CN117648863A - Regional water supply capacity acquisition method under special drought conditions based on water system communication - Google Patents
Regional water supply capacity acquisition method under special drought conditions based on water system communication Download PDFInfo
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Abstract
The regional water supply capacity acquisition method based on the water system communication special drought condition comprises the following steps of 1, accurately identifying historical special drought events and generating different-level drought scenes; step 2, identifying a regional water system communication state in a special drought scene based on remote sensing satellite data and a WRF-Hydro model; step 3, constructing a regional refined water resource allocation model; step 4, regional water resource allocation simulation under the communication state of different special drought scenes and water systems; and 5, calculating the limit water supply capacity and generating a water supply scheme under the special drought scene. According to the invention, through the fine simulation of the communication condition of various water source systems inside and outside the area, the aim of accurately evaluating the water supply capacity of the area under the condition of extra-large drought can be achieved, and the drought loss is reduced. The provided refined allocation model for regional water resources under the super-large drought condition is used for simulating the optimal allocation scheme for regional water resources in different water systems in the communication state under the super-large drought condition in a refined mode, pre-judging the water shortage risk, realizing the accurate assessment of water supply capacity and improving the overall allocation capacity of the water resources.
Description
Technical Field
The invention relates to the field of regional water supply calculation, in particular to a regional water supply capacity acquisition method under an oversized drought condition based on water system communication engineering.
Technical Field
The regional water supply capacity is an important index for reflecting the adaptability of regional water resources supply and demand, and is an important reference for determining the hydraulic engineering planning and construction scheme. Current global climate warming exacerbates water circulation activity and increases the risk of extreme climate events such as extra drought. The regional extra-large drought refers to a serious unbalance event of regional water resource supply and demand caused by long-time lack of precipitation or insufficient precipitation, has the characteristics of long duration, wide influence range, high strength and the like, and severely restricts a series of activities such as agricultural production, economic development, ecological protection and the like of the human society. The water quantity flowing into reservoirs, wetlands and underground aquifers during extra-large drought period is reduced, the communication degree between different water supply sources is reduced along with the time, and the water supply capacity in the area and the water resource transmission capacity among the areas are reduced.
At present, the main stream method for calculating regional water supply capacity is based on a single hydraulic engineering or regional hydraulic engineering group, combines the water supply condition of a water supply system and the regional water consumption condition, and considers the hydraulic engineering operation mode to carry out generalized simulation calculation of the whole scene. The current method does not consider the influence of the water system connectivity reduction and temporary water system communication engineering between the inside of the area and the area on the water supply capacity of the area under the ultra-large drought condition. In order to effectively cope with oversized drought and better coordinate and utilize various drought-resistant water sources in an area to reduce drought loss, a calculation method of water supply capacity of each water source at a water supply side is required to be provided for oversized drought situations.
Disclosure of Invention
Aiming at the problems, the invention aims to provide the regional water supply capacity acquisition method based on the refined description of the communication conditions of various water source water systems inside and outside the region aiming at the regional water supply capacity problem under special dry events. Aiming at the problem of unmatched supply and demand of regional water resources, the feasibility of water system communication of different regions is evaluated by identifying hydraulic connection among different regions under the super-drought condition, the regional water resource transmission capacity under different water system communication states is simulated, and the limit water supply capacity under the super-drought condition is calculated. The water supply scheme of the water resource system can be selected by simulating and calculating the combined scenes of different water supply schemes, so that the overall allocation capacity of the water resource can be improved, and special drought disasters can be resisted. In order to solve the problem, the invention mainly adopts the following technical scheme:
on the one hand, the application provides a regional water supply capacity acquisition method based on water system communication under special drought conditions, which comprises the following steps:
step 1, accurately identifying historical oversized drought events and generating drought scenes of different grades:
collecting historical long-series meteorological hydrological data of a calculation area, constructing a standardized rainfall index (SPI), and accurately identifying historical oversized drought events in the area; for the scene generation problem of the extra-large drought event, identifying regional drought weather characteristic factors through information and Hampel criteria, constructing a VMD-SSA-BILSTM machine learning model to learn the mapping relation between drought weather driving factors and the drought event, and simulating the drought weather driving factors according to the future weather mode to obtain the multi-state extra-large drought event.
Step 2, identifying a regional water system communication state in a special drought scene based on remote sensing satellite data and a WRF-Hydro model:
aiming at the problem of identifying the regional water system communication state in a special drought scene, the WRF-Hydro model is utilized to calculate the simulated regional runoff and water system communication state. The satellite remote sensing image is used as basic data, the ENVI software is used for carrying out preliminary extraction verification on river water systems of the area by using a multiband method, then a decision tree classification method is used for further extracting needed water system connection conditions, and water system morphological index parameter values of different drought events are calculated.
And 3, constructing a regional refined water resource allocation model:
the regional attribute gridding, the regional water demand gridding and the water supply project gridding are used for obtaining the water demand and the water supply capacity of each calculation cell of the region, the topological relation between the water demand of each grid and the corresponding water supply source is established, the basic guarantee water supply and other water supply benefits and related weights are calculated, the water supply economic benefit is maximized, and a regional refined water resource allocation model is established.
Step 4, regional water resource allocation simulation under different special drought scenes and water system communication states:
and (3) carrying out cluster analysis on the special drought scene generated in the step (1) to extract a typical special drought scene based on the regional refined water resource allocation model. And calculating the abundant meeting probability of different water supply pixels through copula, and analyzing the water system connectivity lifting space in a special drought scene. And constructing regional water resource allocation models under different special drought scenes and water system communication states, accurately solving by using a Gurobi solver, and performing regional water resource allocation simulation.
Step 5, calculating the limit water supply capacity and generating a water supply scheme under the special drought scene:
according to the simulation allocation results of water resources of different special drought scenes, the relation between the water system communication capacity lifting cost and the total water supply benefit of the balanced region is comprehensively balanced, the sustainable utilization limit of water sources is considered, the optimal water supply scheme of the region in the special drought scenes is optimized, and the regional limit water supply capacity in the special drought scenes is calculated.
Further, in the above technical scheme, the specific process of accurately identifying the historical oversized drought event in the step 1 and generating the drought scene with different grades is as follows:
step 1.1, collecting and calculating regional historical long-series meteorological hydrologic data, and identifying regional oversized drought historical events by adopting a standardized rainfall index (SPI).
Step 1.2, generating a scene of an extra-large drought event of the region, and identifying a drought weather driving factor of the region through mutual information and Hampel criterion; constructing a VMD-SSA-BILSTM machine learning model, and learning a mapping relation between drought weather driving factors and drought events; and simulating drought weather driving factor data obtained according to a future weather mode on the basis to obtain a multi-state extra-large drought event.
Furthermore, in the above technical solution, the specific process of identifying the special dry event in the step 1.1 is as follows:
And 1.1.1, collecting regional meteorological element data and runoff data.
Step 1.1.2 describes the variation of precipitation by adopting the f distribution probability, and then obtains an SPI value through normal standardization:
wherein:probability of P precipitation, when P>S=1 when 0.5, and P is less than or equal to 0.5; further s= -1, c 0 ＝2.52，c 1 ＝0.80，c 2 ＝0.01，d 1 ＝1.43，d 2 ＝0.19，d 3 ＝0.001。S、c 0 、c 1 、c 2 、d 1 、d 2 、d 3 Are all process parameters in calculation. Calculating values of regional long series SPI
Step 1.1.3, according to drought grade standard, when SPI is more than or equal to-2.0, the drought is special. And determining the regional oversized drought history event by judging the SPI value of the region.
