Ecohydrological process simulation method, device and equipment based on dynamic vegetation

Abstract

The present invention provides a method, device and equipment for simulating eco-hydrological processes based on dynamic vegetation, which relates to the technical field of hydrological process simulation, including: obtaining long-sequence hydrological and meteorological data and satellite remote sensing image data in a target watershed; interpreting and generating a surface average normalized vegetation index sequence corresponding to the target watershed based on the satellite remote sensing image data; simulating the eco-hydrological process of the target watershed under dynamic vegetation conditions based on the long-sequence hydrological and meteorological data and the surface average normalized vegetation index sequence through a pre-constructed watershed hydrological model, so as to obtain the eco-hydrological process simulation result corresponding to the target watershed. The present invention can realize accurate simulation of eco-hydrological processes under dynamic vegetation conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of eco-hydrological process simulation, and in particular to an eco-hydrological process simulation method, device and equipment based on dynamic vegetation. Background Art

[0002] Since the 20th century, the global climate has continued to warm, which has had a certain impact on regional vegetation. Vegetation, as an important component of underlying surface changes, is a natural "link" connecting soil, atmosphere and water, and is inseparable from the ecological and hydrological processes in the basin. Some current studies have shown that vegetation changes the underlying surface conditions of the basin and has a direct impact on surface runoff. Areas with better vegetation are not conducive to the generation of surface runoff. At the same time, vegetation changes can affect vegetation transpiration and evaporation losses to a certain extent. Basins with better vegetation have greater transpiration and evaporation losses. Therefore, basin vegetation changes affect the hydrological process and soil moisture content (soil moisture content) of the basin by affecting surface runoff and evapotranspiration losses. Objectively and accurately simulating eco-hydrological variables such as basin runoff, actual evapotranspiration and soil moisture content under dynamic vegetation conditions is of great significance to basin water resources evaluation, flood control, drought relief and disaster reduction, and basin ecological engineering construction.

[0003] The monitoring data of actual evapotranspiration and soil moisture content in the basin are scarce, and basin hydrological simulation is an important way to evaluate regional water resources, evapotranspiration and soil moisture. However, the simulation of basin eco-hydrological element variables by traditional basin hydrological models (such as RCCC-WBM model, etc.) is mostly based on static vegetation underlying surface conditions, that is, it is assumed that the basin vegetation underlying surface conditions remain unchanged during the simulation period. Therefore, the simulated runoff, soil moisture content, and actual evapotranspiration only reflect the influence of climate factors (temperature, precipitation, etc.), lacking an objective characterization and description of the impact of environmental factors, which brings certain difficulties to supporting basin water resources evaluation, flood control, drought relief and disaster reduction, and ecological impact assessment. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device and equipment for simulating eco-hydrological processes based on dynamic vegetation, which can realize accurate simulation of eco-hydrological processes under dynamic vegetation conditions.

[0005] In a first aspect, an embodiment of the present invention provides a method for simulating an eco-hydrological process based on dynamic vegetation, comprising:

[0006] Obtain long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin;

[0007] Based on satellite remote sensing image data, the average normalized vegetation index series corresponding to the target watershed is generated by interpretation;

[0008] Through the pre-constructed basin hydrological model, based on long-sequence hydrological and meteorological data and surface average normalized vegetation index sequence, the eco-hydrological process of the target basin under dynamic vegetation conditions is simulated to obtain the simulation results of the eco-hydrological process corresponding to the target basin.

[0009] In one embodiment, the eco-hydrological process of the target watershed under dynamic vegetation conditions is simulated by using a pre-constructed watershed hydrological model based on long-sequence hydrological and meteorological data and a surface average normalized vegetation index sequence to obtain the eco-hydrological process simulation results corresponding to the target watershed, including:

[0010] According to the soil moisture content of the target watershed in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the actual evapotranspiration of the target watershed in the current period is determined;

[0011] Furthermore, based on the long-sequence hydrological and meteorological data, the surface average rainfall, snowmelt runoff and groundwater runoff of the target basin in the current period are determined, and according to the soil moisture content of the target basin in the previous period, the surface average rainfall, snowmelt runoff and groundwater runoff of the target basin in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the river runoff of the target basin in the current period is determined;

[0012] Determine the soil moisture content of the target watershed in the current period based on the soil moisture content of the target watershed in the previous period and the actual evapotranspiration and river runoff of the target watershed in the current period;

[0013] Among them, the simulation results of eco-hydrological processes include the actual evapotranspiration, river runoff and soil moisture content of the target basin in each time period.

[0014] In one embodiment, according to the soil moisture content of the target watershed in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the actual evapotranspiration of the target watershed in the current period is determined, including:

[0015] The actual evapotranspiration of the target basin in the current period is determined according to the following formula:

[0016] ;

[0017] in, For the target basin in the current period The actual evapotranspiration of the watershed below, is the vegetation evapotranspiration coefficient, is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index, The target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period The evaporation capacity of the basin below.

