Monthly-scale meteorological drought risk quantitative estimation method based on climate mode forecast

Abstract

The invention provides a monthly-scale meteorological drought risk quantitative estimation method based on climate mode forecasting, and the method comprises the steps: determining an evaluation time period according to the occurrence condition of a drought process in an early-stage rainfall time period of an evaluation region, and carrying out the evaluation of the early-stage rainfall time period through the historical day-by-day rainfall observation data of a meteorological station in the evaluation time period for many years. Counting the number of duration days without effective precipitation and the average percentage of the accumulated precipitation in the evaluation period of each year, calculating a meteorological drought risk index of each year, and determining a grading threshold value of a meteorological drought risk grade by adopting a percentile method; and calculating a meteorological drought risk index of an estimated month and determining a risk level through the estimated data of the evaluation time period. According to the method, the non-effective rainfall duration day index considering the characteristic of less rainfall duration and the accumulated rainfall average percentage index reflecting the characteristic of less rainfall total amount are selected as estimation indexes, the complexity of an existing monthly-scale meteorological drought estimation method is overcome, and the estimation operability and reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of meteorological disaster early warning technology, specifically involving a quantitative prediction method for monthly meteorological drought risk based on climate model forecasts. Background Technology

[0002] Drought refers to a water shortage caused by an imbalance between water intake and expenditure or between supply and demand. Drought can be classified into meteorological drought, agricultural drought, hydrological drought, and socio-economic drought. Meteorological drought is the direct cause of other types of drought and the basis for monitoring and assessment. Conducting meteorological drought risk forecasting, especially for the next month, is of great reference value for disaster prevention and mitigation decision-making by relevant departments such as agriculture and water resources.

[0003] Existing drought disaster risk research mainly focuses on assessing past drought disasters, lacking effective assessments of future drought disasters. Current quantitative forecasting methods for monthly meteorological drought risk primarily use daily precipitation forecasts from numerical models to calculate the daily Comprehensive Meteorological Drought Monitoring Index (MCI) (definition and calculation method are found in GB / T 20481-2017 "Meteorological Drought Classification"). Drought classification and forecasting are then based on the specific values ​​of the daily MCI. The MCI considers the combined effects of effective precipitation (weighted cumulative precipitation) over 60 days, evapotranspiration (relative humidity) over 30 days, and precipitation over quarterly (90-day) and semi-annual (150-day) timeframes, making the calculation complex. Furthermore, the uncertainty of daily precipitation forecasts from numerical models increases with forecast lead time, leading to a rapid decline in accuracy after 10 days. This results in a rapid decrease in the forecast accuracy of the daily comprehensive meteorological drought index after 10 days as well. Therefore, it is necessary to improve existing techniques. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for quantitatively predicting the risk of meteorological drought on a monthly scale based on climate model forecasts.

[0005] The specific technical solution of the present invention is as follows:

[0006] A quantitative prediction method for monthly meteorological drought risk based on climate model forecasts includes the following steps:

[0007] S1. The assessment period is determined based on the occurrence of drought events during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period.

[0008] S2. Obtain historical daily precipitation observation data of meteorological stations over many years during the assessment period, and calculate the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year. Calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year.

[0009] S3. Based on the index of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage index, calculate the annual meteorological drought risk index and form a historical meteorological drought risk index sequence. Use the percentile method to determine the classification threshold of the meteorological drought risk level.

[0010] S4. Obtain the estimated data for the assessment period, which includes daily precipitation observation data from meteorological stations used during the previous precipitation period and daily precipitation data for the next month predicted by climate models used during the assessment month. Statistically analyze the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the estimated data within the assessment period, and identify drought processes. If a drought process is identified, calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly within the assessment period, calculate the estimated monthly meteorological drought risk index, and determine the estimated monthly meteorological drought risk level based on the classification threshold of the meteorological drought risk level.

