Medium and long term runoff prediction method and system for cross-border river area

Abstract

The embodiment of the invention discloses a medium and long term runoff prediction method for a cross-border river area. The method comprises the following steps: obtaining observation data of a hydrological station in the cross-border river area; a preset hydrological model is verified based on the observation data, a space-time simulation database of the cross-border river area is obtained, the preset hydrological model at least comprises an SWAT hydrological model and a J2000 hydrological model, and the reliability of the space-time simulation database is higher than that of the preset hydrological model; a runoff database of the cross-border river area is constructed based on observation data of a hydrometric station of the cross-border river area and simulation data of a space-time simulation database, and the runoff database is used for analyzing space-time distribution characteristics of runoff volume, ice and snow melt water and rainfall runoff of different areas of the cross-border river area; and based on the runoff database of the cross-border river area, predicting medium and long-term runoff of the cross-border river area. According to the method, the medium-and-long-term runoff in the cross-border river area can be accurately predicted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrology, in particular to a method and system for predicting medium and long term runoff in a cross-border river region. BACKGROUND

[0002] Medium and long term runoff prediction is a core supporting technology for water resources management, water power scheduling, flood control and drought resistance, and agricultural irrigation. Due to the complexity of climate or weather prediction, initial water condition judgment, and forecast model construction, the uncertainty of medium and long term runoff prediction is high. In particular, for cross-border rivers, in addition to technical problems, political, legal, cooperation mechanism and data acquisition problems also need to be considered, resulting in less research on medium and long term runoff prediction in cross-border river regions and a lack of valuable solutions. SUMMARY

[0003] The embodiments of the present application provide a method and system for predicting medium and long term runoff in a cross-border river region, which can accurately predict the medium and long term runoff in a cross-border river region and accumulate valuable data for runoff analysis and research.

[0004] In one aspect, the embodiments of the present application provide a method for predicting medium and long term runoff in a cross-border river region, comprising: obtaining observation data of a hydrological station in a cross-border river region; verifying a preset hydrological model based on the observation data to obtain a time and space simulation database of the cross-border river region, wherein the preset hydrological model at least includes a SWAT hydrological model and a J2000 hydrological model, and the reliability of the time and space simulation database is higher than that of the preset hydrological model; based on the observation data of the hydrological station in the cross-border river region and the simulation data of the time and space simulation database, constructing a runoff database of the cross-border river region, wherein the runoff database is used to analyze the time and space distribution characteristics of runoff, snowmelt water and rainfall runoff in different areas of the cross-border river region; based on the runoff database of the cross-border river region, predicting the medium and long term runoff in the cross-border river region.

[0005] Optionally, the input of the runoff database is the observation data of the hydrological station, and the output of the runoff database is the time and space distribution characteristics of runoff, snowmelt water and rainfall runoff in different areas of the cross-border river region. The observation data of the hydrological station includes precipitation data, air temperature data, wind speed data, relative humidity data, sunshine duration data and runoff observation data. The time and space distribution characteristics output by the runoff database include different grade river flow data, different sub-basin evaporation data, different sub-basin runoff depth data, different sub-basin snowmelt water data and different sub-basin rainfall runoff data.

[0006] Optionally, the runoff recharge sources of the J2000 hydrological model include rainfall, snowmelt, glacial melt and groundwater; The runoff components according to the runoff path division include surface runoff RD1, interflow RD2 formed by soil water lateral outflow, groundwater flow RG1 from the upper part of the aquifer and groundwater flow RG2 of the saturated groundwater aquifer; The surface runoff RD1 has the fastest response outflow velocity, and the surface runoff RD1 includes runoff generated when the snow surface and the ground are saturated; the response outflow velocity of the interflow RD2 is less than that of the surface runoff RD1; the groundwater flow RG1 has a stronger infiltration capacity than the lower part of the aquifer based on weathering; The J2000 hydrological model is a process-based hydrological model that considers the snowmelt process. After discretizing the hydrological response unit HRU, the model divides the watershed attributes and obtains meteorological driving data and basic attribute information from the hydrological response unit HRU. The total runoff of each hydrological response unit HRU flows into the river channel and finally converges to the outlet of the watershed. The basic attribute information includes elevation, slope, slope direction and land use.

[0007] Optionally, the J2000 hydrological model includes a snow module, a glacier module, a soil water module and a groundwater module when simulating runoff; The snow module is used to: The size of the snow depends on the air temperature and the amount of precipitation on the day. When the air temperature is lower than the first critical temperature for snowfall, the form of precipitation is snow. When the air temperature is higher than the second critical temperature for rainfall, the form of precipitation is rain. When the air temperature is between the first critical temperature and the second critical temperature, the precipitation is a mixture of rain and snow. Define the critical temperature TRS, when the air temperature reaches the critical temperature TRS, 50% of the precipitation form is snow and 50% of the precipitation form is rain. Define the transition air temperature parameter Trans as the radius of the air temperature width of the mixed area, so as to calculate the snow ratio: Wherein: is the snow ratio; is the critical temperature, and Trans is the transition air temperature parameter; is the daily air temperature with precipitation; Because the initial stage of snow accumulation can store water until the melting temperature value The melting water is released when the temperature exceeds the threshold BaseTemp. The energy required for melting is obtained from three sources: sensible heat input from air temperature, energy input from precipitation and heat transfer from the soil. The energy input from precipitation is difficult to obtain and is corrected using empirical parameters 、 and The amount of snow melting is calculated using the following equation:

