Drought and flood sharp turning feature analysis and trend prediction method based on multi-source data fusion

Abstract

The invention discloses a drought and flood sudden change feature analysis and trend prediction method based on multi-source data fusion, and the method comprises the steps: comprehensively employing a plurality of data analysis methods and models, such as Mann-Kendall trend analysis, an R / S analysis method, an M-K sudden change detection method, a sliding T detection method, a Morlet wavelet analysis method, and an inverse distance interpolation method, through the multi-source data fusion; the spatial-temporal change characteristics of drought and flood sudden turning are systematically and accurately analyzed. The method can comprehensively and accurately analyze the spatial and temporal change characteristics of drought and flood sudden change, reliably predicts the future trend, provides an important scientific basis for regional water resource management and agricultural disaster prevention and reduction, and provides technical support for drought and flood disaster response in related fields.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydrometeorological analysis, and relates to a method for analyzing the characteristics of sudden alternation between drought and flood and predicting trends based on multi-source data fusion. Background Art

[0002] The sudden alternation between drought and flood in a basin refers to the phenomenon that within a short period of time, the basin quickly changes from a drought state to a flood state, or vice versa. Under the background of global warming, the seasonal variation of the sudden alternation between drought and flood is frequent. Existing research shows that the phenomena of "sudden alternation between drought and flood" and "coexistence of drought and flood" represent the inter-annual and intra-annual seasonal variations of precipitation in the basin. The frequent occurrence of sudden alternation between drought and flood in the basin will bring serious disasters and huge economic losses. Due to the characteristics of suddenness and turning of the sudden alternation between drought and flood, and the lag effect of the drought and flood control work on the sudden alternation between drought and flood events in terms of time scale, it is particularly important to clarify the spatio-temporal characteristics of the sudden alternation between drought and flood in the basin for disaster prevention and control.

[0003] Under the background of global climate change, extreme weather events occur frequently, and the sudden alternation between drought and flood has a serious impact on agriculture, water resource management, ecological environment, social economy and other aspects.

[0004] At present, the research on the sudden alternation between drought and flood mainly focuses on the analysis of a single data source (such as meteorological data or hydrological data), lacking the fusion analysis of multi-source data. Traditional methods are difficult to comprehensively and accurately capture the spatio-temporal variation characteristics of the sudden alternation between drought and flood, and the prediction ability is limited, unable to meet the needs of refined management and forward-looking decision-making of drought and flood disasters in practical applications. Therefore, it is of great practical significance to develop a method for analyzing the characteristics of sudden alternation between drought and flood and predicting trends based on multi-source data fusion. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for analyzing the characteristics of sudden alternation between drought and flood and predicting trends based on multi-source data fusion, so as to solve the problems of single method for analyzing the characteristics of sudden alternation between drought and flood and insufficient prediction ability in the prior art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A method for analyzing the characteristics of sudden alternation between drought and flood and predicting trends based on multi-source data fusion, comprising the following steps: Step 1: Obtain multi-source data of a certain time series within the evaluation area; Step 2: Based on the multi-source data of the area, calculate the drought and flood indexes in different fields respectively; Step 3: Use the entropy weight method to fuse the drought and flood indexes calculated in Step 2 to construct a comprehensive drought and flood index (SCDI); Step 4: Calculate the Z value of the Mann - Kendall trend analysis and the Hurst exponent of the R / S analysis method. The Z value is used to test the significance of the time series, and the Hurst exponent is used to predict the persistence of future changes in the rapid alternation of drought and flood in the region. The Z value and the Hurst exponent value are jointly used to evaluate the temporal variation characteristics and future trends of the rapid alternation of drought and flood in the region; Step 5: Calculate the statistical quantities UF and UB curves by the M - K mutation test method, and jointly identify the possible mutation years in combination with the moving T - test. The Morlet wavelet analysis method is used to identify the change period of the rapid alternation of drought and flood index; Step 6: Use the inverse distance interpolation method for interpolation to obtain the occurrence frequency of the rapid alternation of drought and flood in the region.

[0007] As one of the preferred technical solutions, in Step 1, the multi - source data includes: meteorological data, hydrological data, remote sensing data, and social - economic data.

