A method and system for predicting short-term flood discharge in river channels

Abstract

This application relates to the field of river flood flow prediction technology, specifically to a method and system for short-term river flood flow prediction. The method includes: acquiring temperature, rainfall, and topographic data of the study basin; dividing the study basin into grids; predicting the next day's temperature-induced snowmelt runoff based on the degree to which the daily temperature exceeds the snowmelt baseline temperature in each grid, combined with the daily temperature-induced snowmelt runoff and snow cover rate in each grid; analyzing the total snowmelt water volume caused by rainfall under rainfall conditions; and predicting the future flood flow curve at the target river section based on the temperature-induced snowmelt runoff, the total snowmelt water volume, combined with future meteorological forecast data of the study basin, and the topographic data of the study basin. This achieves accurate flood prediction driven by both rainfall and snowmelt factors, solving the defect of the traditional purely rainfall-driven Xin'anjiang model in underestimating flood flow and improving the accuracy and reliability of the Xin'anjiang model in flood flow prediction.

Description

Technical Field

[0001] This application relates to the field of river flood flow prediction technology, specifically to a method and system for predicting short-term river flood flow. Background Technology

[0002] Short-term flood flow forecasting is one of the core components of modern hydrological forecasting. It refers to the forward-looking simulation and forecasting of flood flow changes that will occur in the next few hours or days at a specific river section, such as upstream of a village or reservoir.

[0003] The Xin'anjiang model is one of China's few hydrological models with global influence. Based on rainfall data for future periods within the basin, it calculates the flood discharge curve at the basin outlet to achieve flood discharge prediction. Traditional Xin'anjiang models only consider the increase in water volume due to rainfall, neglecting other factors contributing to water volume. For example, in high-altitude and high-mountain regions, floods originate not only from rainfall but also from snowmelt. Large-scale snowmelt caused by rapid warming in spring leads to significant spring floods. Therefore, flood prediction needs to consider the combined effects of snowmelt and rainfall. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for predicting short-term flood discharge in river channels. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a method for predicting short-term flood discharge in a river channel, the method comprising the following steps: Acquire temperature and rainfall data, as well as topographic data, of the study watershed; grid the study watershed; The snow cover rate of each grid is obtained daily using remote sensing imagery; the difference between the daily average temperature and the preset snow melt reference temperature is obtained; based on the preset snow melt runoff coefficient and preset degree-day factor of each grid daily, combined with the snow cover rate and the difference, the runoff depth generated by snow melt in each grid daily is calculated; based on the runoff depth, grid area, and temperature-induced snow melt runoff of each grid daily, the temperature-induced snow melt runoff of each grid on the following day is predicted. Under rainfall conditions, the total daily snowmelt water volume caused by rainfall in each grid is determined based on the daily average rainfall and daily average temperature. Based on the temperature-induced snowmelt runoff and the total snowmelt water volume, combined with the future meteorological forecast data of the study basin and the topographic data of the study basin, the future flood flow curve at the target river section is predicted.

[0005] In one embodiment, the process of obtaining the runoff depth is as follows: Calculate the difference between the daily average temperature and the preset snow melting baseline temperature, and record it as the daily effective temperature; The runoff depth generated by snow melting in each grid is calculated based on the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the daily effective temperature. The runoff depth is positively correlated with the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the daily effective temperature.

[0006] In one embodiment, the runoff depth is specifically: Obtain the maximum value between the daily effective temperature and 0 degrees Celsius; use the product of the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the maximum value as the daily runoff depth generated by snowmelt in each grid.

[0007] In one embodiment, the process of obtaining the temperature-induced snowmelt runoff is as follows: Calculate the product of the daily runoff depth generated by snowmelt in each grid and the area of ​​each grid, and denote it as the first product; The temperature-to-snowmelt runoff for each grid day is determined based on the first product and the daily temperature-to-snowmelt runoff for each grid day.

