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NPDM及改进模型在淮河流域的月径流模拟研究

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[目的]参数模型大多数基于模型驱动，并且对于水文序列的概率分布和相关关系做了一定程度的假设。然而，水文序列的真实分布复杂多样，相关关系也无法仅用线性相关描述，因此基于模型驱动的传统随机模型难以模拟实际月径流概率分布中的强不对称或多模态性，与变量真实概率分布存在差异。[方法]非参数解集模型(Nonparametric Disaggregation Model，NPDM)从数据驱动出发能有效模拟径流间的随机性和相关性变动规律。改进模型(Improved Nonparametric Disaggregation Model，INPDM)考虑年径流和前期月径流综合影响建立条件概率分布函数，使用基于黄金分割搜索和抛物线插值方法的优化算法以最小二乘交叉验证指标(Least Squares cross Validation，LSCV)为目标函数寻求最优带宽，并结合可变核方法修正边界。为深入探讨两模型在淮河流域的适用性，建立淮河流域1950-2007年吴家渡站、1951-2007年鲁台子站年、月径流随机模拟的NPDM和INPDM模型，利用一系列统计特征值和相关特性对比分析模拟效果。[结果]结果表明:在均值方面，NPDM和INPDM模拟效果均较为理想，实现100％控制在一个均方差标准下;吴家渡站和鲁台子站模拟序列的标准差和变异系数均控制在两个月的均方偏差标准下;INPDM模型对于原序列的最大值、月径流间互相关性和非线性状态相关统计特性的再现能力要优于传统模型，NPDM模型在吴家渡站和鲁台子站的2阶自相关系数无法控制在一个均方差标准下的月份占比分别比INPDM模型高50％和16。6％，1阶自相关系数也呈现出类似结果，表明INPDM模拟序列的自相关系数统计特征值与原序列统计特性更接近;但NPDM模型描述月径流序列偏态系数的能力更强。[结论]综上表明基于优化算法改进后的非参数解集模型能充分再现径流序列的统计特性，可为淮河流域开展年、月径流的随机模拟研究提供参考。
Simulation of monthly runoff in Huaihe River Basin based on NPDM and improved model
[Objective]Most parametric models are model-driven.They make certain assumptions about the probability distribu-tion and correlation relationships of hydrological sequences.However,the true distribution of hydrological series is complex and diverse.The correlation cannot be described solely by linear correlation.Therefore,there are differences between traditional model-driven stochastic models and real probability distributions.It is difficult to simulate the strong asymmetry or multimodality in the probability distribution of actual monthly runoff.[Methods]The Nonparametric Disaggregation Model(NPDM)can effec-tively simulate the randomness and correlation changes between runoff from a data-driven perspective.Considering the compre-hensive impact of annual runoff and previous monthly runoff,the Improved Nonparametric Disaggregation Model(INPDM)estab-lishes a conditional probability distribution function.With the Least Squares Cross Validation(LSCV)as the objective function to seek the optimal bandwidth,the model uses an optimization algorithm based on the golden section search and parabolic interpola-tion method,and combines variable kernel method to correct the boundary.To deeply explore the applicability of the two models in the Huai River Basin,NPDM and INPDM were established for stochastic simulation of annual and monthly runoff at Wujiadu Station from 1950 to 2007 and Lutaizi Station from 1951 to 2007 in the Huai River Basin.A series of statistical characteristic val-ues and related characteristics were used to compare and analyze the simulation effect.[Results]The result show that in terms of mean,both NPDM and INPDM have ideal simulation effects,achieving 100％control under one standard of mean square devia-tion;The standard deviation and variation coefficient of simulated sequences at Wujiadu Station and Lutaizi Station are controlled within the two mean square deviation standards;The INPDM outperforms traditional models in reproducing the maximum values of the original sequence,the correlation between monthly runoff,and the statistical characteristics of nonlinear state correlation,The proportion of months in which the second-order autocorrelation coefficient of NPDM at Wujiadu Station cannot be controlled under a mean square error standard is 50％higher than that of the INPDM model,and 16.6％higher than that of the Lutaizi Station.The first-order autocorrelation coefficient also showed similar result,indicating that the statistical characteristic values of the auto-correlation coefficient of the INPDM simulated sequence are closer to the statistical characteristics of the original sequence.But the NPDM has a stronger ability to describe the skewness coefficient of monthly runoff series.[Conclusion]In summary,it indi-cates that the improved nonparametric disaggregation model based on the optimization algorithm can fully reproduce the statistical characteristics of the runoff series,and ultimately provide a reference for conducting random simulation research on annual and monthly runoff in the Huaihe River Basin.

runoff simulationnonparametric disaggregation modelimproved modelvariable kernelHuaihe River basinapplicability analysisinfluence factor

吴昊昊、倪晋、曾兰婷

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安徽省·水利部淮河水利委员会水利科学研究院,安徽蚌埠 233000

安徽省水利水资源重点实验室,安徽蚌埠 233000

河海大学水利水电学院,江苏南京 210098

径流模拟 非参数解集模型 改进模型 可变核 淮河流域 适用性分析 影响因素

国家重点研发计划安徽省自然科学基金安徽省(水利部淮河水利委员会)水利科学研究院青年科技创新计划(2022)

2017YFC04056022208085US04KY202202

2024

10.13928/j.cnki.wrahe.2024.03.010

水利水电技术(中英文)
水利部发展研究中心

水利水电技术(中英文)

CSTPCD北大核心
影响因子：0.456
ISSN：1000-0860
年,卷(期)：2024.55(3)
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