CN114357811B - Determination method, device and equipment for long-duration drought and flood events - Google Patents
Determination method, device and equipment for long-duration drought and flood events Download PDFInfo
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Abstract
The application provides a determination method, a determination device and determination equipment for a long-duration drought and flood incident, wherein self-correcting pascal index scPDSI data of a first preset time period and alternative data of a second preset time period in a target area are obtained, wherein the alternative data comprise tree wheel data and/or stalagmite data; the second preset time period comprises a first preset time period; establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method; reconstructing scPDSI values based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period; the scPDSI sequence is analyzed to obtain drought and waterlogging events. The method prolongs the length of basic data of the existing climate change research, and can quickly and accurately determine drought and flood events in a long time period (years, hundreds of years, even thousands of years), thereby facilitating the research of climate change and the like of a target area.
Description
Technical Field
The application relates to the technical field of drought and flood monitoring, in particular to a method, a device, equipment and a computer-readable storage medium for determining a long-duration drought and flood event.
Background
Drought and flood are recurrent severe climatic phenomena that have plagued humans throughout the historical civilization period. It can last for weeks, months, years or even centuries, with drought and flood impacts generally ranging in space over other natural disasters. Thus, drought and flood disasters often have devastating effects on agriculture, water resources, the environment, and human life. Based on the method, the time-space distribution and the change characteristics of the drought and waterlogging are known, and the method is favorable for selecting a proper drought and waterlogging relieving strategy and an evaluation scheme to prevent the future drought and waterlogging risk.
Currently, there are a number of methods and monitoring tools to characterize, delineate, and even visualize drought and flood disasters. Among the many methods and means, the drought and waterlogging index is the most central, and the most common drought and waterlogging index mainly comprises: normalized rainfall index (SPI), normalized precipitation and evapotranspiration index (SPEI), normalized vegetation index (NDVI), pascal drought index (PDSI), and self-correcting pascal drought index (scPDSI). Among the conventional drought and flood indexes, PDSI is an index for measuring the effectiveness of soil moisture, and is widely used for drought and flood research, particularly as a main index of the recent drought and flood severity. In addition to precipitation, PDSI also takes into account the significant effect of temperature on evapotranspiration. Therefore, the PDSI provides a comprehensive and scientific method for evaluating the influence of climate change on drought and flood events. Whereas scPDSI proposed in 2004 by Wells et al is considered an improved version of the conventional PDSI. It uses dynamically calculated values to replace empirical constants derived from climate characteristics and duration factors, thereby automatically calibrating the calculation at any location. scPDSI is becoming one of the most reasonable and popular indicators in future trend studies to quantify the potential effectiveness of soil moisture.
The long-time meteorological element measurement record can provide reliable information support for researching drought and waterlogging changes for years, decades or even longer. However, the current related research is limited by the limited length of the existing survey records, only one or a few independent drought and flood events can be concerned, or the research period is only limited to short-term survey records within hundred years or independent climate events, and as compared with the long-term survey records, the stable laws and change characteristics of the long-term changes of the climate events can be recorded more completely, which is not beneficial to fully mining the characteristics and driving mechanisms of the time-space changes of the climate events. Meanwhile, the duration of the drought and waterlogging event faced by people is dozens of years, hundreds of years or longer, and people are not aware of the existence of the drought and waterlogging event, so a method for determining the drought and waterlogging event is urgently needed.
Disclosure of Invention
In view of the above, the present application provides a method, an apparatus, a device and a computer-readable storage medium for determining a long-duration drought and flood event.
In a first aspect, the present application provides a method for determining a long-duration drought and flood event, where the method includes:
acquiring self-correcting pascal index scPDSI data of a first preset time period and substitute data of a second preset time period in a target area, wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period comprises the first preset time period;
Establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method;
reconstructing scPDSI values based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period;
and analyzing the scPDSI sequence to obtain drought and waterlogging events.
In a second aspect, the present application provides an apparatus for determining a long-duration drought and flood event, the apparatus including:
the data acquisition module is used for acquiring self-correcting Primer index scPDSI data of a first preset time period and substitute data of a second preset time period in a target area, wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period comprises the first preset time period;
the model reconstruction module is used for establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method;
the sequence obtaining module is used for carrying out scPDSI value reconstruction based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period;
and the drought and waterlogging event obtaining module is used for analyzing the scPDSI sequence to obtain a drought and waterlogging event.
In a third aspect, an embodiment of the present application provides a terminal device, including: a memory; one or more processors coupled with the memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more application programs being configured to perform the method for determining a long-duration drought-flood event provided by the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a program code is stored in the computer-readable storage medium, and the program code may be invoked by a processor to perform the method for determining a long-duration drought and flood event provided in the first aspect.