[0008] As one of the preferred technical solutions, in Step 1, if there is data missing, the linear interpolation method is used to supplement the missing data to ensure the continuity and integrity of the data, and the dimensional difference is eliminated through data standardization.

[0009] As one of the preferred technical solutions, in Step 2, the drought and flood indices in different fields include: meteorological drought and flood indices (SPEI, SPI), hydrological drought and flood indices (SRI), agricultural drought and flood indices (VSWI, VCI), and social - economic drought and flood indices (SEDI).

[0010] As one of the further preferred technical solutions, in Step 2, the meteorological drought and flood index is calculated by the standardized difference between precipitation and potential evapotranspiration or average precipitation; the calculation method of the hydrological drought and flood index is the same as that of the meteorological drought and flood index, but is based on runoff data; the agricultural drought and flood index is calculated by the ratio of the normalized difference vegetation index to land surface temperature or the historical relative proportion of the normalized difference vegetation index; the social - economic drought and flood index is based on the difference between water supply and water demand data for distribution fitting and standardization transformation.

[0011] As one of the further preferred technical solutions, the meteorological drought and flood index includes the standardized precipitation evapotranspiration index SPEI and the standardized precipitation index SPI, and the calculation methods are as follows: Potential evapotranspiration :

[0012] Where: i is each month within a year, taking values from 1 to 12; K is the correction coefficient calculated based on longitude and latitude; T is the monthly average temperature; I is the annual total heating index; M is the coefficient determined by I; The difference D between precipitation and potential evapotranspiration i :

[0013] where: P i is the precipitation in the i-th month, and PET i is the potential evapotranspiration in the i-th month; For the difference sequence D i perform a normal distribution to obtain the SPEI time series data; Use ArcGIS to perform inverse distance spatial interpolation to obtain SPEI raster data, and classify the drought and flood levels according to the magnitude of SPEI;

[0014] where: t is the probability weighted moment; c0 is the first constant, with a value of 2.515517; c1 is the second constant, with a value of 0.802853; c2 is the third constant, with a value of 0.010328; d1 is the fourth constant, with a value of 10432788; d2 is the fifth constant, with a value of 0.189269; d3 is the sixth constant, with a value of 0.001308; ln(·) is the natural logarithm function; F(x) is the cumulative probability of precipitation distribution calculated according to the Γ distribution; x is the precipitation in a certain period; β and γ are the shape and scale parameters of the Γ distribution function; Calculate the SPI time series within the evaluation area according to the above steps, and use ArcGIS to perform inverse distance interpolation to obtain raster data; The SPI drought and flood level classification is the same as that of SPEI.

[0015] As one of the further preferred technical solutions, the hydrological drought and flood index is the standardized runoff index (SRI), and the calculation method is the same as that of SPI, which is used to describe hydrological drought and flood; the SRI drought and flood level classification is the same as that of SPEI.

[0016] As one of the further preferred technical solutions, the agricultural drought and flood index (VSWI) is an important indicator characterizing the vegetation water supply status, which is used to evaluate the water conditions for vegetation growth and climate disasters such as regional drought and flood. The calculation formula is as follows:

[0017] where: i is the month within a year, with a value range of 1 to 12, NDVI is the monthly normalized difference vegetation index; LST is the average land surface temperature in the i-th month; classify the drought and flood levels according to the magnitude of VSWI; The vegetation condition index (VCI), which effectively reflects the vegetation growth status and thus reflects the agricultural drought and flood situation. The VCI calculation formula is as follows:

[0018] Where: i represents the month within a year, with a value range of 1 to 12; NDVI i is the NDVI value for the i-th month of a certain year; NDVI max and NDVI min are respectively the maximum and minimum values of NDVI for the i-th month over multiple years; The drought and flood levels are classified according to the VCI index value.

[0019] As one of the further preferred technical solutions, the difference between the water supply data and the water demand data is subjected to distribution fitting using the non-parametric kernel density estimation method, and the cumulative frequency distribution of the difference is transformed into a standard normal distribution using the equal-probability transformation to obtain the socio-economic drought and flood index (SEDI). The calculation formula is as follows: The probability density function f(e) of the non-parametric kernel density estimation is:

[0020] Where: n is the total number of samples; h is the window width; e is the difference between the water supply data and the water demand data; e i is the i-th sample value of the difference; K(·) is the kernel function.

