A method for calculating drought index based on crop water shortage

Abstract

The present invention discloses a method for calculating a drought index based on crop water deficit, comprising the following steps: S1, obtaining meteorological data and irrigation water availability data; S2, obtaining crop coefficients for typical crops in a study area; S3, calculating reference evapotranspiration based on the Penman formula; S4, calculating actual evapotranspiration and effective rainfall; S5, calculating crop water requirements; S6, calculating irrigation water deficit based on a water supply-demand balance; S7, determining an optimal fitting distribution function for an irrigation water deficit time series; and S8, calculating a new agricultural drought index based on an inverse normal transformation based on the optimal distribution. The present invention takes into account both the crop's inherent water demand and the irrigation's role in replenishing crop water. It directly utilizes water deficit information, which is most directly related to crop drought stress, to characterize agricultural drought, achieving direct quantification of agricultural drought with the advantage of low error.

Description

Technical Field

[0001] The present invention relates to a calculation method, and in particular to a method for calculating a drought index based on crop water shortage. Background Art

[0002] As global warming continues to intensify, the frequency of extreme climate events is increasing significantly worldwide. Drought, one of the most important types of extreme climate events, is characterized by high frequency, long duration, and wide-ranging impacts, posing a significant threat to agricultural production.

[0003] In the past, agricultural drought monitoring was typically based on assessing the adverse effects of soil moisture deficiency on crop growth. Therefore, constructing drought indices based on soil moisture is the primary method for assessing agricultural drought. Commonly used indicators include the Standardized Soil Moisture Index (SSMI), the Soil Moisture Anomaly Index (SMAPI), the Vertical Drought Index (PDI), and the Apparent Thermal Inertia (ATI). Their calculation methods are shown in the following formula:

[0004]

[0005] Where SM represents the monthly soil moisture value, μ represents the mean monthly soil moisture value, and σ represents the standard deviation of the monthly soil moisture value.

[0006]

[0007] Where θ represents the current soil moisture, and α represents the mean soil moisture over the same period of time over many years.

[0008]

[0009] Where R represents the band reflectance after atmospheric correction, red represents the red light band, nir represents the near-infrared band; M is the slope of the soil line.

[0010]

[0011] Where A is the full-band albedo, ΔT is the temperature difference between day and night, and ΔT is used to represent the moisture content of the soil.

[0012] The above-mentioned agricultural drought indices all use soil moisture as an indicator, and they have the following two main defects: First, they fail to monitor agricultural drought based on the water shortage of crops themselves. They all use soil moisture as an indicator. However, soil moisture is only an indicator reflecting soil moisture conditions and can only describe the degree of water stress that crops may suffer. It is a qualitative and indirect method to reflect agricultural drought. Second, they all use remote sensing surface soil moisture products to identify and monitor agricultural drought at the regional scale. Remote sensing surface soil moisture data products can only reflect surface soil moisture information at a depth of 0-5 cm. However, soil moisture information at the depth of the entire root zone is closely related to crop growth. Therefore, agricultural drought monitoring based on surface soil moisture information has large errors. Summary of the Invention

[0013] In order to solve the shortcomings of the above technologies, the present invention provides a method for calculating the drought index based on the water shortage of crops.

[0014] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for calculating the drought index based on the water shortage of crops, comprising the following steps:

[0015] S1. Obtain meteorological data and irrigation water availability data;

[0016] S2. Obtain crop coefficients of typical crops in the study area;

[0017] S3. Calculate reference evapotranspiration based on the Penman formula;

[0018] S4. Calculate actual evapotranspiration and effective rainfall;

[0019] S5. Calculate crop water requirements;

[0020] S6. Calculate irrigation water deficit based on water supply and demand balance;

[0021] S7. Determine the optimal fitting distribution function for the irrigation water deficit time series;

[0022] In order to determine the optimal distribution function, the distribution function of irrigation water shortage calculated by S6 was subjected to parameter estimation and goodness of fit test to screen the optimal distribution;

[0023] S8. Based on the optimal distribution, calculate a new agricultural drought index based on inverse normal transformation.

[0024] Preferably, in S1, the meteorological data acquired is meteorological data with the same downloading time and spatial resolution, including:

[0025] Daily average relative humidity, daily average surface atmospheric pressure, daily average longwave radiation, daily average shortwave radiation, daily average surface wind speed, daily average near-surface temperature, daily maximum near-surface temperature, daily minimum near-surface temperature, and daily precipitation data;

[0026] Among them, daily precipitation data is used to calculate the daily effective rainfall, and the remaining meteorological data is used to calculate the daily reference evapotranspiration using the Penman formula; and the daily data are accumulated by month to obtain the monthly effective rainfall and reference evapotranspiration.

