Drought disaster assessment method based on multiple climate factors

Abstract

The present invention discloses a drought disaster assessment method based on multiple climate factors, which belongs to the technical field of drought disaster assessment and comprises the following steps: S1: collecting data; S2: calculating the drought disaster rate, drought disaster rate and comprehensive loss rate, as well as the correlation coefficients between the drought disaster rate and drought disaster rate and precipitation, average temperature and graded meteorological drought stations; S3: analyzing the temporal variation characteristics of drought disasters; S4: analyzing the spatial variation characteristics of drought disasters; S5: analyzing the key influencing characteristics between drought disasters and precipitation; S6: analyzing the key influencing characteristics between drought disasters and average temperature; S7: analyzing the key influencing characteristics between drought disasters and meteorological drought stations; S8: constructing a fitting model affecting the drought disaster rate and drought disaster rate; S9: predicting the drought disaster rate and drought disaster rate based on the fitting model, and assessing the drought disaster based on the predicted drought disaster rate and drought disaster rate. The present invention has high assessment accuracy.

Description

Technical Field

[0001] The present 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 is deficient due to insufficient 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 prevention measures and reducing disaster losses.

[0003] The current drought disaster assessment method is generally expert prediction 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 severely damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the planting area of ​​major crops, precipitation data, average temperature data, daily precipitation data, and daily temperature data in various regions of drought disaster areas over several years;

[0008] S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, comprehensive drought loss rate, correlation coefficients between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficients between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficients 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 rate of each region in the drought disaster area over the past few years, analyze the temporal variation characteristics of drought disasters in the drought disaster area;

[0010] S4: Based on the comprehensive loss rate of drought disasters in various regions 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 susceptibility and drought disaster rates and precipitation in various regions 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 susceptibility and drought disaster rates and average temperature in various regions over several years, analyze the key influencing characteristics between drought disasters and average temperature 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 meteorological drought station levels over several years, analyze the key influencing characteristics between drought disasters in drought-disaster areas and meteorological drought stations;

[0014] S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought station number, a fitting model that affects the 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 affecting 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 for the 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 for the drought disaster rate is:

[0023]

[0024] Where: D2 represents the area of ​​major crops affected 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] Where: 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 crops that have no harvest due to 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, drought severity rate and precipitation, and the correlation coefficients between the drought susceptibility rate, drought severity rate and average temperature are as follows:

[0030]

[0031] Where: X i Expressed as drought disaster rate and drought disaster rate, Y i Expressed as precipitation and mean temperature, Indicated as X i The mean of Indicated as 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 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 expressed as seasonal adjustment parameters, 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 the drought disaster rate and drought disaster rate and the 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 that affects the 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, 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 in Yunnan agriculture and the test graph;

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

[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 by month;

[0044] Figure 5 This is a standardized sequence diagram of the agricultural drought disaster rate and disaster rate in Yunnan Province, as well as the precipitation, average temperature, and the number of meteorological drought stations in the key period in May;

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

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

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 severely damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the planting area of ​​major crops, precipitation data, average temperature data, daily precipitation data, and daily temperature data in various regions of drought disaster areas over several years;

[0050] S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, comprehensive drought loss rate, correlation coefficients between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficients between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficients 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 rate of each region in the drought disaster area over the past few years, analyze the temporal variation characteristics of drought disasters in the drought disaster area;

[0052] S4: Based on the comprehensive loss rate of drought disasters in various regions 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 susceptibility and drought disaster rates and precipitation in various regions 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 susceptibility and drought disaster rates and average temperature in various regions over several years, analyze the key influencing characteristics between drought disasters and average temperature 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 meteorological drought station levels over several years, analyze the key influencing characteristics between drought disasters in drought-disaster areas and meteorological drought stations;

[0056] S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought station number, a fitting model that affects the 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 affecting 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 the 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 the drought disaster rate is:

[0065]

[0066] Where: D2 represents the area of ​​major crops affected 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] Where: 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 crops that have no harvest due to 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, the drought disaster rate, and precipitation, and the correlation coefficients between the drought susceptibility rate, the 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, Indicated as X i The mean of Indicated as Y i The mean of .

