A snowmelt runoff estimation method and system at a watershed scale, an electronic device, and a storage medium

Abstract

The application discloses a snowmelt runoff estimation method and system at a basin scale, an electronic device and a storage medium, and relates to the technical field of snowmelt runoff estimation. The method comprises the following steps: collecting reanalysis meteorological data, remote sensing snow monitoring data and digital elevation model data in a target basin range, and preprocessing to generate a basin-scale surface average data sequence; dividing the target basin range into a plurality of calculation units; determining a critical temperature threshold of the entire basin based on the response relationship between the basin average snow cover rate and the basin average air temperature; identifying the overall snowmelt period of the basin 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; performing the improved degree-day factor method on each calculation unit in parallel to calculate the snowmelt rate; and performing confluence calculation on the runoff formed by the snowmelt rate of each calculation unit to output the snowmelt runoff of the outlet section of the basin. The application realizes dynamic and accurate estimation of the snowmelt runoff of the basin, and significantly improves the simulation accuracy and practicality.

Description

Technical Field

[0001] This invention relates to the field of hydrological forecasting and water resources management technology, and in particular to a watershed-scale snowmelt runoff estimation method, system, electronic device and storage medium that adopts a "global discrimination, local calculation and summary output" architecture. Background Technology

[0002] Snowmelt runoff is an important hydrological process in cold-region watersheds, playing a crucial role in spring water supply, agricultural irrigation, and flood control. Traditional methods for estimating snowmelt runoff primarily employ the degree-day factor method, which is simple and practical, but suffers from the following significant shortcomings:

[0003] ① Traditional methods typically use a fixed 0℃ as the starting temperature for snow melting, ignoring the differences in temperature thresholds across different watersheds and seasons;

[0004] ② Fixed temperature thresholds make it difficult to accurately identify the start and end times of snowmelt, leading to deviations in runoff process simulations;

[0005] ③ Traditional methods do not take into account the impact of daily temperature range and changes in snow accumulation characteristics on the snow melting process.

[0006] In addition, traditional estimation methods have significant shortcomings when applied at the watershed scale: they cannot accurately account for the spatial heterogeneity within the watershed; and they lack a systematic computational architecture, which limits the accuracy of the model.

[0007] Therefore, a new method is needed to dynamically determine temperature thresholds and estimate snowmelt runoff at the watershed scale. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a watershed-scale snowmelt runoff estimation method, system, electronic device and storage medium. Through the organic combination of global discrimination and local calculation, accurate estimation of snowmelt runoff can be achieved.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, embodiments of the present invention provide a watershed-scale snowmelt runoff estimation method, employing a computational architecture of "global discrimination, local calculation, and summary output," comprising the following steps:

[0011] 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.

[0012] S2: Calculation Unit Division: The target watershed area is divided into several calculation units based on the digital elevation model;

[0013] 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;

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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;

[0018] The remote sensing snow cover monitoring data includes snow cover rate from MODIS satellite and snow water equivalent data from AMSR-E / AMSR2;

[0019] The digital elevation model data is obtained by using the SRTM digital elevation model to acquire watershed topographic elevation, slope, and aspect parameters.

[0020] The preprocessing includes data quality control, spatiotemporal consistency correction, and missing data interpolation and imputation.

[0021] 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.

[0022] Furthermore, the global critical temperature threshold mentioned in step S3 is determined in the following way:

[0023] Establish a model to demonstrate the response relationship between the average snow cover rate and the average temperature of the watershed:

[0024]

[0025] 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.

[0026] Furthermore, the global snowmelt period identification in step S4 includes:

[0027] 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.

[0028] 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.

[0029] 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:

[0030]

[0031] 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 Δ is the correction factor for watershed temperature fluctuations. 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.

[0032] Furthermore, the watershed snowmelt runoff summary output in step S6 adopts a linear reservoir model:

[0033]

[0034] 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.

[0035] 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:

[0036] 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.

[0037] Calculation unit partitioning module: Based on the digital elevation model, the target watershed area is divided into several calculation units;

[0038] 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;

[0039] 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;

[0040] 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;

[0041] 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.

[0042] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described in any one of the first aspects.

[0043] Fourthly, embodiments of the present invention also provide a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as described in any one of the first aspects.

[0044] As can be seen from the above technical solution, compared with the prior art, the present invention has the following technical advantages:

[0045] This invention achieves dynamic and accurate estimation of watershed snowmelt runoff by combining global discrimination and local calculation, overcoming problems such as fixed temperature threshold and insufficient consideration of spatial heterogeneity in traditional methods, and significantly improving simulation accuracy and practicality. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 A flowchart of the watershed-scale snowmelt runoff estimation method provided by this invention.

[0048] Figure 2 This is a schematic diagram showing the sub-basin division of the Xilin River Basin.

[0049] Figure 3 The graph shows the response model and daily average temperature curves for the Xilin River Basin.

