CN115048354B - Hydrologic model creation and runoff prediction method and device and computer equipment - Google Patents
Hydrologic model creation and runoff prediction method and device and computer equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for creating a hydrologic model and predicting runoff and computer equipment, wherein the method for creating the hydrologic model comprises the following steps: acquiring historical data of runoff detection; obtaining a single vegetation daily leaf area index value based on the historical leaf area index data; inputting the data into a preset initial hydrological model to obtain a simulation result of a grid scale with preset resolution; converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin; and calibrating the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrologic model. According to the embodiment of the invention, the new hydrologic model is constructed based on the processed historical data, so that the hydrologic model capable of more scientifically simulating runoff can be obtained, the simulation precision of the hydrologic model is improved, the hydrologic model is more accurate in describing the hydrologic process, and the knowledge of hydrologic circulation law is promoted.
Description
Technical Field
The invention relates to the technical field of hydrologic information processing, in particular to a hydrologic model creation and runoff prediction method, a hydrologic model creation and runoff prediction device and computer equipment.
Background
The VIC model is a large scale hydrologic model developed by researchers at the university of washington and the university of prinston and is constantly improving and modifying. The VIC model based on the grid is mainly characterized by representing vegetation heterogeneity in the grid, adopting a three-layer soil structure, a variable infiltration capacity curve and a nonlinear base flow. The VIC model calculates each process of the produced stream using the principle of water balance or the principle of energy balance.
The interaction of climate, vegetation and hydrologic processes has become a leading edge of the world-wide field of variation, and the changing environment presents a great challenge for simulating runoff using hydrologic models. Vegetation is an important land surface state variable, and changes in vegetation can have an effect on long-term or seasonal water circulation. There are many parameters related to vegetation in hydrologic models, including roughness length, displacement height, building resistance, minimum air pore conductance, root depth, etc. However, leaf Area Index (LAI) is a vegetation parameter which has the greatest influence on the simulation of a hydrologic model, but only a hydrologic model is constructed by using fixed-period land coverage data and multi-year average month Leaf Area Index data of a certain Area at present, and runoff simulation is carried out. The vegetation data in the future climate change background is represented by using the vegetation data in the historical period, the hydrologic simulation in the future climate change background is carried out, and the runoff amount in the future climate change background cannot be predicted scientifically. In recent years, leaf area index has increased significantly over a large area of the world, and challenges have been presented to accurate simulation of hydrologic models.
Disclosure of Invention
Therefore, the invention aims to solve the technical problem that the runoff quantity in the future climate change background cannot be scientifically simulated in the prior art, so as to provide a method and a device for creating a hydrologic model and predicting runoff quantity and computer equipment.
According to a first aspect, an embodiment of the present invention provides a method for creating a hydrological model, including: acquiring historical data of runoff detection, wherein the historical data comprises historical meteorological data, historical leaf area index data, historical land coverage data and fixed data; obtaining a single vegetation daily leaf area index value based on the historical leaf area index data; inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data into a preset initial hydrological model to obtain a simulation result of grid scale with preset resolution; converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin; and calibrating the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrologic model.
Optionally, the obtaining a single vegetation date leaf area index value based on the historical leaf area index data includes: extracting leaf area index values based on the historical data; and obtaining a single vegetation daily leaf area index value based on the leaf area index value.
Optionally, the obtaining a single vegetation date leaf area index value based on the leaf area index value includes: acquiring vegetation coverage rate in a preset data acquisition range and leaf area index ratio of each vegetation in the preset data acquisition range based on the historical data; and obtaining a single vegetation daily leaf area index value containing each vegetation in the historical data based on the vegetation coverage, the leaf area index ratio and the leaf area index value.
According to a second aspect, an embodiment of the present invention provides a radial flow prediction method based on a hydrological model, including: obtaining prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data; determining a correlation between future weather forecast data and leaf area index forecast data based on a preset weather-leaf area correlation; determining leaf area index prediction data based on the correlation and future weather prediction data; inputting the future meteorological prediction data, the land coverage prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical leaf area index data.
Optionally, the preset hydrologic model is created by using the method for creating a hydrologic model according to the first aspect or any optional embodiment of the first aspect.
Optionally, the preset weather-leaf area correlation is determined by: extracting representative meteorological variable data based on the preset meteorological data; acquiring a leaf area index of a preset vegetation type based on the leaf area index; and calculating the correlation between the representative meteorological variable data and the preset vegetation type leaf area index to obtain the preset meteorological-leaf area correlation of each vegetation.
According to a third aspect, an embodiment of the present invention provides a device for creating a hydrological model, including: the data acquisition module is used for acquiring historical data of runoff detection, wherein the historical data comprise historical meteorological data, historical leaf area index data, historical land coverage data and fixed data; the data processing module is used for obtaining a single vegetation daily leaf area index value based on the historical leaf area index data; the data simulation module is used for inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data historical data into a preset initial hydrological model to obtain a simulation result of a grid scale with preset resolution; the converging acquisition module is used for converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin; and the model creation module is used for calibrating the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrologic model.
According to a fourth aspect, an embodiment of the present invention provides a runoff prediction apparatus based on a hydrological model, including: the future data acquisition module is used for acquiring prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data; the relation determining module is used for determining the relation between the future weather forecast data and the leaf area index forecast data based on a preset weather-leaf area relation; the future data prediction module is used for determining leaf area index prediction data based on the correlation and the future meteorological prediction data; the runoff prediction module is used for inputting the future meteorological prediction data, the land coverage prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical leaf area index data.
According to a fifth aspect, a computer device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of creating a hydrological model as described in the first aspect and any optional embodiment or the method of predicting runoff based on a hydrological model as described in the second aspect and any optional embodiment.
According to a sixth aspect, a computer readable storage medium stores computer instructions for causing the computer to perform the method of creating a hydrological model according to the first aspect and any optional embodiment or the method of predicting runoff based on a hydrological model according to the second aspect and any optional embodiment.
