calculate the SPI and SSI indicators respectively, use the bivariate Copula function to link the best marginal distribution functions of SPI and SSI, estimate the parameters through the MLE method to obtain the joint probability distribution function under different conditions, and finally, obtain the drought-flood conversion index of the simulated area by calculating the MSDFI index, and identify its drought-flood conversion trend based on the calculation results;

[0050] Strategy formulation module: connected to the drought-flood conversion trend acquisition module, used to formulate risk management strategies based on the drought-flood conversion trend of the area to be simulated.

[0051] The embodiments of the present invention have the following technical effects:

[0052] The present invention performs grouping processing on the original data and calculates the corresponding indexes of the observation data and the global model data in the grouped original data. Based on the corresponding indexes, it can not only effectively screen out the observation data combination with high spatial and temporal resolution, but also effectively screen out the global model data combination with good spatial coverage and temporal coverage length, and form a higher-quality data set after integration. Subsequently, in this way, the WRF model can make more full use of the characteristics of the high-quality data set in the subsequent initialization and boundary condition setting process, thereby achieving the effect of significantly improving the spatial resolution and temporal resolution, that is, it can provide the effect of high spatiotemporal resolution in a true sense, thereby not only effectively improving the accuracy of the WRF model for simulation results, but also can further improve the scientific nature of the final risk management strategy formulation on the premise of effectively improving the accuracy of drought-flood conversion trend identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a framework diagram of a high temporal and spatial resolution drought and flood conversion prediction and management system based on WRF provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0056] Example 1: Figure 1 As shown, the present invention provides a high temporal and spatial resolution drought and flood conversion prediction and management system based on WRF, including the following modules:

[0057] Data collection module: used to determine the area to be simulated, collect observation data and global model data of the area to be simulated, and integrate them into a data set;

[0058] It is worth noting that the area to be simulated can be any geographical area that requires weather forecasting and drought and flood management, such as cities or urban agglomerations, river basins, or agricultural areas; the observation data and global model data include but are not limited to: ground meteorological station observation data, such as temperature, humidity, wind speed, precipitation, etc.; satellite remote sensing data, such as surface temperature, vegetation index, soil moisture, etc.; radar observation data, such as precipitation distribution, cloud structure, etc.; global reanalysis data, that is, global model data, from global model data provided by the European Center for Medium-Range Weather Forecasts, the U.S. National Centers for Environmental Prediction, etc., which contains large-scale meteorological variables;

[0059] It is worth further explaining that these data need to be integrated into a unified dataset after preprocessing so that they can be used for the subsequent initialization and boundary condition setting of the WRF model.

[0060] Data analysis module: connected to the data collection module, used to analyze the data set of the area to be simulated and obtain the final data set of the area to be simulated based on the analysis results;

[0061] Data grouping unit: used to divide the data set of the area to be simulated into at least two groups;

[0062] It is worth noting that by grouping the data sets, the quality of each set of data can be evaluated more carefully, so that the optimal data combination can be selected more accurately in the subsequent steps;

[0063] High spatiotemporal resolution adaptability index calculation unit: used to calculate the high spatiotemporal resolution adaptability index of the observation data in the dataset of each group of areas to be simulated;

[0064] It is worth noting that by calculating the high spatiotemporal resolution adaptability index, we can screen out observation data combinations with high spatial and temporal resolution, thereby ensuring the high quality of the WRF model input data;

[0065] Spatial resolution index calculation subunit: used to calculate the spatial resolution index of each set of observation data in the area to be simulated;

[0066] It is worth noting that the spatial resolution quality of each set of observation data is quantified to ensure that the observation data used for initialization and boundary condition setting can capture local detailed changes, which is crucial for identifying drought-flood transition trends in small areas.

[0067] Ideal spatial resolution determination interface: used to determine the ideal spatial resolution of the area to be simulated;

[0068] It is worth noting that the ideal spatial resolution is usually determined based on research needs and available resources. For example, when the area to be simulated is an urban or agricultural area, a higher spatial resolution such as 1 km is required to capture local details.

