CN115048354A - Hydrological model establishing and runoff predicting method, device and computer equipment - Google Patents
Hydrological model establishing and runoff predicting method, device and computer equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device and computer equipment for establishing a hydrological model and predicting runoff, wherein the method for establishing the hydrological 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 the grid scale with a preset resolution; converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in a drainage basin; and calibrating the initial hydrological model based on the historical measured data and the simulation result of the outlet section of the basin to obtain a final hydrological model. According to the embodiment of the invention, a new hydrological model is constructed based on the processed historical data, so that a hydrological model capable of simulating runoff more scientifically can be obtained, the hydrological model simulation precision is improved, the hydrological model can be used for describing a hydrological process more accurately, and the knowledge of hydrological circulation rules is promoted.
Description
Technical Field
The invention relates to the technical field of hydrological information processing, in particular to a hydrological model creating method, a hydrological model creating device, a runoff predicting method, a hydrological model creating device and computer equipment.
Background
The VIC model is a large-scale hydrological model, is developed by researchers at Washington university and Princeton university, and is continuously perfected and improved. The grid-based VIC model is mainly characterized by representing the heterogeneity of vegetation 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 production flow by using a water balance principle or an energy balance principle.
The interaction of climate, vegetation and hydrological processes has become a leading-edge and hot-spot problem in the global change field, and the changing environment brings great challenges for simulating runoff by using a hydrological model. Vegetation is an important terrestrial state variable, and changes in vegetation can have an effect on long-term or seasonal water circulation. There are many parameters in the hydrological model that are related to vegetation, including roughness length, displacement height, building resistance, minimum porosity conductance, root depth, etc. However, the Leaf Area Index (LAI) is a vegetation parameter which has the greatest influence on the hydrological model simulation, but at present, only fixed first-stage land coverage data and perennial average monthly Leaf Area Index data in a certain Area are adopted to construct the hydrological model and develop runoff simulation. Vegetation data in a historical period are used for representing vegetation data under a future climate change background, hydrological simulation under the future climate change background is carried out, and runoff under the future climate change background cannot be predicted scientifically. In recent years, the leaf area index has increased significantly over a wide area of the world, and accurate simulation of the hydrological model has presented challenges.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the defect that the runoff under the future climate change background cannot be scientifically simulated in the prior art, so that a method, a device and computer equipment for creating a hydrological model and predicting runoff are provided.
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 a grid scale with a preset resolution; converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in a drainage basin; and calibrating the initial hydrological model based on the historical actual measurement data and the simulation result of the watershed outlet section to obtain a final hydrological model.
Optionally, said deriving a single vegetation daily leaf area index value based on said historical leaf area index data comprises: extracting a leaf area index value based on the historical data; obtaining a single vegetation daily leaf area index value based on the leaf area index value.
Optionally, said obtaining a single vegetation daily leaf area index value based on said leaf area index value comprises: obtaining vegetation coverage in a preset data acquisition range and leaf area index ratios of all vegetation in the preset data acquisition range based on the historical data; and obtaining a single vegetation daily leaf area index value of each vegetation contained in the historical data based on the vegetation coverage rate, the leaf area index ratio and the leaf area index value.
According to a second aspect, an embodiment of the present invention provides a runoff prediction method based on a hydrological model, including: acquiring prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data; determining the correlation between future meteorological prediction data and leaf area index prediction data based on a preset meteorological-leaf area correlation; determining leaf area index prediction data based on the correlation and future meteorological prediction data; inputting the future meteorological prediction data, the land cover prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrological model is established based on historical meteorological data, historical land cover data, fixed data and historical leaf area index data.
Optionally, the preset hydrological model is created by using the method for creating a hydrological model according to the first aspect or any optional embodiment of the first aspect.
Optionally, the preset meteorological-leaf area correlation relationship is determined by: extracting representative meteorological variable data based on the preset meteorological data; acquiring a preset vegetation type leaf area index based on the leaf area index; and calculating the correlation between the representative meteorological variable data and a preset vegetation type leaf area index to obtain a preset meteorological-leaf area correlation relation of each vegetation.
According to a third aspect, an embodiment of the present invention provides an apparatus for creating a hydrological model, including: the data acquisition module is used for 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; 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 a preset resolution; the confluence acquisition module is used for converging the simulation result to a drainage basin outlet section, and the drainage basin outlet section is the lowest point in the drainage basin; and the model establishing module is used for calibrating the initial hydrological model based on the historical measured data and the simulation result of the watershed outlet section to obtain a final hydrological model.
According to a fourth aspect, an embodiment of the present invention provides a runoff predicting apparatus based on a hydrological model, including: the system comprises a future data acquisition module, a data analysis module and a data analysis module, wherein the future data acquisition module is used for acquiring prediction data of runoff detection, and the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data; the relation determining module is used for determining the correlation between the future meteorological prediction data and the leaf area index prediction data based on a preset meteorological-leaf area correlation; the future data prediction module is used for determining the prediction data of the leaf area index 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 cover prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrological model is created based on historical meteorological data, historical land cover 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 according to the first aspect and any of the optional embodiments or the method of predicting runoff based on a hydrological model according to the second aspect and any of the optional embodiments.