The technical scheme of the invention has the following advantages:
the method for creating the hydrological model provided by the embodiment of the invention comprises the following steps: obtaining historical meteorological data, historical leaf area index data, historical land coverage data and fixed data of runoff detection, obtaining a single vegetation date leaf area index value according to the historical leaf area index data, inputting the historical meteorological data, the single vegetation date leaf area index value, the historical land coverage data and the fixed data into a preset initial hydrological model to obtain a simulation result of grid scale with preset resolution, determining a river basin outlet section according to the lowest point in a river basin, extracting elevation data in the fixed data, determining the water flow direction based on the elevation data, converging the simulation result to the river basin outlet section, and calibrating the initial hydrological model based on the historical actual measurement data and the simulation result of the river basin outlet section to obtain a final hydrological model. According to the embodiment of the invention, the final hydrologic model is obtained by inputting the processed historical data into the preset initial hydrologic model, so that the hydrologic model capable of simulating runoff more accurately can be obtained, the scientificity of hydrologic model simulation is improved, the hydrologic model is more accurate in describing the hydrologic process, and the understanding of hydrologic cycle rules is promoted.
The embodiment of the invention also provides a runoff prediction method based on the hydrological model, which comprises the following steps of: and acquiring future weather forecast data, land coverage forecast data and fixed data for forecasting future runoffs, determining a correlation between the weather data and the leaf area index based on a preset weather-leaf area correlation, determining leaf area index forecast data based on the correlation and the future weather forecast data, and inputting the future weather forecast data, the land coverage forecast data, the fixed data and the leaf area index forecast data into a hydrological model created based on historical weather data, historical land coverage data, fixed data and historical leaf area index data. According to the embodiment of the invention, the future leaf area index data is predicted by establishing the correlation between the future meteorological data and the leaf area index, so that the future leaf area index data can be deduced according to the future climate prediction information, the leaf area index parameter value of the preset hydrologic model is improved, further, the leaf area index parameter data with great influence on the future hydrologic cycle is effectively utilized to carry out accurate prediction, the condition that the leaf area index in the future global large area is obviously increased is effectively utilized, and the scientificity of carrying out runoff prediction based on the hydrologic model is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a specific example of a method of creating a hydrological model according to an embodiment of the present invention;
FIG. 2 is a distribution of land coverage types within each 1/12 DEG x 1/12 DEG grid for each resolution in a method of creating a hydrological model according to an embodiment of the present invention;
FIG. 3 shows the values of the area index of the sun-leaf of three vegetation types, namely, graticule, woodland, grassland, etc., centered at 107.375E, 29.625N, and having a resolution of 0.25X 0.25, between 1982 and 2015 in the examples of the present invention;
FIG. 4 is a graph of average leaf area index for each month for each vegetation type provided by a hydrological model vegetation parameter library employed in the prior art;
FIG. 5 is a flowchart of a specific example of a radial flow prediction method based on a hydrological model according to an embodiment of the present invention;
FIG. 6 is a graph of the daily leaf area index values for three vegetation types, including grid farmland, woodland, grasslands, etc., centered at 107.375E, 29.625N, and having a resolution of 0.25X 0.25, for the period 2106-2115 in an embodiment of the present invention;
FIG. 7 is a connection diagram of a specific example of a device for creating a hydrological model according to an embodiment of the present invention;
FIG. 8 is a connection diagram of a specific example of a hydrological model-based runoff prediction apparatus according to an embodiment of the present invention;
fig. 9 is a specific exemplary configuration diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
According to the embodiment of the invention, a new hydrological model is constructed by inputting historical data into an initial hydrological model, the correlation of the historical meteorological data and the historical leaf area index data is obtained according to the historical data, the correlation between the future meteorological data and the future leaf area index data is obtained based on the correlation mapping of the historical meteorological data and the historical leaf area index data to the future data, and the future runoff simulation is performed based on the correlation between the future meteorological data and the future leaf area index data and the new hydrological model, and the data acquisition in the following embodiments takes the Yangtze river basin as an example.
Fig. 1 shows a flowchart of a method for creating a hydrological model according to an embodiment of the present invention, the method specifically comprising the steps of:
s100: and acquiring historical data of runoff detection, wherein the historical data comprises historical meteorological data, historical leaf area index data, historical land coverage data and fixed data.
Specifically, historical multi-period high space-time resolution data is obtained, wherein the historical multi-period high space-time resolution data comprises historical meteorological data, historical leaf area index data and historical land coverage data, and fixed data of a historical period is obtained, and the fixed data comprises soil data, elevation data, other vegetation parameter data and the like.
For example, the obtained historical meteorological data is a period of one day, the historical leaf area index data is a period of one half month or one 8 days, the historical land coverage data is a period of one year or one few years, the historical meteorological data can be obtained by interpolation of long-time sequence (1982-2015 years) meteorological site data of an actual working area and the periphery, the historical meteorological data comprises daily precipitation (mm), highest temperature (DEG C), lowest temperature (DEG C) and wind speed (m/s), the historical land coverage data can be three-period data based on land utilization/coverage data set (RESDC) provided by resource science and data center of China academy of sciences, and the invention is not limited to the three-period data of 1990, 2000 and 2015 years.
The soil data in the fixed data may be, for example, obtained based on a chinese soil dataset provided by the "large cold and dry district science data center" based on a world soil database (Harmonized World Soil Database, HWSD).
By way of example, elevation data in fixed data may be obtained using 90m resolution STRM DEM (Shuttle Radar Topography Mission Digital Elevation Model) data, for example. Other vegetation parameter data in the fixed data may be obtained from vegetation library files of the hydrological model including, but not limited to, roughness length, displacement height, building resistance, minimum air hole conductance, root depth, and the like.
S200: and obtaining a single vegetation daily leaf area index value based on the historical leaf area index data.