[0018] In one embodiment, the river runoff of the target watershed in the current period is determined according to the soil moisture content of the target watershed in the previous period, the surface average rainfall, snowmelt runoff and underground runoff of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, including:

[0019] According to the soil moisture content of the target watershed in the previous period, the surface average rainfall of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the surface runoff of the target watershed in the current period is determined;

[0020] The sum of snowmelt runoff, underground runoff and surface runoff in the target basin in the current period is determined as the river runoff in the target basin in the current period.

[0021] In one embodiment, the surface runoff of the target watershed in the current period is determined according to the soil moisture content of the target watershed in the previous period, the surface average rainfall of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, including:

[0022] The surface runoff of the target basin in the current period is determined according to the following formula:

[0023] ;

[0024] in, For the target basin in the current period Surface runoff under is the surface runoff coefficient, is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index, The target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period Average rainfall below.

[0025] In one embodiment, the soil moisture content of the target watershed in the current period is determined according to the soil moisture content of the target watershed in the previous period and the actual evapotranspiration and river runoff of the target watershed in the current period, including:

[0026] The soil moisture content of the target basin in the current period is determined according to the following formula:

[0027] ;

[0028] in, For the target basin in the current period The soil moisture content under The target basin in the previous period The soil moisture content under For the target basin in the current period The basin precipitation under For the target basin in the current period The actual evapotranspiration of the watershed below, For the target basin in the current period The amount of river runoff below.

[0029] In one embodiment, the method further comprises:

[0030] Obtain the measured values ​​of eco-hydrological processes corresponding to the target watershed;

[0031] Based on the measured values ​​of eco-hydrological processes and the simulation results of eco-hydrological processes, the evaluation indicators corresponding to the watershed hydrological model are determined, so that the parameters of the watershed hydrological model can be dynamically adjusted using the evaluation indicators. The evaluation indicators include model efficiency indicators and / or relative error indicators.

[0032] In a second aspect, an embodiment of the present invention further provides an eco-hydrological process simulation device based on dynamic vegetation, comprising:

[0033] Data acquisition module, used to obtain long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin;

[0034] Vegetation interpretation module, used to interpret and generate the surface average normalized vegetation index sequence corresponding to the target watershed based on satellite remote sensing image data;

[0035] The hydrological process simulation module is used to simulate the eco-hydrological process of the target basin under dynamic vegetation conditions through a pre-constructed basin hydrological model, based on long-sequence hydrological and meteorological data and surface average normalized vegetation index sequence, so as to obtain the corresponding eco-hydrological process simulation results of the target basin.

[0036] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0038] The embodiment of the present invention provides a method, device and equipment for simulating eco-hydrological processes based on dynamic vegetation, which interprets satellite remote sensing image data to generate a surface average normalized vegetation index sequence, so as to use the surface average normalized vegetation index sequence to reflect the dynamic vegetation conditions of the target basin at different time periods, and then use the basin hydrological model, combined with long-sequence hydrological and meteorological data and the surface average normalized vegetation index sequence, to achieve accurate simulation of the eco-hydrological process of the target basin under dynamic vegetation conditions. The embodiment of the present invention can significantly improve the simulation accuracy of the eco-hydrological process, and thus effectively support the work of water resources evaluation and management in the basin, and can also support the practical work of drought monitoring, drought resistance and disaster reduction, and ecological governance in the basin.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a method for simulating an eco-hydrological process based on dynamic vegetation provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the structure of an RCCC-VWBM model provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the interdecadal process of temperature, precipitation, water surface evaporation, NDVI and measured runoff depth in the Yiluo River Basin provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the annual distribution process of temperature, precipitation, water surface evaporation, NDVI and measured runoff in the Yiluo River Basin provided by an embodiment of the present invention;

[0046] Figure 5 A schematic diagram of the monthly measured and simulated runoff process of Heishiguan Station in the Yiluo River Basin provided by an embodiment of the present invention;

[0047] Figure 6 A schematic diagram of the monthly soil moisture content and evapotranspiration process simulated in the Yiluo River Basin provided by an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of the annual distribution process of soil moisture content and evapotranspiration in the Yiluo River Basin simulated by an embodiment of the present invention;

[0049] Figure 8 A schematic diagram of the structure of an eco-hydrological process simulation device based on dynamic vegetation provided by an embodiment of the present invention;

[0050] Fig. 9 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] The basin hydrological model is a mathematical description of the precipitation, infiltration, and runoff processes in the basin. Its input is meteorological elements such as temperature and rainfall, and its output is mostly the flow process at the outlet section of the basin. Basin hydrological models have played an important role in flood prevention and disaster reduction, water resources management, and ecological and environmental protection.