[0027] Preferably, in S1, the available water volume for regional irrigation is obtained as follows:

[0028] IWS=Runoff×f (5)

[0029]

[0030] in:

[0031] IWS – irrigation water availability;

[0032] Runoff

[0033] f——agricultural water use ratio coefficient.

[0034] Preferably, in S2, the crop coefficients of typical crops in the study area are obtained based on the monthly changes in crop coefficient values ​​provided in the "Study on the Isoline Map of Water Requirement of Major Crops in China".

[0035] Preferably, in S3, the daily reference evapotranspiration of the study area is calculated as follows:

[0036]

[0037] in:

[0038] ET0 – reference evapotranspiration;

[0039] Δ——the slope of the relationship curve between saturated water vapor pressure and temperature. The calculation of Δ is shown in formula (8);

[0040] R n ——Net radiation, R n The calculation of is shown in formula (9);

[0041] G——soil heat flux. The soil heat flux value in daily period should be ignored, that is, G≈0;

[0042] T - near-surface air temperature;

[0043] U2——surface wind speed;

[0044] γ——psychrometric constant, taken as 0.665×10-3 P;

[0045] P——surface atmospheric pressure;

[0046] e s ——Saturated water vapor pressure, calculated as shown in formula (10);

[0047] e a ——actual water vapor pressure, calculated as shown in formula (11);

[0048] Formula (8) is as follows:

[0049]

[0050] in:

[0051] T - near-surface air temperature;

[0052] Formula (9) is as follows:

[0053] R n = R ns + R nl (9)

[0054] in:

[0055] R ns —net shortwave radiation;

[0056] R nl - net longwave radiation;

[0057] Formula (10) is as follows:

[0058]

[0059] in:

[0060] e o (T max )——saturated water vapor pressure at the highest temperature near the surface;

[0061] e o (T min )——saturated water vapor pressure at the lowest temperature near the surface;

[0062] Formula (11) is as follows:

[0063]

[0064] in:

[0065] RH mean ——Relative humidity.

[0066] As a preference, in S4, the actual evaporation ET cThe calculation method is:

[0067] ET c =K c ×ET0 (12)

[0068] in:

[0069] Kc——crop coefficient, obtained in step S2;

[0070] ET0——reference evapotranspiration, obtained from step S3.

[0071] Preferably, in S4, the effective rainfall is estimated using the effective utilization coefficient method:

[0072]

[0073] in:

[0074] pr - daily rainfall.

[0075] As a preferred method, in S5, the crop water requirement CWR is the actual evapotranspiration ET c and effective rainfall P eff The difference:

[0076] CWR=ET c -P eff (14)

[0077] Among them: ET c and P eff Obtained from step S4.

[0078] Preferably, in S6, the irrigation water deficit IWD is the difference between the irrigation water supply IWS and the crop water requirement CWR:

[0079] IWD=IWS-CWR (15)

[0080] Wherein: CWR is obtained in step S5; IWS is obtained in step S1.

[0081] Preferably, in S7, the calculation process of the optimal distribution function is as follows:

[0082] 7.1 Johnson SB distribution, Pearson-III distribution, Erlang distribution, generalized inverse Gaussian distribution and Zhongshanmu distribution are selected for modeling irrigation water deficit time series;

[0083] 7.2 Use the maximum likelihood estimation method to calculate the parameters of each marginal distribution function and fit the irrigation water shortage;

[0084] 7.3 Use KS test to perform goodness of fit test to evaluate whether the distribution of irrigation water shortage conforms to the selected distribution; and use the minimum sum of squared deviations criterion to compare and analyze the fitting effect to compare which function has better fitting effect and determine the optimal distribution function.