[0021] The Gaussian kernel function is used for kernel density estimation, and the default window width h in MATLAB is used. The expression of the Gaussian kernel function K(x) is:

[0022]

[0023] Where: x is the degree of difference between different sample values and the current value in a relative sense.

[0024] The drought and flood levels are classified according to the SEDI index value.

[0025] As one of the preferred technical solutions, in step 3, the weights of the comprehensive drought and flood index (SCDI) are determined by the entropy weight method, specifically including: Standardize each index; Allocate weights according to the dispersion degree of each index; Construct a linear weighted model to fuse each drought and flood index.

[0026] As one of the further preferred technical solutions, the specific process of step 3 is: Use the entropy weight method to perform data fusion on each drought and flood index calculated in step 2, and construct a comprehensive drought and flood index (SCDI). The specific calculation formula is as follows:

[0027] Where: i represents each month within a year, with a value range of 1 to 12;w 1 ~ w 6 are the weight values of the six indicators respectively.

[0028] As one of the preferred technical solutions, in step 4, when calculating the Z value using the Mann-Kendall trend test method, the weighted moving average method is used to preprocess the data, giving higher weights to recent data to enhance sensitivity to recent drought and flood trends; in the sliding window technology, the optimized mutation test algorithm adaptively adjusts the window size according to the fluctuation characteristics of the data, using a smaller window in areas where the data fluctuates violently and a larger window in relatively stable areas, thereby improving the accuracy of mutation detection.

[0029] As one of the further preferred technical solutions, the specific process is: The Z value of the drought-flood abrupt transition index is calculated to test the significance of the time series. Z>0 indicates that the series is on an upward trend, which means that the drought-flood abrupt transition situation has an increasing trend in a certain direction (such as "drought to flood" or "flood to drought"); Z<0 indicates that the series is on a downward trend, that is, the drought-flood abrupt transition characteristics are weakening; when When it is greater than or equal to 1.64, 1.96, and 2.58, it passes the significance test at the confidence level of 90%, 95%, and 99%, respectively, indicating that the trend has a high degree of credibility; The standardized drought-flood abrupt transition index time series data is selected as the basis for calculating the Hurst index (H) of the R / S method; if 0 <H<0.5 ,表示时间序列具有反持续性,未来趋势与过去相反;若H> If H=0.5, it means that the time series is continuous and the future trend is the same as the past; if H=0.5, it means that the time series is random and has no obvious trend characteristics; the calculated Hurst index is used to judge the continuation or reversal trend of the drought-flood sudden change phenomenon in the future as a reference for prediction.

[0030] As one of the preferred technical solutions, in step 5, when there is uncertainty in the sliding test results (such as the test statistics of multiple years are close to the mutation condition), the sliding window step size is reduced and the test is repeated, and the accuracy of mutation identification is enhanced by combining the MK statistic curves UF and UB.

[0031] As one of the further preferred technical solutions, the M-K mutation test method is used to draw the statistical quantity UF and UB curves. By observing the intersection points of the two curves and whether the statistical quantity at the intersection points passes a specific significance test (such as the 95% significance level), the possible mutation years are identified. At the same time, combined with the sliding T test (the sliding test step size n = 5), the mutation points are further verified and accurately identified. If UF and UB intersect in a certain year and the test statistic of that year meets the mutation condition, and the sliding T test also supports the mutation judgment, then that year is the mutation year of the drought-flood abrupt change characteristic.

[0032] As one of the preferred technical solutions, in step 5, for the analysis of the change cycle of the drought-flood abrupt change index, if the Morlet wavelet analysis method identifies multiple periodic signals with similar intensities, considering the regional climate historical data and the recent climate change trend, the most representative main cycle is determined. The specific process of wavelet analysis includes: Select wavelet scale and frequency parameters; Analyze the periodic characteristics of the time series and extract the main periodic signal; Determine the main cycle and secondary cycle of the drought-flood abrupt change phenomenon through variance.

