A Drought Impact Prediction Method and System Based on Hydrothermal Anomaly Characteristics

Abstract

The present invention discloses a drought impact prediction method and system based on hydrothermal anomaly characteristics. The method includes: extracting and calculating the time series of meteorological data in the research area, and performing standardization processing on precipitation and evapotranspiration; calculating the soil water content quantile to identify droughts, completing the division of drought periods, and obtaining hydrothermal anomaly characteristic values and drought formation rates; constructing a soil water resilience index; using a random forest to simulate the drought rate; and evaluating the drought impact. The present invention can improve the accuracy of predicting the occurrence and impact of sudden drought events.

Description

Technical Field

[0001] The present invention relates to a method for predicting the impact of future droughts, and particularly to a method and system for predicting drought impacts based on hydrothermal anomaly characteristics. Background Art

[0002] With the influence of human activities, the global meteorological environment has changed significantly. Droughts have gradually shown a rapid development pattern with the continuous increase in temperature, forming sudden droughts with short durations and high intensities.

[0003] For traditional droughts, the common research method is to analyze the characteristics of droughts by analyzing the duration, area, or crop conditions of droughts, and then construct an index based on meteorological conditions to indicate drought events. Furthermore, the index is associated with the characteristics to complete the prediction of drought characteristics based on meteorological conditions. However, this method ignores the resilience of soil water. In some areas with poor resilience, the same drought may have a more profound impact and cause incalculable losses. The prior art cannot estimate in advance what kind of impact the predicted drought event will have. At the same time, many current drought events are different from traditional droughts and have a faster formation rate under the influence of multiple complex anomalies. This makes it often underestimate the formation rate of droughts when using traditional methods for research. In some extreme environments, there are large errors in using traditional methods to study and evaluate sudden droughts. Summary of the Invention

[0004] Object of the Invention: Aiming at the above problems, the present invention provides a method and system for predicting drought impacts based on hydrothermal anomaly characteristics, which can improve the accuracy of predicting the occurrence and impact of sudden drought events.

[0005] Technical solution: The technical solution adopted by the present invention is a method for predicting the impact of drought based on hydrothermal anomaly characteristics, the method comprising: calculating average precipitation, evapotranspiration and average soil moisture content according to the time series of meteorological data, standardizing the average precipitation and evapotranspiration to obtain standardized average precipitation and evapotranspiration, converting the average soil moisture content into a quantile value to obtain an average soil moisture quantile; identifying drought events according to the average soil moisture quantile, and for each drought event, calculating hydrothermal anomaly characteristics and drought formation rate according to the standardized average precipitation and evapotranspiration; calculating the soil water resilience index according to the average soil moisture quantile, and dividing the level of the soil water resilience index by the natural breakpoint method; training a random forest model according to the hydrothermal anomaly characteristics and the drought formation rate to obtain a drought prediction model; using the trained drought prediction model according to the hydrothermal anomaly characteristics to obtain a drought formation rate prediction value, and calculating the corresponding soil water resilience index prediction value according to the drought formation rate prediction value; judging the future drought impact according to the soil water resilience index prediction value, the soil water resilience index before prediction and the level of the soil water resilience index.

[0006] Meteorological data include daily precipitation data, daily average temperature data, daily wind speed data and daily soil moisture data.

[0007] The conversion of the average soil moisture content into a quantile value includes: calculating the probability distribution function of the average soil moisture content SM; fitting the best probability distribution function using the KS test method and the root mean square error method; converting the soil moisture content into a quantile according to the best probability distribution function to obtain the soil moisture content quantile SMP.

[0008] Identifying drought events based on the average soil moisture quantile includes: when the soil moisture quantile is lower than the set threshold, it is judged to be in a drought state.

[0009] Calculation of water and heat anomaly characteristics includes: in the initial period of drought, calculation of the standardized negative anomaly cumulative value of precipitation ∑SP, the standardized positive anomaly cumulative value of evapotranspiration ∑SE, and the duration of negative anomaly of precipitation ∑T P The duration of positive anomaly of evapotranspiration ΣT E The first time when the soil water content quantile drops to the set threshold is the drought start time T0, and the drought initial period is defined as the several weeks before the drought start time T0 to the drought start time T0;

[0010] Negative anomaly refers to the state where the anomaly value is less than 0, positive anomaly refers to the state where the anomaly value is greater than 0, the cumulative value of negative standardized precipitation anomaly ∑SP and the cumulative value of positive standardized evapotranspiration anomaly ∑SE are the accumulation of the standardized values ​​of negative precipitation anomaly and positive evapotranspiration anomaly, respectively; the anomaly duration refers to the sum of the duration of the abnormal state.