Specifically, calculating according to historical leaf area index data and historical land coverage data, and obtaining a single vegetation daily leaf area index value according to an estimation result. In practical application, average value calculation is performed on the leaf area index data of each vegetation type in the preset acquisition area, and the average value is used for estimating the leaf area index value of each vegetation day with a single preset resolution grid scale, wherein the preset resolution grid scale can be, for example, a grid of 0.25 degrees x 0.25 degrees.
S300: and inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data into a preset initial hydrological model to obtain a simulation result of a grid scale with preset resolution.
Specifically, an initial hydrologic model is preset, the initial hydrologic model is constructed based on historical meteorological data, historical single vegetation daily leaf area index values, historical land coverage data and fixed data, the historical meteorological data, the single vegetation daily leaf area index values, the historical land coverage data and the fixed data are input into the initial hydrologic model, and a simulation result of a grid scale of a preset resolution is obtained, wherein the preset resolution can be 0.25 degree x 0.25 degree grid, for example.
The historical weather data, the land coverage type data, the leaf area index data, the soil data, the DEM elevation data and other vegetation parameters are used for inputting the data into a preset initial hydrologic model, the resolution of the hydrologic model can be, for example, a grid with the simulation resolution of c×c, the simulation resolution can be, for example, 0.25 ° ×0.25° grid, the grid scale division of the hydrologic model can be, for example, setting an origin on the basis of an ArcGIS tool on the acquired integral data image, setting a 'pixel width' and a 'pixel height' on the basis of the integral data image, and simulating on the basis of the resolution to obtain a simulation result.
S400: and converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin.
Specifically, the lowest point position information in the river basin is obtained based on the DEM elevation data, the river basin outlet section is determined based on the lowest point position information in the river basin, the water flow direction of runoffs is determined according to the DEM elevation data, and the simulation result is converged to the river basin outlet section.
S500: and calibrating the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrologic model.
The initial hydrologic model is calibrated according to the historical actual measurement runoff data, the preset initial hydrologic model parameters can be, for example, b=0.3, ds=0.02, dm=8, ws=0.8, d1=0.1, d2=0.5 and d3=1.5, wherein b is a shape parameter of a soil water storage capacity curve, ds is a proportion of a maximum rate of a base stream in nonlinear growth, dm is a maximum rate of the base stream, ws is a proportion of a bottom soil water content of the maximum soil water content in the nonlinear base stream, d1, d2 and d3 are three-layer soil thicknesses respectively, an evaluation index and a threshold value are set, model simulation is then performed by utilizing the preset initial hydrologic model parameters, an evaluation index value is obtained based on the runoff simulation result and the historical actual measurement data, if the evaluation index value does not meet the preset threshold value, and adjusting the preset initial hydrological model parameters, re-using the adjusted parameters for simulation until an evaluation index value calculated based on the simulation result and the historical actual measurement data meets a preset threshold value, determining the parameters as calibration parameters, selecting different periods, performing simulation verification by using the calibration parameters to obtain verification data, obtaining simulated runoff data and historical runoff data based on the verification data, calculating the simulated runoff data and the historical runoff data to obtain the evaluation index value, judging whether the calibration parameters are reasonable or not according to the evaluation index value, adjusting the hydrological model based on the calibration parameters if the calibration parameters are reasonable, and constructing a new hydrological model, otherwise, calibrating the parameters again. In practical applications, the evaluation index may be set as, for example, deviation or nash efficiency coefficient, which is not limited to the present invention.
In the embodiment of the invention, the historical meteorological data, the historical leaf area index data, the historical land coverage data and the fixed data of runoff detection are obtained, a single vegetation date leaf area index value is obtained according to the historical leaf area index data, the historical meteorological data, the single vegetation date leaf area index value, the historical land coverage data and the fixed data are input into a preset initial hydrological model to obtain a simulation result of a grid scale with preset resolution, a river basin outlet section is determined according to the lowest point position in a river basin, elevation data in the fixed data is extracted, the water flow direction is determined based on the elevation data, the simulation result is converged to the river basin outlet section, and the initial hydrological model is calibrated based on the historical actual measurement data and the simulation result of the river basin outlet section to obtain a final hydrological model. According to the embodiment of the invention, the final hydrologic model is obtained by inputting the processed historical data into the preset initial hydrologic model, so that the hydrologic model capable of more scientifically simulating runoff can be obtained, the simulation precision of the hydrologic model is improved, the hydrologic model is more accurate in describing the hydrologic process, and the understanding of hydrologic circulation rules is promoted.
In an alternative embodiment of the present invention, the step S200 obtains a single vegetation daily leaf area index value based on the historical leaf area index data, including the steps of:
(1) Leaf area index values are extracted based on the historical data.
The method comprises the steps of obtaining historical leaf area index data of a preset historical period through satellite remote sensing data, extracting vegetation leaf area index data in a preset data acquisition range based on the historical leaf area index data, and extracting leaf area index data in the preset period according to the vegetation leaf area index data in the preset data acquisition range. The preset period may be, for example, 1982-2015, and the preset data collection range may be, for example, a Yangtze river basin, which is not limited by the present invention.
(2) And obtaining a single vegetation daily leaf area index value based on the leaf area index value.
Exemplary, the simulation estimation is performed according to the land coverage data, the vegetation type in the preset data acquisition range is obtained, and the single vegetation daily leaf area index value is obtained based on the leaf area index value and the vegetation type in the preset data acquisition range. In practical applications, for example, the land coverage data with a spatial resolution of a×a, the leaf area index data with a spatial resolution of b×b, and the grid with a spatial resolution of c×c may be used to perform simulation estimation, and the ArcGIS grid calculation tool is used to calculate a single vegetation leaf area index value of a preset resolution grid scale within a preset data acquisition range, where the preset resolution grid scale may be, for example, 0.25 ° ×0.25 ° grid (spatial resolution of c×c).
