Drainage basin scale snow melting runoff estimation method and system, electronic equipment and storage medium

Abstract

The invention discloses a drainage basin scale snow melting runoff estimation method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting reanalysis meteorological data, remote sensing accumulated snow monitoring data and digital elevation model data in a target drainage basin range, and carrying out the preprocessing to generate a drainage basin scale surface average data sequence; dividing the target drainage basin range into a plurality of calculation units; determining a critical temperature threshold value of the whole drainage basin based on a response relationship between the drainage basin average accumulated snow coverage rate and the drainage basin average temperature; on the basis of comparison of the basin average temperature and the critical temperature threshold value, the basin overall snow melting period is recognized by combining basin average accumulated snow coverage rate changes; an improved degree-day factor method is executed in parallel on each calculation unit, and the snow melting rate is calculated; and confluence calculation is carried out on runoff generated by the snow melting rate of each calculation unit, and the snow melting runoff of the drainage basin outlet section is output. According to the method, dynamic and accurate estimation of the drainage basin snow melting runoff is realized, and the simulation precision and practicability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrological forecasting and water resources management, and particularly relates to a snowmelt runoff estimation method, system and electronic device at a basin scale adopting a "global discrimination, local calculation and summary output" architecture and a storage medium. BACKGROUND

[0002] Snowmelt runoff is an important hydrological process in cold regions, and is of great significance to spring water supply, agricultural irrigation and flood control. The traditional snowmelt runoff estimation method mainly adopts the degree-day factor method, which is simple and practical, but has the following obvious shortcomings: ① The traditional method usually uses a fixed 0℃ as the snowmelt start temperature, ignoring the temperature threshold differences of different basins and different seasons; ② The fixed temperature threshold is difficult to accurately identify the start and end time of snowmelt, resulting in deviation in runoff process simulation; ③ The traditional method does not consider the influence of temperature daily range and snow characteristics change on the snowmelt process.

[0003] In addition, the traditional estimation method also has obvious shortcomings when applied at the basin scale: it cannot finely consider the spatial heterogeneity within the basin; and it lacks a systematic calculation architecture, resulting in limited model accuracy.

[0004] Therefore, it is necessary to develop a new method that can dynamically discriminate the temperature threshold and realize snowmelt runoff estimation at the basin scale. SUMMARY

[0005] The present application aims to overcome the shortcomings of the prior art and provides a snowmelt runoff estimation method, system, electronic device and storage medium at the basin scale, which realizes accurate estimation of snowmelt runoff through the organic combination of global discrimination and local calculation.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a snowmelt runoff estimation method at the basin scale, which adopts a "global discrimination, local calculation and summary output" calculation architecture, including the following steps: S1: Basin-scale data acquisition and preprocessing: acquiring reanalysis meteorological data, remote sensing snow monitoring data and digital elevation model data within the target basin range, and preprocessing to generate basin-scale surface average data sequences and spatial distribution data; the surface average data sequence includes: basin average snow cover rate and basin average air temperature; S2: Calculation unit division: dividing the target basin range into a plurality of calculation units based on the digital elevation model; S3: Global temperature threshold determination: Based on the response relationship between the average snow cover rate and the average air temperature of the basin, determine the critical temperature threshold applicable to the entire basin; S4: Global Snow Melting Period Identification: Based on the comparison between the average temperature of the watershed and the critical temperature threshold, combined with the change in the average snow cover of the watershed, the overall snow melting period of the watershed is identified. S5: Local snowmelt rate calculation: During the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit. S6: Watershed Snowmelt Runoff Summary Output: Based on the watershed river network structure, the runoff generated by the snowmelt rate of each calculation unit is further calculated and the snowmelt runoff at the watershed outlet section is finally output.

[0007] Furthermore, the reanalysis meteorological data mentioned in step S1 includes daily average temperature, daily maximum temperature, daily minimum temperature and precipitation data from the ERA5 reanalysis dataset; The remote sensing snow cover monitoring data includes snow cover rate from MODIS satellite and snow water equivalent data from AMSR-E / AMSR2; The digital elevation model data is obtained by using the SRTM digital elevation model to acquire watershed topographic elevation, slope, and aspect parameters. The preprocessing includes data quality control, spatiotemporal consistency correction, and missing data interpolation and imputation.

