Drought disaster assessment method based on multiple climatic factors

Abstract

The invention discloses a drought disaster assessment method based on multiple climatic factors, and belongs to the technical field of drought disaster assessment, and the method comprises the following steps: S1, collecting data; s2, calculating a drought disaster rate, a disaster rate, a comprehensive loss rate and correlation coefficients between the drought disaster rate and the drought disaster rate and rainfall, average temperature and graded meteorological drought stations; s3, analyzing drought disaster time change characteristics; s4, analyzing drought disaster space change characteristics; s5, key influence characteristics between drought disasters and rainfall are analyzed; s6, key influence characteristics between the drought disaster and the average temperature are analyzed; s7, analyzing key influence characteristics between drought disasters and meteorological drought stations; s8, constructing a fitting model influencing the drought disaster rate and the drought disaster forming rate; s9, predicting a drought disaster rate and a drought disaster forming rate based on the fitting model, and evaluating a drought disaster based on the predicted drought disaster rate and the drought disaster forming rate; the method is high in evaluation precision.

Description

Technical Field

[0001] The invention belongs to the technical field of drought disaster assessment, and in particular relates to a drought disaster assessment method based on multiple climate factors. Background Art

[0002] Drought disaster refers to a disaster in which soil moisture deficiency is caused by insufficient precipitation or uneven precipitation distribution, which in turn affects crop growth, water supply and ecosystem health. The correct assessment of drought disaster is of great significance for formulating drought resistance measures and reducing disaster losses.

[0003] The current method of drought disaster assessment is generally for experts to make predictions based on historical data. This method is highly subjective and the accuracy of the assessment needs to be improved.

[0004] In view of this, a drought disaster assessment method based on multiple climate factors was designed to solve the above problems. Summary of the invention

[0005] In order to solve the problems raised in the above background technology, the present invention provides a drought disaster assessment method based on multiple climate factors, which has the characteristics of high assessment accuracy.

[0006] To achieve the above object, the present invention provides the following technical solution: a drought disaster assessment method based on multiple climate factors, comprising the following steps:

[0007] S1: Collect data on the area of ​​major crops affected by drought disasters, the area of ​​major crops damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the area of ​​major crops planted, precipitation data, average temperature data, daily precipitation data and daily temperature data in various regions of drought disaster areas for several years;

[0008] S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, drought disaster comprehensive loss rate, correlation coefficient between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficient between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficient between drought susceptibility rate and drought disaster rate and graded meteorological drought stations in each region of the drought disaster area for several years;

[0009] S3: Based on the disaster incidence and disaster incidence rates of various regions in drought disaster areas over the past few years, analyze the temporal variation characteristics of drought disasters in drought disaster areas;

[0010] S4: Based on the comprehensive loss rate of drought disasters in various regions of drought disaster areas over the past few years, analyze the spatial variation characteristics of drought disasters in drought disaster areas;

[0011] S5: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and precipitation over the past few years, analyze the key influencing characteristics between drought disasters and precipitation in drought disaster areas;

[0012] S6: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and average temperatures over the past few years, analyze the key influencing characteristics between drought disasters and average temperatures in drought disaster areas;

[0013] S7: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and graded meteorological drought stations in recent years, the key influencing characteristics between drought disasters in drought disaster areas and meteorological drought stations are analyzed;

[0014] S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought stations, a fitting model that affects drought disaster rate and drought disaster rate is constructed. The expression is:

[0015] I1=-1.89P K -0.31T K +4.82N K +11.92

[0016] I2=-0.62P K -0.11T K +2.91N K +5.76

[0017] Where: P K Precipitation data expressed as key influencing features, T K Expressed as the average temperature data of key influencing characteristics, N K Meteorological drought station data expressed as key impact characteristics;

[0018] S9: Predict the drought susceptibility rate and drought disaster rate based on the constructed fitting model that affects the drought susceptibility rate and drought disaster rate, and evaluate drought disasters based on the predicted drought susceptibility rate and drought disaster rate.

[0019] Furthermore, in step S2, the calculation formula of drought disaster rate is:

[0020]

[0021] Where: D1 represents the area of ​​major crops affected by drought disasters, and A represents the planting area of ​​major crops.

[0022] Furthermore, in step S2, the calculation formula of drought disaster rate is:

[0023]

[0024] Where: D2 represents the area of ​​major crops damaged by drought, and A represents the planting area of ​​major crops.

[0025] Furthermore, in step S2, the calculation formula for the comprehensive loss rate of drought disaster is:

[0026] L=I3×90%+(I2-I3)×55%+(I1-I2)×20%

[0027]

[0028] In the formula: I3 represents the drought crop failure rate, I2 represents the drought disaster rate, I1 represents the drought disaster rate, D3 represents the area of ​​major crop failure caused by drought disasters, and A represents the planting area of ​​major crops.