By way of example, a hydrographic simulation may be performed using a grid with a resolution of 0.25 ° x 0.25 ° (spatial resolution c x c), and a single vegetation solar leaf area index value estimated using the GIMMS (Global Inventory Modeling and Mapping Studies) LAI3g product from 1982-2015. The spatial resolution of GIMMS LAI3g product was 1/12 ° ×1/12 ° (corresponding to spatial resolution b×b), the temporal resolution was half a month, for 12 months/year×2 period/month×34 year=816 period. When the hydrologic model is adopted for simulation, the daily leaf area index value of cultivated land, forest land and grassland type in each grid with the resolution of 0.25 degrees multiplied by 0.25 degrees (corresponding to the spatial resolution of c multiplied by c) is required to be input, and the daily leaf area index value of each half month of the grid with the resolution of 1/12 degrees multiplied by 1/12 degrees can only be obtained from a GIMMSLAI 3g product.
In the embodiment of the invention, the relation between the historical meteorological data and the historical solar leaf area index is established through estimating the historical data, so that future meteorological data can be obtained through estimating the historical data, and future solar leaf area index values can be obtained based on the relation between the historical meteorological data and the historical solar leaf area index and the future meteorological prediction data, thereby the hydrologic model can comprehensively simulate based on the historical data and the future data, further, the influence of the hydrologic model on hydrologic water resources caused by climate change and vegetation change is enhanced, and the scientificity of the hydrologic model simulation and the rationality of the simulation result are improved.
In an alternative embodiment of the present invention, the process of obtaining the leaf area index value of the single vegetation date based on the leaf area index value mainly includes the following steps:
(1) And acquiring vegetation coverage rate in a preset data acquisition range and leaf area index ratio of each vegetation in the preset data acquisition range based on the historical data.
Specifically, the vegetation type in the preset data acquisition range is obtained, and the vegetation coverage and the area index ratio of each vegetation leaf in the preset data acquisition range are calculated based on the vegetation type and the historical land coverage data, wherein the vegetation coverage can be, for example, the vegetation coverage of each vegetation in the Yangtze river basin or the vegetation coverage of each vegetation in an actual working area taking the Yangtze river basin as a main body. In practical application, all vegetation type data in a preset data acquisition range are acquired, classifying is carried out according to vegetation types, whether the coverage rate of the acquired vegetation types is larger than a set threshold value is judged, if the coverage rate of the acquired vegetation types is larger than the set threshold value, the acquired vegetation types are regarded as one of representative leaf area index values of the area, the ratio among the vegetation types is calculated, for example, one vegetation (cultivated land, woodland and grassland) with the resolution of 1/12 degrees x 1/12 degrees in a certain type of the vegetation (cultivated land, woodland and grassland) in a grid with the coverage area of more than 80 percent can be considered, the leaf area index value of the grid with the resolution of 1/12 degrees x 1/12 degrees is regarded as one of the representative leaf area index values of the vegetation types of the area, and the leaf area index ratio of each vegetation in the area is calculated as LAI 1 ∶LAI 2 ∶LAI 3 ∶…＝m 1 ∶m 2 ∶m 3 …, the grid may be, for example, a leaf area index data grid with a resolution of 1/12×1/12, which is constructed by taking Yangtze river basin as a main body, and the invention is not limited thereto.
For example, a plurality of grids with different resolutions are arranged in the preset data acquisition range, vegetation coverage in the grids with different resolutions in the preset acquisition range is calculated based on the vegetation type and the historical land coverage data, for example, 1/12 degree×1/12 degree grids and 0.25 degree×0.25 degree grids can be arranged, 1/12 degree×1/12 degree grids are used as examples, a plurality of grids with 1/12 degree×1/12 degree are arranged in the preset data acquisition range, a plurality of land coverage type data units with 1km×1km are arranged in the preset data acquisition range, different colors represent different vegetation, and grid calculation is performed on a 1km×1km resolution scale by using an arcgigrid calculation function. In ArcGIS software, each grid with resolution of 1/12 degree x 1/12 degree is assigned a unique number, such as a certain grid number 235607, each land coverage type corresponds to a unique number, for example, a land coverage data set (RESDC) provided by national academy of sciences resource science and data center can be adopted, the numbers of each vegetation type are shown in the following table 1, namely, all of codes 1 are classified as cultivated land, all of codes 2 are classified as woodland, all of codes 3 are classified as grassland, all of codes 4 are classified as water area, all of codes 5 are classified as urban and rural, industrial and mining and residential land, all of codes 6 are classified as unused land, and all of codes 99 (999) are classified as new types of land reclamation.
TABLE 1
By using the ArcGIS grid calculator function and setting the resolution to be consistent with the minimum grid in the formula, for example, the resolution may be set to be consistent with the 1km×1km of land cover type data resolution, and in this embodiment the land cover type is encoded as 2 bits, so the formula is used: grid number×1000+land cover type classification codes, as shown in table 2 below, calculate the value and number of new grids (resolution 1km×1 km).
TABLE 2
The first 6 bits of the new code obtained through calculation represent 1/12 degree x 1/12 degree grid numbers, the last 2 bits represent land cover types, and the percentage of each vegetation type is calculated according to the land cover type codes provided in table 1.
(2) And obtaining a single vegetation daily leaf area index value containing each vegetation in the historical data based on the vegetation coverage, the leaf area index ratio and the leaf area index value.