The method, the device, the equipment and the computer-readable storage medium for determining the long-duration drought and flood events acquire self-correcting pascal index scPDSI data of a first preset time period and substitute data of a second preset time period in a target area, wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period comprises a first preset time period; establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method; reconstructing scPDSI values based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period; the scPDSI sequence is analyzed to obtain drought and waterlogging events.
In the method for determining the long-duration drought and flood event in the embodiment, a random forest regression algorithm is adopted, and multi-source substitute data is used for reconstructing a complete scPDSI sequence of the target area for a long duration (namely, a second preset time period), wherein the scPDSI sequence can reflect the drought and flood condition. The method prolongs the length of basic data of the existing climate change research, and can quickly and accurately determine drought and flood events in a long time period (years, hundreds of years, even thousands of years), thereby facilitating the research of climate change and the like of a target area.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic application scenario of a method for determining a long-duration drought-waterlogging event according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for determining a long-duration drought-flood event according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating a method for determining a long duration drought-flood event according to another embodiment of the present application;
FIG. 4 is a block diagram of a determination device for drought and flood events provided by one embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer-readable storage medium provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely below, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For a more detailed description of the present application, a method, an apparatus, a terminal device and a computer-readable storage medium for determining a long-duration drought-waterlogging event provided by the present application are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating an application scenario of the determination method for a long-duration drought and flood event provided by the embodiment of the present application, where the application scenario includes a terminal device 100 provided by the embodiment of the present application, and the terminal device 100 may be various electronic devices (such as a structural diagram of 102, 104, 106, and 108) having a display screen, including but not limited to a smart phone and a computer device, where the computer device may be at least one of a desktop computer, a portable computer, a laptop computer, a tablet computer, and the like. The user operates the terminal device 100 to send out an indication for determining the drought and flood events, and the terminal device 100 executes the method for determining the long-duration drought and flood events of the application.
Next, the terminal device 100 may be generally referred to as one of a plurality of terminal devices, and the present embodiment is only illustrated by the terminal device 100. Those skilled in the art will appreciate that the number of terminal devices may be greater or fewer. For example, the number of the terminal devices may be only a few, or the number of the terminal devices may be tens or hundreds, or more, and the number and the type of the terminal devices are not limited in the embodiment of the present application. The terminal device 100 may be used to execute a determination method for a long-duration drought-waterlogging event provided in the embodiments of the present application.
In an optional implementation manner, the application scenario may include a server in addition to the terminal device 100 provided in the embodiment of the present application, where a network is provided between the server and the terminal device. The network is used to provide a medium for a communication link between the terminal device and the server. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
It should be understood that the number of terminal devices, networks, and servers is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server may be a server cluster composed of a plurality of servers. Wherein, the terminal device interacts with the server through the network to receive or send messages and the like. The server may be a server that provides various services. Wherein the server may be adapted to perform the steps of a method for determining a long-term drought-flood event as provided in the embodiments of the present application. In addition, when the terminal device executes the determination method for the drought and flood event for a long time provided in the embodiment of the present application, a part of the steps may be executed at the terminal device, and a part of the steps may be executed at the server, which is not limited herein.
Based on the above, the embodiment of the application provides a determination method for a long-duration drought and waterlogging event. Referring to fig. 2, fig. 2 is a schematic flow chart illustrating a method for determining a long-duration drought and flood event according to an embodiment of the present application, which is described by taking the method as an example for being applied to the terminal device in fig. 1, and includes the following steps:
step S110, acquiring self-correcting handkerchief merr index scPDSI data of a first preset time period and substitute data of a second preset time period in the target area.
Wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period includes the first preset time period.
In particular, the target area may be an area where drought or flood event identification is required. The target area may be a national (e.g., china) area, a provincial area, a region, etc. In this embodiment, the whole region of china may be used as the target region.
The first preset time period and the second preset time period are preset time periods, and in general, the second preset time period is longer than the first preset time period, and the second preset time period includes the first preset time period. For example, the first preset time period may be from 1951 to 2000 years; the second preset time period may be from 56 to 2000 b.c.
The self-correcting parmer index scPDSI was proposed by Wells in 2004, and is considered to be an improved version of the traditional PDSI, widely used to assess drought status. Self-correcting the pascal index scPDSI calibrates the effect of self-search at any location by using dynamically calculated values instead of climate feature and duration factors derived from empirical constants.
scPDSI data refers to measured climate data stored in a scPDSI dataset derived from a climate dataset compiled by the Climate Research Unit (CRU) of the university of england, a total of 3725 grid points with a spatial resolution of 0.5 ° × 0.5 °. The data set is based on climate data of CRU TS 3.10.01 version, has higher precision, and can be downloaded from British weather data center. This data set has mainly 3 advantages: 1) under various climatic conditions, the scPDSI has similar variation range, so that the scPDSI is more suitable for analyzing effective moisture in different regions; 2) calculating the evaporation capacity by using a Penman-Monteith formula with clearer physical significance, replacing a reference crop with actual vegetation, and interpreting data (1 km resolution ratio) for satellite remote sensing by using vegetation data from the American geological survey bureau; 3) seasonal snow dynamics are taken into account when calculating the moisture balance. In general, the data set is suitable for drought reconstruction and trend analysis. In this embodiment, scPDSI data of 1951-.