[0053] The RCCC-WBM model is a basin hydrological model developed and gradually improved by the team of the Climate Change Research Center of the Ministry of Water Resources. The model input includes temperature, precipitation, water surface evaporation and measured runoff at the basin outlet control station in each period. Among them, the input measured runoff process is used for model parameter calibration. The runoff simulated by the model includes three components, namely surface runoff, underground runoff and snowmelt runoff. Among them, surface runoff is linearly related to the previous soil moisture content and the rainfall in the period; basin evapotranspiration and soil moisture are the intermediate state variables of the model. The model assumes that evapotranspiration is proportional to the previous soil moisture content and the basin evapotranspiration capacity; the soil moisture content in the period is calculated according to the water balance of the basin. The model has the advantages of simple structure, few parameters (5 parameters, namely maximum soil water storage Smax, surface runoff coefficient Ks, underground runoff coefficient Ks, snowmelt runoff coefficient Ksn, evaporation coefficient Ke) and easy to understand. It has been widely used in environmental change impact attribution analysis and climate change impact assessment.

[0054] However, the simulation of the variables of watershed eco-hydrological elements by the RCCC-WBM model is mostly based on static vegetation underlying surface conditions, that is, it is assumed that the conditions of the watershed vegetation underlying surface remain unchanged during the simulation period. Therefore, the simulated runoff, soil moisture content, and actual evapotranspiration only reflect the impact of climate factors (temperature, precipitation, etc.), and do not reflect the impact of dynamic changes in watershed vegetation on eco-hydrological elements. There is a lack of objective characterization and description of the impact of environmental factors, which brings certain difficulties to supporting watershed water resources evaluation, flood control, drought relief, and ecological impact assessment.

[0055] Based on this, the present invention provides a method, device and equipment for simulating eco-hydrological processes based on dynamic vegetation, which can realize accurate simulation of eco-hydrological processes under dynamic vegetation conditions.

[0056] To facilitate understanding of this embodiment, firstly, a method for simulating an eco-hydrological process based on dynamic vegetation disclosed in an embodiment of the present invention is introduced in detail, see Figure 1 The flowchart of a method for simulating an eco-hydrological process based on dynamic vegetation is shown, and the method mainly includes the following steps S102 to S106:

[0057] Step S102, obtaining long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin.

[0058] Among them, the long-sequence hydrological and meteorological data may include runoff, precipitation, water surface evaporation and temperature, and the satellite remote sensing image data may be satellite remote sensing image data such as GIMMS (Glaobal Inventory Modelling and Mapping Studies) covering the target watershed.

[0059] Step S104, based on the satellite remote sensing image data, interpret and generate a surface average normalized vegetation index sequence corresponding to the target watershed.

[0060] Among them, the surface average normalized difference vegetation index sequence includes the surface average normalized difference vegetation index of the target watershed in multiple time periods, referred to as the surface average NDVI (Normalized Difference Vegetation Index) sequence. In one example, based on satellite remote sensing image data, the normalized difference vegetation index of the pixel is interpreted and generated, and the surface average normalized difference vegetation index sequence of the target watershed is calculated using the normalized difference vegetation index of the pixel.

[0061] Step S106, using a pre-constructed watershed hydrological model, based on long-sequence hydrological and meteorological data and surface average normalized vegetation index sequence, simulate the eco-hydrological process of the target watershed under dynamic vegetation conditions to obtain the eco-hydrological process simulation results corresponding to the target watershed.

[0062] Among them, the watershed hydrological model is an improvement of the traditional RCCC-WBM model, and the improved model can be called the RCCC-VWBM model. Compared with the traditional RCCC-WBM model, the RCCC-VWBM model provided by the embodiment of the present invention only adds the average normalized vegetation index sequence of the model input item surface, and does not increase the model parameters; and, in the scenario where dynamic vegetation is not considered, that is: When the value is set to 0, the RCCC-VWBM model is completely consistent with the RCCC-WBM model. is the current period in the surface average normalized vegetation index series The corresponding surface average normalized vegetation index. The simulation results of the ecohydrological process include the actual evapotranspiration, river runoff, and soil moisture content of the target basin in each period.

[0063] In one example, the actual evapotranspiration of the target basin in the current period can be determined based on the soil water content of the target basin in the previous period output by the RCCC-VWBM model, combined with the surface average normalized vegetation index corresponding to the current period; at the same time, the river runoff of the target basin in the current period can be determined based on the soil water content of the target basin in the previous period output by the RCCC-VWBM model, combined with the surface average rainfall, snowmelt runoff and groundrunoff at the current moment output by the RCCC-VWBM model based on long-sequence hydrometeorological data, and the surface average normalized vegetation index corresponding to the current period; finally, the soil moisture content of the target basin in the previous period is updated using the actual evapotranspiration, river runoff and basin precipitation of the target basin in the current period to obtain the soil moisture content of the target basin in the current period, and the actual evapotranspiration, river runoff and soil moisture content of the target basin in multiple periods are used as the simulation results of the eco-hydrological process.