Furthermore, in the above technical solution, the specific process of generating the oversized drought scene in the step 1.2 is as follows:
step 1.2.1 first calculates the correlation between the meteorological elements. Let the probability distribution of the continuous variable x be P (x), the probability distribution of the continuous variable y be P (x), P (x, y) represent the joint probability distribution of two random variables x, y, then the interaction entropy of the finite sampling sequence of two continuous variables is:
wherein: n is the sampling number of the two-variable sequence, P (x i ) Represents x i Probability of occurrence, P (y i ) Representing y i Probability of occurrence, P (x i ，y i ) Represents x i ，y i Probability of occurrence. The interactive entropy reflects the dependency relationship between the two variables, and the interactive entropy of the dependency relationship between all meteorological elements is obtained through calculation.
The meteorological elements comprise precipitation, air temperature, air pressure, humidity, wind speed, wind direction, evaporation capacity and sunlight intensity.
Step 1.2.2, performing outlier calculation according to Hampel criterion according to the calculated interaction entropy, as follows:
wherein: h j Is Hampel distance, 1.4826 is normalization factor, d j D, for each absolute position of interaction entropy from the center interaction entropy j (0.5) Is d j M is the cross entropy of each factor, M (0.5) Is the median of the interactive entropy values in each meteorological element. According to the standard deviation 3 sigma criterion, when the Hampel distance is larger than 3, the corresponding meteorological elements are selected as drought meteorological driving factors.
Step 1.2.3 performing Variation Modal Decomposition (VMD) on the selected drought weather driving factor long series data, and decomposing the data into a plurality of eigenmode functions u k And find each u k Is a single spectrum of (1), for each u k Frequency modulation is carried out, and finally constraint conditions are added to obtain a constrained variation model:
wherein omega is k The frequency center of each order modal component obtained by the decomposition is assumed; mu (mu) k Is the modal component of the kth signal component; f is drought weather driving factor long series data; delta (t) is a pulse function;indicating a deviation of t. The above problem is converted into an unconstrained variational problem by a lagrangian function:
Wherein alpha is penalty factor; lambda is the lagrange multiplier. And then solving by using an alternate multiplier direction algorithm, updating the center frequency and the bandwidth of each data signal component, and extracting the periodicity rule of each candidate factor.
And 1.2.4, taking the modal periodic component of the selected drought weather driving factor as the input quantity of the machine learning model and the regional SPI value as the output quantity, and establishing a BiLSTM bidirectional long-short-term memory network model. The first 2/3 data are selected from the samples as training samples, and the last 1/3 data are selected as test samples. And (3) carrying out normalization processing on the sample data, and then inputting the sample data into a BiLSTM model for learning training. The calculation is as follows:
wherein: p is p * Normalizing the data; p raw data; p is p min The minimum value of the original data; p is p max Raw data maximum.
And 1.2.5, performing parameter optimization of a two-way long-short-term memory network (BiLSTM) model by using a Sparrow Search Algorithm (SSA) to obtain optimal parameters of the model, and constructing a mapping function relation between drought weather driving factors and oversized drought events.
And 1.2.6, calculating to obtain a regional drought weather driving factor change sequence of 100 years in the future by using a regional weather mode model, and inputting the regional drought driving factor change sequence into a mapping function relation to generate a multi-state oversized drought event.
Further, in the above technical solution, the specific process of identifying the regional water system communication state in the special drought scene in step 2 is as follows:
and 2.1, using satellite remote sensing images as basic data, using ENVI software to preliminarily extract river systems of areas by a multiband method, further extracting area water system connection conditions by using decision tree classification, and calculating water system morphological index parameter values of different drought events.
And 2.2, simulating and calculating the relation between regional runoff and water system communication conditions by constructing a WRF-Hydro coupling model, verifying and calibrating by utilizing the result in the last step, and identifying the water system communication states under different drought events.
Furthermore, in the above technical solution, the specific process of the step 2.1 of preliminary extraction of the river water system in the area is as follows:
and 2.1.1, acquiring a remote sensing image picture of the calculated area, and performing splicing processing.
And 2.1.2, screening according to the spliced regional remote sensing images and combining the regional historical drought event time period obtained in the step 1.
Step 2.1.3, performing radiation correction and geometric correction on the screened regional remote sensing image through EVNI, wherein the sensor radiation calibration is converted into atmospheric reflectivity for calculation:
Wherein L is λ Is the spectral emissivity; d is the distance between the day and the earth in astronomical units; ESUN (electronic service unit) λ Is the average value of the apparent solar emissivity; θ S Is the solar altitude in degrees. Atmospheric radiation correction is performed by the dark pixel method:
wherein R is the surface reflectivity of the ground object, L is apparent radiance, L p For the purpose of range radiation, T φ Is the atmospheric transmissivity of the ground object to the reflection direction of the sensor, T θ E for atmospheric transmittance in the direction of incidence of solar radiation 0 Solar spectrum irradiance of corresponding wavelength outside the atmosphere layer, theta is solar zenith angle and E D For spectral irradiance from sky light to ground surface, φ is the zenith angle of the satellite sensor. And obtaining a corrected regional remote sensing image.
Step 2.1.4, calculating the normalized differential water index to extract the water information in the image:
wherein Green represents a Green light band; NIR stands for near infrared band.
And 2.1.5, extracting the water system based on a decision tree of a knowledge expert classifier. Starting from the root node, for each non-leaf node, finding out an attribute in the corresponding sample set, testing the sample set, dividing the training sample set into a plurality of sub-sample sets according to different test results, forming a new leaf node by each sub-sample set, and repeating the dividing process for the new leaf node, so that the process is continuously circulated until a specific termination condition is reached.
And 2.1.6, constructing a water system graph model, and judging whether the graph model is a connected graph or not. For two points V in the water system diagram 1 And V 2 If two points are connected by a path, defining the communication between the two points; if no path is connected between the two points, the two points are defined to be not communicated. For all the peaks in the water system diagram, if any two points are connected by a path, the water system diagram is called a connected diagram, otherwise, the water system diagram is called a non-connected diagram.
And 2.1.7, calculating structural connectivity of the water system in the regional river network. The water system ring reflects the capability of each node material energy exchange in the river network water system:
wherein n is the number of connecting lines in the graph model, and n is more than or equal to 3; v is the number of nodes in the graph model. The node connection rate reflects the strength of each node in the river network water system in connection with the water system:
β＝n/v
the connectivity of the water systems reflects the connectivity strength and the water transfer capacity between river network water systems:
χ＝n/3(v-2)
the point connectivity is the number of cut points removed by the connectivity map becoming non-connectivity map.
Furthermore, in the above technical solution, the specific process of identifying the water system connection state under different drought events in the step 2.2 is as follows:
step 2.2.1 running WPS (WRF Preprocessing System) module, WRF pre-processing is performed on the calculation region. Creating static ground data (such as terrain, soil type, and land utilization, etc.), reading meteorological data and interpolating its level into a pattern field. Version WRFV3.8.1 was employed as a fully compressible non-static mesoscale mode using ArakawaC grid points in the horizontal direction and topography following the static barometric coordinate system in the vertical direction, simulating an atmospheric process on the scale of hundreds to thousands of kilometers.
