An evaluation method and device for the impact of extreme drought events on the flood process in a basin

Abstract

The present invention relates to the technical field of water conservancy engineering, and discloses an evaluation method and device for the impact of extreme drought events on the flood process of a basin. The method includes obtaining meteorological and hydrological data of the target basin, determining the rapid alternation of drought and flood events based on the meteorological and hydrological data of the target basin; calculating the vegetation greenness value of the basin after the rapid alternation of drought and flood events; obtaining the vegetation greenness value of the basin before the rapid alternation of drought and flood events, and respectively performing flood process simulation on the vegetation greenness value of the basin before the rapid alternation of drought and flood events and the vegetation greenness value of the basin after the rapid alternation of drought and flood events to obtain an evaluation result of the impact of extreme drought events on the flood process of the basin. The present invention realizes the quantitative evaluation of the change in vegetation leaf area and the contribution of soil to the flood process caused by the rapid alternation of drought and flood in the target basin, and provides technical support for flood control dispatching and disaster warning of the basin.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and particularly relates to an evaluation method and device for the impact of extreme drought events on the flood process of a basin. Background Art

[0002] An extreme drought event refers to a meteorological and hydrological event in which a region is in a drought state. Studying the impact of vegetation greenness on the flood process of a basin during extreme drought events is of great significance in aspects such as improving disaster warning, reducing losses, protecting the ecology, promoting scientific research progress, and coping with climate change.

[0003] However, relevant flood prediction methods do not quantify the impact of vegetation greenness changes on the flood process of a basin during extreme drought events, and cannot use the vegetation greenness changes during extreme drought events to provide technical support for basin flood control scheduling and disaster warning. Summary of the Invention

[0004] In view of this, the present invention provides an evaluation method and device for the impact of extreme drought events on the flood process of a basin to solve the problem that relevant flood prediction methods cannot use the vegetation greenness changes during extreme drought events to provide technical support for basin flood control scheduling and disaster warning.

[0005] In a first aspect, the present invention provides an evaluation method for the impact of extreme drought events on the flood process of a basin. The method includes:

[0006] Obtain the meteorological and hydrological data of the target basin, and determine the rapid alternation event of drought and flood based on the meteorological and hydrological data of the target basin;

[0007] Calculate the vegetation greenness value of the basin after the rapid alternation event of drought and flood;

[0008] Obtain the vegetation greenness value of the basin before the rapid alternation event of drought and flood, and respectively simulate the flood process of the basin for the vegetation greenness value of the basin before the rapid alternation event of drought and flood and the vegetation greenness value of the basin after the rapid alternation event of drought and flood to obtain the evaluation result of the impact of extreme drought events on the flood process of the basin.

[0009] The evaluation method for the impact of extreme drought events on the basin flood process provided by this embodiment obtains the meteorological and hydrological data of the target basin, determines the rapid alternation events of drought and flood based on the meteorological and hydrological data of the target basin; calculates the vegetation greenness value of the basin after the rapid alternation events of drought and flood; obtains the vegetation greenness value of the basin before the rapid alternation events of drought and flood, respectively simulates the basin flood process for the vegetation greenness value of the basin before the rapid alternation events of drought and flood and the vegetation greenness value of the basin after the rapid alternation events of drought and flood, and obtains the evaluation result of the impact of extreme drought events on the basin flood process; by simulating the basin flood process for the vegetation greenness value of the basin before the rapid alternation events of drought and flood and the vegetation greenness value of the basin after the rapid alternation events of drought and flood, it realizes the quantitative evaluation of the change of vegetation leaf area and the contribution of soil to the flood process in the target basin, and provides technical support for basin flood control scheduling and disaster warning.

[0010] In an alternative embodiment, determining the rapid alternation events of drought and flood based on the meteorological and hydrological data of the target basin includes:

[0011] Calculating the standardized rapid alternation index of drought and flood based on the meteorological data of the target basin;

[0012] Comparing the standardized rapid alternation index of drought and flood with the evaluation threshold of rapid alternation of drought and flood, and determining the rapid alternation events of drought and flood based on the comparison result.

[0013] The evaluation method for the impact of extreme drought events on the basin flood process provided by this embodiment calculates the standardized rapid alternation index of drought and flood, compares the standardized rapid alternation index of drought and flood with the evaluation threshold of rapid alternation of drought and flood, and determines the rapid alternation events of drought and flood based on the comparison result, realizing the accurate identification of the rapid alternation events of drought and flood in the target basin, and laying a foundation for the evaluation of the impact of extreme drought events on the basin flood process.

[0014] In an alternative embodiment, calculating the vegetation greenness value of the basin after the rapid alternation events of drought and flood includes:

[0015] Obtaining the relationship between the vegetation greenness value of the basin and the standardized precipitation index;

[0016] Determining the standardized precipitation index based on the meteorological data of the target basin; the standardized precipitation index corresponds to the rapid alternation events of drought and flood;

[0017] Based on the standardized precipitation index, using the relationship between the vegetation greenness value of the basin and the standardized precipitation index to determine the vegetation greenness value of the basin after the rapid alternation events of drought and flood.

