GNSS (Global Navigation Satellite System) and wind cloud satellite assisted meteorological and hydrological coupling runoff forecasting method

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) and wind cloud satellite assisted meteorological and hydrological coupled runoff forecasting method. The method comprises the following steps: 1, GNSS two-dimensional water vapor inversion; 2, performing water vapor inversion on the Fengyun No.4; 3, performing three-dimensional variation assimilation on the precipitable water volume inverted by the GNSS and the precipitable water volume inverted by the FY-4 star A based on a WRF meteorological mode; and 4, runoff forecasting is carried out based on the WRF-Hydro hydrological model. The method is simple in step and reasonable in design, three-dimensional variational assimilation is performed on the precipitable water volume inversed by the GNSS and the precipitable water volume inversed by the FY-4 star A based on the WRF meteorological mode, accurate meteorological driving data is provided for a WRF-Hydro hydrological model, the dependence of WRF meteorological mode data assimilation on conventional meteorological observation data in traditional meteorological and hydrological coupling forecast is avoided, and the method is suitable for popularization and application. And the applicability of the WRF and WRF-Hydro model coupling mode in the meso-and small-scale watershed in the conventional data scarcity region is further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of runoff simulation and forecasting, and in particular relates to a runoff forecasting method coupled with GNSS and Fengyun satellite-assisted meteorological and hydrological data. Background Art

[0002] Precipitation data is the most important meteorological driving factor affecting the hydrological process. Whether it can accurately describe the temporal and spatial distribution characteristics of precipitation will directly affect the temporal and spatial evolution of key factors such as basin evapotranspiration, soil moisture, and runoff, and thus affect the accuracy of hydrological simulation and flood forecasting. Due to the high temporal and spatial variability of precipitation, it is difficult to accurately estimate it. The precipitation data used in traditional hydrological forecasting mainly comes from ground rain gauge observations and is only applicable to areas where the observation sites are evenly and densely distributed. For small and medium-sized basins with complex terrain, the lack of high-quality station observation precipitation data greatly limits the rainfall-runoff forecast of the basin.

[0003] As a high-resolution distributed hydrological model based on clear physical mechanisms, multi-scale and multi-parameterization, the WRF-Hydro model can fully consider the impact of changes in underlying surface conditions and meteorological environmental factors on hydrological processes, and is easy to combine with numerical weather forecast models. Currently, the WRF-Hydro model is coupled with the WRF model to analyze the interaction between the atmosphere and hydrological processes. However, WRF / WRF-Hydro coupling research is mostly concentrated in areas with rich meteorological and hydrological data. It relies on conventional meteorological observations, radar observations and other data, and is less applicable to small and medium-sized basins in areas with scarce data.

[0004] Therefore, a reasonably designed GNSS and FY satellite-assisted meteorological-hydrological coupled runoff forecasting method is needed. Based on the WRF meteorological model, three-dimensional variational assimilation of the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A can be performed to provide accurate meteorological driving data for the WRF-Hydro hydrological model, avoiding the dependence of WRF meteorological model data assimilation on conventional meteorological observation data in traditional meteorological-hydrological coupled forecasting, and further improving the applicability of the WRF and WRF-Hydro model coupling modes in small and medium-scale basins in data-scarce areas. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a runoff forecasting method for GNSS and FY satellite-assisted meteorological and hydrological coupling in view of the deficiencies in the above-mentioned prior art. The method has simple steps and reasonable design. Based on the WRF meteorological model, three-dimensional variational assimilation is performed on the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A, so as to provide accurate meteorological driving data for the WRF-Hydro hydrological model, thereby avoiding the dependence of WRF meteorological model data assimilation on conventional meteorological observation data in traditional meteorological and hydrological coupling forecasting, and further improving the applicability of WRF and WRF-Hydro model coupling modes in small and medium-scale basins in data-scarce areas.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a runoff forecasting method coupled with GNSS and Fengyun satellite-assisted meteorological and hydrological data, characterized in that the method comprises the following steps:

[0007] Step 1: GNSS 2D water vapor inversion:

[0008] Step 101: using a computer to obtain the precipitable water volume PWV inverted by the GNSS station according to the formula PWV=Π·ZWD; wherein ZWD represents the zenith wet delay, and Π represents the atmospheric water vapor conversion coefficient;

[0009] Step 102, repeating step 101 multiple times to obtain the precipitable water inverted by multiple GNSS stations; wherein the precipitable water inverted by the j-th GNSS station is recorded as PWV(j), and the longitude, latitude and altitude corresponding to the j-th GNSS station are obtained and recorded as lat1(j), lon1(j) and h1(j), respectively;

[0010] Step 2: FY-4 water vapor inversion:

[0011] Step 201: Use a computer to calculate the Get the hourly precipitable water volume PWV of a single grid point based on the inversion of FY-4A satellite hourly ; Among them, TPW f represents the total amount of atmospheric water vapor at the fth grid point; F represents the total amount of atmospheric water vapor within one hour, F = 4, 1 ≤ f ≤ F;

[0012] Step 202: Repeat step 201 multiple times to obtain the hourly precipitation at multiple grid points; the hourly precipitation at the e-th grid point is recorded as PWV hourly (e), and obtain the longitude, latitude and altitude corresponding to the e-th grid point, which are recorded as lat2(e), lon2(e) and h2(e) respectively; e is a positive integer;

[0013] Step 3: Perform three-dimensional variational assimilation of the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF meteorological model:

[0014] Step 301: Use a computer to convert the longitude lat1(j), latitude lon1(j), altitude h1(j), precipitable water volume PWV(j), and the first error Δ1(j) corresponding to the j-th GNSS station and the longitude lat2(e), latitude lon2(e), altitude h2(e), and hourly precipitable water volume PWV corresponding to the e-th grid point inverted based on the FY-4A satellite into the value of hourly (e), the second error Δ2(e) is converted into the precipitable water data in Little_R format;

[0015] Step 302: Using a computer, the Little_R formatted precipitable water data is input into the observation value processor OBSPROC module of the WRF meteorological model and processed into an observation value file for WRFDA;

[0016] Step 303: using a computer to input the observation value file for WRFDA into the WRFDA module of the WRF meteorological model for three-dimensional variational assimilation, and outputting a meteorological data file in NetCDF format;

[0017] Step 4: Runoff forecast based on WRF-Hydro hydrological model:

[0018] Step 401: Using a computer, input a meteorological data file in NetCDF format into an NCL preprocessing tool for data screening and processing to obtain a meteorological driving data file in NetCDF format;

[0019] Step 402: using a computer to input the meteorological driving data file in NetCDF format into the WRF-Hydro model, and setting the runoff parameters, soil parameters and channel parameters in the calibration parameters for simulation, and outputting the runoff data file in NetCDF format;

[0020] Step 403: Obtain runoff data from a runoff data file in NetCDF format to implement runoff forecasting.

[0021] The above-mentioned GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method is characterized in that: the acquisition of the zenith wet delay ZWD and the atmospheric water vapor conversion coefficient Π in step 101 is specifically carried out as follows:

[0022] Step A, using a computer to solve the GNSS station data using GAMIT / GLOBK software to obtain the zenith tropospheric total delay ZTD;

[0023] Step B: Using the Saastamoinen model using a computer The zenith dry delay ZHD is obtained; where P represents the surface pressure at the GNSS station location, represents the latitude of the GNSS station, and H represents the geodetic height of the GNSS station;

[0024] Step C, using a computer to obtain the zenith wet delay ZWD according to the formula ZWD=ZTD-ZHD;