And 2.2.2, dividing the regional WRF mode setting into a 4-layer nesting mode, setting two-way feedback between different nesting layers, and performing WRF process simulation. The main physical process parameterization scheme is as follows: the planetary boundary layer scheme adopts a Yonsei University (YSU) scheme, the long wave radiation scheme adopts an RRTM scheme, the short wave radiation scheme adopts a Dudhia scheme, the land process scheme adopts a Noah scheme, the microphysics process scheme adopts a Thompson scheme, and the cloud accumulation convection adopts a Kain-Fritsch (new Eta) scheme.
And 2.2.3, constructing a regional WRF-Hydro complete distributed hydrologic model, and performing parameter calibration and verification of the model by utilizing the water system communication condition extracted based on the remote sensing satellite and the collected actual measurement runoff data in the step 2.1.
And 2.2.4, simulating runoff conditions of different special drought scenes of the generated area by using the rated WRF-Hydro model and taking climate mode data as input, and identifying the regional water system communication state of the special drought event by using the method in the step 2.1.5.
Further, in the above technical scheme, the specific process of constructing the refined water resource allocation model in the step 3 is as follows:
and 3.1, regional attribute meshing, regional water demand meshing and water supply engineering meshing are carried out to obtain regional water demand and water supply capacity, regional basic guaranteed water supply and other functional water supply benefits and related weights are calculated, and a topological relation between the water demand of each calculated cell and corresponding water supply sources is established.
And 3.2, constructing an area refined water resource allocation model with the aim of maximizing the economic benefit of water supply.
Furthermore, in the above technical solution, the specific process of calculating the water supply and demand in the step 3.1 is as follows:
and 3.1.1, collecting remote sensing data of the land utilization type and the planting structure of the area and carrying out regional statistics on the annual photo.
And 3.1.2, performing superposition processing on the regional land utilization type and the planting structure remote sensing data obtained in the step 3.1.1 by utilizing the ArcGIS software processing function, and generating a regional gridding result. Correction is performed by checking with regional statistics annual certificates and checking on site.
And 3.1.3, calculating the basic assurances of each grid point, the water demand of other requirements and the two-level water supply benefits according to the regional grid result and the corresponding water quota in the previous step. And calculating the water benefit coefficient of the water-requiring unit according to the weight of the two-level water supply benefits of the region.
Step 3.1.4 the analysis area may develop a water source type based on the collected data, including river and water supply distribution and profile data. And determining the water supply range of each water source through the regional grid result and the regional water system map, and performing regional water supply meshing.
And 3.1.5, constructing a topological relation between the water supply and demand pixels and the water supply pixels according to the regional water supply and demand grids obtained in the steps.
Furthermore, in the above technical solution, the specific process of model construction in the step 3.2 is as follows:
step 3.2.1 is based on supply and demand water topology relations to hierarchically represent the topological relation between the water demand of each unit and the available water supply of each water source:
X i ＝Y 1 +Y 2 +Ω+Y j
wherein: y is Y j Indicating the type of water supply source for the i-th unit water demand.
Step 3.2.2, analyzing water resource allocation constraint conditions: including restrictions on the water supply unit; a limitation of the total water supply amount; non-negative constraints of water supply variables; limiting the water demand of users; restriction of water supply order; and (5) limitation of water supply range.
Step 3.2.3, taking the maximum economic benefit of regional water supply as an objective function:
wherein: j is the number of water sources; i is the total number of cells; b i The water benefit coefficient of the ith water-requiring unit; c i A water supply cost factor for the ith unit; alpha i Supplying the ith unit with a priority coefficient, lambda i The water supply sequence coefficient of the j-th water source, x ij And supplying water supply quantity of the ith unit cell for the jth water source. And constructing and obtaining a regional refined water resource allocation model.
Further, in the above technical solution, the specific process of the regional water resource allocation simulation in the step 4 is as follows:
and 4.1, carrying out cluster analysis on the special drought scene to extract a typical special drought scene, calculating the abundant meeting probability of different water supply pixels through copula, and analyzing the water system communication capacity lifting space under the special drought scene.
And 4.2, constructing regional water resource allocation models under different special drought scenes and water system communication states based on the regional refined water resource allocation models, accurately solving by using a Gurobi solver, and performing regional water resource allocation simulation.
Furthermore, in the above technical solution, the specific process of the typical scenario extraction and water system communication lifting capacity analysis in the step 4.1 is as follows:
step 4.1.1, carrying out maximum normalization processing on the regional drought scene obtained in the step 1 to obtain a normalized scene set { W }; k scene objects are selected as initial clustering centers, and then the mixing metric of all scene objects in { W } and the initial clustering centers is measured:
MLX＝ωD(x,y)-(1-ω)cos(x,y)
and dividing the similar classification according to the minimum allocation principle, repeating calculation to obtain a clustering center, and outputting the typical special drought scene of the optimal region when the clustering center is not changed. Wherein ω is a weight coefficient, D (x, y) is the euclidean distance of the two feature drought scenes, and cos (x, y) is the cosine similarity of the two feature drought scenes.
And 4.1.2, firstly constructing an edge distribution function of regional water resource allocation water supply unit runoff, calculating a two-dimensional joint distribution function between water supply units without water system communication relation, and optimizing by using a square root error method (RMSE), an AIC criterion method and a BIC method.
And 4.1.3, calculating the probability of the combined risk of the abundant withering between the two water supply units according to the copula function of the two-dimensional combined distribution of the different water supply units. And determining a threshold value of the water system communication capacity lifting space among the water supply units according to the comprehensive area abundant probability. Wherein, the combined risk probability of the full-length and full-length is same as that of the full-length and full-length.
Furthermore, in the above technical solution, the specific process of the regional water resource allocation simulation in the step 4.2 is as follows:
and 4.2.1, based on the regional water resource refined allocation model in the step 3, taking the runoff quantity and the unit layering water demand quantity under a typical drought scene as input, taking different water system communication schemes as constraints, maximizing the economic benefit of regional water supply as an objective function, and constructing the regional water resource allocation model under different water system communication schemes.
And 4.2.2, compiling and solving the model by utilizing a Gurobi solver package in Python to obtain regional water resource allocation simulation results under different water system communication schemes.
Further, in the above technical solution, the specific process of calculating the regional limit water supply capacity and generating the water supply scheme in the step 5 is as follows:
and 5.1, comprehensively analyzing the relation between the regional water system communication capacity lifting cost and the regional basic guaranteed water supply and other functional water supply benefits according to the water resource simulation allocation results of different special drought scenes.
And 5.2, considering the sustainable utilization limit of the water source, preferably selecting an optimal water supply scheme of the area in the ultra-large drought scene, and calculating to obtain the limiting water supply capacity of the area in the ultra-drought scene.
Furthermore, in the above technical solution, the specific process of analyzing the relationship between the cost and benefit of improving the regional water system connectivity in the step 5.1 is as follows:
and 5.1.1, carrying out water system communication engineering cost accounting on the different water system communication construction schemes of the area obtained by simulation in the step 4, and obtaining scheme construction cost for improving different water system communication capacity.