[0064] The method for simulating eco-hydrological processes based on dynamic vegetation provided in the embodiment of the present invention interprets satellite remote sensing image data to generate a surface average normalized vegetation index sequence, so as to use the surface average normalized vegetation index sequence to reflect the dynamic vegetation conditions of the target watershed at different time periods, and then use the watershed hydrological model, combined with long-sequence hydrological and meteorological data and the surface average normalized vegetation index sequence, to achieve accurate simulation of the eco-hydrological process of the target watershed under dynamic vegetation conditions. The embodiment of the present invention can significantly improve the simulation accuracy of the eco-hydrological process, and thus effectively support the work of watershed water resources evaluation and management, and can also support the practical work of watershed drought monitoring, drought relief and disaster reduction, and watershed ecological governance.

[0065] In one embodiment, the embodiment of the present invention is based on the RCCC-WBM model. By considering the physical mechanism of the influence of the underlying vegetation on surface runoff and evapotranspiration, a surface runoff and evapotranspiration calculation method based on dynamic vegetation information is proposed, and then an eco-hydrological process simulation method for dynamic vegetation is constructed to achieve accurate simulation of eco-hydrological processes such as runoff, evapotranspiration and soil moisture content under dynamic vegetation conditions.

[0066] For ease of understanding, an embodiment of the present invention provides a specific implementation of an eco-hydrological process simulation method based on dynamic vegetation.

[0067] For the aforementioned step S102, an embodiment of the present invention provides an implementation method for obtaining long-sequence hydrological and meteorological data and satellite remote sensing image data in the target watershed, including: collecting long-sequence monthly hydrological and meteorological data in the watershed, including: runoff, precipitation, water surface evaporation and temperature; collecting satellite remote sensing image data such as GIMMS covering the study watershed.

[0068] For the aforementioned step S104, the embodiment of the present invention provides an implementation method for interpreting and generating a surface average normalized vegetation index sequence corresponding to a target watershed based on satellite remote sensing image data, including: interpreting and generating a normalized vegetation index of a pixel based on satellite remote sensing image data, and calculating a surface average normalized vegetation index sequence of the target watershed using the normalized vegetation index of the pixel. Specifically, the normalized vegetation index of each pixel is calculated according to the following formula (1):

[0069] Formula (1): ;

[0070] Among them, NDVI is the normalized vegetation index; NIR is the near-infrared band reflectance; Red is the red band reflectance.

[0071] In one example, based on the aforementioned long-sequence hydrological and meteorological data and the surface average normalized difference vegetation index sequence, the interdecadal process and annual distribution characteristics of the surface average temperature, precipitation, water surface evaporation, NDVI and runoff in the target basin can be analyzed.

[0072] For the aforementioned step S106, the embodiment of the present invention first explains the basin hydrological model. The embodiment of the present invention is based on the RCCC-WBM model and combines the actual evapotranspiration of the basin. and surface runoff , establish the RCCC-VWBM model based on NDVI, such as Figure 2 The figure shows a schematic diagram of the structure of an RCCC-VWBM model, in which air temperature, precipitation, and water surface evaporation are traditional RCCC-WBM model inputs, and vegetation index is a new model input; soil moisture content, river runoff, and actual evapotranspiration in the basin (abbreviated as actual evaporation) are the eco-hydrological variable outputs of the model.

[0073] On this basis, the embodiment of the present invention provides a specific implementation method of using the above RCCC-VWBM model to simulate the eco-hydrological process of the target watershed under dynamic vegetation conditions, including the following (i) to (iii):

[0074] (I) According to the soil moisture content of the target basin in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, determine the actual evapotranspiration of the target basin in the current period. Specifically, the actual evapotranspiration of the target basin in the current period can be determined according to the following formula (2):

[0075] Formula (2): ;

[0076] in, For the target basin in the current period Actual evapotranspiration of the watershed below; is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index is obtained based on the interpretation of satellite remote sensing images; The target basin in the previous period The soil moisture content under is the state variable of the watershed and is obtained by calculating the water balance in the RCCC-WBM model; For the target basin in the current period The evaporation capacity of the watershed under the condition is generally obtained by measuring the value of E601 evaporation dish or calculating it by using Penman's formula; and are model parameters, is the vegetation evapotranspiration coefficient, is the maximum soil water storage capacity, determined by the model optimization rate.

[0077] (ii) Determine the surface average rainfall, snowmelt runoff and groundrunoff in the target basin in the current period based on the long-sequence hydrological and meteorological data, and determine the river runoff in the target basin in the current period based on the soil moisture content in the target basin in the previous period, the surface average rainfall, snowmelt runoff and groundrunoff in the target basin in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence.