[0011] Calculating the drought formation rate includes: calculating the average soil water level decline rate RI during the drought formation period. mean When the quantile of soil water content first drops to the set threshold, it is the drought start time t0. When the quantile of soil water content drops to the inflection point, it is the drought formation time t. d The time period from the drought start time t0 to the drought formation time t d is defined as the drought formation period.

[0012] The formula for calculating the average soil water level decline rate RI mean is as follows:

[0013]

[0014] In the formula, RI mean is the average soil water level decline rate, d is the total duration from the drought start time to the drought formation time, t i is the i-th moment, t i+1 is the next moment of the i-th moment, t0 is the drought start time, t d is the drought formation time, and SMP is the quantile of soil water content.

[0015] Calculating the soil water resilience index includes: first calculating the soil water adaptability index and the inter-annual variation value of the average soil water level decline rate, and then calculating the soil water resilience index based on the inter-annual variation value of the average soil water level decline rate and the standardized soil water adaptability index;

[0016] The soil water adaptability index is the slope of the linear trend line of the inter-annual variation of the quantile of soil water content. The calculation formula is:

[0017]

[0018] Among them, A is the soil water adaptability index, n represents the total number of years, x represents the specific year, and y represents the annual change rate of the quantile of soil water content;

[0019] The calculation formula for the inter-annual variation value of the average soil water level decline rate is:

[0020]

[0021]

[0022]

[0023] The calculation formula for the soil water resilience index is:

[0024] λ = SA - RI year

[0025] In the formula, λ is the soil water resilience index, and SA is the standardized soil water adaptability index.

[0026] According to the predicted value of the soil water resilience index, the soil water resilience index before prediction, and the level of the soil water resilience index, the judgment of future drought impacts includes: when the predicted value of the soil water resilience index is less than the soil water resilience index before prediction, calculate the predicted value of the soil water resilience index and the soil water resilience index before prediction. If the levels of the two soil water resilience indices change, it is determined that the predicted drought has a significant impact on the study area; if the levels of the two soil water resilience indices do not change, further calculate the absolute value of the difference between the two. If the absolute value of the difference exceeds k% of the absolute value of the range of the soil water resilience index level, it is determined that the predicted drought has a significant impact on the study area; otherwise, it is determined that the predicted drought has no significant impact on the study area.

[0027] The present invention provides a drought impact prediction system based on hydrothermal anomaly characteristics, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the drought impact prediction method based on hydrothermal anomaly characteristics is implemented.

[0028] Beneficial effects: Compared with the prior art, the present invention has the following advantages: Compared with the prior art, the present invention has the following advantages: The present invention calculates the average soil water decline rate as the drought formation rate, combines the drought development rate revealed by soil water changes with the interannual changes of soil water itself, and uses a random forest model with the standardized precipitation negative anomaly cumulative value, the standardized evapotranspiration positive anomaly cumulative value, the precipitation negative anomaly duration, and the evapotranspiration positive anomaly duration as model inputs to learn and predict the drought formation rate, improving the accuracy of predicting the occurrence of drought events, especially flash drought events; at the same time, the present invention introduces the soil water resilience index, completes the resilience level division through historical data, and combines the accuracy of evaluation. Description of the Drawings

[0029] Figure 1 is a flowchart of the drought impact prediction method based on hydrothermal anomaly characteristics according to the present invention. Detailed Embodiments

[0030] The technical solutions of the present invention will be further described below with reference to the drawings and embodiments.

