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CN-121980153-A - Summer corn drought detection method and system

CN121980153ACN 121980153 ACN121980153 ACN 121980153ACN-121980153-A

Abstract

The invention relates to the technical field of hydrology and discloses a method and a system for detecting drought of summer corns, wherein the method comprises the steps of obtaining daily precipitation and evaporation capacity data in a certain area, obtaining effective precipitation and actual water demand of the summer corns, obtaining the difference value between the effective precipitation and the actual water demand of the summer corns in a certain time Performing trend fitting on the difference D t by using a smooth spline to replace the position parameters of the Log-logistic distribution to obtain time-varying position parameters According to time-varying position parameters Acquiring time-varying Estimating parameters by using a probability weighted distance method PWMs based on experience frequency for Log-logistic distribution of the time sequence, and normalizing drought detection indexes; the invention provides a new summer maize non-stationarity drought index, which considers the difference of the moisture demand and drought tolerance of different crop growth stages and can realize the daily-scale summer maize drought refined detection.

Inventors

  • SUN PENG
  • YAO RUI
  • GU XIHUI
  • LIU RONGHUA
  • ZHANG XIAOLEI
  • Yuan Qianyu

Assignees

  • 安徽师范大学

Dates

Publication Date
20260505
Application Date
20251126

Claims (10)