[0018] The evaluation method for the impact of extreme drought events on the basin flood process provided in this embodiment realizes the accurate calculation of the vegetation greenness value of the basin after the drought-flood rapid transition event by obtaining the relationship between the vegetation greenness value of the basin and the standardized precipitation index, and based on the standardized precipitation index corresponding to the drought-flood rapid transition event, using the relationship between the vegetation greenness value of the basin and the standardized precipitation index, which lays a foundation for measuring the impact of the vegetation greenness value of the basin on the basin flood process in extreme drought events.

[0019] In an alternative implementation, obtaining the relationship between the vegetation greenness value of the basin and the standardized precipitation index includes:

[0020] Obtain the target research area, divide the target research area to obtain multiple spatial grids;

[0021] Obtain the vegetation greenness value and grid meteorological data corresponding to each spatial grid, and calculate the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data;

[0022] Based on the vegetation greenness value corresponding to each spatial grid and the standardized precipitation index corresponding to each spatial grid, use a machine learning algorithm to determine the relationship between the vegetation greenness value of the basin and the standardized precipitation index.

[0023] The evaluation method for the impact of extreme drought events on the basin flood process provided in this embodiment divides the target research area to obtain multiple spatial grids, calculates the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data, and then uses a machine learning algorithm to determine the relationship between the vegetation greenness value of the basin and the standardized precipitation index, which lays a foundation for accurately evaluating the impact of extreme drought events on the basin flood process.

[0024] In an alternative implementation, respectively perform basin flood process simulations on the vegetation greenness value of the basin before the drought-flood rapid transition event and the vegetation greenness value of the basin after the drought-flood rapid transition event to obtain the evaluation results of the impact of extreme drought events on the basin flood process, including:

[0025] Construct a basin eco-hydrological model;

[0026] Based on the vegetation greenness value of the basin before the drought-flood rapid transition event and the vegetation greenness value of the basin after the drought-flood rapid transition event, use the basin eco-hydrological model to calculate the basin flood change data; wherein, the basin flood change data includes flood duration change data, flood peak change data, and flood volume change data;

[0027] Based on the basin flood change data, determine the evaluation results of the impact of extreme drought events on the basin flood process.

[0028] The evaluation method for the impact of extreme drought events on the basin flood process provided by this embodiment quantifies and evaluates the impact of vegetation greenness changes on the basin flood process in drought-flood abrupt alternation events by constructing a basin eco-hydrological model and using the basin eco-hydrological model to calculate the basin flood change data. It has important value for comprehensively understanding the role of vegetation in drought-flood abrupt alternation events and helps improve the accuracy of basin hydrological forecasting and early warning.

[0029] In an alternative embodiment, based on the basin vegetation greenness value before the drought-flood abrupt alternation event and the basin vegetation greenness value after the drought-flood abrupt alternation event, the basin eco-hydrological model is used to calculate the basin flood change data; wherein, the calculation formula for the basin flood change data is:

[0030]

[0031] Wherein, represents mild, moderate, severe, and extreme drought-flood abrupt alternation events, represents the flood duration change data, represents the flood peak change data, represents the flood volume change data, represents the basin vegetation greenness value after the drought-flood abrupt alternation event, represents the basin vegetation greenness value before the drought-flood abrupt alternation event, represents using the basin eco-hydrological model to simulate the basin flood process corresponding to the basin vegetation greenness value after the drought-flood abrupt alternation event, represents using the basin eco-hydrological model to simulate the basin flood process corresponding to the basin vegetation greenness value before the drought-flood abrupt alternation event, represents the standardized drought-flood abrupt alternation index after the drought-flood abrupt alternation event, represents the standardized precipitation index after the drought-flood abrupt alternation event.

[0032] In a second aspect, the present invention provides an evaluation device for the impact of extreme drought events on the basin flood process, and the device includes:

[0033] A determination module, configured to obtain the meteorological and hydrological data of the target basin and determine the drought-flood abrupt alternation event based on the meteorological and hydrological data of the target basin;

[0034] A calculation module, configured to calculate the basin vegetation greenness value after the drought-flood abrupt alternation event;

[0035] A simulation module, configured to obtain the basin vegetation greenness value before the drought-flood abrupt alternation event, and respectively simulate the basin flood process for the basin vegetation greenness value before the drought-flood abrupt alternation event and the basin vegetation greenness value after the drought-flood abrupt alternation event, so as to obtain the evaluation result of the impact of extreme drought events on the basin flood process.

[0036] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to implement the method for evaluating the impact of extreme drought events on the basin flood process according to the first aspect or any corresponding embodiment thereof.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the method for evaluating the impact of extreme drought events on the basin flood process according to the first aspect or any corresponding embodiment thereof.

[0038] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to cause a computer to execute the method for evaluating the impact of extreme drought events on the basin flood process according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 FIG. is a schematic flowchart of a method for evaluating the impact of extreme drought events on the basin flood process according to an embodiment of the present invention;

[0041] Figure 2 FIG. is a schematic flowchart of another method for evaluating the impact of extreme drought events on the basin flood process according to an embodiment of the present invention;

[0042] Figure 3 FIG. is a schematic flowchart of yet another method for evaluating the impact of extreme drought events on the basin flood process according to an embodiment of the present invention;

[0043] Figure 4 FIG. is a schematic flowchart of still another method for evaluating the impact of extreme drought events on the basin flood process according to an embodiment of the present invention;

[0044] Figure 5 FIG. is a structural block diagram of an apparatus for evaluating the impact of extreme drought events on the basin flood process according to an embodiment of the present invention;