[0025] Step D: Using a computer based on Get the weighted average temperature T m ; Where I represents the number of atmospheric layers, i is the layer number, i and I are both positive integers, and 1≤i≤I; T i represents the absolute temperature of the top of the ith atmospheric layer, e i represents the water vapor pressure at the top of the i-th atmospheric layer, Δh i represents the thickness of the i-th atmospheric layer;

[0026] Step E: Use a computer to calculate the The atmospheric water vapor conversion coefficient Π is obtained; where ρ w represents the density of liquid water, R v represents the water vapor ratio gas constant, T m represents the weighted average temperature, k2' represents the first constant of atmospheric refractive index, k3 represents the second constant of atmospheric refractive index,

[0027] The above-mentioned GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method is characterized in that: the total atmospheric water vapor TPW of the fth single grid point in step 201 is f The specific process of obtaining is as follows:

[0028] Step 2011: Use a computer to calculate the The total amount of atmospheric water vapor TPW at a single grid point based on the inversion of FY-4A is obtained; where g is the gravitational acceleration, q1 is the water vapor mixing ratio at the surface of a single grid point, q2 is the water vapor mixing ratio at 900 hPa, q3 is the water vapor mixing ratio at 700 hPa, q4 is the water vapor mixing ratio at 300 hPa, p1 is the surface pressure at a single grid point, p2 is the 900 hPa pressure, p3 is the 700 hPa pressure, and p4 is the 300 hPa pressure;

[0029] Step 212: According to the method of step 211, multiple atmospheric water vapor totals of a single grid point within one hour are obtained, and the fth atmospheric water vapor total of the single grid point is recorded as TPW f ; Wherein, f is a positive integer.

[0030] The above-mentioned GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method is characterized in that: the acquisition of the first error Δ1(j) and the second error Δ2(e) in step 301 is specifically carried out as follows:

[0031] The precipitable water obtained by the radiosonde is taken as the true value. The error between the precipitable water inverted by the j-th GNSS station and the true value is obtained by computer and recorded as Δ1(j), and the error between the hourly precipitable water at the e-th grid point and the true value is recorded as Δ2(e).

[0032] The above-mentioned GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method is characterized in that: the runoff parameters in step 402 include infiltration parameters and control deep drainage coefficient, and the infiltration parameter value range is 0.1 to 3.0, and the step length is 0.5; the control deep drainage coefficient value range is 0.0 to 1.0, and the step length is 0.2;

[0033] Soil parameters include pore size distribution index and soil saturated hydraulic conductivity. The pore size distribution index ranges from 0.4 to 1.9 with a step length of 0.2; the soil saturated hydraulic conductivity ranges from 0.1 to 2.0 with a step length of 0.2.

[0034] The channel parameter is Manning's roughness, which ranges from 0.3 to 2.0 with a step size of 0.5.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] 1. The method of the present invention has simple steps and reasonable design. It solves the problem of dependence on conventional meteorological observation data in traditional hydrological forecasting technology and meteorological-hydrological coupling forecasting. It can be applied to small and medium-sized river basins in data-scarce areas, and fully makes up for the sparse and uneven distribution of conventional meteorological observation sites. This method introduces unconventional observation data assimilation into the WRF meteorological model, and couples its precipitable water with the hydrological model WRF-Hydro, further improving the hydrological forecasting ability and applicability of the WRF / WRF-Hydro coupling, which can extend the forecast period of flood forecasting, and is of great significance for the forecast and warning of flash floods in small and medium-sized river basins in data-scarce areas.

[0037] 2. The present invention performs three-dimensional variational assimilation on the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF model, which can effectively improve the initial field quality of the WRF meteorological model in areas where conventional observation data are scarce, further enhance the precipitation forecast capability of the model, and provide meteorological driving data for meteorological-hydrological coupled forecasting.

