Hydropower station reservoir area monthly water temperature prediction method and device
By combining the exponential decay model with meteorological data, a monthly water temperature calculation model was established, which solved the problem of insufficient water temperature prediction accuracy in large reservoirs and achieved efficient and accurate water temperature distribution prediction, which is suitable for temperature control design and management of hydropower stations.
Patent Information
- Application Number
- CN202510793650.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-13
- Publication Date
- 2025-10-17
AI Technical Summary
Existing water temperature prediction methods lack accuracy in dam design and management, especially in large reservoirs. They cannot accurately reflect water depth changes and seasonal fluctuations, resulting in large errors in prediction results.
An exponential decay model is used in combination with meteorological data to calculate the annual average water temperature, annual temperature variation and water temperature phase difference at the reservoir surface and at different water depths, and a monthly water temperature calculation model is established to predict the monthly water temperature distribution in the hydropower station reservoir area.
It improves the accuracy and simplicity of water temperature prediction, can quickly provide water temperature distribution at different water depths and time points, and provides an important reference for dam temperature control design and operation management. It is suitable for hydropower stations under different climatic conditions.
Smart Images
Figure CN120804648A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to a method and device for predicting monthly water temperature in a reservoir area of a hydropower station, and belongs to the technical field of water temperature research in a reservoir area of a hydropower station. BACKGROUND
[0002] In the design and operation process of a hydropower station, the water temperature in the reservoir area of the hydropower station is an important influencing factor. In particular, in the temperature stress calculation and temperature control design of a dam, the distribution of the water temperature in the reservoir area is directly related to the structural safety and operation efficiency of the dam. However, the water temperature in the reservoir area is not only affected by meteorological conditions, but also by a variety of factors such as reservoir depth, sunlight, seasonal changes, etc. In particular, in a large reservoir, due to the large water depth, the vertical distribution of the water temperature and its variation characteristics become more complex. Therefore, accurately predicting the water temperature at each depth of the reservoir, especially the monthly variation of the water temperature, is of great significance for the temperature control of the dam, the long-term operation of equipment, and the protection of the water ecological environment.
[0003] Traditional water temperature prediction methods mostly rely on field measurements and empirical formulas, and can usually only provide rough estimates, which have limited reference value for dam design and management. In addition, some existing methods lack sufficient consideration of water depth changes and seasonal fluctuations, resulting in large errors in the prediction results. With the development of computer technology and mathematical models, more and more researches have begun to try to combine mathematical models with actual data to predict water temperature, among which the commonly used methods include using an exponential decay model to simulate the vertical distribution of water temperature. Although these methods have improved the prediction accuracy of water temperature to some extent, there are still some problems, such as complex calculation process, poor adaptability to meteorological changes, etc. SUMMARY
[0004] To solve the above technical problems, the application provides a method and device for predicting monthly water temperature in a reservoir area of a hydropower station.
[0005] The application is implemented by the following technical solutions:
[0006] In a first aspect, a method for predicting monthly water temperature in a reservoir area of a hydropower station includes the following steps:
[0007] Step 101: Obtain meteorological data of the area where the reservoir is located, calculate the annual average water temperature of the surface layer of the reservoir based on the annual average air temperature, and calculate the annual average water temperature at any water depth using an exponential decay model;
[0008] Step 102: Obtain the temperature annual amplitude of the surface layer of the reservoir, and calculate the water temperature annual amplitude at each water depth of the reservoir according to the temperature annual amplitude of the surface layer of the reservoir;
[0009] Step 103, the water temperature phase difference at different water depths of the reservoir is calculated by using an exponential decay relationship, and a monthly water temperature calculation model is established by combining the annual variation amplitude of water temperature and the annual average water temperature at an arbitrary water depth;
[0010] Step 104, according to the monthly water temperature calculation model, the target month and water depth information are inputted to predict the monthly water temperature distribution at different elevations in the reservoir area of the hydropower station.
[0011] The meteorological data in the step 101 includes the annual average temperature, the average temperature in July, the average temperature in January, the sunshine duration and intensity of the area where the reservoir is located.