[0033] As one of the further preferred technical solutions, through wavelet transform of the index sequence, the real part of the wavelet transform and the variance are analyzed. The fluctuation of the real part reflects the periodic change characteristics of the drought-flood abrupt change at different time scales, and the variance can be used to measure the intensity of each cycle. Extract the vibration signals at different time scales, determine their coverage range and intensity, find the cycle with obvious peaks as the main cycle, and obtain the periodic law of the drought-flood abrupt change phenomenon for long-term prediction and law summary.

[0034] As one of the preferred technical solutions, the specific process of step 6 is: the inverse distance interpolation method. According to the geographical locations of each rain gauge station and the drought-flood abrupt change frequency data, based on the inverse distance weighting principle, the closer the station is to the interpolation point, the greater the influence on the interpolation point and the higher the weight. Through this method, a spatial distribution map is generated to visually display the spatial distribution differences of the regional drought-flood abrupt change phenomenon.

[0035] The calculation formula of the inverse distance interpolation method is as follows:

[0036] Where: Z j is the predicted value of the drought-flood abrupt change occurrence frequency at the spatial interpolation point j; d i,j is the distance between the known point i in space and the interpolation point j; Z i is the drought-flood abrupt change frequency value of the i-th known point; n is the number of known sample points participating in the interpolation calculation.

[0037] As one of the preferred technical solutions, in step 6, if there are local anomalies in the spatial distribution results obtained by the inverse distance interpolation method (such as isolated high or low value points), adjust the form of the distance weight function (such as changing from a simple inverse ratio function to an exponential decay function) or increase the weight of local data, and re-perform interpolation calculations to make the spatial distribution results more reasonable.

[0038] The beneficial effects of the present invention are as follows: The present invention discloses a method for analyzing the characteristics of rapid alternation between drought and flood and predicting trends based on multi-source data fusion, including the following steps: Step 1: Obtain multi-source data of a certain time series within the evaluation area; Step 2: Based on the multi-source data of this area, calculate drought and flood indices in different fields respectively; Step 3: Use the entropy weight method to fuse the drought and flood indices calculated in step 2 to construct a comprehensive drought and flood index (SCDI); Step 4: Calculate the Z value of the Mann-kendall trend analysis and the Hurst index of the R / S analysis method. The Z value is used to test the significance of the time series, and the Hurst index is used to predict the persistence of future changes in the rapid alternation between drought and flood in the region. The Z value and the Hurst index value are jointly used to evaluate the temporal variation characteristics and future trends of the rapid alternation between drought and flood in the region; Step 5: Calculate the statistical quantity UF and UB curves by the M-K mutation test method, and jointly identify possible mutation years in combination with the sliding T test, and use the Morlet wavelet analysis method to identify the change period of the rapid alternation between drought and flood index; Step 6: Use the inverse distance interpolation method for interpolation to obtain the occurrence frequency of the rapid alternation between drought and flood in this area. The present invention can comprehensively and accurately analyze the spatio-temporal variation characteristics of the rapid alternation between drought and flood, and reliably predict its future trends, providing an important scientific basis for regional water resources management and agricultural disaster prevention and mitigation, and providing technical support for related fields to cope with drought and flood disasters.

[0039] Compared with the known existing technologies, the present invention has the following advantages: 1. Through multi-source data fusion, it can comprehensively and accurately identify the spatio-temporal variation characteristics of the rapid alternation between drought and flood, overcoming the limitations of single data source analysis.

[0040] 2. By comprehensively using various data analysis methods and models such as Mann-Kendall trend analysis, R / S analysis method, M-K mutation test method, sliding T test, Morlet wavelet analysis method, inverse distance interpolation method, etc., the spatio-temporal variation characteristics of the rapid alternation between drought and flood are systematically and accurately analyzed. Compared with traditional single methods, the reliability and comprehensiveness of the analysis results are improved.

[0041] 3. By constructing a prediction model, it realizes the prediction of the future change trend of the rapid alternation between drought and flood in the region, provides sufficient time for relevant departments to formulate coping strategies in advance, effectively reduces the losses caused by drought and flood disasters, and ensures the stable development of social economy and the safety of the ecological environment. Description of the Drawings

[0042] To make the objectives, technical solutions, and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration: Figure 1 It is a schematic flowchart of the present invention.