According to a sixth aspect, a computer-readable storage medium stores computer instructions for causing a computer to perform the method for creating a hydrological model according to the first aspect and any of the alternative embodiments or the method for predicting runoff based on a hydrological model according to the second aspect and any of the alternative embodiments.
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 cover data and fixed data of runoff detection, obtaining a single vegetation daily leaf area index value according to the historical leaf area index data, inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land cover data and the fixed data into a preset initial hydrological model, obtaining a simulation result of a grid scale with preset resolution, determining a watershed outlet section according to the lowest point position in a watershed, extracting elevation data in the fixed data, determining the flow direction of water flow based on the elevation data, converging the simulation result to the watershed outlet section, and carrying out actual measurement and calibration on the initial hydrological model based on the historical data and the simulation result of the watershed outlet section to obtain a final hydrological model. In the embodiment of the invention, the final hydrological model is obtained by inputting the processed historical data into the preset initial hydrological model, so that the hydrological model capable of simulating the runoff more accurately can be obtained, the scientificity of hydrological model simulation is improved, the hydrological model can describe the hydrological process more accurately, and the knowledge of hydrological circulation rules is promoted.
The embodiment of the invention also provides a runoff prediction method based on the hydrological model, which comprises the following steps: the method comprises the steps of obtaining future meteorological prediction data, land cover prediction data and fixed data for predicting future runoff, determining the correlation between the meteorological data and a leaf area index based on a preset meteorological-leaf area correlation, determining leaf area index prediction data based on the correlation and the future meteorological prediction data, and inputting the future meteorological prediction data, the land cover prediction data, the fixed data and the leaf area index prediction data into a hydrological model created based on historical meteorological data, historical land cover data, the fixed data and the 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 calculated according to the future climate prediction information, the leaf area index parameter value of the preset hydrological model is improved, further, the leaf area index parameter data which has large influence on future water circulation is effectively utilized to carry out accurate prediction, the condition that the leaf area index is remarkably increased in a future global large-area range is effectively responded, and the scientificity of runoff prediction based on the hydrological model is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart illustrating a specific example of a method for creating a hydrological model according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the distribution of land cover types within a grid having a resolution of 1/12 ° x 1/12 ° for each of the methods for creating a hydrological model according to embodiments of the present invention;
FIG. 3 is a graph showing the daily leaf area index values of three vegetation types, such as cultivated land, woodland and grassland, of a grid, with a resolution of 0.25 DEG x 0.25 DEG, centered at 107.375 DEG E and 29.625 DEG N, in 1982-2015 in the example of the present invention;
FIG. 4 is a mean value of leaf area indices 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 illustrating an example embodiment of a method for hydrologic model based runoff prediction;
FIG. 6 is a graph showing the leaf-by-leaf area index values of three vegetation types, such as grid cultivated land, forest land and grassland, with the center at 107.375E and 29.625N and the resolution at 0.25 multiplied by 0.25 during 2106 and 2115 years in the future in accordance with 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 runoff predicting device based on a hydrological model according to an embodiment of the present invention;
fig. 9 is a diagram showing a specific example of a computer device according to the embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention is to input historical data into an initial hydrological model, construct a new hydrological model, obtain the correlation between historical meteorological data and historical leaf area index data according to the historical data, map the correlation between the historical meteorological data and the historical leaf area index data to future data to obtain the correlation between the future meteorological data and the future leaf area index data, and perform future runoff simulation based on the correlation between the future meteorological data and the future leaf area index data and the new hydrological model.
Fig. 1 shows a flowchart of a method for creating a hydrological model according to an embodiment of the present invention, which specifically includes the following steps:
s100: acquiring historical data of runoff detection, wherein the historical data comprises historical meteorological data, historical leaf area index data, historical land cover data and fixed data.
Specifically, historical multi-period high-space-time resolution data including historical meteorological data, historical leaf area index data and historical land cover data are obtained, and fixed data including soil data, elevation data, other vegetation parameter data and the like in historical periods are obtained.
Illustratively, the acquired historical meteorological data is one period per day, the historical leaf area index data is one period per half month or 8 days, and the historical land cover data is one period per year or every few years, the historical meteorological data can be obtained by interpolation of long-time sequence (1982) and 2015 years) meteorological site data of an actual working area and the periphery, the historical meteorological data comprises daily precipitation (mm), maximum temperature (DEG C), minimum temperature (DEG C) and wind speed (m/s), the historical land cover data can be three-period data based on a land utilization/cover data set (DC) provided by a resource environmental science and data center of Chinese academy of sciences, wherein the three-period data are 1990, 2000 and 2015 year data respectively, and the invention is not limited by the data.
Exemplarily, the Soil data in the fixed data may be obtained based on a chinese Soil data set of a World Soil Database (HWSD) provided by "han-upland area science big data center".
Illustratively, Elevation data in the fixed data may be obtained, for example, using 90m resolution STRM DEM (short radius Topographic Session Digital Elevation Model) data. Other vegetation parameter data in the fixed data may be obtained from a vegetation library file of the hydrological model, including but not limited to roughness length, displacement height, building resistance, minimum stomatal 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, calculation is carried out according to historical leaf area index data and historical land coverage data, and a single vegetation daily leaf area index value is obtained according to an estimation result. In practical application, the leaf area index data of each vegetation type in a preset acquisition area is subjected to mean calculation, and the single vegetation daily leaf area index value of a preset resolution grid scale is estimated through the mean, wherein the preset resolution grid scale can be 0.25 degrees multiplied by 0.25 degrees of a grid, for example.
