Air temperature and cold wave index evaluation method for ice condition of water conveyance canal in cold region

Abstract

The invention relates to an air temperature and cold wave index evaluation method for the ice condition of a water conveyance canal in a needle cold region. The method comprises the following steps: collecting and collecting air temperature data along a canal; calculating the probability distribution of the daily average minimum temperature change; calculating a temperature cold wave index TCI; defining cold wave classification grade indexes and threshold values; and TCI analysis. According to the method, a three-parameter Log-logistic distribution function is adopted to describe the change of the air temperature, then normalization processing is carried out, and finally the cumulative frequency of the air temperature is introduced into river and canal line cold wave classification. On the basis of researching climate and air temperature characteristics and distribution rules along the canal, an air temperature and cold wave index method and a cold wave grade classification standard are established, and corresponding relations among different grades of cold waves, negative accumulated temperature thresholds and ice condition occurrence and development conditions are analyzed. The accumulated negative accumulated temperature threshold value and the cold wave grade classification method proposed by the air temperature and cold wave index method are more suitable for judgment and prediction of the ice condition development process, and a powerful scientific basis is provided for prediction of disaster prevention and reduction of river and canal ice conditions.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the temperature cold snap index for ice conditions in water conveyance channels, which is a method for evaluating and predicting hydrological data and a method for evaluating the influence of climate on channel water conveyance. Background Art

[0002] Although IPCC and Nature reports show that the temperature has an obvious upward trend in the past 30 years and the global river ice process has weakened, the increasing extreme events are still frequent. As an extreme atmospheric event, a cold snap will be accompanied by weather phenomena such as cooling, strong wind, rain and snow, triggering secondary natural disasters such as freezing, ice jam and flood in rivers or channels. Therefore, a suitable cold snap evaluation method provides scientific support for predicting natural disasters and freezing events caused by cold snaps.

[0003] At present, the definition of cold snap in the Chinese national standard "Cold Snap Grade" (GB / T 21987—2017) is as follows: the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥8°C within 24 hours, or ≥10°C within 48 hours, or ≥12°C within 72 hours, and the daily minimum temperature in this place ≤4°C; Severe cold snap: the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥10°C within 24 hours, or ≥12°C within 48 hours, or ≥14°C within 72 hours, and the daily minimum temperature in this place ≤2°C; Extra-severe cold snap: the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥12°C within 24 hours, or ≥14°C within 48 hours, or ≥16°C within 72 hours, and the daily minimum temperature in this place ≤0°C. This definition is widely recognized and widely used. It mainly emphasizes the drop amplitude of the minimum temperature and the minimum temperature value within a certain period of time. However, the development of river channel ice conditions begins when the air temperature and the cumulative negative air temperature are low enough to lower the water temperature to the freezing point. The range of the daily minimum temperature value ≤4°C defined in this cold snap evaluation index is relatively large. When the river channel water temperature is higher than zero degree, the canal water cannot freeze. Therefore, this cold snap grade cannot provide a clear reference index for the development of river and canal ice conditions. The current research on cold snaps mostly focuses on the relationship between climate change and cold snaps on the time scale and the influence of cold air on cold snaps. The commonly used research methods are mostly to obtain the local cold snap change law through the induction of historical data. Research shows that thermal driving factors such as air temperature are the key to determining river channel ice conditions. Cold snaps under sufficient negative accumulated temperature are the driving force for the generation of bank ice, floating ice and ice cover. However, the traditional method for evaluating the temperature cold snap index does not consider this key factor and cannot provide a clear index for the development of river and canal ice conditions. How to change this phenomenon and thus establish a new cold snap evaluation index system is a problem that needs to be solved. Summary of the Invention

[0004] To overcome the problems of the existing technology, the present invention proposes a method for evaluating the air temperature cold wave index for ice conditions in water conveyance channels in cold regions. The method uses an appropriate method to evaluate the air temperature thresholds and cold wave grades corresponding to the occurrence and development of ice conditions in the areas along the river and canal, providing a strong support basis for solving the problem of the difference between the existing cold wave evaluation index and the ice conditions of the river and canal and for accurately forecasting ice conditions.