[0029] Furthermore, in step S2, the calculation formulas for the correlation coefficients between the drought susceptibility rate and drought disaster rate and precipitation, and the correlation coefficients between the drought susceptibility rate and drought disaster rate and average temperature are:

[0030]

[0031] Where: X i Expressed as drought disaster rate and drought disaster rate, Y i Expressed as precipitation and mean temperature, Represented as X i The mean of Represented by Y i The mean of .

[0032] Furthermore, in step S2, the calculation steps of the correlation coefficients between the drought disaster rate and the drought disaster rate and the drought grade station times are as follows:

[0033] The daily meteorological drought comprehensive index is calculated based on the daily precipitation data and daily temperature data. The calculation formula is:

[0034] MCI=K a ·(a·SPIW 60 +b·MI 30 +c SPI 90 +d SPI `150 )

[0035] Where: K a It is represented as seasonal adjustment parameter, a, b, c and d are empirical coefficients, SPIW 60Expressed as the standardized weighted precipitation index for the past 60 days, MI 30 Expressed as the relative humidity index for the past 30 days, SPI 90 and SPI 150 Expressed as the standardized precipitation index for the past 90 and 150 days;

[0036] The meteorological drought level classification method is used to classify the meteorological drought stations in the drought disaster area based on the daily meteorological drought comprehensive index;

[0037] The calculation formula for the correlation coefficient between drought disaster rate, drought disaster rate and drought grade station number is as above.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention analyzes the key influencing characteristics between drought disasters and precipitation, average temperature and meteorological drought stations in drought disaster areas based on data, constructs a fitting model affecting drought susceptibility rate and drought disaster rate based on the three key influencing characteristics, predicts the drought susceptibility rate and drought disaster rate based on the constructed fitting model affecting drought susceptibility rate and drought disaster rate, and evaluates drought disasters based on the drought susceptibility rate and drought disaster rate, with high evaluation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is the inter-annual variation of the drought disaster rate and disaster rate of Yunnan agriculture and the test graph;

[0041] Figure 2 This is the spatial distribution map of comprehensive drought loss rate in 16 states (cities) of Yunnan Province at different time periods in the present invention;

[0042] Figure 3 This is a schematic diagram showing the correlation between the drought disaster rate and disaster rate in Yunnan and the precipitation and temperature from January to December in the present invention;

[0043] Figure 4 This is a schematic diagram showing the correlation between the disaster-affected rate and the disaster-stricken rate and the number of stations with moderate drought or above in each month;

[0044] Figure 5 The standardized sequence diagram of the disaster-stricken rate and disaster-stricken rate of agricultural drought in Yunnan Province and the precipitation, average temperature and the number of meteorological drought stations in the key period in May of the present invention;

[0045] Figure 6 This is a schematic diagram of the main crop planting area, effective irrigation area ratio, number of reservoirs and reservoir capacity in Yunnan Province according to the present invention;

[0046] Figure 7 This is a schematic diagram of the agricultural drought disaster rate and disaster rate in Yunnan Province calculated and fitted by the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The present invention provides the following technical solution: a drought disaster assessment method based on multiple climate factors, comprising the following steps:

[0049] S1: Collect data on the area of ​​major crops affected by drought disasters, the area of ​​major crops damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the area of ​​major crops planted, precipitation data, average temperature data, daily precipitation data and daily temperature data in various regions of drought disaster areas for several years;

[0050] S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, drought disaster comprehensive loss rate, correlation coefficient between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficient between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficient between drought susceptibility rate and drought disaster rate and graded meteorological drought stations in each region of the drought disaster area for several years;

[0051] S3: Based on the disaster incidence and disaster incidence rates of various regions in drought disaster areas over the past few years, analyze the temporal variation characteristics of drought disasters in drought disaster areas;

[0052] S4: Based on the comprehensive loss rate of drought disasters in various regions of drought disaster areas over the past few years, analyze the spatial variation characteristics of drought disasters in drought disaster areas;

[0053] S5: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and precipitation over the past few years, analyze the key influencing characteristics between drought disasters and precipitation in drought disaster areas;

[0054] S6: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and average temperatures over the past few years, analyze the key influencing characteristics between drought disasters and average temperatures in drought disaster areas;

[0055] S7: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and graded meteorological drought stations in recent years, the key influencing characteristics between drought disasters in drought disaster areas and meteorological drought stations are analyzed;

[0056] S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought stations, a fitting model that affects drought disaster rate and drought disaster rate is constructed. The expression is:

[0057] I1=-1.89P K -0.31T K +4.82N K +11.92

[0058] I2=-0.62P K -0.11T K +2.91N K +5.76

[0059] Where: P K Precipitation data expressed as key influencing features, T K Expressed as the average temperature data of key influencing characteristics, N K Meteorological drought station data expressed as key impact characteristics;

[0060] S9: Predict the drought susceptibility rate and drought disaster rate based on the constructed fitting model that affects the drought susceptibility rate and drought disaster rate, and evaluate drought disasters based on the predicted drought susceptibility rate and drought disaster rate.

[0061] Specifically, in step S2, the calculation formula for drought disaster rate is:

[0062]

[0063] Where: D1 represents the area of ​​major crops affected by drought disasters, and A represents the planting area of ​​major crops.