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 the grid scale with preset resolution.
Specifically, an initial hydrological model is preset, the initial hydrological model is constructed based on historical meteorological data, historical single vegetation daily leaf area index values, historical land cover data and fixed data, the historical meteorological data, the historical single vegetation daily leaf area index values, the historical land cover data and the fixed data are input into the initial hydrological model, and a simulation result of a grid scale with a preset resolution is obtained, wherein the preset resolution can be 0.25 degrees multiplied by 0.25 degrees of a grid for example.
Illustratively, historical period meteorological data, land cover type data, leaf area index data, soil data, DEM elevation data and other vegetation parameters are used, the data are input into a preset initial hydrological model, the resolution of the hydrological model can be, for example, a grid with a simulation resolution of c × c, the simulation resolution can be, for example, a grid with a 0.25 ° × 0.25 ° resolution, the grid scaling of the hydrological model can be, for example, based on an ArcGIS tool, setting an origin point on an acquired overall data image, setting a pixel width and a pixel height on the overall data image based on the origin point, and performing simulation based on the resolution to obtain a simulation result, which is not limited by the invention.
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, minimum point position information in the drainage basin is obtained based on the DEM elevation data, a drainage basin outlet section is determined based on the minimum point position information in the drainage basin, the water flow direction of the drainage basin is determined according to the DEM elevation data, and the simulation result is converged to the drainage basin outlet section.
S500: and calibrating the initial hydrological model based on the historical actual measurement data and the simulation result of the watershed outlet section to obtain a final hydrological model.
Illustratively, the initial hydrological model is calibrated according to historical measured runoff data, and preset initial hydrological model parameters may be, for example, b is 0.3, Ds is 0.02, Dm is 8, Ws is 0.8, d1 is 0.1, d2 is 0.5, and d3 is 1.5, where b is a shape parameter of a soil water storage capacity curve, Ds is a ratio of a rate of a base flow when the base flow is nonlinearly increased to a maximum rate thereof, Dm is a maximum rate of the base flow, Ws is a ratio of a bottom layer soil water content to a maximum soil water content when the nonlinear base flow occurs, d1, d2, and d3 are three-layer soil thicknesses respectively, an evaluation index and a threshold are set, model simulation is performed by using the preset initial hydrological model parameters, an index value evaluation is obtained by calculating based on the runoff simulation result and the historical measured data, and if the evaluation index value does not satisfy the preset threshold, the initial hydrological model is adjusted, and carrying out simulation by using the adjusted parameters again 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 rating parameters, then selecting different periods, carrying out simulation verification by using the rating parameters to obtain verification data, obtaining simulation runoff data and historical runoff data based on the verification data, calculating by using the simulation runoff data and the historical runoff data to obtain the evaluation index value, judging whether the rating parameters are reasonable or not according to the evaluation index value, if the rating parameters are reasonable, adjusting the hydrological model based on the rating parameters to construct a new hydrological model, and if not, carrying out rating on the parameters again. In practical applications, the evaluation index may be set to, for example, a deviation or a nash efficiency coefficient, and the present invention is not limited thereto.
In the embodiment of the invention, by acquiring historical meteorological data, historical leaf area index data, historical land cover data and fixed data of runoff detection, obtaining a single vegetation daily leaf area index value according to the historical leaf area index data, inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land cover data and the fixed data into a preset initial hydrological model to obtain a simulation result of a grid scale with a preset resolution, determining a watershed outlet section according to the lowest point position in the watershed, and extracting elevation data in the fixed data, determining the flow direction of water flow based on the elevation data, converging the simulation result to a drainage basin outlet section, and calibrating the initial hydrological model based on the historical measured data and the simulation result of the drainage basin outlet section to obtain a final hydrological model. In the embodiment of the invention, the final hydrological model is obtained by inputting the processed historical data into the preset initial hydrological model, so that the hydrological model capable of simulating runoff more scientifically can be obtained, the simulation precision of the hydrological model is improved, the hydrological model can be used for describing a hydrological process more accurately, and the knowledge of hydrological cycle rules is promoted.
In an optional embodiment of the present invention, in the step S200, obtaining a single vegetation daily leaf area index value based on the historical leaf area index data includes the following steps:
(1) and extracting a leaf area index value based on the historical data.
Illustratively, historical leaf area index data of a preset historical period are obtained through satellite remote sensing data, vegetation leaf area index data in a preset data acquisition range are extracted based on the historical leaf area index data, and leaf area index data in the preset period are extracted 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, the Yangtze river basin, which is not limited by the invention.
(2) Obtaining a single vegetation daily leaf area index value based on the leaf area index value.
Illustratively, simulation estimation is carried out according to the land cover data, a vegetation type in a preset data acquisition range is obtained, and a 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 cover data with spatial resolution of a × a, the leaf area index data with spatial resolution of b × b, and the grid with spatial resolution of c × c may be used to perform simulation estimation, and an ArcGIS grid calculation tool is used to calculate a single vegetation leaf area index value with 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 is c × c).