For example, an actual working area may be set as a grid centered at 107.375 ° E,29.625 ° N and having a resolution of 0.25 ° ×0.25 °, a single vegetation daily leaf area index value in the data collection area may be calculated based on the history data, averaged from 9 1/12 ° ×1/12 ° leaf area index data values in the grid, and a ratio (FRACTION) of each vegetation in the grid Cultivated land 、FRACTION Woodlands 、FRACTION Grassland Statistical analysis is performed by using an ArcGIS grid analysis tool), and the leaf area index values of three vegetation types such as arable land, woodland and grassland in the grid are obtained by separating the ratio of leaf area indexes of vegetation types in the area where the grid belongs, and are calculated by adopting the following formula:
LAI cultivated land ×FRACTION Cultivated land +LAI Woodlands ×FRACTION Woodlands +LAI Grassland ×FRACTION Grassland
＝LAI Grid net (107.375°E，29.625°N)
Illustratively, as shown in Table 3 below, the horizontal row represents the first 19 stages of data collected, the first column represents the average leaf area index data for the collected arable land, the second column represents the average leaf area index data for the collected woodland, and the third column represents the average leaf area index data for the collected grassland, and multi-stage historical leaf area index data is obtained, e.g., if the coverage area of a type of vegetation (arable land, woodland, grassland) within a grid having a resolution of 1/12 DEG x 1/12 DEG is greater than 80%, then the leaf area index value for the grid having a resolution of 1/12 DEG x 1/12 DEG is considered one of the representative leaf area index values for that type of vegetation in that region. In this region, for each period of leaf area index within a preset periodThe data calculate the average leaf area index value of each vegetation type, so as to represent the ratio of various vegetation types in a preset period, for example, the average leaf area index value can be cultivated land: forest land: grassland = x 1 ：x 2 ：x 3 。
TABLE 3 Table 3
| Cultivated land | | Grassland | |
| Stage | |||
| 1 | 0.2804 | 1.1532 | 0.1947 |
| |
0.3209 | 0.9153 | 0.1919 |
| |
0.5449 | 1.2285 | 0.2189 |
| |
0.3052 | 0.8884 | 0.1152 |
| |
1.1404 | 1.8944 | 0.3490 |
| |
0.9966 | 1.5605 | 0.2520 |
| Stage 7 | 1.1703 | 1.9601 | 0.3619 |
| Stage 8 | 1.0951 | 1.8236 | 0.3188 |
| Stage 9 | 0.8242 | 2.0218 | 0.2885 |
| |
0.7267 | 2.3063 | 0.3150 |
| Stage 11 | 0.4306 | 1.2148 | 0.2845 |
| Stage 12 | 1.2725 | 2.4940 | 0.6433 |
| Stage 13 | 1.3574 | 2.4859 | 1.0522 |
| Stage 14 | 1.5669 | 2.6658 | 1.3595 |
| Stage 15 | 1.6008 | 2.2915 | 1.1647 |
| Stage 16 | 1.5875 | 2.4898 | 1.2243 |
| Stage 17 | 0.8976 | 1.4634 | 0.8338 |
| Stage 18 | 1.1281 | 2.5396 | 0.8003 |
| Stage 19 | 0.8600 | 2.4604 | 0.5285 |
Illustratively, in the prior art, global land utilization/coverage data developed by the university of maryland was commonly used to construct hydrologic models, and the data set was made using AVHRR (Advanced Very High Resolution Radiometer) data from 1992-1993. Vegetation coverage is totally divided into 14 types including water bodies (water bodies), evergreen conifer (needleleaf evergreen forest), evergreen broadleaf forest (broadleaf evergreen forest), fallen conifer (needleleaf deciduous forest), fallen broadleaf forest (broadleaf deciduous forest), mixed forest (mixed forest), woodland (woodland), woodland grassland (wooded grassland), closed shrub (closed shrub) bush (open shrub) grassland (grassland), cultivated land (crop land), bare land (bare land), city and building (uban/building-up), but this data has only one period, and is not sufficiently accurate for description of land utilization/coverage types in China, therefore, the present embodiment adopts three-period data of land utilization/data set (vidc) provided by the national academy resource science and data center of china with 1km resolution (grid corresponding to spatial resolution a), which are 1990, 2000, three-period data, and 2015-year data input model of improved data of land utilization/data, respectively. The land utilization/coverage data of 1990 is used to represent land utilization conditions of 1982-1990, the land utilization/coverage data of 2000 is used to represent land utilization conditions of 1991-2000, and the land utilization/coverage data of 2015 is used to represent land utilization conditions of 2001-2015. The land utilization/coverage type data set used in the invention contains 6 primary land utilization/coverage types, namely 3 types of cultivated lands, woodlands, grasslands, water areas, urban and rural areas, industrial and mining areas, residential and unused lands, wherein the vegetation types are cultivated lands, woodlands, grasslands and the like. The land utilization/coverage data developed and developed by the university of maryland (hereinafter abbreviated as UMD data) and the national academy of sciences resource environment science of china were compared with the land utilization/coverage data of year 2000 of china (hereinafter abbreviated as RESDC data) provided by the data center, the comparison data are shown in the following table 4,
TABLE 4 Table 4
Specifically, each vegetation coverage rate of the grid based on a preset resolution is calculated based on the leaf area index value and the ratio, and each vegetation daily leaf area index value is obtained. In the existing application, as shown in fig. 4, the leaf area index of each month of each vegetation type provided by the vegetation parameter library of the hydrological model is generally used to represent the leaf area index of the grid vegetation with the resolution of 0.25 degrees x 0.25 degrees, but the method cannot reflect the annual change condition of leaf area index data, and meanwhile, because the area of a research area is larger, the annual difference of leaf area indexes of the same vegetation type in different areas is also larger, therefore, in the practical application, the embodiment of the invention adopts the 1982-2015 annual Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product to calculate. The spatial resolution of GIMMS LAI3g product was 1/12 ° ×1/12 °, the temporal resolution was half a month, and total 12 months/year×2 period/month×34 year=816 period. When the hydrologic model of the embodiment of the invention is adopted for simulation, the daily leaf area index of cultivated land, woodland and grassland types with the resolution of 0.25 degrees multiplied by 0.25 degrees is required to be input, and only the total daily leaf area index value of 1/12 degrees multiplied by 1/12 degrees of the grid per half month can be obtained from a GIMMS LAI3g product.
In the embodiment of the invention, the leaf area index value is obtained based on the historical leaf area index data, the coverage rate of each vegetation type in the preset resolution grid is obtained based on the historical land coverage data, the leaf area index ratio of each vegetation in the preset data acquisition range is obtained by utilizing the historical leaf area index data and the historical land coverage data, and the single vegetation daily leaf area index value is obtained by calculating an interpolation method based on the coverage rate of each vegetation type, the leaf area index ratio of each vegetation and the leaf area index value in the preset resolution grid, as shown in fig. 3, for example, the interpolation method can be a cubic spline interpolation method. According to the embodiment of the invention, the single vegetation daily degree leaf area index value of the vegetation in the preset resolution grid can be obtained more accurately through the data processing of the historical data, and the invention is not limited to the single vegetation daily degree leaf area index value.