[0064] Specifically, in step S2, the calculation formula for drought disaster rate is:

[0065]

[0066] Where: D2 represents the area of ​​major crops damaged by drought, and A represents the planting area of ​​major crops.

[0067] Specifically, in step S2, the calculation formula for the comprehensive loss rate of drought disaster is:

[0068] L=I3×90%+(I2-I3)×55%+(I1-I2)×20%

[0069]

[0070] In the formula: I3 represents the drought crop failure rate, I2 represents the drought disaster rate, I1 represents the drought disaster rate, D3 represents the area of ​​major crop failure caused by drought disasters, and A represents the planting area of ​​major crops.

[0071] Specifically, in step S2, the calculation formulas for the correlation coefficients between the drought susceptibility rate and drought disaster rate and precipitation, and the correlation coefficients between the drought susceptibility rate and drought disaster rate and average temperature are:

[0072]

[0073] Where: X i Expressed as drought disaster rate and drought disaster rate, Y i Expressed as precipitation and mean temperature, Represented as X i The mean of Represented by Y i The mean of .

[0074] Specifically, in step S2, the calculation steps of the correlation coefficients between the drought disaster rate and the drought disaster rate and the drought grade station times are as follows:

[0075] The daily meteorological drought comprehensive index is calculated based on the daily precipitation data and daily temperature data. The calculation formula is:

[0076] MCI=K a ·(a·SPIW 60 +b·MI 30 +c SPI 90 +d SPI `150 )

[0077] Where: K a It is represented as seasonal adjustment parameter, a, b, c and d are empirical coefficients, SPIW 60 Expressed as the standardized weighted precipitation index for the past 60 days, MI 30 Expressed as the relative humidity index for the past 30 days, SPI 90 and SPI 150 Expressed as the standardized precipitation index for the past 90 and 150 days;

[0078] The meteorological drought level classification method is used to classify the meteorological drought stations in the drought disaster area based on the daily meteorological drought comprehensive index;

[0079] The calculation formula for the correlation coefficient between drought disaster rate, drought disaster rate and drought grade station number is as above. Specific embodiments

[0081] Take the agricultural drought in Yunnan as an example:

[0082] See attached Figure 1 , where (a) represents the interannual variation of the drought disaster incidence and disaster rate of Yunnan agriculture, and (b) represents the sliding t test;

[0083] From (a), we can see that the overall disaster rate and disaster rate are both decreasing, with the rates of decrease being 0.49% / 10a and 0.09% / 10a respectively. The disaster-affected area and disaster-affected area reflect the areas of mild and moderate agricultural drought disasters to a certain extent. Therefore, it can be said that the risk of mild agricultural drought disasters in Yunnan is decreasing, while the trend of moderate agricultural drought disaster risk is not obvious. This is inconsistent with the research conclusions of Bai Shuming and Han Lanying based on the agricultural drought disaster loss data of Yunnan from 1950 to 2002 and 1949 to 2012 respectively. Their research pointed out that: Yunnan The increase in the rates of agricultural drought disasters and disasters is mainly due to the different analysis periods, indicating that with the update of data, new changes in agricultural drought disasters in Yunnan have emerged. The maximum values ​​of the disaster rate and disaster rate from 1978 to 2022 were in 2010, which were 45.9% and 33.2% respectively, followed by 2005, which were 33.9% and 19.7% respectively. Only in 2018 were the disaster rate and disaster rate both 0, which was the minimum value. This shows that since 1978, Yunnan has suffered losses from drought disasters every year except 2018, but the degree of loss is different.

[0084] From (b), we can see that although the overall drought disaster rate and disaster rate in Yunnan Province showed a decreasing trend from 1978 to 2022, there was an obvious mutation feature. The disaster rate and disaster rate both experienced a mutation from less to more around 2004, and from more to less around 2013. Both mutations passed the 0.05 reliability test. This can also be seen from (a) and Table 1. The drought disaster rate and disaster rate in Yunnan Province fluctuated from 1978 to 2004, with an average value of 11.28% and 5.02%, respectively, which was lower than the average value from 1978 to 2022. The values ​​are relatively close. 2005-2013 is the period with the maximum disaster rate and disaster rate, with average values ​​of 20.11% and 11.44% respectively, which are 8.23% and 5.67% higher than the average values ​​of 1978-2022. The disaster rate and disaster rate in 2014-2022 are the lowest since 1978, with average values ​​of 6.1% and 2.38% respectively, which are 5.78% and 3.39% lower than the average values ​​of 1978-2022. In other words, there are obvious changes in drought disasters before and after the mutation, and agricultural drought disasters in Yunnan have been significantly reduced in the past 9 years.