[0085] The present invention discloses a method for calculating a drought index based on crop water deficit. This method takes into account the crop's own demand for water and the effect of irrigation on crop water replenishment, directly utilizes water deficit information that is most directly related to crop drought stress to characterize agricultural drought, thereby achieving direct quantification of agricultural drought and having the advantage of low error. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 This is a flow chart of the agricultural water shortage index calculation process of the present invention. DETAILED DESCRIPTION

[0087] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0088] Traditional agricultural drought indices are known to use soil moisture as a metric, and regional soil moisture often relies on remote sensing data products. However, these traditional drought indices only provide a qualitative and indirect indicator of agricultural drought, failing to consider the crop's inherent water needs or the role of irrigation in replenishing crop water. Irrigation is the most widely used method for managing agricultural drought. Therefore, agricultural drought is related not only to precipitation and soil moisture, but also to the crop's inherent water requirements and the regulatory effects of irrigation. Furthermore, remote sensing surface soil moisture data products only reflect surface soil moisture information at a depth of 0-5 cm. Therefore, drought indices calculated based on this data are subject to significant uncertainty and error.

[0089] In response to the above situation, the present invention proposes a new method for calculating the drought index. This drought index is a new agricultural drought index that can directly reflect the water shortage characteristics of the crop growth process and the impact of agricultural irrigation. It takes into account the crop's own demand for water and the role of irrigation in replenishing crop water. It is a completely different approach to identifying and monitoring agricultural drought from the traditional drought index based on soil moisture.

[0090] like Figure 1 As shown, it mainly includes the following steps:

[0091] S1. Obtain meteorological data and irrigation water availability data;

[0092] S2. Obtain crop coefficients of typical crops in the study area;

[0093] S3. Calculate reference evapotranspiration based on the Penman formula;

[0094] S4. Calculate actual evapotranspiration and effective rainfall;

[0095] S5. Calculate crop water requirements;

[0096] S6. Calculate irrigation water deficit based on water supply and demand balance;

[0097] S7. Determine the optimal fitting distribution function for the irrigation water deficit time series;

[0098] S8. Based on the optimal distribution, calculate a new agricultural drought index based on inverse normal transformation.

[0099] The specific implementation methods of the drought index calculation method based on crop water shortage are as follows:

[0100] (1) Obtain meteorological data and irrigation water availability data;

[0101] Download meteorological data with the same temporal and spatial resolution, including: daily average relative humidity, daily average surface atmospheric pressure, daily average longwave radiation, daily average shortwave radiation, daily average surface wind speed, daily average near-surface temperature, daily maximum near-surface temperature, daily minimum near-surface temperature, and daily precipitation data; among them, daily precipitation data is used to calculate daily effective rainfall, and all other meteorological data are used to calculate daily reference evapotranspiration according to the Penman formula provided by the Food and Agriculture Organization of the United Nations; and the daily data are accumulated by month to obtain the monthly effective rainfall and reference evapotranspiration.

[0102] Obtain the runoff data simulated by the hydrological model and calculate the agricultural water use ratio coefficient based on the water resources data in the Water Resources Bulletin to obtain the regional irrigation water supply:

[0103] IWS=Runoff×f (5)

[0104]

[0105] in:

[0106] IWS – irrigation water availability;

[0107] Runoff

[0108] f——agricultural water use ratio coefficient.

[0109] (2) Obtaining crop coefficients of typical crops in the study area

[0110] According to the monthly changes in crop coefficient values ​​provided in the "Study on the Isoline Map of Water Requirements of Major Crops in China", the crop coefficients of typical crops in the study area were obtained. Some data are shown in Table 1:

[0111] Table 1 Crop coefficients of typical crops

[0112]

[0113] (3) Calculation of reference evapotranspiration

[0114] The Penman formula, a reference evapotranspiration calculation method provided by the Food and Agriculture Organization (FAO), was used in this study to calculate the daily reference evapotranspiration of the study area. The specific calculation method is as follows:

[0115]

[0116] in:

[0117] ET0 – reference evapotranspiration;

[0118] Δ——the slope of the relationship curve between saturated water vapor pressure and temperature. The calculation of Δ is shown in formula (8);

[0119] R n ——Net radiation, R n The calculation of is shown in formula (9);

[0120] G - soil heat flux. The soil heat flux value in daily period can be ignored, that is, G≈0;

[0121] T - near-surface air temperature;

[0122] U2——surface wind speed;

[0123] γ——psychrometric constant, taken as 0.665×10 -3 P;

[0124] P——surface atmospheric pressure;

[0125] e s ——Saturated water vapor pressure, calculated as shown in formula (10);

[0126] e a ——actual water vapor pressure, calculated as shown in formula (11);

[0127] Formula (8) is as follows:

[0128]

[0129] in:

[0130] T - near-surface air temperature;

[0131] Formula (9) is as follows:

[0132] R n = R ns + R nl (9)

[0133] in:

[0134] R ns —net shortwave radiation;

[0135] R nl - net longwave radiation;

[0136] Formula (10) is as follows:

[0137]

[0138] in:

[0139] e o (T max )——saturated water vapor pressure at the highest temperature near the surface;

[0140] e o (T min )——saturated water vapor pressure at the lowest temperature near the surface;

[0141] Formula (11) is as follows:

[0142]

[0143] in:

[0144] RH mean ——Relative humidity.