[0069] Interface for determining the total area of ​​the area to be simulated: used to determine the total area of ​​the area to be simulated using GIS tools;

[0070] It is worth noting that the detailed process of using GIS tools to determine the total area of ​​the area to be simulated is as follows: first, obtain the boundary data of the area to be simulated. The boundary data includes data in vector format or raster format. At the same time, the boundary data usually includes administrative boundaries, watershed boundaries or other custom geographic areas. Then, ensure that all data used are projected into the same coordinate system to avoid area calculation errors caused by different coordinate systems. Commonly used projection coordinate systems include UTM, Lambert Conformal Conic, etc.; then, open the project in the GIS software and load the boundary data of the area to be simulated. During this process, the integrity of the boundary data needs to be checked to ensure that there are no geometric errors, such as self-intersections or multi-part faces; after the inspection is completed, the total area of ​​the area to be simulated is calculated. During the calculation process, when the boundary data is in vector format, method one: direct attribute table calculation method is used. The following is the specific calculation process:

[0071] First, right click on the layer -> "Open Attribute Table";

[0072] Then in the attribute table, right-click on the blank column header and select "Add Field" to create a new field to store the area value (for example, named "Area" and of type Double).

[0073] Then right-click the new field again, select "Calculate Geometry", then select "Area" and set the appropriate unit (such as square meters or square kilometers). At this time, the total area of ​​the area to be simulated is calculated.

[0074] When the boundary data is in raster format, then method 2 is used: area calculation based on raster data. The specific calculation process is as follows:

[0075] First convert the raster data to vector format using the "Raster to Polygon" tool (in ArcGIS) or the "Raster layer to vector" tool (in QGIS).

[0076] Then, the total area of ​​the area to be simulated can be calculated according to the above-mentioned vector data method, that is, method one.

[0077] Observation point number statistics interface: used to count the number of observation points in the data set of each group of areas to be simulated;

[0078] Equivalent resolution calculation interface: used to calculate the equivalent resolution of each set of observation data in the area to be simulated based on the total area of ​​the area to be simulated and the number of observation points in each set of observation data in the area to be simulated;

[0079] The calculation formula for the equivalent resolution of each set of observation data in the simulated area is:

[0080] ;

[0081] Where, represents the equivalent resolution of the observation data of the i-th group of regions to be simulated; Represents the total area of ​​the region to be simulated; The number of observation points representing the observation data of the i-th group of regions to be simulated;

[0082] It is worth noting that this formula is used to quantify the distribution density of each set of observation data in the area to be simulated. A higher equivalent resolution means fewer observation points and a larger coverage area; a lower equivalent resolution means more observation points and finer coverage.

[0083] Spatial resolution index calculation interface: used to calculate the spatial resolution index of each set of observation data in the area to be simulated based on the ideal spatial resolution of the area to be simulated and the equivalent resolution of each set of observation data in the area to be simulated;

[0084] The calculation formula for the spatial resolution index of each set of observation data in the simulated area is:

[0085] ;

[0086] Where, Represents the spatial resolution index of the observation data of the i-th group of simulated regions; represents the ideal spatial resolution of the area to be simulated; represents the equivalent resolution of the observation data of the i-th group of regions to be simulated;

[0087] It is worth noting that this index measures the quality of each observation data relative to the ideal spatial resolution. The closer the value is to 1, the closer the spatial resolution of the observation data of the current group is to the ideal value and the higher the quality.