[0011] A further design of the present invention is that, in S1, determining the assessment period based on the occurrence of drought events during the preceding precipitation period in the assessment area specifically involves:

[0012] The month following the estimated date is designated as the estimated month. The period of preceding precipitation is determined according to the following scenarios:

[0013] Scenario 1: If no drought event occurs in the month prior to the forecast, then the month prior to the forecast shall be considered as the period of previous precipitation.

[0014] Scenario 2: If a drought event occurs within one month prior to the forecast date, and the forecast date is still within a drought period, then the period from the date of the drought event to the forecast date will be considered as the period of previous precipitation.

[0015] Scenario 3: If a drought event occurs one month before the forecast date, and the drought event has ended by the forecast date, then the period from the end of the drought event to the forecast date is considered as the period of previous precipitation.

[0016] A further design of the present invention is that the index of consecutive days without effective precipitation is calculated by the following formula:

[0017]

[0018] In the formula, This is the index of the number of consecutive days without effective precipitation at a single station; This refers to the number of consecutive days without effective precipitation at a single station. The threshold value for the number of consecutive days without effective precipitation is used in this invention. sky; This represents the maximum number of consecutive days without effective precipitation across all stations in history.

[0019] A further design of the present invention is that the cumulative precipitation anomaly percentage index is calculated by the following formula:

[0020]

[0021] In the formula, The percentage of cumulative precipitation anomaly at a single station; This represents the percentage of cumulative precipitation anomaly at a single station. The threshold value for the percentage of precipitation anomaly is used when the assessment period is less than or equal to 2 months. When the evaluation period exceeds 2 months, ; This is the minimum percentage of historical precipitation anomalies among all stations.

[0022] A further design of the present invention is that the mathematical model for calculating the annual meteorological drought risk index is as follows:

[0023]

[0024] In the formula, R is the meteorological drought risk index; The index represents the number of consecutive days without effective precipitation. It is the percentage index of precipitation anomaly; The weighting coefficient for the index of consecutive days without effective precipitation; This is the weighting coefficient for the percentage of precipitation anomaly index.

[0025] A further design of the present invention is that the weight coefficients corresponding to the two predicted indicators are determined by the information entropy weighting method, the expert scoring method, or the principal component analysis method.

[0026] A further design of the present invention is that, in S3, the calculation of the annual meteorological drought risk index yields a historical meteorological drought risk index sequence, and the percentile method is used to determine the classification threshold for the meteorological drought risk level, specifically as follows:

[0027] From the historical meteorological drought risk index series, samples with risk indices not equal to 0 were selected, and the percentile method was used to sort them according to the index values ​​from smallest to largest to determine the threshold for each risk level.

[0028] A quantitative prediction system for monthly meteorological drought risk based on climate model forecasts includes the following modules:

[0029] The module for determining the assessment period determines the assessment period based on the occurrence of drought processes during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period.

[0030] The historical data processing module acquires historical daily precipitation observation data from meteorological stations over many years during the assessment period, calculates the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year, and calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year.

[0031] The risk level threshold determination module calculates the annual meteorological drought risk index and forms a historical meteorological drought risk index sequence based on the index of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage index, and uses the percentile method to determine the classification threshold of the meteorological drought risk level.

[0032] The meteorological drought risk prediction module acquires prediction data for the assessment period, including daily precipitation observation data from meteorological stations used during the previous precipitation period and daily precipitation data for the next month predicted by climate models used during the prediction month period. It statistically analyzes the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the prediction data within the assessment period, and identifies drought processes. If a drought process is identified, it calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly within the assessment period, calculates the meteorological drought risk index for the predicted month, and determines the meteorological drought risk level for the predicted month based on the classification threshold of the meteorological drought risk level.

[0033] The present invention also provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, the processor being used to call and run the computer program stored in the memory to perform the method described above.