[0008] Where: is the amount of snow melting (mm); is the air temperature factor; is the precipitation energy factor; is the surface heat flux factor, is the air temperature of the hydrological response unit; The glacier module is configured to: Due to the limitation of the observation data on the plateau, the melting of the glacier is considered by the degree-day factor method. When the critical melting temperature Tcrit is lower than the melting temperature Tmelt, the glacier ablation occurs and is calculated as follows:

[0009] Where: is the maximum air temperature; is the average air temperature; is the melting rate; is the glacier melting coefficient; is the glacier radiation melting factor; is the time step; is the critical glacier melting temperature; is the melting temperature; If there is moraine on the surface of the glacier, the melting rate of the glacier will be accelerated, and the melting rate at this time is: Where: is the melting rate of the moraine coverage; is the moraine melting factor.

[0010] Optionally, the soil water module is configured to: The soil water is divided into the first pore storage water Mps stored in a preset diameter range and the second pore storage water Lps with a soil diameter greater than a preset size in the J2000 hydrological model. When the amount of infiltrated water exceeds the amount of infiltrated water in the saturated soil, the excess water will be stored in the depression on the surface to form the depression storage DPS. When the amount of depression storage exceeds the maximum storage capacity, surface runoff will be formed.​​ where: is the actual infiltration amount; is the saturated infiltration amount; and represent the actual and maximum water storage of the first soil pore, respectively; and represent the actual and maximum water storage of the second soil pore, respectively; The amount of water infiltrated will be distributed into the MPS and LPS, calculated by: where: is the infiltration amount into the MPS; is the infiltration amount into the LPS; is the infiltration coefficient; The vertical infiltration and lateral flow of groundwater occurs in the large soil pores (LPS), and the outflow is calculated as follows: where: is the outflow of the LPS (mm); is the relative saturation of the soil (mm); is the actual water amount of the soil (mm); The groundwater module is used to: In the case of weathered material with high permeability in the upper groundwater layer and low permeability bedrock fissures in the lower layer, the base flow is divided into a fast runoff component RG1 in the upper groundwater layer and a slow runoff component RG2 in the lower layer. The outflow of groundwater is calculated by a linear drainage function, which reflects the dynamic changes of groundwater, and the outflows of RG1 and RG2 are corrected by the parameter factors gwRG1 and gwRG2, respectively, which are calculated as follows: where: and are the outflows of RG1 and RG2 (mm), respectively; and are the retention coefficients of RG1 and RG2, respectively; and are the correction parameters of RG1 and RG2, respectively; and are the current water contents of RG1 and RG2, respectively; Considering the dynamic change of the groundwater in the basin, the infiltration flow in the soil module is distributed by correcting the parameters and considering the slope effect, and then the inflow of the groundwater is obtained: wherein: and are the inflow of RG1 and RG2 respectively; slop is the slope of the hydrological response unit; are the correction parameters of RG1 and RG2.

[0011] Optionally, the preset hydrological model is verified based on the observation data to obtain a time-space simulation database of the cross-border river region, comprising: When verifying the preset hydrological model, the calibration period and the verification period of the model need to be set. When using the SWAT model to simulate runoff in the cross-border river basin, the model parameters are determined in combination with the measured data rate of the hydrological station, the time-space distribution of the corrected and improved precipitation input is extracted through artificial intelligence, and the accuracy of the flood simulation is improved; In the mountainous small watershed, the J2000 model is used, the measured data is used to model the groundwater recharge process, and the evaporation estimation is improved through machine learning to build a data-driven correction algorithm, and the hydrological model output is dynamically optimized to make up for the lack of traditional model parameterization; After verifying the preset hydrological model, the method further comprises evaluating the calibration results by using the following method: Generally, the correlation coefficient and the Nash-Sutcliffe simulation efficiency coefficient NS are used to evaluate the applicability of the model: Correlation coefficient The calculation formula is: wherein: is the measured average value; is the simulated average value; The value range is 0-1, and the closer the value is to 1, the closer the simulation value is to the measured value; Nash-Sutcliffe simulation efficiency coefficient NS, the calculation formula is: Wherein, the value range of NS is 0-1; the closer the value of NS is to 1, the closer the simulation value is to the observation value.

[0012] Optionally, based on the runoff database of the cross-border river region, the medium and long term runoff of the cross-border river region is predicted, comprising: The system uses a runoff database based on the transboundary river region to forecast the medium- and long-term runoff of the transboundary river region, and forecasts the spatial evolution and amplitude variation characteristics of the runoff at seasonal, interannual ENSO, and interdecadal PDO scales.