[0088] Time resolution index calculation subunit: used to calculate the time resolution index of each set of observation data in the area to be simulated;

[0089] It is worth noting that by recording the time interval between each observation and calculating the average, the temporal resolution of the observation data can be evaluated, which is very important for capturing rapidly changing atmospheric phenomena such as heavy rain and thunderstorms;

[0090] Ideal time resolution determination interface: used to determine the ideal time resolution of the area to be simulated;

[0091] It is worth noting that the ideal temporal resolution is often determined by the characteristics of the meteorological phenomena in the area to be simulated. The determination process needs to be obtained through existing literature records. For example, rapidly changing phenomena such as thunderstorms and heavy rains require a higher temporal resolution, such as hourly level; or slowly changing phenomena such as droughts and long-term precipitation trends can accept a lower temporal resolution, such as daily or weekly level.

[0092] Time interval acquisition interface: used to obtain the timestamp information of each set of observation data of the area to be simulated at the time of observation, and record the time interval of each set of observation data of the area to be simulated at each observation based on the timestamp information;

[0093] Actual time interval calculation interface: used to calculate the mean of all time intervals and obtain the actual time interval of each set of observation data in the area to be simulated;

[0094] Time resolution index calculation interface: used to calculate the time resolution index of each group of observation data in the area to be simulated based on the ideal time resolution of the area to be simulated and the actual time interval of the observation data in each group of the area to be simulated;

[0095] The calculation formula for the time resolution index of each set of regional observation data to be simulated is:

[0096] ;

[0097] Where, represents the temporal resolution index of the observation data in the dataset of the i-th group of regions to be simulated; represents the desired temporal resolution for the area to be simulated; represents the actual time interval of the observation data of the i-th group of regions to be simulated;

[0098] It is worth noting that this index measures the quality of each set of observation data relative to the ideal time resolution. The closer the value is to 1, the closer the time resolution of the current group of observation data is to the ideal value and the higher the quality.

[0099] High spatiotemporal resolution adaptability index calculation subunit: used to combine the spatial resolution index and the temporal resolution index to obtain the high spatiotemporal resolution adaptability index of the observation data in the dataset of each group of areas to be simulated;

[0100] The calculation formula for the high temporal and spatial resolution adaptability index of the observation data in the dataset of each group of simulated areas is:

[0101] ;

[0102] Where, represents the high spatiotemporal resolution adaptability index of the observation data in the dataset of the i-th group of regions to be simulated; Represents the spatial resolution index of the observation data in the dataset of the i-th group of simulated areas; represents the temporal resolution index of the observation data in the dataset of the i-th group of regions to be simulated;

[0103] It is worth noting that this index comprehensively considers the impact of the spatial resolution and temporal resolution of each set of observation data, and provides an assessment of the overall quality of each set of observation data.

[0104] Spatiotemporal coverage capability index calculation unit: used to calculate the spatiotemporal coverage capability index of each set of global model data for the area to be simulated;

[0105] It is worth noting that by calculating the spatiotemporal coverage capability index, global model data combinations with good spatial coverage and temporal coverage can be screened out. This can not only effectively ensure the accuracy of the WRF model during initialization and boundary condition setting, but also effectively ensure that there is effective external condition support throughout the subsequent simulation period.

[0106] Spatial coverage index calculation subunit: used to calculate the spatial coverage index of each set of global model data for the area to be simulated;

[0107] It is worth noting that by quantifying the spatial coverage of global model data, accurate meteorological background information can be provided on a large scale, which is crucial to the overall accuracy of subsequent simulation results.

[0108] Coverage determination interface: used to determine the coverage of the global model data for each group of areas to be simulated through the coverage map of the global model data;

[0109] Coverage area calculation interface: used to determine the boundary of the area to be simulated, and use GIS tools to superimpose the boundary of the area to be simulated with the coverage of the global model data of each group of areas to be simulated, calculate the area of ​​the overlapping part after superposition, and obtain the coverage area of ​​the global model data of each group of areas to be simulated;

[0110] Spatial coverage index calculation interface: used to calculate the spatial coverage index of the global model data for each group of areas to be simulated based on the coverage area of ​​the global model data for each group of areas to be simulated and the total area of ​​the areas to be simulated;

[0111] The calculation formula for the spatial coverage index of the global model data for each group of simulated areas is:

[0112] ;

[0113] Where, represents the spatial coverage index of the global model data for the i-th group of simulated regions; represents the coverage area of ​​the global model data for the i-th group of regions to be simulated; Represents the total area of ​​the region to be simulated;

[0114] It is worth noting that this index measures the coverage of each group of global model data in the area to be simulated. The closer the value is to 1, the more complete the coverage of the global model data of the current group is.