[0043] Figure 2 It is the frequency of the SPEI drought phenomenon.

[0044] Figure 3 It is the frequency of the SPI flood phenomenon.

[0045] Figure 4 It is the frequency of the SRI drought phenomenon.

[0046] Figure 5 It is the frequency of the VSWI flood phenomenon.

[0047] Figure 6 It is the frequency of the VCI drought phenomenon.

[0048] Figure 7 It is the frequency of the SEDI flood phenomenon.

[0049] Figure 8 It is the future change trend of the overall drought-to-flood phenomenon in Shandong Province.

[0050] Figure 9 It is the future change trend of the overall flood-to-drought phenomenon in Shandong Province.

[0051] Figure 10 It is the occurrence frequency of the overall drought-to-flood phenomenon in Shandong Province.

[0052] Figure 11 It is the occurrence frequency of the overall flood-to-drought phenomenon.

[0053] In the drawings, N is a compass. Detailed Embodiment

[0054] Next, the preferred embodiments of the present invention will be described in detail with reference to the drawings.

[0055] The embodiments of the present invention analyze the characteristics and future change trends of drought-flood rapid alternation in Shandong Province using multi-source data in Shandong Province, and the research area is Shandong Province. Shandong Province is located in the eastern coastal area and the lower reaches of the Yellow River in China. It has a mild climate and distinct seasons. There are significant differences in annual precipitation, and the precipitation distribution within a year is uneven. In recent years, the phenomenon of drought-flood rapid alternation has occurred frequently in Shandong Province, causing serious impacts on agricultural production, water resource management, and social economy. Therefore, developing a scientific and effective method for analyzing the characteristics of drought-flood rapid alternation and predicting trends is of great significance for disaster prevention and reduction work in Shandong Province. Embodiment

[0056] Please refer to Figure 1, the method for analyzing the characteristics and predicting the trend of rapid alternation between drought and flood in Shandong Province provided by this embodiment includes the following steps: Step 1: Obtain multi-source data of a certain time series within the evaluation area, including meteorological data, hydrological data, remote sensing data, and socio-economic data.

[0057] Among them, the time series of meteorological data is from 1991 to 2020, and the data comes from the data of 83 ground meteorological stations in Shandong Province. If there are missing data, linear interpolation method is used to supplement the missing data to ensure the continuity and integrity of the data. The hydrological data uses the monthly runoff data of Shandong Province from 1991 to 2018; the remote sensing data uses MODIS image data from 2001 to 2020 (such as vegetation coverage NDVI, land surface temperature LST), and the NDVI data and LST data from 1991 to 2000 are selected as Landsat5 data; the socio-economic data includes grid water supply, population density data, and GDP data; other data includes land use data, traffic road network data, and atmospheric circulation data.

[0058] Step 2: Calculate the meteorological drought and flood index (SPEI, SPI), hydrological drought and flood index (SRI), agricultural drought and flood index (VSWI, VCI), and socio-economic drought and flood index (SEDI) respectively.

[0059] By integrating multi-source data such as meteorology, remote sensing, and historical disaster conditions, coupling various algorithms and technologies, multi-source data such as precipitation, soil moisture, meteorology, and remote sensing in Shandong Province for many years are used to establish the comprehensive drought and flood index (SCDI) of Shandong Province based on the standardized precipitation evapotranspiration index (SPEI), standardized precipitation index (SPI), standardized runoff index (SRI), vegetation water supply index (VSWI), vegetation condition index (VCI), and socio-economic drought and flood index (SEDI), and the rapid alternation between drought and flood in Shandong Province is identified and quantitatively analyzed. Among them, the SPEI index and SPI index are calculated using meteorological station data, the SRI index is calculated according to runoff grid data, the VSWI index and VCI index are calculated based on NDVI and LST remote sensing data, and the SEDI data is obtained according to the grid water supply data and the Shandong Statistical Yearbook. Finally, the entropy weight method is applied to construct the comprehensive drought and flood index (SCDI) applicable to Shandong Province.