[0013] Secondly, a medium- to long-term runoff prediction system for transboundary river regions is provided, including: The acquisition module is used to acquire observation data from hydrological stations in transboundary river areas; The verification module is used to verify the preset hydrological model based on the observation data to obtain a spatiotemporal simulation database of the transboundary river area. The preset hydrological model includes at least the SWAT hydrological model and the J2000 hydrological model. The reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model. The module is used to construct a runoff database for a cross-border river region based on observation data from hydrological stations in the region and simulation data from the spatiotemporal simulation database. The runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt, and rainfall runoff in different areas of the cross-border river region. The prediction module is used to predict the medium- and long-term runoff of the cross-border river region based on the runoff database of the cross-border river region.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including: a memory and a processor, wherein the memory and the processor are connected; Memory, used to store computer programs; A processor is used to invoke a computer program stored in memory to perform any of the methods described above.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when run by a computer, performs the methods described in any of the above-mentioned embodiments.

[0016] The medium- and long-term runoff prediction method for transboundary river regions provided in this invention includes: acquiring observation data from hydrological stations in the transboundary river region; verifying a preset hydrological model based on the observation data to obtain a spatiotemporal simulation database for the transboundary river region, wherein the preset hydrological model includes at least a SWAT hydrological model and a J2000 hydrological model, and the reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model; constructing a runoff database for the transboundary river region based on the observation data from the hydrological stations in the transboundary river region and the simulation data from the spatiotemporal simulation database, wherein the runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt, and rainfall runoff in different areas of the transboundary river region; and predicting the medium- and long-term runoff of the transboundary river region based on the runoff database.

[0017] This method can accurately predict medium- and long-term runoff in transboundary river regions, accumulating valuable data for runoff analysis research.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0021] Figure 1 A flowchart illustrating a medium- to long-term runoff prediction method for transboundary river regions provided in this embodiment of the invention; Figure 2 A block diagram of a medium- to long-term runoff prediction system for transboundary river regions provided in an embodiment of the present invention. Detailed Implementation

[0022] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0023] The following specific embodiments will be used to explain and illustrate the solution of this patent application.

[0024] One embodiment of this patent application provides a method for medium- to long-term runoff prediction in transboundary river regions, such as... Figure 1As shown, steps 101-104 are included: Step 101: Obtain observation data from hydrological stations in the transboundary river area; Step 102: Verify the preset hydrological model based on the observation data to obtain a spatiotemporal simulation database of the transboundary river region. The preset hydrological model includes at least the SWAT hydrological model and the J2000 hydrological model. The reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model. Step 103: Based on the observation data of hydrological stations in the transboundary river region and the simulation data of the spatiotemporal simulation database, construct a runoff database for the transboundary river region. The runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt, and precipitation runoff in different areas of the transboundary river region. Step 104: Based on the runoff database of the transboundary river region, predict the medium- and long-term runoff of the transboundary river region.

[0025] The input to the runoff database is the observation data from the hydrological station, and the output of the runoff database is the spatiotemporal distribution characteristics of runoff, snowmelt, and rainfall runoff in different areas of the transboundary river region. It should be noted that the observation data from the hydrological station includes: precipitation data, temperature data, wind speed data, relative humidity data, sunshine duration data, and runoff observation data; The spatiotemporal distribution characteristics output by the runoff database include: flow data of rivers at different levels, evaporation data of different sub-basins, runoff depth data of different sub-basins, snowmelt data of different sub-basins, and rainfall-runoff data of different sub-basins.

[0026] Optionally, the runoff recharge sources of the J2000 hydrological model include rainfall, snowmelt, glacial meltwater, and groundwater; Runoff components classified according to runoff path include surface runoff RD1, interflow formed by lateral outflow of soil water RD2, groundwater flow from the upper part of the aquifer RG1, and groundwater flow from the saturated groundwater aquifer RG2. The surface runoff RD1 has the fastest outflow velocity, and the surface runoff RD1 includes runoff generated by snow and ice surfaces and ground saturation; the outflow velocity of the interflow RD2 is less than that of the surface runoff RD1; the groundwater flow RG1, based on weathering, has a stronger infiltration capacity compared to the lower part of the aquifer. The J2000 hydrological model is a process-based hydrological model that takes into account the snowmelt process. The model discretizes the hydrological response units (HRUs), divides the watershed attributes, and obtains meteorological driving data and basic attribute information from the HRUs. The runoff of each HRU is calculated, and the total runoff of the HRUs flows into the river channel and eventually converges at the watershed outlet. The basic attribute information includes: elevation, slope, aspect, and land use.