Illustratively, for example, a grid with a resolution of 0.25 ° × 0.25 ° (spatial resolution c × c) may be used for hydrologic simulation, and the single vegetation daily leaf area index value is estimated using the 1982-2015 inter-year GIMMS (Global investment Modeling and Mapping students) LAI3g product. The GIMMS LAI3g product has a spatial resolution of 1/12 ° × 1/12 ° (corresponding to a spatial resolution of b × b), and a temporal resolution of half a month, for a total of 12 months/year × 2/month × 34 years, 816. When a hydrological model is adopted for simulation, the day-by-day leaf area index values of the types of cultivated land, forest land and grassland in each grid with the resolution of 0.25 degrees multiplied by 0.25 degrees (the corresponding spatial resolution is c multiplied by c) need to be input, while the leaf area index value of 1/12 degrees multiplied by 1/12 degrees in each half month of the grid can only be obtained from the GIMMS LAI3g product, the invention adopts the following method to process the total leaf area index value of 1/12 degrees multiplied by 1/12 degrees in each half month of the grid, so as to obtain the day-by-day leaf area index of the types of vegetation such as cultivated land, forest land and grassland in the 0.25 degrees multiplied by 0.25 degrees of the grid.
In the embodiment of the invention, the relation between the historical meteorological data and the historical daily-degree leaf area index is established by estimating the historical data, so that future meteorological data can be obtained by estimating the historical data, and the future daily-degree leaf area index value is obtained based on the relation between the historical meteorological data and the historical daily-degree leaf area index and the future meteorological prediction data, so that the hydrological model can be comprehensively simulated based on the historical data and the future data, further, the influence of the hydrological model on hydrological water resources caused by climate change and vegetation change is enhanced, and the scientificity of the hydrological model simulation and the rationality of a simulation result are improved.
In an optional embodiment of the present invention, the process of obtaining a single vegetation daily leaf area index value based on the leaf area index value mainly includes the following steps:
(1) and acquiring the vegetation coverage rate in a preset data acquisition range and the 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 acquired, and the vegetation coverage and the ratio of the area index 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 the actual working area with the Yangtze river basin as the main body. In practical application, all vegetation type data in a preset data acquisition range are acquired, classification is performed according to the vegetation types, whether the coverage rate of the acquired vegetation types is greater than a set threshold value or not is judged, if the coverage rate of the acquired vegetation types is greater than the set threshold value, the collected vegetation types are regarded as one of the representative leaf area index values of the region, the ratio between the vegetation is calculated, for example, the leaf area index value of a 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 region when the coverage area of a certain type of vegetation (cultivated land, woodland and grassland) in the grid with the resolution of 1/12 degrees x 1/12 degrees is greater than 80 percent, and the leaf area index ratio of the vegetation types in the region is calculated as LAI 1 ∶LAI 2 ∶LAI 3 ∶…＝m 1 ∶m 2 ∶m 3 …, the grid can be, for example, a 1/12 ° x 1/12 ° leaf area index data grid constructed with the Yangtze river basin as the main body, and the invention is not limited thereto.
Illustratively, several grids with different resolutions are arranged in a preset data acquisition range, and based on the vegetation types and the historical land cover data, the vegetation coverage rate in the grids with different resolutions in the preset acquisition range is calculated, for example, 1/12 ° x 1/12 ° grids, 0.25 ° x 0.25 ° grids, 1/12 ° x 1/12 ° grids are taken as an example, as shown in fig. 2, several grids with a resolution of 1/12 ° x 1/12 ° are arranged in the preset data acquisition range, a plurality of land cover type data units with a resolution of 1 × km 1km are arranged inside the preset data acquisition range, different colors represent different vegetation, and the grid calculation is performed at a resolution scale of 1km × 1km by using the ArcGIS grid calculation function. In the ArcGIS software, each grid with a resolution of 1/12 ° × 1/12 ° is assigned a unique number, for example, a grid with a number of 235607 and each type of land cover corresponds to a unique number, for example, a land cover data set (RESDC) provided by resource, environmental, scientific and data center of the chinese academy of sciences may be used, and the numbers of the types of vegetation are shown in table 1 below, i.e., 1 is coded as land, 2 is coded as forest land, 3 is coded as grass land, 4 is coded as water, 5 is coded as urban and rural, industrial and mining, residential land, 6 is coded as unused land, and 99(999) is coded as a new type generated by sea reclamation.
TABLE 1
Using the ArcGIS grid calculator function and setting the resolution to stay the same as the smallest grid in the formula, for example, the resolution may be set to a grid that stays the same as the land cover type data resolution of 1km × 1km, which is encoded in 2 bits in this embodiment, so the formula is used: the grid number × 1000+ land cover type classification code, as shown in table 2 below, was calculated to obtain the value and number of new grids (resolution 1km × 1 km).
TABLE 2
The first 6 digits of the new code obtained by calculation represent 1/12 degrees multiplied by 1/12 degrees grid numbers, the second 2 digits represent land cover types, and the percentage of each vegetation type is calculated according to the land cover type codes provided in the table 1.
(2) And obtaining a single vegetation daily leaf area index value of each vegetation in the historical data based on the vegetation coverage rate, the leaf area index ratio and the leaf area index value.