The substitute data may be tree wheel data and/or stalagmite data. Alternatively, 234 alternative data from between 400-1944 could be collected, including 231 sets of tree wheel width data and 3 sets of stalagmite delta 18O isotope data. The 231 sets of tree rotation data are obtained from an international tree rotation database. And three groups of stalagmite data are also downloaded from a shared data website respectively. In addition, to maximize the period of overlap of the substitute data with the scPDSI data, all the substitute data can be extended to 2000 a-year by RegEM algorithm, where 94 substitute data are extended, and the maximum and average lengths of the extensions are 10 and 5 years, respectively.
And step S120, establishing an scPDSI reconstruction model by adopting a random forest regression method according to the scPDSI data and the substitute data.
The random forest is an improved classification and regression tree method, and has strong robustness and flexibility in simulating input and output function relations. Such methods include a set of regression trees trained using different pilot samples of training data. Each tree itself acts as a regression function and the final output is taken as the average of the individual tree outputs. Furthermore, due to the RFR (random forest regression) built-in cross-validation capability with the help of the out-of-bag samples, realistic prediction error estimates are provided during the training process and are therefore suitable for real-time processing. RFR is being applied to various fields such as language modeling, bioinformatics, species distribution modeling, and ecosystem modeling.
Due to the fact that tree wheel data or stalagmite data are closely related to drought changes. Therefore, in the embodiment of the present application, based on the assumption that the relationship between the substitute data and the corresponding drought during the weather change is always stable on the thousand-year scale, the relationship between the substitute data and the pascal drought index is established to form an scPDSI reconstruction model. In order to reconstruct the scPDSI, a gridded scPDSI data set is used, and the substitute data also has a significant correlation with the scPDSI data.
And step S130, carrying out scPDSI value reconstruction based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period.
Specifically, the substitute data on the grid points passing the model test are input into the corresponding reconstruction models, and the scPDSI values are reconstructed respectively, so that a complete reconstructed scPDSI sequence in a second preset time period (namely from 56 years to 2000 years) is obtained.
And step S140, analyzing the scPDSI sequence to obtain drought and waterlogging events.
The drought and flood events include drought events and flood (or waterlogging) events. The scPDSI has a dry-wet grade division standard, and the drought and waterlogging events can be identified by analyzing the scPDSI sequence according to the standard.
The scPDSI dry and wet grade classification standard refers to the following table 1:
TABLE 1 scPDSI Dry and Wet rating Scale
The method for determining the long-duration drought and flood event comprises the steps of firstly obtaining self-correcting pascal index scPDSI data of a first preset time period and alternative data of a second preset time period in a target area, wherein the alternative data comprises tree wheel data and/or stalagmite data; the second preset time period comprises a first preset time period; then establishing an scPDSI reconstruction model by adopting a random forest regression method according to the scPDSI data and the substitute data; reconstructing scPDSI values based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period; the scPDSI sequence is analyzed to obtain drought and waterlogging events.
In the method for determining the long-duration drought and flood event in the embodiment, a random forest regression algorithm is adopted, and multi-source substitute data are used for reconstructing a complete scPDSI sequence of the target area for a long duration (namely, a second preset time period), wherein the scPDSI sequence can reflect drought and flood conditions. The method prolongs the length of basic data of the existing climate change research, and can quickly and accurately determine drought and flood events in a long time period (years, hundreds of years, even thousands of years), thereby facilitating the research of climate change and the like of a target area.
In one embodiment, step S120 is executed to establish an scPDSI reconstruction model according to the scPDSI data and the substitute data by using a random forest regression method, including: dividing a second preset time period into a plurality of reconstruction time periods and verification time periods according to the quantity of the substitute data of the second preset time period; the checking time period is the same as the first preset time period; respectively taking the substitute data of each reconstruction time interval in the verification time interval as an independent variable and scPDSI data as a dependent variable to form a plurality of groups of modeling data; and randomly and unreplaceably extracting the modeling data of the first preset group number and inputting the modeling data into a random forest regression model to establish an scPDSI reconstruction model.
Specifically, the entire study period is divided into five sections according to the number of the replacement data at different times: the time sequence comprises a verification time period and a plurality of reconstruction time periods, wherein the reconstruction time periods are respectively recorded as a first reconstruction time period, a second reconstruction time period, a third reconstruction time period and a fourth reconstruction time period.