[0045] Figure 6 FIG. is a schematic hardware structure diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0047] In a narrow sense, drought-flood transition events usually refer to meteorological and hydrological events in which a region is in a drought state in the early stage and forms floods due to high-intensity precipitation in the later stage. Drought-flood transition events are different from single drought and flood events. They emphasize the rapid transition between drought and flood states in a region in a short period of time. The research on drought-flood transition events focuses on their definition, identification methods, temporal and spatial changes, cause analysis, and the response laws of runoff changes to different drought-flood transition types. There are many studies on runoff changes caused by vegetation construction or land use changes, and the impact of vegetation on runoff is usually obtained based on similarity analogy, model simulation and other methods. However, related methods pay less attention to the impact of extreme drought events on the flood process in the basin due to the impact of extreme drought on the vegetation greenness in the basin. Generally, in extreme drought events, the vegetation greenness will decline to a certain extent. After the extreme drought event, the vegetation greenness of the ecological basin can recover within a period of time through the self-recovery ability of the ecosystem. However, after the extreme drought event, the vegetation does not have time to recover to its original state, and the impact of this part on the flood process in the basin cannot be ignored.

[0048] As the drought continues, the water in the soil cannot be replenished in time, and water becomes the dominant factor limiting vegetation transpiration; under the combined effects of water shortage and high temperature, tree leaves gradually lose their physiological activity, wither and turn yellow, and trees gradually die; when the natural state of the target basin changes dramatically, the leaf area of ​​vegetation changes accordingly, and the underlying surface characteristics such as soil change significantly, which has a significant impact on the basin's runoff generation and convergence process, resulting in flash floods, sudden rise of river water, river intrusion and other phenomena; under the same total rainfall, the floods brought about by extreme drought events are often faster and more severe; in this case, there is no relevant quantitative evaluation method for the changes in vegetation leaf area in the target basin and the contribution of soil to the flood process.

[0049] To solve the above technical problems, an embodiment of the present invention provides a method for evaluating the impact of extreme drought events on the flood process of a basin. It should be noted that the execution subject of the method for evaluating the impact of extreme drought events on the flood process of a basin provided by the embodiment of the present invention can be a device for evaluating the impact of extreme drought events on the flood process of a basin. This device for evaluating the impact of extreme drought events on the flood process of a basin can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. Among them, the electronic device can be a server or a terminal. Among them, the server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application can be other intelligent hardware devices such as a smart phone, a personal computer, a tablet computer, a wearable device, and a smart robot. In the following method embodiments, the execution subject is taken as an electronic device as an example for illustration.

[0050] According to an embodiment of the present invention, an embodiment of a method for evaluating the impact of extreme drought events on the flood process of a basin is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0051] In this embodiment, a method for evaluating the impact of extreme drought events on the flood process of a basin is provided, which can be used for the above-mentioned electronic device. Figure 1 It is a flowchart of a method for evaluating the impact of extreme drought events on the flood process of a basin according to an embodiment of the present invention, as Figure 1 shown, the process includes the following steps:

[0052] Step S101, obtain the meteorological and hydrological data of the target basin, and determine the drought-flood rapid transition event based on the meteorological and hydrological data of the target basin.

[0053] Specifically, the meteorological and hydrological data includes precipitation, temperature, wind speed, air pressure, sunshine, and underlying surface data of relative humidity. Among them, the underlying surface data of relative humidity includes: area, elevation, longitude and latitude, leaf area index, soil properties, land use type, and impervious area.

[0054] Step S102, calculate the vegetation greenness value of the basin after the drought-flood rapid transition event.

[0055] Step S103, obtain the vegetation greenness value of the basin before the drought-flood rapid transition event, and perform flood process simulation on the vegetation greenness value of the basin before the drought-flood rapid transition event and the vegetation greenness value of the basin after the drought-flood rapid transition event respectively, to obtain the evaluation result of the impact of extreme drought events on the flood process of the basin.

[0056] The evaluation method for the impact of extreme drought events on the basin flood process provided in this embodiment determines drought-flood abrupt alternation events based on the meteorological and hydrological data of the target basin by obtaining the meteorological and hydrological data of the target basin; calculates the vegetation greenness value of the basin after the drought-flood abrupt alternation event; obtains the vegetation greenness value of the basin before the drought-flood abrupt alternation event, and respectively simulates the basin flood process for the vegetation greenness value of the basin before the drought-flood abrupt alternation event and the vegetation greenness value of the basin after the drought-flood abrupt alternation event to obtain the evaluation result of the impact of extreme drought events on the basin flood process; by simulating the basin flood process for the vegetation greenness value of the basin before the drought-flood abrupt alternation event and the vegetation greenness value of the basin after the drought-flood abrupt alternation event, it realizes the quantitative evaluation of the change in vegetation leaf area and the contribution of soil to the flood process in the target basin, providing technical support for basin flood control scheduling and disaster warning.

[0057] In this embodiment, an evaluation method for the impact of extreme drought events on the basin flood process is provided, which can be used for the above-mentioned electronic device. Figure 2 It is a flowchart of the evaluation method for the impact of extreme drought events on the basin flood process according to an embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0058] Step S201: Obtain the meteorological and hydrological data of the target basin, and determine the drought-flood abrupt alternation event based on the meteorological and hydrological data of the target basin.