[0008] In one embodiment, the temperature-induced snowmelt runoff specifically refers to: Set the decay coefficient for the next day of each day for each grid, which is positively correlated with the daily temperature and snowmelt runoff of each grid; The difference between the natural number 1 and the decay coefficient is used as the weight of the first product, and the decay coefficient is used as the weight of the daily temperature snowmelt runoff for each grid. The weighted fusion value of the first product and the daily temperature-to-snowmelt runoff of each grid is used as the temperature-to-snowmelt runoff of each grid for the next day.

[0009] In one embodiment, the process of predicting future flood discharge at the target river section is as follows: The weighted rainfall for each grid in the future is determined based on the forecast rainfall for each grid in the future and the day before. The total weighted snowmelt water volume caused by rainfall in each grid for each future day is obtained and used to correct the weighted rainfall volume to obtain the corrected total input water volume for each grid for each future day. This corrected total input water volume is then input into the Xin'anjiang model to obtain the rainfall runoff for each grid for each future day. The temperature and snowmelt runoff of each grid for the next few days are obtained and used to correct the rainfall runoff, resulting in the corrected rainfall runoff for each grid for the next few days. This is then used to perform subsequent calculations for the Xin'anjiang model and obtain the flood discharge curve at the target river section for the next few days. The input data for the Xin'anjiang model also includes topographic data of the study basin.

[0010] In one embodiment, the weighted rainfall for each grid in the future days is the weighted sum of the forecast rainfall for each grid in the future days and the forecast rainfall for the previous day, wherein the weighted rainfall for the first day in the future for each grid is the forecast rainfall for the first day in the future for each grid.

[0011] In one embodiment, the corrected total input water volume for each grid in the future days is the sum of the weighted total snowmelt water volume caused by rainfall in each grid in the future days and the weighted rainfall volume.

[0012] In one embodiment, the corrected precipitation runoff for each grid in the future days is the sum of the temperature-induced snowmelt runoff and precipitation runoff for each grid in the future days.

[0013] Secondly, embodiments of this application also provide a short-term flood flow prediction system for rivers, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] The embodiments of this application have at least the following beneficial effects: This application analyzes two factors that cause floods in high-altitude and high-latitude regions: snowmelt and rainfall, calculates the total snowmelt runoff caused by temperature and the total snowmelt water volume caused by rainfall, and incorporates the calculation results into the Xin'anjiang model to calculate the flood flow of the target river section, thus solving the defect of the traditional rainfall-driven Xin'anjiang model that underestimates the flood flow. This application optimizes the input data of the Xin'anjiang model by dividing the study basin into multiple grids and considering that the data of each grid on the future day t is also affected by the data of the previous day t-1. This solves the problem that the traditional Xin'anjiang model does not take into account the temporal dependence of grid data, and can improve the accuracy and reliability of the traditional Xin'anjiang model in flood flow prediction. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a method for predicting short-term flood discharge in a river channel, as provided in one embodiment of this application; Figure 2A flowchart illustrating the steps involved in predicting future flood flow curves. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a short-term flood flow prediction method and system for rivers proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for predicting short-term flood flow in rivers provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for predicting short-term flood discharge in a river channel according to an embodiment of this application. The method includes the following steps: Step S1: Obtain temperature and rainfall data, as well as topographic data, for the study watershed; grid the study watershed.

[0021] Obtain temperature and precipitation data for the study basin: Download the temperature and precipitation data for the study basin from the Xihe Energy Meteorological Big Data Platform. Specifically, select the location on the map and customize the time range before downloading.

[0022] Obtain elevation data for the study watershed: Obtain elevation data for the study watershed from the global digital elevation model Copernicus GLO-30.

[0023] Furthermore, the obtained elevation data consists of elevation values ​​acquired at fixed intervals within the study watershed. The acquisition method involves setting up a monitoring point every 50 meters within the study watershed, for a total of M monitoring points, and obtaining the elevation values ​​of these M points. It should be noted that the implementer can set the value of M according to the actual situation; this application does not impose specific restrictions. In this embodiment, M is taken as 50000.