[0008] Furthermore, the calculation unit division in step S2 adopts the hydrological response unit method: extracting watershed boundaries and sub-watersheds based on the SRTM digital elevation model; dividing elevation zones according to the preset elevation range; dividing sunny slopes, shady slopes, and semi-sunny / semi-shady slopes according to slope aspect; and generating hydrological response units as basic calculation units through GIS overlay analysis.

[0009] Furthermore, the global critical temperature threshold mentioned in step S3 is determined in the following way: Establish a model to demonstrate the response relationship between the average snow cover rate and the average temperature of the watershed:

[0010] in, The average snow cover rate of the watershed. The average daily temperature in the basin. This is the critical temperature threshold. a This represents the maximum snow cover. c This refers to the residual snow cover. b This is the ablation rate coefficient.

[0011] Furthermore, the global snowmelt period identification in step S4 includes: The conditions for the start of snow melting are: the average temperature of the watershed surface is higher than the critical temperature threshold for several consecutive days, and the average snow cover of the watershed surface is higher than the first preset threshold. The snow melting ends when the average temperature of the watershed is below the critical temperature threshold for several consecutive days, or the average snow cover of the watershed is below the second preset threshold.

[0012] Furthermore, the calculation of the local snowmelt rate in step S5 employs a distributed calculation using the improved degree-day factor method, as shown in the formula:

[0013] in, M i For the first i Snow melting rate of each computing unit; DDF This serves as the baseline daily factor for the watershed. For the first i The daily average temperature of each calculation unit; T 0 represents the critical temperature threshold of the watershed; K This is the correction factor for watershed temperature fluctuations; Δ T i For the first i The daily temperature varies across different calculation units; f ( SWE i ) is the first i Snow water equivalent correction function for each calculation unit.

[0014] Furthermore, the watershed snowmelt runoff summary output in step S6 adopts a linear reservoir model:

[0015] in, Let t be the snowmelt runoff at the outlet of the basin; For the first i The output flow rate of each computing unit A i For the first i The area of ​​each calculation unit; For the first i The convergence time of each computing unit; α i , β i For the first i The confluence parameters of each calculation unit are calibrated using historical runoff observation data; t For time.

[0016] Secondly, embodiments of the present invention also provide a watershed-scale snowmelt runoff estimation system for implementing the watershed-scale snowmelt runoff estimation method as described in any of the first aspects, the system comprising: The basin scale data acquisition and preprocessing module: acquires reanalysis meteorological data, remote sensing snow monitoring data and digital elevation model data in the target basin range, and pre-processes to generate basin scale surface average data sequence and spatial distribution data; the surface average data sequence includes: basin average snow cover rate and basin average air temperature; The calculation unit division module: divides the target basin range into a plurality of calculation units based on the digital elevation model; The global temperature threshold determination module: determines a critical temperature threshold suitable for the entire basin based on the response relationship between the basin average snow cover rate and the basin average air temperature; The global snowmelt period identification module: identifies the overall basin snowmelt period based on the comparison between the basin average air temperature and the critical temperature threshold, combined with the change of the basin average snow cover rate; The local snowmelt rate calculation module: in the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit; The basin snowmelt runoff summary output module: based on the basin river network structure, the runoff formed by the snowmelt rate of each calculation unit is calculated again, and finally the snowmelt runoff of the outlet section of the basin is output.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the steps of the method according to any one of the first aspect.

[0018] In a fourth aspect, an embodiment of the present application further provides a storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to realize the steps of the method according to any one of the first aspect.

[0019] According to the above technical solution, compared with the prior art, the present application has the following technical advantages: The present application realizes dynamic and accurate estimation of basin snowmelt runoff by combining global determination and local calculation, overcomes the problems of fixed temperature threshold and insufficient consideration of spatial heterogeneity in traditional methods, and significantly improves the simulation accuracy and practicality. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0021] Figure 1A flow basin scale snowmelt runoff estimation method flow chart provided by the present application.