In the present embodiment, the first predetermined time period is 1951 to 2000 years (i.e. the verification time period), and the second predetermined time period is 56 to 2000 years. The corresponding substitute data in the four reconstruction periods in 1951-2000 are respectively used as independent variables, and scPDSI data is used as dependent variables to form four groups of modeling data. Modeling data of the randomly not replaced extraction 3/4 (37 years, namely the first preset group number) is input into RFR (mtry = 78, ntree = 1500), so as to construct the scPDSI reconstruction model. In addition, the prediction results generated when the scPDSI reconstruction model is constructed can be subjected to correlation analysis with the dependent variables in the modeling data of 3/4, so that the training precision of the model can be verified.
In one embodiment, in step S130, scPDSI value reconstruction is performed based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period, which includes: selecting modeling data of a second preset group number to be input into the scPDSI reconstruction model, and performing model verification; inputting the substitute data which passes the model test in each reconstruction period into an scPDSI reconstruction model, and reconstructing scPDSI values respectively; the scPDSI values within each reconstruction period are concatenated to form a scPDSI sequence.
After the scPDSI reconstruction model is constructed, the accuracy of the scPDSI reconstruction model needs to be verified, namely independent variables in the residual 1/4 modeling data are input into the scPDSI reconstruction model, training data are reconstructed by using the scPDSI reconstruction model, and the model prediction result and dependent variables in a second preset group of modeling data are subjected to correlation analysis so as to verify the test accuracy of the model; in order to ensure the accuracy of the reconstruction result, points which meet the requirements of the training precision and the testing precision simultaneously are selected from all grid points to carry out the next reconstruction. The specific verification process comprises the following steps: the arguments of the remaining 1/4 modeling data (13 years, i.e., the second predetermined set number) in the sets of modeling data are input into the reconstruction model, and the training data is reconstructed using the reconstructed model. And (4) carrying out correlation analysis on the model prediction result and the dependent variable in the modeling data of 1/4 so as to verify the testing accuracy of the model. Conventional accuracy and performance metrics are also employed herein to evaluate the reliability of the reconstruction, including the Reduced Error (RE) and efficiency Coefficient (CE) during the validation period. Meanwhile, the uncertainty of model reconstruction is measured by using the standard deviation of the reconstructed value and the dependent variable residual error in the verification period. In order to ensure the accuracy of the reconstruction result, the point of 3725 grid points with the training precision (n = 37, r > 0.2709, p-value < 0.1) and the testing precision (n = 13, r > 0.4575, p-value < 0.1) meeting the requirements at the same time is selected for further reconstruction.
And after the model verification is completed, inputting the substitute data on the grid points passing the model test in the four reconstruction periods into corresponding reconstruction models, and reconstructing the scPDSI values respectively. In addition, in order to make the lengths of all reconstructed data of 3725 grid points consistent, the estimated values of scPDSI at all unqualified points are calculated by using kriging spatial interpolation according to the reconstructed values of scPDSI every year. Finally, the reconstruction results at all grid points in the four time intervals are spliced together in sequence to obtain a complete reconstructed scPDSI sequence (i.e. from 56 to 2000 b.c.), and the specific process is shown in fig. 3.
It should be noted that both the first preset group number and the second preset group number may be preset values, and may be specifically adjusted according to actual needs, as long as the sum of the first preset group number and the second preset group number does not exceed the total group number of the modeling data.
By adopting the method, the scPDSI reconstruction model can be conveniently, quickly and accurately established, and then a complete scPDSI sequence is obtained.
In one embodiment, before constructing the plurality of sets of modeling data by using the replacement data of each reconstruction period in the check period as an independent variable and the scPDSI data as a dependent variable, the method further includes: selecting the substitute data corresponding to each reconstruction time interval in the verification time interval respectively; performing Pierce-Sum correlation analysis on the selected substitute data and the scPDSI data; and selecting the substitute data with the correlation coefficient larger than a preset threshold value as an independent variable.
Specifically, the corresponding replacement data and scPDSI data in the four reconstruction periods within 1951-2000 are respectively selected for Pearson correlation analysis.
Wherein the substitute data points for the four reconstruction periods are 234 (first reconstruction period), 36 (second reconstruction period), 15 (third reconstruction period), and 5 (fourth reconstruction period), respectively. In this embodiment, Pearson correlation analysis can be performed on the 1951-. In order to ensure that at least one alternative data is significantly related to the scPDSI at each grid point, the maximum correlation coefficient of the grid point and all the alternative data is selected as the correlation analysis result of the point. After the pearson correlation analysis is performed, alternative data with a correlation coefficient greater than a preset threshold value can be selected as the independent variable to establish the scPDSI reconstruction model. Wherein the preset threshold may be 0.2795.