[0024] Data preprocessing: The elevation data is gridded. In GIS software, a resampling tool is used to set the acquired elevation data to a grid size of 500m × 500m (each edge of the grid contains the elevation values ​​of 11 detection points). Temperature and rainfall data are gridded daily (24 hours). Based on the gridded elevation data, the central temperature and central rainfall of each grid are calculated. Specifically, the average daily temperature within the study basin is used as the central temperature of all grids, and the average daily rainfall within the study basin is used as the central rainfall of all grids. It should be noted that this implementation uses a 500m × 500m grid size and a daily time step. In other embodiments of this application, the implementer can set the grid size and time step according to the actual situation.

[0025] It should be noted that this application requires at least three days of historical data before subsequent calculations can be performed in order to successfully predict future flood flows in the research basin.

[0026] Step S2: Obtain the daily snow cover rate of each grid using remote sensing imagery; obtain the difference between the daily average temperature and the preset snow melting baseline temperature caused by the temperature; calculate the daily runoff depth generated by snow melting in each grid based on the preset snowmelt runoff coefficient and preset degree-day factor for each grid, combined with the snow cover rate and the difference; predict the temperature-induced snowmelt runoff of each grid for the next day based on the daily runoff depth, grid area, and temperature-induced snowmelt runoff for each grid.

[0027] In high-latitude, high-mountain basins, in addition to rainfall, snowmelt is another crucial factor in the formation of floods. The factors affecting snowmelt fall into two categories: first, air temperature, the higher the temperature, the faster the snow melts; and second, rainfall, the greater the rainfall, the faster the snow melts.

[0028] Specifically, the first consideration is the impact of air temperature on snow accumulation. In high-altitude areas, snow temperatures are typically below 0°C. Melting snow requires overcoming its internal cold storage capacity, meaning raising the overall temperature of the snow to its melting point (0°C). Only then can a sustained positive accumulated temperature (the cumulative value of air temperature above the baseline temperature of 0°C) be used to generate meltwater. However, in reality, even if the ambient temperature can initiate the warming process of snow, the rate at which snow at -10°C melts and accumulates into a noticeable water flow is extremely slow and negligible. Therefore, the actual baseline temperature for snow melting needs to be considered. Here, the melting point refers to the temperature at which a substance transforms from a solid to a liquid state.

[0029] Next is the impact of rainfall on snow. Rain melts snow through the heat brought by the rainwater. At this time, the heat transfer medium changes from air to rainwater. The heat transfer efficiency of rainwater is significantly greater than that of air. Therefore, under the influence of rainfall, the benchmark temperature for snow melting can be taken as 0℃. In addition, under the same rainwater temperature, light rain, moderate rain, heavy rain and rainstorm have different effects on snow melting. Therefore, the impact of different rainfall amounts also needs to be considered.

[0030] To investigate the impact of air temperature on snow cover, a baseline temperature for snowmelt is obtained, and the snowmelt runoff caused by air temperature is calculated. Specifically: (1) When the melting rate of snow reaches 10-20 mm per hour, it may cause flooding. Therefore, it is necessary to obtain the temperature at which the melting rate of snow reaches 10-20 mm per hour as the reference temperature for snow melting caused by air temperature. In this embodiment of the application, the reference temperature for snow melting caused by air temperature is specifically set to 1.5 degrees Celsius.

[0031] It should be noted that the melting speed of 10 mm per hour and the melting depth of 10 mm both refer to the depth of the water produced by melting reaching 10 mm per unit area.

[0032] (2) Calculate the ambient temperature when it reaches Snowmelt runoff at times of 100 and above.

