Multi-scale SPEI prediction and drought monitoring method fusing site and grid point data

Abstract

The invention discloses a multi-scale SPEI prediction and drought monitoring method fusing station and lattice point data, relates to the technical field of meteorological disaster monitoring, and aims to fuse monthly scale lattice point SPEIGrid data with synchronous meteorological station SPEISstation data. In order to fully retain the characteristics of the weather station SPEISstation in the fusion process, the number of stations during Anuspin interpolation is controlled at the same time, grid points around the weather station are screened with the weather station as the center, and the grid points beyond 20 km are reserved as encryption points of the weather station; and then, fusing the screened grid points with meteorological stations, and generating 1km-resolution SPEIGS data sets with the scale of 1 / 3 / 6 / 12 / 24 months in batches by adopting an Anuspin meteorological interpolation model and taking topographic features as covariants. And on the basis of the data set, sudden change characteristics, change trends and sustainability of meteorological drought in a specified area are analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological disaster monitoring, and in particular to a multi-scale SPEI prediction and drought monitoring method integrating station and grid data. Background Art

[0002] Drought is a phenomenon of imbalance between water supply and demand caused by abnormally low or no precipitation for a long period of time. It is one of the meteorological disasters with the greatest impact on social economy and ecological environment. Against the background of global warming, my country's karst areas are mainly distributed in southwestern provinces, and the frequency and intensity of droughts in these areas have increased significantly. Therefore, monitoring the evolution characteristics of drought in the southwestern karst areas has important reference value for guiding the government's disaster prevention and mitigation.

[0003] At present, the standardized precipitation evapotranspiration index (SPEI) is widely used in meteorological drought monitoring. This index not only considers the impact of precipitation and evapotranspiration on meteorological drought, but also has multi-scale characteristics. At present, the commonly used SPEI datasets for meteorological drought monitoring include: ① SPEIbasev2.9 monthly scale dataset, with a spatial resolution of 0.5 degrees, a time span of 1901-2022, spatial coverage of the world, and a time scale of 1-48 months; ② Based on the SPEIbasev2.7 monthly scale dataset, the environmental parameters are comprehensively considered and downscaled to generate a 6-month scale SPEI dataset with a resolution of 1 km from 1901 to 2021; ③ SPEI-RF dataset, with a spatial resolution of 1 km, a time span of 2001-2020, spatial coverage of China, and a time scale of 1 / 3 / 6 / 9 / 12 / 24 months; ④ The SPEI index is calculated based on meteorological stations, and then spatial interpolation is used to generate a raster SPEI dataset. In summary, the gridded SPEI dataset with a spatial resolution of 0.5° can reflect the long-term change characteristics of drought, but it has certain limitations in depicting the detailed information of drought changes; although the downscaled SPEI dataset can reflect the local detailed information of drought to a certain extent, there is a certain uncertainty due to the environmental changes and downscaling methods selected during downscaling; although the 1km resolution SPEI dataset obtained by SPEI-RF can effectively reflect the detailed information of drought and has high accuracy, its time series is only from 2001 to 2020, and the time series is relatively short, and it cannot fully reflect the evolution characteristics of meteorological drought before and after the ecological restoration project in the southwestern karst area; and the direct interpolation method of SPEI at meteorological stations has a certain influence on the accuracy of the interpolation results due to the location and density of the stations. Summary of the invention

[0004] In order to solve the shortcomings of the existing SPEI dataset, the present invention provides a multi-scale SPEI prediction and drought monitoring method that integrates station and grid data. The following technical solutions are adopted:

[0005] A multi-scale SPEI prediction and drought monitoring method integrating station and grid data includes the following steps:

[0006] Step 1: Processing meteorological station data, obtaining daily-scale meteorological data sets of meteorological stations in a set time period, and synthesizing daily-scale data into monthly-scale meteorological data;

[0007] Step 2: Calculate the SPEI of the meteorological station at 1 / 3 / 6 / 12 / 24 months Station value;

[0008] Step 3: Filter the grid points that meet the set conditions and regard each grid point as a site; take the meteorological site in the study area as the center and filter the grid points outside the set distance of the meteorological site as the encryption points of the site;

[0009] Step 4: Extract the grid 1 / 3 / 6 / 12 / 24 month scale SPEI Grid Based on the results of step 3, extract the 1 / 3 / 6 / 12 / 24-month SPEI values ​​that meet the conditions from the SPEIbase dataset. Grid value;

[0010] Step 5: Fusion Grid SPEI Grid With site SPEI Station Based on the results of step 2 and step 4, several stations are randomly selected from the meteorological stations as the validation data set; then the remaining stations are fused with the grid points to obtain the 1 / 3 / 6 / 12 / 24 month scale SPEI GS Fusion point data, and convert the fusion point data into xls format;

[0011] Step 6: Generate 1 / 3 / 6 / 12 / 24-month 1km resolution SPEI using Anusplin interpolation GS Dataset.