Fig. 5 is a flowchart of a runoff prediction method based on a hydrological model, which is provided in an embodiment of the present invention, and specifically includes the following steps:
s10: and obtaining prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data.
The fixed data includes, by way of example, elevation data, soil data, such as may be obtained from a world soil database (Harmonized World Soil Database, HWSD) based chinese soil dataset provided by the "arid region science big data center", other vegetation parameter data in the fixed data, such as may be obtained from a vegetation library file of the hydrologic model, including but not limited to roughness length, displacement height, building resistance, minimum air hole conductivity, root depth, etc., and future weather forecast data, such as may be obtained from an isiminip 2b project, CMIP6 project simulation, the ISIMIP2b (Inter-Sectoral Impact Model Intercomparison Project 2 b) project is used for analyzing the influence contribution of climate change to a low-emission climate change scene (a scene at 1.5 ℃) and discussing the influence of global change to the surface process and human society, the CMIP6 project is used for world climate research planning, the CMIP6 project is used for answering a new scientific problem faced by the current climate change field, the land coverage prediction data can be, for example, a related land coverage model, such as FLUS (Future Land Use Simulation) model, obtained by directly acquiring the related land coverage model through a related website and simulating according to the related land coverage model, so as to obtain the land coverage prediction data, and interpolating the data to a 0.25 degree x 0.25 degree grid by adopting an interpolation method.
Illustratively, future meteorological data used in this example is an ECHAM6-3-LR dataset provided by HAPPI project, the scenario is selected to be 2.0 ℃ hot relative to the period prior to industrialization, the time is 2106-2115 years, the time resolution is daily, and the spatial resolution is 0.5 DEG x 0.5 deg. Weather prediction data of a grid with a spatial resolution of 0.5 degree x 0.5 degree, which is centered at 107.375 degrees E and 29.625 degrees N and has a resolution of 0.25 degrees x 0.25 degrees, is used to represent weather conditions of the grid in 2106-2115 years, and the present invention is not limited thereto. In practical application, since different initial states are set, the data sets include 20 subsets in total, and the first subset is taken as an example in this example. At least 4 daily variables are needed for the VIC model to run: precipitation, maximum temperature, minimum temperature, wind speed, the data set has been subjected to deviation correction, so the present example does not perform deviation correction when using the data set, and the HAPPI project evaluates the difference of climate influence between 1.5 ℃ and 2 ℃ by using targeted multi-integration simulation, which is not limited by the present invention.
S20: and determining the correlation between future weather forecast data and the leaf area index based on the preset weather-leaf area correlation.
Specifically, based on the correlation between the historical meteorological data and the future meteorological prediction data and the correlation between the historical meteorological data and the leaf area index data, the correlation between the future meteorological prediction data and the future leaf area index is obtained.
S30: leaf area index prediction data is determined based on the correlation and weather prediction data.
Specifically, according to the correlation between the historical meteorological data and the historical single vegetation daily leaf area index value, the correlation between the future meteorological data and the future single vegetation daily leaf area index value is obtained, and the future single vegetation daily leaf area index predicted value is obtained based on the correlation between the future meteorological data and the future single vegetation daily leaf area index value and the future meteorological predicted data, wherein the predicted result is shown in fig. 6.
S40: inputting the future meteorological prediction data, the land coverage prediction data, the fixed data and the single vegetation daily leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical single vegetation daily leaf area index data.
Specifically, the obtained future weather forecast data, land coverage forecast data, fixed data and single vegetation daily leaf area index forecast data are input into the hydrologic model created in the embodiment, and runoff simulation is performed based on the hydrologic model to obtain the future runoff forecast data.
Illustratively, future weather forecast data, future land coverage data, future single vegetation daily leaf area index data, and fixed data including, but not limited to, soil data, elevation data, and other vegetation parameter data are utilized as model input data to drive a hydrologic model to simulate a grid centered at 107.375E, 29.625N with a resolution of 0.25 x 0.25 for 2106-2115 years, and a daily runoff sequence of the grid is calculated to obtain future runoff forecast data.
In the embodiment of the invention, future weather forecast data, land coverage forecast data and fixed data for forecasting future runoffs are obtained, a correlation between the future weather forecast data and a future single vegetation leaf area index value is determined based on a preset weather-leaf area correlation, a single vegetation daily leaf area index forecast value is determined based on the correlation and the future weather forecast data, the land coverage forecast data, the fixed data and the single vegetation daily leaf area index forecast value are input into a hydrological model created based on historical weather data, historical land coverage data, fixed data and the historical single vegetation daily leaf area index value.
According to the embodiment of the invention, the correlation between the meteorological data and the single vegetation daily leaf area index value is established, and the future single vegetation daily leaf area index value is predicted, so that the daily leaf area index value of each vegetation type in the future can be deduced according to the future meteorological prediction information, the leaf area index parameter value of a preset hydrological model is improved, and the scientificity of runoff prediction based on the hydrological model is improved.
In an alternative embodiment of the invention, the predetermined weather-blade area correlation is determined by:
(1) Extracting representative meteorological variable data based on the preset historical meteorological data;
(2) Acquiring a preset vegetation type leaf area index based on the historical leaf area index;
(3) And calculating the correlation between the representative meteorological variable data and the preset vegetation type leaf area index to obtain the preset meteorological-leaf area correlation of each vegetation.