[0022] Figure 2 A sub-basin division schematic diagram for the Xilinhe River Basin.

[0023] Figure 3 A response model and daily average temperature curve for the Xilinhe River Basin.

[0024] Figure 4 A flow basin scale snowmelt runoff estimation system block diagram provided by the present application.

[0025] Figure 5 An electronic computing device structure diagram provided by the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0027] Embodiment 1: Referring to Figure 1 The embodiments of the present application disclose a flow basin scale snowmelt runoff estimation method, which adopts an innovative architecture of "global discrimination, local calculation, and summary output", including: ① global discrimination layer: determining key parameters based on the overall state of the basin; ② local calculation layer: performing core calculation on each calculation unit in parallel; and ③ summary output layer: integrating the results of each unit to output the final runoff process. The specific implementation steps are as follows: S1: flow basin scale data acquisition and preprocessing: collecting reanalysis meteorological data, remote sensing snow monitoring data, and digital elevation model data within the target basin range, and preprocessing to generate flow basin scale surface average data sequence and spatial distribution data; the surface average data sequence includes: basin average snow cover rate and basin average air temperature; Specifically: taking the Xilinhe River Basin (area 10700 km²) as an example, the multi-source data within the target basin range includes: ① meteorological data adopts ERA5 reanalysis data set, including daily average temperature (Tmean) T mean ), daily maximum temperature (Tmax) T max ), daily minimum temperature (Tmin) T min ), and precipitation (P) P , spatial resolution 0.25x0.25, time resolution daily; ②Snow cover data were obtained using the MODIS MOD10A1 snow cover product to obtain the average snow cover rate. SCA Spatial resolution 500m; average snow water equivalent was obtained using AMSR-E / AMSR2 passive microwave data. SWE ), spatial resolution 25km; ③ The terrain data was obtained using the SRTM digital elevation model, acquiring elevation, slope, and aspect information with a spatial resolution of 90m. Data preprocessing included quality control and outlier removal; calculation of the watershed average series; and time series interpolation to fill in missing data.

[0028] S2: Calculation Unit Division: Based on the digital elevation model, the target watershed area is divided into several calculation units to establish a watershed spatial calculation framework; The basin boundary and 28 sub-basins were extracted based on the SRTM digital elevation model, such as Figure 2 As shown; divided by elevation: four elevation zones: <1000m, 1000~1200m, 1200~1400m, and >1400m; divided by slope aspect: sunny slope (135°~225°), shady slope (315°~45°), and semi-sunny / semi-shady slope; 336 hydrological response units were generated as basic calculation units through GIS overlay analysis.

[0029] S3: Global temperature threshold determination: Based on the response relationship between the average snow cover rate and the average air temperature of the basin, determine the critical temperature threshold applicable to the entire basin; Response modeling using basin-averaged data:

[0030] in, SCA mean Snow cover rate (%) T mean The daily average temperature (°C) T 0 represents the critical temperature threshold (°C); a This represents the maximum snow cover rate (%), which is the stable snow cover rate of the watershed when the temperature is far below the critical threshold (during the severe winter period). The value range is usually 70-95%. c Residual snow cover (%) represents the snow cover that can still exist in the watershed for a long time when the temperature is much higher than the critical threshold (late spring). It is mainly distributed in high-altitude shaded areas and the value range is usually 0-15%. b Indicates the ablation rate coefficient (°C) -1 This refers to the parameter controlling the steepness of the curve, which reflects the sensitivity of the snowmelt process to temperature changes. b A higher value indicates a more vigorous and rapid ablation process; the value typically ranges from 0.3 to 1.5℃. -1 .

[0031] The parameters were determined by fitting the curve using a nonlinear least squares method, and the temperature corresponding to the inflection point of the curve was taken. T 0 is used as the critical temperature threshold. T 0 reflects the temperature conditions under which snow begins to melt significantly.

[0032] In the specific implementation in the Xilin River Basin, the following results were obtained based on spring data from 2014 to 2018:

[0033] The goodness of fit R² = 0.696, and the critical temperature threshold T0 = 1.01℃ is determined. Figure 3 As shown, the horizontal axis represents the daily average temperature, and the vertical axis represents the snow cover rate. The red line segment in the figure is the fitted curve.