The results of the pearson correlation analysis are: correlation coefficients of the substitute data of the first reconstruction period and 3725 grid points are all greater than 0.2759 (n = 50, p-value < 0.05), and 3678 grid points having correlation coefficients greater than 0.3575 (n = 50, p-value < 0.01). According to the relevant analysis results, the proportion of grid points meeting the reconstruction requirement (r > 0.2329, n = 50, p-value < 0.1) at this stage is 100%. The substitution data for the second reconstruction period had 3665 grid points with correlation coefficients greater than 0.2329 for 3725 grid points, 3488 grid points with correlation coefficients greater than 0.2795, and 2637 grid points greater than 0.3575. The proportion of grid points that meet the reconstruction requirements at this stage is 98.39%. The substitute data for the third reconstruction period has 3423 grid points with correlation coefficients greater than 0.2329 for all grid points, 2984 grid points with correlation coefficients greater than 0.2795 and 1905 grid points greater than 0.3575. The proportion of grid points that meet the reconstruction requirements at this stage is 91.89%. The substitute data for the fourth reconstruction period has 2463 grid points with correlation coefficients greater than 0.2329 for all grid points, 1862 grid points with correlation coefficients greater than 0.2795, and 1004 grid points greater than 0.3575. The proportion of grid points that meet the reconstruction requirements at this stage is 66.12%. Based on this, the above-mentioned substitute data conforms to the requirements of the scPDSI reconstruction model.
In one embodiment, in executing step S140, the scPDSI sequence is analyzed to obtain a drought-waterlogging event, including: calculating the average value of the reconstruction values on each scPDSI grid point every year; determining the annual variation of the average scPDSI according to the average value; and determining the drought and waterlogging events according to the average annual change of the scPDSI.
Specifically, after the scPDSI sequence is completely reconstructed, in order to visually reflect the annual dry-wet change rule, an average value of the reconstructed values at 3725 grid points per year can be calculated, and an annual change curve of the scPDSI average in nearly two thousand years in china is formed. From the annual average of the reconstructed scPDSI, it can be seen that china has been in a state of partial drought overall for nearly two thousand years. Then counting the number of grid points of severe drought (scPDSI < = -3) which occurs every year, and calculating the proportion of the grid points in the total grid points, thereby representing the severe drought degree every year in China.
In this example, based on the relationship between the reconstructed scPDSI sequence and its multi-year mean, 19 significant drought periods and 18 wetting periods were identified in the last two thousand years in china, see table 2. As can be seen from table 2, there were 13 significant wetting periods before the 1000 b.c. and only 5 after the 1000 b.c. period. And the number of significant drought periods (9) before the 1000 years of the Gongyuan is also smaller than that (10) after the 1000 years of the Gongyuan. According to historical data, the reconstruction result of the scPDSI in the application basically accords with historical reality, so that the identification method of the drought and waterlogging events is effective.
TABLE 2 statistical table of significant drought (flood) periods
Further, a plurality of periods of historically significant drought are identified according to the relationship between the reconstructed scPDSI value and the multi-year average value thereof, and the most representative period, namely the period of drought which is recorded most in historical documents, is selected for further analysis. And respectively averaging the reconstructed scPDSI values on each grid point in the representative time period, and then performing Krigin spatial interpolation on the average values on each grid point in the same period by means of ArcGIS software to respectively obtain the drought space distribution map of the representative time period.
By adopting the method, the drought and waterlogging events and the severity of the drought and waterlogging events can be determined for years.
Further, an embodiment for analyzing changes in drought and flood events is provided and described in detail below.
In one embodiment, the method further comprises: extracting IMF components from the scPDSI sequence by adopting EMD, wherein the IMF components comprise an annual scale component, a perennial scale component, a century scale component and a long-term trend component; and analyzing the change condition of the drought and waterlogging events according to the IMF component.
Specifically, the reconstructed scPDSI sequence may be deeply analyzed by using an emd (empirical Mode decomposition), and fluctuations or trends of different scales (frequencies) are gradually decomposed from the original signal according to natural oscillation of the signal, so as to obtain a series of IMF (intrinsic Mode function) components of all sequences with different scales, including an annual scale, a perennial scale, a century scale and a long-term trend, where each IMF component corresponds to a fluctuation condition of scPDSI of one band, and reflects a change process of a period and an amplitude of scPDSI with time at the scale. The lowest frequency component represents the trend term of the original signal, and the trend term represents the linear term or the slowly changing component with the period larger than the signal data length existing in the signal; and simultaneously acquiring the distribution and change conditions of the main period and the amplitude in the time domain on different scales of the scPDSI sequence. The variation rule of each IMF component conforms to the nonlinear variation characteristic of a natural signal, and the fluctuation has a relatively stable quasi-period called a main period; the influence degree of each scale signal on the overall characteristics of the raw data is greatly different, and the square of the amplitude of each component can reflect the signal strength and the energy of the component in the raw data.
In the embodiment, the IMF component analysis shows that the amplitudes of the three periods are larger in 0-400 years, 1000-1400 years and 1900-2000 years, which indicates that the dry-wet change condition of the three periods is more severe. According to the analysis result, the following results are obtained: the dry-wet cycle variation of the perennial scale component is approximately 30-50 years per cycle. Wherein the amplitude is larger than other time periods during the 800-1300 years. The period is just the middle-aged warming period, and the global temperature is in a warmer stage.