[0034] The present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method described above.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] This invention presents a quantitative prediction method for monthly meteorological drought risk based on climate model forecasts. It scientifically employs the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly as prediction indicators. Since meteorological drought manifests as a water balance imbalance caused by evapotranspiration exceeding precipitation within a certain period, its main characteristic is a persistently abnormally low precipitation. This invention, in constructing its prediction indicators, selects the number of consecutive days without effective precipitation, which considers the duration of low precipitation, and the percentage of cumulative precipitation anomaly, which reflects the characteristic of low total precipitation. This overcomes the complexity of existing monthly meteorological drought prediction methods, improves prediction efficiency and operability, and becomes a key technology for the quantitative prediction of monthly meteorological drought. Drought disaster risk assessment is the prediction of the probability and severity of drought disasters. This invention, based on the actual occurrence of meteorological drought in a certain region and considering the performance of climate model forecasts, selects the above two meteorological drought risk assessment indicators. It uses the climate model forecast to predict the daily precipitation for the next month, and transforms it into a prediction of the number of consecutive days without precipitation and the cumulative precipitation deviation for the next month. From the perspective of the predictability of climate forecast models, it can more effectively extract predictable information from the models and improve the reliability and accuracy of the prediction results.

[0037] Based on the inventor's long-term monitoring and research, prolonged periods without effective precipitation can directly reflect the length of a drought process and are the direct cause of drought formation and development; the cumulative precipitation anomaly percentage can reflect the severity of drought in a specific period and effectively quantify drought intensity; in actual drought monitoring operations, these two indicators complement each other and can be used together to fully reflect the characteristics of a drought.

[0038] This invention dynamically sets the assessment period according to different scenarios. Since drought is a cumulative process, it is related not only to current precipitation but also to previous precipitation. Therefore, the forecast not only analyzes the forecast monthly precipitation but also considers previous precipitation, improving the comprehensiveness of drought risk forecasting and reducing forecast bias. The determined assessment period includes two parts: the previous precipitation period and the future forecast period. The previous precipitation period uses measured data that reflects the actual drought situation. Based on whether a drought process actually occurred in the previous period, the previous precipitation period is reasonably determined. If no drought process occurred in the month before the forecast date, then the month before the forecast date is used as the previous precipitation period; if a drought process occurred in the month before the forecast date and the forecast date is still in a drought process, then the period from the occurrence of the drought process to the forecast date is used as the previous precipitation period; if a drought process occurred in the month before the forecast date and the drought process has ended by the forecast date, then the period from the end of the drought process to the forecast date is used as the previous precipitation period.

[0039] Drought disasters occur only after precipitation has been consistently below a certain level. Therefore, this invention employs piecewise functions to construct predictive indices, making the prediction results more reasonable. Different piecewise functions are used to construct index models when considering threshold values ​​for different predictive indices. The resulting index models for the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage can more effectively determine the predictive indices. Furthermore, based on the actual occurrence of meteorological drought in the assessment region, the threshold values ​​for the predictive indices are reasonably determined, making it more applicable to regional drought prediction.

[0040] This invention constructs a meteorological drought risk index model based on two forecasting indicators, and improves the accuracy of the forecast results by scientifically allocating the weights of the continuous days without effective precipitation index and the precipitation anomaly percentage index. The meteorological drought risk index model can calculate specific index values, achieving a quantitative forecast of meteorological drought risk.

[0041] This invention uses historical observation data from previous years and employs the percentile method to accurately provide the risk level classification thresholds when determining the risk level classification thresholds for meteorological drought. When forecasting meteorological drought risk, it combines observational data with future data predicted by climate models to make predictions and classify risk levels, effectively reducing prediction bias. Attached Figure Description

[0042] Figure 1 This is the control flowchart for Example 1;

[0043] Figure 2 Legend for the meteorological drought risk forecast and cumulative drought intensity in May 2019 in Test Example 1;

[0044] Figure 3 Legend for the meteorological drought risk forecast and cumulative drought intensity in November 2019 in Test Example 2; Detailed Implementation

[0045] Example 1:

[0046] like Figure 1 As shown, the method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts of the present invention includes the following steps:

[0047] S1. The assessment period is determined based on the occurrence of drought events during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period.