Illustratively, the actual working area may be set to 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 within the data collection area may be calculated based on the historical data, averaged from the 9 1/12 ° × 1/12 ° leaf area index data values within the grid, and calculated as a percentage of each plant within the grid (FRACTION) Cultivation of land 、FRACTION Woodlands 、FRACTION Grass land And is obtained by statistics of ArcGIS grid analysis tools), and the ratio of the leaf area index of each vegetation type in the area to which the grid belongs, the leaf area index values of three vegetation types such as cultivated land, forest land and grassland in the grid are obtained by separation, and the following formula is adopted for calculation:
LAI cultivation of land ×FRACTION Cultivation of land +LAI Woodlands ×FRACTION Woodlands +LAI Grass land ×FRACTION Grass land
＝LAI Grid mesh (107.375°E，29.625°N)
Illustratively, as shown in Table 3 below, the horizontal rows represent the first 19 collected phase data, the first column represents the mean data of the leaf area index of the collected farmland, the second column represents the mean data of the leaf area index of the collected forest land, the third column represents the mean data of the leaf area index of the collected grassland, and the multi-phase historical leaf area index data is obtained, for example, if one resolution is usedThe coverage area of a certain type of vegetation (cultivated land, woodland and grassland) in the grid with the resolution of 1/12 degrees multiplied by 1/12 degrees is more than 80 percent, and the leaf area index value of the grid with the resolution of 1/12 degrees multiplied by 1/12 degrees is regarded as one of the representative leaf area index values of the type of vegetation in the area. In the area, the average leaf area index value of each vegetation type is calculated according to the leaf area index data of each period in the preset period, so as to represent the ratio of various types of vegetation in the preset period, such as cultivated land: forest land: grass field x 1 ：x 2 ：x 3 。
TABLE 3
| Cultivation of land | Woodlands | | |
| 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, the global land use/coverage data developed and developed at Maryland university is usually used to construct the hydrological model, and the dataset is made using AVHRR (advanced Very High Resolution radiometer) data obtained in 1992-1993. Vegetation coverage is divided into 14 types in total, including water (water bodies), evergreen coniferous forest (needleaf evergreen forest), evergreen broadleaf forest (broadleaf evergreen forest), deciduous coniferous forest (needleaf deciduous forest), deciduous broadleaf forest (broadleaf deciduous forest), mixed forest (mixed forest), woodland (woodland), woodland grassland (woodland), closed shrub (closed shrub), shrub (open shrub), grassland (grassland), cultivated land (cropland), bare land (bare land), city and building (urban/building-up), but this data is only one phase, and the resolution is described for the land utilization/coverage type in our country, so the resolution is described using the three phases of the spatial grid (1) and the environmental coverage data (dc) of the scientific dc center, three-phase data, 1990, 2000, 2015, respectively, for improving land use/coverage data entered by the VIC model. The method adopts 1990 land use/coverage data to represent the 1990 land use situation of 1982-2000, 2000 land use/coverage data to represent the 1991-2000 land use situation of 2015, and 2015 land use/coverage data to represent the 2001-2015 land use situation. The land utilization/coverage type data set used by the invention comprises 6 primary land utilization/coverage types, namely cultivated land, forest land, grassland, water area, urban and rural area, industrial and mining area, residential land and unused land, wherein the vegetation types are 3 types of cultivated land, forest land, grassland and the like. The land use/coverage data developed and developed by the university of maryland (hereinafter abbreviated as UMD data) and the land use/coverage data of China year 2000 (hereinafter abbreviated as RESDC data) provided by the resource and environment science and data center of the national academy of sciences are compared, and the comparison data is shown in the following table 4,
TABLE 4
Specifically, based on the coverage rate of each vegetation of the preset resolution grid, and meanwhile based on the leaf area index value and the ratio, the leaf area index value of each vegetation day degree is obtained. In the prior art, as shown in fig. 4, the leaf area index of each month of each vegetation type provided by the hydrological model vegetation parameter library usually represents the leaf area index of the grid vegetation with the resolution of 0.25 ° × 0.25 °, but the method cannot reflect the annual variation condition of the leaf area index data, and the annual difference of the leaf area indexes of the same vegetation type in different regions is large due to the large area of the research region, so in the practical application, the embodiment of the invention adopts the 1982 + 2015 inter-year Global Inventory Modeling and Mapping Students (GIMMS) LAI3g product for estimation. The gimmlai 3g product had a spatial resolution of 1/12 ° × 1/12 ° and a temporal resolution of half a month for a total of 12 months/year × 2 years/month × 34 years of 816. When the hydrological model of the embodiment of the invention is adopted for simulation, the area indexes of the day leaves of the types of cultivated land, forest land and grassland in the 0.25 degree multiplied by 0.25 degree grid are required to be input, but the total value of the area indexes of the day leaves of the 1/12 degree multiplied by 1/12 degree grid per half month can only be obtained from the GIMMS LAI3g product, and the embodiment of the invention processes the total leaf area index value of the 1/12 degree multiplied by 1/12 degree grid per half month to obtain the single vegetation day leaf area index value of the types of cultivated land, forest land and grassland in the 0.25 degree multiplied by 0.25 degree grid.
In the embodiment of the present invention, a leaf area index value is obtained based on historical leaf area index data, coverage of each vegetation type in a preset resolution grid is obtained based on the historical land coverage data, a leaf area index ratio of each vegetation in a preset data acquisition range is obtained by using the historical leaf area index data and the historical land coverage data, and a single vegetation daily leaf area index value is obtained by calculating through an interpolation method based on the coverage 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, the interpolation method may adopt, for example, a cubic spline interpolation method. The embodiment of the invention can more accurately obtain the single vegetation daily leaf area index value of the vegetation in the grid with the preset resolution ratio by processing the historical data, and the invention is not limited by the invention.