Specifically, the preset meteorological data can be, for example, the historical daily precipitation (unit: mm), the highest temperature (unit: DEG C), the lowest temperature (unit: DEG C) and the wind speed (unit: m/s), the preset vegetation types can be, for example, three vegetation types of cultivated land, woodland and grassland, and the correlation between the preset meteorological data and the single vegetation daily leaf area index of the preset vegetation type can be calculated by adopting a multiple regression method, and the correlation between the single vegetation daily leaf area index of the preset vegetation type and the preset meteorological variable can be respectively obtained. In practical application, multiple regression method is adopted to analyze single vegetation daily Leaf Area Index (LAI) of three vegetation types of cultivated land, woodland and grassland Cultivated land 、LAI Woodlands 、LAI Grassland ) Relation with Precipitation (PREC), maximum Temperature (TMAX), minimum Temperature (TMIN), WIND speed (WIND).
In the embodiment of the invention, the correlation between the historical meteorological data and the historical single vegetation daily leaf area index value is obtained by utilizing multiple regression calculation, so that the correlation between the future meteorological data and the future single vegetation daily leaf area index value can be scientifically obtained based on the correlation between the historical meteorological data and the historical single vegetation daily leaf area index value, further, the leaf area index parameter data with great influence on the future water circulation is effectively utilized for carrying out accurate prediction, the condition that the leaf area index in the future global large area range is obviously increased is effectively utilized, and the scientificity of carrying out runoff prediction based on a hydrological model is improved.
As shown in fig. 7, an embodiment of the present invention further provides a device for creating a hydrological model, including: a data acquisition module 1, a data processing module 2, a data simulation module 3, a confluence acquisition module 4 and a model creation module 5, wherein,
the data acquisition module 1 is configured to acquire historical data of runoff detection, where the historical data includes historical meteorological data, historical leaf area index data, historical land coverage data, and fixed data, and details can be found in the related description of step S100 of any method embodiment;
The data processing module 2 is configured to obtain a single vegetation date leaf area index value based on the historical leaf area index data, and details can be seen from the related description of step S200 of any of the above method embodiments;
the data simulation module 3 is configured to input the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data historical data into a preset initial hydrological model to obtain a simulation result of a grid scale with a preset resolution, and details can be seen from the related description of step S300 of any method embodiment;
the converging obtaining module 4 is configured to converge the simulation result to a drainage basin outlet section, where the drainage basin outlet section is a lowest point in a drainage basin, and details of the converging obtaining module can be found in the related description of step S400 of any method embodiment;
the model creation module 5 is configured to rate the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section, so as to obtain a final hydrologic model, and details can be seen from the related description of step S500 of any method embodiment.
In the embodiment of the invention, the historical meteorological data, the historical leaf area index data, the historical land coverage data and the fixed data of runoff detection are obtained, a single vegetation date leaf area index value is obtained according to the historical leaf area index data, the historical meteorological data, the single vegetation date leaf area index value, the historical land coverage data and the fixed data are input into a preset initial hydrological model to obtain a simulation result of a grid scale with preset resolution, a river basin outlet section is determined according to the lowest point position in a river basin, elevation data in the fixed data is extracted, the water flow direction is determined based on the elevation data, the simulation result is converged to the river basin outlet section, and the initial hydrological model is calibrated based on the historical actual measurement data and the simulation result of the river basin outlet section to obtain a final hydrological model. According to the embodiment of the invention, the final hydrologic model is obtained by inputting the processed historical data into the preset initial hydrologic model, so that the hydrologic model capable of simulating runoff more accurately can be obtained, the scientificity of hydrologic model simulation is improved, the hydrologic model is more accurate in describing the hydrologic process, and the understanding of hydrologic cycle rules is promoted.
As shown in fig. 8, an embodiment of the present invention further provides a runoff prediction apparatus based on a hydrological model, including: a future data acquisition module 10, a relationship determination module 20, a future data prediction module 30, a runoff prediction module 40, wherein,
a future data obtaining module 10, configured to obtain prediction data of runoff detection, where the prediction data includes future weather prediction data, land coverage prediction data, and fixed data, and details of the description of step S10 of any of the foregoing method embodiments may be referred to;
the relationship determining module 20 is configured to determine a relationship between the meteorological data and the leaf area index based on a preset meteorological-leaf area relationship, and details can be found in the related description of step S20 of any of the above method embodiments;
the future data prediction module 30 is configured to determine future single vegetation daily leaf area index prediction data based on the correlation and the future weather prediction data, and details can be found in the description related to step S30 of any of the above method embodiments;
the runoff prediction module 40 is configured to input the future weather prediction data, the land coverage prediction data, the fixed data, and the single vegetation daily leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical single vegetation daily leaf area index data, and details can be found in the related description of step S40 of any of the above method embodiments.
In the embodiment of the invention, future weather forecast data, land coverage forecast data and fixed data for forecasting future runoffs are obtained, the correlation between the weather data and the leaf area index is determined based on a preset weather-leaf area correlation, future single vegetation daily leaf area index forecast data is determined based on the correlation and the future weather forecast data, the land coverage forecast data, the fixed data and the single vegetation daily leaf area index forecast data are input into a hydrological model created based on historical weather data, historical land coverage data, historical fixed data and historical single vegetation daily leaf area index data.
According to the embodiment of the invention, the correlation between the historical period meteorological data and the historical single vegetation daily leaf area index is established, so that the future single vegetation daily leaf area index value is predicted, the future single vegetation daily leaf area index sequence can be deduced according to the future climate prediction information, the leaf area index parameter value of a preset hydrological model is improved, further, the leaf area index parameter data with great influence on future water circulation is effectively utilized to accurately predict, the condition that the leaf area index is obviously increased in a large global area in the future is effectively utilized, and the accuracy of radial flow prediction based on the hydrological model is improved.
For specific limitations and advantageous effects of the creation device of the hydrologic model and the radial flow prediction device based on the hydrologic model, reference may be made to the above limitations of the creation method of the hydrologic model and the radial flow prediction method based on the hydrologic model, and the description thereof will not be repeated here. The above-described means for creating a hydrological model and the respective modules of the radial flow prediction device based on the hydrological model may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or independent of a processor in the electronic device, or may be stored in software in a memory in the electronic device, so that the processor may call and execute operations corresponding to the above modules.