[0012] By adopting the above technical solution, it is planned to convert the 0.5° resolution SPEIbaseV2.9 monthly grid SPEI Grid Data and SPEI of meteorological stations in the same period Station In order to fully retain the SPEI of the meteorological station StationThe characteristics of the Anusplin interpolation are taken into consideration, and the number of stations is controlled during the Anusplin interpolation. The grid points around the meteorological station are screened, and the grid points 20 km away are retained as the densification points of the meteorological station. Then, the screened grid points are fused with the meteorological station, and the Anusplin meteorological interpolation model is used with terrain features as covariates to batch generate 1 km resolution SPEI at 1 / 3 / 6 / 12 / 24 month scales. GS Dataset.

[0013] This technical solution not only makes full use of the observation data of meteorological stations, but also selects grid points to encrypt meteorological stations, which can effectively improve SPEI. GS The accuracy of the dataset; the predicted SPEI at 1km resolution and 1 / 3 / 6 / 12 / 24 months GS The dataset can effectively capture the occurrence, development and end time of meteorological drought, and monitor the sudden change, changing trend and persistence of meteorological drought. At the same time, this dataset has a long time series, which can effectively describe the spatiotemporal variation characteristics of meteorological drought in some specific areas, such as the karst area in the southwest.

[0014] Optionally, in step 1, the daily-scale meteorological data set includes precipitation, maximum temperature, minimum temperature, average temperature, average wind speed, and sunshine hours variables.

[0015] Optionally, in step 2, based on the monthly meteorological data, the monthly potential evapotranspiration is first calculated using the Penman-Monteith formula recommended by FAO-56; the difference between the monthly precipitation and the potential evapotranspiration is then calculated, and then the 1 / 3 / 6 / 12 / 24-month cumulative water deficit is calculated, and finally the SPEI at the 1 / 3 / 6 / 12 / 24-month scale is calculated. Station value.

[0016] Optionally, in step 3, the gridded SPEIbase data of 0.5° in the study area are converted into point data.

[0017] Optionally, in step 6, based on the result of step 5, the 1 / 3 / 6 / 12 / 24 month scale SPEI is calculated. GS The fused point data were imported into SPSS and exported into batch files in dat format. Based on the Anusplin meteorological interpolation model, the 1km resolution elevation of the study area was used as a covariate to generate 1 / 3 / 6 / 12 / 24-month scale SPEI in batches. GS Dataset.

[0018] Optionally, step 7, SPEI GS Fusion point data interpolation result test: Select three indicators: correlation coefficient (r), bias (BIAS) and root mean square error (RMSE) to test the accuracy of the interpolation results.

[0019] Optionally, step 8 is also included, based on SPEI GS The meteorological drought mutation test is based on the annual scale SPEI of the region. GS -12 data, and the Mann-Kendall mutation test method was used to detect whether there was a mutation point in the meteorological drought time series in the set time period.

[0020] Optionally, step 9 is also included, based on SPEI GS The change trend and persistence analysis of meteorological drought in China is based on the SPEI of the regional annual scale. GS -12 data were used to analyze the temporal variation characteristics of meteorological drought on an annual scale, and the significance and persistence of the long-term variation trend of meteorological drought were analyzed by combining the Theil-Sen Median, Mann-Kendall significance test and Hurst index.