  1. 1. The method for detecting the drought of the summer corns is characterized by comprising the following steps of: step S1, acquiring daily precipitation and evaporation capacity data in a certain area, and acquiring effective precipitation and actual water demand of summer corns; s2, obtaining the difference between the effective precipitation amount and the actual water demand amount of the summer corns in a certain time ; Step S3, performing trend fitting on the difference D t by using a smooth spline to replace the position parameters of the Log-logistic distribution to obtain time-varying position parameters ; Step S4, according to the time-varying position parameters Acquiring time-varying And estimating parameters by using a probability weighted distance method PWMs based on experience frequency for Log-logistic distribution of the time sequence, and normalizing drought detection indexes.
  2. 2. The method for detecting drought in summer corns according to claim 1, wherein the step S1 is specifically as follows: Step S1.1, calculating the potential evapotranspiration by Penman-Montetith method The calculation formula is as follows: In the formula, Is the potential evapotranspiration, mm/d; is the slope of the saturated water vapor pressure-temperature curve, kPa/° C; for the surface net radiation, MJ/(m2.d), Is soil heat flux; The average daily air temperature is expressed as a unit of DEG C; the wind speed is 2m high, m/s; Saturated water vapor pressure, kPa; is the actual water vapor pressure, kPa; Is dry-wet surface constant, kPa/° C; Step S1.2, calculating the actual water demand of crops: In the middle of The water is required by crops, and the water is mm/d; Is the coefficient of summer corn crops; step S1.3, calculating the effective precipitation amount of the summer corns, wherein the effective precipitation amount is as follows: In the formula, For effective precipitation, in mm/d, The actual precipitation is given in mm/d.
  3. 3. The method for detecting drought in summer corns according to claim 1, wherein the step S2 is specifically as follows: and calculating the difference between the daily precipitation and the water demand. In the formula, For the difference between the precipitation and the actual evapotranspiration, In order to effectively reduce the water content day by day, Is the actual amount of steaming per day.
  4. 4. The method for detecting drought in summer corns according to claim 1, wherein the step S3 is specifically as follows: step S3.1, determining the sequence data using a smooth spline function Smoothing Splines Is a linear or nonlinear trend fit to Fitting was performed as follows: In the formula, Is solar radiation; Time is; is a smoothing parameter; is the highest air temperature; is the lowest air temperature; Is that Is a linear fitting function of (2); The time-varying position parameters are as follows: 。
  5. 5. The method for detecting drought in summer corns according to claim 1, wherein the step S4 is specifically as follows: Based on time variation The Log-logistic distribution of the time series is: Wherein, the 、 、 The parameters of the size, shape and position of the Log-logidtic distribution function are estimated by using a probability weighted distance method PWMs based on the empirical frequency: In the formula, Is a PWM of s-order, in which =4, Is the number of data points; The mean value is transformed along with the time sequence, the trend value fitted by the smooth spline function, namely the position parameter, is continuously changed, the position parameter is kept unchanged only when the mean value is unchanged, then NSPEI-corn and SPEI values are kept consistent, and K-S is utilized to judge whether the Log-logistic distribution is met or not: wherein: For frequency estimation, when ≤0.5, Is that When (1) >0.5, Then Other parameters are =2.515517, =0.802853, =0.01028, =1.432788, =0.189269, = 0.001308, Calculate NSPEI-core, and if positive NSPEI-core value indicates wetting, negative NSPEI-core value indicates drought.
  6. 6. A summer corn drought detection system is characterized by comprising: The data acquisition module is used for acquiring daily precipitation and evaporation capacity data in a certain area and acquiring effective precipitation and actual water demand of summer corns; the difference value obtaining module obtains the difference value between the effective precipitation amount and the actual water demand amount of the summer corns in a certain time ; The trend fitting module is used for carrying out trend fitting on the difference D t by utilizing a smooth spline to replace the position parameters of the Log-logistic distribution so as to obtain time-varying position parameters ; Drought index detection module based on time-varying position parameters Acquiring time-varying And estimating parameters by using a probability weighted distance method PWMs based on experience frequency for Log-logistic distribution of the time sequence, and normalizing drought detection indexes.
  7. 7. The summer maize drought detection system of claim 6 wherein: the specific processing procedure of the data acquisition module is as follows: the potential evapotranspiration is calculated by Penman-Montetith method The calculation formula is as follows: In the formula, Is the potential evapotranspiration, mm/d; is the slope of the saturated water vapor pressure-temperature curve, kPa/° C; for the surface net radiation, MJ/(m2.d), Is soil heat flux; The average daily air temperature is expressed as a unit of DEG C; the wind speed is 2m high, m/s; Saturated water vapor pressure, kPa; is the actual water vapor pressure, kPa; Is dry-wet surface constant, kPa/° C; Secondly, calculating the actual water demand of crops: In the middle of The water is required by crops, and the water is mm/d; Is the coefficient of summer corn crops; and finally calculating the effective precipitation amount of the summer corns, wherein the effective precipitation amount is as follows: In the formula, For effective precipitation, in mm/d, The actual precipitation is given in mm/d.
  8. 8. The summer corn drought detection system according to claim 6, wherein the specific processing procedure of the difference value acquisition module is as follows: and calculating the difference between the daily precipitation and the water demand. In the formula, For the difference between the precipitation and the actual evapotranspiration, In order to effectively reduce the water content day by day, Is the actual amount of steaming per day.
  9. 9. The summer maize drought detection system of claim 6, wherein the trend fitting module comprises: determining the sequence data using a smooth spline function Smoothing Splines Is a linear or nonlinear trend fit to Fitting was performed as follows: In the formula, Is solar radiation; Time is; is a smoothing parameter; is the highest air temperature; is the lowest air temperature; Is that Is a linear fitting function of (2); The time-varying position parameters are as follows: Wherein Loess is local weighted regression, which is a non-parametric regression method.
  10. 10. The system for detecting drought in summer corn according to claim 6, wherein the drought index detection module comprises a time-varying based processing process The Log-logistic distribution of the time series is: Wherein, the 、 、 The parameters of the size, shape and position of the Log-logidtic distribution function are estimated by using a probability weighted distance method PWMs based on the empirical frequency: In the formula, Is a PWM of s-order, in which =4, Is the number of data points; The mean value is transformed along with the time sequence, the trend value fitted by the smooth spline function, namely the position parameter, is continuously changed, the position parameter is kept unchanged only when the mean value is unchanged, then NSPEI-corn and SPEI values are kept consistent, and K-S is utilized to judge whether the Log-logistic distribution is met or not: wherein: For frequency estimation, when ≤0.5, Is that When (1) >0.5, Then Other parameters are =2.515517, =0.802853, =0.01028, =1.432788, =0.189269, = 0.001308, Calculate NSPEI-core, and if positive NSPEI-core value indicates wetting, negative NSPEI-core value indicates drought.