Next, an embodiment of time and space analysis of the scPDSI sequence is also given, and the detailed description is as follows:
in one embodiment, the method further comprises: and carrying out time and space analysis on the scPDSI sequence to obtain the time and space distribution of existence of the drought and waterlogging events.
Or: and comparing and analyzing the air temperature sequence components of the region represented by the scPDSI sequence and the scPDSI sequence to obtain the time effect relation between the humidity change condition and the air temperature change of the represented region. And then the reasonability of the result of the reconstruction sequence can be verified through the relation of the amplitude and the frequency.
Or: and performing REOF decomposition on the scPDSI sequence, dividing the region represented by the scPDSI sequence into a plurality of dry and wet characteristic subareas according to variance contribution and accumulated explained variance contribution obtained after main component rotation obtained by the REOF decomposition and REOF rotation load vectors, and then judging the rationality of a reconstructed sequence result on spatial characteristic representation according to historical data.
Referring to fig. 3, the scPDSI sequence may be analyzed temporally and spatially to obtain temporal and spatial distribution of existence of drought and flood events. The specific process is as follows: and decomposing the reconstructed scPDSI sequence by a Rotation Empirical Orthogonal Function (REOF) to obtain variance contribution and accumulated interpretation variance contribution after principal component rotation, manufacturing a space distribution diagram and displaying space modes according to a REOF rotation load vector, and dividing the region into a plurality of dry-wet characteristic sub-regions according to the distribution characteristics of high load regions of the modes in space, wherein the adjacent regions basically have no overlapped part.
According to the distribution characteristics of high load areas of various modes in space, Chinese is divided into 9 dry-wet characteristic areas: northwest, Xinjiang, southwest, southeast, loess plateau, Central China, south Tibetan, east of China, and northeast. The adjacent areas basically have no overlapped parts, and the partitioning result reasonably reflects the actual situation of the Chinese terrain.
For further analysis of the history of dry-wet variation (between 56 and 2000 B.C.), the scPDSI values of the grid points included in each feature region may be averaged and then a 10-year sliding average may be calculated. From the perspective of the multi-year mean of the scPDSI sequences of each subregion (between 56 and 2000 B.C.), all regions are negative, except the east China. It shows that the areas in China except east China are basically dry and drought, and the drought situation in the northwest is the most severe. Comparing the multi-year mean of each subregion with the multi-year mean of the whole country, it can be seen that the multi-year mean of the southeast region, east China region and northeast region is higher than that of the whole country, i.e. the three regions are relatively humid regions. The three regions are the wet regions of China, wherein the annual average precipitation of the east China and the southeast China is more than 800mm, and the annual average precipitation of the northeast China is more than 400 mm. In addition, the average value of the China and the loess plateau for many years is almost equal to the average value of the China for many years, which shows that the dry and wet conditions of the China and the North China reflect the dry and wet conditions of the China as a whole. The average value of the northwest region and the Xinjiang region is the lowest, which indicates that the northwest region is the region with the most severe drought situation in China all the time. The characteristic is just in line with the actual situation of China. Therefore, the reconstruction sequence results of the present study are also reasonably reliable in the characterization of spatial features from the perspective of the dry-wet variation characteristics of the individual subregions.
By adopting the method, the distribution area and the distribution event of the drought and flood events can be quickly determined.
Further, in order to determine the scPDSI reconstruction effect evaluation based on RF (random forest regression), a conventional Linear Regression (LR) can be used to reconstruct scPDSI of a long time sequence, and compared with an actually calculated value (i.e., AC), a density distribution graph of the change of the mean annual value of RF, LR and AC and the density distribution graph between different scPDSI categories is obtained, and various statistical factors are used to evaluate the stability of different methods, including mean, range, pearson correlation coefficient (r), Nash-Sutcliffe efficiency coefficient (NSE) and percentage deviation (PBIAS,%). Higher r and NES values and lower PBIAS values indicate good performance per scPDSI sequence constructed using RF in the examples of this application.
It should be understood that although the steps in the flowcharts of fig. 2 to 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
The embodiment disclosed in the present application describes a method for determining a long-duration drought-waterlogging event in detail, and the method disclosed in the present application can be implemented by various types of devices, so that the present application also discloses a device for determining a drought-waterlogging event corresponding to the method, and the following specific embodiment is given in detail.
Referring to fig. 4, a device for determining drought and flood events disclosed in the embodiment of the present application mainly includes:
the data acquisition module 410 is configured to acquire self-correcting pascal index scPDSI data of a first preset time period and substitute data of a second preset time period in the target area, where the substitute data includes tree wheel data and/or stalagmite data; the second preset time period includes the first preset time period.
