CN107423496A - A kind of random generation method of new catchment - Google Patents
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Abstract
本发明涉及一种新的降雨事件随机生成方法，包括：(1)根据降雨事件的定义，分割降雨事件，并提取降雨事件的降雨特征和降雨过程线；(2)基于Copula方法随机模拟降雨特征值；(3)基于K‑means聚类分析方法对降雨过程线进行分类，并引入聚类有效判别法来优选降雨雨型的最佳分类数；(4)根据降雨雨型的出现概率，利用转换的蒙特卡罗方法随机生成降雨雨型；(5)利用多步组合法，将随机模拟的降雨特征值和降雨雨型合并，重组降雨事件。本发明方法考虑了降雨量和降雨历时的相依关系，使模拟更符合实际情况，提高模拟精度；考虑不同的降雨雨型，为政府采取不同的调蓄水响应策略提供技术支撑；可显著增加降雨事件的序列长度。
The present invention relates to a new random generation method of rainfall events, comprising: (1) according to the definition of rainfall events, segmenting rainfall events, and extracting the rainfall characteristics and rainfall process lines of rainfall events; (2) randomly simulating rainfall characteristics based on Copula method (3) Based on the K-means cluster analysis method, the rainfall process lines are classified, and the effective clustering discriminant method is introduced to optimize the optimal classification number of rainfall patterns; (4) according to the occurrence probability of rainfall patterns, using The converted Monte Carlo method randomly generates rainfall patterns; (5) Using the multi-step combination method, the random simulated rainfall eigenvalues and rainfall patterns are combined to reorganize rainfall events. The method of the present invention considers the dependence relationship between rainfall amount and rainfall duration, makes the simulation more in line with the actual situation, and improves the simulation accuracy; considers different rainfall patterns, and provides technical support for the government to adopt different water regulation and storage response strategies; it can significantly increase rainfall The sequence length of the event.
Description
技术领域technical field
本发明涉及降雨事件的随机模拟技术领域，具体的说是一种将降雨雨型分类模拟与降雨特征模拟相结合的新的降雨事件随机生成方法。The invention relates to the technical field of random simulation of rainfall events, in particular to a new random generation method of rainfall events which combines rainfall classification simulation and rainfall characteristic simulation.
背景技术Background technique
降雨是水文循环的一个重要组成部分，它直接影响着下渗、土壤湿度和径流过程。降雨事件数据对于农业取用水计划、水库和大坝工程建设以及水资源可持续利用规划与管理等与水相关的工程研究是必不可少的，而降雨事件数据的长短又直接影响着这些研究的可靠性和准确性。因此，对降雨事件进行快速、准确地模拟以增加序列长度是十分重要的。Rainfall is an important component of the hydrological cycle, directly affecting infiltration, soil moisture and runoff processes. Rainfall event data is essential for water-related engineering research such as agricultural water withdrawal planning, reservoir and dam engineering construction, and sustainable use planning and management of water resources, and the length of rainfall event data directly affects the quality of these studies. reliability and accuracy. Therefore, it is very important to quickly and accurately simulate rainfall events to increase the sequence length.
降雨事件的模拟涉及其发生的次数、降雨量、降雨历时和降雨过程线(即降雨强度随时间的变化)等方面。通常，降雨特征(降雨量和降雨历时)之间存在一定的相关关系。目前虽然降雨事件的随机模拟方法多样，但都存在一定的局限性。这些模型大多采用的是平均降雨强度或者平均降雨强度曲线，而不考虑不同的降雨过程线(降雨雨型)对水利工程措施(如，蓄放水策略)的影响。有些模型甚至不考虑降雨特征间的相依关系，脱离了实际情况。The simulation of rainfall events involves the number of occurrences, rainfall amount, rainfall duration, and rainfall hydrograph (that is, the change of rainfall intensity with time). Usually, there is a certain correlation between rainfall characteristics (rainfall amount and rainfall duration). Although there are various stochastic simulation methods for rainfall events, they all have certain limitations. Most of these models use the average rainfall intensity or the average rainfall intensity curve, without considering the impact of different rainfall hydrographs (rainfall patterns) on hydraulic engineering measures (such as water storage and discharge strategies). Some models do not even take into account the interdependence of rainfall features, which is divorced from the actual situation.