[0005] The object of the present invention is achieved as follows: A method for evaluating the air temperature cold wave index for ice conditions in water conveyance channels in cold regions, and the steps of the method are as follows:

[0006] Step 1, collect and acquire the air temperature data along the river and canal: The acquired hydrological data includes: the air temperature change data and ice condition data of each past year in the studied river section, and acquire the air temperature change data of the current year. Especially when the first cold wave period occurs, closely monitor the ice conditions along the river and canal;

[0007] Step 2, calculate the probability distribution of the daily average minimum air temperature change: Use the three-parameter Log-logistic probability distribution function to describe the daily average minimum air temperature change, and the distribution function F(x) is:

[0008]

[0009] where: α is the shape parameter; β is the scale parameter; γ is the location parameter;

[0010]

[0011] γ = w0 - αΓ(1 + 1 / β)Γ(1 - 1 / β)

[0012]

[0013] In the formula: w s is the probability distance weight, s = 0, 1, 2; x i is the air temperature sequence arranged in ascending order, that is, x1 ≤ x2 …… ≤ x n . N is the maximum value of n; Γ is the gamma function;

[0014] Step 3, calculate the air temperature cold wave index TCI:

[0015] P is the cumulative distribution function after normal standardization:

[0016] P = 1 - F(x)

[0017] When P ≤ 0.5, the probability weighted moment w is:

[0018]

[0019] The air temperature cold wave index TCI is:

[0020]

[0021] When P > 0.5, the probability weighted moment w is as follows:

[0022]

[0023] The temperature cold wave index TCI is as follows:

[0024]

[0025] Where c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, d3 = 0.001308;

[0026] Step 4, define the cold wave classification levels and the cold and warm winter level criteria: Based on the TCI method, the cold wave classification levels are 3 levels: cold wave, strong cold wave, and extreme strong cold wave. When the minimum temperature drops by greater than or equal to 6 °C within 48 hours and the 7-day average daily variation of the minimum temperature (7DAVDMAT) is not less than the corresponding threshold range for 3 consecutive days, it corresponds to the cold wave level, where:

[0027] The TCI value corresponding to the cold wave is: -1 to -0.5, and the interval occurrence probability is: 15%;

[0028] The TCI value corresponding to the strong cold wave is: -1.5 to -1, and the interval occurrence probability is: 9%;

[0029] The TCI value corresponding to the extreme strong cold wave is: -∞ to -1.5, and the interval occurrence probability is: 7%;

[0030] The threshold of the 7-day cumulative value of the daily minimum temperature (7DAVDMAT) corresponding to the TCI value varies according to the different geographical locations of different river channels;

[0031] Step 5, TCI analysis: Adopt the cold wave judgment standard of the TCI method. When the first cold wave period occurs, closely monitor the ice conditions along the river channel, and combine the water temperature fine simulation and the water temperature forecast results of the forecast model to provide a basis for ice condition early warning and forecasting, so as to formulate practical anti-icing measures.

[0032] The advantages and beneficial effects of the present invention are as follows: The present invention uses a 3-parameter Log-logistic distribution function to describe the variation distribution of air temperature, then performs normalization processing, and finally introduces the cumulative frequency of air temperature into the cold wave classification of the river channel line. On the basis of studying the climate and air temperature characteristics and distribution laws along the river channel, an air temperature cold wave index method and a cold wave grade classification standard are established, and the corresponding relationships between different grades of cold waves, negative accumulated temperature thresholds and the occurrence and development of ice conditions are analyzed. The cumulative negative accumulated temperature threshold and cold wave grade classification method proposed by this air temperature cold wave index method are more suitable for the judgment and prediction of the ice condition development process, providing a strong scientific basis for the prediction of flood prevention and disaster reduction of river channel ice conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below with reference to the drawings and embodiments.