[0145] The above parameters all come from the meteorological data obtained in step S1.

[0146] (4) Calculate actual evapotranspiration and effective rainfall

[0147] Actual evaporation:

[0148] Calculate actual evapotranspiration ET using crop coefficient Kc and reference evapotranspiration ET0 c :

[0149] ET c =K c ×ET0 (12)

[0150] in:

[0151] Kc——crop coefficient, obtained in step S2;

[0152] ET0——reference evapotranspiration, obtained from step S3.

[0153] Effective rainfall:

[0154] Effective rainfall refers to the amount of rainfall that can be effectively utilized by crops during the crop growth period, that is, the water actually supplied to the plant root layer soil from natural precipitation. It is estimated using the effective utilization coefficient method:

[0155]

[0156] in:

[0157] pr - daily rainfall.

[0158] (5) Calculate crop water requirements:

[0159] The difference between actual evapotranspiration and effective rainfall is the crop water requirement (CWR):

[0160] CWR=ET c -P eff (14)

[0161] Among them: ET c and P eff Obtained from step S4.

[0162] (6) Calculate crop water shortage

[0163] According to the supply and demand balance, the difference between irrigation water supply (IWS) and crop water requirement (CWR) is the irrigation water deficit (IWD):

[0164] IWD=IWS-CWR (15)

[0165] Wherein: CWR is obtained in step S5; IWS is obtained in step S1.

[0166] (7) Determine the optimal fitting distribution function of the crop water shortage time series

[0167] To determine the optimal distribution function, rigorous parameter estimation and goodness-of-fit testing are required for the crop water deficit distribution function. This process consists of three main steps: selecting a distribution function to model crop water deficit; parameter estimation; and performing goodness-of-fit testing and evaluation to select the optimal distribution. The specific calculation process is as follows:

[0168] 7.1 Johnson SB distribution, Pearson-III distribution, Erlang distribution, generalized inverse Gaussian distribution and Zhongshanmu distribution are selected for modeling irrigation water deficit time series;

[0169] 7.2 Use the maximum likelihood estimation method to calculate the parameters of each marginal distribution function and fit the irrigation water shortage;

[0170] 7.3 Use KS test to perform goodness of fit test to evaluate whether the distribution of irrigation water shortage conforms to the selected distribution; and use the minimum sum of squared deviations criterion to compare and analyze the fitting effect to compare which function has better fitting effect and determine the optimal distribution function.

[0171] (8) Calculation of a new agricultural drought index based on inverse normal transformation

[0172] Based on step S7, the continuous time series of irrigation water deficit can be considered to follow a Johnson SB distribution. The corresponding cumulative probability function is then derived, and then converted to a standard normal distribution using the cumulative probability function. After the conversion, the x-axis value of the standard normal distribution corresponding to the cumulative probability of each irrigation water deficit value is the agricultural water deficit drought index. Based on this agricultural water deficit drought index, agricultural drought can be identified and monitored.

[0173] It can be seen from this that the present invention, starting from the perspective of directly estimating the amount of irrigation water shortage during crop growth, proposes a new method for calculating the agricultural drought index that can directly reflect the water shortage characteristics of the crop growth process and the impact of agricultural irrigation. The biggest feature of the present invention is that this index takes into account the crop's own demand for water and the role of irrigation in replenishing crop water. Using runoff data, combined with the water resource utilization coefficient and the agricultural water use ratio coefficient, the irrigation water supply is calculated to obtain the available irrigation water. Further, based on meteorological data, the crop water requirement is calculated using the crop coefficient and the reference evapotranspiration method. According to the balance of supply and demand, the irrigation water shortage is obtained, thereby achieving direct quantification of agricultural drought. The new agricultural drought index proposed by the present invention directly uses the water shortage information that is most directly related to the drought stress of crops to characterize agricultural drought, which is also more reasonable in theory.