传统的这些模型仅仅考虑了降雨事件某一方面的特征，而没有将具有相依关系的降雨特征值与降雨雨型结合起来，不能反映降雨发生的真实状况，导致模拟精度不足。These traditional models only consider the characteristics of a certain aspect of rainfall events, but do not combine the dependent rainfall characteristic values with rainfall patterns, which cannot reflect the real situation of rainfall occurrence, resulting in insufficient simulation accuracy.
发明内容Contents of the invention
为解决现有技术的不足，本发明的目的在于提供一种将降雨雨型分类模拟与降雨特征模拟相结合的新的降雨事件随机生成方法，以增加降雨序列长度，并为工程设计和政府蓄放水策略提供更为精确、有效的技术支撑。In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a new random generation method for rainfall events that combines rainfall classification simulation with rainfall characteristic simulation, so as to increase the length of the rainfall sequence and provide more information for engineering design and government storage. The water release strategy provides more accurate and effective technical support.
为实现上述目标，一种新的降雨事件随机生成方法，其特征在于，包括如下步骤：For realizing above-mentioned goal, a kind of new random generation method of rainfall event is characterized in that, comprises the following steps:
1)搜集站点的连续降雨序列数据，根据降雨事件的定义提取不同场次的降雨事件，并统计分析降雨事件的年发生次数以及每场降雨事件的降雨量、降雨历时和降雨过程线(降雨强度随时间的变化过程线)；1) Collect the continuous rainfall sequence data of the station, extract different rainfall events according to the definition of rainfall events, and statistically analyze the annual occurrence times of rainfall events as well as the rainfall, rainfall duration and rainfall process line of each rainfall event (rainfall intensity varies with Time change process line);
2)统计分析降雨特征(降雨量和降雨历时)服从的分布线型，使用Copula方法构建降雨量和降雨历时的联合分布函数，以此联合分布函数随机模拟降雨量和降雨历时；2) Statistically analyze the distribution line type of rainfall characteristics (rainfall amount and rainfall duration), use the Copula method to construct a joint distribution function of rainfall and rainfall duration, and use this joint distribution function to randomly simulate rainfall and rainfall duration;
3)先将降雨过程线无量纲化，即去除降雨量和降雨历时的影响，然后使用K-means聚类分析方法对无量纲的降雨累积过程线进行分类，并引入聚类有效判别法确定降雨雨型的最佳分类数；3) First, the rainfall hydrograph is dimensionless, that is, the influence of rainfall and rainfall duration is removed, and then the K-means cluster analysis method is used to classify the dimensionless rainfall accumulation hydrograph, and the effective clustering discriminant method is introduced to determine the rainfall The optimal classification number of rain types;
4)使用转换过的蒙特卡罗模拟方法，构建指定降雨雨型的随机生成模型；4) Use the converted Monte Carlo simulation method to construct a random generation model of the specified rainfall pattern;
5)根据提取出的降雨事件，统计分析不同降雨量和降雨历时下的各降雨雨型出现的概率；5) According to the extracted rainfall events, statistically analyze the probability of occurrence of each rainfall pattern under different rainfall amounts and rainfall durations;
6)根据步骤1)中降雨事件的年发生次数，采用Poisson分布随机生成N年(如，30年)降雨事件的年发生次数，每年中降雨事件的降雨量和降雨历时根据步骤2)中构建的联合分布函数随机模拟。在此基础上，各降雨事件的雨型根据步骤5)中不同雨型出现的概率随机生成。6) According to the annual occurrence frequency of rainfall events in step 1), the annual occurrence frequency of rainfall events in N years (for example, 30 years) is randomly generated using Poisson distribution, and the rainfall and rainfall duration of rainfall events in each year are constructed according to step 2) Stochastic simulation of the joint distribution function of . On this basis, the rain type of each rainfall event is randomly generated according to the probability of occurrence of different rain types in step 5).
7)根据多步组合法，将步骤6)中随机模拟的降雨量、降雨历时和降雨雨型合并，重组成完整的降雨事件。7) According to the multi-step combination method, the rainfall amount, rainfall duration and rainfall pattern randomly simulated in step 6) are combined to form a complete rainfall event.