[0059] Specifically, the above step S201 includes:

[0060] Step S2011: Calculate the standardized drought-flood abrupt alternation index based on the meteorological data of the target basin.

[0061] Specifically, the standardized drought-flood abrupt alternation index (Standardized Drought-Wetness Abrupt Alternation Index, abbreviated as SDWAI) is applicable to the assessment of regional drought-flood abrupt alternation in the target basin and can be calculated through the meteorological data of the target basin.

[0062] Furthermore, the standardized drought-flood abrupt alternation index has the following calculation formula:

[0063] (1)

[0064] where, is the drought-flood index sequence of the meteorological and hydrological variables after standardized transformation, is the absolute distance of the drought-flood index sequence, and its calculation formula is as follows:

[0065] (2)

[0066] where, is the sequence length.

[0067] In step S2012, compare the standardized dry-wet abrupt change index with the dry-wet abrupt change evaluation threshold, and determine the dry-wet abrupt change event based on the comparison result.

[0068] Specifically, according to the value I of SDWAI SDWAI compare with the dry-wet abrupt change evaluation threshold, and identify the dry-wet abrupt change event according to the comparison result; for example, when -0.5 < I SDWAI < 0.5, it is a normal situation, when I SDWAI ≤ -0.5, it is a change from drought to flood, when I SDWAI ≥ 0.5, it is a change from flood to drought. Both of the above two situations include four levels: mild, moderate, severe, and extreme, that is, extreme drought to flood (I SDWAI ≤ -2.0), severe drought to flood (-2.0 < I SDWAI ≤ -1.5), moderate drought to flood (-1.5 < I SDWAI ≤ -1.0) and mild drought to flood (-1.0 < I SDWAI ≤ -0.5), extreme flood to drought (I SDWAI ≥ 2.0), severe flood to drought (1.5 ≤ I SDWAI < 2.0), moderate flood to drought (1.0 ≤ I SDWAI < 1.5) and mild flood to drought (0.5 ≤ I SDWAI < 1.0).

[0069] Furthermore, compare the standardized dry-wet abrupt change index calculated by the above formula (1) with the above dry-wet abrupt change evaluation threshold, and obtain the standardized dry-wet abrupt change index after the dry-wet abrupt change event , indicating mild, moderate, severe, and extreme dry-wet abrupt change events.

[0070] In step S202, calculate the vegetation greenness value of the basin after the dry-wet abrupt change event. For details, please refer to Figure 1 step S102 of the illustrated embodiment, which will not be elaborated here.

[0071] In step S203, obtain the vegetation greenness value of the basin before the dry-wet abrupt change event, and perform basin flood process simulation on the vegetation greenness value of the basin before the dry-wet abrupt change event and the vegetation greenness value of the basin after the dry-wet abrupt change event respectively, to obtain the evaluation result of the impact of extreme drought events on the basin flood process. For details, please refer to Figure 1 step S103 of the illustrated embodiment, which will not be elaborated here.

[0072] The evaluation method for the impact of extreme drought events on the flood process of a basin provided in this embodiment calculates the standardized drought-flood abrupt transition index, compares the standardized drought-flood abrupt transition index with the drought-flood abrupt transition evaluation threshold, and determines the drought-flood abrupt transition events based on the comparison results, achieving accurate identification of the drought-flood abrupt transition events in the target basin and laying a foundation for evaluating the impact of extreme drought events on the flood process of the basin.

[0073] In this embodiment, an evaluation method for the impact of extreme drought events on the flood process of a basin is provided, which can be used for the above-mentioned electronic device. Figure 3 It is a flowchart of the evaluation method for the impact of extreme drought events on the flood process of a basin according to an embodiment of the present invention, as Figure 3 shown, and this process includes the following steps:

[0074] Step S301, obtain the meteorological and hydrological data of the target basin, and determine the drought-flood abrupt transition events based on the meteorological and hydrological data of the target basin. For details, please refer to Figure 2 step S201 of the shown embodiment, which will not be elaborated here.

[0075] Step S302, calculate the vegetation greenness value of the basin after the drought-flood abrupt transition event.

[0076] Specifically, identify the drought-to-flood transition events in the target basin through the SDWAI index, determine the standardized precipitation index SPI m after the drought-flood abrupt transition event according to the drought-to-flood transition events in the target basin, and calculate the vegetation greenness value LAI of the basin after the drought-flood abrupt transition event by using the relationship between the vegetation greenness value of the basin and the standardized precipitation index. m

[0077] Specifically, the above step S302 includes:

[0078] Step S3021, obtain the relationship between the vegetation greenness value of the basin and the standardized precipitation index.

[0079] Specifically, drought has a significant impact on vegetation greenness, which is mainly reflected in three aspects: 1) Weakening photosynthesis: Under drought conditions, the reduction of soil moisture will lead to a decline in the photosynthesis ability of plants. Photosynthesis is the basis of plant growth, which directly affects the growth rate and health status of plants. Therefore, during drought, the photosynthesis of plants weakens, resulting in a decrease in vegetation greenness; 2) Limited vegetation growth: Lack of water will limit the growth of plants, making it impossible for plants to maintain their normal growth rate and biomass accumulation. This manifestation of growth limitation is the reduction of vegetation greenness in remote sensing images; 3) Change in vegetation coverage: Drought will cause some vegetation to wither or even die, thus reducing the vegetation coverage. The change in coverage is directly reflected in the vegetation greenness, making the greenness in drought-stricken areas generally low.