[0078] First, the surface runoff of the target basin in the current period is determined based on the soil moisture content of the target basin in the previous period, the surface average rainfall of the target basin in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence. Specifically, the surface runoff of the target basin in the current period can be determined according to the following formula (3):

[0079] Formula (3): ;

[0080] in, For the target basin in the current period Surface runoff under is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index is obtained based on the interpretation of satellite remote sensing images; The target basin in the previous period The soil moisture content under is the state variable of the watershed and is obtained by calculating the water balance in the RCCC-WBM model; For the target basin in the current period The surface average rainfall under the RCCC-WBM model is obtained by dividing snow and rain according to temperature and precipitation. and are model parameters, is the surface runoff coefficient, is the maximum soil water storage capacity, determined by the model optimization rate.

[0081] Then, the sum of the snowmelt runoff, underground runoff and surface runoff in the target basin in the current period is determined as the river runoff in the target basin in the current period. Specifically, the river runoff in the target basin in the current period can be determined according to the following formula (4):

[0082] Formula (4): ;

[0083] The current period of the simulation of river runoff; is the current period calculated based on dynamic NDVI The surface runoff is calculated by formula (3); and The current period of calculation The snowmelt runoff and groundwater runoff were simulated by the RCCC-WBM model.

[0084] (III) Determine the soil moisture content of the target basin in the current period based on the soil moisture content of the target basin in the previous period, as well as the actual evapotranspiration and river runoff of the target basin in the current period. Specifically, the soil moisture content of the target basin in the current period can be determined according to the following formula (5):

[0085] Formula (5): ;

[0086] in, For the target basin in the current period The soil moisture content under The target basin in the previous period Soil moisture content below; For the target basin in the current period The basin precipitation under is calculated based on the measured precipitation at the basin stations; For the target basin in the current period The actual evapotranspiration of the basin under is calculated according to the constructed formula (2); For the target basin in the current period The river runoff under is calculated according to the constructed formula (4).

[0087] The embodiment of the present invention can use the RCCC-VWBM model to simulate eco-hydrological processes such as basin runoff, actual evapotranspiration and soil moisture content under dynamic vegetation conditions, and statistically analyze the annual distribution and interdecadal variation characteristics of runoff, actual evaporation and soil moisture content.

[0088] Furthermore, an embodiment of the present invention also provides a specific implementation method for optimizing the RCCC-VWBM model, including: obtaining the measured values ​​of the eco-hydrological processes corresponding to the target watershed; based on the measured values ​​of the eco-hydrological processes and the simulation results of the eco-hydrological processes, determining the evaluation indicators corresponding to the watershed hydrological model, so as to dynamically adjust the parameters of the watershed hydrological model using the evaluation indicators, and the evaluation indicators include model efficiency indicators and / or relative error indicators.

[0089] In the specific implementation, the Nash-Sutcliffe model efficiency index NSE and relative error index RE are used as the objective function to optimize and calibrate the RCCC-VWBM model parameters; if NSE is greater than 60% and the relative error RE is less than 5%, the model is considered to have good adaptability to the study basin. Among them, the expression of the Nash-Sutcliffe model efficiency index NSE is shown in the following formula (6), and the expression of the relative error index RE is shown in the following formula (7):

[0090] Formula (6): ;

[0091] Formula (7): ;

[0092] Where: is the simulated average runoff, is the measured average runoff, is the simulated runoff volume, is the measured runoff volume, and N is the total length of the runoff series, i.e. the total number of time periods.

[0093] Dynamics of watershed interpretation and the assumed value (value is 0) drives the RCCC-VWBM model, optimizes the model parameters, compares the NSE and RE of runoff simulation, and analyzes the effectiveness of considering the dynamics of basin vegetation on basin hydrological simulation.

[0094] In summary, the method for simulating eco-hydrological processes based on dynamic vegetation provided by the embodiment of the present invention has at least the following characteristics:

[0095] (1) Based on the RCCC-WBM model, the embodiment of the present invention proposes a basin eco-hydrological process simulation method based on dynamic vegetation (RCCC-VWBM model) by coupling the basin dynamic vegetation information (NDVI). It can provide the simulation accuracy of runoff to a certain extent, thereby effectively supporting the work of basin water resources evaluation and management.

[0096] (2) The soil moisture content and actual evapotranspiration of a watershed under dynamic vegetation conditions simulated by the (RCCC-VWBM model provided by the embodiment of the present invention can support practical work in aspects such as watershed drought monitoring, drought relief and disaster reduction, and watershed ecological management.