上述技术方案中，步骤1)降雨事件定义为：In the above-mentioned technical scheme, step 1) rainfall event is defined as:
(1)连续的降雨时段被一个干旱时期隔开，这个连续的降雨时段被定义为一个降雨事件；(1) Continuous rainfall periods are separated by a dry period, and this continuous rainfall period is defined as a rainfall event;
(2)一个时段内，降雨深大于1mm的即被认为是降雨时段，否则被认为是干旱时段；(2) In a period of time, if the rainfall depth is greater than 1mm, it is considered as a rainfall period, otherwise it is considered as a drought period;
(3)连续两场降雨事件间的干旱时期要大于1d；(3) The drought period between two consecutive rainfall events should be greater than 1 day;
步骤2)中所述的降雨特征的分布线型需根据实际情况选定。分布线型可采用如指数分布、正态分布、对数分布以及各种极值分布等多种分布形式。降雨量和降雨历时最佳的边缘分布可根据K-S检验、卡方检验、T检验等方法确定。The distribution line type of the rainfall characteristics described in step 2) needs to be selected according to the actual situation. The distribution line type can adopt various distribution forms such as exponential distribution, normal distribution, logarithmic distribution and various extreme value distributions. The optimal marginal distribution of rainfall and rainfall duration can be determined by K-S test, chi-square test, T test and other methods.
步骤2)中所述的Copula方法可采用椭圆型、阿基米德型以及二次型等多种形式的Copula函数。拟合最优的联合概率分布函数可根据AIC(赤池信息量准则法)以及OLS(离差平方和最小准则法)拟合优度评估结果选取。The Copula method described in step 2) can adopt various forms of Copula functions such as elliptic type, Archimedes type and quadratic type. The optimal joint probability distribution function can be selected according to the goodness of fit evaluation results of AIC (Akaike information criterion) and OLS (sum of squared deviation criterion).
步骤3)中所述的降雨过程线无量纲化，具体方法为：The rainfall hydrograph described in step 3) is dimensionless, and the specific method is:
τ＝t/d；Aτ＝Dt/Dd τ = t/d; A τ = D t /D d
其中，d为降雨历时，τ为对应于时刻t的无量纲时刻，τ∈(0,1]；Dd为总降雨量，Dt为时刻t的累积降雨量，Aτ为相应的无量纲累积降雨量，Aτ∈[0，1]。对于每场降雨事件的无量纲累积降雨曲线被均分为K个时段(ΔT＝d/K)，K值根据站点的降雨特性选取(一般为12-20)。此时，第i个时刻(i＝1,2,…,K)对应的无量纲累积降雨量为Ai(i＝1,2,…,K)，第i个时间间隔所对应的降雨量增量为Ii＝(Ai-Ai-1)。Among them, d is the duration of rainfall, τ is the dimensionless time corresponding to time t, τ∈(0,1]; D d is the total rainfall, D t is the cumulative rainfall at time t, A τ is the corresponding dimensionless Cumulative rainfall, A τ ∈ [0, 1]. For each rainfall event, the dimensionless cumulative rainfall curve is divided into K periods (ΔT = d/K), and the K value is selected according to the rainfall characteristics of the station (generally 12-20). At this time, the dimensionless cumulative rainfall corresponding to the ith moment (i=1,2,...,K) is A i (i=1,2,...,K), and the ith time interval The corresponding rainfall increment is I i =(A i -A i-1 ).
步骤3)中所引入的聚类有效判别法通过综合评估划分的不同降雨雨型间的离散度(越大越好)和同一雨型内的紧密度(越小越好)来最终优选降雨雨型分类数。XB指标是聚类有效判别法中的一种判别指标，XB指标如下：The clustering effective discriminant method introduced in step 3) finally optimizes the rainfall pattern by comprehensively evaluating the dispersion between different rainfall patterns (the larger the better) and the closeness within the same rain pattern (the smaller the better) number of categories. The XB index is a discriminant index in the clustering effective discriminant method, and the XB index is as follows:
其中，N是所有的降雨过程线；nc是降雨雨型分类数；ujk为第j条降雨过程线在第k个雨型中的所属关系；d(x,y)为x与y之间的欧式距离；xj为第j条降雨过程线；ci为第i个雨型的聚类中心。XB指标越小，表明聚类效果越好。Among them, N is all the rainfall process lines; nc is the classification number of rainfall and rain types; u jk is the affiliation relationship of the j-th rainfall process line in the k-th rain type; d(x,y) is the relationship between x and y The Euclidean distance of ; x j is the j-th rainfall process line; ci is the cluster center of the i -th rain type. The smaller the XB index, the better the clustering effect.