And 5.1.2, calculating to obtain regional water resource allocation benefits under different water system communication schemes according to different weight coefficients of regional foundation guaranteed water supply and other functional water supply requirements based on the water resource simulation allocation results of different special drought scenes.
And 5.1.3, constructing mapping relations between regional water resource allocation cost and benefits under different water system communication schemes according to the calculation results of the cost and the benefits of the steps.
Furthermore, in the above technical solution, the specific process of calculating the regional limit water supply capacity in the special drought scene in step 5.2 is as follows:
and 5.2.1, calculating a long-term supplement and discharge relation and runoff trend of the water supply source according to the long-series runoff data of the regional water supply unit, defining a water supply threshold of the water supply unit, and eliminating the scheme which does not accord with the sustainable utilization principle of water resources.
And 5.2.2, based on simulation results of different water system communication schemes of a typical special drought scene, improving cost and regional water resource allocation benefit according to different water system communication capabilities, and comprehensively comparing and selecting to obtain a regional optimal water system communication scheme.
And 5.2.3, inputting the typical oversized drought scene and the optimal water system communication scheme into the regional water resource refined allocation model, and calculating and counting to obtain regional water supply capacity.
On the other hand, the application also provides a regional water supply capacity acquisition system based on water system communication under special drought conditions, which comprises a processor and a memory; the processor is in communication connection with the memory, and the processor is used for reading the computer program from the memory and executing the computer program so as to realize the regional water supply capacity acquisition method based on water system communication under the special drought condition.
The beneficial effects of the invention are as follows:
(1) For drought-resistant work of the area under the super-drought scene, the invention can achieve the aim of accurately evaluating the water supply capacity of the area under the super-drought condition by finely simulating the communication condition of various water source systems inside and outside the area. Various water sources in the region can be better coordinated and utilized in drought-resistant work, and a water supply scheme for improving regional water supply capacity under special drought events is determined, so that drought loss is reduced.
(2) The invention provides a complete set of regional water resource refined allocation model based on a water system communication super-drought condition based on a water system communication concept. Through the model, the optimal allocation scheme of the regional water resources under different water system communication states of the super-drought conditions can be obtained through fine simulation calculation, the water shortage risk of the region under the super-drought conditions is prejudged, the precise assessment of the regional water supply capacity under the super-drought conditions is realized, the overall allocation capacity of the water resources is improved, and the super-drought disasters are resisted. Compared with a conventional water resource allocation model and a water supply capacity acquisition method under the condition of extra-large drought, the method has obvious advantages.
Drawings
Fig. 1 is a schematic overall flow diagram of a regional water supply capacity acquisition method under special drought conditions based on water system communication.
Fig. 2 is a diagram of a water resource unit allocation node in Chuxiong state in an embodiment.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples, it being noted that the examples herein are intended to provide a more thorough understanding and explanation of the invention, and are not intended to limit the invention.
The invention provides a regional water supply capacity acquisition method based on water system communication under special drought conditions, which aims to solve the problem of regional drought-resistant water source overall allocation maximization utilization under special drought conditions, and specifically comprises the following steps:
and (3) accurately identifying historical oversized drought events and generating drought scenes of different grades.
In this example, the calculation is performed in Chuxiong area. Specifically, the allocation calculation units of the Chuxiong water resource system are divided according to a water resource system generalization method, the allocation diagram is shown in fig. 2, and the water demand of the relevant allocation units is obtained.
Further, the specific implementation process of the step is as follows:
step 1.1, collecting and calculating historical long-line meteorological hydrologic data and literature records of the Chuxiong state, and constructing a standardized rainfall index (SPI) which is used for identifying extra-large drought historical events of the Chuxiong state.
The method specifically comprises the following steps:
and 1.1.1, collecting weather element data and runoff data of Chuxiong state.
Step 1.1.2 describes the variation of precipitation by adopting the gamma distribution probability, and obtains SPI value by normal standardization:
wherein:p is the probability of precipitation, when P>At 0.5, s=1; when P is less than or equal to 0.5, S= -1. In addition c 0 ＝2.52，c 1 ＝0.80，c 2 ＝0.01，d 1 ＝1.43，d 2 ＝0.19，d 3 ＝0.001。S、c 0 、c 1 、c 2 、d 1 、d 2 、d 3 Are all process parameters in calculation. Calculating the value of Chuxiong state length series SPI.
Step 1.1.3 determines an oversized drought history event in Chuxiong state according to drought grade criteria, and in the embodiment, when SPI is greater than or equal to-2.0, the oversized drought history event is identified.
Step 1.2, for scene generation of a Chuxiong extra-large drought event, identifying and calculating an area drought weather driving factor through mutual information and Hampel criteria, constructing a VMD-SSA-BILSTM machine learning model to learn a mapping relation between the drought weather driving factor and the drought event, and constructing the multi-state extra-large drought event according to simulated drought weather driving factor data obtained by a future climate mode.
The step 1.2 specifically comprises the following steps:
and step 1.2.1, calculating the correlation among weather elements in Chuxiong state. Let the probability distribution of the continuous variable x be P (x), the probability distribution of the continuous variable y be P (x), P (x, y) represent the joint probability distribution of two random variables x, y, then the interaction entropy of the finite sampling sequence of two continuous variables is:
Wherein: n is the sampling number of the two-variable sequence, p (x i ) Represents x i Probability of occurrence, p (y i ) Representing y i Probability of occurrence, p (x i ，y i ) Represents x i ，y i Probability of occurrence. And calculating to obtain the interactive entropy of the dependent relation among all the meteorological elements.
The meteorological elements comprise precipitation, air temperature, air pressure, humidity, wind speed, wind direction, evaporation capacity and sunlight intensity.
Step 1.2.2, performing outlier calculation according to Hampel criterion according to the calculated interaction entropy, as follows:
d j ＝|M-M (0.5) |
wherein: h j Is Hampel distance, 1.4826 is normalization factor, d j D, for each absolute position of interaction entropy from the center interaction entropy j (0.5) Is d j M is the cross entropy of each factor, i.e. the result calculated in step 1.2.1, M (0.5) Is the median of the interactive entropy values in each meteorological element. And according to a standard deviation 3 sigma criterion, when the Hampel distance is larger than 3, selecting the corresponding meteorological elements as drought meteorological driving factors in Chuxiong state.
Wherein, the drought weather driving factors of the Chuxiong state include precipitation, air temperature, humidity and evaporation capacity.
Step 1.2.3, carrying out Variational Modal Decomposition (VMD) on the selected Chuxiong drought weather driving factor long series data, and respectively decomposing the data into a plurality of eigenmode functions u k And find each u k Is a single spectrum of (1), for each u k Frequency modulation is carried out, and finally constraint conditions are added to obtain a constrained variation model:
wherein omega is k The frequency center of each order modal component obtained by the decomposition is assumed; mu (mu) k Is the modal component of the kth signal component; f is drought weather driving factor long series data; delta (t) is a pulse function;indicating that t is biased and t is time. The above problem is converted into an unconstrained variational problem by a lagrangian function:
wherein alpha is penalty factor; lambda is the Lagrangian multiplier; f (t) is total long series data, u k (t) denotes the kthA signal component. And then solving by using an alternate multiplier direction algorithm, updating the center frequency and the bandwidth of each data signal component, and extracting the periodicity rule of the Chuxiong drought weather driving factor.