[0074] Specifically, in step S2, the calculation steps for 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 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 expressed as seasonal adjustment parameters, 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 the drought disaster rate and drought disaster rate and the 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 agricultural drought disaster incidence and disaster rate in Yunnan, and (b) represents the sliding t-test;

[0083] From (a), we can see that both the disaster-affected rate and the disaster-stricken rate are generally showing a decreasing trend, with the rates of decrease being 0.49% / 10a and 0.09% / 10a respectively. The affected area and the disaster-stricken 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 conclusions of Bai Shuming and Han Lanying based on the agricultural drought disaster loss data of Yunnan from 1950 to 2002 and from 1949 to 2012 respectively. Their research pointed out that: Yunnan The increase in the agricultural drought disaster and disaster rate 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 disaster rate and disaster rate from 1978 to 2022 were both in 2010, at 45.9% and 33.2% respectively, followed by 2005, at 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 extent of the losses has varied.

[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 was the period with the maximum disaster rate and disaster rate, with average values ​​of 20.11% and 11.44% respectively, which were 8.23% and 5.67% higher than the average values ​​of 1978-2022. 2014-2022 was the period with the minimum disaster rate and disaster rate since 1978, with average values ​​of 6.1% and 2.38% respectively, which were 5.78% and 3.39% lower than the average values ​​of 1978-2022. In other words, there was a significant change in drought disasters before and after the mutation, and agricultural drought disasters in Yunnan have been significantly reduced in the past nine years.

[0085] Table 1: Statistics on drought disaster incidence and severity 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) of Yunnan from 1996 to 2022, (b) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) of Yunnan from 1996 to 2004, (c) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) of Yunnan from 2005 to 2013, and (d) represents the spatial distribution of the comprehensive loss rate of drought disasters in 16 prefectures (cities) of 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 spatial characteristics analysis 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 was the highest in Baoshan, Dali, and Chuxiong, at 6-7%, followed by Lijiang and Yuxi, at 5%-6%, and the lowest in Nujiang and Dehong, with a comprehensive loss rate 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. The rest of the prefectures (cities) were all less than the average 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 from 2005 to 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 from 1996 to 2022;

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

[0094] For the convenience of analysis, years with a comprehensive loss rate of agricultural drought of less than 3% are defined as light disaster years, years with a loss rate of 3%-6% are defined as moderate disaster years, and years with a 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 the 16 prefectures (cities) of Yunnan from 1996 to 2022. It can be found that Dali has the most severely affected 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. Checking the data, it is found that the comprehensive loss rate of Baoshan in 2010 was 45.5%, ranking first among the 16 prefectures (cities), and the average subsequent value is in the same range as Chuxiong and Dali;

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

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

[0099]

[0100]

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

[0102] From a spatial perspective, the comprehensive loss rate of drought disasters increased in five prefectures (cities) from 1996 to 2022, while it decreased in 11 prefectures (cities). Dali, Chuxiong, and Baoshan in Yunnan had the highest comprehensive loss rates. Severe agricultural drought disasters affected a large part of the province from 2005 to 2013. Except for Xishuangbanna and Nujiang, agricultural drought losses in the remaining 14 prefectures (cities) were higher than the 1996-2022 average. During the period of the mildest drought disasters from 2014 to 2022, 13 of the 16 prefectures (cities) experienced drought losses lower than the 1996-2022 average.

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

[0104] From 1978 to 2022, the correlation coefficients between Yunnan's agricultural drought susceptibility rate and disaster rate and 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 susceptibility rate passed the 0.05 confidence test, and the correlation with precipitation was significantly higher than that with average temperature. This indicates that precipitation has a greater impact on agricultural drought disasters in Yunnan than average temperature, and annual average temperature has no significant impact on agricultural drought disasters in Yunnan.

[0105] Research shows that drought losses are often closely related to changes in temperature and precipitation during certain key periods. Therefore, we calculated the correlation between agricultural drought susceptibility and disaster rates and precipitation and average temperature from January to December.

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

[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 coefficients are greater than those of annual precipitation and annual temperature, indicating that they are key influencing factors 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 Yunnan's geographical location on a low-latitude plateau. May is the transition period between dry and wet seasons in Yunnan, and it is a critical period for water demand for major crops. Summer corn is in the sowing and seedling stage, tobacco and rice are in the transplanting stage, and coffee is in full bloom. Agricultural water demand increases rapidly. After the six-month dry season, the water storage in reservoirs and ponds drops sharply. Therefore, the impact of precipitation and temperature during this period is often greater than that in other periods.

[0110] Drought can be categorized as meteorological, agricultural, hydrological, and socioeconomic. Meteorological drought is the root cause of the other three types of drought. In drought monitoring in Southwest and South China, the MCI outperforms other drought indices in terms of both effectiveness and monitoring capability. Therefore, the MCI is used as a meteorological drought 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 Province was counted according to the meteorological drought 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 disaster rate and 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 between 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 moderate or above drought 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 of April among January to September did not pass the reliability test, and the correlation coefficients of the remaining months with the disaster rate all passed the 0.05 reliability test. The disaster rate did not pass the reliability test in May and August, and the negative correlations from October to December did not pass the reliability test.