[0034] S4: Global Snow Melting Period Identification: Based on the comparison between the average temperature of the watershed and the critical temperature threshold, combined with the change in the average snow cover of the watershed, the overall snow melting period of the watershed is identified. Based on the overall state of the watershed, the conditions are as follows: ①Starting condition: 3 consecutive days and ; ② Termination condition: 5 consecutive days or ; This method can accurately identify the actual snowmelt period each year, avoiding errors caused by using fixed dates or fixed temperature thresholds.

[0035] Specific applications in the Xilin River Basin in 2018: Snow melt begins: March 22 (3 consecutive days of T) mean >1.01℃, SCA mean =76.7%>10%) Snowmelt ended: April 25 (5 consecutive days of SCA) mean =4.3%<5%); Total snow melting period: 34 days.

[0036] S5: Local snowmelt rate calculation: During the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit. During the snowmelt period, each computing unit performs calculations in parallel:

[0037] in, M i For the first i Snow melting rate (mm / d) of each calculation unit; DDFThe baseline degree-day factor (mm / ℃·d) for the watershed was calibrated using historical snowmelt observation data. For the first i The daily average temperature (°C) of each calculation unit; T 0 is the critical temperature threshold (°C) of the watershed, which is determined by step S3; K This is a correction factor for watershed temperature fluctuations. An empirical value can be used, with a commonly used reference range of 0.05~0.15℃. -1 ;Δ T i For the first i The daily temperature range (the difference between the daily maximum and minimum temperatures Δ) for each calculation unit T i = T maxi - T mini ); f ( SWE i ) is the first i The piecewise function for snow water equivalent correction of the first calculation unit, based on the first... i Different correction coefficients are set for different snow water equivalent ranges in each calculation unit to reflect the impact of snow depth on snowmelt efficiency. In the Xilin River Basin, based on existing research and historical field snow depth observation data, the following calibration is performed: DDF = 3.5 mm / ℃·d, K = 0.06. f (SWE i Use piecewise functions:

[0038] S6: Watershed Snowmelt Runoff Summary Output: Based on the watershed river network structure, the runoff generated by the snowmelt rate of each calculation unit is further calculated and the snowmelt runoff at the watershed outlet section is finally output.

[0039] Specifically, a distributed flow-convergence model can be used to calculate the flow rate of each unit: Determine the convergence path and time for each unit. ;Summarize the contributions of each unit:

[0040] in, Let t be the snowmelt runoff at the outlet of the basin (m³) 3 / s); For the first i The output flow rate of each computing unit (m³) 3 / s), M i For the first i Snow melting rate (mm / d) of each calculation unit. A i For the first iArea of ​​each computing unit (km²) 2 ); For the first i The convergence time (d) of each computing unit; α i , β i For the first i The confluence parameters of each calculation unit are calibrated using historical runoff observation data; t For time.

[0041] In the application in the Xilin River Basin in 2018, the confluence parameters were calibrated using runoff data from 2014 to 2018. α =0.18, β =0.35; total runoff volume is 158 million m³, runoff depth is 14.8 mm; runoff coefficient is 0.19.

[0042] The watershed-scale snowmelt runoff estimation method provided by this invention significantly improves the accuracy of snowmelt period identification and snowmelt volume calculation by dynamically determining the watershed-specific critical temperature threshold and employing an improved degree-day factor method that considers diurnal temperature range and snowmelt equivalent. Furthermore, by dividing the watershed into hydrological response units (HRUs) and performing parallel computation, it meticulously considers the spatial differences within the watershed caused by factors such as elevation and slope aspect, thus better reflecting actual hydrological processes.