And the model reconstruction module 420 is used for establishing an scPDSI reconstruction model by adopting a random forest regression method according to the scPDSI data and the substitute data.
And the sequence obtaining module 430 is configured to reconstruct an scPDSI value based on the scPDSI reconstruction model to obtain an scPDSI sequence with a complete second preset time period.
And the drought and flood event obtaining module 440 is configured to analyze the scPDSI sequence to obtain a drought and flood event.
In one embodiment, the model reconstruction module 420 is configured to divide a second preset time period into a plurality of reconstruction periods and verification periods according to the amount of the substitute data of the second preset time period; the checking time period is the same as the first preset time period; respectively taking the substituted data of each reconstruction time interval in the verification time interval as independent variables and scPDSI data as dependent variables to form a plurality of groups of modeling data; and randomly and unreplaceably extracting the modeling data of the first preset group number and inputting the modeling data into a random forest regression model to establish an scPDSI reconstruction model.
In one embodiment, the sequence obtaining module 430 is configured to select a second preset number of sets of modeling data to be input into the scPDSI reconstruction model for model verification; inputting the substitute data which passes the model test in each reconstruction time period into an scPDSI reconstruction model, and reconstructing scPDSI values respectively; and splicing the scPDSI values in each reconstruction period to form an scPDSI sequence.
In one embodiment, the apparatus further comprises:
and the substitute data selection module is used for respectively selecting the substitute data corresponding to each reconstruction time interval in the verification time interval.
The correlation analysis module is used for carrying out Pierce correlation analysis on the selected substitute data and the scPDSI data;
and the independent variable selection module is used for selecting the substitute data with the correlation coefficient larger than a preset threshold value as the independent variable.
In one embodiment, the drought and flood event obtaining module 440 is configured to calculate an average of the reconstructed values at each grid point of the scPDSI per year; determining the annual variation of the average scPDSI according to the average value; and determining the drought and waterlogging event according to the average scPDSI annual change.
In one embodiment, the apparatus further comprises:
and the IMF component extraction module is used for extracting IMF components from the scPDSI sequence by adopting EMD, wherein the IMF components comprise an annual scale component, a century scale component and a long-term trend component.
And the drought and waterlogging change analysis module is used for analyzing the change condition of the drought and waterlogging event according to the IMF component.
In one embodiment, the apparatus further comprises:
the drought and flood distribution determining module is used for carrying out time and space analysis on the scPDSI sequence to obtain the time and space distribution of drought and flood events;
or: and the comparison analysis module is used for comparing and analyzing the temperature sequence components of the represented region of the scPDSI sequence and the scPDSI sequence to obtain the time effect relationship between the humidity change condition and the temperature change of the represented region. And then the reasonability of the result of the reconstruction sequence can be verified through the relation between the amplitude and the frequency.
Or: and the dry-wet characteristic subregion dividing module is used for performing REOF decomposition on the scPDSI sequence, and dividing the region represented by the scPDSI sequence into a plurality of dry-wet characteristic subregions according to the variance contribution and the accumulated explained variance contribution after the main component is rotated and the REOF decomposition and the REOF rotation load vector. The reasonableness of the reconstruction sequence result on the spatial feature characterization can be judged according to the historical data.
Specific limitations of the determination means regarding drought or flood events can be found in the above limitations of the method, which are not described herein. The various modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent of a processor in the terminal device, and can also be stored in a memory in the terminal device in a software form, so that the processor can call and execute operations corresponding to the modules.
Referring to fig. 5, fig. 5 is a block diagram illustrating a structure of a terminal device according to an embodiment of the present disclosure. The terminal device 50 may be a computer device. The terminal device 50 in the present application may include one or more of the following components: a processor 52, a memory 54, and one or more applications, wherein the one or more applications may be stored in the memory 54 and configured to be executed by the one or more processors 52, the one or more applications configured to perform the methods described in the above-described method embodiments of determining a long duration drought or flood event.
The Memory 54 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 54 may be used to store instructions, programs, code sets, or instruction sets. The memory 54 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The storage data area may also store data created by the terminal device 50 in use, and the like.
Those skilled in the art will appreciate that the structure shown in fig. 5 is a block diagram of only a portion of the structure relevant to the present application, and does not constitute a limitation on the terminal device to which the present application is applied, and a particular terminal device may include more or less components than those shown in the drawings, or combine certain components, or have a different arrangement of components.
In summary, the terminal device provided in the embodiment of the present application is used to implement the method for determining a long-duration drought/flood event in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Referring to fig. 6, a block diagram of a computer-readable storage medium according to an embodiment of the present disclosure is shown. The computer readable storage medium 60 has stored therein program code that can be invoked by a processor to perform the method described in the above-described method embodiment of determining a long-duration drought-flood event.