[0048] S2. Obtain historical daily precipitation observation data of meteorological stations over many years during the assessment period, and calculate the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year. Calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year.

[0049] S3. Based on the index of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage index, calculate the annual meteorological drought risk index and form a historical meteorological drought risk index sequence. Use the percentile method to determine the classification threshold of meteorological drought risk level.

[0050] S4. Obtain the forecast data for the assessment period. The forecast data includes the daily precipitation observation data of the meteorological station used during the previous precipitation period and the daily precipitation data of the climate model forecast for the next month used during the forecast month period. Statistically analyze the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the forecast data within the assessment period, and identify drought processes. If a drought process is identified, calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly in the assessment period, calculate the meteorological drought risk index for the forecast month, and determine the meteorological drought risk level for the forecast month based on the classification threshold of the meteorological drought risk level.

[0051] This method selects the number of days without effective precipitation, which takes into account the duration of low precipitation, and the percentage of cumulative precipitation anomaly, which reflects the characteristics of low total precipitation, as the prediction indicators. This overcomes the complexity of existing monthly-scale meteorological drought prediction methods and improves the operability and reliability of the prediction.

[0052] Example 2:

[0053] This example, based on Example 1, further designs the following: In S1, the assessment period is determined according to the occurrence of drought events during the previous precipitation period in the assessment area. Specifically:

[0054] The month following the estimated date is designated as the estimated month. The period of preceding precipitation is determined according to the following scenarios:

[0055] Scenario 1: If no drought event occurs in the month prior to the forecast, then the month prior to the forecast shall be considered as the period of previous precipitation.

[0056] Scenario 2: If a drought event occurs within one month prior to the forecast date, and the forecast date is still within a drought period, then the period from the date of the drought event to the forecast date will be considered as the period of previous precipitation.

[0057] Scenario 3: If a drought event occurs one month before the forecast date, and the drought event has ended by the forecast date, then the period from the end of the drought event to the forecast date is considered as the period of previous precipitation.

[0058] The determination of drought process is based on the single-station drought process monitoring method in Chapter 4 of the industry standard QX / T 597-2021 "Regional Drought Process Monitoring and Assessment Methods".

[0059] Example 3:

[0060] This example further designs the concept of the number of consecutive days without effective precipitation, based on the above embodiments, using the following formula:

[0061]

[0062] In the formula, This is the index of the number of consecutive days without effective precipitation at a single station; This refers to the number of consecutive days without effective precipitation at a single station. The threshold value for the number of consecutive days without effective precipitation is used in this invention. sky; This represents the maximum number of consecutive days without effective precipitation across all stations in history.

[0063] The cumulative precipitation anomaly percentage index is calculated using the following formula:

[0064]

[0065] In the formula, The percentage of cumulative precipitation anomaly at a single station; This represents the percentage of cumulative precipitation anomaly at a single station. The threshold value for the percentage of precipitation anomaly is used when the assessment period is less than or equal to 2 months. When the evaluation period exceeds 2 months, ; This is the minimum percentage of historical precipitation anomalies among all stations.

[0066] The mathematical model for calculating the annual meteorological drought risk index is as follows:

[0067]

[0068] In the formula, R is the meteorological drought risk index; The index represents the number of consecutive days without effective precipitation. It is the percentage index of precipitation anomaly; The weighting coefficient for the index of consecutive days without effective precipitation; This is the weighting coefficient for the percentage of precipitation anomaly index.

[0069] The weight coefficients corresponding to the two predicted indicators are determined using the information entropy weighting method, expert scoring method, or principal component analysis method. And they satisfy... For example, using expert scoring to determine .