[0080] Furthermore, there is a close interaction relationship between vegetation greenness and drought, that is, drought will affect the growth and health of vegetation, resulting in a decrease in vegetation greenness, and the change of vegetation greenness can be used as an indicator of drought and an evaluation index of ecological restoration effect.

[0081] In some alternative embodiments, the above step S3021 includes:

[0082] Step a1, obtain the target study area, divide the target study area to obtain multiple spatial grids.

[0083] Step a2, obtain the vegetation greenness value and grid meteorological data corresponding to each spatial grid, and calculate the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data.

[0084] Specifically, the leaf area index (Leaf Area Index, abbreviated as LAI) is used to represent the vegetation greenness value, and the change of vegetation greenness in the target basin is monitored by satellite remote sensing technology.

[0085] Furthermore, the target study area is divided into spatial grids such as 0.1°×0.1°, and software such as arcgis (geographic information system software platform) is used to divide the vegetation greenness value and meteorological data into 0.1°×0.1° spatial grid data, so as to obtain the vegetation greenness value and grid meteorological data corresponding to each spatial grid, calculate the SPI (Standardized Precipitation Index, abbreviated as the standardized precipitation index) according to the grid meteorological data, and then obtain the vegetation greenness value and standardized precipitation index corresponding to each spatial grid.

[0086] Step a3, based on the vegetation greenness value corresponding to each spatial grid and the standardized precipitation index corresponding to each spatial grid, use a machine learning algorithm to determine the relationship between the vegetation greenness value of the basin and the standardized precipitation index.

[0087] Specifically, assuming that the machine learning algorithm uses the BP (Back Propagation) neural network algorithm, taking the SPI and time (such as month) corresponding to each spatial grid as input data, and taking the vegetation greenness value LAI corresponding to each spatial grid as output data, use the above input data and output data to train the BP neural network to obtain the quantitative relationship between the vegetation greenness value of the basin and the standardized precipitation index. The expression of the relationship between the vegetation greenness value of the basin and the standardized precipitation index is as follows:

[0088] (3)

[0089] Furthermore, drought levels are measured by drought indices such as the Standardized Precipitation Index and the Standardized Precipitation-Evapotranspiration Index (SPEI for short). The SPI measures the shortage of precipitation at different time scales based on probability distributions, identifies meteorological drought events, and has advantages such as simple calculation, strong spatial comparability, and variable time scales. Taking the SPI as an example, the meteorological drought levels are divided as follows. The criteria for judging the severity of meteorological drought by dividing the magnitude of the SPI into meteorological drought levels are shown in Table 1:

[0090] Table 1:

[0091]

[0092] The above criteria are applicable to drought monitoring, assessment services, and scientific research in related fields such as meteorology, agriculture, and hydrology. They have good spatio-temporal adaptability and have been widely used, and are specifically divided into five categories: no drought, light drought, moderate drought, severe drought, and extreme drought.

[0093] Step S3022: Determine the Standardized Precipitation Index based on the meteorological data of the target basin. The Standardized Precipitation Index corresponds to the rapid alternation of drought and flood events.

[0094] Specifically, calculating the Standardized Precipitation Index based on the meteorological data of the target basin and combining the drought-to-flood events in the target basin with the criteria for drought severity in Table 1 can make the Standardized Precipitation Index SPI m correspond to the drought-to-flood events in the target basin.

[0095] Step S3023: Based on the Standardized Precipitation Index, determine the vegetation greenness value of the basin after the rapid alternation of drought and flood events using the relationship between the vegetation greenness value of the basin and the Standardized Precipitation Index.

[0096] Specifically, based on the Standardized Precipitation Index, use the relationship between the vegetation greenness value of the basin and the Standardized Precipitation Index in the above formula (3) to determine the vegetation greenness value of the basin after the rapid alternation of drought and flood events .

[0097] Step S303: Obtain the vegetation greenness value of the basin before the rapid alternation of drought and flood events, and perform basin flood process simulations on the vegetation greenness value of the basin before the rapid alternation of drought and flood events and the vegetation greenness value of the basin after the rapid alternation of drought and flood events respectively to obtain the evaluation result of the impact of extreme drought events on the basin flood process. For details, please refer to Figure 2 Step S203 of the embodiment shown, which will not be elaborated here.

[0098] The evaluation method for the impact of extreme drought events on the basin flood process provided by this embodiment realizes the accurate calculation of the basin vegetation greenness value after the drought-flood abrupt transition event by obtaining the relationship between the basin vegetation greenness value and the standardized precipitation index, and using the relationship between the basin vegetation greenness value and the standardized precipitation index based on the standardized precipitation index corresponding to the drought-flood abrupt transition event, laying a foundation for measuring the impact of the basin vegetation greenness value on the basin flood process in extreme drought events; by dividing the target research area to obtain multiple spatial grids, calculating the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data, and then using the machine learning algorithm to determine the relationship between the basin vegetation greenness value and the standardized precipitation index, laying a foundation for accurately evaluating the impact of extreme drought events on the basin flood process.