[0038] 3. The present invention is based on the coupling of WRF meteorological model and WRF-Hydro hydrological model for runoff forecasting, which can analyze the runoff process under different meteorological conditions, and overcome the limitation of complex terrain and topography of small and medium-sized river basins on the development of meteorological-hydrological forecasting technology. In addition, the regional and spatiotemporal resolution settings of the WRF model are more flexible, which helps to obtain high-precision and high-resolution meteorological driving data in local areas, so as to carry out refined WRF / WRF-Hdyro coupled runoff forecasting, and further realize efficient and accurate flood forecasting and warning for small and medium-sized river basins.

[0039] In summary, the method of the present invention has simple steps and reasonable design. It performs three-dimensional variational assimilation on the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF meteorological model, provides accurate meteorological driving data for the WRF-Hydro hydrological model, avoids the dependence of WRF meteorological model data assimilation on conventional meteorological observation data in traditional meteorological and hydrological coupled forecasting, and further improves the applicability of the WRF and WRF-Hydro model coupling modes in small and medium-scale basins in data-scarce areas.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0042] like Figure 1 As shown, the present invention provides a GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method, comprising the following steps:

[0043] Step 1: GNSS 2D water vapor inversion:

[0044] Step 101: using a computer to obtain the precipitable water volume PWV inverted by the GNSS station according to the formula PWV=Π·ZWD; wherein ZWD represents the zenith wet delay, and Π represents the atmospheric water vapor conversion coefficient;

[0045] Step 102, repeating step 101 multiple times to obtain the precipitable water inverted by multiple GNSS stations; wherein the precipitable water inverted by the j-th GNSS station is recorded as PWV(j), and the longitude, latitude and altitude corresponding to the j-th GNSS station are obtained and recorded as lat1(j), lon1(j) and h1(j), respectively;

[0046] Step 2: FY-4 water vapor inversion:

[0047] Step 201: Use a computer to calculate the Get the hourly precipitable water volume PWV of a single grid point based on the inversion of FY-4A satellitehourly ; Among them, TPW f represents the total amount of atmospheric water vapor at the fth grid point; F represents the total amount of atmospheric water vapor within one hour, F = 4, 1 ≤ f ≤ F;

[0048] Step 202: Repeat step 201 multiple times to obtain the hourly precipitation at multiple grid points; the hourly precipitation at the e-th grid point is recorded as PWV hourly (e), and obtain the longitude, latitude and altitude corresponding to the e-th grid point, which are recorded as lat2(e), lon2(e) and h2(e), respectively; e is a positive integer;

[0049] Step 3: Perform three-dimensional variational assimilation of the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF meteorological model:

[0050] Step 301: Use a computer to convert the longitude lat1(j), latitude lon1(j), altitude h1(j), precipitable water volume PWV(j), and the first error Δ1(j) corresponding to the j-th GNSS station and the longitude lat2(e), latitude lon2(e), altitude h2(e), and hourly precipitable water volume PWV corresponding to the e-th grid point inverted based on the FY-4A satellite into the value of hourly (e), the second error Δ2(e) is converted into the precipitable water data in Little_R format;

[0051] Step 302: Using a computer, the Little_R formatted precipitable water data is input into the observation value processor OBSPROC module of the WRF meteorological model and processed into an observation value file for WRFDA;

[0052] Step 303: using a computer to input the observation value file for WRFDA into the WRFDA module of the WRF meteorological model for three-dimensional variational assimilation, and outputting a meteorological data file in NetCDF format;

[0053] Step 4: Runoff forecast based on WRF-Hydro hydrological model:

[0054] Step 401: Using a computer, input a meteorological data file in NetCDF format into an NCL preprocessing tool for data screening and processing to obtain a meteorological driving data file in NetCDF format;

[0055] Step 402: using a computer to input the meteorological driving data file in NetCDF format into the WRF-Hydro model, and setting the runoff parameters, soil parameters and channel parameters in the calibration parameters for simulation, and outputting the runoff data file in NetCDF format;

[0056] Step 403: Obtain runoff data from a runoff data file in NetCDF format to implement runoff forecasting.