[0021] By adopting the above technical solutions, the Mann-Kendall mutation test, Theil-Sen Median trend analysis, Mann-Kendall significance test and Hurst index can be used to analyze the SPEI of specific areas, such as the karst area in the southwest. GS The spatiotemporal evolution characteristics of the

[0022] In summary, the present invention includes at least one of the following beneficial technical effects:

[0023] The present invention can provide a multi-scale SPEI prediction and drought monitoring method integrating station and grid data, and proposes to integrate the 0.5° resolution SPEIbaseV2.9 monthly grid SPEI Grid Data and SPEI of meteorological stations in the same period Station In order to fully retain the SPEI of the meteorological station Station The characteristics of the Anusplin interpolation are taken into consideration, and the number of stations is controlled during the Anusplin interpolation. The grid points around the meteorological station are screened, and the grid points 20 km away are retained as the densification points of the meteorological station. Then, the screened grid points are fused with the meteorological station, and the Anusplin meteorological interpolation model is used with terrain features as covariates to batch generate 1 km resolution SPEI at 1 / 3 / 6 / 12 / 24 month scales. GS Finally, the Mann-Kendall mutation test, Theil-Sen Median trend analysis, Mann-Kendall significance test and Hurst index were used to analyze the SPEI in the karst region of Southwest China. GS The spatiotemporal evolution characteristics of the BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a multi-scale SPEI prediction and drought monitoring method integrating station and grid data of the present invention;

[0025] Figure 2 In the specific embodiment of the present invention, SPEI GS Results test chart (1 / 3 / 6 / 12 / 24 month scale);

[0026] Figure 3a and Figure 3b In the specific embodiment of the present invention, SPEI GS -03 and SPEIbase-03 monthly data comparison in the karst area of ​​Southwest China;

[0027] Figure 4a-4d In the specific embodiment of the present invention, SPEI GS -12 Mutation test map in the karst region of the southwest; DETAILED DESCRIPTION

[0028] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0029] The embodiment of the present invention discloses a multi-scale SPEI prediction and drought monitoring method integrating station and grid data.

[0030] Reference Figure 1 , a multi-scale SPEI prediction and drought monitoring method integrating station and grid data, comprising the following steps:

[0031] Step 1: Processing meteorological station data, obtaining daily-scale meteorological data sets of meteorological stations in a set time period, and synthesizing daily-scale data into monthly-scale meteorological data;

[0032] Step 2: Calculate the SPEI of the meteorological station at 1 / 3 / 6 / 12 / 24 months Station value;

[0033] Step 3: Filter the grid points that meet the set conditions and regard each grid point as a site; take the meteorological site in the study area as the center and filter the grid points outside the set distance of the meteorological site as the encryption points of the site;

[0034] Step 4: Extract the grid 1 / 3 / 6 / 12 / 24 month scale SPEI Grid Based on the results of step 3, extract the 1 / 3 / 6 / 12 / 24-month SPEI values ​​that meet the conditions from the SPEIbase dataset. Grid value;

[0035] Step 5: Fusion Grid SPEI Grid With site SPEIStation Based on the results of step 2 and step 4, several stations are randomly selected from the meteorological stations as the validation data set; then the remaining stations are fused with the grid points to obtain the 1 / 3 / 6 / 12 / 24 month scale SPEI GS Fusion point data, and convert the fusion point data into xls format;

[0036] Step 6: Generate 1 / 3 / 6 / 12 / 24-month 1km resolution SPEI using Anusplin interpolation GS Dataset.

[0037] It is planned to convert the 0.5° resolution SPEIbaseV2.9 monthly grid-point SPEI Grid Data and SPEI of meteorological stations in the same period Station In order to fully retain the SPEI of the meteorological station Station The characteristics of the Anusplin interpolation are taken into consideration, and the number of stations is controlled during the Anusplin interpolation. The grid points around the meteorological station are screened, and the grid points 20 km away are retained as the densification points of the meteorological station. Then, the screened grid points are fused with the meteorological station, and the Anusplin meteorological interpolation model is used with terrain features as covariates to batch generate 1 km resolution SPEI at 1 / 3 / 6 / 12 / 24 month scales. GS Dataset.

[0038] This technical solution not only makes full use of the observation data of meteorological stations, but also selects grid points to encrypt meteorological stations, which can effectively improve SPEI. GS The accuracy of the dataset; the predicted SPEI at 1km resolution and 1 / 3 / 6 / 12 / 24 months GS The dataset can effectively capture the occurrence, development and end time of meteorological drought, and monitor the sudden change, changing trend and persistence of meteorological drought. At the same time, this dataset has a long time series, which can effectively describe the spatiotemporal variation characteristics of meteorological drought in some specific areas, such as the karst area in the southwest.

[0039] In step 1, the daily-scale meteorological dataset includes precipitation, maximum temperature, minimum temperature, average temperature, average wind speed, and sunshine hours variables.