[0099] In this embodiment, an evaluation method for the impact of extreme drought events on the basin flood process is provided, which can be used for the above-mentioned electronic device. Figure 4 It is a flowchart of the evaluation method for the impact of extreme drought events on the basin flood process according to an embodiment of the present invention, as Figure 4 shown. The process includes the following steps:

[0100] Step S401, obtain the meteorological and hydrological data of the target basin, and determine the drought-flood abrupt transition event based on the meteorological and hydrological data of the target basin. For details, please refer to Figure 3 Step S301 of the embodiment shown, which will not be elaborated here.

[0101] Step S402, calculate the basin vegetation greenness value after the drought-flood abrupt transition event. For details, please refer to Figure 3 Step S302 of the embodiment shown, which will not be elaborated here.

[0102] Step S403, obtain the basin vegetation greenness value before the drought-flood abrupt transition event, and respectively simulate the basin flood process for the basin vegetation greenness value before the drought-flood abrupt transition event and the basin vegetation greenness value after the drought-flood abrupt transition event, to obtain the evaluation result of the impact of extreme drought events on the basin flood process.

[0103] Specifically, the above step S403 includes:

[0104] Step S4031, construct a basin eco-hydrological model.

[0105] Specifically, the watershed eco-hydrological model can adopt WaSSI (Water Supply Stress Index model), DTVGM-CASACNP (eco-hydrological two-way coupling model), CHANGE (change model), RHESSys (regional hydrological ecological simulation system), CEVSA model (carbon exchange between vegetation, soil and atmosphere model, a biogeochemical model that simulates the carbon exchange between vegetation, soil and atmosphere based on processes such as plant photosynthesis, respiration and soil microbial activities), BEPS model (borealecosystem productivity simulator, a computer simulation system for simulating the carbon balance of terrestrial ecosystems), IBIS model (Input / Output Buffer Information Specification, a method for quickly and accurately modeling I / OBUFFER based on the V / I curve), CASA (Carnegie-Ames-Stanford approach, a process-based remote sensing model), VIP model (vegetation interface process model), etc.; among them, the watershed eco-hydrological model can adopt WaSSI, which integrates a potential evapotranspiration model, a snowmelt model and the Sacramento model, and fully considers key water-carbon elements such as terrain undulation, soil moisture, vegetation conditions and water division, and has achieved good application results.

[0106] Furthermore, the watershed eco-hydrological model needs to calculate the reference evapotranspiration using the FAO Penman-Monteith (potential evapotranspiration in high-latitude regions) formula based on the input data. , and then, based on the internal relationship between the reference evapotranspiration and meteorological (precipitation, temperature, radiation, humidity, etc.) and vegetation characteristics (leaf area index), estimate the vegetation evapotranspiration potential of 10 different land use types in the watershed, including farmland, deciduous forest, evergreen forest, mixed forest, grassland, shrub, wetland, town, bare land and water area, without considering the soil moisture condition. The calculation formula for the vegetation evapotranspiration potential is as follows:

[0107] (4)

[0108] Where, represents the evapotranspiration potential considering the vegetation effect, that is, the vegetation evapotranspiration potential, is the precipitation, is the potential evapotranspiration, - is the empirical parameter.

[0109] Further, the Sacramento Hydrological Model (Sacramento Soil Moisture Accounting model, abbreviated as SAC model) is used to simulate the flood process of the basin, and main hydrological parameters such as actual evapotranspiration, surface runoff, subsurface flow and baseflow can be obtained through simulation. Among them, considering the influence of soil moisture on evapotranspiration, the value is used as the input parameter of the evaporation potential of the SAC model. The water for actual evapotranspiration in the carbon cycle module comes from the upper soil and the bound water in the lower layer. At the same time, the soil parameters required for the SAC model can be estimated using soil texture data. Among them, the SAC model is a lumped parameter type continuous operation deterministic model and a standard soil moisture content calculation model. The SAC model is based on the storage, infiltration, migration and evapotranspiration characteristics of soil moisture, and uses a series of mathematical expressions with certain physical concepts to describe each process of runoff formation. The state variables in the model represent a relatively independent characteristic in the hydrological cycle, and the model parameters have clear physical meanings and can be derived according to basin characteristics, rainfall and flow data.

[0110] Further, after simulating the flood process of the basin corresponding to the vegetation greenness value of the basin before and after the drought-flood rapid transition event using the basin eco-hydrological model, the flood change amount, peak flood change amount and flood volume change amount are obtained.

[0111] Step S4032, based on the vegetation greenness value of the basin before the drought-flood rapid transition event and the vegetation greenness value of the basin after the drought-flood rapid transition event, calculate the basin flood change data using the basin eco-hydrological model. Among them, the basin flood change data includes flood duration change data, peak flood change data and flood volume change data.

[0112] Specifically, select mild, moderate, severe and extreme drought-to-flood events, calculate the corresponding SPI and LAI, and calculate the corresponding flood process changes before and after considering the drought impact on LAI through the basin eco-hydrological model. The flood process impact is represented by the flood duration change , peak flood change , flood volume change . Among them, the calculation formula for the basin flood change data is:

[0113] (5)

[0114] (6)

[0115] (7)

[0116] Among them, represents mild, moderate, severe and extreme drought-flood rapid transition events, represents the vegetation greenness value of the basin after the drought-flood rapid transition event, Indicates the vegetation greenness value of the basin before the drought-flood abrupt alternation event. Indicates the basin flood process corresponding to the vegetation greenness value of the basin after simulating the drought-flood abrupt alternation event using the basin eco-hydrodynamic model. Indicates the basin flood process corresponding to the vegetation greenness value of the basin before simulating the drought-flood abrupt alternation event using the basin eco-hydrodynamic model. Indicates the standardized drought-flood abrupt alternation index after the drought-flood abrupt alternation event. Indicates the standardized precipitation index after the drought-flood abrupt alternation event.