[0097] Furthermore, an embodiment of the present invention also provides an application example of an eco-hydrological process simulation method based on dynamic vegetation. The core of the basin eco-hydrological process simulation method based on dynamic vegetation includes: ① constructing a surface runoff and basin evapotranspiration calculation formula based on vegetation information, and establishing an RCCC-VWBM model that couples vegetation information based on the RCCC-WBM model; ② simulating basin runoff, soil moisture content and actual evapotranspiration based on basin dynamic vegetation information. The Yiluo River is a first-level tributary of the middle reaches of the Yellow River, with a basin area of ​​18,563 km2 above the Heishiguan Station; taking the Yiluo River as an example, the specific calculation process includes the following steps:

[0098] (1) Collect monthly precipitation, temperature, water surface evaporation and monthly measured runoff at the Heishiguan Station in the Yiluo River Basin from 1982 to 2015, calculate the monthly average precipitation, average temperature and water surface evaporation in the basin; collect GIMMS and other satellite remote sensing image data covering the study basin.

[0099] (2) Based on the GIMMS satellite remote sensing image data, the Normalized Difference Vegetation Index (NDVI) sequence of each pixel is interpreted and generated, and the NDVI of the pixel is used to calculate the average NDVI sequence of the watershed.

[0100] (3) Analyze the interdecadal process and annual distribution characteristics of the runoff at Heishiguan Station and the average temperature, precipitation, water surface evaporation and NDVI in the Yiluo River Basin. Figure 3 A schematic diagram of the interdecadal process of temperature, precipitation, water surface evaporation, NDVI and measured runoff depth in the Yiluo River Basin is shown. Figure 3 (a) is the interdecadal process of temperature, (b) is the interdecadal process of precipitation, (c) is the interdecadal process of water surface evaporation, (d) is the interdecadal process of NDVI, and (e) is the interdecadal process of measured runoff depth; and, Figure 4 A schematic diagram of the annual distribution of temperature, precipitation, water surface evaporation, NDVI and measured runoff in the Yiluo River Basin is shown. Figure 4 (a) is the annual distribution process of temperature, (b) is the annual distribution process of precipitation, (c) is the annual distribution process of water surface evaporation, (d) is the annual distribution process of NDVI, and (e) is the annual distribution process of measured runoff depth.

[0101] (4) Constructing the actual evapotranspiration of the basin considering the vegetation index ( ) calculation method, see formula (2).

[0102] (5) Constructing surface runoff considering vegetation index ( ) calculation method, see formula (3).

[0103] (6) Based on the RCCC-WBM model, combined and , the RCCC-VWBM model based on NDVI was established.

[0104] (7) Taking the NSE coefficient and relative error RE as the objective function, the RCCC-VWBM model parameters were calibrated using the data from 1982 to 2015. The NSE coefficient of the monthly runoff simulation at the Heishiguan station was 74.8%, and the relative error RE was 2.2%. Figure 5 The figure shows a schematic diagram of the monthly measured and simulated runoff process at the Heishiguan Station in the Yiluo River Basin. The measured and simulated runoffs are generally well matched, and the RCCC-VWBM model has good adaptability to the Yiluo River Basin.

[0105] (8) Using static vegetation conditions ( The RCCC-VWBM model was driven by the meteorological factors from 1982 to 2015, and the model parameters were optimized. The NSE coefficient of the monthly runoff simulation at the Heishiguan station was 59.2%, and the relative error RE was 1.9%. It can be seen from the comparison that the model's simulation effect on runoff is significantly improved by considering dynamic vegetation conditions.

[0106] (9) Using the RCCC-VWBM model, the eco-hydrological processes such as watershed runoff, actual evapotranspiration and soil moisture content under dynamic vegetation conditions are simulated. Figure 6 A schematic diagram of the monthly soil moisture content and evapotranspiration process simulated in the Yiluo River Basin is shown. Figure 6 (a) shows the simulated monthly soil moisture process, and (b) shows the simulated evapotranspiration process. Based on this, the annual distribution characteristics of runoff, evaporation, and soil moisture are analyzed, such as Figure 7 The diagram shows a simulated annual distribution process of soil moisture and evapotranspiration in the Yiluo River Basin. Figure 7 (a) shows the simulated annual distribution process of soil moisture content, and (b) shows the simulated annual distribution process of evapotranspiration.

[0107] Example conclusion: (1) The surface runoff and evapotranspiration calculation formula coupled with vegetation information (NDVI) was constructed ( and ), based on the RCCC-WBM model, combined with and , an RCCC-VWBM model based on NDVI was established. (2) Based on the dynamic vegetation information of the Yiluo River, the NSE coefficient of the RCCC-VWBM model for the monthly runoff at the Heishiguan Station was 74.8%, and the relative error RE was 2.2%; under static vegetation conditions, the NSE coefficient of the model for the monthly runoff at the Heishiguan Station was 59.2%, and the relative error RE was 1.9%; considering the dynamic vegetation information, the model's simulation effect on runoff was significantly improved.