[0034] Figure 1 is a flowchart of the method described in the embodiment of the present invention;

[0035] Figure 2 is an application example described in the embodiment of the present invention, a schematic diagram of the main canal of the Middle Route of the South-to-North Water Diversion Project;

[0036] Figure 3 is an application example described in the embodiment of the invention, the cumulative distribution probability of the lowest air temperature and 7DAVDMAT at Shijiazhuang Station;

[0037] Figure 4 is an application example described in the embodiment of the invention, the cumulative distribution probability of the lowest air temperature and 7DAVDMAT at Xingtai Station;

[0038] Figure 5 is an application example described in the embodiment of the invention, the cumulative distribution probability of the lowest air temperature and 7DAVDMAT at Baoding Station;

[0039] Figure 6 is an application example described in the embodiment of the invention, obtaining the cold wave classification of Baoding by using the TCI method. DETAILED DESCRIPTION OF THE INVENTION

[0040] Embodiment:

[0041] This embodiment is a method for evaluating the air temperature cold wave index for the ice condition of the water conveyance channel. The method introduced uses a 3-parameter Log-logistic distribution function to describe the variation of air temperature, then performs normalization processing, and finally introduces the cumulative frequency of air temperature into the cold wave classification of the Middle Route of the South-to-North Water Diversion Project. On the basis of studying the climate and air temperature characteristics and distribution laws along the line, an air temperature cold wave index method and a cold wave grade classification standard are established, and the corresponding relationships between different grades of cold waves, negative accumulated temperature thresholds and the occurrence and development of ice conditions are analyzed.

[0042] The so-called 3-parameter log-logistic distribution (LLD3) has good statistical properties and has attracted more and more attention from scholars. It has been applied in industries such as economy, water conservancy, and atmosphere. In this embodiment, the skewed distribution characteristics of the 3-parameter log-logistic distribution function are used to express the temperature distribution law, and its cumulative probability density function is calculated and normalized to define a new method for classifying cold snaps with the temperature threshold as an index. The following takes the section of the Middle Route of the South-to-North Water Diversion Project north of the Yellow River as an example to illustrate the principle and operation process of the method described in this embodiment. The specific steps of the method are as follows, and the process is as Figure 1 shown:

[0043] Step 1: Collect and acquire the temperature data along the river channel: The hydrological data collected includes the temperature change data and ice regime data of previous years in the studied river section, and the temperature change data of the current year. Especially when the first cold snap occurs, closely monitor the ice regime along the river channel.

[0044] The main canal of the Middle Route of the South-to-North Water Diversion Project spans from south to north across latitudes 33° to 40°. The temperature decreases continuously along the way. During winter operation, the 700-km canal section north of the Yellow River will be affected by cold temperatures and will have varying degrees of ice jam problems (see Figure 2 ). Through on-site observations after operation of the bank ice, floating ice, and ice cover spatial distribution of the Middle Route main canal, it is found that the ice jam impact area of the Middle Route main canal is mainly the 363-km canal section from the Qilihe Inverted Siphon to the Beijuma River Culvert. Among them, the section from the Qilihe River to the Hutuo River is mainly bank ice, which has little impact on the water conveyance in the main flow area. The ice jam risk area is the 217-km canal section from the Hutuo River to the Beijuma River (i.e., the Beijing-Shijiazhuang section), that is, the area north of Shijiazhuang is the key area for winter ice prevention, and the closer to Beijing, the more serious the ice regime phenomenon. Baoding area is the national meteorological station with long-term meteorological data and the closest to Beijing. Therefore, the research on cold snaps in the Middle Route of the South-to-North Water Diversion Project is based on the long-term meteorological data in Baoding since 1956.