[0033] The SRM model was used to calculate the daily temperature-induced snowmelt runoff for each grid cell. The formula is as follows: In the formula, This represents the snowmelt runoff caused by the temperature on day n+1 within the Kth grid, denoted as temperature-induced snowmelt runoff, with units of [unit missing]. ; This represents the runoff depth caused by snowmelt on day n within the Kth grid, in units of... ; The snowmelt runoff coefficient is a dimensionless ratio between 0 and 1, indicating what proportion of the total water generated by snow accumulation can actually be converted into surface runoff and enter the river channel, while the rest is lost due to evaporation, infiltration, and other reasons. This represents the day-to-day factor, in units of 1. This indicates how many centimeters of snow (calculated as equivalent water depth) can melt within one degree-day for every 1°C increase in temperature. This represents the effective temperature on day n, in °C. This represents the average temperature on day n. This indicates the baseline temperature at which snow melts due to air temperature. This represents the maximum value between the effective temperature on day n and 0 degrees Celsius, indicating when the air temperature is below the base temperature. At that time, no snow melting occurs due to temperature changes; It represents the snow cover rate on day n within the Kth grid, and is a dimensionless ratio between 0 and 1; This represents the area of ​​the Kth grid cell, in units of... ; Used for unit conversion, converting the daily runoff depth within the Kth grid from units. Convert to units ; This represents the portion of the newly generated snowmelt runoff in the Kth grid on day n that does not immediately decay and directly contributes to the snowmelt runoff on day n+1; among which, This is denoted as the first product; The decay coefficient on day n+1 within the Kth grid is a dimensionless value between 0 and 1. This represents the decay contribution of the snowmelt runoff on day n within the Kth grid to the snowmelt runoff on day n+1. This represents the temperature-to-snowmelt runoff on day n within the Kth grid, in units of... .

[0034] It should be noted that the snowmelt runoff coefficient in the above formula... Day-to-day factor a, recession coefficient and snow cover The acquisition process for these parameters is publicly known and can be calculated in the following ways: a) Snowmelt runoff coefficient The following was calculated using historical data and water balance: In the formula, This represents the snowmelt runoff on day n-1 within the Kth grid. This represents the daily snowmelt amount on day n-1 within the Kth grid. This represents the area of ​​the Kth grid cell.

[0035] b) The formula for calculating the day-to-day factor a is: In the formula, It is the average density of snow (usually taken as...). ); It is the density of water (usually taken as...) It should be noted that the coefficient "1.1" in this formula is a unit value, and its unit is "". ".

[0036] c) Decline coefficient Use a formula related to the previous day's snowmelt runoff: In the formula, x and y represent the decay constants, which are set to 0.95 and 0.05 respectively in this embodiment; This represents the snowmelt runoff on day n within the Kth grid. It is understandable that the formula... The input data is purely numerical; for example, let... If so, then only the value 10 is substituted into the formula for calculation. It should be noted that if... The unit is other units, such as It should be converted to First, determine the unit, then substitute its numerical value into the formula for calculation.

[0037] d) Snow cover Analysis of hyperspectral remote sensing imagery reveals that snow has high reflectance in the near-infrared band, while rock surfaces have low reflectance. This is due to the different absorption characteristics of snow and rock in the near-infrared band. Hyperspectral remote sensing imagery within the Kth grid is acquired. This imagery contains the reflectance of each pixel in different frequency bands. This method uses near-infrared reflectance and classifies the reflectance of each pixel in the near-infrared band into two categories. The average reflectance of each category is calculated, with the category with the highest average reflectance representing snow and the category with the lowest average reflectance representing rock. The ratio of snow-covered pixels to the total number of pixels in the image is then calculated to obtain the snow coverage in the Kth grid.

[0038] In this embodiment, the specific binary classification method is as follows: K-means clustering, where K=2, can group pixels into two classes. In other embodiments of this application, implementers may also use other existing clustering algorithms to perform binary classification of pixels.

[0039] Step S3: Under rainfall conditions, determine the total daily snowmelt water volume caused by rainfall in each grid based on the daily average rainfall and daily average temperature.

[0040] Furthermore, the impact of rainfall on snow accumulation is analyzed. People often equate rainwater temperature with ambient air temperature because the difference between them is minimal. This scheme uses the same method to design rainwater temperature. Under the same rainwater temperature, different rainfall amounts have varying degrees of impact on snow melting. Heavy rain has a significantly greater impact than torrential rain because the rainfall volume of heavy rain is greater than that of torrential rain. In reality, rainwater is affected by wind, causing the impact force per unit area to change constantly, making it difficult to statistically predict its patterns. However, rainfall volume can be accurately calculated and predicted both before and after rainfall occurs. Moreover, rainfall volume is positively correlated with the total heat provided by the rainwater temperature; that is, the greater the rainfall volume, the more heat the rainwater provides, and the faster the snow melts. Therefore, different rainfall volumes are used to calculate the total snowmelt water volume.