[0115] Time coverage length index calculation subunit: used to calculate the time coverage length index of each group of global model data in the area to be simulated;

[0116] It is worth noting that by evaluating the temporal coverage of global model data, it is possible to ensure that boundary conditions are regularly updated throughout the subsequent simulation period to reflect the latest atmospheric changes, thereby improving the real-time and accuracy of subsequent simulation results.

[0117] The total simulation time required determination interface is used to determine the total simulation time required for the area to be simulated;

[0118] Time series information acquisition interface: used to obtain time series information of global pattern data;

[0119] Start time and end time acquisition interface: used to record the start time and end time of each group of global model data for the area to be simulated based on the time series information of the global model data;

[0120] Coverage time length calculation interface: used to subtract the start time from the end time of each group of global model data for the area to be simulated to obtain the coverage time length of each group of global model data for the area to be simulated;

[0121] Time coverage length index calculation interface: used to calculate the time coverage length index of the global model data for each group of areas to be simulated based on the coverage time length of the global model data for each group of areas to be simulated and the total simulation time required for the areas to be simulated;

[0122] The calculation formula for the time coverage length index of each group of global model data in the simulated area is:

[0123] ;

[0124] Where, represents the time coverage length index of the global model data for the i-th group of regions to be simulated; represents the coverage time length of the global model data for the i-th group of regions to be simulated; Represents the total time required to simulate the area to be simulated;

[0125] It is worth noting that this index measures the temporal coverage of each group of global model data during the study period. The closer the value is to 1, the more complete the temporal coverage of the global model data of the current group is.

[0126] The spatiotemporal coverage capability index calculation subunit is used to combine the spatial coverage index and the temporal coverage length index to obtain the spatiotemporal coverage capability index of the global model data in the dataset of each group of simulated areas;

[0127] The calculation formula for the spatiotemporal coverage capability index of the global model data in each set of data sets for the simulated area is:

[0128] ;

[0129] Where, Represents the spatiotemporal coverage capability index of the global model data in the dataset of the i-th group of simulated regions; represents the spatial coverage index of the global model data in the dataset of the i-th group of simulated areas; represents the temporal coverage length index of the global model data in the dataset of the i-th group of simulated regions;

[0130] It is worth noting that this index comprehensively considers the impact of the spatial coverage and temporal coverage length of each set of global model data, and provides an assessment of the overall quality of each set of global model data.

[0131] Data combination unit: used to obtain the optimal observation data combination of the area to be simulated and the optimal global model data combination of the area to be simulated based on the high temporal and spatial resolution adaptability index and the temporal and spatial coverage capability index;

[0132] It is worth noting that by screening out the optimal data combination to form a higher-quality dataset, the WRF model can fully utilize the characteristics of the high-quality dataset during subsequent initialization and boundary condition setting, significantly improving the spatial and temporal resolution.