An embodiment of the present invention further provides a computer device, as shown in fig. 9, where fig. 9 is a schematic structural diagram of a computer device provided in an alternative embodiment of the present invention, and the computer device may include at least one processor 41, at least one communication interface 42, at least one communication bus 43, and at least one memory 44, where the communication interface 42 may include a Display screen (Display), a Keyboard (Keyboard), and the optional communication interface 42 may further include a standard wired interface and a wireless interface. The memory 44 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 44 may alternatively be at least one memory device located remotely from the aforementioned processor 41. Wherein the processor 41 may be the apparatus described in connection with fig. 7, 8, the application program is stored in the memory 44, and the processor 41 invokes the program code stored in the memory 44 for performing the steps of the method of creating a hydrological model and the radial flow prediction method based on a hydrological model of any of the method embodiments described above.
The communication bus 43 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The communication bus 43 may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 9, but not only one bus or one type of bus.
Wherein the memory 44 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid state disk (english: solid-state drive, abbreviated as SSD); memory 44 may also include a combination of the types of memory described above.
The processor 41 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
The processor 41 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviated: FPGA), a general-purpose array logic (English: generic array logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 44 is also used for storing program instructions. The processor 41 may invoke program instructions to implement the method of creating a hydrologic model as shown in the fig. 1 embodiment of the invention and the method of radial flow prediction based on a hydrologic model as shown in the fig. 5 embodiment of the invention.
Embodiments of the present invention also provide a non-transitory computer storage medium storing computer-executable instructions that are operable to perform the method of any of the method embodiments described above. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (6)
1. The method for creating the hydrological model is characterized by comprising the following steps of:
acquiring historical data of runoff detection, wherein the historical data comprises historical meteorological data, historical leaf area index data, historical land coverage data and fixed data, and the fixed data comprises soil data, elevation data and other vegetation parameter data;
obtaining a single vegetation daily leaf area index value based on the historical data;
inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data into a preset initial hydrological model to obtain a simulation result of grid scale with preset resolution;
converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin;
calibrating the initial hydrological model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrological model;
the obtaining a single vegetation date leaf area index value based on the historical data comprises:
extracting leaf area index values based on the historical leaf area index data in the historical data;
acquiring coverage rate of each vegetation type in a grid with preset resolution based on the historical land coverage data in the historical data;
Acquiring the ratio of each vegetation leaf area index in a preset data acquisition range by utilizing the historical land coverage data and the historical leaf area index data;
and calculating by an interpolation method based on the coverage rate of each vegetation type in the preset resolution grid, the leaf area index ratio of each vegetation in the preset data acquisition range and the leaf area index value to obtain the single vegetation daily leaf area index value.
2. A method of radial flow prediction based on a hydrological model, the method comprising:
obtaining prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data;
determining a correlation between future weather forecast data and a leaf area index based on a preset weather-leaf area correlation;
determining leaf area index prediction data based on the correlation of the future weather prediction data and the leaf area index and the future weather prediction data;
inputting the future meteorological prediction data, the land coverage prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical leaf area index data, and is created by using the method for creating the hydrologic model according to claim 1;
The preset weather-leaf area correlation is determined by the following steps:
extracting representative meteorological variable data based on the preset meteorological data;
acquiring a leaf area index of a preset vegetation type based on the leaf area index;
and calculating the correlation between the representative meteorological variable data and the preset vegetation type leaf area index to obtain the preset meteorological-leaf area correlation of each vegetation.
3. A hydrological model creation apparatus, characterized by comprising:
the data acquisition module is used for acquiring historical data of runoff detection, wherein the historical data comprise historical meteorological data, historical leaf area index data, historical land coverage data and fixed data, and the fixed data comprise soil data, elevation data and other vegetation parameter data;
the data processing module is used for obtaining a single vegetation daily leaf area index value based on the historical data, and comprises the following components: extracting leaf area index values based on the historical leaf area index data in the historical data; acquiring coverage rate of each vegetation type in a grid with preset resolution based on the historical land coverage data in the historical data; acquiring the ratio of each vegetation leaf area index in a preset data acquisition range by utilizing the historical land coverage data and the historical leaf area index data; calculating by an interpolation method based on each vegetation type coverage rate in the preset resolution grid, each vegetation leaf area index ratio in the preset data acquisition range and the leaf area index value to obtain a single vegetation daily leaf area index value;
The data simulation module is used for inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land coverage data and the fixed data historical data into a preset initial hydrological model to obtain a simulation result of grid scale with preset resolution;
the converging acquisition module is used for converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in the drainage basin;
and the model creation module is used for calibrating the initial hydrologic model based on the historical actual measurement data and the simulation result of the drainage basin outlet section to obtain a final hydrologic model.
4. A hydrologic model-based runoff prediction device, comprising:
the future data acquisition module is used for acquiring prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data;
the relation determining module is used for determining the relation between the future weather forecast data and the leaf area index forecast data based on a preset weather-leaf area relation, and the preset weather-leaf area relation is determined through the following steps:
extracting representative meteorological variable data based on the preset meteorological data;
Acquiring a leaf area index of a preset vegetation type based on the leaf area index;
calculating the correlation of the representative meteorological variable data and the preset vegetation type leaf area index to obtain a preset meteorological-leaf area correlation of each vegetation;
the future data prediction module is used for determining leaf area index prediction data based on the correlation between the future weather prediction data and the leaf area index and the future weather prediction data;
the runoff prediction module is used for inputting the future meteorological prediction data, the land coverage prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrologic model is created based on historical meteorological data, historical land coverage data, fixed data and historical leaf area index data, and is created by using the method for creating the hydrologic model according to claim 1.
5. A computer device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to perform the method of creating a hydrological model as claimed in claim 1 or the method of radial flow prediction based on a hydrological model as claimed in claim 2.
6. A computer-readable storage medium storing computer instructions for causing the computer to perform the method of creating a hydrological model as claimed in claim 1 or the method of predicting runoff based on a hydrological model as claimed in claim 2.
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