[0041] The calculation of the total snowmelt water volume caused by rainfall is a well-known method, and the formula is as follows: The calculation applies if and only if the temperature... Calculations should be performed on time, otherwise : In the formula, This represents the total snowmelt water volume caused by rainfall on day n within the Kth grid. This total snowmelt water volume refers to the total amount of liquid water produced during the snowmelt process, and the unit is [unit missing]. ; This represents the snowmelt factor based on rainfall temperature, measured in units of... ; This represents the average rainfall on day n, in units of... ; This is a preset value in °C. Based on prior knowledge, it is known that when the temperature difference is 1 °C, every 80 mm of rainfall can melt 1 mm of snow (calculated using equivalent water depth). Therefore, in this embodiment... ; This represents the average temperature on day n, in degrees Celsius (°C).

[0042] It should be noted that the calculation result of snow melt caused by temperature is snowmelt runoff, while the calculation result of snow melt caused by rainfall is the total snowmelt water volume. The reason for the difference is that the runoff volume generated during rainfall includes both rainwater and snowmelt, making it impossible to calculate the snowmelt runoff during rainfall separately.

[0043] Step S4: Based on the temperature-induced snowmelt runoff and the total snowmelt water volume, combined with the future meteorological forecast data of the study basin and the topographic data of the study basin, predict the future flood flow curve at the target river section.

[0044] The Xin'anjiang model divides the entire basin into multiple block-shaped unit basins (this scheme divides the study basin into multiple grids) and performs runoff generation and runoff calculations for each unit basin to obtain the outflow (rainfall runoff) process of the unit basin. Then, it performs river flood calculations: by adding the outflow processes of each unit basin, the total outflow process of the basin outlet (the target river section in this scheme) can be obtained.

[0045] The Xin'anjiang model was used to predict flood flow at the target river section under the influence of both snowmelt and rainfall. The inputs were: the weighted rainfall for each grid in the watershed for the next 3 days, the weighted total snowmelt water volume caused by the rainfall for each grid in the watershed for the next 3 days, the area of ​​each grid in the watershed, the average slope of the watershed, and the initial soil moisture content in the watershed.

[0046] Before inputting the data, the total weighted snowmelt water volume caused by the daily rainfall of each grid in the study basin for the next 3 days is added to the weighted rainfall volume of each grid in the study basin for the next 3 days. This yields the corrected total input water volume for each grid in the study basin for the next 3 days, and the corrected result is then input into the Xin'anjiang model.

[0047] Furthermore, during the process of inputting the corrected total water volume into the Xin'anjiang model and performing calculations, the daily rainfall runoff for each grid over the next three days will be calculated in the intermediate calculation steps. At this point, the calculation process of the Xin'anjiang model is temporarily interrupted, and the daily temperature-induced snowmelt runoff for each grid in the study basin over the next three days is added to the rainfall runoff of the corresponding grid to obtain the corrected rainfall runoff for each grid over the next three days. The corrected rainfall runoff is then used for the subsequent calculation process of the Xin'anjiang model.

[0048] It should be noted that during rainfall, surface runoff includes rainwater and snowmelt. Calculating snowmelt runoff in this case would result in lower accuracy. Therefore, the total snowmelt water volume is used to overlay the rainfall data for correction. If the snow melts due to temperature, the calculated snowmelt runoff is more accurate. In this case, the snowmelt runoff is overlaid on the rainfall runoff calculated in the Xin'anjiang model for correction.