And 1.2.4, taking the modal periodic component of the selected Chuxiong drought weather driving factor as the input quantity of the machine learning model and the regional SPI value as the output quantity, and establishing a BiLSTM bidirectional long-short-term memory network model. The first 2/3 data are selected from the samples as training samples, and the last 1/3 data are selected as test samples. And (3) carrying out normalization processing on the sample data, and then inputting the sample data into a BiLSTM model for learning training. The calculation is as follows:
Wherein: p is p * Normalizing the data; p raw data; p is p min The minimum value of the original data; p is p max Raw data maximum.
In the embodiment, the samples are long-series data of drought driving meteorological factors in various Chuxiong states, and can be obtained from a national meteorological website.
And 1.2.5, performing parameter optimization of a two-way long-short-term memory network (BiLSTM) model by using a Sparrow Search Algorithm (SSA) to obtain optimal parameters of the model, and constructing a mapping function relation between drought weather driving factors and oversized drought events in the Chu-Xiong area.
Step 1.2.6, predicting a drought weather driving factor change sequence in the Chu-Xiong state in the future 100 years by using a regional climate model, and inputting the obtained drought weather driving factor sequence into a mapping function relation to generate a multi-state extra-large drought event in the Chu-Xiong state.
And 2, identifying the regional water system communication state in the special drought scene based on remote sensing satellite data and the WRF-Hydro model.
In this example, step 2 specifically includes:
and 2.1, primarily extracting river water systems of Chuxiong states by using a multiband method by using an ENVI software by using satellite remote sensing images as basic data, further extracting the connection condition of the water systems of the Chuxiong states by using a decision tree classification method, and calculating morphological index parameter values of the water systems of the Chuxiong states of different drought events.
And 2.1.1, obtaining a calculated Chuxiong remote sensing image picture, and performing splicing treatment.
And 2.1.2, screening according to the spliced Chuxiong state remote sensing image and combining the Chuxiong state historical drought event time period obtained in the step 1.
Step 2.1.3, performing radiation correction and geometric correction on the screened Chuxiong state remote sensing image through EVNI, wherein the sensor radiation correction is converted into atmospheric reflectivity for calculation:
wherein L is λ Is the spectral emissivity; d is the distance between the day and the earth in astronomical units; ESUN (electronic service unit) λ Is the average value of the apparent solar emissivity; θ S Is the solar altitude in degrees. Atmospheric radiation correction is performed by the dark pixel method:
wherein R is the surface reflectivity of the ground object, L is apparent radiance, L p For the purpose of range radiation, T φ Is the atmospheric transmissivity of the ground object to the reflection direction of the sensor, T θ E for atmospheric transmittance in the direction of incidence of solar radiation 0 Solar spectrum irradiance of corresponding wavelength outside the atmosphere layer, theta is solar zenith angle and E D For spectral irradiance from sky light to ground surface, φ is the zenith angle of the satellite sensor. And obtaining the corrected Chuxiong state remote sensing image.
Step 2.1.4, calculating the normalized differential water index to extract the water information in the image:
Wherein Green represents a Green light band; NIR stands for near infrared band.
And 2.1.5, extracting Chuxiong water system based on a decision tree of a knowledge expert classifier. Starting from the root node, for each non-leaf node, finding out an attribute in the corresponding sample set, testing the sample set, dividing the training sample set into a plurality of sub-sample sets according to different test results, forming a new leaf node by each sub-sample set, and repeating the dividing process for the new leaf node, so that the process is continuously circulated until a specific termination condition is reached.
And 2.1.6, constructing a Chuxiong state water system diagram model, and judging whether the diagram model is a connected diagram. For two points V in the water system diagram 1 And V 2 If two points are connected by a path, the two points are defined to be communicated, if no path is connected between the two points, the two points are defined to be not communicated, and if any two points are connected by a path, the water system diagram is called a communication diagram, otherwise, the water system diagram is called a non-communication diagram.
And 2.1.7, calculating structural connectivity of the water system in the Chu-Xiongzhou river network. The water system ring reflects the capability of each node material energy exchange in the river network water system:
Wherein n is the number of connecting lines in the graph model, and n is more than or equal to 3; v is the number of nodes in the graph model. The node connection rate reflects the strength of each node in the river network water system in connection with the water system:
b＝n/v
the connectivity of the water systems reflects the connectivity strength and the water transfer capacity between river network water systems:
c＝n/3(v-2)
the point connectivity is the number of cut points removed by the connectivity map becoming non-connectivity map.
And 2.2, calculating and simulating the relation between the runoff of Chu-Xiongzhou and the water system communication condition by constructing a WRF-Hydro coupling model, verifying and calibrating by utilizing the result in the last step, and identifying the water system communication state under different drought events.
Step 2.2.1 runs WPS (WRFP reprocessing System) module to perform WRF pretreatment on the calculated Chu. Creating static ground data (such as terrain, soil type, and land utilization, etc.), reading meteorological data and interpolating its level into a pattern field. Version WRFV3.8.1 is used in this embodiment as a fully compressible non-static mesoscale mode, using ArakawaC grid points in the horizontal direction and topography following the static barometric coordinate system in the vertical direction, simulating an atmospheric process of hundreds to thousands of kilometers in scale.
And 2.2.2, dividing the Chuxiong WRF mode setting into a 4-layer nesting mode, setting different nesting layers into two-way feedback, and performing WRF process simulation. The main physical process parameterization scheme is as follows: the planetary boundary layer scheme adopts a Yonsei University (YSU) scheme, the long wave radiation scheme adopts an RRTM scheme, the short wave radiation scheme adopts a Dudhia scheme, the land process scheme adopts a Noah scheme, the microphysics process scheme adopts a Thompson scheme, and the cloud accumulation convection adopts a Kain-Fritsch (new Eta) scheme.
And 2.2.3, constructing a Chuxiong WRF-Hydro complete distributed hydrologic model, and performing parameter calibration and verification of the model by utilizing the water system communication condition extracted in the step 2.1 based on the remote sensing satellite and the collected measured runoff data.
And 2.2.4, simulating and generating runoff conditions in different special drought scenes of Chu-XiongZhou by using the WRF-Hydro model after the calibration and taking climate pattern data as input, and identifying the water system communication state of the Chu-XiongZhou in the special drought event by using the method in the step 2.1.5.
And 3, constructing a regional refined water resource allocation model.
In this example, step 3 specifically includes:
and 3.1, performing grid formation on the property of Chuxiong state, performing grid formation on water demand of Chuxiong state and performing grid formation on water supply to obtain water demand and water supply quantity of Chuxiong state, calculating basic guaranteed water supply and other functional water supply benefits and related weights of the areas, and establishing a topological relation between the water demand of each calculation cell and corresponding water supply sources.
The method specifically comprises the step of collecting remote sensing data of land utilization types and planting structures in Chuxiong state and statistics annual certificates in Chuxiong state in step 3.1.1.
And 3.1.2, performing superposition processing on the land utilization type and the planting structure remote sensing data of the Chuxiong state obtained in the step 3.1.1 by utilizing the ArcGIS software processing function, and generating a grid result of the Chuxiong state. And correcting by checking with Chuxiong statistical annual book and checking on site.