[0117] Step S4033: Determine the evaluation result of the impact of extreme drought events on the basin flood process based on the basin flood change data.

[0118] The evaluation method for the impact of extreme drought events on the basin flood process provided in this embodiment constructs a basin eco-hydrodynamic model, calculates the basin flood change data using the basin eco-hydrodynamic model, and quantitatively evaluates the impact of vegetation greenness change on the basin flood process during the drought-flood abrupt alternation event. It has important value for comprehensively understanding the role of vegetation in the drought-flood abrupt alternation event and helps improve the accuracy of basin hydrological forecasting and early warning.

[0119] In this embodiment, an evaluation device for the impact of extreme drought events on the basin flood process is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0120] This embodiment provides an evaluation device for the impact of extreme drought events on the basin flood process, as Figure 5 shown. The device includes:

[0121] A determination module 501, configured to obtain the meteorological and hydrological data of the target basin and determine the drought-flood abrupt alternation event based on the meteorological and hydrological data of the target basin.

[0122] A calculation module 502, configured to calculate the vegetation greenness value of the basin after the drought-flood abrupt alternation event.

[0123] A simulation module 503, configured to obtain the vegetation greenness value of the basin before the drought-flood abrupt alternation event, simulate the basin flood process for the vegetation greenness value of the basin before the drought-flood abrupt alternation event and the vegetation greenness value of the basin after the drought-flood abrupt alternation event respectively, and obtain the evaluation result of the impact of extreme drought events on the basin flood process.

[0124] In some alternative implementation manners, the determination module 501 includes:

[0125] A first calculation unit for calculating a standardized drought-flood abrupt change index based on meteorological data of a target basin;

[0126] A comparison unit for comparing the standardized drought-flood abrupt change index with a drought-flood abrupt change assessment threshold and determining a drought-flood abrupt change event based on the comparison result.

[0127] In some alternative embodiments, the calculation module 502 includes:

[0128] An acquisition unit for acquiring the relationship between the vegetation greenness value of a basin and the standardized precipitation index.

[0129] A first determination unit for determining the standardized precipitation index based on meteorological data of the target basin; the standardized precipitation index corresponds to a drought-flood abrupt change event.

[0130] A second determination unit for determining the vegetation greenness value of the basin after a drought-flood abrupt change event based on the standardized precipitation index and using the relationship between the vegetation greenness value of the basin and the standardized precipitation index.

[0131] In some alternative embodiments, the acquisition unit includes:

[0132] A sub-division unit for acquiring a target study area, dividing the target study area, and obtaining a plurality of spatial grids.

[0133] A calculation sub-unit for acquiring the vegetation greenness value and grid meteorological data corresponding to each spatial grid and calculating the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data.

[0134] A determination sub-unit for determining the relationship between the vegetation greenness value of the basin and the standardized precipitation index by using a machine learning algorithm based on the vegetation greenness value corresponding to each spatial grid and the standardized precipitation index corresponding to each spatial grid.

[0135] In some alternative embodiments, the simulation module 503 includes:

[0136] A construction unit for constructing a basin eco-hydrological model.

[0137] A second calculation unit for calculating basin flood change data by using the basin eco-hydrological model based on the vegetation greenness value of the basin before a drought-flood abrupt change event and the vegetation greenness value of the basin after the drought-flood abrupt change event; wherein, the basin flood change data includes flood duration change data, flood peak change data, and flood volume change data.

[0138] A third determination unit for determining an evaluation result of the impact of an extreme drought event on the basin flood process based on the basin flood change data.

[0139] In some alternative embodiments, the calculation formula for the basin flood change data in the simulation module 503 is:

[0140]

[0141] Among them, represents mild, moderate, severe, and extreme drought-flood rapid alternation events, represents flood duration change data, represents flood peak change data, represents flood volume change data, represents the vegetation greenness value of the basin after the drought-flood rapid alternation event, represents the vegetation greenness value of the basin before the drought-flood rapid alternation event, represents the basin flood process corresponding to the vegetation greenness value of the basin after simulating the drought-flood rapid alternation event using the basin eco-hydrological model, represents the basin flood process corresponding to the vegetation greenness value of the basin before simulating the drought-flood rapid alternation event using the basin eco-hydrological model, represents the standardized drought-flood rapid alternation index after the drought-flood rapid alternation event, represents the standardized precipitation index after the drought-flood rapid alternation event.

[0142] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0143] The evaluation device for the impact of extreme drought events on the basin flood process in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0144] This embodiment of the present invention also provides a computer device having the above Figure 5 shown evaluation device for the impact of extreme drought events on the basin flood process.

[0145] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 6As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses can be used together with multiple memories if needed. Similarly, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 6 In the figure, a processor 10 is taken as an example.

[0146] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0147] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0148] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0149] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0150] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 can be connected through a bus or other means.Figure 6 Take the bus connection as an example.