[0027] In one embodiment, the J2000 hydrological model for simulating runoff includes: a snow accumulation module, a glacier module, a soil water module, and a groundwater module; The snow accumulation module is used for: Since the amount of snow accumulation depends on the temperature and precipitation of the day, when the temperature is below the first critical temperature for snowfall, the precipitation is snowfall; when the temperature is above the second critical temperature for precipitation, the precipitation is precipitation; precipitation between the first and second critical temperatures is a mixture of rain and snow. Define a critical temperature TRS, where 50% of precipitation is snow and 50% is rain. Define a transformed temperature parameter Trans, which serves as the radius of the temperature width of the mixed region, thereby calculating the proportion of snowfall. in: The proportion of snowfall; Where is the critical temperature, and Trans is the transformed temperature parameter; The temperature of the day when precipitation occurs; Because snow can store water in the initial stage of accumulation until the melting temperature reaches a certain value. Snowmelt is released once the snowmelt temperature exceeds the BaseTemp threshold. The energy required for snowmelt is obtained through three methods: sensible heat from air temperature input, energy input from rainfall, and heat transfer through the soil. Due to the difficulty in obtaining sufficient input data, empirical parameters are used. , and After correction, the amount of melted snow is calculated using the following formula:

[0028] in: Snowmelt volume (mm); : represents the temperature factor; : is the precipitation energy factor; It is the surface heat flux factor. The air temperature of the hydrological response unit; The glacier module is used for: Due to limitations in high-altitude observation data, the day-de-day factor method is used to consider glacier melting, when the critical melting temperature... Below the melting temperature At that time, glacial melting occurred, calculated as follows:

[0029] in: The highest temperature; Average temperature; This refers to the rate of ice melting; This refers to the glacier melting coefficient; It is a glacier radiation melting factor; For time step; This is the critical glacier melting temperature; Melting temperature; If glacial till exists on the glacier surface, the melting rate of the glacier will accelerate, and the melting rate at this time will be: in: The melting rate of the ice deposits; Glacial moraine melting factor.

[0030] In one embodiment, the soil water module is used for: In the J2000 hydrological model, soil water is divided into a first pore water storage volume (MPS) stored within a preset diameter range and a second pore water storage volume (LPS) with a soil diameter greater than a preset size. When the infiltration rate exceeds the saturated soil infiltration rate, the excess water will be stored in surface depressions, forming depression storage (DPS). When the depression storage exceeds its maximum storage capacity, it will form surface runoff. in: This represents the actual amount of infiltration. This represents the saturated infiltration rate. and These represent the actual water storage capacity and maximum water storage capacity of the first pore of the soil, respectively; and These represent the actual water storage capacity and maximum water storage capacity of the second pore of the soil, respectively; The amount of infiltrated water will be allocated to MPS and LPS, calculated using the following formula: in: This refers to the amount of water that seeps into the MPS. The amount of infiltration into LPS; : represents the infiltration coefficient; Vertical infiltration and lateral flow of groundwater occur in the soil macropores (LPS), and the outflow rate is calculated as follows: in: The output flow rate of LPS is (mm). Relative soil saturation (mm); The actual soil moisture content (mm); Groundwater module, used for: The upper layer of groundwater consists of weathered material with high permeability, while the lower layer consists of bedrock fissures with low permeability. The baseflow is divided into a fast runoff component RG1 in the upper layer and a slow runoff component RG2 in the lower layer. The groundwater outflow rate was calculated using a linear drainage function. To reflect the dynamic changes in groundwater, the outflow rates of RG1 and RG2 were corrected using parameter factors gwRG1 and gwRG2, respectively, as follows: in: and The outflow rates (mm) for RG1 and RG2 are respectively. and The retention coefficients for RG1 and RG2 are respectively; and These are the correction parameters for RG1 and RG2, respectively; and These are the current water contents of RG1 and RG2, respectively. Considering the dynamic changes in groundwater in the watershed, the seepage flow in the soil module is allocated by adjusting parameters and taking into account the effect of slope, thereby obtaining the groundwater inflow situation: in: and , , represent the inflow rates of RG1 and RG2, respectively; slop represents the slope of the hydrological response unit; These are the correction parameters for RG1 and RG2.

[0031] In one embodiment, the step of verifying the preset hydrological model based on the observation data to obtain a spatiotemporal simulation database for the transboundary river region includes: When verifying the preset hydrological model, it is necessary to set the model's calibration period and validation period. When using the SWAT model to simulate runoff in cross-border river basins, the model parameters are determined by combining the measured data rate of hydrological stations. Artificial intelligence is used to extract and correct the spatiotemporal distribution of precipitation input to improve the accuracy of flood simulation. The J2000 model was used in a small watershed in the mountainous area to model the groundwater recharge process using measured data. Evaporation estimation was improved through machine learning, and a data-driven correction algorithm was constructed to dynamically optimize the hydrological model output in order to make up for the lack of parameterization in traditional models. After verifying the preset hydrological model, the method further includes evaluating the verification results using the following methods: The correlation coefficient is usually selected. The Nash-Sutcliffe simulation efficiency coefficient (NS) is used to evaluate the applicability of the model. Correlation coefficient The calculation formula is: in: This is the average value of the measured values; This is a simulated average value; The value ranges from 0 to 1. The closer the value is to 1, the closer the simulated value is to the measured value. The efficiency coefficient NS in the Nash-Sutcliffe simulation is calculated using the following formula: The value of NS ranges from 0 to 1; the closer the value of NS is to 1, the closer the simulated value is to the observed value.