[0031] The drought impact prediction method based on hydrothermal anomaly characteristics according to the present invention has a flowchart as Figure 1As shown. The method includes: calculating the average precipitation P, evapotranspiration E and average soil moisture content SM according to the time series of meteorological data, standardizing the average precipitation P and evapotranspiration E to obtain the standardized average precipitation P and evapotranspiration E, converting the average soil moisture content SM into a quantile value to obtain the average soil moisture content quantile; identifying drought events according to the average soil moisture content quantile, and for each drought event, calculating the hydrothermal anomaly characteristics and the drought formation rate according to the standardized average precipitation P and evapotranspiration E; calculating the soil water resilience index according to the average soil moisture content quantile, and dividing the soil water resilience index into grades by the natural breakpoint method; training a random forest model according to the hydrothermal anomaly characteristics and the drought formation rate to obtain a drought prediction model; using the trained drought prediction model according to the hydrothermal anomaly characteristics, obtaining a drought formation rate prediction value, and calculating the corresponding soil water resilience index prediction value according to the drought formation rate prediction value; judging the future drought impact according to the soil water resilience index prediction value and the soil water resilience index grade.

[0032] The following is divided into five parts to explain each step in detail.

[0033] 1: Based on the time series of meteorological data, calculate the average precipitation P, evapotranspiration E and average soil moisture content SM corresponding to each grid area.

[0034] Spatial data is obtained by spatial interpolation of station data. The extracted meteorological data includes daily precipitation data, daily average temperature data, daily wind speed data, and daily soil moisture data. The required calculation data are average precipitation P, average evapotranspiration E, and average soil moisture SM.

[0035] The average precipitation P, average evapotranspiration E, and average soil moisture content SM were obtained on a weekly scale using the extracted daily precipitation data and daily soil moisture data using daily values.

[0036] The evapotranspiration data was calculated using the Penman-Monteith formula, as follows:

[0037]

[0038] Where, E is evapotranspiration; R n is the net radiation of crops; G is the soil heat flux; T is the average temperature at a height of 2 meters; u2 is the wind speed at a height of 2 meters; es and ea are the saturated water vapor pressure and actual water vapor pressure respectively; Δ is the slope of the saturated water vapor pressure curve; r is the hygrometer constant.

[0039] Then the average precipitation P and evapotranspiration E need to be standardized. The standardization process uses Z-score standardization, and the formula is as follows:

[0040]

[0041] Among them, P is the average precipitation. Replacing P with the evapotranspiration E is the standardization process of the evapotranspiration E. μ is the mean of the corresponding data, and σ is the standard deviation of the corresponding data.

[0042] Second: Identify drought events according to the soil water content quantiles. For each drought event, calculate the hydrothermal anomaly characteristics and the drought formation rate according to the average precipitation P, the evapotranspiration E, and the average soil water content SM.

[0043] Fit the probability distribution function of the average soil water content data; Use the K-S test method and the root mean square error method to optimize the best probability distribution function; According to the best probability distribution function, convert the soil water content value into a quantile value, and then identify drought events according to the soil water content quantiles. First, it is considered that when the quantile is lower than the 40% threshold, it enters the drought state. Subsequently, divide the time period. When the quantile just drops to the 40% threshold, it is considered the drought start time t0; When the quantile drops to a smaller change range or is about to rise continuously (take the time closest to t0) is the drought formation time t d . Define T 0-7 (the seven-week period before t0) to t0 as the initial drought period, and T 0-d (after t0 to t d ) is defined as the drought formation period. In addition, it should be noted that only events with a quantile lower than 20% for no less than two weeks can be considered drought events.

[0044] Calculate the hydrothermal anomaly characteristics: In the T 0-7 time period, calculate the cumulative value of the standardized precipitation negative anomaly ∑SP, the cumulative value of the standardized evapotranspiration positive anomaly ∑SE, the precipitation negative anomaly duration ΣT P and the evapotranspiration positive anomaly duration ΣT E ; Calculate the drought formation rate: The average soil water level decline rate RI 0-d in the T mean .

[0045] Among them, the negative anomaly refers to the state where the anomaly value is less than 0, and the positive anomaly refers to the state where the anomaly value is greater than 0. The cumulative value of the standardized precipitation negative anomaly ∑SP and the cumulative value of the standardized evapotranspiration positive anomaly ∑SE are respectively the sum of the standardized values of the precipitation negative anomaly and the evapotranspiration positive anomaly in the T 0-7 time period; The anomaly duration refers to the total duration of the abnormal state in the T 0-7 time period. For example, for precipitation, if there are a total of three periods with standard values lower than 0 in the T 0-7 time period, which are T P1 , T P2 and T P3 , then ΣT P= T P1 + T P2 + T P3 。

[0046] The average annual decline rate RI of soil water mean is calculated as follows:

[0047]

[0048] III: Construct the soil water resilience index according to the quantiles of the average soil water content and divide the resilience levels.