[0040] In step 2, based on the monthly meteorological data, the monthly potential evapotranspiration is first calculated using the Penman-Monteith formula recommended by FAO-56; then the difference between monthly precipitation and potential evapotranspiration is calculated, and then the 1 / 3 / 6 / 12 / 24-month cumulative water deficit is calculated, and finally the 1 / 3 / 6 / 12 / 24-month SPEI is calculated. Station value.

[0041] In step 3, the gridded SPEIbase data of 0.5° in the study area are converted into point data.

[0042] In step 6, based on the results of step 5, the 1 / 3 / 6 / 12 / 24 month scale SPEI is calculated. GS The fused point data were imported into SPSS and exported into batch files in dat format. Based on the Anusplin meteorological interpolation model, the 1km resolution elevation of the study area was used as a covariate to generate 1 / 3 / 6 / 12 / 24-month scale SPEI in batches. GS Dataset.

[0043] Also includes step 7, SPEI GS Fusion point data interpolation result test: Select three indicators: correlation coefficient (r), bias (BIAS) and root mean square error (RMSE) to test the accuracy of the interpolation results.

[0044] Also includes step 8, based on SPEI GS The meteorological drought mutation test is based on the annual scale SPEI of the region. GS -12 data, and the Mann-Kendall mutation test method was used to detect whether there was a mutation point in the meteorological drought time series in the set time period.

[0045] Also includes step 9, based on SPEI GS The change trend and persistence analysis of meteorological drought in China is based on the SPEI of the regional annual scale. GS -12 data were used to analyze the temporal variation characteristics of meteorological drought on an annual scale, and the significance and persistence of the long-term variation trend of meteorological drought were analyzed by combining the Theil-Sen Median, Mann-Kendall significance test and Hurst index.

[0046] The Mann-kendall mutation test, Theil-Sen Median trend analysis, Mann-kendall significance test and Hurst index methods can be used to monitor the spatiotemporal evolution characteristics of SPEIGS in specific areas, such as the southwestern karst area.

[0047] The following specific examples are used to illustrate the implementation principle of a multi-scale SPEI prediction and drought monitoring method that integrates station and grid data of the present invention:

[0048] Step 1: Meteorological data processing;

[0049] Obtain daily-scale meteorological data sets from 1982 to 2019 at stations in the southwestern karst region, including variables such as precipitation, maximum temperature, minimum temperature, average temperature, average wind speed, and sunshine hours, and synthesize daily-scale data into monthly-scale meteorological data through Python programming;

[0050] Step 2: Calculate the 1 / 3 / 6 / 12 / 24-month SPEI of the site Station value;

[0051] Based on the results of step 1, the monthly potential evapotranspiration is first calculated using the Penman-Monteith formula recommended by FAO-56; then the difference between monthly precipitation and potential evapotranspiration is calculated, and then the 1 / 3 / 6 / 12 / 24-month cumulative water deficit is calculated, and finally the SPEI on the 1 / 3 / 6 / 12 / 24-month scale is calculated. Station The specific calculation formulas 1.1-1.6 are as follows:

[0052] ① Based on the Penman-Monteith calculation, the monthly potential evapotranspiration (PET) of the station is:

[0053]

[0054] Where PET is the potential evapotranspiration, Δ is the slope of the saturated water vapor pressure curve, Rn is the net radiation of the surface, G is the soil heat flux, γ is the dry-wet constant, T is the temperature, U2 is the average wind speed, and e s is the saturated water vapor pressure, e a is the actual water vapor pressure.

[0055] ② Calculate the difference between monthly precipitation and potential evapotranspiration at the station (D):

[0056] D i =P i -PET i (1.2)

[0057] Where Pi is the precipitation in the i-th month, and PETi is the potential evapotranspiration in the i-th month.

[0058] ③Construct a multi-time scale water surplus and deficit cumulative sequence and calculate its probability distribution.

[0059]

[0060] Where k is the monthly time scale, 1≤k≤24, and n represents a month.

[0061] ④Use Log-logistic distribution to The water surplus and deficit series were fitted with three parameters and normalized, and the cumulative probability of a given time scale was calculated.

[0062] The three-parameter Log-logistic probability density function is:

[0063]

[0064] Where α, β, and γ are scale, shape, and location parameters, respectively, which can be fitted using the linear moment method. Based on the Log-logistic probability distribution function, the cumulative probability of a given time scale can be calculated as follows:

[0065]

[0066] ⑤ Standardize the probability distribution F(x) to obtain the site SPEI value.