步骤4)中所构建的降雨雨型随机生成模型，其本质是生成无量纲累积降雨曲线Ai(1,2,…,K)，又可转换成生成各时段降雨增量Ii(1,2,…,K)的生成问题。降雨雨型随机生成模型必须满足以下约束条件：(1)I1+I2+…+IK＝1且0≤I1≤I2≤…≤IK＝1；(2)0≤Ii≤1,i＝1,2,…,K。无量纲降雨增量I通常是相关非正态变量。The essence of the random generation model of rainfall pattern constructed in step 4) is to generate dimensionless cumulative rainfall curves A i (1,2,...,K), which can be transformed into generating rainfall increments I i (1, 2,...,K) generation problem. The random generation model of rainfall and rain must meet the following constraints: (1) I 1 +I 2 +…+I K ＝1 and 0≤I 1 ≤I 2 ≤…≤I K ＝1; (2) 0≤I i ≤1, i=1,2,...,K. The dimensionless rainfall increment I is usually a correlated non-normal variable.
构建的降雨雨型随机生成模型，具体过程为：The constructed random generation model of rainfall and rain, the specific process is as follows:
(1)将受约束的相关非正态多变量经对数转换转换为不受约束的相关非正态多变量。(1) Transform the restricted correlated non-normal multivariate into unconstrained correlated non-normal multivariate by logarithmic transformation.
(2)将相关非正态多变量经正态转换转换为相关标准正态多变量。(2) Transform the correlated non-normal multivariate into correlated standard normal multivariate through normal transformation.
(3)将相关的标准正态多变量经正交转换转换为独立的标准正态多变量。(3) Transform the correlated standard normal multivariate into independent standard normal multivariate by orthogonal transformation.
(4)采用蒙特卡罗模拟法随机生成独立的正态随机变量，然后经过(4) Use the Monte Carlo simulation method to randomly generate independent normal random variables, and then
(3)-(1)的逆运算，得到最终所需要的受约束条件的相关非正态多变量。The inverse operation of (3)-(1) obtains the relevant non-normal multivariate subject to the final required conditions.
该过程中，步骤(1)中所述的对数转换首先要指定一个无量纲的降雨增量Ii*(如i*＝4)，对数转换为Yi＝log(Ii/Ii*)i＝1,2,…,K i≠i*，即Ii∈[0,1]转化为Yi∈(-∞,+∞)。In this process, the logarithmic transformation described in step (1) will at first specify a dimensionless rainfall increment I i* (such as i*=4), and the logarithmic transformation is Y i =log(I i /I i * )i=1,2,...,K i≠i * , that is, I i ∈[0,1] is transformed into Y i ∈(-∞,+∞).
步骤(2)所述的正态转换采用的是Johnson分布系统，What the normal conversion described in step (2) adopts is the Johnson distribution system,
其中，X为原非正态变量，Z为标准正态变量，γ,δ,ξ和λ分别为模型的四个参数，γ和δ为形状参数，ξ为位置参数，λ为尺度参数。四参数根据Yi的样本矩(均值，标准差，偏度和峰度)估计得到。 Among them, X is the original non-normal variable, Z is the standard normal variable, γ, δ, ξ and λ are the four parameters of the model respectively, γ and δ are the shape parameters, ξ is the position parameter, and λ is the scale parameter. The four parameters are estimated from the sample moments (mean, standard deviation, skewness and kurtosis) of Y i .
步骤(3)所述的正交转换过程为：The described orthogonal conversion process of step (3) is:
(1)计算权利要求11中获得的正态变量Zi(有K-1个Z变量)的相关系数矩阵，求此矩阵的正交转换矩阵T；(1) calculate the correlation coefficient matrix of the normal variable Z i (has K-1 Z variables) that obtains in claim 11, ask the orthogonal transformation matrix T of this matrix;
(2)将具有相关关系的正态随机变量Z分解为不相关的变量Z′，即Z′＝T-1Z。(2) Decompose the correlated normal random variable Z into uncorrelated variable Z', that is, Z'=T- 1 Z .
步骤5)中所述的各降雨雨型在不同降雨量和降雨历时下的出现概率，计算为：其中l为雨型总数，ni为各雨型在指定降雨量和降雨历时下的出现次数，Pi为各雨型出现的概率。The occurrence probability of each rainfall type described in step 5) under different rainfall amounts and rainfall durations is calculated as: Among them, l is the total number of rain types, n i is the number of occurrences of each rain type under the specified rainfall amount and rainfall duration, and P i is the probability of occurrence of each rain type.