[0108] Based on the above embodiments, the present invention provides an eco-hydrological process simulation device based on dynamic vegetation. Figure 8 The schematic diagram of the structure of an eco-hydrological process simulation device based on dynamic vegetation is shown, and the device mainly includes the following parts:

[0109] Data acquisition module 802, used to acquire long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin;

[0110] The vegetation interpretation module 804 is used to interpret and generate a surface average normalized vegetation index sequence corresponding to the target watershed based on satellite remote sensing image data;

[0111] The hydrological process simulation module 806 is used to simulate the eco-hydrological process of the target basin under dynamic vegetation conditions through a pre-constructed basin hydrological model, based on long-sequence hydrological and meteorological data and surface average normalized vegetation index sequence, so as to obtain the eco-hydrological process simulation results corresponding to the target basin.

[0112] The eco-hydrological process simulation device based on dynamic vegetation provided by the embodiment of the present invention interprets satellite remote sensing image data to generate a surface average normalized vegetation index sequence, so as to use the surface average normalized vegetation index sequence to reflect the dynamic vegetation conditions of the target watershed at different time periods, and then use the watershed hydrological model, combined with long-sequence hydrological and meteorological data and the surface average normalized vegetation index sequence, to achieve accurate simulation of the eco-hydrological process of the target watershed under dynamic vegetation conditions. The embodiment of the present invention can significantly improve the simulation accuracy of the eco-hydrological process, and thus effectively support the work of watershed water resources evaluation and management, and can also support the practical work of watershed drought monitoring, drought relief and disaster reduction, and watershed ecological governance.

[0113] In one embodiment, the hydrological process simulation module 806 is specifically used to:

[0114] According to the soil moisture content of the target watershed in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the actual evapotranspiration of the target watershed in the current period is determined;

[0115] Furthermore, based on the long-sequence hydrological and meteorological data, the surface average rainfall, snowmelt runoff and groundwater runoff of the target basin in the current period are determined, and according to the soil moisture content of the target basin in the previous period, the surface average rainfall, snowmelt runoff and groundwater runoff of the target basin in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the river runoff of the target basin in the current period is determined;

[0116] Determine the soil moisture content of the target watershed in the current period based on the soil moisture content of the target watershed in the previous period and the actual evapotranspiration and river runoff of the target watershed in the current period;

[0117] Among them, the simulation results of eco-hydrological processes include the actual evapotranspiration, river runoff and soil moisture content of the target basin in each time period.

[0118] In one embodiment, the hydrological process simulation module 806 is specifically used to:

[0119] The actual evapotranspiration of the target basin in the current period is determined according to the following formula:

[0120] ;

[0121] in, For the target basin in the current period The actual evapotranspiration of the watershed below, is the vegetation evapotranspiration coefficient, is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index, The target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period The evaporation capacity of the basin below.

[0122] In one embodiment, the hydrological process simulation module 806 is specifically used to:

[0123] According to the soil moisture content of the target watershed in the previous period, the surface average rainfall of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the surface runoff of the target watershed in the current period is determined;

[0124] The sum of snowmelt runoff, underground runoff and surface runoff in the target basin in the current period is determined as the river runoff in the target basin in the current period.

[0125] In one embodiment, the hydrological process simulation module 806 is specifically used to:

[0126] The surface runoff of the target basin in the current period is determined according to the following formula:

[0127] ;

[0128] in, For the target basin in the current period Surface runoff under is the surface runoff coefficient, is the current period in the surface average normalized vegetation index series The corresponding surface average normalized difference vegetation index, The target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period Average rainfall below.

[0129] In one embodiment, the hydrological process simulation module 806 is specifically used to:

[0130] The soil moisture content of the target basin in the current period is determined according to the following formula:

[0131] ;

[0132] in, For the target basin in the current period The soil moisture content under The target basin in the previous period The soil moisture content under For the target basin in the current period The basin precipitation under For the target basin in the current period The actual evapotranspiration of the watershed below, For the target basin in the current period The amount of river runoff below.

[0133] In one embodiment, a model adjustment module is further included, which is used to:

[0134] Obtain the measured values ​​of eco-hydrological processes corresponding to the target watershed;

[0135] Based on the measured values ​​of eco-hydrological processes and the simulation results of eco-hydrological processes, the evaluation indicators corresponding to the watershed hydrological model are determined, so that the parameters of the watershed hydrological model can be dynamically adjusted using the evaluation indicators. The evaluation indicators include model efficiency indicators and / or relative error indicators.

[0136] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0137] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0138] Fig. 9 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 90, a memory 91, a bus 92 and a communication interface 93, wherein the processor 90, the communication interface 93 and the memory 91 are connected via the bus 92; the processor 90 is used to execute an executable module stored in the memory 91, such as a computer program.