[0012] The calculation formula of the annual average water temperature of the reservoir surface layer in the step 101 is as follows:
[0013] T s =T am +Δb,
[0014] Wherein, T s is the annual average water temperature of the reservoir surface layer, in Celsius; T am is the annual average temperature, in Celsius; Δb is the temperature increment of the annual average water temperature of the reservoir surface layer brought by sunshine, in Celsius.
[0015] The calculation formula of the annual average water temperature at an arbitrary water depth in the step 101 by using an exponential decay model is as follows:
[0016] T m (y)=c+(T s -c)e -0.04y ,
[0017] c=(T b -T s g) / (1-g),
[0018] g=e -0.04H ,
[0019] Wherein, T m (y) is the annual average water temperature at water depth y, in Celsius; T s is the annual average water temperature of the reservoir surface layer, in Celsius; T b is the reservoir bottom water temperature, in Celsius; H is the maximum water depth of the reservoir, in meters; c and g are intermediate variables.
[0020] The calculation formula of the temperature annual variation amplitude of the reservoir surface layer in the step 102 is as follows:
[0021]
[0022] Wherein, A0 is the temperature annual variation amplitude of the reservoir surface layer, T7 and T1 are the average air temperature of the reservoir area in July and January respectively, and the unit is Celsius.
[0023] The calculation formula of the water temperature annual variation amplitude of each water depth in the reservoir in the step 102 is as follows:
[0024] A(y) = A0e -0.018y ,
[0025] Wherein, A(y) is the water temperature annual variation amplitude at the water depth y, and the unit is Celsius; e is the base of natural logarithm, and is about equal to 2.71828; 0.018 is the attenuation coefficient, and is used for describing the attenuation rate of the water temperature annual variation amplitude with the increase of the depth.
[0026] The calculation formula of the water temperature phase difference of different water depths in the reservoir in the step 103 is as follows:
[0027] ε = 2.15-1.30e -0.085y ,
[0028] Wherein, ε is the phase difference of the water temperature annual variation relative to the air temperature variation at the water depth y, and the unit is month; 0.085 is the attenuation coefficient, and is used for describing the attenuation rate of the phase difference with the increase of the depth.
[0029] The calculation formula of the monthly water temperature calculation model in the step 103 is as follows:
[0030] T(y, τ) = T m (y) + A(y)cosω(τ-τ0-ε),
[0031] Wherein, T(y, τ) is the water temperature at the water depth y in the month τ, and the unit is Celsius; τ0 is the month with the highest air temperature; ω is the angular frequency.
[0032] The second aspect is a water temperature prediction device for a reservoir area of a hydropower station, comprising:
[0033] A first calculation module is used for running the method in the step 101;
[0034] A second calculation module is used for running the method in the step 102;
[0035] A third calculation module is used for running the method in the step 103;
[0036] A fourth calculation module is used for running the method in the step 104.
[0037] The third aspect is an electronic device, comprising a processor and a memory connected with the processor in communication;
[0038] The memory stores computer execution instructions;
[0039] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.
[0040] In a fourth aspect, a computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed on a processor, the processor executes the method described in the first aspect.
[0041] In a fifth aspect, a computer program product comprises computer execution instructions, which, when executed on a processor, cause the processor to execute the method described in the first aspect.
[0042] The beneficial effects of the present invention are as follows: First, based on meteorological data and the physical variation law of reservoir water temperature, an exponential decay model is used to calculate the annual average water temperature at different water depths, which can more accurately reflect the vertical distribution characteristics of reservoir water temperature. Secondly, by establishing a calculation method for the annual amplitude of water temperature variation, and combining it with the variation law of water temperature phase difference, the seasonal fluctuation of water temperature can be more finely portrayed, thereby improving the prediction accuracy. Thirdly, the calculation process of the present invention is simple and efficient. Only basic meteorological and reservoir parameters need to be input to quickly obtain the monthly water temperature distribution at different water depths. It is suitable for hydropower stations in different regions and climatic conditions. Finally, the present invention can not only provide an important reference for dam temperature control design and operation period management, but can also be widely used in water resources scheduling, water ecological protection and other fields, and has high engineering practical value. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] Figure 1 Schematic diagram of the flow of the method for predicting monthly water temperature in a hydropower station reservoir area according to the present invention;
[0044] Figure 2 It is a structural schematic diagram of the monthly water temperature prediction device for the hydropower station reservoir area of the present invention.