Fig. 5 is a flowchart of a runoff predicting method based on a hydrological model according to an embodiment of the present invention, which includes the following specific steps:
s10: and acquiring prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land cover prediction data and fixed data.
Illustratively, the fixed data includes elevation data, Soil data, such as obtained from a chinese Soil data set based on a World Soil Database (HWSD) provided by the "Hanhai-arid region science big data center", and other vegetation parameter data, such as obtained from a vegetation library file of a hydrological Model, including but not limited to roughness length, displacement height, building resistance, minimum pore conductance, root depth, etc., and future weather prediction data, such as obtained from simulation of the isimp 2b Project, the CMIP6 Project, the isimp 2b (Inter-sector Impact Model Inter composition Project 2b) Project discusses the influence contribution of climate change to lower emission climate change scenarios (1.5 ℃) and discusses the influence of global climate change on the surface processes and human society, the CMIP6 project is a world climate research plan, the CMIP6 project is a project for answering new scientific questions faced in the current climate change field, the Land cover prediction data may be, for example, a related Land cover model, such as a plus (future Land Use simulation) model, directly obtained through a related website, and simulated according to the related Land cover model, so as to obtain Land cover prediction data, and the data is interpolated to a grid of 0.25 ° × 0.25 ° by using an interpolation method, which is not limited by the present invention.
Illustratively, the future meteorological data used in this example employs the ECHAM6-3-LR dataset provided by the HAPPI project, with a 2.0C temperature increase, time 2106 and 2115 years, time resolution on a daily basis, and spatial resolution of 0.5 deg.. times 0.5 deg.. Weather prediction data for a grid with a spatial resolution of 0.5 ° × 0.5 ° grid centered at 107.375 ° E, 29.625 ° N and having a resolution of 0.25 ° × 0.25 ° grid is used to represent the weather conditions of the grid in 2106 + 2115 years, but the invention is not limited thereto. In practical applications, the data set includes 20 subsets, as different initial states are set, and the first subset is taken as an example in this example. The VIC model requires at least 4 daily variables to run: precipitation, maximum temperature, minimum temperature, wind speed, and the data set has been corrected for drift, so the example does not correct for drift when using the data set, and the HAPPI project evaluates the climate impact difference between 1.5 ℃ and 2 ℃ using targeted multiple integration simulation, which is not intended to limit the invention.
S20: and determining the correlation relation between the future meteorological prediction data and the leaf area index based on the preset meteorological-leaf area correlation relation.
Specifically, the correlation between the future meteorological predicted data and the future leaf area index is obtained based on the correlation between the historical meteorological data and the future meteorological predicted data and the correlation between the historical meteorological data and the leaf area index data.
S30: and determining leaf area index prediction data based on the correlation and meteorological 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 a prediction value of the future single vegetation daily leaf area index 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 prediction data, and the prediction result is shown in fig. 6.
S40: inputting the future meteorological prediction data, the land cover 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 hydrological model is established based on historical meteorological data, historical land cover data, fixed data and historical single vegetation daily leaf area index data.
Specifically, the obtained future meteorological prediction data, land cover prediction data, fixed data and single vegetation daily-scale leaf area index prediction data are input into the hydrological model created in the above embodiment, and runoff simulation is performed based on the hydrological model to obtain future runoff prediction data.
Illustratively, future meteorological predicted data, future land cover 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 used as model input data, a hydrological model is driven by the input data to simulate a grid which is centered at 107.375 DEG E, 29.625 DEG N and has a resolution of 0.25 DEG multiplied by 0.25 DEG in 2106 + 2115 years, and a daily runoff sequence of the grid is calculated, so as to obtain future runoff predicted data.
In the embodiment of the invention, future meteorological prediction data, land cover prediction data and fixed data for predicting future runoff are obtained, the correlation between the future meteorological prediction data and a future single vegetation leaf area index value is determined based on a preset meteorological-leaf area correlation, a single vegetation day leaf area index prediction value is determined based on the correlation and the future meteorological prediction data, the land cover prediction data, the fixed data and the single vegetation day leaf area index prediction value are input into a hydrological model created based on historical meteorological data, historical land cover data, the fixed data and the historical single vegetation day leaf area index value.
According to the embodiment of the invention, the future single vegetation daily leaf area index value is predicted by establishing the correlation between the meteorological data and the single vegetation daily leaf area index value, so that the future daily leaf area index value of each vegetation type can be calculated according to the future meteorological prediction information, the leaf area index parameter value of the preset hydrological model is improved, and the scientificity of runoff prediction based on the hydrological model is improved.
In an optional embodiment of the present invention, the preset meteorological-leaf area correlation relationship 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 species leaf area index to obtain the preset meteorological-leaf area correlation relation of each vegetation.