[0043] Example 2: This invention also provides a watershed-scale snowmelt runoff estimation system to implement the watershed-scale snowmelt runoff estimation method as described in Example 1, referring to... Figure 4 As shown, the system includes: Watershed-scale data acquisition and preprocessing module: Acquires reanalysis meteorological data, remote sensing snow cover monitoring data and digital elevation model data within the target watershed area, and performs preprocessing to generate watershed-scale surface average data sequences and spatial distribution data; Calculation Unit Division Module: Based on the digital elevation model, the target watershed area is divided into several calculation units to establish a watershed spatial calculation framework; Global temperature threshold discrimination module: Based on the response relationship between the average snow cover rate and the average air temperature of the basin, determine the critical temperature threshold applicable to the entire basin; Global snowmelt period identification module: Based on the comparison between the average temperature sequence of the watershed and the critical temperature threshold, combined with the change in the average snow cover of the watershed, the overall snowmelt period of the watershed is identified; Local snowmelt rate calculation module: During the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit; A basin snowmelt runoff collection and output module: based on the basin river network structure, the runoff formed by the snowmelt rate of each calculation unit is calculated again, and the snowmelt runoff of the outlet section of the basin is finally output.

[0044] Embodiment 3: Based on the same inventive concept, the application also provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus; a memory for storing a computer program; The processor is used for executing the program stored on the memory, and can realize the real-time detection method of the spam message as any one of the embodiments 1.

[0045] As Figure 5 shown, the electronic device can include a processor 10, a communication interface 20, a memory 30 and a communication bus 40, wherein the processor 10, the communication interface 20 and the memory 30 complete communication with each other through the communication bus 40. The processor 10 can call the logical instructions in the memory 30 to execute the real-time detection of the spam message, and the method comprises: S1: basin scale data acquisition and pretreatment: collecting reanalysis meteorological data, remote sensing snow monitoring data and digital elevation model data in the target basin range, and pretreating to generate basin scale surface average data sequence and spatial distribution data; the surface average data sequence comprises: basin average snow cover rate and basin average temperature; S2: calculation unit division: dividing the target basin range into a plurality of calculation units based on the digital elevation model; S3: global temperature threshold judgment: based on the response relationship between the basin average snow cover rate and the basin average temperature, determining a critical temperature threshold suitable for the whole basin; S4: global snowmelt period identification: based on the comparison between the basin average temperature and the critical temperature threshold, and combined with the change of the basin average snow cover rate, identifying the global snowmelt period of the basin; S5: local snowmelt rate calculation: in the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit; S6: basin snowmelt runoff collection and output: based on the basin river network structure, the runoff formed by the snowmelt rate of each calculation unit is calculated again, and the snowmelt runoff of the outlet section of the basin is finally output.

[0046] Embodiment 4: The embodiment of the application also provides a computer readable storage medium, and a program stored in the computer readable storage medium is used for executing the snowmelt runoff estimation method of the basin scale in the above embodiment 1, and the program can be executed on the processor.

[0047] Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0048] The program stored in the medium is loaded into the memory of the processor for execution to complete various functions. The storage medium is connected with the hardware device, so that the computer can execute the steps of embodiment 1 described above.

[0049] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0050] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating snowmelt runoff at the watershed scale, characterized in that, The computational architecture employs a "global discrimination, local calculation, and summary output" approach, including the following steps: S1: Watershed-scale data acquisition and preprocessing: Acquire reanalysis meteorological data, remote sensing snow cover monitoring data, and digital elevation model data within the target watershed area, and perform preprocessing to generate watershed-scale surface average data sequences and spatial distribution data; the surface average data sequences include: watershed average snow cover and watershed average temperature. S2: Calculation Unit Division: The target watershed area is divided into several calculation units based on the digital elevation model; S3: Global temperature threshold determination: Based on the response relationship between the average snow cover rate and the average air temperature of the basin, determine the critical temperature threshold applicable to the entire basin. S4: Global Snow Melting Period Identification: Based on the comparison between the average temperature of the watershed and the critical temperature threshold, combined with the change in the average snow cover of the watershed, the overall snow melting period of the watershed is identified. S5: Local snowmelt rate calculation: During the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit. S6: Watershed Snowmelt Runoff Summary Output: Based on the watershed river network structure, the runoff generated by the snowmelt rate of each calculation unit is further calculated and the snowmelt runoff at the watershed outlet section is finally output.