[0085] Table 1: Statistics on drought disaster incidence and disaster rate in Yunnan from 1978 to 2022 (unit: %)

[0086]

[0087] See attached Figure 2 , (a) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) in Yunnan from 1996 to 2022, (b) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) in Yunnan from 1996 to 2004, (c) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) in Yunnan from 2005 to 2013, (d) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) in Yunnan from 2014 to 2022;

[0088] Because the interannual variation series showed obvious mutations around 2004 and 2013 and the state (city) data started in 1996, the analysis of spatial characteristics of drought disasters was conducted for four periods: 1996-2022, 1996-2004, 2005-2013, and 2014-2022;

[0089] As shown in (a), the average comprehensive loss rate of drought disasters in Yunnan from 1996 to 2022 is the highest in Baoshan, Dali and Chuxiong, which is 6-7%, followed by Lijiang and Yuxi, which is 5%-6%, and the lowest is Nujiang and Dehong, with a comprehensive loss rate of drought of less than 2%;

[0090] As shown in (b), from 1996 to 2004, the comprehensive loss rate of drought disasters in Chuxiong was 6%-7%, while that in Dehong and Nujiang was less than 2%, which was the same as the average value from 1996 to 2022, and that in other prefectures (cities) was less than the average value from 1996 to 2022;

[0091] As shown in (c), from 2005 to 2013, Dali and Baoshan had the highest comprehensive losses, at 11%-12%, followed by Chuxiong at 10%-11%, Zhaotong at 9%-10%, Nujiang, Dehong, Lincang and Xishuangbanna at less than 6%, and the rest of the prefectures (cities) between 6% and 9%;

[0092] That is, the severe drought disaster in 2005-2013 was widespread across the province. Except for Xishuangbanna and Nujiang, the comprehensive loss rate of the remaining 14 prefectures (cities) was about 2%-6% higher than the average in 1996-2022;

[0093] As shown in (d), the comprehensive loss rate of drought in Lijiang from 2014 to 2022 is 6%-7%, which is higher than the average of 1996-2022. Dehong and Nujiang are the same as the average of 1996-2022. The comprehensive loss rates of the other 13 states (cities) are all lower than the average of 1996-2022, especially in several states (cities) in the west and east, where the comprehensive loss rates of drought are all less than 2%;

[0094] For the convenience of analysis, the years with a comprehensive loss rate of agricultural drought of less than 3% are defined as light disaster years, the years with a comprehensive loss rate of 3%-6% are defined as moderate disaster years, and the years with a comprehensive loss rate of more than 6% are defined as severe disaster years.

[0095] Table 2 shows the statistics of the number of years with disasters of different degrees in 16 prefectures (cities) in Yunnan from 1996 to 2022. It can be found that Dali has the most severe disaster years, with 12 years, followed by Yuxi and Chuxiong, both with 10 years, and Dehong has the least, with only 1 year;

[0096] Compare with Figure 2 , it can be found that the average comprehensive loss rate of Baoshan from 1996 to 2022 is between 6% and 7% as that of Chuxiong and Dali, but Baoshan has fewer severe and moderate disaster years than Chuxiong and Dali. Looking at the data, it is found that the comprehensive loss rate of Baoshan in 2010 was 45.5%, ranking first among the 16 states (cities), and the average value is in the same range as Chuxiong and Dali;

[0097] It can also be seen from Table 2 that the comprehensive drought loss rate in 11 prefectures (cities) in Yunnan showed a decreasing trend, while in 5 prefectures (cities) including Yuxi, Dali, Dehong, Lijiang and Lincang, it showed an increasing trend, mainly in the western part of Yunnan. The most obvious decrease was in Baoshan, with a decreasing rate of 1.48% / 10a, and the most obvious increase was in Lijiang, with an increasing rate of 2.21% / 10a.

[0098] Table 2: Statistics of different severity years of drought disasters in 16 prefectures (cities) in Yunnan (unit: year) and climate tendency rate of comprehensive loss rate (unit: % / 10a)

[0099]

[0100]

[0101] From the above analysis, we can see that, in terms of time, in Yunnan Province, drought disasters caused agricultural losses in 44 of the 45 years from 1978 to 2022. The worst year was 2010, when 45.9% of the area was affected by drought and 33% of the area was a disaster. The drought disaster rate and disaster rate in the province were both decreasing, but there were two obvious mutations around 2004 and 2013, from less to more in 2004 and from more to less in 2013.

[0102] From a spatial perspective, from 1996 to 2022, the comprehensive loss rate of drought disasters in 5 states (cities) showed an increasing trend, and in 11 states (cities) it showed a decreasing trend. The comprehensive loss rate of drought disasters in Dali, Chuxiong and Baoshan in Yunnan Province was the highest. From 2005 to 2013, the severe agricultural drought disaster was widespread across the province. Except for Xishuangbanna and Nujiang, the agricultural drought losses in the remaining 14 states (cities) were higher than the average from 1996 to 2022. In the period of the lightest drought disaster from 2014 to 2022, the drought disaster losses in 13 of the 16 states (cities) were lower than the average from 1996 to 2022.