[0045] The prototype observation of the ice and water regime in winter of the Middle Route of the South-to-North Water Diversion Project started in 2011 and is divided into three stages: The first stage is from 2011 to 2015, mainly carrying out the observation of parameters such as meteorology, flow, water level, flow velocity, and water temperature in the discharge aqueduct, and initially identifying the ice regime characteristics of typical canal sections during the initial emergency water conveyance and full-line water supply; The second stage is from 2015 to 2019, and the observation scope of ice regime characteristics is expanded to about 500 kilometers of the main canal from the Anyang River Inverted Siphon to the Beijuma River Culvert. The third stage is after December 2019, refining the ice regime impact canal section of the Middle Route main canal to the 363-kilometer canal section from the Qilihe Inverted Siphon to the Beijuma River, and carrying out the automated collection of meteorological, ice and water regime elements of five typical canal pools. Therefore, the ice regime data used in the research are all from the records of the prototype observations of the Middle Route main canal in winter from 2011 to 2023.

[0046] Step 2, calculate the probability distribution of the daily minimum temperature change: The 3-parameter Log-logistic probability distribution function is used to describe the daily minimum temperature change, and the distribution function F(x) is:

[0047]

[0048] where: α is the shape parameter; β is the scale parameter; γ is the location parameter;

[0049]

[0050] γ = w0 - αΓ(1 + 1 / β)Γ(1 - 1 / β)

[0051]

[0052] In the formula: w s is the probability distance weight, s = 0, 1, 2; x i is the temperature sequence arranged in ascending order, i.e., x1 ≤ x2 …… ≤ x n . N is the maximum value of n; Γ is the gamma function.

[0053] Step 3, calculate the temperature cold wave index TCI:

[0054] P is the cumulative distribution function after normal standardization:

[0055] P = 1 - F(x)

[0056] When P ≤ 0.5, the probability weighted moment w is:

[0057]

[0058] The temperature cold wave index TCI is:

[0059]

[0060] When P > 0.5, the probability weighted moment w is:

[0061]

[0062] The temperature cold wave index TCI is:

[0063]

[0064] Since the air temperature has seasonal characteristics in terms of data distribution and belongs to a skewed distribution, a three-parameter Log-logistic probability distribution function is used to describe the change in the daily minimum air temperature. Through normal standardization, the cold wave grades are divided by the cumulative frequency distribution of the air temperature, and are restricted by defining the temperature drop amplitude and the temperature drop duration, thus obtaining the South-to-North Water Diversion Temperature Cold wave Index (TCI).

[0065] Assume two random variables x and y, which are related through logarithmic transformation

[0066]

[0067] In the formula, the parameters α, β, and γ are the shape, scale, and location parameters respectively; α is the shape (horizontal scaling) parameter, which controls the horizontal stretching of the function. When α increases, the entire function graph becomes flatter, and when α decreases, the graph becomes steeper. It affects the growth rate of the logarithmic function; γ is the location (translation) parameter, which affects the translation of the function on the x-axis (to ensure the function is meaningful, make x - γ > 0). If γ increases, the entire function graph will move to the right along the x-axis, and if γ decreases, the entire function graph will move to the left along the x-axis; β is the scale (vertical scaling) parameter, which is mainly used to adjust the output of the model and control the change rate of the logarithmic function. It determines the response degree of y to x.

[0068] Assume that y has the property of a logical distribution (LD), that is

[0069]

[0070] where g(y) is the probability density function of y, and solve

[0071] x = αe y / β + γ (3)

[0072] Take the reciprocal of y

[0073]

[0074]

[0075] Then the probability density function f X (Probability Density Function, PDF) is:[[]]END

[0076]

[0077] That is

[0078]

[0079] Given by the linear moment estimation method

[0080]

[0081] γ = w0 - αΓ(1 + 1 / β)Γ(1 - 1 / β)

[0082]

[0083] where w s is the probability distance weight, s = 0, 1, 2; x i is the ascending order of the air temperature sequence, i.e., x1 ≤ x2 …… ≤ x n . Thus, the expression of the Cumulative Distribution Function (CDF) at a given time scale can be obtained as follows:

[0084]

[0085] Since f X (t) is 0 when t <= γ, we can change the lower limit from -∞ to γ, let then t = αu + γ, dt = αdu. When t = γ, u = 0; when t = x, then the integral becomes

[0086]

[0087] That is, the cumulative distribution function is obtained as

[0088]

[0089] After normalizing the cumulative distribution function, let

[0090] p = 1 - F(x)

[0091] When P ≤ 0.5, the probability weighted moment w is:

[0092]

[0093] The temperature cold wave index (TCI) is obtained as

[0094]

[0095] When P > 0.5, the probability weighted moment w is:

[0096]

[0097] The temperature cold wave index (TCI) is obtained as

[0098]

[0099] In the formula, c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, d3 = 0.001308.