[0133] Threshold calculation subunit: used for calculating the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index respectively according to the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index;

[0134] First mean and standard deviation calculation interface: used to calculate the first mean of the high spatiotemporal resolution adaptability index of each group of observation data in the area to be simulated, and obtain the first standard deviation of the high spatiotemporal resolution adaptability index of each group of observation data in the area to be simulated based on the first mean;

[0135] High spatiotemporal resolution adaptability index threshold calculation interface: used to calculate the threshold of the high spatiotemporal resolution adaptability index based on the first mean and the first standard deviation;

[0136] Second mean and standard deviation calculation interface: used to calculate the second mean of the spatiotemporal coverage capability index of each set of global model data for the simulated area, and obtain the second standard deviation of the spatiotemporal coverage capability index of each set of global model data for the simulated area based on the second mean;

[0137] Temporal and spatial coverage capability index threshold calculation interface: used to calculate the threshold of the temporal and spatial coverage capability index based on the second mean and the second standard deviation;

[0138] Data group screening subunit: used to screen the groups of observation data of the area to be simulated and the groups of global model data of the area to be simulated according to the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index;

[0139] Observation data screening interface: used to retain the observation data of the current group of to-be-simulated areas if the high spatiotemporal resolution adaptability index of the observation data is greater than or equal to the threshold of the high spatiotemporal resolution adaptability index; if the high spatiotemporal resolution adaptability index of the observation data of the current group of to-be-simulated areas is less than the threshold of the high spatiotemporal resolution adaptability index, then remove the observation data of the current group of to-be-simulated areas;

[0140] Global model data screening interface: used to retain the global model data of the current group of to-be-simulated areas if the spatiotemporal coverage capability index of the global model data is greater than or equal to the spatiotemporal coverage capability index threshold; if the spatiotemporal coverage capability index of the current group of to-be-simulated areas is less than the spatiotemporal coverage capability index threshold, then remove the global model data of the current group of to-be-simulated areas;

[0141] Data combination subunit: used to integrate all the groups of observation data of the area to be simulated to obtain the optimal combination of observation data of the area to be simulated, and to integrate all the groups of global model data of the area to be simulated to obtain the optimal combination of global model data of the area to be simulated;

[0142] Final data set acquisition unit: used to integrate the optimal observation data combination of the area to be simulated with the optimal global model data combination of the area to be simulated to obtain the final data set of the area to be simulated;

[0143] It is worth noting that by integrating the best observational data and global model data, the quality of the WRF model during initialization and boundary condition setting is ensured, which not only improves the continuity and stability of the entire subsequent simulation process, but also significantly improves the accuracy of the simulation results.

[0144] Model setting module: connected to the data analysis module, used to build the WRF model and use the final data set of the area to be simulated to initialize the WRF model and set the boundary conditions to obtain the completed WRF model;

[0145] It is worth noting that, first of all, according to the characteristics of the area to be simulated, appropriate microphysical processes, radiation transfer, boundary layer parameterization and other schemes are selected. Then, the scope and resolution of the area to be simulated are set. In the process, a nested grid structure is required to improve the spatial resolution of key areas. Then, the optimal global model data combination in the final data set of the area to be simulated is used as the initial field, and the atmospheric state at the start of the simulation of the area to be simulated is set. These data provide more accurate large-scale meteorological background information than before, such as the spatial distribution of variables such as temperature, humidity, wind speed, and air pressure. The optimal observation data combination is then integrated into the initial field, thereby significantly improving the spatial resolution and temporal resolution of the initial field. Specifically, observational data can fill in blank areas in global model data and provide a more detailed description of surface features. At the same time, it can enable the initial field to more accurately reflect changes in atmospheric conditions over a short period of time, thereby improving temporal resolution. The boundary condition setting process includes using the optimal global model data combination to provide lateral boundary conditions to ensure that the large-scale circulation characteristics outside the area to be simulated evolve reasonably during the entire simulation period. Subsequently, the boundary conditions provided by the outer grid are improved by using the inner grid of the nested grid structure in the WRF model and combining it with the optimal observation data combination, thereby improving the resolution of the entire area to be simulated. After completing the above preparations, the WRF model is obtained after the settings are completed.