[0116] Further calculations of the correlation between the drought impact rate and the drought severity rate and the cumulative number of stations experiencing moderate or above drought from January to March and from May to September showed correlation coefficients of 0.63 and 0.58, respectively, both passing the 0.001 reliability test, much higher than the correlation with the number of stations experiencing moderate or above drought 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 greater than that of the whole year, and these are the key periods of impact;

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

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

[0120] However, during the 9 years from 2014 to 2022, Yunnan’s May precipitation was negative for 6 years, the average temperature in May was positive for 7 years, and the meteorological drought station was positive for 5 years. That is, the drought disaster-causing factors during the critical period are conducive to the occurrence of severe drought disasters. However, from a temporal perspective, the drought disaster was positive only in 2019, and the spatial distribution in most areas was also relatively mild. At the same time, from the perspective of the surrounding 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 during the critical period showed an increasing trend, which is inconsistent with the changing trends of the drought disaster incidence and disaster rate.

[0121] The correlations between precipitation, average temperature, and the number of meteorological drought stations in the critical period and the disaster rate and disaster severity rate in May from 1978 to 2022, 1978 to 2013, and 2014 to 2022 were calculated, as shown in Table 3. It was found that the correlations between precipitation, average temperature, and the number of meteorological drought stations in the critical period and the disaster rate and disaster severity rate in May from 1978 to 2022 and 1978 to 2013 all passed the 0.01 reliability test. In 2014 to 2022, the correlations between precipitation in May and the disaster severity rate, and between the average temperature in May and the disaster rate and disaster severity rate only passed the 0.05 reliability test, while the correlations between precipitation in May and the disaster rate, and between the number of drought stations in the critical period and the disaster rate and disaster severity rate all failed the reliability test. Therefore, it can be concluded that precipitation and temperature in May from 2014 to 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 Yunnan in May, as well as the number of meteorological drought stations and the disaster rate and disaster rate at different times

[0123]

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

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

[0126] The above analysis shows that the drought susceptibility and severity rates in Yunnan Province showed a decreasing trend from 1978 to 2022, while the climate factors during the critical period showed changes that were unfavorable for reducing drought disasters. This characteristic became more obvious after 2014. This indicates that the extent of drought disasters is affected by meteorological factors as well as other social factors such as crop planting area and drought disaster prevention and control capabilities.

[0127] Effective irrigation area is an important indicator reflecting the drought resistance of agricultural production in different regions and a major factor affecting the stability of grain production in arid regions. Therefore, we use a formula to calculate the proportion of effective irrigation area and count the corresponding number of reservoirs and reservoir capacity to analyze the social factors affecting the changes in drought disasters 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 major crop planting areas and effective irrigation areas 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 has an increasing trend, with an increasing 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 effectively irrigated area in Yunnan has shown an increasing trend, with an average 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 effectively irrigated area was between 21% and 25% from 1978 to 2013, and between 25% and 29% after 2014, indicating that the effectively irrigated area has increased significantly since 2014.

[0132] From (b), we can find that both are showing an increasing trend, with a significant increase after 2014. From 1987 to 2022, the average number of reservoirs and the storage capacity were 5,504 and 28.3 billion cubic meters, respectively. From 1987 to 2013, they were 5,079 and 10.4 billion cubic meters, respectively. From 2014 to 2022, they were 6,781 and 82.1 billion cubic meters, respectively. These are 1,277 more reservoirs and 53.8 billion cubic meters than from 1987 to 2022, respectively. The storage capacity of reservoirs is more than twice the average from 1987 to 2022.

[0133] From the above analysis, we can see that after 2014, against the backdrop of an increase in the area of ​​drought-affected areas and a heavier occurrence of meteorological drought, the enhanced comprehensive drought disaster defense capability is one of the important reasons for the reduction in agricultural drought disasters in Yunnan. The enhanced defense capability 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 severe four-year drought from 2009 to 2012, and the direct economic losses due to the drought amounted to 39.6 billion yuan. 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 rapidly at an annual rate of 40% from 2010 to 2013, and the cumulative investment in four years exceeded 70 billion yuan, effectively improving the province's drought disaster prevention and mitigation capabilities and the level of water conservancy infrastructure security.