[0103] Precipitation and temperature are the most important disaster-causing factors of meteorological drought, and are closely related to the distribution pattern and development trend of drought disaster risk. Therefore, the relationship between the drought disaster rate, disaster rate, precipitation and temperature in Yunnan from 1978 to 2022 is first analyzed;

[0104] From 1978 to 2022, the correlation coefficients of the agricultural drought disaster rate and disaster rate in Yunnan with annual precipitation were -0.37 and -0.27, respectively, and the correlation coefficients with annual average temperature were 0.12 and 0.17, respectively. Only the correlation between precipitation and disaster rate passed the 0.05 confidence test, and the correlation with precipitation was significantly higher than the average temperature, which shows that the impact of precipitation on agricultural drought disasters in Yunnan is greater than that of average temperature, and the annual average temperature has no significant impact on agricultural drought disasters in Yunnan.

[0105] Studies have shown that drought disaster losses are often closely related to changes in temperature and precipitation during certain key periods. Therefore, the correlation between the agricultural drought disaster rate and disaster rate and precipitation and average temperature from January to December was calculated.

[0106] As attached Figure 3 As shown, it can be seen that the disaster rate and disaster rate are negatively (positively) correlated with precipitation (temperature) in most months;

[0107] The disaster-affected rate and disaster-stricken rate are negatively correlated with the precipitation in January-February, April-August and November, and positively correlated with the precipitation in other months. The negative correlation with precipitation in May is the most significant, which is -0.45 and -0.38 respectively, both passing the reliability test of 0.01. The disaster-affected rate and disaster-stricken rate are positively correlated with the temperature in January-February and April-August, and negatively correlated with the precipitation in other months. The correlation with the average temperature in May is the most significant, which is 0.46 and 0.4 respectively, both passing the reliability test of 0.01.

[0108] From the above analysis, we can see that the agricultural drought disaster rate and disaster rate in Yunnan are significantly negatively correlated and positively correlated with the precipitation and average temperature in May, respectively, and the correlation coefficient is greater than the annual precipitation and annual temperature, which is the key influencing factor of drought disasters;

[0109] Zhang Qiang's analysis shows that drought disasters in the south (including southwest and south China) are related to precipitation in April, June, and July and high temperatures in June, July, and December. This is different from the conclusion of this paper, indicating that the key influencing factors of agricultural drought disasters in Yunnan are significantly different from those in the whole south. This is related to the geographical location of Yunnan in the low-latitude plateau. May is the transition period between dry and wet seasons in Yunnan, and it is the key period for water demand of major crops. Summer corn is in the sowing and germination stage, flue-cured tobacco and rice are in the transplanting stage, coffee is in the flowering stage, etc. The demand for agricultural water increases rapidly. After a dry season lasting half a year, the water storage in reservoirs and ponds has dropped seriously, so the impact of precipitation and temperature during this period is often greater than that of other periods.

[0110] Drought can be divided into meteorological drought, agricultural drought, hydrological drought, socio-economic drought, etc. Meteorological drought is the source of the other three types of drought disasters. In drought monitoring in Southwest and South China, the MCI index is superior to other drought indices in terms of effectiveness and monitoring capability. Therefore, MCI is used as a meteorological drought characterization indicator.

[0111] The daily MCI index from 1978 to 2022 was calculated using daily precipitation and temperature data. The number of meteorological drought stations of different levels in Yunnan in each year and month was counted according to the meteorological drought grade classification standards. The correlation between Yunnan's drought disaster rate and disaster rate and the number of drought stations of different levels in each year was then calculated.

[0112] The results show that the correlation between the disaster rate and disaster rate and the stations with mild drought or above (including mild drought, moderate drought, severe drought and extreme drought) is 0.46 and 0.40 respectively, and the correlation between the stations with moderate drought or above (including moderate drought, moderate drought and extreme drought) is 0.49 and 0.43 respectively, both reaching the reliability test of 0.01;

[0113] Compared with the correlation with annual precipitation and temperature, the correlation coefficient between meteorological drought stations and agricultural drought disaster rate and disaster rate is more obvious, which shows that drought disasters are relatively more affected by meteorological drought;

[0114] In order to analyze the relationship between agricultural drought disasters and monthly meteorological drought, the correlation coefficients between the disaster-affected rate and the disaster-stricken rate and the number of stations with droughts above medium or above in Yunnan from January to December were calculated.

[0115] As attached Figure 4 As shown, it can be found that meteorological drought and drought disasters are positively correlated from January to September, and negatively correlated from October to December. The correlation coefficient in April among January to September did not pass the reliability test, and the correlations with the disaster rate in the remaining months all passed the reliability test of 0.05. The disaster rate did not pass the reliability test in May and August, and the negative correlations in October to December did not pass the reliability test.

[0116] The correlation between drought disaster rate and disaster rate and the cumulative sum of drought-affected stations from January to March and from May to September was further calculated. The correlation coefficients were 0.63 and 0.58 respectively, both of which passed the reliability test of 0.001, which was much higher than the correlation with the drought-affected stations throughout the year.