[0146] Simulation result acquisition module: connected to the model setting module, used to simulate the area to be simulated using the set WRF model to obtain the simulation results of the area to be simulated;

[0147] It is worth noting that when a certain watershed is selected as the area to be simulated, various types of data for the area to be simulated should be collected first, including but not limited to static data such as terrain data, land use type, vegetation cover, soil type, and meteorological forcing data that changes with time, such as solar radiation and sea surface temperature. These data should be preprocessed and used as input data for the set WRF model. Next, the WRF model will output corresponding simulation results based on these input data. For example, the simulation results show the changes in meteorological elements such as hourly precipitation, soil water data, temperature, and wind speed in a certain watershed over the next 7 days.

[0148] Drought-flood conversion trend acquisition module: connected to the simulation result acquisition module, used to calculate the drought-flood index of the area to be simulated based on the simulation results of the area to be simulated, and identify the drought-flood conversion trend of the area to be simulated according to the calculation results;

[0149] Specifically, for example, the precipitation and soil water data of the area to be simulated are obtained, the SPI and SSI indicators are calculated respectively, the bivariate Copula function is used to link the optimal marginal distribution functions of SPI and SSI, and the parameters are estimated by the MLE method to obtain the joint probability distribution function under different conditions. Finally, by calculating the MSDFI index, the drought-flood conversion index of the area to be simulated is obtained, and its drought-flood conversion trend is identified based on the calculation results; the following is the specific calculation method, first, based on the precipitation and soil moisture of the area to be simulated, the SPI and SSI are calculated respectively, and the n value with the maximum Pearson correlation coefficient is selected as the response time of soil moisture to meteorological dryness and wetness, different binary copulas are used to link the optimal marginal distribution functions of SPI and SSI, and the parameters are estimated by the MLE method to obtain the joint probability distribution function under different conditions.

[0150] ;

[0151] In the formula is a Copula function, and where are the cumulative distribution functions of SPI and SSI, respectively, and X and Y are the univariate thresholds for SPI and SSI, respectively. Based on this, the optimal bivariate copula was selected based on the minimum AIC and BIC values. Finally, an inverse normal transformation was performed on the optimal joint distribution function to obtain the SDM Idraught / Flood for drought and wet conditions.

[0152] ;

[0153] in is the inverse normal distribution function, is the SDMI value that is less than the threshold condition, is the SDMI greater than the threshold condition.

[0154] Finally, the MSDFI was calculated based on the SDMI, and the run theory was used to identify flood-to-drought events and drought-to-flood events.

[0155] ;

[0156] In the formula is the SDMI value of the i-th time series, is the SDMI value of the i+RT time series, R is the deviation between the maximum SDMI and the minimum SDMI, n is the length of the series, Drought-flood conversion events are divided by 2 to maintain consistency with the drought-flood index classification threshold. It is an objective weighted item to avoid missing drought-flood transition events. It reflects the difference in severity of drought-flood conversion. Under flood conditions, when MSDFI ≥ 0.5, it is considered as the occurrence of drought-flood conversion, and when MSDFI ≤ -0.5, it is considered as the occurrence of flood-drought conversion. The larger the absolute value of the drought-flood conversion index, the more severe the drought-flood conversion event.

[0157] Strategy formulation module: connected to the drought-flood conversion trend acquisition module, used to formulate risk management strategies based on the drought-flood conversion trend of the simulated area;

[0158] It is worth noting that based on the drought-flood conversion trend in a certain river basin, it is shown that the basin may face flood risks within the next month. Therefore, the corresponding risk management strategy should be to issue early warnings and recommend local governments to strengthen river dredging, reservoir scheduling, prepare emergency supplies, organize evacuation drills and other measures to deal with possible flood disasters.

[0159] It should be noted that the terms used in the present invention are only for describing specific embodiments and are not intended to limit the scope of this application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular and may also include the plural. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method or device comprising the elements.