[0049] Specifically, the contribution of the rainfall on day t in the Kth grid to the river flow includes not only the rainfall on that day but also the effective rainfall input correction from the rainfall on the previous t-1 days. The closer to day t, the greater the impact of the effective rainfall input correction, aiming to simulate rapid confluence or rainfall lag effects not captured by the model. Therefore, the rainfall on day t input in the Xin'anjiang model should be: In the formula, This represents the weighted rainfall on the t-th day in the future, expressed as the input to the K-th grid of the Xin'anjiang model. This represents the predicted rainfall for the Kth grid on the tth day. This represents the predicted rainfall for the Kth grid on the (t-1)th day. The weights are in the range of [0.9, 0.95], and implementers can set them according to their actual needs. The value of is not specifically limited in this application; in this embodiment... ; This means setting the weighted rainfall for the first day in the future to the predicted rainfall for the first day in the future for the Kth grid.

[0050] Furthermore, based on the forecast rainfall and forecast temperature of the Kth grid on the tth day, the total snowmelt water volume caused by the rainfall on the tth day of the Kth grid is determined; based on the total snowmelt water volume caused by the rainfall on the tth and t-1th days of the Kth grid, the weighted total snowmelt water volume caused by the rainfall on the tth day of the Kth grid is calculated using the same calculation method as the weighted rainfall volume.

[0051] Furthermore, based on the temperature and snowmelt runoff of the Kth grid on the future day t and the forecast temperature of the Kth grid on the future day t, combined with the grid area and snow cover rate, the temperature and snowmelt runoff of the Kth grid on the future day t+1 is calculated.

[0052] Other key input data for the Xin'anjiang model include: the average slope of the study basin is obtained by averaging the slopes within each grid cell; the initial soil moisture content within the study basin is obtained by averaging the soil moisture content within each grid cell. The calculation processes for the average slope and initial soil moisture content of the study basin are well-known. Specifically, the expression for the average slope of the study basin can be: In the formula, The average slope of the study watershed is represented by N; N represents the number of grid cells within the study watershed. This represents the vertical elevation difference between the highest and lowest points within the Kth grid. This represents the horizontal distance between the highest and lowest elevation points within the Kth grid.

[0053] Specifically, if the vertical height difference between the highest and lowest points in the Kth grid is less than the preset height value H, then the slope of that grid is directly set to 0, i.e. The value of H is directly set to 0. In this embodiment, H is set to 1 meter. In other embodiments of this application, the implementer can set the value of H according to the actual situation.

[0054] The process of obtaining the initial soil moisture content in the watershed can be as follows: the initial soil moisture content can be obtained by taking the soil water storage state value stored at the end of the previous calculation period of the model, or the soil pre-wetness index estimated based on the rainfall in the previous period can be used as the initial soil moisture content.

[0055] The Xin'anjiang model outputs the flow process curve at the target river section for the next 72 hours. Based on the flow process curve, the future flood flow at the target river section can be predicted in advance.

[0056] A flowchart illustrating the steps involved in predicting future flood flow curves is shown below. Figure 2 As shown.

[0057] Based on the same inventive concept as the above method, this application embodiment also provides a river short-term flood flow prediction system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described river short-term flood flow prediction methods.

[0058] In summary, this application provides a method for predicting short-term flood flow in river channels. By analyzing two factors that cause floods in high-altitude and high-latitude regions—snowmelt and rainfall—it calculates the total snowmelt runoff caused by temperature and the total snowmelt volume caused by rainfall. The calculation results are then incorporated into the Xin'anjiang model to calculate the flood flow at the target river section, thus solving the problem of the traditional rainfall-driven Xin'anjiang model underestimating flood flow. By dividing the study basin into multiple grids, and considering that the data for day t in the future of each grid is also affected by the data for day t-1 in the previous period, the input data of the Xin'anjiang model is optimized. This solves the problem that the traditional Xin'anjiang model does not consider the temporal dependence of grid data, thereby improving the accuracy and reliability of the traditional Xin'anjiang model in flood flow prediction.