[0057] In this embodiment, the specific process of obtaining the zenith wet delay ZWD and the atmospheric water vapor conversion coefficient Π in step 101 is as follows:

[0058] Step A, using a computer to solve the GNSS station data using GAMIT / GLOBK software to obtain the zenith tropospheric total delay ZTD;

[0059] Step B: Using the Saastamoinen model using a computer The zenith dry delay ZHD is obtained; where P represents the surface pressure at the GNSS station location, represents the latitude of the GNSS station, and H represents the geodetic height of the GNSS station;

[0060] Step C, using a computer to obtain the zenith wet delay ZWD according to the formula ZWD=ZTD-ZHD;

[0061] Step D: Using a computer based on Get the weighted average temperature T m ; Where I represents the number of atmospheric layers, i is the layer number, i and I are both positive integers, and 1≤i≤I; T i represents the absolute temperature of the top of the ith atmospheric layer, e i represents the water vapor pressure at the top of the i-th atmospheric layer, Δh i represents the thickness of the i-th atmospheric layer;

[0062] Step E: Use a computer to calculate the The atmospheric water vapor conversion coefficient Π is obtained; where ρ w represents the density of liquid water, R v represents the water vapor ratio gas constant, T m represents the weighted average temperature, k2' represents the first constant of atmospheric refractive index, k3 represents the second constant of atmospheric refractive index,

[0063] In this embodiment, the total amount of atmospheric water vapor TPW at the fth grid point in step 201 is f The specific process of obtaining is as follows:

[0064] Step 2011: Use a computer to calculate the The total amount of atmospheric water vapor TPW at a single grid point based on the inversion of FY-4A is obtained; where g is the gravitational acceleration, q1 is the water vapor mixing ratio at the surface of a single grid point, q2 is the water vapor mixing ratio at 900 hPa, q3 is the water vapor mixing ratio at 700 hPa, q4 is the water vapor mixing ratio at 300 hPa, p1 is the surface pressure at a single grid point, p2 is the 900 hPa pressure, p3 is the 700 hPa pressure, and p4 is the 300 hPa pressure;

[0065] Step 212: According to the method of step 211, multiple atmospheric water vapor totals of a single grid point within one hour are obtained, and the fth atmospheric water vapor total of the single grid point is recorded as TPW f ; Wherein, f is a positive integer.

[0066] In this embodiment, the specific process of obtaining the first error Δ1(j) and the second error Δ2(e) in step 301 is as follows:

[0067] The precipitable water obtained by the radiosonde is taken as the true value. The error between the precipitable water inverted by the j-th GNSS station and the true value is obtained by computer and recorded as Δ1(j), and the error between the hourly precipitable water at the e-th grid point and the true value is recorded as Δ2(e).

[0068] In this embodiment, the runoff parameters in step 402 include an infiltration parameter and a control deep drainage coefficient, and the infiltration parameter value range is 0.1 to 3.0, with a step length of 0.5; the control deep drainage coefficient value range is 0.0 to 1.0, with a step length of 0.2;

[0069] Soil parameters include pore size distribution index and soil saturated hydraulic conductivity. The pore size distribution index ranges from 0.4 to 1.9 with a step length of 0.2; the soil saturated hydraulic conductivity ranges from 0.1 to 2.0 with a step length of 0.2.

[0070] The channel parameter is Manning's roughness, which ranges from 0.3 to 2.0 with a step size of 0.5.

[0071] In this embodiment, during specific implementation, the meteorological driving data file in step 401 includes precipitation, temperature, air pressure, wind U / V component, specific humidity, shortwave radiation flux and vegetation ratio.

[0072] In this embodiment, during the specific implementation, the unit of the surface pressure P corresponding to the GNSS station position in step B is hPa, and the latitude of the GNSS station is The unit of is rad, and the unit of the GNSS station geodetic height H is km.