Specifically, the preset meteorological data may be, for example, historical daily precipitation (unit: mm), maximum temperature (unit: DEG C), minimum temperature (unit: DEG C), and wind speed (unit: m/s), the preset vegetation type may be, for example, three vegetation types of cultivated land, forest land, and grassland, and the correlation between the preset meteorological data and the single vegetation daily leaf area index of the preset vegetation type may be calculated, for example, by using a multiple regression method, to obtain the correlation between the single vegetation daily leaf area index of the preset vegetation type and the preset meteorological variable, respectively. In practical application, the single vegetation daily Leaf Area Index (LAI) of three vegetation types of arable land, forest land and grassland is respectively analyzed by adopting a multiple regression method Cultivation of land 、LAI Woodlands 、 LAI Grass land ) And 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-index value is obtained by utilizing multiple regression calculation, so that the correlation between the future meteorological data and the future single vegetation daily-index value can be scientifically obtained based on the correlation between the historical meteorological data and the historical single vegetation daily-index value, further, the leaf area index parameter data which has large influence on future water circulation is effectively utilized to carry out accurate prediction, the condition that the leaf area index is remarkably increased in the future global large-area range is effectively responded, and the scientificity of 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 cover data, and fixed data, and the details may be referred to in the related description of step S100 of any of the above method embodiments;
a data processing module 2, configured to obtain a single vegetation daily leaf area index value based on the historical leaf area index data, for details, see the related description of step S200 in 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 the details of which may be referred to the related description of step S300 in any of the above method embodiments;
a converging obtaining module 4, configured to converge the simulation result to a cross section of a drainage basin outlet, where the cross section of the drainage basin outlet is a lowest point in a drainage basin, and details of the converging obtaining module may refer to the related description of step S400 in any of the above method embodiments;
a model creating module 5, configured to calibrate the initial hydrological model based on the historical measured data and the simulation result of the watershed outlet section, so as to obtain a final hydrological model, where details of the initial hydrological model may refer to the related description of step S500 in any of the above method embodiments.
In the embodiment of the invention, by acquiring historical meteorological data, historical leaf area index data, historical land cover data and fixed data of runoff detection, obtaining a single vegetation daily leaf area index value according to the historical leaf area index data, inputting the historical meteorological data, the single vegetation daily leaf area index value, the historical land cover data and the fixed data into a preset initial hydrological model to obtain a simulation result of a grid scale with a preset resolution, determining a watershed outlet section according to the lowest point position in the watershed, and extracting elevation data in the fixed data, determining the flow direction of the water flow based on the elevation data, converging the simulation result to a drainage basin outlet section, and calibrating the initial hydrological model based on historical measured data and the simulation result of the drainage basin outlet section to obtain a final hydrological model. In the embodiment of the invention, the final hydrological model is obtained by inputting the processed historical data into the preset initial hydrological model, so that the hydrological model capable of simulating the runoff more accurately can be obtained, the scientificity of hydrological model simulation is improved, the hydrological model can describe the hydrological process more accurately, and the knowledge of hydrological circulation rules is promoted.
As shown in fig. 8, an embodiment of the present invention further provides a runoff predicting 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 acquisition module 10, configured to acquire prediction data of runoff detection, where the prediction data includes future meteorological prediction data, land cover prediction data, and fixed data, and details can be referred to in the related description of step S10 of any of the above method embodiments;
a relation determining module 20, configured to determine a correlation between the meteorological data and the leaf area index based on a preset meteorological-leaf area correlation, for details, see the relevant description of step S20 in any of the above method embodiments;
a future data prediction module 30, configured to determine future single vegetation daily foliage area index prediction data based on the correlation and the future weather prediction data, for details, see the relevant description of step S30 in any of the above method embodiments;
the runoff prediction module 40 is used for inputting the future meteorological prediction data, the land cover 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 hydrological model is created based on historical meteorological data, historical land cover data, fixed data and historical single vegetation daily leaf area index data, and the details can be referred to the related description of step S40 of any of the above method embodiments.
In the embodiment of the invention, future meteorological prediction data, land cover prediction data and fixed data for predicting future runoff are obtained, the correlation between the meteorological data and the leaf area index is determined based on a preset meteorological-leaf area correlation, future single vegetation day leaf area index prediction data is determined based on the correlation and the future meteorological prediction data, the land cover prediction data, the fixed data and the single vegetation day leaf area index prediction data are input into a hydrological model created based on historical meteorological data, historical land cover data, historical fixed data and historical single vegetation day leaf area index data.
According to the embodiment of the invention, the future single vegetation day leaf area index value is predicted by establishing the correlation between the historical period meteorological data and the historical single vegetation day leaf area index, so that the future single vegetation day leaf area index sequence can be calculated according to the future climate prediction information, the leaf area index parameter value of the preset hydrological model is improved, further, the leaf area index parameter data which has large influence on future water circulation is effectively utilized to carry out accurate prediction, the condition that the leaf area index is obviously increased in the future global large-area range is effectively responded, and the accuracy of runoff prediction based on the hydrological model is improved.
For specific limitations and beneficial effects of the device for creating a hydrological model and the runoff predicting device based on the hydrological model, reference may be made to the above limitations on the method for creating a hydrological model and the runoff predicting method based on the hydrological model, and details are not repeated here. The modules of the device for creating the hydrological model and the device for predicting runoff based on the hydrological model can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the electronic device, or can be stored in a memory in the electronic device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 9 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, where 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 (Display) and a Keyboard (Keyboard), and the alternative communication interface 42 may also include a standard wired interface and a standard wireless interface. The Memory 44 may be a high-speed RAM 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 combined with the apparatus described in fig. 7 and 8, the memory 44 stores an application program, and the processor 41 calls the program code stored in the memory 44 for executing the steps of the method for creating a hydrological model and the method for predicting runoff based on a hydrological model according to any of the above-mentioned method embodiments.