[0060] Step 3: Use the entropy weight method to fuse the data of each drought and flood index calculated in step (2) to construct the comprehensive drought and flood index (SCDI). The specific calculation formula is as follows:

[0061] In the formula: W 1 ~ W 6They are the weight values of six indicators respectively. In this embodiment, the weight values calculated by the entropy weight method are 0.3015, 0.2076, 0.2187, 0.1067, 0.0224, and 0.1431 respectively.

[0062] In this embodiment, the phenomenon of rapid alternation of drought and flood is defined from the perspective of time compounding, that is, the mutation between drought and flood in adjacent months. The rapid alternation of drought and flood magnitude index (S) is used to measure the intensity of the rapid alternation of drought and flood phenomenon. The specific calculation formula is as follows:

[0063] In the formula: S a is the difference between the maximum value and the minimum value in the comprehensive drought and flood index sequence, .

[0064] According to the calculation results, the intensity levels of the rapid alternation of drought and flood are divided according to the magnitude index of the rapid alternation of drought and flood. When S ≤ -1.8, it is extremely drought-to-flood; when -1.8 < S ≤ -1.2, it is severely drought-to-flood; when -1.2 < S ≤ -0.8, it is moderately drought-to-flood; when -0.8 < S ≤ -0.4, it is slightly drought-to-flood; when -0.4 < S ≤ 0.4, it is normal; when S > 1.8, it is extremely flood-to-drought; when 1.2 < S ≤ 1.8, it is severely flood-to-drought; when 0.8 < S ≤ 1.2, it is moderately flood-to-drought; when 0.4 < S ≤ 0.8, it is slightly flood-to-drought.

[0065] Step 4: Calculate the Z value of the Mann-Kendall trend analysis and the Hurst index of the R / S analysis method: The Z value is used to test the significance of the time series, and the Hurst index is used to predict the persistence of future changes in the regional rapid alternation of drought and flood; the Z value and the Hurst index value are jointly used to evaluate the time variation characteristics and future trends of the regional rapid alternation of drought and flood phenomenon.

[0066] Calculate the Z value of the rapid alternation of drought and flood index to test the significance of the time series. Z > 0 indicates that the sequence shows an upward trend, meaning that the rapid alternation of drought and flood situation has an increasing trend in a certain direction (such as "drought-to-flood" or "flood-to-drought"); Z < 0 indicates that the sequence shows a downward trend, that is, the characteristics of the rapid alternation of drought and flood are weakening. When is greater than or equal to 1.64, 1.96, 2.58, it has passed the significance tests with confidence levels of 90%, 95%, 99% respectively, indicating that the trend has a high credibility.

[0067] Select the time series data of the drought-flood abrupt alternation index after standardization as the basis to calculate the Hurst index (H) of the R / S method. If 0 < H < 0.5, it indicates that the time series has anti-persistence and the future trend is opposite to the past; if H > 0.5, it indicates that the time series has persistence and the future trend is the same as the past; if H = 0.5, it indicates that the time series is random and has no obvious trend characteristics. Judge the future continuous or reverse trend of the drought-flood abrupt alternation phenomenon according to the calculated Hurst index, which serves as a reference basis for prediction.

[0068] Perform spatial overlay analysis on the Z value and H value of the statistical quantities of drought-flood abrupt alternation phenomena at each level in Shandong Province. It can be seen that the overall development trend of the drought-flood abrupt alternation phenomenon is towards a good direction. Only in a small part of Qingdao and Liaocheng cities, the harmfulness of the overall drought-to-flood phenomenon has increased. When the Z value shows an insignificant trend change, the Hurst value can provide information about the potential long-term persistence or anti-persistence of the sequence. The combination of the two can comprehensively grasp the trend characteristics of the drought-flood abrupt alternation and avoid misjudgment or omission that may exist in a single method.

[0069] Step 5: Calculate the M-K mutation test method to draw the statistical quantities UF and UB curves, and combine with the moving T test to jointly identify the possible mutation years, and use the Morlet wavelet analysis method to identify the change period of the drought-flood abrupt alternation index.