[0134] The above analysis shows that the agricultural drought disaster susceptibility and severity rates in Yunnan have a good correspondence with precipitation, temperature, and drought stations during the critical period, which is better than the correlation with the annual climate factors. Therefore, fitting equations were established for precipitation in May, average temperature, meteorological drought stations during the critical period, and their combination with the agricultural drought susceptibility and severity rates in Yunnan. The correlation, standard error, and mean absolute error of the predicted values ​​and statistical values ​​of the fitting models were calculated, as shown in Tables 4 and 5.

[0135] Table 4: Parameters related to the estimated and statistical values ​​of the disaster rate fitting model based on 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, and *** represents passing the 0.001 reliability test.

[0139] Table 5: Parameters related to the estimated and statistical values ​​of the disaster rate fitting model based on 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, and *** represents passing the 0.001 reliability test.

[0142] It can be found that the correlation between the estimated values ​​and statistical values ​​calculated by the fitting model established using only May precipitation, average temperature, and the number of meteorological drought stations during the key period is smaller than that of the fitting model after the three factors are integrated. The correlation between the estimated values ​​and statistical values ​​of the fitting model with the 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 mean absolute error are also smaller than the calculated values ​​of the fitting model established by the individual factors.

[0143] Comparisons were made between fitting models based solely on annual climate factors and those built using multiple factors. The results showed that the correlation between the estimated values ​​and statistical values ​​of the fitting model built based solely on annual meteorological factors was also smaller than that of the fitting model built using multiple factors. Furthermore, the correlation coefficients (accurate error and mean absolute error) between the estimated values ​​and statistical values ​​of the model built based on annual factors were smaller (larger) than those of the fitting model built based on critical period factors.

[0144] Therefore, we finally used three factors, namely, May precipitation, May average temperature, and the number of meteorological drought stations during the key period, 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 factors in the key period are closer to the statistical values, especially in 2010, 2005, 1979, and 2019, years with severe drought disasters. The drought disaster rate and disaster rate based on the fitting of the key period are closer to the statistical values ​​than the estimated values ​​based on the meteorological factors throughout the year. From 1978 to 2013, the estimated values ​​in the same years were generally lower than the statistical values. After 2013, the estimated values ​​in the years with mild drought disasters were 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 factors and agricultural drought disasters in Yunnan has changed;

[0150] In summary, the estimated values ​​of the model 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 the fitting model of any single factor 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 is consistent with the above analysis, further proving that the critical period climate factors have a greater impact on drought disasters than the annual scale climate factors.

[0151] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A drought disaster assessment method based on multiple climate factors, characterized by: The following steps are involved: S1: Collect data on the area of ​​major crops affected by drought disasters, the area of ​​major crops severely damaged by drought disasters, the area of ​​major crops completely lost due to drought disasters, the planting area of ​​major crops, precipitation data, average temperature data, daily precipitation data, and daily temperature data in various regions of drought disaster areas over several years; S2: Based on the collected data, calculate the drought susceptibility rate, drought disaster rate, drought crop failure rate, comprehensive drought loss rate, correlation coefficients between drought susceptibility rate and drought disaster rate and precipitation, correlation coefficients between drought susceptibility rate and drought disaster rate and average temperature, and correlation coefficients 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 rate of each region in the drought disaster area over the past few years, analyze the temporal variation characteristics of drought disasters in the drought disaster area; S4: Based on the comprehensive loss rate of drought disasters in various regions 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 susceptibility and drought disaster rates and precipitation in various regions 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 susceptibility and drought disaster rates and average temperature in various regions over several years, analyze the key influencing characteristics between drought disasters and average temperature 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 meteorological drought station levels over several years, analyze the key influencing characteristics between drought disasters in drought-disaster areas and meteorological drought stations; S8: Based on the key influencing characteristics of precipitation, average temperature and meteorological drought station number, a fitting model that affects the 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 affecting 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, characterized in that: In step S2, the calculation formula for the 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 for the drought disaster rate is: Where: D2 represents the area of ​​major crops affected 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, 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% Where: 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 crops that have no harvest due to 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, characterized in that: In step S2, the calculation formulas for the correlation coefficients between the drought susceptibility rate, the drought disaster rate, and precipitation, and the correlation coefficients between the drought susceptibility rate, the drought disaster rate, and average temperature are as follows: Where: X i Expressed as drought disaster rate and drought disaster rate, Y i Expressed as precipitation and mean temperature, Indicated as X i The mean of Indicated as Y i The mean of .

6. The drought disaster assessment method based on multiple climate factors according to claim 1, 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 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 expressed as seasonal adjustment parameters, 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 the drought disaster rate and drought disaster rate and the drought grade station number is as above.

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