[0117] This shows that the impact of meteorological drought on agricultural drought disasters from January to March and from May to September is higher than that of the whole year, and it is the key period of impact;

[0118] The above analysis shows that precipitation and average temperature in May, meteorological drought from January to March and from May to September are the key influencing periods of drought disasters in Yunnan. To further analyze the relationship between them, the standardized time series of agricultural drought disasters’ susceptibility rate, disaster rate and the cumulative sum of precipitation and average temperature in May, and stations with moderate drought or above from January to March and from May to September from 1978 to 2022 are calculated;

[0119] As attached Figure 5 As shown in the figure, when the drought disaster rate and disaster rate are negative, most of the years correspond to more precipitation and lower average temperature in May in Yunnan, and the number of stations with moderate drought or above in the critical period is negative, while the opposite is true when it is positive;

[0120] However, during the 9 years from 2014 to 2022, Yunnan's May precipitation was negative in 6 years, the average temperature in May was positive in 7 years, and the meteorological drought station was positive in 5 years. That is, the drought disaster-causing factors in the critical period are conducive to the occurrence of severe drought disasters. However, from a temporal perspective, drought disasters were positive only in 2019, and the spatial distribution was also light in most areas. At the same time, from the perspective of the surrounding areas, Figure 5 It can also be seen that the precipitation in Yunnan in May showed a decreasing trend, while the average temperature in May and the number of drought stations in the critical period showed an increasing trend, which is inconsistent with the changing trend of the drought disaster incidence and disaster rate;

[0121] The correlations between precipitation, average temperature and meteorological drought stations in May of 1978-2022, 1978-2013 and 2014-2022 and the disaster rate and disaster severity rate in the corresponding period were calculated respectively, as shown in Table 3. It was found that the correlations between precipitation, average temperature and meteorological drought stations in May of 1978-2022 and 1978-2013 and the disaster rate and disaster severity rate all passed the reliability test of 0.01. In 2014-2022, the correlations between precipitation in May and disaster severity rate, and between average temperature in May and disaster severity rate and disaster severity rate only passed the reliability test of 0.05, while the correlations between precipitation in May and disaster severity rate, and between drought stations in the critical period and disaster severity rate and disaster severity rate all failed to pass the reliability test. Therefore, it can be concluded that precipitation and temperature in May of 2014-2022 are still the key influencing factors of drought disasters in Yunnan, but the degree of influence has weakened, while meteorological drought in the critical period has no obvious corresponding relationship with drought disasters.

[0122] Table 3: Correlation between precipitation and average temperature in May in Yunnan and the number of meteorological drought stations and the disaster rate and disaster rate at different times

[0123]

[0124] Note: * represents passing the 0.05 reliability test, ** represents passing the 0.01 reliability test

[0125] In summary, the correlation between May precipitation, May average temperature, and the number of meteorological drought stations from January to March and May to September and drought disasters in Yunnan is the best, which is significantly better than the correlation with the annual climate elements. They are the key influencing factors of drought disasters in Yunnan. However, after 2014, when the drought disaster rate and disaster rate were significantly reduced, the corresponding relationship between them weakened.

[0126] The above analysis shows that the drought disaster rate and disaster rate in Yunnan from 1978 to 2022 are decreasing, while the climate factors in the critical period are not conducive to the reduction of drought disasters. This feature is more obvious after 2014, which shows that the extent of drought disasters is affected by meteorological factors, and may also be affected by other social factors such as crop planting area and drought disaster prevention and control capabilities;

[0127] The effective irrigation area is an important indicator reflecting the drought resistance of agricultural production in different regions and is the main influencing factor for the stability of grain production in arid climate areas. Therefore, the formula is used to calculate the proportion of effective irrigation area and count the corresponding number of reservoirs and reservoir capacity to analyze the social factors of drought disaster changes in Yunnan. The expression is as follows:

[0128]

[0129] Where: I r It is expressed as the ratio of effective irrigation area, S represents the effective irrigation area, and A represents the main crop planting area;

[0130] As attached Figure 6 As shown, (a) represents the proportion of main crop planting area and effective irrigation area in Yunnan from 1978 to 2022, and (b) represents the number of reservoirs and reservoir capacity from 1987 to 2022;

[0131] From (a), we can see that the crop planting area is increasing, with an increase rate of 82.5hm2 / 10a. The average value from 1978 to 2022 is 547.4hm2, from 1978 to 2013 is 511.4hm2, and from 2014 to 2022 is 691.6hm2, which is 144.2hm2 more than the average from 1978 to 2022. That is, the exposure of crop drought disasters in Yunnan has increased since 2014. In the same period, The proportion of effective irrigation area in Yunnan shows an increasing trend, with an average value of 24.8% from 1978 to 2022, 24.2% from 1978 to 2013, and 27.2% from 2014 to 2022, 2.4% higher than the average from 1978 to 2022. The proportion of effective irrigation area from 1978 to 2013 was between 21% and 25%, and after 2014 it was between 25% and 29%, that is, the effective irrigation area after 2014 has increased significantly compared with before;

[0132] From (b), we can find that both of them show an increasing trend, with a significant increase after 2014. From 1987 to 2022, the average number of reservoirs and 28.3 billion cubic meters were 5,504, 5,079 and 10.4 billion cubic meters from 1987 to 2013, and 678.1 and 82.1 billion cubic meters from 2014 to 2022, respectively. Compared with 1987-2022, there are 1,277 more reservoirs and 53.8 billion cubic meters, respectively. The reservoir capacity is more than twice the average of 1987-2022.