[0045] In the figure: 100 - first computing module, 200 - second computing module, 300 - third computing module, 400 - fourth computing module. DETAILED DESCRIPTION
[0046] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.
[0047] like Figure 1 As shown, in a first aspect, a method for predicting monthly water temperature in a hydropower station reservoir area according to the present invention comprises the following steps:
[0048] Step 101: Obtain meteorological data for the area where the reservoir is located, calculate the annual average water temperature of the reservoir surface based on the annual average air temperature, and calculate the annual average water temperature at any water depth using an exponential decay model.
[0049] Step 101 is the basis of the water temperature prediction method for the reservoir area of a hydropower station. The purpose is to obtain meteorological data of the reservoir area, calculate the annual average water temperature of the reservoir surface layer, and further calculate the annual average water temperature at any water depth using an exponential decay model.
[0050] The specific implementation process of step 101 is as follows:
[0051] First, meteorological data of the reservoir area need to be collected, which is the basis for subsequent calculations. Meteorological data mainly includes the following contents:
[0052] (1) Annual average temperature: The annual average temperature of the reservoir area is one of the key parameters for calculating the annual average water temperature of the reservoir surface layer. This data can be obtained from the observation data of the meteorological station or from the climate data published by the local meteorological department.
[0053] (2) Average temperature in July and January: These two parameters are used to calculate the temperature annual amplitude of the reservoir surface layer. July and January represent the months with the highest and lowest temperatures respectively, and their average temperature data can also be obtained from the meteorological station or the meteorological department.
[0054] (3) Sunshine hours and intensity: Sunshine has a significant impact on the water temperature of the reservoir surface layer. Sunshine hours and intensity data can be used to estimate the solar warming amount, so as to more accurately calculate the annual average water temperature of the reservoir surface layer.
[0055] The annual average water temperature T s of the reservoir surface layer is calculated based on the annual average temperature T am and the solar warming amount Δb. The specific calculation formula is as follows:
[0056] T s = T am + Δb,
[0057] Where T s is the annual average water temperature of the reservoir surface layer, in Celsius; T am is the annual average temperature, in Celsius; Δb is the temperature increment of the annual average water temperature of the reservoir surface layer caused by sunshine, in Celsius.
[0058] In practical applications, Δb can be estimated according to the local sunshine conditions and the experience data of similar reservoirs. For example, in the case of a certain hydropower station, the annual average temperature at the dam site is 12.02℃, and the temperature increment caused by sunshine is 3℃, so the annual average water temperature of the reservoir surface layer is 15.02℃.
[0059] Further, the exponential decay model is used to calculate the annual average water temperature at different water depths of the reservoir. The specific steps are as follows:
[0060] First, the maximum depth of the reservoir and the bottom water temperature are obtained. The maximum depth of the reservoir H and the bottom water temperature T b are the key parameters for calculating the annual average water temperature at any depth. These data can be obtained through field measurement or design data.
[0061] For example, according to the book "Mass Concrete Temperature Stress and Temperature Control" by Academician Zhu Bofang, the bottom water temperature T b of the reservoir under different climate conditions can be obtained.
[0062] Further, the calculation formula for calculating the annual average water temperature at any depth in step 101 is as follows:
[0063] T m (y) = c + (T s -c)e -0.04y ,
[0064] c = (T b -T s g) / (1-g),
[0065] g = e -0.04H ,
[0066] where T m (y) is the annual average water temperature at depth y, in Celsius; T s is the annual average water temperature of the surface layer of the reservoir, in Celsius; T b is the bottom water temperature, in Celsius; H is the maximum depth of the reservoir, in meters; and c and g are intermediate variables.