[0059] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0060] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0061] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting short-term flood discharge in a river channel, characterized in that, The method includes the following steps: Acquire temperature and rainfall data, as well as topographic data, of the study watershed; grid the study watershed; The snow cover rate of each grid is obtained daily using remote sensing imagery; the difference between the daily average temperature and the preset snow melt reference temperature is obtained; based on the preset snow melt runoff coefficient and preset degree-day factor of each grid daily, combined with the snow cover rate and the difference, the runoff depth generated by snow melt in each grid daily is calculated; based on the runoff depth, grid area, and temperature-induced snow melt runoff of each grid daily, the temperature-induced snow melt runoff of each grid on the following day is predicted. Under rainfall conditions, the total daily snowmelt water volume caused by rainfall in each grid is determined based on the daily average rainfall and daily average temperature. Based on the temperature-induced snowmelt runoff and the total snowmelt water volume, combined with the future meteorological forecast data of the study basin and the topographic data of the study basin, the future flood flow curve at the target river section is predicted.

2. The method for predicting short-term flood discharge in a river as described in claim 1, characterized in that, The process for obtaining the runoff depth is as follows: Calculate the difference between the daily average temperature and the preset snow melting baseline temperature, and record it as the daily effective temperature; The runoff depth generated by snow melting in each grid is calculated based on the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the daily effective temperature. The runoff depth is positively correlated with the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the daily effective temperature.

3. The method for predicting short-term flood discharge in a river as described in claim 2, characterized in that, The runoff depth is specifically: Obtain the maximum value between the daily effective temperature and 0 degrees Celsius; use the product of the snowmelt runoff coefficient, the degree-day factor, the snow cover rate, and the maximum value as the daily runoff depth generated by snowmelt in each grid.

4. The method for predicting short-term flood discharge in a river as described in claim 1, characterized in that, The process of obtaining the temperature-induced snowmelt runoff is as follows: Calculate the product of the daily runoff depth generated by snowmelt in each grid and the area of ​​each grid, and denote it as the first product; The temperature-to-snowmelt runoff for each grid day is determined based on the first product and the daily temperature-to-snowmelt runoff for each grid day.

5. The method for predicting short-term flood discharge in a river as described in claim 4, characterized in that, The specific snowmelt runoff from the temperature is as follows: Set the decay coefficient for the next day of each day for each grid, which is positively correlated with the daily temperature and snowmelt runoff of each grid; The difference between the natural number 1 and the decay coefficient is used as the weight of the first product, and the decay coefficient is used as the weight of the daily temperature snowmelt runoff for each grid. The weighted fusion value of the first product and the daily temperature-to-snowmelt runoff of each grid is used as the temperature-to-snowmelt runoff of each grid for the next day.

6. The method for predicting short-term flood discharge in a river as described in claim 1, characterized in that, The process for predicting future flood discharge at the target river section is as follows: The weighted rainfall for each grid in the future is determined based on the forecast rainfall for each grid in the future and the day before. The total weighted snowmelt water volume caused by rainfall in each grid for each future day is obtained and used to correct the weighted rainfall volume to obtain the corrected total input water volume for each grid for each future day. This corrected total input water volume is then input into the Xin'anjiang model to obtain the rainfall runoff for each grid for each future day. The temperature and snowmelt runoff of each grid for the next few days are obtained and used to correct the rainfall runoff, resulting in the corrected rainfall runoff for each grid for the next few days. This is then used to perform subsequent calculations for the Xin'anjiang model and obtain the flood discharge curve at the target river section for the next few days. The input data for the Xin'anjiang model also includes topographic data of the study basin.

7. The method for predicting short-term flood discharge in a river as described in claim 6, characterized in that, The weighted rainfall for each grid in the future is the weighted sum of the forecast rainfall for each grid in the future and the forecast rainfall for the previous day, wherein the weighted rainfall for the first day in the future for each grid is the forecast rainfall for the first day in the future for each grid.

8. The method for predicting short-term flood discharge in a river as described in claim 6, characterized in that, The corrected total input water volume for each grid in the future days is the sum of the weighted total snowmelt water volume caused by rainfall in each grid in the future days and the weighted rainfall volume.

9. The method for predicting short-term flood discharge in a river as described in claim 6, characterized in that, The corrected precipitation runoff for each grid in the future days is the sum of the temperature-induced snowmelt runoff and precipitation runoff for each grid in the future days.

10. A short-term flood flow prediction system for a river channel, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

Images