[0133] From the above analysis, we can know that after 2014, under the background of the increase in the area of ​​drought disasters and the occurrence of severe meteorological drought, the enhancement of comprehensive drought disaster defense capabilities is one of the important reasons for the reduction of agricultural drought disasters in Yunnan. The enhancement of defense capabilities is mainly reflected in the expansion of effective irrigation area caused by the increase in the number and capacity of reservoirs. This may be because Yunnan suffered a serious four-year drought from 2009 to 2012, and the direct economic losses due to drought reached 39.6 billion yuan. In order to reduce the losses caused by subsequent drought disasters, government departments at all levels continued to increase investment in water conservancy infrastructure construction. According to statistics, the scale of water conservancy investment in the province increased at a high speed of 40% per year from 2010 to 2013, and the cumulative investment in four years exceeded 70 billion yuan, which effectively improved the province's drought disaster prevention and mitigation capabilities and water conservancy basic guarantee level;

[0134] From the above analysis, it can be seen that the agricultural drought disaster susceptibility rate and disaster rate in Yunnan have a good correspondence with the precipitation, temperature and drought stations in the key period, which is better than the correlation with the annual climate elements. Therefore, the fitting relationship between the precipitation in May, the average temperature, the meteorological drought stations in the key period and their combination and the agricultural drought susceptibility rate and disaster rate in Yunnan is established, and the correlation, standard error and mean absolute error between the predicted value and the statistical value of the fitting model are calculated, as shown in Table 4 and Table 5;

[0135] Table 4: Parameters related to the estimated values ​​and statistical values ​​of the disaster rate fitting model based on the temperature, precipitation and drought stations throughout the year and during the critical period

[0136]

[0137]

[0138] Note: * represents passing the 0.05 reliability test, ** represents passing the 0.01 reliability test, *** represents passing the 0.001 reliability test

[0139] Table 5: Parameters related to the estimated values ​​and statistical values ​​of the disaster rate fitting model based on the temperature, precipitation and drought stations throughout the year and during the critical period

[0140]

[0141] Note: * represents passing the 0.05 reliability test, ** represents passing the 0.01 reliability test, *** represents passing the 0.001 reliability test

[0142] It can be found that the correlation between the estimated value and the statistical value calculated by the fitting model established by using the May precipitation, average temperature and the key period meteorological drought station is smaller than that of the fitting model after the three factors are integrated. The correlation between the estimated value and the statistical value of the fitting model with three factors is 0.66 and 0.59 respectively, both reaching the reliability test of 0.001. At the same time, the standard error and the mean absolute error are also smaller than the calculated values ​​of the fitting model established by the single factor.

[0143] The fitting model based on annual climate factors alone and multi-factors was compared. The results showed that the correlation between the estimated value and the statistical value of the fitting model based on annual meteorological factors alone was also smaller than that of the multi-factor integrated fitting model. At the same time, the correlation coefficient (accurate error and mean absolute error) between the estimated value and the statistical value of the model based on annual factors was smaller (larger) than that of the fitting model based on key period factors.

[0144] Therefore, the three elements of May precipitation, May average temperature, and key period meteorological drought stations were finally used to construct a fitting model for the agricultural drought disaster rate and disaster rate in Yunnan:

[0145] I1=-1.89P K -0.31T K +4.82N K +11.92

[0146] I2=-0.62P K -0.11T K +2.91N K +5.76

[0147] Where: P K Precipitation data expressed as key influencing features, T K Expressed as the average temperature data of key influencing characteristics, N K Meteorological drought station data expressed as key impact characteristics;

[0148] As attached Figure 7 As shown, (a) represents the statistical and fitted agricultural drought disaster rate in Yunnan from 1978 to 2022, and (b) represents the statistical and fitted agricultural drought disaster rate in Yunnan from 1978 to 2022;

[0149] It can be seen that the drought disaster rate and disaster rate fitted by the climate elements in the key period are closer to the statistical values, especially in 2010, 2005, 1979, and 2019, the years with severe drought disasters, the drought disaster rate and disaster rate based on the key period fitting are closer to the statistical values ​​than the estimated values ​​based on the meteorological elements throughout the year. From 1978 to 2013, the estimated values ​​in the same years are generally lower than the statistical values. After 2013, the estimated values ​​in the years with mild drought disasters are generally higher than the statistical values. This is consistent with the analysis in Part 3, that is, starting around 2014, the corresponding relationship between meteorological elements and agricultural drought disasters in Yunnan has changed;

[0150] In summary, the model estimates based on the multi-factor fitting of the critical period climate have a good correspondence with the statistical values, which shows that the model has a good effect on estimating the losses of agricultural drought disasters in Yunnan. At the same time, the correlation coefficient between the estimated values ​​and statistical values ​​of any single-factor fitting model is lower than that of the multi-factor combination. The correlation coefficient between the estimated values ​​and statistical values ​​of the multi-factor fitting model based on the critical period is higher than that of the fitting model established with the full-year factors. On the one hand, this shows that the multi-factor fitting model is better than the single-factor fitting model. On the other hand, it shows that the model established based on the critical period is better than the model established with annual climate factors. This corresponds to the above analysis, which further proves that the climate factors in the critical period have a greater impact on drought disasters than the climate factors at the annual scale.