[0032] In another embodiment, forecasting the medium- and long-term runoff of the transboundary river region based on the runoff database of the transboundary river region includes: The system uses a runoff database based on the transboundary river region to forecast the medium- and long-term runoff of the transboundary river region, and forecasts the spatial evolution and amplitude variation characteristics of the runoff at seasonal, interannual ENSO, and interdecadal PDO scales.

[0033] The J2000 hydrological model used in the embodiments of the present invention is described below: In this embodiment of the invention, the geographic input data used in constructing the J2000 hydrological model mainly includes topographic data, land use data, and soil data. Elevation data can be obtained using 90 m SRTM DEM data. Based on the DEM data, hydrological analysis is performed using the ArcGIS platform to obtain information including elevation range, slope aspect, and river network. Land use data can be obtained using the 2009 European Space Agency Global Land Use Dataset (http: / / due.esrin.esa.int / page_globcover.php). Glacier distribution data is obtained using RGI (Randolph glacierinventory) glacier catalog data, downloadable from http: / / www.glims.org / . Combined with global land use data, this data is then converted into codes for land use types in the model, thus obtaining the watershed's land use types. Soil texture data were obtained from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Science (IIASA) in Vienna. This database was used to obtain the distribution of soil types in the watershed. Then, based on the soil types obtained from HWSD, the soil attribute information required for the model was derived, mainly including: soil type, soil layer thickness, field capacity, permanent wilting point, saturated hydraulic conductivity, and soil organic matter content. Based on the prepared spatial data, HRU (Hyper-Round Root) subdivisions were performed, and then the J2000 hydrological model for runoff simulation was constructed.

[0034] The J2000 hydrological model, when simulating runoff, includes: a snow cover module, a glacier module, a soil water module, and a groundwater module. Each module is described below: 1. Snow accumulation module The amount of snow accumulation depends primarily on the day's temperature and precipitation. When the temperature is below the critical temperature for snowfall, the precipitation is snow. When the temperature is above the critical temperature for rainfall, the precipitation is rainfall. Precipitation between these two threshold temperatures is a mixture of rain and snow. To determine the critical temperature and thus the size of the mixing zone, a critical temperature TRS is first defined, which corresponds to 50% snowfall and 50% rainfall. A parameter Trans is also defined as the radius of the temperature width of the mixing zone. The proportion of snowfall is then calculated. in: The percentage of snowfall (%) is the critical temperature (°C); Trans is the converted air temperature parameter (°C). The temperature (°C) on the day when precipitation occurs.

[0035] Because snow can initially store water after it falls and accumulates, it eventually melts at a certain temperature. Meltwater release only begins after the snowmelt temperature threshold (BaseTemp) is exceeded. The energy required for snowmelt is primarily obtained through three different mechanisms: sensible heat from air temperature input, energy input from rainfall, and heat transfer through the soil. Given that the input data required for these methods is often difficult to obtain, the model uses empirical parameters for these methods. , and Corrections are made. The amount of melted snow is calculated using the following formula:

[0036] in: Snowmelt volume (mm); : represents the temperature factor; : is the precipitation energy factor; It is the surface heat flux factor. The temperature (°C) is the unit temperature for the hydrological response.

[0037] 2. Glacier Module Due to limitations in high-altitude observation data, glacier melting is typically considered using the day-degree factor method, only when the critical melting temperature (tbase) is below a certain threshold. The timeframe for glacial melting is calculated as follows:

[0038] in: Maximum temperature (°C); Average temperature (°C); : Ice melting rate (mm / d); Glacier melting coefficient; Glacier radiation melting factor; : Time step (day).

[0039] If glacial till exists on the glacier surface, the melting rate of the glacier will accelerate, and the melting rate at this time will be: in: Melting rate of glacial moraine cover (mm / d) 3. Soil and water module In models, soil water is typically categorized into mesopore water storage (MPS) with diameters of 0.2–50 μm and macropore water storage (MPS) with diameters greater than 50 μm. The saturated infiltration volume of the soil is calculated using empirical formulas. When the infiltration volume exceeds this saturated volume, the excess water is stored in surface depressions, forming depression storage (DPS). When the DPS exceeds its maximum storage capacity, surface runoff will occur.

[0040] in: Actual infiltration rate (mm / d); Saturated infiltration rate (mm / d); and These represent the actual water storage capacity and the maximum water storage capacity (mm) in the soil pores, respectively. and These represent the actual water storage capacity and maximum water storage capacity (mm) of the soil macropores, respectively.

[0041] The amount of infiltrated water will be allocated to MPS and LPS, calculated using the following formula: 10.195 10.196 in: This refers to the amount of water that seeps into the MPS. The amount of infiltration into LPS; : is the infiltration coefficient.

[0042] Vertical infiltration and lateral flow of groundwater occur only in the macropores (LPS) of the soil, and the outflow rate is calculated as follows: 10.197 in: The output flow rate of LPS is (mm). Relative soil saturation (mm); The actual soil moisture content (mm).