[0100] The air temperature cold wave index method considering ice regime characteristics uses the accumulated air temperature, the temperature drop amplitude, and the continuity of the temperature drop as the index parameters for cold wave classification. Considering the forecast lead time of the air temperature at the meteorological station and taking into account the response time of the South-to-North Water Diversion Project operation, the 7Days' Accumulated Value of Daily Minimum Air Temperature (7DAVDMAT) is used as the index parameter for cold wave grade division. In the calculation of the air temperature cold wave index method, the 3-parameter Log-logistic distribution function is used as the probability density function. The empirical values and calculated values of the cumulative distribution probabilities of the daily minimum air temperatures and 7DAVDMAT at three meteorological stations from south to north in Hebei Province (the locations are shown in Figure 2 ) Shijiazhuang, Xingtai, and Baoding stations are distributed as Figures 3 - 5 shown. The root mean square errors of the empirical values and calculated values of the cumulative distribution probabilities of the daily minimum air temperatures at the three stations are 0.006, 0.005, and 0.010 respectively, and the correlation coefficients are 0.9998, 0.9999, and 0.9996 respectively; the root mean square errors of the empirical values and calculated values of the cumulative distribution probabilities of 7DAVDMAT are 0.010, 0.009, and 0.012 respectively, and the correlation coefficients are 0.9996, 0.9997, and 0.9992 respectively. The root mean square error is about 0.01, and the correlation coefficient is almost equal to 1. The correlations between the empirical values and calculated values are all very good, indicating that the 3-parameter Log-logistic distribution function can be used to describe the skewed distribution law of air temperature and 7DAVDMAT.

[0101] Step 4, define the cold wave classification grades: In this embodiment, based on the TCI, the cold wave is divided into three grades: cold wave, strong cold wave, and extreme strong cold wave:

[0102] The temperature cold wave index TCI values are respectively taken -0.5, -1.0, and -1.5 as the threshold boundaries for defining the cold wave levels (see Table 1). The corresponding 7DAVDMAT values for TCI values of -0.5, -1, and -1.5 are -47.0 °C, -61.0 °C, and -77.0 °C respectively. The occurrence probabilities of the threshold intervals corresponding to cold waves, strong cold waves, and extreme cold waves are 15%, 9%, and 7% respectively. In addition, a cold wave reflects a weather activity of significant temperature drop. Therefore, the classification of cold wave levels described in this embodiment not only reflects the cumulative value of negative temperatures but also takes into account the temperature drop amplitude. The condition for the temperature drop intensity of the cold wave is defined as "the temperature drop amplitude within 48 hours of the daily minimum temperature is greater than or equal to 6 °C", and the persistence of the temperature drop is also considered.

[0103] In summary, the classification level of the cold wave based on the TCI method is defined as follows: within 48 hours, the minimum temperature drop is greater than or equal to 6 °C, and the 7DAVDMAT for three consecutive days is not less than the corresponding threshold interval in Table 1 for the corresponding cold wave level. Taking the Baoding temperature of the Middle Route Project of the South-to-North Water Diversion as the judgment basis, for example: A cold wave occurred on January 11, 2018. The 7DAVDMAT values from January 9 to 11 were -56 °C, -55 °C, and -63.9 °C respectively. The minimum value of the 7DAVDMAT for three consecutive days was -55 °C, and it met the condition that the temperature drop amplitude within 48 hours of the daily minimum temperature was greater than 6 °C. According to Table 1, the cold wave level of this cold wave was determined to be a cold wave; The cold wave on January 2, 2021 was defined. The 7DAVDMAT values from December 31 to January 2 were -73.1 °C, -69.5 °C, and -65.4 °C respectively. The minimum value of the 7DAVDMAT for three consecutive days was -65.4 °C, and it met the condition that the temperature drop amplitude within 48 hours of the daily minimum temperature was greater than 6 °C. According to Table 1, the cold wave level of this cold wave was determined to be a strong cold wave. It should be specifically noted that the seven-day cumulative value of the daily minimum temperature on the current day (7DAVDMAT) refers to the sum of the daily minimum temperatures for seven consecutive days from the current day and the previous six days. For example, the cumulative temperature on January 7 refers to the cumulative value of the daily minimum temperatures from January 1 to January 7.