2. The method according to claim 1, characterized in that, The reanalysis meteorological data mentioned in step S1 includes daily average temperature, daily maximum temperature, daily minimum temperature and precipitation data from the ERA5 reanalysis dataset; The remote sensing snow cover monitoring data includes snow cover rate from MODIS satellite and snow water equivalent data from AMSR-E / AMSR2; The digital elevation model data is obtained by using the SRTM digital elevation model to acquire watershed topographic elevation, slope, and aspect parameters. The preprocessing includes data quality control, spatiotemporal consistency correction, and missing data interpolation and imputation.

3. The method according to claim 1, characterized in that, The calculation unit division in step S2 adopts the hydrological response unit method: the watershed boundary and sub-watershed are extracted based on the SRTM digital elevation model; the elevation zone is divided according to the preset elevation range; the slope aspect is divided into sunny slope, shady slope, and semi-sunny / semi-shady slope; and hydrological response units are generated as basic calculation units through GIS overlay analysis.

4. The method according to claim 1, characterized in that, The global critical temperature threshold mentioned in step S3 is determined in the following way: Establish a model to demonstrate the response relationship between the average snow cover rate and the average temperature of the watershed: in, The average snow cover rate of the watershed. The average daily temperature in the basin. This is the critical temperature threshold. a This represents the maximum snow cover. c Residual snow cover; b This is the ablation rate coefficient.

5. The method according to claim 1, characterized in that, The global snowmelt period identification in step S4 includes: The conditions for the start of snow melting are: the average temperature of the watershed surface is higher than the critical temperature threshold for several consecutive days, and the average snow cover of the watershed surface is higher than the first preset threshold. The snow melting ends when the average temperature of the watershed is below the critical temperature threshold for several consecutive days, or the average snow cover of the watershed is below the second preset threshold.

6. The method according to claim 1, characterized in that, The local snowmelt rate calculation in step S5 uses a distributed calculation based on the improved degree-day factor method, and the formula is as follows: in, M i For the first i Snow melting rate of each computing unit; DDF This serves as the baseline daily factor for the watershed. For the first i The daily average temperature of each calculation unit; T 0 represents the critical temperature threshold of the watershed; K This is the correction factor for watershed temperature fluctuations; Δ T i For the first i The daily temperature varies across different calculation units; f ( SWE i ) is the first i Snow water equivalent correction function for each calculation unit.

7. The method according to claim 6, characterized in that, The watershed snowmelt runoff summary output in step S6 adopts a linear reservoir model: in, Let t be the snowmelt runoff at the outlet of the basin; For the first i The output flow rate of each computing unit A i For the first i The area of ​​each calculation unit; For the first i The convergence time of each computing unit; α i , β i For the first i The confluence parameters of each calculation unit are calibrated using historical runoff observation data; t For time.

8. A watershed-scale snowmelt runoff estimation system, characterized in that, For implementing the watershed-scale snowmelt runoff estimation method as described in any one of claims 1-7, the system comprises: Watershed-scale data acquisition and preprocessing module: Acquires reanalysis meteorological data, remote sensing snow cover monitoring data, and digital elevation model data within the target watershed area, and preprocesses them to generate watershed-scale surface average data sequences and spatial distribution data; the surface average data sequences include: watershed average snow cover and watershed average temperature. Calculation unit partitioning module: Based on the digital elevation model, the target watershed area is divided into several calculation units; Global temperature threshold discrimination module: Based on the response relationship between the average snow cover rate and the average air temperature of the basin, determine the critical temperature threshold applicable to the entire basin; Global snowmelt period identification module: Based on the comparison between the average temperature of the watershed and the critical temperature threshold, combined with the change in the average snow cover of the watershed, the overall snowmelt period of the watershed is identified; Local snowmelt rate calculation module: During the identified snowmelt period, the improved degree-day factor method is executed in parallel on each calculation unit to calculate the snowmelt rate of each unit; Watershed snowmelt runoff aggregation and output module: Based on the watershed river network structure, the runoff generated by the snowmelt rate of each calculation unit is further calculated and finally output as the snowmelt runoff at the watershed outlet section.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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