And 3.1.3, calculating the basic assurances of each grid point, the water demand of other requirements and the two-level water supply benefits according to the Chu-Xue grid result and the corresponding water quota in the previous step. And calculating the water benefit coefficient of the water-requiring unit according to the weight of the water supply benefits of the two layers of Chu-Xiong.
And 3.1.4, analyzing the type of the developable water source in Chuxiong state according to the distribution and profile data of the river and water supply engineering in Chuxiong state. And determining the water supply range of each water source through the Chuxiong grid result and the Chuxiong water system diagram, and performing Chuxiong water supply gridding.
And 3.1.5, constructing a topological relation between the water supply and demand pixels and the water supply pixels according to the Chuxiong water supply and demand grids obtained in the steps.
And 3.2, constructing a Chuxiong refined water resource allocation model with the aim of maximizing water supply economic benefit.
Step 3.2.1 is based on supply and demand water topology relations to hierarchically represent the topological relation between the water demand of each unit and the available water supply of each water source:
X i ＝Y 1 +Y 2 +…+Y j
wherein: y is Y j Indicating the type of water supply source for the i-th unit water demand.
Step 3.2.2, analyzing water resource allocation constraint conditions: including restrictions on the water supply unit; a limitation of the total water supply amount; non-negative constraints of water supply variables; limitation of water demand capability of users; restriction of water supply order; and (5) limitation of water supply range.
Step 3.2.3, taking the maximum economic benefit of water supply in Chuxiong state as an objective function:
wherein: j is the number of water sources; i is the total number of cells; b i The water benefit coefficient of the ith water-requiring unit; c i A water supply cost factor for the ith unit; alpha i Supplying the ith unit with a priority coefficient, lambda i The water supply sequence coefficient of the j-th water source, x ij And supplying water supply quantity of the ith unit cell for the jth water source. And constructing to obtain the Chuxiong refined water resource allocation model.
And 4, regional water resource allocation simulation under the communication state of different special drought scenes and water systems.
In this example, step 4 specifically includes:
and 4.1, carrying out cluster analysis on the special drought scene to extract a typical special drought scene, calculating the abundant meeting probability of different water supply pixels through copula, and analyzing the water system communication capacity lifting space under the special drought scene.
Step 4.1.1, carrying out maximum normalization processing on the Chuxiong drought scene obtained in the step 1 to obtain a normalized scene set { W }; k scene objects are selected as initial clustering centers, and then the mixing metric of all scene objects in { W } and the initial clustering centers is measured:
MLX＝ωD(x,y)-(1-ω)cos(x,y)
and dividing the similar classification according to the minimum allocation principle, repeating calculation to obtain a clustering center, and outputting an optimal Chuxiong typical special drought scene when the clustering center is not changed. Wherein ω is a weight coefficient, D (x, y) is the euclidean distance of the two feature drought scenes, and cos (x, y) is the cosine similarity of the two feature drought scenes.
And 4.1.2, firstly, constructing an edge distribution function of the runoff of the Chuxiong water resource allocation water supply units, calculating a two-dimensional joint distribution function among water supply units without water system communication relation, and optimizing by using a square root error method (RMSE), an AIC criterion method and a BIC method.
And 4.1.3, calculating the probability of the combined risk of the abundant withering between the two water supply units according to the copula function of the two-dimensional combined distribution of the different water supply units. And determining a threshold value of a water system communication capacity lifting space between the water supply units according to the comprehensive and abundant probability of Chuxiong. Wherein, the combined risk probability of the full-length and full-length is same as that of the full-length and full-length.
And 4.2, constructing a Chuxiong state water resource allocation model under the communication state of different special drought scenes and a water system based on the Chuxiong state refined water resource allocation model, accurately solving by using a Gurobi solver, and performing the Chuxiong state water resource allocation simulation.
And 4.2.1, based on the Chu-Xiongzhou water resource refined allocation model in the step 3, taking the runoff quantity and the unit layering water demand under a typical drought scene as input, taking different water system communication schemes as constraints, maximizing the economic benefit of the Chu-Xiongzhou water supply as an objective function, and constructing the Chu-Xiongzhou water resource allocation model under different water system communication schemes.
And 4.2.2, compiling and solving the model by utilizing a Gurobi solver package in Python to obtain Chu-Xuan water resource allocation simulation results under different water system communication schemes.
And 5, calculating the limit water supply capacity and generating a water supply scheme under the special drought scene.
In this example, step 5 specifically includes:
and 5.1, comprehensively analyzing the relationship between the lifting cost of the communication capability of the Chu-Xiongzhou water system and the regional foundation guaranteed water supply and other functional water supply benefits according to the water resource simulation allocation results of different special drought scenes.
And 5.1.1, carrying out water system communication engineering cost accounting on the different water system communication construction schemes in Chuxiong state obtained by simulation in the step 4, and obtaining scheme construction cost for improving different water system communication capacity.
And 5.1.2, calculating to obtain the Chuxiong state water resource allocation benefit under different water system communication schemes according to different weight coefficients of regional foundation guaranteed water supply and other functional water supply requirements based on water resource simulation allocation results of different special drought scenes.
And 5.1.3, constructing mapping relations between the Chuxiong state water resource allocation cost and the benefit under different water system communication schemes according to the calculation results of the cost and the benefit of the step.
And 5.2, considering the sustainable utilization limit of a water source, preferably selecting an optimal water supply scheme of Chuxiong state in a super drought scene, and calculating to obtain the limit water supply capacity of the Chuxiong state in the super drought scene.
And 5.2.1, calculating a long-term supplement and discharge relation and runoff trend of a water supply source according to the long-series runoff data of the Chuxiong water supply unit, defining a water supply threshold of the water supply unit, and eliminating the scheme which does not accord with the sustainable utilization principle of water resources.
And 5.2.2, based on simulation results of different water system communication schemes of a typical special drought scene, improving cost and water resource allocation benefits in Chu-Xiong according to different water system communication capabilities, and comprehensively selecting an optimal water system communication scheme in Chu-Xiong.
And 5.2.3, inputting the typical oversized drought scene and the optimal water system communication scheme into a Chuxiong state water resource refined allocation model, and calculating and counting to obtain the water supply capacity of the Chuxiong state.
The technical scheme provided by the invention is not limited by the embodiment, and all the technical schemes formed by using the structure and the mode of the invention through transformation and substitution are within the protection scope of the invention.
Claims (10)
1. A regional water supply capacity acquisition method under special drought conditions based on water system communication is characterized by comprising the following steps of: comprises the steps of,
step 1, accurately identifying historical oversized drought events and generating drought scenes of different grades;
step 2, identifying a regional water system communication state in a special drought scene based on remote sensing satellite data and a WRF-Hydro model;
Step 3, constructing a regional refined water resource allocation model;
step 4, regional water resource allocation simulation is carried out under the communication state of different special drought scenes and water systems;
and 5, calculating the limit water supply capacity and generating a water supply scheme under the special drought scene.
2. The method for acquiring regional water supply capacity under special drought conditions based on water system communication according to claim 1, wherein the method comprises the following steps: step 3 comprises the steps of,
step 3.1, regional attribute meshing, regional water demand meshing and water supply engineering meshing are carried out to obtain regional water demand and water supply capacity, regional basic guaranteed water supply and other functional water supply benefits and related weights are calculated, and a topological relation between the water demand of each calculated cell and corresponding water supply sources is established;
and 3.2, constructing an area refined water resource allocation model with the aim of maximizing the economic benefit of water supply.
3. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 2, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: wherein, the step 3.1 comprises the following steps,
step 3.1.1, collecting remote sensing data of the land utilization type and the planting structure of the area and carrying out area statistics on the annual photo;
step 3.1.2, performing superposition processing on the regional land utilization type and the planting structure remote sensing data obtained in the step 3.1.1 by utilizing an ArcGIS software processing function to generate a regional gridding result, and correcting by a method of checking with a regional statistics annual survey and checking on site;
Step 3.1.3 calculates the basic assurances of each grid point and the water demand of other demands and two-level water supply benefits according to the grid result of the region and the corresponding water quota in step 3.1.2, and calculates the water benefit coefficient of the water demand unit according to the weight of the two-level water supply benefits of the region;
step 3.1.4, analyzing the type of the area-developable water source according to the collected data including river and water supply engineering distribution and profile data, determining the water supply range of each water source through the area grid result and the area water system map, and performing area water supply gridding;
step 3.1.5, constructing a topological relation between the water supply and demand pixels and the water supply pixels according to the regional water supply and demand grid obtained in the step 3.1.4;
wherein, the step 3.2 comprises the following steps,
step 3.2.1 is based on supply and demand water topology relations to hierarchically represent the topological relation between the water demand of each unit and the available water supply of each water source:
X i ＝Y 1 +Y 2 +…+Y j
wherein: y is Y j A water supply source type indicating the water demand of the i-th unit;
step 3.2.2, analyzing water resource allocation constraint conditions: the method comprises the steps of limiting a water supply unit, limiting the total water supply amount, non-negative constraint of a water supply variable, limiting the water demand of a user, limiting the water supply order and limiting the water supply range;
Step 3.2.3, taking the maximum economic benefit of regional water supply as an objective function:
wherein: j is the number of water sources; i is the total number of cells; b i The water benefit coefficient of the ith water-requiring unit; c i A water supply cost factor for the ith unit; alpha i Supplying the ith unit with a priority coefficient, lambda j The water supply sequence coefficient of the j-th water source, x ij And constructing and obtaining a regional refined water resource allocation model for the water supply quantity of the jth water source for supplying water to the ith unit.
4. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 2, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 4 comprises the steps of,
step 4.1, carrying out cluster analysis on the special drought scene to extract a typical special drought scene, calculating the abundant meeting probability of different water supply pixels through copula, and analyzing the water system communication capacity lifting space under the special drought scene;
and 4.2, constructing regional water resource allocation models under different special drought scenes and water system communication states based on the regional refined water resource allocation models, accurately solving by using a Gurobi solver, and performing regional water resource allocation simulation.
5. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 4, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 4.1 comprises the steps of,
Step 4.1.1, carrying out maximum normalization processing on the regional drought scene obtained in the step 1 to obtain a normalized scene set { W }; k scene objects are selected as initial clustering centers, the mixing metric of all scene objects in { W } and the initial clustering centers is calculated,
MLX＝ωD(x,y)-(1-ω)cos(x,y)
wherein ω is a weight coefficient, D (x, y) is a euclidean distance of the two characteristic drought scenes, cos (x, y) is a cosine similarity of the two characteristic drought scenes;
dividing similar classification according to a minimum allocation principle, and repeatedly calculating to obtain a clustering center, and outputting an optimal region typical special drought scene when the clustering center is not changed;
step 4.1.2, constructing an edge distribution function of runoff of regional water resource allocation water supply units, calculating a two-dimensional joint distribution function among water supply units without water system communication relation, and optimizing by using a square root error method (RMSE), an AIC criterion method and a BIC method;
step 4.1.3, calculating the probability of the combined risk of the two water supply units for the full-scale according to the optimized copula function of the two-dimensional combined distribution of the different water supply units, and determining the threshold value of the water system communication capacity lifting space among the water supply units according to the comprehensive full-scale probability of the region;
step 4.2 comprises the steps of,
step 4.2.1, based on the regional water resource refined allocation model in step 3, taking the runoff quantity and the unit layering water demand quantity under a typical drought scene as input, taking different water system communication schemes as constraint, maximizing the economic benefit of regional water supply as an objective function, and constructing a regional water resource allocation model under different water system communication schemes;
And 4.2.2, compiling and solving the model by utilizing a Gurobi solver package in Python to obtain regional water resource allocation simulation results under different water system communication schemes.
6. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 4, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 5 comprises the steps of,
step 5.1, comprehensively analyzing the relation between the regional water system communication capacity lifting cost and the layered water demand benefits according to the water resource simulation allocation results of different special drought scenes;
and 5.2, considering the sustainable utilization limit of the water source, preferably selecting an optimal water supply scheme of the area in the ultra-large drought scene, and calculating to obtain the limiting water supply capacity of the area in the ultra-drought scene.
7. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 6, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 5.1 comprises the steps of,
step 5.1.1, carrying out water system communication engineering cost accounting on the different water system communication schemes of the area obtained by simulation in the step 4, and obtaining scheme construction cost for improving the communication capacity of different water systems;
step 5.1.2, calculating to obtain regional water resource allocation benefits under different water system communication schemes according to different weight coefficients of the water demands of the two layers based on water resource simulation allocation results of different special drought scenes;
Step 5.1.3, constructing mapping relations between regional water resource allocation cost and benefit under different water system communication schemes according to the cost and benefit calculation results of the step 5.1.1 and the step 5.1.2;
step 5.2 comprises the steps of,
step 5.2.1, calculating a long-term supplement and discharge relation and runoff trend of a water supply source according to the long-series runoff data of the regional water supply unit, defining a water supply threshold of the water supply unit, and eliminating a scheme which does not accord with the sustainable utilization principle of water resources;
step 5.2.2, based on simulation results of different water system communication schemes of a typical special drought scene, improving cost and regional water resource allocation benefit according to different water system communication capabilities, and comprehensively comparing and selecting to obtain a regional optimal water system communication scheme;
and 5.2.3, inputting the typical oversized drought scene and the optimal water system communication scheme into the regional water resource refined allocation model, and calculating and counting to obtain regional water supply capacity.
8. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to any one of claims 1 to 7, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 1 comprises the steps of,
step 1.1, collecting historical long-series meteorological hydrologic data of a calculated area, and identifying an oversized drought historical event of the area by adopting a standardized rainfall index (SPI);
Step 1.2, for the scene generation of the regional oversized drought event, identifying regional drought weather driving factors through mutual information and Hampel criteria, constructing a VMD-SSA-BILSTM machine learning model, learning the mapping relation between the drought weather driving factors and the drought event, and simulating the drought weather driving factors obtained according to the future weather mode to obtain the multi-state oversized drought event;
wherein, the step 1.1 comprises the following steps,
step 1.1.1, collecting regional weather element data and runoff data;
step 1.1.2 describes the change of precipitation by adopting the gamma distribution probability, and obtains an SPI value through normal standardization:
step 1.1.3, determining an extra-large drought history event of the area through judging the SPI value of the area, wherein the SPI is more than or equal to-2.0 as extra-large drought according to a drought grade standard;
wherein, the step 1.2 comprises the following steps,
step 1.2.1, calculating the correlation among all the meteorological elements to obtain the interactive entropy among all the meteorological elements;
step 1.2.2, calculating outliers according to Hampel criteria and selecting drought weather driving factors according to the calculated interaction entropy;
step 1.2.3, respectively decomposing the selected drought weather driving factors into a plurality of eigenmode functions u k And find each u k Is a single spectrum of (1), for each u k Frequency modulation is carried out, and finally constraint conditions are added to obtain a constrained variation model:
wherein omega is k The frequency center of each order modal component obtained by the decomposition is assumed; mu (mu) k Is the modal component of the kth signal component; f is drought weather driving factor long series data; delta (t) is a pulse function;indicating that t is biased and t is time. The above problem is converted into an unconstrained variational problem by a lagrangian function:
wherein alpha is penalty factor; lambda is the Lagrangian multiplier; f (t) is the total long series data; u (u) k (t) marking a kth signal component, solving by using an alternate multiplier direction algorithm, updating the center frequency and bandwidth of each data signal component, and extracting the periodicity rule of each drought driving factor of the drought weather;
step 1.2.4, taking the modal periodic component of the selected drought weather driving factor as the input quantity of the machine learning model and the regional SPI value as the output quantity, establishing a BiLSTM bidirectional long-short-term memory network model, selecting part of data from the samples as training samples, taking the other part of data as test samples, normalizing the sample data, and inputting the normalized sample data into the BiLSTM model for learning training;
Step 1.2.5, performing parameter optimization of a two-way long-short-term memory network (BiLSTM) model by using a Sparrow Search Algorithm (SSA) method to obtain optimal parameters of the model, and constructing a mapping function relation between drought weather driving factors and oversized drought events;
and 1.2.6, calculating to obtain a weather element change sequence of a future multi-year region by using a weather mode model, inputting the obtained drought weather driving factor sequence into a mapping function relation, and generating a multi-state oversized drought event.
9. The regional water supply capacity acquisition method based on water system communication under special drought conditions according to claim 8, wherein the regional water supply capacity acquisition method is characterized by comprising the following steps of: step 2 comprises the steps of,
step 2.1, using satellite remote sensing images as basic data, initially extracting river water systems of areas by using ENVI software through a multiband method, classifying and extracting needed water system communication conditions by using decision trees, and calculating water system morphological index parameter values of different drought events;
step 2.2, constructing a WRF-Hydro coupling model to simulate and calculate the condition of regional runoff and water system communication, verifying and calibrating by utilizing the result of the step 2.1, and identifying the water system communication state under different drought events;
wherein, the step 2.1 comprises the following steps,
Step 2.1.1, acquiring a remote sensing image picture of a calculation area, and performing splicing treatment;
step 2.1.2, screening according to the spliced regional remote sensing images and combining the regional historical drought event time period obtained in the step 1;
step 2.1.3, carrying out radiation correction and geometric correction on the screened regional remote sensing image through EVNI, wherein the sensor radiation correction is converted into atmospheric reflectivity for calculation, and the atmospheric radiation correction is carried out through a dark pixel method;
step 2.1.4, calculating the normalized differential water index to extract the water information in the image:
step 2.1.5, extracting water system based on a decision tree method of a knowledge expert classifier;
step 2.1.6, constructing a water system graph model, and judging whether the graph model is a connected graph or not;
step 2.1.7, performing structural connectivity calculation on the water system in the regional river network, wherein the structural connectivity calculation comprises water system circularity, node connection rate, water system connectivity and point connectivity;
step 2.2 comprises the steps of,
step 2.2.1, running a WPS (WRF Preprocessing System) module, and carrying out WRF pretreatment on the calculation area;
step 2.2.2, dividing the regional WRF mode setting into a 4-layer nesting mode, setting two-way feedback between different nesting layers, and performing WRF process simulation;
2.2.3, constructing a regional WRF-Hydro complete distributed hydrologic model, and performing parameter calibration and verification of the model by utilizing the water system communication condition extracted in the step 2.1 based on the remote sensing satellite and the collected actual measurement runoff data;
and 2.2.4, simulating runoff conditions of different special drought scenes of the generated area by using the rated WRF-Hydro model and taking climate mode data as input, and identifying the regional water system communication state of the special drought event by using the method in the step 2.1.5.
10. Regional water supply ability acquisition system based on water system intercommunication under special drought condition, its characterized in that: including a processor and a memory; the processor is communicatively connected to the memory, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any of claims 1 to 9.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014326A (en) * | 2024-04-09 | 2024-05-10 | 水利部水利水电规划设计总院 | Method and system for planning river basin water resource scheduling in water network system |
| CN118095973A (en) * | 2024-04-29 | 2024-05-28 | 水利部交通运输部国家能源局南京水利科学研究院 | Hydrological abundant encounter probability calculation method based on coupling dimension reduction theory |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018107245A1 (en) * | 2016-12-16 | 2018-06-21 | The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development | Detection of environmental conditions |
| CN111797129A (en) * | 2020-06-01 | 2020-10-20 | 武汉大学 | Hydrologic drought assessment method under climate change situation |
| CN115049313A (en) * | 2022-07-20 | 2022-09-13 | 江苏省水利科学研究院 | Method for converting historical agriculture disaster-inflicted due to drought under condition of lack of historical data in humid region |
| CN116108956A (en) * | 2022-11-21 | 2023-05-12 | 河海大学 | River basin water and wind-solar resource integrated forecasting method based on physical-data dual driving |
-
2023
- 2023-12-04 CN CN202311641579.5A patent/CN117648863B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018107245A1 (en) * | 2016-12-16 | 2018-06-21 | The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development | Detection of environmental conditions |
| CN111797129A (en) * | 2020-06-01 | 2020-10-20 | 武汉大学 | Hydrologic drought assessment method under climate change situation |
| CN115049313A (en) * | 2022-07-20 | 2022-09-13 | 江苏省水利科学研究院 | Method for converting historical agriculture disaster-inflicted due to drought under condition of lack of historical data in humid region |
| CN116108956A (en) * | 2022-11-21 | 2023-05-12 | 河海大学 | River basin water and wind-solar resource integrated forecasting method based on physical-data dual driving |
Non-Patent Citations (2)
| Title |
|---|
| YIMIN WANG 等: "Assessing the drought mitigation ability of the reservoir in the downstream of the Yellow River", 《SCIENCE OF THE TOTAL ENVIRONMENT 646 (2019)》, 23 July 2018 (2018-07-23), pages 1327 * |
| 王春学 等: "气象干旱综合指数MCI 在四川省的适用性分析及修订", 《中国农学报》, vol. 35, no. 9, 31 March 2019 (2019-03-31), pages 115 - 121 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118014326A (en) * | 2024-04-09 | 2024-05-10 | 水利部水利水电规划设计总院 | Method and system for planning river basin water resource scheduling in water network system |
| CN118014326B (en) * | 2024-04-09 | 2024-05-31 | 水利部水利水电规划设计总院 | Method and system for planning river basin water resource scheduling in water network system |
| CN118095973A (en) * | 2024-04-29 | 2024-05-28 | 水利部交通运输部国家能源局南京水利科学研究院 | Hydrological abundant encounter probability calculation method based on coupling dimension reduction theory |
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