[0104] Table 1 Classification Table of Temperature Cold Wave Index

[0105] Cold wave level TCI value TCI value corresponding to 7DAVDMAT Interval occurrence probability Cold wave -1—-0.5 -61.0℃—-47.0℃ 15％ Strong cold wave -1.5—-1 -77.0℃—-61.0℃ 9％ Extra - strong cold wave -∞—-1.5 -∞—-77.0 7％

[0106] Note: The occurrence probability of the TCI value being less than -0.5 is 31%, less than -1 is 16%, and less than -1.5 is 7%.

[0107] Step 5, TCI analysis: Using the TCI method cold wave judgment standard, when the first cold wave period occurs, closely monitor the ice conditions along the river and canal, and combine the water temperature fine simulation and the water temperature forecast results of the forecast model to provide a basis for ice condition early warning and forecasting, so as to formulate practical anti-icing measures.

[0108] Cold Wave Analysis by TCI Method:

[0109] The cold snap classification in Baoding is obtained by using the TCI method as Figure 6 shown. According to the TCI method, there were 103 cold snaps in 64 years. Cold snaps mainly occurred in December, January, and February, with the most cold snaps (61 times) occurring in January. In the 20 years before 1980, there were 44 cold snaps in total, with extra-strong cold snaps, strong cold snaps, and cold snaps appearing alternately. From 1980 to 2010, in the 31-year period showing the characteristics of climate warming, there were 15 cold snaps in total, and no extra-strong cold snaps or strong cold snaps occurred. After 2010, cold snaps occurred frequently, with a total of 44 times, among which 13 extra-strong cold snaps and strong cold snaps occurred. Super cold snaps mainly occurred in the 1960s and after 2010. Comparing with the existing national standard cold snap classification method, the total number of cold snaps in the three categories is similar, and extra-strong cold snaps are also distributed in the 1960s - 1970s of the last century and after 2010, basically occurring in strongly cold winter years, which is consistent with the regional climate change trend. The TCI method emphasizes the combined effects of cumulative negative air temperature, temperature drop, and the continuity of temperature drop. The cold snap classification results obtained by using the TCI method show that cold snaps mainly occur in December and January of the following year, which is consistent with the occurrence time of the ice conditions in the South-to-North Water Diversion Project. In the years with the strongest and most cold snaps, the development of ice conditions is also the most serious.

[0110] Relationship between the cold snap grade of the South-to-North Water Diversion Project and ice conditions based on the TCI method:

[0111] Since the full-line water transfer in December 2014, the project has implemented the winter operation and dispatch of "high water level, low flow velocity, and water conveyance under the ice cover" in accordance with the "Ice Period Water Conveyance Dispatch Plan for the Middle Route Main Canal of the South-to-North Water Diversion Project". To prevent ice jam and other ice-related accidents, the winter water conveyance flow rate in the main canal section from Beijing to Shijiazhuang is only 30% - 47% of the designed flow rate [7] , which has significantly reduced the water conveyance capacity and restricted the exertion of water conveyance benefits. Continuous multi-year observations show that it is easy to form bank ice, floating ice, and local ice covers in the main canal section from Beijing to Shijiazhuang north of the Hutuo River inverted siphon in the Middle Route of the South-to-North Water Diversion Project. Especially, the 85 km section from Gangtou to Beijuma Sluice is a section where ice covers occur frequently. On January 21, 2016, due to the influence of a rare cold snap, the air temperature dropped by 10℃ within 48 hours, and the lowest air temperature dropped suddenly to -18.3℃, setting the second lowest value of the winter air temperature in Baoding since 1970. As a result, serious ice jams occurred in the section from Caohe Aqueduct to Gangtou Tunnel and from Xiacheting Tunnel to Nan Juma River Inverted Siphon. The ice jam affected a 110 km section of the canal from Puyang River Inverted Siphon to Beijuma River Culvert. The average water backwater caused by the ice jam was 0.50 m, and the maximum water backwater was 0.73 m. From January 4th to 7th, 2021, affected by the cold snap, the air temperature dropped continuously by 16℃, and the lowest air temperature dropped to -22.0℃, setting a new historical low again. As a result, 50.5% of the canal section from Gangtou Tunnel to Beijuma River Culvert was continuously frozen, the thickness of the floating ice accumulation was 20 - 45 cm, the thickness of the ice cover was 3 - 16 cm, and the ice cover in the Beijuma River canal section was the thickest, reaching 16 cm. The lower the cumulative negative temperature in January and the greater the temperature drop, the easier it is for the main canal to form floating ice and ice covers, and the higher the risk of ice jams caused by extreme sudden cold snaps.

[0112] The annual cold snap statistics in Baoding during the operation period of the South-to-North Water Diversion Project are shown in Table 3. According to the classification results of the TCI method: after the full-line water transfer in 2014, the number of cold snaps in 2015 - 2016, when the ice situation of the South-to-North Water Diversion Project was the most serious, reached a record high of 7 times; in the second most serious 2020 - 2021, there were 2 extremely strong cold snaps in January, and the total number of cold snaps was 5 times; the number of cold snaps in 2017 - 2018 and 2018 - 2019, when the ice drift and freezing length were the second most serious, was 4 times and 5 times respectively. Generally speaking, the number and intensity of cold snaps classified by the TCI method can reflect the severity of the ice situation of the South-to-North Water Diversion Project. The statistical results using the existing national standard method are as follows: there were 2 cold snaps in 2015 - 2016, and the total number of cold snaps in 2020 - 2021 was 4 times. In the years with a relatively large number of cold snaps, the ice situation was not significant or even no ice situation occurred. Obviously, the cold snap classification method of the existing national standard method cannot reflect the development of the ice situation. It should be noted that in the middle and late December 2023, there was a temperature drop that exceeded the historical extreme, forming two super strong cold snaps. However, the cooling time was short, and the cooling process occurred in December, at an earlier time, and did not cause ice drift or shore ice.

[0113] Table 3 Cold Snap Statistics during the Operation Period of the South-to-North Water Diversion Project

[0114]

[0115] Based on the 13-year ice period observation data from 2011 to 2023, the corresponding relationship between the occurrence of cold snaps and the ice situation is listed in Table 5. It is found through analysis that the initial ice time occurs during the first cold snap of the year or in the cumulative temperature range where the seven-day negative accumulated temperature first reaches the annual cold snap, that is, the seven-day cumulative negative accumulated temperature is less than -47.0°C (see Table 1); the ice drift time is always during the first cold snap of the year or the first strong cold snap; the freezing occurs during the first strong cold snap of the year or during consecutive cold snaps. In summary, based on the daily minimum temperature in Baoding and using the cold snap judgment standard of the TCI method, when the first cold snap occurs, the ice situation in the Beijing-Shijiazhuang section should be closely monitored, and a practical dispatching plan and anti-icing measures should be formulated in combination with the water temperature prediction results of the water temperature fine simulation and prediction model of the research group, which can provide a basis for the ice situation dispatching operation and early warning and prediction of the South-to-North Water Diversion Project.

[0116] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred layout scheme, those of ordinary skill in the art should understand that the technical solution of the present invention (such as the form of the river channel system, the application of various formulas, the sequence of steps, etc.) can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention.