[0151] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0152] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0153] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be invoked or provided. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include but are not limited to source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0154] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the impact of extreme drought events on flood processes in a river basin, characterized in that: The method comprises: Acquiring meteorological and hydrological data of a target watershed, and determining drought-flood sudden change events based on the meteorological and hydrological data of the target watershed; Calculate the greenness value of watershed vegetation after the drought-flood sudden change event; The greenness value of the watershed vegetation before the drought-flood sudden transition event is obtained, and the watershed flood process is simulated for the watershed vegetation greenness value before the drought-flood sudden transition event and the watershed vegetation greenness value after the drought-flood sudden transition event, respectively, to obtain the evaluation result of the impact of the extreme drought event on the watershed flood process; The basin flood process simulation is performed on the basin vegetation greenness value before the drought-flood sudden transition event and the basin vegetation greenness value after the drought-flood sudden transition event to obtain the evaluation result of the impact of the extreme drought event on the basin flood process, including: Constructing a watershed ecohydrological model; Based on the greenness value of the watershed vegetation before the drought-flood sudden change event and the greenness value of the watershed vegetation after the drought-flood sudden change event, the watershed flood change data is calculated using the watershed eco-hydrological model; wherein the watershed flood change data includes flood duration change data, flood peak change data and flood volume change data; An evaluation result of the impact of the extreme drought event on the flood process in the basin is determined based on the basin flood change data.

2. The method according to claim 1, characterized in that The determining of drought-flood sudden change events based on the meteorological and hydrological data of the target watershed includes: Calculating a standardized drought-flood transition index based on the meteorological data of the target watershed; The standardized drought-flood transition index is compared with a drought-flood transition assessment threshold, and the drought-flood transition event is determined based on the comparison result.

3. The method according to claim 1, characterized in that The calculating of the watershed vegetation greenness value after the drought-flood sudden change event includes: Obtain the relationship between the watershed vegetation greenness value and the standardized precipitation index; Determine a standardized precipitation index based on the meteorological data of the target watershed; the standardized precipitation index corresponds to the drought-flood abrupt transition event; Based on the standardized precipitation index, the greenness value of the watershed vegetation after the drought-flood sudden change event is determined by utilizing the relationship between the watershed vegetation greenness value and the standardized precipitation index.

4. The method according to claim 3, characterized in that The method of obtaining the relationship between the watershed vegetation greenness value and the standardized precipitation index includes: Acquire a target research area, and divide the target research area into multiple spatial grids; Obtaining vegetation greenness values ​​and grid meteorological data corresponding to each spatial grid, and calculating the standardized precipitation index corresponding to each spatial grid based on the grid meteorological data; Based on the vegetation greenness value corresponding to each spatial grid and the standardized precipitation index corresponding to each spatial grid, a machine learning algorithm is used to determine the relationship between the vegetation greenness value of the watershed and the standardized precipitation index.

5. The method according to claim 1, characterized in that The basin flood change data is calculated based on the basin vegetation greenness value before the drought-flood sudden change event and the basin vegetation greenness value after the drought-flood sudden change event using the basin eco-hydrological model; wherein the calculation formula for the basin flood change data is: in, Indicates mild, moderate, severe and extreme drought-flood transition events. Indicates flood duration change data, Indicates flood peak change data, Indicates flood volume change data, It indicates the greenness value of watershed vegetation after the drought-flood sudden change event. It indicates the greenness value of watershed vegetation before the drought-flood transition event. It indicates the basin flood process corresponding to the basin vegetation greenness value after the drought-flood sudden change event is simulated using the basin eco-hydrological model. It indicates that the basin flood process corresponding to the basin vegetation greenness value before the drought-flood sudden change event is simulated by using the basin eco-hydrological model. It represents the standardized drought-flood transition index after the drought-flood transition event. Represents the Standardized Precipitation Index after a drought-flood transition event.

6. An evaluation device for the impact of extreme drought events on flood processes in a river basin, characterized in that: The device comprises: A determination module, used for acquiring meteorological and hydrological data of a target watershed, and determining a drought-flood sudden change event based on the meteorological and hydrological data of the target watershed; A calculation module, used to calculate the greenness value of watershed vegetation after the drought-flood sudden change event; A simulation module is used to obtain the greenness value of the watershed vegetation before the drought-flood sudden transition event, and to simulate the watershed flood process for the watershed vegetation greenness value before the drought-flood sudden transition event and the watershed vegetation greenness value after the drought-flood sudden transition event, so as to obtain an evaluation result of the impact of the extreme drought event on the watershed flood process; The simulation modules include: Construction unit, used to construct the watershed ecohydrological model; The second calculation unit is used to calculate the basin flood change data based on the basin vegetation greenness value before the drought-flood sudden change event and the basin vegetation greenness value after the drought-flood sudden change event using the basin eco-hydrological model; wherein the basin flood change data includes flood duration change data, flood peak change data and flood volume change data; The third determination unit is used to determine the evaluation result of the impact of extreme drought events on the flood process in the basin based on the basin flood change data.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for evaluating the impact of extreme drought events on flood processes in a watershed as described in any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for evaluating the impact of extreme drought events on basin flood processes according to any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for evaluating the impact of extreme drought events on flood processes in a watershed as claimed in any one of claims 1 to 5.

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