[0067] Through the above calculation, the annual average water temperature distribution of the reservoir from the surface layer to the bottom can be obtained, providing basic data for subsequent water temperature prediction.
[0068] Step 102, obtain the temperature annual amplitude of the surface layer of the reservoir, and calculate the water temperature annual amplitude at each depth of the reservoir according to the temperature annual amplitude of the surface layer of the reservoir.
[0069] The purpose of step 102 is to obtain the temperature annual amplitude of the surface layer of the reservoir, and calculate the water temperature annual amplitude at each depth of the reservoir based on this. This process is crucial for understanding the seasonal variation of the water temperature of the reservoir.
[0070] The following is the specific implementation process of step 102:
[0071] The temperature annual amplitude A0 of the surface layer of the reservoir is calculated by the average temperature of the surface layer of the reservoir in July T7 and the average temperature in January T1. These two parameters represent the highest and lowest months of the year, respectively, and can effectively reflect the seasonal variation of the water temperature of the surface layer of the reservoir. The specific calculation formula is as follows:
[0072]
[0073] where A0 is the annual temperature variation amplitude of the reservoir surface layer, T7 and T1 are the average air temperatures in July and January, respectively, in the area where the reservoir is located, and the units are both in degrees Celsius.
[0074] This formula calculates half of the difference between the average air temperatures in July and January to obtain the variation amplitude of the reservoir surface layer water temperature throughout the year. This method is simple and intuitive and can effectively reflect the seasonal variation characteristics of the reservoir surface layer water temperature.
[0075] Suppose the average air temperature in July in the area where the reservoir is located is 19.3°C, and the average air temperature in January is 3.3°C. The calculated annual temperature variation amplitude of the reservoir surface layer is 8°C.
[0076] Further, the annual temperature variation amplitude A(y) at each depth of the reservoir is calculated based on the annual temperature variation amplitude A0 of the reservoir surface layer and the water depth y. Since the annual variation amplitude of the reservoir water temperature gradually decreases with increasing depth, an exponential decay relationship is used to describe this variation law. The specific calculation formula is as follows:
[0077] A(y) = A0e -0.018y ,
[0078] where A(y) is the annual temperature variation amplitude at water depth y, the unit is degrees Celsius; e is the base of natural logarithm, approximately equal to 2.71828; 0.018 is the decay coefficient, used to describe the decay rate of the annual temperature variation amplitude with increasing depth.
[0079] This formula associates the annual temperature variation amplitude of the reservoir surface layer with the water depth through an exponential decay model, so that the annual temperature variation amplitude at any depth of the reservoir can be calculated. This method can effectively reflect the law that the annual temperature variation amplitude gradually decreases with increasing depth, providing important parameter support for subsequent water temperature prediction.
[0080] Through the above step 102, the annual temperature variation amplitude of the reservoir surface layer and the annual temperature variation amplitude at each water depth can be accurately calculated, laying a solid foundation for establishing a monthly water temperature calculation model. This process not only considers the seasonal variation characteristics of the reservoir water temperature, but also reasonably describes the variation law of the annual temperature variation amplitude with depth through an exponential decay model, which has high scientificity and practicality.
[0081] Step 103, calculate the water temperature phase difference at different water depths of the reservoir using an exponential decay relationship, combine the annual temperature variation amplitude and the annual average water temperature at any water depth, and establish a monthly water temperature calculation model.
[0082] Step 103 is the core step of the water temperature prediction method for the reservoir area of a hydropower station. The purpose is to calculate the water temperature phase difference at different water depths in the reservoir, and combine the annual water temperature amplitude and the annual average water temperature to establish a monthly water temperature calculation model that can accurately predict the water temperature at any water depth and time point.
[0083] The following is the specific implementation process of step 103:
[0084] The water temperature phase difference ε refers to the lag time of the annual change of water temperature at different water depths in the reservoir relative to the change of air temperature. This parameter reflects the time difference between water temperature change and air temperature change, and is of great significance for accurately predicting the water temperature in the reservoir area of a hydropower station. The specific calculation formula is as follows:
[0085] ε = 2.15 - 1.30e -0.085y ,
[0086] where ε is the phase difference of the annual change of water temperature at water depth y relative to the change of air temperature, with units of months; 0.085 is the attenuation coefficient, used to describe the attenuation rate of the phase difference with increasing depth.