[0160] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A high spatiotemporal resolution drought and flood conversion prediction and management system based on WRF, characterized by: Includes the following modules: Data collection module: used to determine the area to be simulated, collect observation data and global model data of the area to be simulated, and integrate them into a data set; Data analysis module: connected to the data collection module, used to analyze the data set of the area to be simulated and obtain the final data set of the area to be simulated based on the analysis results; Data grouping unit: used to divide the data set of the area to be simulated into at least two groups; High spatiotemporal resolution adaptability index calculation unit: used to calculate the high spatiotemporal resolution adaptability index of the observation data in the dataset of each group of areas to be simulated; Spatial resolution index calculation subunit: used to calculate the spatial resolution index of each set of observation data of the area to be simulated; Ideal spatial resolution determination interface: used to determine the ideal spatial resolution of the area to be simulated; Interface for determining the total area of ​​the area to be simulated: used to determine the total area of ​​the area to be simulated using GIS tools; Observation point number statistics interface: used to count the number of observation points of each group of observation data in the area to be simulated; Equivalent resolution calculation interface: used to calculate the equivalent resolution of each set of observation data in the area to be simulated based on the total area of ​​the area to be simulated and the number of observation points of each set of observation data in the area to be simulated; Spatial resolution index calculation interface: used to calculate the spatial resolution index of each set of observation data in the area to be simulated based on the ideal spatial resolution of the area to be simulated and the equivalent resolution of the observation data in each set of the area to be simulated; Time resolution index calculation subunit: used to calculate the time resolution index of each set of observation data in the area to be simulated; Ideal time resolution determination interface: used to determine the ideal time resolution of the area to be simulated; Time interval acquisition interface: used to obtain the timestamp information of the observation data of each group of areas to be simulated at the time of observation, and record the time interval of each observation of the observation data of each group of areas to be simulated according to the timestamp information; Actual time interval calculation interface: used to calculate the mean of all time intervals and obtain the actual time interval of observation data for each group of areas to be simulated; Time resolution index calculation interface: used to calculate the time resolution index of each set of observation data in the area to be simulated based on the ideal time resolution of the area to be simulated and the actual time interval of each set of observation data in the area to be simulated; High spatiotemporal resolution adaptability index calculation subunit: used to combine the spatial resolution index and the temporal resolution index to obtain the high spatiotemporal resolution adaptability index of each set of observation data in the area to be simulated; Spatiotemporal coverage capability index calculation unit: used to calculate the spatiotemporal coverage capability index of the global model data in the dataset of each group of areas to be simulated; Data combination unit: used to obtain the optimal observation data combination of the area to be simulated and the optimal global model data combination of the area to be simulated based on the high temporal and spatial resolution adaptability index and the temporal and spatial coverage capability index; Final data set acquisition unit: used to integrate the optimal observation data combination of the area to be simulated with the optimal global model data combination of the area to be simulated to obtain the final data set of the area to be simulated; Model setting module: connected to the data analysis module, used to build the WRF model and use the final data set of the area to be simulated to initialize the WRF model and set the boundary conditions to obtain the completed WRF model; Simulation result acquisition module: connected to the model setting module, used to simulate the area to be simulated using the set WRF model to obtain the simulation results of the area to be simulated; Drought-flood conversion trend acquisition module: connected to the simulation result acquisition module, used to calculate the drought-flood index of the area to be simulated based on the simulation results of the area to be simulated, and identify the drought-flood conversion trend of the area to be simulated according to the calculation results; Strategy formulation module: connected to the drought-flood conversion trend acquisition module, used to formulate risk management strategies based on the drought-flood conversion trend of the area to be simulated.