步骤6)中所述的降雨雨型的随机生成是在考虑降雨量和降雨历时模拟的基础上进行的，使降雨雨型出现的规律更服从实际情况。The random generation of the rainfall pattern described in step 6) is carried out on the basis of considering the rainfall amount and the rainfall duration simulation, so that the law of the rainfall pattern is more subject to the actual situation.
步骤7)所述的将降雨特征和降雨雨型合并的多步组合法，具体为：Step 7) the described multi-step combination method that rainfall feature and rainfall type are merged, specifically:
(1)无量纲的降雨积累过程线(即降雨雨型)乘以降雨深，无量纲时间乘以降雨历时，到在各时刻t(t＝ΔT,2ΔT,…,KΔT)下的降雨累积过程线；(1) Multiply the dimensionless rainfall accumulation process line (i.e. rainfall type) by rainfall depth, dimensionless time by rainfall duration, to the rainfall accumulation process at each time t (t = ΔT, 2ΔT, ..., KΔT) Wire;
(2)将降雨累积过程线转换成降雨事件，即各时段内的降雨量(假设各间隔时间ΔT内的降雨强度是均匀的)。(2) Convert the rainfall accumulation process line into rainfall events, that is, the rainfall in each time period (assuming that the rainfall intensity in each interval time ΔT is uniform).
上述技术方案，首次将Copula随机模拟的具有相依关系的降雨特征值与不同降雨雨型结合起来。The above-mentioned technical scheme combines the dependent rainfall characteristic values of Copula stochastic simulation with different rainfall patterns for the first time.
通过采用上述技术手段，本发明的有益效果为：By adopting above-mentioned technical means, the beneficial effect of the present invention is:
(1)Copula随机模拟降雨量和降雨历时，可以有效地保留两者间的相关关系；(1) Copula randomly simulates rainfall and rainfall duration, which can effectively preserve the correlation between the two;
(2)对降雨雨型进行分类和模拟，可以为政府制定不同的蓄放水策略提供信息支撑。(2) Classifying and simulating rainfall patterns can provide information support for the government to formulate different water storage and release strategies.
(3)采用聚类有效判别法来优选降雨雨型分类数，使降雨雨型的分类更客观化；(3) Use the clustering effective discriminant method to optimize the classification number of rainfall and rain patterns, so that the classification of rainfall and rain patterns is more objective;
(4)将Copula模拟的降雨特征值与降雨雨型相结合，并考虑雨型在降雨特征值下的出现概率，使降雨事件的发生在数值上和过程上更符合实际情况，提高了模拟的效率和计算精度。(4) Combine the rainfall eigenvalues simulated by Copula with the rainfall patterns, and consider the occurrence probability of rain patterns under the rainfall eigenvalues, so that the occurrence of rainfall events is more in line with the actual situation in terms of value and process, and improves the simulation accuracy. efficiency and computational accuracy.
附图说明Description of drawings
图1为本发明的一个流程示意图；Fig. 1 is a schematic flow chart of the present invention;
图2为降雨雨型的分类结果；Fig. 2 is the classification result of rainfall type;
图3为本发明中降雨雨型模拟的流程示意图；Fig. 3 is the schematic flow sheet of rainfall rain type simulation among the present invention;
图4为各降雨雨型随机模拟的结果；Fig. 4 is the result of random simulation of each rainfall pattern;
图5为降雨特征与降雨雨型结合后的降雨事件示意图。Figure 5 is a schematic diagram of rainfall events after combining rainfall characteristics and rainfall patterns.
具体实施方式detailed description
下面通过实例，并结合附图，对本发明的技术方案做进一步详细说明。The technical scheme of the present invention will be described in further detail below through examples and in conjunction with the accompanying drawings.
如图1所示，本发明涉及一种新的降雨事件随机生成方法，其包括以下步骤：As shown in Figure 1, the present invention relates to a kind of new random generation method of rainfall event, and it comprises the following steps:
(1)收集到某站1962-2011年共50年的日降雨数据，根据降雨事件的定义，提取到2671场降雨事件。其中，包括小、中、大以及极值降雨事件。然后将这些降雨事件的降雨量、降雨历时和降雨过程线统计出来。(1) The daily rainfall data of a certain station from 1962 to 2011 were collected for 50 years, and 2671 rainfall events were extracted according to the definition of rainfall events. Among them, small, medium, large and extreme rainfall events are included. Then the rainfall, rainfall duration and rainfall process line of these rainfall events are counted.