[0087] This formula associates the water temperature phase difference with the water depth y through an exponential decay model, effectively reflecting the rule that the water temperature phase difference gradually approaches a fixed value as the depth increases. The introduction of this model makes the water temperature prediction more consistent with the actual physical process.
[0088] Further, based on the calculated water temperature phase difference, water temperature annual amplitude, and annual average water temperature at any water depth, the monthly water temperature calculation model is established. This model can predict the water temperature at any water depth and time point in the reservoir, and the specific formula is as follows:
[0089] T(y, τ) = T m (y) + A(y)cosω(τ - τ0 - ε),
[0090] where T(y, τ) is the water temperature at water depth y in month τ, with units of degrees Celsius; τ0 is the month with the highest air temperature; ω is the angular frequency, taken as 2π / 12. The highest temperature is usually in the middle of July, so τ0 can be taken as 6.5 months.
[0091] This model takes into account the annual average water temperature, water temperature annual amplitude, and phase lag effect, and can accurately describe the seasonal variation of water temperature in the reservoir.
[0092] Through the above step 103, the water temperature phase difference at different water depths in the reservoir can be accurately calculated, and an accurate monthly water temperature calculation model is established. This model not only can effectively predict the water temperature at any water depth and time point in the reservoir, but also provides important technical support for the construction and operation management of hydropower stations.
[0093] Step 104, according to the monthly water temperature calculation model, input the target month and water depth information, predict the monthly water temperature distribution at different elevations in the reservoir area of the hydropower station.
[0094] Based on the monthly water temperature calculation model established in the previous step, the water temperature at any water depth and time point of the reservoir can be calculated. By inputting the target month τ and water depth y, the water temperature T(y, τ) at the corresponding time point can be calculated.
[0095] In addition, the prediction results can be displayed in the form of charts to intuitively reflect the monthly water temperature distribution at different elevations. These prediction results can provide important reference for the construction and operation management of the hydropower station.
[0096] Through the above steps, the monthly water temperature distribution at different elevations of the hydropower station can be accurately predicted, providing important technical support for the construction and operation management of the hydropower station. This method not only has high prediction accuracy, but also has strong practicality and adaptability.
[0097] As shown in FIG. Figure 2 The second aspect is a hydropower station reservoir monthly water temperature prediction device, which comprises:
[0098] The first calculation module 100 is used to run the method of step 101, that is, to calculate the annual average water temperature of the reservoir surface layer based on the meteorological data of the area where the reservoir is located and the annual average air temperature, and to calculate the annual average water temperature at any water depth using the exponential decay model.
[0099] The second calculation module 200 is used to run the method of step 102, that is, to obtain the temperature annual amplitude of the reservoir surface layer, and to calculate the water temperature annual amplitude at each water depth of the reservoir according to the temperature annual amplitude of the reservoir surface layer.
[0100] The third calculation module 300 is used to run the method of step 103, that is, to calculate the water temperature phase difference at different water depths of the reservoir using the exponential decay relationship, and to establish a monthly water temperature calculation model by combining the water temperature annual amplitude and the annual average water temperature at any water depth.
[0101] The fourth calculation module 400 is used to run the method of step 104, that is, to input the target month and water depth information according to the monthly water temperature calculation model, and to predict the monthly water temperature distribution at different elevations in the reservoir area of the hydropower station.
[0102] The third aspect is an electronic device comprising a processor and a memory connected in communication with the processor.
[0103] The memory stores computer execution instructions.
[0104] The processor executes the computer execution instructions stored in the memory to implement the method of the first aspect.
[0105] In a fourth aspect, a computer readable storage medium stores computer executing instructions, which when executed on a processor, cause the processor to perform the method of the first aspect.
[0106] In a fifth aspect, a computer program product includes computer executing instructions, which when executed on a processor, cause the processor to perform the method of the first aspect.