[0174] The above embodiments are not limitations of the present invention, and the present invention is not limited to the above examples. Any changes, modifications, additions or substitutions made by technicians in this technical field within the scope of the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for calculating drought index based on crop water shortage, characterized by: The method comprises the following steps: S1. Obtain meteorological data and irrigation water availability data; In S1, the available water for regional irrigation is obtained as: ， ， in: IWS – irrigation water availability; Runoff ——Agricultural water use ratio coefficient; S2. Obtain crop coefficients of typical crops in the study area; S3. Calculate reference evapotranspiration based on the Penman formula; S4. Calculate actual evapotranspiration and effective rainfall; In S4, actual evapotranspiration ET c The calculation method is: ， in: Kc——crop coefficient, obtained in step S2; ET0——reference evapotranspiration, obtained in step S3; In S4, effective rainfall is estimated using the effective utilization coefficient method: ， in: pr - daily rainfall; S5. Calculate crop water requirements; In S5, crop water requirement CWR is the actual evapotranspiration and effective rainfall The difference: ， Including: Actual evapotranspiration and effective rainfall Obtained by step S4; S6. Calculate irrigation water deficit based on water supply and demand balance; The irrigation water deficit IWD is the difference between the irrigation water supply IWS and the crop water requirement CWR: ， Wherein: the crop water requirement CWR is obtained in step S5; the irrigation water supply IWS is obtained in step S1; S7. Determine the optimal fitting distribution function for the irrigation water deficit time series; In order to determine the optimal distribution function, parameter estimation and goodness of fit test were performed on the distribution function of irrigation water shortage calculated by S6 to screen the optimal distribution. The calculation process of the optimal distribution function is as follows: 7.1 Johnson SB distribution, Pearson-III distribution, Erlang distribution, generalized inverse Gaussian distribution, and Zhongshanmu distribution are selected for modeling irrigation water deficit time series; 7.2 Use the maximum likelihood estimation method to calculate the parameters of each marginal distribution function and fit the irrigation water shortage; 7.3 Use the KS test to perform a goodness of fit test to assess whether the distribution of irrigation water shortages conforms to the selected distribution. Compare and analyze the fitting results using the minimum sum of squared deviations criterion to determine which function has the best fitting effect and determine the optimal distribution function. S8. Based on the optimal distribution, calculate a new agricultural drought index based on inverse normal transformation.

2. The method for calculating drought index based on crop water shortage according to claim 1, characterized in that: In S1, the meteorological data obtained is the meteorological data with the same download time and spatial resolution, including: Daily average relative humidity, daily average surface atmospheric pressure, daily average longwave radiation, daily average shortwave radiation, daily average surface wind speed, daily average near-surface temperature, daily maximum near-surface temperature, daily minimum near-surface temperature, and daily precipitation data; Among them, daily precipitation data is used to calculate the daily effective rainfall, and the remaining meteorological data is used to calculate the daily reference evapotranspiration using the Penman formula; and the daily data are accumulated by month to obtain the monthly effective rainfall and reference evapotranspiration.

3. The method for calculating drought index based on crop water shortage according to claim 1, characterized in that: In S2, the crop coefficients of typical crops in the study area were obtained based on the monthly changes in crop coefficient values ​​provided in the Study on the Isoline Map of Water Requirements of Major Crops in China.

4. The method for calculating drought index based on crop water shortage according to claim 1, characterized in that: In S3, the daily reference evapotranspiration for the study area is calculated as follows: ， in: ET0 – reference evapotranspiration; ——the slope of the curve of the relationship between saturated water vapor pressure and temperature, The calculation of is shown in formula (8); - net radiation, The calculation of is shown in formula (9); ——Soil heat flux, the soil heat flux value in daily period should be ignored, that is ; T——mean surface temperature; —mean surface wind speed; ——Psychrometer constant, take ; P——Surface atmospheric pressure ——Saturated water vapor pressure, calculated as shown in formula (10); ——actual water vapor pressure, calculated as shown in formula (11); Formula (8) is as follows: ， in: T - near-surface air temperature; Formula (9) is as follows: ， in: R ns —net shortwave radiation; R nl - net longwave radiation; Formula (10) is as follows: ， in: ——the saturated water vapor pressure at the highest temperature near the surface; ——the saturated water vapor pressure at the lowest temperature near the surface; Formula (11) is as follows: ， in: ——Relative humidity.

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