[0139] The memory 91 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 93 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0140] The bus 92 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0141] Among them, the memory 91 is used to store programs, and the processor 90 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiments of the present invention can be applied to the processor 90 or implemented by the processor 90.

[0142] The processor 90 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 90. The above processor 90 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 91, and the processor 90 reads the information in the memory 91 and completes the steps of the above method in combination with its hardware.

[0143] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0144] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0145] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for simulating eco-hydrological processes based on dynamic vegetation, characterized in that: include: Obtain long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin; Based on the satellite remote sensing image data, interpret and generate a surface average normalized vegetation index sequence corresponding to the target watershed; By using a pre-constructed watershed hydrological model, based on the long sequence hydrological and meteorological data and the surface average normalized difference vegetation index sequence, the eco-hydrological process of the target watershed under dynamic vegetation conditions is simulated to obtain the eco-hydrological process simulation results corresponding to the target watershed; Through the pre-constructed watershed hydrological model, based on the long sequence hydrological and meteorological data and the surface average normalized vegetation index sequence, the eco-hydrological process of the target watershed under dynamic vegetation conditions is simulated to obtain the eco-hydrological process simulation results corresponding to the target watershed, including: Determine the actual evapotranspiration of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence; and, determining the surface average rainfall, snowmelt runoff and underground runoff of the target watershed in the current period based on the long sequence hydrometeorological data, and determining the river runoff of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, the surface average rainfall, snowmelt runoff and underground runoff of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence; Determine the soil moisture content of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, and the actual evapotranspiration and river runoff of the target watershed in the current period; The eco-hydrological process simulation results include the actual evapotranspiration of the target watershed in each time period, the river runoff, and the soil moisture content; According to the soil moisture content of the target watershed in the previous period and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, the actual evapotranspiration of the target watershed in the current period is determined, including: determining the actual evapotranspiration of the target watershed in the current period according to the following formula: ;in, For the target basin in the current period The actual evapotranspiration of the watershed below, is the vegetation evapotranspiration coefficient, The current period in the surface average normalized vegetation index sequence The corresponding surface average normalized difference vegetation index, For the target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period The evaporation capacity of the basin below; Determining the river runoff of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, the surface average rainfall, the snowmelt runoff and the underground runoff of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, including: determining the surface runoff of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, the surface average rainfall of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence; determining the sum of the snowmelt runoff, the underground runoff and the surface runoff of the target watershed in the current period as the river runoff of the target watershed in the current period; Determining the surface runoff of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, the surface average rainfall of the target watershed in the current period, and the surface average normalized vegetation index corresponding to the current period in the surface average normalized vegetation index sequence, including: determining the surface runoff of the target watershed in the current period according to the following formula: ;in, For the target basin in the current period Surface runoff under is the surface runoff coefficient, The current period in the surface average normalized vegetation index sequence The corresponding surface average normalized difference vegetation index, For the target basin in the previous period The soil moisture content under is the maximum soil water storage capacity, For the target basin in the current period The average rainfall for the surface below.

2. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Determining the soil moisture content of the target watershed in the current period according to the soil moisture content of the target watershed in the previous period, and the actual evapotranspiration and river runoff of the target watershed in the current period, includes: The soil moisture content of the target watershed in the current period is determined according to the following formula: ； in, For the target basin in the current period The soil moisture content under For the target basin in the previous period The soil moisture content under For the target basin in the current period The basin precipitation under For the target basin in the current period The actual evapotranspiration of the watershed below, For the target basin in the current period The amount of river runoff below.

3. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: The method further comprises: Obtaining measured values ​​of eco-hydrological processes corresponding to the target watershed; Based on the measured values ​​of the eco-hydrological process and the simulation results of the eco-hydrological process, the evaluation indicators corresponding to the watershed hydrological model are determined, so as to dynamically adjust the parameters of the watershed hydrological model using the evaluation indicators, and the evaluation indicators include model efficiency indicators and / or relative error indicators.

4. An eco-hydrological process simulation device based on dynamic vegetation, characterized in that: The device is used to implement the method for simulating eco-hydrological processes based on dynamic vegetation as described in claim 1, and the device comprises: Data acquisition module, used to obtain long-sequence hydrological and meteorological data and satellite remote sensing image data in the target basin; A vegetation interpretation module, used for interpreting and generating a surface average normalized vegetation index sequence corresponding to the target watershed based on the satellite remote sensing image data; The hydrological process simulation module is used to simulate the eco-hydrological process of the target basin under dynamic vegetation conditions through a pre-constructed basin hydrological model, based on the long sequence hydrological and meteorological data and the surface average normalized vegetation index sequence, so as to obtain the eco-hydrological process simulation results corresponding to the target basin.

5. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 3.

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