[0049] First, calculate the slope of the linear trend line of the interannual change of the soil water content quantiles to represent the change state of soil water under various abnormal influences, which is defined as the soil water adaptability index A. The formula is as follows:

[0050] y = Ax + B

[0051]

[0052] where x represents the specific year, y represents the annual change rate of the average soil water content SM, and B represents the intercept of the linear trend line of the interannual change;

[0053] Subsequently, calculate RI year . Since there is an RI for each drought mean , a sequence of RIs corresponding to the total time span for calculating the soil water adaptability index A can be obtained, and its interannual change is calculated. The formula is as follows: mean

[0054]

[0055] where represents the average value of RI in the i-th year mean , represents the average value of the total RImean sequence.

[0056] Define λ = SA - RI year , where SA is the standardized adaptability index of the soil water adaptability index A. The larger the value of λ, the stronger the soil water resilience, and the less affected the area is by droughts with high RI mean . The resilience levels are divided using the natural breakpoint method, and the assessment of the sensitivity of the study area to flash droughts can be completed. The levels divided by the natural breakpoint method are usually not evenly distributed. For example, the first level is from -1 to -0.5, and the second level is from -0.5 to -0.3.

[0057] IV: Use random forest to simulate the drought formation rate.

[0058] Take ∑SP, ∑SE, ΣT P and ΣTE Respectively as independent variables X1, X2, X3, and X4, the average soil level decline rate RI mean As the dependent variable Y is imported into the random forest model, and 70% of the samples are randomly selected as the training set and 30% of the samples are used as the test set, a prediction model for the drought formation rate based on hydrothermal anomaly characteristics can be obtained.

[0059] In the future, when drought is detected at the beginning or when the soil water content value reaches the 40% quantile, this prediction model can be used to input ∑SP, ∑SE, ΣT P and ΣT E to simulate the drought formation rate RI mean .

[0060] V: Judging the impact of future drought.

[0061] The average soil level decline rate of a future drought simulated by the random forest is incorporated into the calculation of the interannual change value RI yeaR That is, this drought is incorporated into the calculation as the last drought in the full time span. After obtaining the predicted value of λ, it is compared with the original actual value (i.e., the λ value calculated in step three). If the predicted value becomes larger, no subsequent evaluation is done. If the predicted value becomes smaller, it indicates that the predicted drought has a certain impact on the region. When the change value of λ exceeds 50% of the grade segment it belongs to, it is considered that this drought has a relatively serious impact on the study area.

[0062] It should exceed 50% of the grade segment it belongs to. Taking the first level from -1 to -0.5 and the second level from -0.5 to -0.3 as an example, if the λ value changes from -0.5 to -0.75 (the change value of λ exceeds 50% of 0.5, that is, it decreases by more than 0.25), it is considered to have a significant impact.

[0063] Setting 50% as the threshold for the grade segment is not absolute. The most rigorous approach is to select different thresholds according to the study area, substitute the drought times with greater historical impacts into the calculation, and determine the threshold based on the calculated change range. Generally speaking, 50% is on the high side.