The communication bus 43 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 43 may be divided 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 this does not indicate only one bus or one type of bus.
The memory 44 may include a volatile memory (RAM), such as a random-access memory (RAM); the memory may also include a non-volatile memory (such as a flash memory), a hard disk (HDD) or a solid-state drive (SSD); the memory 44 may also comprise a combination of the above-mentioned kinds of memories.
The processor 41 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an 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. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof.
Optionally, the memory 44 is also used to store program instructions. Processor 41 may invoke program instructions to implement a method of creating a hydrological model as shown in the fig. 1 embodiment of the present invention and a method of predicting runoff based on a hydrological model as shown in the fig. 5 embodiment of the present invention.
Embodiments of the present invention further provide a non-transitory computer storage medium storing computer-executable instructions, where the computer-executable instructions may perform the method in any of the above method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for creating a hydrological model is characterized by comprising the following steps:
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 a grid scale with a preset resolution;
converging the simulation result to a drainage basin outlet section, wherein the drainage basin outlet section is the lowest point in a drainage basin;
and calibrating the initial hydrological model based on the historical measured data and the simulation result of the outlet section of the basin to obtain a final hydrological model.
2. The method for creating the hydrological model according to claim 1, wherein the obtaining a single vegetation daily leaf area index value based on the historical leaf area index data comprises:
extracting a leaf area index value based on the historical data;
obtaining a single vegetation daily leaf area index value based on the leaf area index value.
3. The method of creating a hydrological model according to claim 2, wherein said deriving a single vegetation daily leaf area index value based on said leaf area index value comprises:
obtaining vegetation coverage in a preset data acquisition range and leaf area index ratios of all vegetation in the preset data acquisition range based on the historical data;
and obtaining a single vegetation daily leaf area index value of each vegetation contained in the historical data based on the vegetation coverage rate, the leaf area index ratio and the leaf area index value.
4. A runoff predicting method based on a hydrological model, which is characterized by comprising the following steps:
acquiring prediction data of runoff detection, wherein the prediction data comprises future meteorological prediction data, land cover prediction data and fixed data;
determining the correlation between future meteorological prediction data and a leaf area index based on a preset meteorological-leaf area correlation;
determining leaf area index prediction data based on the correlation and future meteorological prediction data;
inputting the future meteorological prediction data, the land cover prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrological model is created based on historical meteorological data, historical land cover data, fixed data and historical leaf area index data.
5. The hydrological model based runoff prediction method of claim 4, wherein the preset hydrological model is created using the method of creating a hydrological model as claimed in any one of claims 1 to 3.
6. A hydrologic model based runoff prediction method according to claim 4 wherein said preset meteorological-leaf area correlation relationship is determined by:
extracting representative meteorological variable data based on the preset meteorological data;
acquiring a preset vegetation type leaf area index based on the leaf area index;
and calculating the correlation between the representative meteorological variable data and a preset vegetation type leaf area index to obtain a preset meteorological-leaf area correlation relation of each vegetation.
7. An apparatus for creating a hydrological model, comprising:
the data acquisition module is used for 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;
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 numerical 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;
the confluence acquisition module is used for converging the simulation result to a drainage basin outlet section, and the drainage basin outlet section is the lowest point in the drainage basin;
and the model establishing module is used for calibrating the initial hydrological model based on the historical measured data and the simulation result of the outlet section of the drainage basin to obtain a final hydrological model.
8. A runoff predicting apparatus based on a hydrological model is characterized by comprising:
the system comprises a future data acquisition module, a data acquisition module and a data processing module, wherein the future data acquisition module is used for acquiring prediction data of runoff detection, and the prediction data comprises future meteorological prediction data, land coverage prediction data and fixed data;
the relation determining module is used for determining the correlation between the future meteorological prediction data and the leaf area index prediction data based on a preset meteorological-leaf area correlation;
the future data prediction module is used for determining the prediction data of the leaf area index 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 cover prediction data, the fixed data and the leaf area index prediction data into a preset hydrological model to obtain runoff prediction data; the preset hydrological model is created based on historical meteorological data, historical land cover data, fixed data and historical leaf area index data.
9. 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 a method of creation of a hydrological model according to any of claims 1-3 or a method of runoff prediction based on a hydrological model according to any of claims 4-6.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of creating a hydrological model according to any one of claims 1 to 3 or the method of predicting a runoff based on a hydrological model according to any one of claims 4 to 6.
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| CN117057174A (en) * | 2023-10-13 | 2023-11-14 | 长江三峡集团实业发展(北京)有限公司 | Runoff prediction method for data-missing area |
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| CN117172037A (en) * | 2023-11-03 | 2023-12-05 | 长江三峡集团实业发展(北京)有限公司 | Distributed hydrologic forecasting method, device, computer equipment and medium |
| CN117172037B (en) * | 2023-11-03 | 2024-01-26 | 长江三峡集团实业发展(北京)有限公司 | Distributed hydrologic forecasting method, device, computer equipment and medium |
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