(2)用线性矩系数图和K-S检验结合的方法判断降雨量和降雨历时服从的分布线型。此本实例中，采用的分布线型有：皮尔逊III型分布(P-III)、3参数-对数分布(LN3)、3参数-广义帕累托分布(GPA)和广义极值分布(GEV)。结果为降雨量服从GPA分布，降雨历时服从P-III分布。(2) Use the linear moment coefficient diagram and the K-S test to judge the distribution line type of rainfall and rainfall duration. In this example, the distribution line types used are: Pearson Type III distribution (P-III), 3-parameter-logarithmic distribution (LN3), 3-parameter-generalized Pareto distribution (GPA) and generalized extreme value distribution ( GEV). The result is that the rainfall follows the GPA distribution, and the rainfall duration follows the P-III distribution.
(3)采用Pearson相关分析得到降雨量和降雨历时间的相关系数为0.7，说明降雨量和降雨历时存在较强的相关关系。此实例中Copula采用Elliptical型Copula家族中的Gaussian和t Copula，以及Archimedean型Copula家族中的Clayton、Frank和GumbelCopula来构建两者间的联合分布函数，并用AIC和OLS准则最终选定t Copula对两者的拟合最优，并以此tCopula随机模拟降雨量和降雨历时。(3) Using Pearson correlation analysis, the correlation coefficient between rainfall and rainfall duration is 0.7, indicating that there is a strong correlation between rainfall and rainfall duration. In this example, the Copula uses Gaussian and t Copula in the Elliptical Copula family, and Clayton, Frank, and Gumbel Copula in the Archimedean Copula family to construct the joint distribution function between the two, and finally selects the t Copula for the two The fit of the one is the best, and the rainfall and rainfall duration are randomly simulated with this tCopula.
(4)将降雨累积过程线无量纲化，并将其均分为12段(K＝12)。然后用K-means聚类方法将其分为5、6、7类，结果为图2所示。然后用XB指标最终确定东阳站降雨雨型的最佳分类为6类。A型：降雨峰值靠前；C型：降雨峰值在中间；U型：降雨强度在整个降雨过程比较均匀；D型：降雨峰值靠后。(4) Make the rainfall accumulation process line dimensionless and divide it into 12 segments (K=12). Then use the K-means clustering method to divide it into 5, 6, and 7 categories, and the results are shown in Figure 2. Then use the XB index to finally determine the best classification of Dongyang Station's rainfall patterns into 6 categories. Type A: the peak of rainfall is in front; Type C: the peak of rainfall is in the middle; Type U: the intensity of rainfall is relatively uniform throughout the rainfall process; Type D: the peak of rainfall is behind.
(5)图3为降雨雨型的随机生成流程图。根据该流程可以随机生成这6类雨型中任意一类的降雨过程线，结果示意图为图4。(5) Figure 3 is a flowchart of random generation of rainfall patterns. According to this process, the rainfall hydrographs of any of the six types of rain types can be randomly generated, and the schematic diagram of the results is shown in Figure 4.
(6)采用Possion分布随机生成1年的年降雨事件次数，为521场。用t Copula随机生成521场的降雨量和降雨历时，然后根据统计的降雨雨型在降雨量和降雨历时下的发生概率，随机生成521场的降雨雨型；(6) Possion distribution is used to randomly generate the number of annual rainfall events in one year, which is 521 events. Use t Copula to randomly generate 521 rainfall and rainfall durations, and then randomly generate 521 rainfall patterns according to the statistical occurrence probability of rainfall and rainfall patterns under rainfall amount and rainfall duration;
(7)将步骤(6)中的降雨量、降雨历时与降雨雨型整合，最终得到521场日降雨事件。结果见图5，仅挑选6场降雨事件进行展示。(7) Integrate the rainfall amount, rainfall duration and rainfall pattern in step (6), and finally get 521 daily rainfall events. The results are shown in Figure 5, and only 6 rainfall events are selected for display.
以上所述仅对本发明的实例实施而已，并不用于限制本发明。本发明中对降雨事件的定义，也可根据不同的研究问题具体制定。对于本领域的研究者来说，本发明可以有各种更改和变化。凡是在本发明的权利要求限定范围内，所做的任何修改、等同替换、改进等，均应在本发明的保护范围之内。The above description is only implemented as an example of the present invention, and is not intended to limit the present invention. The definition of rainfall events in the present invention can also be specifically formulated according to different research questions. For researchers in the field, various modifications and changes of the present invention are possible. Any modifications, equivalent replacements, improvements, etc. made within the scope of the claims of the present invention shall fall within the protection scope of the present invention.
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