[0067]

[0068] When P≤0.5, P=1-F(x); when P>0.5, P=1-P, and the sign of SPEI is reversed. Wherein C0=2.515517, C1=0.802853, C2=0.010328, d1=1.432788, d2=0.189269, d3=0.001308.

[0069] Step 3: Screen the grid points that meet the conditions

[0070] The 0.5° grid point SPEIbase data of the study area was converted into point data. Each grid point was regarded as a station. With the meteorological station in the study area as the center, the grid points located 20 km away from the meteorological station were selected as the encryption points of the meteorological station.

[0071] Step 4: Extract the grid 1 / 3 / 6 / 12 / 24 month scale SPEI Grid value

[0072] Based on the results of step 3, extract the 1 / 3 / 6 / 12 / 24-month SPEI grid points that meet the conditions from the SPEIbase data. Grid Value, the time span is 1982-2019;

[0073] Step 5: Fusion Grid SPEI Grid With site SPEI Station

[0074] Based on the results of steps 2 and 4, 100 stations are randomly selected from the meteorological stations as the validation data set; then the remaining stations are fused with the grid points to obtain the 1 / 3 / 6 / 12 / 24-month scale SPEI Grid Fusion point data, and convert the fusion point data into xls format;

[0075] Step 6: Generate 1km resolution SPEI at 1 / 3 / 6 / 12 / 24 months using Anusplin interpolation Grid Dataset

[0076] Based on the results of step 5, the 1 / 3 / 6 / 12 / 24 month scale SPEI is calculated. Grid The fused point data were imported into SPSS and exported into batch files in dat format. Based on the Anusplin meteorological interpolation model, the 1km resolution elevation of the study area was used as a covariate to generate 1 / 3 / 6 / 12 / 24-month scale SPEI in batches. Grid Datasets;

[0077] Step 7: SPEI GS Dataset result verification

[0078] The three indicators of correlation coefficient (r), mean absolute error (MAE) and root mean square error (RMSE) are selected to test the accuracy of the interpolation results. The specific formulas 1.7-1.9 are as follows:

[0079]

[0080] Where xi is the site SPEI Station , yi is SPEI GS The interpolation result is and is the mean value and n is the number of samples.

[0081] Step 8: Based on SPEI GS Test of meteorological drought mutation

[0082] Based on the annual scale SPEI in the karst area of ​​Southwest China GS -12 data, the Mann-Kendall mutation test method is used to detect whether there is a mutation point in the meteorological drought time series from 1982 to 2019; the specific formulas 1.10 to 1.14 are as follows:

[0083] ①Construction order column:

[0084]

[0085] In the formula

[0086] ②Define statistics:

[0087]

[0088] In the formula

[0089]

[0090] UFk is a statistical sequence calculated according to the order of the time series. Given a significance level a, if UFk>Ua, it indicates that the sequence has an obvious trend of change. Reverse the order of the time series and calculate UBk. Let UB k =UF k (k=n,n-1,…,1). Given a significant level α=0.05, U0.05=±1.96. If UFk and UBk exceed the critical line, it indicates a significant upward or downward trend. If the two curves of UFk and UBk intersect between the critical lines, the intersection is the mutation point of the time series.

[0091] Step 9: Based on SPEI GS Analysis on the changing trend and persistence of meteorological drought in China

[0092] Based on the annual scale SPEI in the karst area of ​​Southwest China GS -12 data, analyze the temporal variation characteristics of meteorological drought in the period from 1982 to 2019, before (1982-1999) and after (2000-2019) the ecological restoration project, and use the Theil-Sen Median trend analysis, Mann-Kendall significance test and Hurst index combined method to analyze the significance and persistence of the long-term trend of meteorological drought. The specific calculation process is as follows:

[0093] 1Theil-Sen Median trend degree β calculation, formula 1.15 is as follows:

[0094]

[0095] Where β is the pixel-by-pixel SPEI GS -12, i and j are time series; SPEI i and SPEI j The SPEI of the pixel at time i and j respectively GS -12 value; when β>0, it indicates that the time series is in an upward trend, otherwise it is in a downward trend.