[0043] 4. Groundwater module The upper layer of groundwater consists of weathered material with high permeability, while the lower layer is bedrock fissures with low permeability. Therefore, the baseflow is divided into a fast runoff component (RG1) in the upper layer and a slow runoff component (RG2) in the lower layer. The groundwater outflow is calculated using a linear drainage function. To reflect the dynamic changes in groundwater, the outflows of RG1 and RG2 are corrected using parameter factors gwRG1 and gwRG2, respectively, as follows:

[0044] in: and The outflow rates (mm) for RG1 and RG2 are respectively. and The retention coefficients for RG1 and RG2 are respectively; and These are the correction parameters for RG1 and RG2, respectively; and These are the current moisture contents (mm) of RG1 and RG2, respectively.

[0045] To account for the dynamic changes in groundwater in the watershed, the seepage flow in the soil module is allocated by correcting parameters and taking into account the effect of slope, thereby obtaining the groundwater inflow situation.

[0046] in: and , respectively, represent the inflow rates (mm) of RG1 and RG2; slop represents the slope of the hydrological response unit; These are the correction parameters for RG1 and RG2.

[0047] In one embodiment, the contribution of snowmelt and rainfall to runoff can be quantified using the following method.

[0048] To discuss the relative contributions of snowmelt and rainfall to runoff variation at different time scales (monthly, seasonal, and annual), and to quantify the relative magnitudes of different runoff sources on runoff variation, the following methods and formulas can be used: 1) Analysis at different time scales ① Lunar scale: Data decomposition: Monthly precipitation, temperature, and runoff data are decomposed to identify the contributions of snowmelt and rainfall to runoff.

[0049] formula: in, Monthly runoff, Contribute to snowmelt Contributed to rainfall Contributes to the base current.

[0050] ②Seasonal scale Seasonal analysis: Group the data by season (e.g., spring, summer) and analyze the impact of snowmelt and rainfall on runoff in different seasons.

[0051] formula: in, Seasonal runoff, For the first Months of snowmelt contribution, For the first Monthly rainfall contribution

[0052] ③ Annual scale Long-term trends: Analyze annual data to study the impact of snowmelt and rainfall on long-term runoff changes.

[0053] formula: 10.228 in, Annual runoff For the first Seasonal runoff.

[0055] 2) Impact of snowmelt and rainfall on total runoff Snowmelt model: Calculating snowmelt amount using the degree-day method. in, For snowmelt volume, For the sun's melting snow factor, The daily average temperature This is the reference temperature.

[0056] Rainfall-runoff model: Calculate rainfall-runoff volume using a rainfall-runoff model (such as the SCS-CN method). Where Q is the rainfall runoff, P is the rainfall, S is the water storage, and CN is the curve number.

[0057] 3) Quantify different runoff sources Water balance equation: comprehensively considering the relationship between precipitation, evapotranspiration, water storage, and runoff. in, For precipitation, Evaporation amount This refers to changes in water storage.

[0058] Contribution rate calculation: By separating runoff from different sources, the relative contribution rate is calculated. in, For the runoff from a specific source, This represents the total runoff.

[0059] Using the methods and formulas described above, we can systematically study the relative contributions of snowmelt and rainfall to runoff changes, and quantify the relative magnitude of different runoff sources to runoff changes.

[0060] This invention provides a medium- to long-term runoff prediction system for transboundary river regions, such as... Figure 2 As shown, it includes: The acquisition module 201 is used to acquire observation data from hydrological stations in transboundary river areas; The verification module 202 is used to verify the preset hydrological model based on the observation data to obtain a spatiotemporal simulation database of the transboundary river area. The preset hydrological model includes at least the SWAT hydrological model and the J2000 hydrological model. The reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model. The construction module 203 is used to construct a runoff database for the cross-border river region based on the observation data of hydrological stations in the cross-border river region and the simulation data of the spatiotemporal simulation database. The runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt and precipitation runoff in different areas of the cross-border river region. The prediction module 204 is used to predict the medium- and long-term runoff of the cross-border river region based on the runoff database of the cross-border river region.

[0061] Thirdly, embodiments of the present invention provide an electronic device, including: a memory and a processor, wherein the memory and the processor are connected; Memory, used to store computer programs; A processor is used to invoke a computer program stored in memory to perform any of the methods described above.

[0062] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when run by a computer, performs the methods described in any of the above-mentioned embodiments.

[0063] The present invention provides a method and system for predicting medium- and long-term runoff in transboundary river areas, comprising: acquiring observational data from hydrological stations in the transboundary river area; verifying a preset hydrological model based on the observational data to obtain a spatiotemporal simulation database of the transboundary river area, wherein the preset hydrological model includes at least a SWAT hydrological model and a J2000 hydrological model, and the reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model; constructing a runoff database of the transboundary river area based on the observational data from the hydrological stations in the transboundary river area and the simulation data from the spatiotemporal simulation database, wherein the runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt, and rainfall runoff in different areas of the transboundary river area; and predicting the medium- and long-term runoff of the transboundary river area based on the runoff database. This method and system can accurately predict the medium- and long-term runoff of transboundary river areas, accumulating valuable data for runoff analysis research.