[0050] Figure 4 A block diagram of the watershed-scale snowmelt runoff estimation system provided by this invention.

[0051] Figure 5 A structural diagram of the electronic computing device provided by the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1:

[0054] Reference Figure 1 As shown in the figure, this invention discloses a watershed-scale snowmelt runoff estimation method, employing an innovative architecture of "global discrimination, local calculation, and summary output," including: ① a global discrimination layer: determining key parameters based on the overall watershed state; ② a local calculation layer: performing core calculations in parallel on each calculation unit; and ③ a summary output layer: integrating the results from each unit to output the final runoff process. The specific implementation steps are as follows:

[0055] 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.

[0056] Specifically: Taking the Xilin River Basin (area 10,700 km²) as an example, the collection of multi-source data within the target basin area includes:

[0057] ① Meteorological data were obtained using the ERA5 reanalysis dataset, including daily average temperature ( T mean ), Daily maximum temperature ( T max ), daily minimum temperature ( T min ) and precipitation ( P Spatial resolution 0.25×0.25, temporal resolution daily;

[0058] 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;

[0059] 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.

[0060] 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;

[0061] 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.

[0062] 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;

[0063] Response modeling using basin-averaged data:

[0064]

[0065] 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 .

[0066] 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.

[0067] In the specific implementation in the Xilin River Basin, the following results were obtained based on spring data from 2014 to 2018:

[0068]

[0069] 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.

[0070] 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.

[0071] Based on the overall state of the watershed, the conditions are as follows:

[0072] ①Starting condition: 3 consecutive days and ;

[0073] ② Termination condition: 5 consecutive days or ;

[0074] This method can accurately identify the actual snowmelt period each year, avoiding errors caused by using fixed dates or fixed temperature thresholds.

[0075] Specific applications in the Xilin River Basin in 2018:

[0076] Snow melt begins: March 22 (3 consecutive days of T) mean >1.01℃, SCA mean =76.7%>10%)

[0077] Snowmelt ended: April 25 (5 consecutive days of SCA) mean =4.3%<5%);

[0078] Total snow melting period: 34 days.

[0079] 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.

[0080] During the snowmelt period, each computing unit performs calculations in parallel:

[0081]

[0082] in, M i For the first i Snow melting rate (mm / d) of each calculation unit; DDF The 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... iDifferent 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:

[0083]

[0084] 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.

[0085] 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:

[0086]

[0087] 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 i Area 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.

[0088] 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.

[0089] 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.

[0090] Example 2:

[0091] This invention also provides a watershed-scale snowmelt runoff estimation system to implement the watershed-scale snowmelt runoff estimation method as described in Embodiment 1, referring to... Figure 4 As shown, the system includes:

[0092] 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;

[0093] 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;

[0094] 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;

[0095] 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 of the average snow cover of the watershed, the overall snowmelt period of the watershed is identified;

[0096] 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;

[0097] 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.

[0098] Example 3:

[0099] Based on the same inventive concept, the present invention also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0100] Memory, used to store computer programs;

[0101] When the processor executes the program stored in the memory, it is able to implement the real-time spam SMS detection method as described in any one of Embodiment 1.

[0102] like Figure 5 As shown, the electronic device may 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 communicate with each other via the communication bus 40. The processor 10 can call logical instructions in the memory 30 to perform real-time detection of spam text messages. The method includes:

[0103] 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.

[0104] S2: Calculation Unit Division: The target watershed area is divided into several calculation units based on the digital elevation model;

[0105] 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;

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Example 4:

[0110] This invention also provides a computer-readable storage medium containing a program for executing the watershed-scale snowmelt runoff estimation method of Embodiment 1 described above. This program can be executed on a processor.

[0111] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] The program stored on this medium is loaded into the processor's memory and executed to perform various functions. This storage medium, connected to hardware devices, enables the computer to perform the steps of Embodiment 1 described above.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0114] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded 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; including: establishing a response relationship model between the average snow cover rate and the average air temperature of the basin. 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 The ablation rate coefficient is used; the parameter is determined by fitting using the nonlinear least squares method, and the temperature corresponding to the inflection point of the curve is taken. This serves as a critical temperature threshold; it reflects the temperature conditions at which snow begins to melt significantly. 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 Snow Melting Rate Calculation: During the identified snow melting period, the improved day-degree factor method is executed in parallel on each calculation unit to calculate the snow melting rate of each unit; including: during the snow melting period, each calculation unit performs parallel calculations: in, M i For the first i Snow melting rate of each computing unit; DDF 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 Δ is the correction factor for watershed temperature fluctuations. T i For the first i The daily temperature varies across the calculation units; f ( SWE i ) is the first i Snow water equivalent correction function for each calculation 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 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.

5. The method according to claim 1, 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.

6. 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 1-5, 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.

7. 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 5.

8. 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 5.

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