[0151] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A drought disaster assessment method based on multiple climate factors, characterized in that: The following steps are involved: S1: Collect data on the area of ​​major crops affected by drought disasters, the area of ​​major crops damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the area of ​​major crops planted, precipitation data, average temperature data, daily precipitation data and daily temperature data in various regions of drought disaster areas for several years; S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, drought disaster comprehensive loss rate, correlation coefficient between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficient between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficient between drought susceptibility rate and drought disaster rate and graded meteorological drought stations in each region of the drought disaster area for several years; S3: Based on the disaster incidence and disaster incidence rates of various regions in drought disaster areas over the past few years, analyze the temporal variation characteristics of drought disasters in drought disaster areas; S4: Based on the comprehensive loss rate of drought disasters in various regions of drought disaster areas over the past few years, analyze the spatial variation characteristics of drought disasters in drought disaster areas; S5: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and precipitation over the past few years, analyze the key influencing characteristics between drought disasters and precipitation in drought disaster areas; S6: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and average temperatures over the past few years, analyze the key influencing characteristics between drought disasters and average temperatures in drought disaster areas; S7: Based on the temporal and spatial variation characteristics of drought disasters in drought disaster areas, as well as the correlation coefficients between drought disaster rates and drought disaster rates in various regions of drought disaster areas and graded meteorological drought stations in recent years, the key influencing characteristics between drought disasters in drought disaster areas and meteorological drought stations are analyzed; S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought stations, a fitting model that affects drought disaster rate and drought disaster rate is constructed. The expression is: I1=-1.89P K -0.31T K +4.82N K +11.92 I2=-0.62P K -0.11T K +2.91N K +5.76 Where: P K Precipitation data expressed as key influencing features, T K Expressed as the average temperature data of key influencing characteristics, N K Meteorological drought station data expressed as key impact characteristics; S9: Predict the drought susceptibility rate and drought disaster rate based on the constructed fitting model that affects the drought susceptibility rate and drought disaster rate, and evaluate drought disasters based on the predicted drought susceptibility rate and drought disaster rate.

2. The drought disaster assessment method based on multiple climate factors according to claim 1 is characterized in that: In step S2, the calculation formula of drought disaster rate is: Where: D1 represents the area of ​​major crops affected by drought disasters, and A represents the planting area of ​​major crops.

3. The drought disaster assessment method based on multiple climate factors according to claim 1, characterized in that: In step S2, the calculation formula of drought disaster rate is: Where: D2 represents the area of ​​major crops damaged by drought, and A represents the planting area of ​​major crops.

4. The drought disaster assessment method based on multiple climate factors according to claim 1 is characterized in that: In step S2, the calculation formula for the comprehensive loss rate of drought disaster is: L=I3×90%+(I2-I3)×55%+(I1-I2)×20% In the formula: I3 represents the drought crop failure rate, I2 represents the drought disaster rate, I1 represents the drought disaster rate, D3 represents the area of ​​major crop failure caused by drought disasters, and A represents the planting area of ​​major crops.

5. The drought disaster assessment method based on multiple climate factors according to claim 1 is characterized in that: In step S2, the calculation formulas for the correlation coefficients between the drought susceptibility rate and drought disaster rate and precipitation, and the correlation coefficients between the drought susceptibility rate and drought disaster rate and average temperature are: Where: X i Expressed as drought disaster rate and drought disaster rate, Y i Expressed as precipitation and mean temperature, Represented as X i The mean of Represented by Y i The mean of .

6. The drought disaster assessment method based on multiple climate factors according to claim 1 is characterized in that: In step S2, the calculation steps of the correlation coefficients between the drought disaster rate and the drought disaster rate and the drought grade station times are as follows: The daily meteorological drought comprehensive index is calculated based on the daily precipitation data and daily temperature data. The calculation formula is: MCI=K a ·(a·SPIW 60 +b·MI 30 +c·SPI 90 +d·SPI `150 ) Where: K a It is represented as seasonal adjustment parameter, a, b, c and d are empirical coefficients, SPIW 60 Expressed as the standardized weighted precipitation index for the past 60 days, MI 30 Expressed as the relative humidity index for the past 30 days, SPI 90 and SPI 150 Expressed as the standardized precipitation index for the past 90 and 150 days; The meteorological drought level classification method is used to classify the meteorological drought stations in the drought disaster area based on the daily meteorological drought comprehensive index; The calculation formula for the correlation coefficient between drought disaster rate, drought disaster rate and drought grade station number is as above.

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