[0064] It should be noted that the content of the method embodiments and system embodiments provided in this patent application corresponds one-to-one with each other. The content involved in any embodiment can be referenced or combined with other embodiments to form part of that embodiment. For ease of description, this patent application focuses on explaining the method embodiments. The description of the relevant technical features and solutions of the device embodiments and pump station system embodiments can be referred to the relevant content in the method embodiments.

[0065] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0068] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for predicting medium- to long-term runoff in transboundary river regions, characterized in that, include: Obtain observational data from hydrological stations in transboundary river areas; The preset hydrological model is validated based on the observation data to obtain a spatiotemporal simulation database of the transboundary river region. The preset hydrological model includes at least the SWAT hydrological model and the J2000 hydrological model. The reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model. Based on the observation data of hydrological stations in the transboundary river region and the simulation data of the spatiotemporal simulation database, a runoff database for the transboundary river region is constructed. The runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt and precipitation runoff in different areas of the transboundary river region. Based on the runoff database of the transboundary river region, the medium- and long-term runoff of the transboundary river region is predicted.

2. The method according to claim 1, characterized in that, The input to the runoff database is the observation data from the hydrological station, and the output of the runoff database is the spatiotemporal distribution characteristics of runoff, snowmelt, and precipitation runoff in different areas of the transboundary river region. The observation data from the hydrological station include: precipitation data, temperature data, wind speed data, relative humidity data, sunshine duration data, and runoff observation data. The spatiotemporal distribution characteristics output by the runoff database include: flow data of rivers at different levels, evaporation data of different sub-basins, runoff depth data of different sub-basins, snowmelt data of different sub-basins, and rainfall-runoff data of different sub-basins.

3. The method according to claim 1, characterized in that, The runoff sources of the J2000 hydrological model include rainfall, snowmelt, glacial meltwater, and groundwater; Runoff components classified according to runoff path include surface runoff RD1, interflow formed by lateral outflow of soil water RD2, groundwater flow from the upper part of the aquifer RG1, and groundwater flow from the saturated groundwater aquifer RG2. The surface runoff RD1 has the fastest outflow velocity, and the surface runoff RD1 includes runoff generated by snow and ice surfaces and ground saturation; the outflow velocity of the interflow RD2 is less than that of the surface runoff RD1; the groundwater flow RG1, based on weathering, has a stronger infiltration capacity compared to the lower part of the aquifer. The J2000 hydrological model is a process-based hydrological model that takes into account the snowmelt process. The model discretizes the hydrological response units (HRUs), divides the watershed attributes, and obtains meteorological driving data and basic attribute information from the HRUs. The runoff of each HRU is calculated, and the total runoff of the HRUs flows into the river channel and eventually converges at the watershed outlet. The basic attribute information includes: elevation, slope, aspect, and land use.

4. The method according to claim 1, characterized in that, The J2000 hydrological model, when simulating runoff, includes: a snow accumulation module, a glacier module, a soil water module, and a groundwater module; The snow accumulation module is used for: Since the amount of snow accumulation depends on the temperature and precipitation of the day, when the temperature is below the first critical temperature for snowfall, the precipitation is snowfall; when the temperature is above the second critical temperature for precipitation, the precipitation is precipitation; precipitation between the first and second critical temperatures is a mixture of rain and snow. Define a critical temperature TRS, where 50% of precipitation is snow and 50% is rain. Define a transformed temperature parameter Trans, which serves as the radius of the temperature width of the mixed region, thereby calculating the proportion of snowfall. ； ； in: The proportion of snowfall; Where Trans is the critical temperature, and Trans is the transformed temperature parameter; The temperature of the day when precipitation occurs; Because snow can store water in the initial stage of accumulation until the melting temperature reaches a certain value. Snowmelt is released once the snowmelt temperature exceeds the BaseTemp threshold. The energy required for snowmelt is obtained through three methods: sensible heat from air temperature input, energy input from rainfall, and heat transfer through the soil. Due to the difficulty in obtaining sufficient input data, empirical parameters are used. , and After correction, the amount of melted snow is calculated using the following formula: ； in: Snowmelt volume (mm); : represents the temperature factor; : is the precipitation energy factor; It is the surface heat flux factor. The air temperature of the hydrological response unit; The glacier module is used for: Due to limitations in high-altitude observation data, the day-de-day factor method is used to consider glacier melting, when the critical melting temperature... Below the melting temperature At that time, glacial melting occurred, calculated as follows: ； ； in: The highest temperature; Average temperature; This refers to the rate of ice melting; This refers to the glacier melting coefficient; It is a glacier radiation melting factor; For time step; This is the critical glacier melting temperature; Melting temperature; If glacial till exists on the glacier surface, the melting rate of the glacier will accelerate, and the melting rate at this time will be: ； in: The melting rate of the ice deposits; Glacial moraine melting factor.