[0070] Example 4:

[0071] This example, based on the above embodiments, further designs the following: In S3, the annual meteorological drought risk index is calculated to obtain the historical meteorological drought risk index sequence, and the percentile method is used to determine the classification threshold of the meteorological drought risk level, specifically as follows:

[0072] From the historical meteorological drought risk index series, samples with risk indices not equal to 0 are selected. The percentile method is used to sort the index values ​​from smallest to largest and determine the threshold for each risk level. For example, the 50th, 75th and 90th percentiles are taken as the classification thresholds for low, medium and high meteorological drought risk levels.

[0073] Example 5:

[0074] This example provides a quantitative prediction system for monthly meteorological drought risk based on climate model forecasts, including the following modules:

[0075] The module for determining the assessment period determines the assessment period based on the occurrence of drought processes during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period.

[0076] The historical data processing module acquires historical daily precipitation observation data from meteorological stations over many years during the assessment period, calculates the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year, and calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year.

[0077] The risk level threshold determination module calculates the annual meteorological drought risk index based on the continuous days without effective precipitation index and the cumulative precipitation anomaly percentage index, and forms a historical meteorological drought risk index sequence. The percentile method is used to determine the classification threshold of the meteorological drought risk level.

[0078] The meteorological drought risk prediction module acquires prediction data for the assessment period, including daily precipitation observation data from meteorological stations used during the previous precipitation period and daily precipitation data for the next month predicted by climate models used during the prediction month period. It statistically analyzes the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the prediction data within the assessment period, and identifies drought processes. If a drought process is identified, it calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly within the assessment period, calculates the meteorological drought risk index for the predicted month, and determines the meteorological drought risk level for the predicted month based on the classification threshold of meteorological drought risk levels.

[0079] Example 6:

[0080] This example provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to perform the method described above.

[0081] This example also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method described above.

[0082] Example 7:

[0083] This invention provides a quantitative prediction method for monthly meteorological drought risk based on climate model forecasts, comprising the following steps:

[0084] Step 1: Determine the assessment period

[0085] The assessment period is divided into two parts: the period of previous precipitation and the period of the estimated month.

[0086] The assessment period is determined based on the occurrence of drought events during the preceding precipitation period. The preceding precipitation period is considered in three scenarios, with the month following the forecast date set as the forecast month. The preceding precipitation period is determined according to the following scenarios:

[0087] Scenario 1: If no drought event occurs in the month prior to the forecast, then the month prior to the forecast shall be considered as the period of previous precipitation.

[0088] Scenario 2: If a drought event occurs within one month prior to the forecast date, and the forecast date is still within a drought period, then the period from the date of the drought event to the forecast date will be considered as the period of previous precipitation.

[0089] Scenario 3: If a drought event occurs one month before the estimated date, and the drought event has ended by the estimated date, then the period from the end of the drought event to the estimated date is considered the period of previous precipitation.

[0090] Step 2: Processing historical data

[0091] Using historical daily precipitation data since the meteorological station was established, the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage were statistically analyzed for each assessment period. The number of consecutive days without effective precipitation refers to the number of consecutive days with daily precipitation less than 5 mm. The precipitation anomaly percentage was calculated according to Appendix A of GB / T 20481-2017 "Meteorological Drought Classification". Based on this, the annual index of consecutive days without effective precipitation was calculated using the following formula. and cumulative precipitation anomaly percentage index .

[0092] The index of the number of days without effective precipitation during the assessment period is calculated using the following formula (1):

[0093]

[0094] In the formula, This is the index of the number of consecutive days without effective precipitation at a single station; This refers to the number of consecutive days without effective precipitation at a single station. The threshold value for the number of consecutive days without effective precipitation is used in this invention. sky; This represents the maximum number of consecutive days without effective precipitation across all stations in history.