[0070] Use the combined test algorithm based on the M-K mutation test method and the moving T test, and integrate it into the data analysis software to draw the statistical quantities UF and UB curves. Set the moving test step size n = 5, and comprehensively scan and judge the possible mutation years according to the algorithm. For each year, calculate the test statistic in detail and compare it with the preset significance level. If the UF and UB curves of the M-K mutation test of the drought-flood abrupt alternation intensity intersect in a certain year and the test statistic of the moving T test in that year meets the mutation condition (less than the specific significance threshold 0.05), then determine that year as the mutation year and clarify the reliability of its mutation. The combined use of the M-K mutation test and the moving T test effectively refines the mutation points and avoids misjudgment that may be caused by a single method.

[0071] Using the Morlet wavelet analysis method, according to the time series characteristics of the drought-flood abrupt change index, reasonably select the parameters of wavelet transform, such as wavelet scale, central frequency, etc. Through multiple experiments and comparative analysis, determine the optimal parameter combination to improve the accuracy and resolution of periodic analysis. Perform wavelet transform on the occurrence frequencies of different grades of drought-flood abrupt change phenomena in Shandong Province, deeply analyze the real part and variance of the wavelet transform, and accurately determine the coverage range and intensity of each period by extracting the vibration signals at different time scales. In the wavelet real part diagram, the white part indicates that the real part of the wavelet is positive, indicating a relatively high occurrence frequency of the drought-flood abrupt change phenomenon; the black part indicates that the real part of the wavelet is negative, indicating a relatively low occurrence frequency of the drought-flood abrupt change phenomenon. The wavelet variance diagram represents the periodic changes of the drought-flood abrupt change phenomenon at different time scales. During the analysis process, draw the real part diagram and variance diagram of the wavelet transform to visually display the results of periodic analysis, which is convenient for researchers to understand and analyze.

[0072] Use the inverse distance interpolation method to interpolate the space to obtain the occurrence frequency of drought-flood abrupt change in this area; On the ArcGIS platform, use the inverse distance interpolation method to perform spatial interpolation on the occurrence frequencies of drought-flood abrupt change events at each rain gauge station to judge the drought-flood abrupt change phenomenon in Shandong Province. During the interpolation process, reasonably adjust parameters such as the distance decay coefficient according to the geographical characteristics of Shandong Province and the distribution of rain gauge stations. In areas with complex terrain or sparse distribution of rain gauge stations, appropriately reduce the distance decay coefficient to improve the accuracy of interpolation; in areas with relatively flat terrain and uniform distribution of rain gauge stations, the distance decay coefficient can be appropriately increased to speed up the calculation. Through interpolation calculation, generate the spatial distribution map of drought-flood abrupt change, visually presenting the spatial distribution differences of the drought-flood abrupt change phenomenon in Shandong Province.

[0073] From Figure 2 、 Figure 3 It can be seen that the regional differences in meteorological drought in Shandong Province are not obvious. The frequencies of meteorological drought phenomena are relatively high in the central and southern parts of Shandong Province, and relatively low in the northwest and northeast regions. The frequency of meteorological flood phenomena gradually increases from the northwest to the southeast. Figure 4 It shows that the hydrological drought phenomenon in Shandong Province presents an obvious northwest-southeast increasing distribution pattern, and the occurrence frequencies of drought phenomena in Linyi and Rizhao are relatively high. Figure 5 、 Figure 6 It shows that the overall agricultural flood phenomenon in Shandong Province presents a northwest-southeast increasing distribution pattern, and the high-frequency occurrence areas of agricultural drought are mainly concentrated in the northwest. Figure 7 It presents a distribution pattern of gradually decreasing northwest-southeast of the social and economic flood phenomenon in Shandong Province.

[0074] Figure 8 It shows that the drought-to-flood phenomenon in Shandong Province generally presents a benign development trend, and in the future, the risk of drought-to-flood only increases in a small number of areas. Figure 9It shows that the overall trend of the future waterlogging-to-drought phenomenon in Shandong Province is a non-obvious benign development trend, and only a small part of the regions show a significant increasing trend.

[0075] From Figure 10 , Figure 11 It can be seen that the phenomenon of rapid alternation between drought and waterlogging in Shandong Province has the characteristics of regionality and group occurrence. The events of drought-to-waterlogging and waterlogging-to-drought both show the characteristic of more in the southwest and less in the northeast in space, and there are very obvious differences in the occurrence frequencies of rapid alternation between drought and waterlogging among different regions.