[0073] In this embodiment, during the specific implementation, the value of I in step D is 37, and the specific stratification refers to the 37 pressure layers of the ERA5 high-altitude ground data.

[0074] In this embodiment, during the specific implementation, the density of liquid water in step E is w , the value is 1.0×10 3 kg / m 3 , R v The value of T is 461.495 J / (kg·K), m The unit is K, and the value of k2′ is 17±10K / hPa, and the value of k3 is (3.776±0.04)×10 5 K 2 / hPa.

[0075] In this embodiment, during the specific implementation, the determination coefficient R 2 , Nash coefficient NSE, flood peak relative error Dp, flood volume relative error, and peak time error are used to evaluate the coupled runoff data of the present invention.

[0076] In summary, the method of the present invention has simple steps and reasonable design. It performs three-dimensional variational assimilation on the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF meteorological model, provides accurate meteorological driving data for the WRF-Hydro hydrological model, avoids the dependence of WRF meteorological model data assimilation on conventional meteorological observation data in traditional meteorological and hydrological coupled forecasting, and further improves the applicability of the WRF and WRF-Hydro model coupling modes in small and medium-scale basins in data-scarce areas.

[0077] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A runoff forecasting method using GNSS and Fengyun satellite-assisted meteorological and hydrological coupling, characterized in that: The method comprises the following steps: Step 1: GNSS 2D water vapor inversion: Step 101: using a computer to obtain the precipitable water volume PWV inverted by the GNSS station according to the formula PWV=Π·ZWD; wherein ZWD represents the zenith wet delay, and Π represents the atmospheric water vapor conversion coefficient; Step 102, repeating step 101 multiple times to obtain the precipitable water inverted by multiple GNSS stations; wherein the precipitable water inverted by the j-th GNSS station is recorded as PWV(j), and the longitude, latitude and altitude corresponding to the j-th GNSS station are obtained and recorded as lat1(j), lon1(j) and h1(j), respectively; Step 2: FY-4 water vapor inversion: Step 201: Use a computer to calculate the Get the hourly precipitable water volume PWV of a single grid point based on the inversion of FY-4A satellite hourly ; Among them, TPW f represents the total amount of atmospheric water vapor at the fth grid point; F represents the total amount of atmospheric water vapor within one hour, F = 4, 1 ≤ f ≤ F; Step 202: Repeat step 201 multiple times to obtain the hourly precipitation at multiple grid points; the hourly precipitation at the e-th grid point is recorded as PWV hourly (e), and obtain the longitude, latitude and altitude corresponding to the e-th grid point, which are recorded as lat2(e), lon2(e) and h2(e), respectively; e is a positive integer; Step 3: Perform three-dimensional variational assimilation of the precipitable water inverted by GNSS and the precipitable water inverted by FY-4A based on the WRF meteorological model: Step 301: Use a computer to convert the longitude lat1(j), latitude lon1(j), altitude h1(j), precipitable water volume PWV(j), and the first error Δ1(j) corresponding to the j-th GNSS station and the longitude lat2(e), latitude lon2(e), altitude h2(e), and hourly precipitable water volume PWV corresponding to the e-th grid point inverted based on the FY-4A satellite into the value of hourly (e), the second error Δ2(e) is converted into the precipitable water data in Little_R format; Step 302: Using a computer, the Little_R formatted precipitable water data is input into the observation value processor OBSPROC module of the WRF meteorological model and processed into an observation value file for WRFDA; Step 303: using a computer to input the observation value file for WRFDA into the WRFDA module of the WRF meteorological model for three-dimensional variational assimilation, and outputting a meteorological data file in NetCDF format; Step 4: Runoff forecast based on WRF-Hydro hydrological model: Step 401: Using a computer, input a meteorological data file in NetCDF format into an NCL preprocessing tool for data screening and processing to obtain a meteorological driving data file in NetCDF format; Step 402: using a computer to input the meteorological driving data file in NetCDF format into the WRF-Hydro model, and setting the runoff parameters, soil parameters and channel parameters in the calibration parameters for simulation, and outputting the runoff data file in NetCDF format; Step 403: Obtain runoff data from a runoff data file in NetCDF format to implement runoff forecasting.