2. The high spatiotemporal resolution drought-flood conversion prediction and management system based on WRF according to claim 1 is characterized in that: The calculation of the spatiotemporal coverage capability index of the global model data in the data set of each group of regions to be simulated comprises: Spatial coverage index calculation subunit: used to calculate the spatial coverage index of each set of global model data for the area to be simulated; Coverage determination interface: used to determine the coverage of the global model data for each group of areas to be simulated through the coverage map of the global model data; Coverage area calculation interface: used to determine the boundary of the area to be simulated, and use GIS tools to superimpose the boundary of the area to be simulated with the coverage of each set of global model data for the area to be simulated, calculate the area of ​​the overlapping part after superposition, and obtain the coverage area of ​​the global model data for each set of areas to be simulated; Spatial coverage index calculation interface: used to calculate the spatial coverage index of the global model data for each group of areas to be simulated based on the coverage area of ​​the global model data for each group of areas to be simulated and the total area of ​​the areas to be simulated; Time coverage length index calculation subunit: used to calculate the time coverage length index of each group of global model data in the area to be simulated; The total simulation time required determination interface is used to determine the total simulation time required for the area to be simulated; Time series information acquisition interface: used to obtain time series information of global pattern data; Start time and end time acquisition interface: used to record the start time and end time of each group of global model data for the area to be simulated based on the time series information of the global model data; Coverage time length calculation interface: used to subtract the start time from the end time of each group of global model data for the area to be simulated to obtain the coverage time length of each group of global model data for the area to be simulated; Time coverage length index calculation interface: used to calculate the time coverage length index of the global model data for each group of areas to be simulated based on the coverage time length of the global model data for each group of areas to be simulated and the total simulation time required for the areas to be simulated; The spatiotemporal coverage capability index calculation subunit is used to combine the spatial coverage range index and the temporal coverage length index to obtain the spatiotemporal coverage capability index of each set of global model data for the area to be simulated.

3. The high spatiotemporal resolution drought-flood conversion prediction and management system based on WRF according to claim 1 is characterized in that: The method of obtaining the optimal observation data combination and the optimal global model data combination for the area to be simulated based on the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index respectively includes: Threshold calculation subunit: used for calculating the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index respectively according to the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index; First mean and standard deviation calculation interface: used to calculate the first mean of the high spatiotemporal resolution adaptability index of the observation data in the dataset of each group of areas to be simulated, and obtain the first standard deviation of the high spatiotemporal resolution adaptability index of the observation data in each group of areas to be simulated based on the first mean; High spatiotemporal resolution adaptability index threshold calculation interface: used to calculate the threshold of the high spatiotemporal resolution adaptability index based on the first mean and the first standard deviation; Second mean and standard deviation calculation interface: used to calculate the second mean of the spatiotemporal coverage capability index of each set of global model data for the simulated area, and obtain the second standard deviation of the spatiotemporal coverage capability index of each set of global model data for the simulated area based on the second mean; Temporal and spatial coverage capability index threshold calculation interface: used to calculate the threshold of the temporal and spatial coverage capability index based on the second mean and the second standard deviation; Data group screening subunit: used to screen the groups of observation data of the area to be simulated and the groups of global model data of the area to be simulated according to the thresholds of the high spatiotemporal resolution adaptability index and the spatiotemporal coverage capability index; Observation data screening interface: used to retain the observation data of the current group of areas to be simulated if the high spatiotemporal resolution adaptability index of the observation data is greater than or equal to the threshold of the high spatiotemporal resolution adaptability index; if the high spatiotemporal resolution adaptability index of the observation data of the current group of areas to be simulated is less than the threshold of the high spatiotemporal resolution adaptability index, then remove the observation data of the current group of areas to be simulated; Global model data screening interface: used to retain the global model data of the current group of to-be-simulated areas if the spatiotemporal coverage capability index of the global model data is greater than or equal to the spatiotemporal coverage capability index threshold; if the spatiotemporal coverage capability index of the current group of to-be-simulated areas is less than the spatiotemporal coverage capability index threshold, then remove the global model data of the current group of to-be-simulated areas; Data combination subunit: used to integrate all the groups of observation data of the reserved areas to be simulated to obtain the optimal combination of observation data of the areas to be simulated, and to integrate all the groups of global model data of the reserved areas to be simulated to obtain the optimal combination of global model data of the areas to be simulated.

Images