Claims
1. A method for predicting monthly water temperature in a hydropower station reservoir area, characterized by: The following steps are involved: Step 101: Obtain meteorological data for the reservoir area, calculate the annual average water temperature of the reservoir surface based on the annual average air temperature, and calculate the annual average water temperature at any water depth using an exponential decay model; Step 102: Obtain the annual temperature variation of the reservoir surface layer, and calculate the annual temperature variation of the water at each water depth of the reservoir based on the annual temperature variation of the reservoir surface layer; Step 103: Calculate the phase difference of water temperature at different water depths in the reservoir using an exponential decay relationship, and establish a monthly water temperature calculation model based on the annual water temperature variation and the annual average water temperature at any water depth; Step 104: According to the monthly water temperature calculation model, the target month and water depth information are input to predict the monthly water temperature distribution at different elevations in the hydropower station reservoir area.
2. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The meteorological data in step 101 include the annual average temperature, the average temperature in July, the average temperature in January, and the hours and intensity of sunshine in the area where the reservoir is located.
3. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula for the annual average water temperature of the reservoir surface in step 101 is as follows: T s =T am +Δb, Among them, T s is the annual average water temperature of the reservoir surface, in degrees Celsius; T am is the annual average air temperature, in degrees Celsius; Δb is the temperature increment of the annual average water temperature of the reservoir surface brought by sunshine, in degrees Celsius.
4. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula for calculating the annual average water temperature at any water depth using the exponential decay model in step 101 is as follows: T m (y)=c+(T s -c)e -0.04y , c=(T b -T s g) / (1-g), g=e -0.04H , Among them, T m (y) is the annual mean water temperature at depth y, in degrees Celsius; T s is the annual average water temperature of the reservoir surface, in degrees Celsius; T b is the water temperature at the bottom of the reservoir, in degrees Celsius; H is the maximum water depth of the reservoir, in meters; c and g are both intermediate variables.
5. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula for the annual temperature variation of the reservoir surface in step 102 is as follows: Among them, A0 is the annual temperature variation of the reservoir surface, T7 and T1 are the average temperature in July and January of the reservoir area, respectively, and the unit is degrees Celsius.
6. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula for the annual variation of water temperature at each water depth in the reservoir in step 102 is as follows: A(y)=A0e -0.018y , Where A(y) is the annual water temperature variation at depth y, in degrees Celsius; e is the base of the natural logarithm, approximately equal to 2.71828; and 0.018 is the attenuation coefficient, which describes the rate at which the annual water temperature variation decays with increasing depth.
7. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula for calculating the water temperature phase difference at different water depths in the reservoir using the exponential decay relationship in step 103 is as follows: ε=2.15-1.30e -0.085y , Where ε is the phase difference of the annual change of water temperature at depth y relative to the change of air temperature, expressed in months; 0.085 is the attenuation coefficient, which describes the decay rate of the phase difference with increasing depth.
8. The method for predicting monthly water temperature in a hydropower station reservoir area according to claim 1, wherein: The calculation formula of the monthly water temperature calculation model in step 103 is as follows: T(y,τ)=T m (y)+A(y)cosω(τ-τ0-ε), where T(y,τ) is the water temperature at depth y in month τ, in degrees Celsius; τ0 is the month with the highest temperature; and ω is the angular frequency.
9. A monthly water temperature prediction device for a hydropower station reservoir, characterized by: include: A first computing module (100), configured to execute the method of step 101 according to any one of claims 1 to 8; A second computing module (200), configured to execute the method of step 102 according to any one of claims 1 to 8; A third computing module (300), configured to execute the method of step 103 according to any one of claims 1 to 8; A fourth computing module (400) is configured to execute the method of step 104 as claimed in any one of claims 1 to 8.
10. An electronic device, characterized in that: comprising a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.
11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed on a processor, the processor is caused to execute the method according to any one of claims 1 to 8.
12. A computer program product, characterized in that: The method comprises computer-executable instructions, which, when executed on a processor, cause the processor to execute the method according to any one of claims 1 to 8.