[0076] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for analyzing the characteristics of rapid alternation between drought and flood and predicting trends based on multi-source data fusion, characterized in that, It includes the following steps: Step 1: Obtain multi-source data of a certain time series within the evaluation area; Step 2: Based on the multi-source data of this area, calculate drought and flood indices in different fields respectively; Step 3: Use the entropy weight method to fuse the data of each drought and flood index calculated in Step 2 to construct a comprehensive drought and flood index; Step 4: Calculate the Z value of the Mann-kendall trend analysis and the Hurst index of the R / S analysis method. The Z value is used to test the significance of the time series, and the Hurst index is used to predict the persistence of future changes in the abrupt change of regional drought and flood. The Z value and the Hurst index value are jointly used to evaluate the time variation characteristics and future trends of the regional drought and flood abrupt change phenomenon; Step 5: Calculate the statistical quantity UF and UB curves by the M-K mutation test method, and jointly identify possible mutation years in combination with the moving T test. Use the Morlet wavelet analysis method to identify the change period of the drought and flood abrupt change index; Step 6: Use the inverse distance interpolation method for interpolation to obtain the occurrence frequency of the drought and flood abrupt change in this area.

2. The method according to claim 1, characterized in that, In Step 1, the multi-source data includes: meteorological data, hydrological data, remote sensing data, and social and economic data.

3. The method according to claim 1, wherein In Step 1, if there is data missing, the linear interpolation method is used to supplement the missing data to ensure the continuity and integrity of the data, and the dimension difference is eliminated through data standardization.

4. The method according to claim 1, wherein In Step 2, the drought and flood indices in different fields include: meteorological drought and flood index, hydrological drought and flood index, agricultural drought and flood index, and social and economic drought and flood index.

5. The method according to claim 4, characterized in that, In Step 2, the meteorological drought and flood index is calculated by the standardized difference between precipitation and potential evapotranspiration or average precipitation; the calculation method of the hydrological drought and flood index is the same as that of the meteorological drought and flood index, but it is realized based on runoff data; the agricultural drought and flood index is calculated by the ratio of the normalized difference vegetation index to the land surface temperature or the historical relative ratio of the normalized difference vegetation index; the social and economic drought and flood index is based on the difference between water supply and water demand data for distribution fitting and standardized transformation.

6. The method according to claim 1, wherein In Step 3, the weight of the comprehensive drought and flood index is determined by the entropy weight method, specifically including: Perform standardized processing on each index; Allocate weights according to the dispersion degree of each index; Construct a linear weighted model to fuse each drought and flood index.

7. The method according to claim 1, wherein In Step 4, when calculating the Z value by the Mann-Kendall trend test method, the weighted moving average method is used to preprocess the data, and higher weights are assigned to the recent data to enhance the sensitivity to the recent drought and flood change trend; In the optimized mutation test algorithm in the sliding window technique, the window size is adaptively adjusted according to the fluctuation characteristics of the data. A smaller window is used in the area with severe data fluctuations, and a larger window is used in the relatively stable area to improve the accuracy of mutation detection.

8. The method according to claim 1, wherein In Step 5, when the sliding test result is uncertain, reduce the sliding window step size and re-perform the test, and jointly enhance the accuracy of mutation identification in combination with the M-K statistic curves UF and UB.

9. The method according to claim 1, wherein In Step 5, for the analysis of the change period of the drought and flood abrupt change index, if the Morlet wavelet analysis method identifies multiple periodic signals with similar intensities, considering the regional climate historical data and the recent climate change trend, determine the most representative main period; the specific process of wavelet analysis includes: Select wavelet scale and frequency parameters; Analyze the periodic characteristics of the time series and extract the main periodic signal; Determine the main period and secondary period of the phenomenon of rapid alternation between drought and flood through variance.

10. The method according to claim 1, characterized in that, The specific process of step 6 is as follows: inverse distance interpolation method. According to the geographical locations of each rain gauge station and the data of the frequency of rapid alternation between drought and flood, based on the principle of inverse distance weighting, the closer the station is to the interpolation point, the greater the influence on the interpolation point and the higher the weight.

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