[0095] The cumulative precipitation anomaly percentage index for the assessment period over the years is calculated using the following formula (2):

[0096]

[0097] In the formula, The percentage of cumulative precipitation anomaly at a single station; This represents the percentage of cumulative precipitation anomaly at a single station. The threshold value for the percentage of precipitation anomaly is used when the assessment period is less than or equal to 2 months. When the evaluation period exceeds 2 months, ; This is the minimum percentage of historical precipitation anomalies among all stations.

[0098] Step 3: Determining the risk level threshold

[0099] Use the historical number of consecutive days without effective precipitation obtained in step 2 to calculate the evaluation period. and cumulative precipitation anomaly percentage index The meteorological drought risk index is calculated according to the following formula (3):

[0100]

[0101] In the formula, R is the meteorological drought risk index. The index represents the number of consecutive days without effective precipitation. The precipitation anomaly percentage index. These are the weighting coefficients corresponding to the two predicted indicators mentioned above, and they satisfy... This example was determined using an expert scoring method. .

[0102] Based on the above results, the historical meteorological drought risk indexes were used to form a historical meteorological drought risk index sequence. Samples with risk indices not equal to 0 were selected from this sequence. Using the percentile method, the index values ​​were sorted from smallest to largest, and the 50th, 75th, and 90th percentiles were taken as the grading thresholds for low, medium, and high meteorological drought risks.

[0103] Table 1 Thresholds for Meteorological Drought Risk Levels

[0104] Step 4: Meteorological Drought Risk Assessment

[0105] The assessment period is divided into two parts: the pre-precipitation period and the estimated monthly period. Daily precipitation data combining actual observations and model forecasts are used as the estimated data for each period. The number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly within the assessment period are statistically analyzed. Meteorological drought processes are identified based on the number of consecutive days without effective precipitation. A meteorological drought process is determined when the number of consecutive days without effective precipitation reaches or exceeds 20 days. When a meteorological drought process is determined to occur, the index of consecutive days without effective precipitation within the assessment period is calculated according to the method described in step 2. and cumulative precipitation anomaly percentage index Then, calculate the meteorological drought risk index according to the method described in step 3, and determine the estimated monthly meteorological drought risk level based on the meteorological drought risk level threshold obtained in step 3.

[0106] The following test case focuses on the drought in Anhui Province in 2019, which mainly occurred from early May to early June (spring drought) and from mid-August to late November (summer-autumn drought). May (when no drought process had formed in the early stage) and November (when a drought process had already started in the early stage) were selected as the estimated months for the following related tests.

[0107] Test Example 1:

[0108] This test case is based on Example 7. Using the monthly-scale meteorological drought risk quantitative prediction method based on climate model forecasts of this invention, the steps for predicting the meteorological drought risk in May 2019 are as follows:

[0109] Step 1: Determine the assessment period: Since a drought process has not yet formed by the end of April, the assessment period is determined to be from April 1 to May 31.

[0110] Step 2: Processing historical data: Using historical daily precipitation observation data from 1961 to 2018, the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly from April 1st to May 31st of each year were statistically analyzed. Based on this, the index of consecutive days without effective precipitation was calculated according to the above formulas (1) and (2). and cumulative precipitation anomaly percentage index .

[0111] Step 3: Risk Level Threshold Determination: Use the historical data obtained in Step 2 to determine the number of consecutive days without effective precipitation from April 1st to May 31st of each year. and cumulative precipitation anomaly percentage index The meteorological drought risk index is calculated according to the above formula (3). In the meteorological drought risk index series of previous years, samples with risk indices not equal to 0 are screened. The percentile method is used to sort the index values ​​from smallest to largest. The 50th, 75th and 90th percentiles are taken as the classification thresholds for the three levels of low, medium and high meteorological drought risk, which are 0.1117, 0.2228 and 0.4107, respectively.