2. A GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method according to claim 1, characterized in that: The specific process of obtaining the zenith wet delay ZWD and the atmospheric water vapor conversion coefficient Π in step 101 is as follows: Step A, using a computer to solve the GNSS station data using GAMIT / GLOBK software to obtain the zenith tropospheric total delay ZTD; Step B: Using the Saastamoinen model using a computer The zenith dry delay ZHD is obtained; where P represents the surface pressure at the GNSS station location, represents the latitude of the GNSS station, and H represents the geodetic height of the GNSS station; Step C, using a computer to obtain the zenith wet delay ZWD according to the formula ZWD=ZTD-ZHD; Step D: Using a computer based on Get the weighted average temperature T m ; Where I represents the number of atmospheric layers, i is the layer number, i and I are both positive integers, and 1≤i≤I; T i represents the absolute temperature of the top of the ith atmospheric layer, e i represents the water vapor pressure at the top of the i-th atmospheric layer, Δh i represents the thickness of the i-th atmospheric layer; Step E: Use a computer to calculate the The atmospheric water vapor conversion coefficient Π is obtained; where ρ w represents the density of liquid water, R v represents the water vapor ratio gas constant, T m represents the weighted average temperature, k′2 represents the first constant of the atmospheric refractive index, and k3 represents the second constant of the atmospheric refractive index.

3. A GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method according to claim 1, characterized in that: The total atmospheric water vapor TPW of the fth grid point in step 201 is f The specific process of obtaining is as follows: Step 2011: Use a computer to calculate the The total atmospheric water vapor TPW of a single grid point based on the inversion of FY-4A is obtained; Among them, ρ w represents the density of liquid water, g is the acceleration of gravity, q1 is the water vapor mixing ratio at the surface of a single grid point, q2 is the water vapor mixing ratio at 900hPa, q3 is the water vapor mixing ratio at 700hPa, q4 is the water vapor mixing ratio at 300hPa, p1 is the air pressure at the surface of a single grid point, p2 is the air pressure at 900hPa, p3 is the air pressure at 700hPa, and p4 is the air pressure at 300hPa; Step 212: According to the method of step 211, multiple atmospheric water vapor totals of a single grid point within one hour are obtained, and the fth atmospheric water vapor total of the single grid point is recorded as TPW f ; Wherein, f is a positive integer.

4. A GNSS and Fengyun satellite-assisted meteorological and hydrological coupled runoff forecasting method according to claim 1, characterized in that: The specific process of obtaining the first error Δ1(j) and the second error Δ2(e) in step 301 is as follows: The precipitable water obtained by the radiosonde is taken as the true value. The error between the precipitable water inverted by the j-th GNSS station and the true value is obtained by computer and recorded as Δ1(j), and the error between the hourly precipitable water at the e-th grid point and the true value is recorded as Δ2(e).

5. The method for runoff forecasting by coupling GNSS and Fengyun satellite-assisted meteorological and hydrological data according to claim 1, characterized in that: In step 402, the runoff parameters include an infiltration parameter and a control deep drainage coefficient, and the infiltration parameter value range is 0.1 to 3.0, with a step length of 0.5; the control deep drainage coefficient value range is 0.0 to 1.0, with a step length of 0.2; Soil parameters include pore size distribution index and soil saturated hydraulic conductivity. The pore size distribution index ranges from 0.4 to 1.9 with a step length of 0.2; the soil saturated hydraulic conductivity ranges from 0.1 to 2.0 with a step length of 0.

2. The channel parameter is Manning's roughness, which ranges from 0.3 to 2.0 with a step size of 0.5.

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