Claims

1. A drought impact prediction method based on hydrothermal anomaly characteristics, characterized in that, The method includes: calculating the average precipitation, evapotranspiration, and average soil water content based on the time series of meteorological data, standardizing the average precipitation and evapotranspiration to obtain the standardized average precipitation and evapotranspiration, converting the average soil water content into quantile values to obtain the average soil water content quantiles; identifying drought events based on the average soil water content quantiles, and for each drought event, calculating the hydrothermal anomaly characteristics and drought formation rate based on the standardized average precipitation and evapotranspiration; calculating the soil water resilience index based on the average soil water content quantiles, and dividing the grades of the soil water resilience index by the natural breakpoint method; training a random forest model based on the hydrothermal anomaly characteristics and drought formation rate to obtain a drought prediction model; using the trained drought prediction model based on the hydrothermal anomaly characteristics to obtain the predicted value of the drought formation rate, and calculating the corresponding predicted value of the soil water resilience index based on the predicted value of the drought formation rate; judging the future drought impact based on the predicted value of the soil water resilience index, the soil water resilience index before prediction, and the grades of the soil water resilience index. The calculated hydrothermal anomaly characteristics include: in the initial period of drought, calculating the cumulative value of the standardized precipitation negative anomaly ∑SP, the cumulative value of the standardized evapotranspiration positive anomaly ∑SE, and the precipitation negative anomaly duration ΣT P and the evapotranspiration positive anomaly duration ΣT E ; when the quantile of soil water content first drops to the set threshold, it is the drought start time T0, and several weeks before the drought start time T0 to the drought start time T0 are defined as the initial drought period; Negative anomaly refers to the state where the anomaly value is less than 0, and positive anomaly refers to the state where the anomaly value is greater than 0. The cumulative value of standardized precipitation negative anomaly ∑SP and the cumulative value of standardized evapotranspiration positive anomaly ∑SE are the sums of the standardized values of precipitation negative anomaly and evapotranspiration positive anomaly respectively; the anomaly duration refers to the total duration showing the abnormal state.

2. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 1, wherein: The meteorological data includes daily precipitation data, daily average temperature data, daily wind speed data, and daily soil water content data.

3. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 1, characterized in that: Converting the average soil water content into quantile values includes: calculating the probability distribution function of the average soil water content SM; fitting the best probability distribution function using the K-S test method and the root mean square error method; converting the soil water content into quantiles according to the best probability distribution function to obtain the soil water content quantiles SMP.

4. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 1, characterized in that: Identifying drought events based on the average soil water content quantiles includes: judging that it enters the drought state if the soil water content quantiles are lower than the set threshold.

5. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 1, characterized in that: Calculating the drought formation rate includes: calculating the average soil water level decline rate RI during the drought formation period mean ; The moment when the quantile of soil water content first drops to the set threshold is the drought start time t0, and the moment when the quantile of soil water content drops to the inflection point is the drought formation time t d , from the drought start time t0 to the drought formation time t d is defined as the drought formation period.

6. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 5, characterized in that: Average soil level decline rate RI mean The calculation formula is as follows: where RI mean is the average soil level decline rate, d is the total duration from the start time of drought to the formation time of drought, t i is the i-th moment, t i+1 is the next moment of the i-th moment, t0 is the start time of drought, t d is the formation time of drought, and SMP is the soil water content quantile.

7. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 1, characterized in that: Calculating the soil water resilience index includes: first calculating the soil water adaptability index and the interannual change value of the average annual decline rate of soil water, and then calculating the soil water resilience index based on the interannual change value of the average annual decline rate of soil water and the standardized soil water adaptability index; The soil water adaptability index is the slope of the linear trend line of the interannual change of the soil water content quantiles, and the calculation formula is: where A is the soil water adaptability index, n represents the total number of years, x represents the specific year, and y represents the annual change rate of the soil water content quantiles; The calculation formula for the interannual change value of the average annual decline rate of soil water is: where RI year is the inter-annual variation value of the average annual soil level decline rate, represents the average value of RI in the xth year mean , represents the average value of the total RI mean sequence; The calculation formula for the soil water resilience index is: λ = SA - RI year In the formula, λ is the soil water resilience index, and SA is the standardized soil water adaptability index.

8. The drought impact prediction method based on hydrothermal anomaly characteristics according to claim 7, characterized in that: Based on the predicted value of the soil water resilience index, the soil water resilience index before prediction, and the level of the soil water resilience index, the judgment of future drought impacts includes: when the predicted value of the soil water resilience index is less than the soil water resilience index before prediction, calculate the predicted value of the soil water resilience index and the soil water resilience index before prediction. If the levels of the soil water resilience index where they are located change, it is judged that the predicted drought has a significant impact on the study area; if the levels of the soil water resilience index where they are located do not change, further calculate the absolute value of the difference between the two. If the absolute value of the difference exceeds k% of the absolute value of the range of the soil water resilience index level where they are located, it is judged that the predicted drought has a significant impact on the study area; otherwise, it is judged that the predicted drought has no significant impact on the study area.

9. A drought impact prediction system based on hydrothermal anomaly characteristics, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the drought impact prediction method based on hydrothermal anomaly characteristics described in any one of claims 1 to 8.

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