[0117] 1. A method for evaluating the air temperature cold snap index for the ice situation of water conveyance channels in cold regions, characterized in that the steps of the method are as follows:

[0118] Step 1: Collect and acquire the air temperature data along the river channels: The acquired hydrological data includes the air temperature change data and ice regime data of previous years in the studied river section, and the air temperature change data of the current year.

[0119] Step 2: Calculate the probability distribution of the daily minimum air temperature change: The three-parameter Log-logistic probability distribution function is used to describe the daily minimum air temperature change, and the distribution function F(x) is:

[0120]

[0121] where: α is the shape parameter; β is the scale parameter; γ is the location parameter;

[0122]

[0123] γ = w0 - αΓ(1 + 1 / β)Γ(1 - 1 / β)

[0124]

[0125] In the formula: w s is the probability distance weight, s = 0, 1, 2; x i is the air temperature sequence arranged in ascending order, that is, x1 ≤ x2 …… ≤ x n . N is the maximum value of n; Γ is the gamma function;

[0126] Step 3: Calculate the temperature cold wave index TCI:

[0127] P is the cumulative distribution function after normal standardization:

[0128] P = 1 - F(x)

[0129] When P ≤ 0.5, the probability weighted moment w is:

[0130]

[0131] The temperature cold wave index TCI is:

[0132]

[0133] When P > 0.5, the probability weighted moment w is:

[0134]

[0135] The temperature cold wave index TCI is:

[0136]

[0137] Where \(c_0 = 2.515517\), \(c_1 = 0.802853\), \(c_2 = 0.010328\), \(d_1 = 1.432788\), \(d_2 = 0.189269\), \(d_3 = 0.001308\).

Claims

1. A method for evaluating the temperature cold wave index for ice conditions in water delivery channels in cold regions, characterized in that: The steps of the method are as follows: Step 1: Collect and collect temperature data along the river channel: Collect hydrological data including: temperature change data and ice condition data of the studied river section in the past years, and collect temperature change data of the current year; Step 2, calculate the probability distribution of the daily average minimum temperature change: use the 3-parameter Log-logistic probability distribution function to describe the daily average minimum temperature change, the distribution function F(x) is: Among them: α is the shape parameter; β is the scale parameter; γ is the location parameter; γ=w0-αΓ(1+1 / β)Γ(1-1 / β) Where: w s is the probability distance weight, s = 0, 1, 2; x i The temperature sequence is arranged in ascending order, that is, x1≤x2……≤x n ; N is the maximum value of n; Γ is the gamma function; Step 3, calculate the temperature cold wave index TCI: P is the cumulative distribution function after normal standardization: P=1-F(x) When P≤0.5, the probability weighted moment w is: The temperature cold wave index TCI is: When P>0.5, the probability weighted moment w is: The temperature cold wave index TCI is: Wherein, c0=2.515517, c1=0.802853, c2=0.010328, d1=1.432788, d2=0.189269, d3=0.001308; Step 4, define the cold wave classification level and cold and warm winter level standards: The cold wave classification level based on the TCI method is divided into 3 levels: cold wave, severe cold wave, and extremely severe cold wave: the minimum temperature drop within 48 hours is greater than or equal to 6°C, and the 7DAVDMAT for 3 consecutive days is not less than the corresponding threshold interval as the corresponding cold wave level, where: The TCI value corresponding to the cold wave is: -1--0.5, and the probability of occurrence in the interval is: 15%; The TCI value corresponding to a severe cold wave is: -1.5—-1, and the probability of occurrence in the interval is: 9%; The TCI value corresponding to the extremely strong cold wave is: -∞—-105, and the probability of occurrence in the interval is: 7%; The TCI value corresponds to the seven-day cumulative value of the daily minimum temperature. The 7DAVDMAT threshold value varies according to the geographical location of different rivers and canals; Step 5, TCI analysis: Use the TCI method to judge cold waves. When the first cold wave occurs, pay close attention to the ice conditions along the rivers and canals, and combine the water temperature forecast results of the water temperature fine simulation and forecast model to provide a basis for ice warning and forecast, so as to formulate practical anti-icing measures.

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