Description

Summer corn drought detection method and system Technical Field The invention relates to the technical field of hydrology, in particular to a summer corn drought detection method. Background The sixth evaluation report of IPCC indicated a continuous rise in global surface temperature, 1.1 ℃ global warming compared to 1850-1990a, with increased greenhouse gas emissions leading to extreme weather increases. In this scenario, future global water circulation further changes will lead to stronger precipitation, flooding and more severe drought. Drought is a weather disaster with high frequency, long duration and wide influence range, and has serious influence on the economic development of the world, in particular on agricultural production. Drought indexes can quantitatively describe drought, represent the occurrence severity of drought, and are the basis and the core of drought monitoring and evaluation. In 2010 vicenter-Serrano established a new Standardized Precipitation Evaporation Index (SPEI) based on precipitation and temperature transpiration differences. SPEI not only considers the influence of temperature on drought severity, but also retains the multi-scale and multi-space characteristics of SPI. Sun Peng and other researches find that SPEI is sensitive to temperature, drought intensity and duration can be overestimated when future drought changes are evaluated, NSPEI can make up for the defect that SPEI is sensitive to temperature and has deviation to detection of non-stationary drought, and drought conditions are reflected more truly. However, these commonly used drought indices are only suitable for evaluation when drought occurs at a single stage, and most studies are drought prediction and evaluation on a month scale or longer, and are insufficient in drought applicability on short time scales such as the growing season, the key fertility period, etc. of the studied crops. From the monitoring of the month scale, drought may occur in one month from no drought to abnormal drought, and it is obvious that drought on the month scale or longer cannot accurately capture the drought occurrence time. In addition, many related researches on drought only consider factors such as precipitation, air temperature and the like, but not factors such as the growth period of crops, and the complexity of drought occurrence is ignored. Drought in soil during any growth period results in reduced yield in maize, while drought from the male stage to the dairy stage has the greatest effect on yield, indicating that crops have great differences in water demand and drought tolerance during different development periods. And drought indexes of crops in growth period and demand law of each growth period are not considered, so that the influence of drought on crops cannot be accurately estimated, and the applicability of drought risk estimation is further affected. In order to accurately identify short-term drought occurring in the growth and development process of crops and accurately capture drought conditions of crops in each growth period, monitoring and early warning are needed according to drought indexes of the development days of crop coefficients in each growth period of crops. Disclosure of Invention The invention aims to provide a method and a system for detecting drought of summer corns, which solve the technical problem that the drought index estimated by the prior art cannot accurately evaluate the influence of drought on crops, thereby influencing the applicability of drought risk evaluation. The method comprises the following steps: step S1, acquiring daily precipitation and evaporation capacity data in a certain area, and acquiring effective precipitation and actual water demand of summer corns; s2, obtaining the difference between the effective precipitation amount and the actual water demand amount of the summer corns in a certain time ; Step S3, performing trend fitting on the difference D t by using a smooth spline to replace the position parameters of the Log-logistic distribution to obtain time-varying position parameters; Step S4, according to the time-varying position parametersAcquiring time-varyingAnd estimating parameters by using a probability weighted distance method PWMs based on experience frequency for Log-logistic distribution of the time sequence, and normalizing drought detection indexes. A summer corn drought detection system comprising: The data acquisition module is used for acquiring daily precipitation and evaporation capacity data in a certain area and acquiring effective precipitation and actual water demand of summer corns; the difference value obtaining module obtains the difference value between the effective precipitation amount and the actual water demand amount of the summer corns in a certain time ; The trend fitting module is used for carrying out trend fitting on the difference D t by utilizing a smooth spline to replace the position parameters of the Log-logistic distribut