[0112] Step 4: Meteorological Drought Risk Assessment: Using daily precipitation observation data from April 1st to April 29th, 2019, combined with daily precipitation data output from the climate model from April 30th to May 31st (reported starting April 30th), the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly during the assessment period are statistically analyzed. When the number of consecutive days without effective precipitation reaches or exceeds 20 days, a meteorological drought event is determined, and the index of consecutive days without effective precipitation during the assessment period is statistically analyzed according to the method in Step 2. and cumulative precipitation anomaly percentage index Then, the meteorological drought risk index is calculated, and the risk level is determined based on the obtained grading threshold.

[0113] The predicted results are as follows Figure 2 As shown, Figure 2 Figures (a) and (b) show the meteorological drought risk forecast results and the actual monitoring of cumulative drought intensity in May 2019, respectively. As can be seen from the figures, the forecast results using the method of this invention are consistent with the actual monitoring results and can relatively accurately reflect the intensity and spatial distribution of meteorological drought in the forecast month.

[0114] Test Example 2:

[0115] This test case is based on Example 7. Using the monthly-scale meteorological drought risk quantitative prediction method based on climate model forecasts of this invention, the steps for predicting the meteorological drought risk in November 2019 are as follows:

[0116] Step 1: Determine the assessment period: There was already a significant drought in October, and a drought process was formed. The drought process started in early September, so the assessment period is determined to be from September 1 to November 30.

[0117] Step 2: Processing historical data: Using historical daily precipitation observation data from 1961 to 2018, the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly from September 1st to November 30th of each year were statistically analyzed. Based on this, the index of consecutive days without effective precipitation was calculated according to the above formulas (1) and (2). and cumulative precipitation anomaly percentage index .

[0118] Step 3: Risk Level Threshold Determination: Use the historical data obtained in Step 2 to determine the number of consecutive days without effective precipitation from September 1st to November 30th of each year. and cumulative precipitation anomaly percentage index The meteorological drought risk index is calculated according to the above formula (3). In the meteorological drought risk index series of previous years, samples with risk indices not equal to 0 are screened. The percentile method is used to sort the index values ​​from smallest to largest. The 50th, 75th and 90th percentiles are taken as the classification thresholds for the three levels of low, medium and high meteorological drought risk, which are 0.1127, 0.2234 and 0.3809, respectively.

[0119] Step 4: Meteorological Drought Risk Assessment: Using daily precipitation observation data from September 1 to October 30, 2019, combined with daily precipitation data output from the climate model from October 31 to November 30 (reported starting October 31), the index of consecutive days without effective precipitation during the assessment period is calculated. and cumulative precipitation anomaly percentage index Then, the meteorological drought risk index is calculated according to the above formula (3), and the risk level is determined according to the grading threshold obtained in the previous step.

[0120] The predicted results are as follows Figure 3 As shown. Figure 3 Figures (a) and (b) show the meteorological drought risk forecast and the actual cumulative drought intensity monitoring results for November 2019, respectively. As can be seen from the figures, the forecast results using the method of this invention are largely consistent with the actual monitoring results, and can relatively accurately reflect the intensity and spatial distribution of the meteorological drought in the forecast month.

Claims

1. A method for quantitatively predicting the risk of meteorological drought on a monthly scale based on climate model forecasts, characterized in that, Includes the following steps: S1. The assessment period is determined based on the occurrence of drought events during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period. S2. Obtain historical daily precipitation observation data of meteorological stations over many years during the assessment period, and statistically analyze the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year. Calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year. S3. Based on the index of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage index, calculate the annual meteorological drought risk index and form a historical meteorological drought risk index sequence. Use the percentile method to determine the classification threshold of the meteorological drought risk level. S4. Obtain the estimated data for the assessment period. The estimated data includes daily precipitation observation data from meteorological stations used during the previous precipitation period and daily precipitation data for the next month predicted by climate models used during the estimated monthly period. Statistically analyze the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the estimated data within the assessment period, and identify drought processes. If a drought process is identified, calculate the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly within the assessment period, calculate the estimated monthly meteorological drought risk index, and determine the estimated monthly meteorological drought risk level based on the classification threshold of the meteorological drought risk level.

2. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 1, characterized in that, In S1, the determination of the assessment period based on the occurrence of drought events during the previous precipitation period in the assessment area is specifically as follows: The month following the estimated date is designated as the estimated month. The period of preceding precipitation is determined according to the following scenarios: Scenario 1: If no drought event occurs in the month prior to the forecast, then the month prior to the forecast shall be considered as the period of previous precipitation. Scenario 2: If a drought event occurs within one month prior to the forecast date, and the forecast date is still within a drought period, then the period from the date of the drought event to the forecast date will be considered as the period of previous precipitation. Scenario 3: If a drought event occurs one month before the estimated date, and the drought event has ended by the estimated date, then the period from the end of the drought event to the estimated date is considered the period of previous precipitation.

3. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 2, characterized in that, The index of days without effective precipitation is calculated using the following formula: ； In the formula, This is the index of the number of consecutive days without effective precipitation at a single station; This refers to the number of consecutive days without effective precipitation at a single station. This represents the threshold value for the number of consecutive days without effective precipitation. This represents the maximum number of consecutive days without effective precipitation across all stations in history.

4. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 3, characterized in that, The cumulative precipitation anomaly percentage index is calculated using the following formula: ； In the formula, The percentage of cumulative precipitation anomaly at a single station; This represents the percentage of cumulative precipitation anomaly at a single station. This is the threshold value for the percentage of precipitation anomaly. This is the minimum percentage of historical precipitation anomalies among all stations.

5. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 4, characterized in that, The mathematical model for calculating the annual meteorological drought risk index is as follows: ； In the formula, R is the meteorological drought risk index; The index represents the number of consecutive days without effective precipitation. It is the percentage index of precipitation anomaly; The weighting coefficient for the index of consecutive days without effective precipitation; This is the weighting coefficient for the percentage of precipitation anomaly index.

6. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 5, characterized in that, The weight coefficients corresponding to the two predicted indicators are determined using the information entropy weighting method, expert scoring method, or principal component analysis method.

7. The method for quantitative prediction of monthly meteorological drought risk based on climate model forecasts according to claim 1, characterized in that, In S3, the calculation of the annual meteorological drought risk index yields a historical sequence of meteorological drought risk indices. The percentile method is then used to determine the threshold for classifying the meteorological drought risk level. Specifically: From the historical meteorological drought risk index series, samples with risk indices not equal to 0 were selected, and the percentile method was used to sort them according to the index values ​​from smallest to largest to determine the threshold for each risk level.

8. A quantitative prediction system for monthly meteorological drought risk based on climate model forecasts, characterized in that, Includes the following modules: The module for determining the assessment period determines the assessment period based on the occurrence of drought processes during the previous precipitation period in the assessment area. The assessment period is divided into the previous precipitation period and the estimated monthly period. The historical data processing module acquires historical daily precipitation observation data from meteorological stations over many years during the assessment period, calculates the number of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage during the assessment period each year, and calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly percentage for each year. The risk level threshold determination module calculates the annual meteorological drought risk index and forms a historical meteorological drought risk index sequence based on the index of consecutive days without effective precipitation and the cumulative precipitation anomaly percentage index, and uses the percentile method to determine the classification threshold of the meteorological drought risk level. The meteorological drought risk prediction module acquires prediction data for the assessment period, including daily precipitation observation data from meteorological stations used during the previous precipitation period and daily precipitation data for the next month predicted by climate models used during the prediction month period. It statistically analyzes the number of consecutive days without effective precipitation and the percentage of cumulative precipitation anomaly in the prediction data within the assessment period, and identifies drought processes. If a drought process is identified, it calculates the index of consecutive days without effective precipitation and the index of cumulative precipitation anomaly within the assessment period, calculates the meteorological drought risk index for the predicted month, and determines the meteorological drought risk level for the predicted month based on the classification threshold of the meteorological drought risk level.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7 above.

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