5. The method according to claim 4, characterized in that: The soil water module is used for: In the J2000 hydrological model, soil water is divided into a first pore water storage volume (MPS) stored within a preset diameter range and a second pore water storage volume (LPS) with a soil diameter greater than a preset size. When the infiltration rate exceeds the saturated soil infiltration rate, the excess water will be stored in surface depressions, forming depression storage (DPS). When the depression storage exceeds its maximum storage capacity, it will form surface runoff. ； ； in: This represents the actual amount of infiltration. This represents the saturated infiltration rate. and These represent the actual water storage capacity and maximum water storage capacity of the first pore of the soil, respectively; and These represent the actual water storage capacity and maximum water storage capacity of the second pore of the soil, respectively; The amount of infiltrated water will be allocated to MPS and LPS, calculated using the following formula: ； ； in: This refers to the amount of water that seeps into the MPS. The amount of infiltration into LPS; : represents the infiltration coefficient; Vertical infiltration and lateral flow of groundwater occur in the soil macropores (LPS), and the outflow rate is calculated as follows: ； in: The output flow rate of LPS is (mm). Relative soil saturation (mm); The actual soil moisture content (mm); Groundwater module, used for: The upper layer of groundwater consists of weathered material with high permeability, while the lower layer consists of bedrock fissures with low permeability. The baseflow is divided into a fast runoff component RG1 in the upper layer and a slow runoff component RG2 in the lower layer. The groundwater outflow rate was calculated using a linear drainage function. To reflect the dynamic changes in groundwater, the outflow rates of RG1 and RG2 were corrected using parameter factors gwRG1 and gwRG2, respectively, as follows: ； ； in: and The outflow rates (mm) for RG1 and RG2 are respectively. and The retention coefficients for RG1 and RG2 are respectively. and These are the correction parameters for RG1 and RG2, respectively; and These are the current water contents of RG1 and RG2, respectively; Considering the dynamic changes in groundwater in the watershed, the seepage flow in the soil module is allocated by adjusting parameters and taking into account the effect of slope, thereby obtaining the groundwater inflow situation: ； ； in: and , , represent the inflow rates of RG1 and RG2, respectively; slop represents the slope of the hydrological response unit; These are the correction parameters for RG1 and RG2.

6. The method according to claim 1, characterized in that, The process of validating the preset hydrological model based on the observed data to obtain a spatiotemporal simulation database for transboundary river regions includes: When verifying the preset hydrological model, it is necessary to set the model's calibration period and validation period. When using the SWAT model to simulate runoff in cross-border river basins, the model parameters are determined by combining the measured data rate of hydrological stations. Artificial intelligence is used to extract and correct the spatiotemporal distribution of precipitation input to improve the accuracy of flood simulation. The J2000 model was used in a small watershed in the mountainous area to model the groundwater recharge process using measured data. Evaporation estimation was improved through machine learning, and a data-driven correction algorithm was constructed to dynamically optimize the hydrological model output in order to make up for the lack of parameterization in traditional models. After verifying the preset hydrological model, the method further includes evaluating the verification results using the following methods: The correlation coefficient is usually selected. The Nash-Sutcliffe simulation efficiency coefficient (NS) is used to evaluate the applicability of the model. Correlation coefficient The calculation formula is: ； in: This is the average value of the measured values; This is a simulated average value; The value ranges from 0 to 1. The closer the value is to 1, the closer the simulated value is to the measured value. The efficiency coefficient NS in the Nash-Sutcliffe simulation is calculated using the following formula: ； The value of NS ranges from 0 to 1; the closer the value of NS is to 1, the closer the simulated value is to the observed value.

7. The method according to any one of claims 1 to 6, characterized in that, The forecasting of medium- and long-term runoff in the transboundary river region based on the runoff database of the transboundary river region includes: The system uses a runoff database based on the transboundary river region to forecast the medium- and long-term runoff of the transboundary river region, and forecasts the spatial evolution and amplitude variation characteristics of the runoff at seasonal, interannual ENSO, and interdecadal PDO scales.

8. A medium- to long-term runoff prediction system for transboundary river regions, characterized in that, include: The acquisition module is used to acquire observation data from hydrological stations in transboundary river areas; The verification module is used to verify the preset hydrological model based on the observation data to obtain a spatiotemporal simulation database of the transboundary river area. The preset hydrological model includes at least the SWAT hydrological model and the J2000 hydrological model. The reliability of the spatiotemporal simulation database is higher than that of the preset hydrological model. The module is used to construct a runoff database for a cross-border river region based on observation data from hydrological stations in the region and simulation data from the spatiotemporal simulation database. The runoff database is used to analyze the spatiotemporal distribution characteristics of runoff, snowmelt, and rainfall runoff in different areas of the cross-border river region. The prediction module is used to predict the medium- and long-term runoff of the cross-border river region based on the runoff database of the cross-border river region.

9. An electronic device, characterized in that, include: Memory and processor, and the connection between memory and processor; Memory, used to store computer programs; A processor for invoking a computer program stored in memory to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a computer, performs the method as described in any one of claims 1 to 7.

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