The computer-readable storage medium 60 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 60 includes a non-transitory computer-readable storage medium. The computer readable storage medium 60 has storage space for program code 62 for performing any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. The program code 62 may be compressed, for example, in a suitable form.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A method for determining a long-duration drought or flood event, the method comprising:
acquiring self-correcting pascal index scPDSI data of a first preset time period and substitute data of a second preset time period in a target area, wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period comprises the first preset time period;
establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method; wherein the scPDSI data is a modified value of PDSI, a value that is calibrated self-retrieved at an arbitrary location;
Reconstructing scPDSI values based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period;
analyzing the scPDSI sequence to obtain drought and waterlogging events;
the method for establishing the scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method comprises the following steps:
dividing a second preset time period into a plurality of reconstruction time periods and verification time periods according to the quantity of the substitute data of the second preset time period; wherein the check time period is the same as the first preset time period;
respectively taking the substituted data of each reconstruction time interval in the verification time interval as independent variables and the scPDSI data as dependent variables to form a plurality of groups of modeling data;
and randomly and unreplaceably extracting a first preset group of modeling data and inputting the modeling data into a random forest regression model to establish the scPDSI reconstruction model.
2. The method of claim 1, wherein performing scPDSI value reconstruction based on the scPDSI reconstruction model to obtain a complete scPDSI sequence of a second preset time period comprises:
selecting a second preset group number of modeling data to input into the scPDSI reconstruction model for model verification;
Inputting the substitute data which passes the model test in each reconstruction time period into the scPDSI reconstruction model, and reconstructing scPDSI values respectively;
splicing the scPDSI values in each reconstruction period to form the scPDSI sequence.
3. The method as claimed in claim 1, wherein before the step of constructing the plurality of sets of modeling data by using the substitute data of each reconstruction period in the check period as an independent variable and the scPDSI data as a dependent variable, respectively, further comprises:
selecting the substitute data corresponding to each reconstruction time interval in the verification time interval respectively;
performing Pierce-Sun correlation analysis on the selected substitute data and the scPDSI data;
and selecting the substitute data with the correlation coefficient larger than a preset threshold value as the independent variable.
4. The method according to any one of claims 1 to 3, wherein said analyzing said scPDSI sequence for drought or waterlogging events comprises:
calculating the average value of the reconstruction values of each scPDSI grid point every year;
determining an average scPDSI annual variation from the average;
determining the drought-waterlogging event from the average scPDSI annual change.
5. The method according to any one of claims 1-3, further comprising:
Extracting IMF components from the scPDSI sequence by adopting EMD, wherein the IMF components comprise an annual scale component, a perennial scale component, a century scale component and a long-term trend component;
and analyzing the change condition of the drought and waterlogging event according to the IMF component.
6. The method according to any one of claims 1-3, further comprising:
carrying out time and space analysis on the scPDSI sequence to obtain the time and space distribution of drought and waterlogging events;
or
Comparing and analyzing the air temperature sequence components of the represented region of the scPDSI sequence and the scPDSI sequence to obtain the time effect relation between the dry-wet change condition and the air temperature change of the represented region;
or
And performing REOF decomposition on the scPDSI sequence, and dividing the region represented by the scPDSI sequence into a plurality of dry-wet characteristic subareas according to variance contribution and accumulated explained variance contribution after main component rotation obtained by the REOF decomposition and REOF rotation load vectors.
7. An apparatus for determining a long-duration drought or flood event, the apparatus comprising:
the data acquisition module is used for acquiring self-correcting Primer index scPDSI data of a first preset time period and substitute data of a second preset time period in a target area, wherein the substitute data comprises tree wheel data and/or stalagmite data; the second preset time period comprises the first preset time period;
The model reconstruction module is used for establishing an scPDSI reconstruction model according to the scPDSI data and the substitute data by adopting a random forest regression method; wherein the scPDSI data is a modified value of PDSI, a value calibrated self-retrieved at an arbitrary position;
the sequence obtaining module is used for carrying out scPDSI value reconstruction based on the scPDSI reconstruction model to obtain a complete scPDSI sequence in a second preset time period;
the drought and flood event acquisition module is used for analyzing the scPDSI sequence to obtain a drought and flood event;
the model reconstruction module is used for dividing a second preset time period into a plurality of reconstruction time periods and verification time periods according to the quantity of the substitute data of the second preset time period; the checking time period is the same as the first preset time period; respectively taking the substitute data of each reconstruction time interval in the verification time interval as an independent variable and scPDSI data as a dependent variable to form a plurality of groups of modeling data; and randomly and unreplaceably extracting the modeling data of the first preset group number and inputting the modeling data into a random forest regression model to establish an scPDSI reconstruction model.
8. A terminal device, comprising:
a memory; one or more processors coupled with the memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-6.
9. A computer-readable storage medium, having stored thereon program code that can be invoked by a processor to perform the method according to any one of claims 1 to 6.
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