[0096] ②Mann-Kendall judges the significance of the change trend. Formulas 1.16 to 1.19 are as follows:

[0097] Define the test statistic Z

[0098]

[0099] In the formula,

[0100]

[0101] Where: Z is the normal distribution statistic, Var(S) is the variance, and sgn is the sign function. At a given confidence level of α, if |Z| ≥ Z 1-α / 2 , it indicates that there is a significant change in the time series at the α level. To judge the significance of the change trend of the SPEI GS -12 time series at the confidence level of α = 0.05.

[0102] ③ The Hurst index is used to judge the persistence of the change trend. The formulas 1.20 to 1.24 are as follows:

[0103] Define the mean sequence:

[0104] Cumulative deviation:

[0105] Range:

[0106] Standard deviation:

[0107] Calculation of the Hurst index: For the ratio If there is a relationship R / S ∝ τ H , then the value of H can be obtained by fitting with the formula 1.24 in the double logarithmic coordinate system (ln(τ), ln(R / S)).

[0108] ln(R / S) = ln(c) + Hln(τ) (1.24)

[0109] Where c is a constant and H is the Hurst index. When H = 0.5, it indicates that the SPEI GS -12 sequence is a random sequence, without persistence and no dependence on past trends; when 0 < H < 0.5, it indicates that the SPEI GS -12 future changes are opposite to the past, and the smaller H is, the stronger the anti-persistence; when 0.5 < H < 0.75, it indicates that the SPEI GS -12 future changes are consistent with the past, with weak persistence; when H ≥ 0.75, it indicates that the SPEI GS -12 future changes are consistent with the past, with strong persistence, and the larger H is, the stronger the persistence.

[0110] To verify the results of the present invention, a 1 km resolution SPEI dataset at 1 / 3 / 6 / 12 / 24-month scales in the southwestern karst region was generated, and the results were evaluated using the validation dataset. The results are as GS shown. From Figure 2 Figure 2It can be seen that the 1 / 3 / 6 / 12 / 24-month scale SPEI predicted by the method of the present invention is GS The data set accuracy is good, R 2 The SPEI is between 0.595 and 0.760, the RMSE is between 0.485 and 0.624, and the MAE is between 0.363 and 0.494. GS -03 is taken as an example, and the monthly data of SPEIbase-03 in eight provinces and regions in southwest China are compared, and the results are shown in FIG3. As can be seen from FIG3, the SPEI predicted by the present invention GS -03 data in the eight provinces and autonomous regions of Southwest China show a high consistency with the monthly change trend of SPEIbase-03 data (r>0.95, RMSE<0.3, MAE<0.3), which can effectively describe the characteristics of dry and wet changes. Taking the drought event in the karst area of ​​Southwest China from 2009 to 2010 as an example, SPEI GS -03 is used to reflect the monthly evolution trend of drought, and compared with SPEIbase-03 data. It can be understood that the general evolution process of the drought event in the southwestern karst area in 2009-2010: it appeared in Guizhou Province in July 2009, and the drought area gradually expanded and intensified from August to September; then from October to March 2010, the drought area further expanded and the degree of drought was the most serious; from April 2010, the drought area gradually decreased and the intensity eased; it basically ended in June. The SPEI predicted by the invention GS The drought evolution and spatial distribution pattern reflected by the SPEIbase-03 data are well consistent with the SPEIbase-03 data, indicating that the SPEI predicted by the method of the present invention is GS The data set is highly accurate and reliable. Figure 4a-4d It can be seen that the dry-wet alternation in the southwestern karst region was quite obvious from 1982 to 2019, and both UF and UB had intersections, and the change trend showed certain differences; after the mutation point appeared in most areas, the UF and UB curves did not exceed ±1.96, and the change trend was not significant. Figure 4aTaking Guangxi as an example, the period before 1995 was mainly a drought period. Between 1988 and 1992, the UF curve was less than -1.96 for 4 years, indicating a significant trend of drought during this period; from 1996 to 2006, the UF curve was greater than 0 and intersected with the UB curve many times, but the trend of change was not significant; from 2007 to 2014, the UF curve was mainly less than 0, intersecting with the UB curve in 2011, and then it showed an upward trend until 2019, but the trend of change did not pass the 0.05 significance test. The trend of meteorological drought in the karst area of ​​Southwest China at different times and its persistence show obvious spatial differences. From 1982 to 2019, the meteorological drought in the study area was mainly weak and persistent, with no significant decrease (43.34%), weak and persistent, with no significant increase (28.61%), and strong and persistent, with no significant decrease (17.96%). The areas with weak and persistent, with no significant decrease were mainly distributed in western Guizhou, eastern and southern Yunnan, areas outside western Sichuan, and parts of Hubei, Hunan, and Chongqing; the areas with weak and persistent, with no significant increase were concentrated in Guangxi, Guizhou, Hunan, and Guangdong; the areas with strong and persistent, with no significant decrease were mainly distributed in Yunnan and western Sichuan. Before the ecological restoration project from 1982 to 1999, meteorological drought in the western part of the study area was mainly weak, persistent and insignificantly increasing (30.88%), and strong, persistent and insignificantly increasing (30.49%) in the eastern part; after the ecological restoration project from 2000 to 2019, meteorological drought in the south and north of the study area was mainly weak, persistent and insignificantly increasing (44.86%), and strong, persistent and insignificantly decreasing (15.91%) in the west, and strong, persistent and insignificantly increasing (13.09%) in the border areas of Guizhou, Guangxi and Hunan, and weak, persistent and insignificantly decreasing (12.51%) in the northeastern and southwestern regions.

[0111] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-scale SPEI prediction and drought monitoring method integrating station and grid data, characterized in that: The following steps are involved: Step 1: Processing meteorological station data, obtaining daily-scale meteorological data sets of meteorological stations in a set time period, and synthesizing daily-scale data into monthly-scale meteorological data; Step 2: Calculate the SPEI of the meteorological station at 1 / 3 / 6 / 12 / 24 months Station value; Step 3: Filter the grid points that meet the set conditions and regard each grid point as a site; take the meteorological site in the study area as the center and filter the grid points outside the set distance of the meteorological site as the encryption points of the site; Step 4: Extract the grid 1 / 3 / 6 / 12 / 24 month scale SPEI Grid Based on the results of step 3, extract the 1 / 3 / 6 / 12 / 24-month SPEI values ​​that meet the conditions from the SPEIbase dataset. Grid value; Step 5: Fusion Grid SPEI Grid With site SPEI Station Based on the results of step 2 and step 4, several stations are randomly selected from the meteorological stations as the validation data set; then the remaining stations are fused with the grid points to obtain the 1 / 3 / 6 / 12 / 24 month scale SPEI GS Fusion point data, and convert the fusion point data into xls format; Step 6: Anusplin interpolation generates 1 / 3 / 6 / 12 / 24 month scale 1km resolution SPEI GS Dataset.

2. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: In step 1, the daily-scale meteorological dataset includes precipitation, maximum temperature, minimum temperature, average temperature, average wind speed, and sunshine hours variables.

3. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 2, characterized in that: In step 2, based on the monthly meteorological data, the Penman-Monteith formula recommended by FAO-56 is first used to calculate the monthly potential evapotranspiration; secondly, the difference between monthly precipitation and potential evapotranspiration is calculated, and then the 1 / 3 / 6 / 12 / 24-month cumulative water deficit is calculated, and finally the SPEI at the 1 / 3 / 6 / 12 / 24-month scale is calculated. Station value.

4. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: In step 3, the gridded SPEIbase data of 0.5° in the study area are converted into point data.

5. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: In step 6, based on the results of step 5, the 1 / 3 / 6 / 12 / 24 month scale SPEI is calculated. GS The fusion point data were imported into SPSS and exported into batch files in dat format. Based on the Anusplin meteorological interpolation model, the 1km resolution elevation of the study area was used as a covariate to generate 1 / 3 / 6 / 12 / 24-month scale SPEI in batches. GS Dataset.

6. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: Also includes step 7, SPEI GS Fusion point data interpolation result test: Select three indicators: correlation coefficient (r), bias (BIAS) and root mean square error (RMSE) to test the accuracy of the interpolation results.

7. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: Also includes step 8, based on SPEI GS The meteorological drought mutation test is based on the annual scale SPEI of the region. GS -12 data, and the Mann-Kendall mutation test method was used to detect whether there was a mutation point in the meteorological drought time series in the set time period.

8. The multi-scale SPEI prediction and drought monitoring method integrating station and grid data according to claim 1, characterized in that: Also includes step 9, based on SPEI GS The change trend and persistence analysis of meteorological drought in China is based on the SPEI of the regional annual scale. GS -12 data were used to analyze the temporal variation characteristics of meteorological drought on an annual scale, and the significance and persistence of the long-term variation trend of meteorological drought were analyzed by combining the Theil-SenMedian, Mann-Kendall significance test and Hurst index.

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