嫩江流域中长期径流预报方法比较研究
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摘要
本文选题于国家自然基金项目《基于水文气象因素耦合作用的中长期径流预报机理及方法研究》(50879028)。
我国位于气候环境激烈变化的东亚季风区,独特的季风环流决定了区域(流域)水文循环的复杂性和多变性,具体表现为降水时空分布的不均匀。相对其他国家和地区而言,中长期径流预报作为解决来水和用水矛盾的非工程性措施,能够解决防洪与抗旱、蓄水与弃水等矛盾,通过统筹规划,可以缓解水资源供需矛盾、服务于洪水资源化和流域水资源统一调度的现实需求,以及实现水利水电工程经济合理地运行,在我国进行这项研究具有重要的现实意义,尤其在水资源供需矛盾日益突出的情况下,近年来越来越受到水资源管理部门的重视。
选择合理的预报方法是做出准确预报的前提。本文结合嫩江长系列水文气象资料,并引用嫩江流域径流对于主要影响因素的敏感性分析结果,首先,比较了时间序列分析方法和回归分析方法模拟精度和预报检验精度,以评价数理统计方法和物理成因分析方法的优劣；进而在物理成因分析基础上,比较多元回归分析和人工神经网络方法的模拟精度和预报检验精度,以评价线性分析和非线性分析方法在因变量和自变量相关关系识别中的优劣。通过这些比较研究,初步确定基于物理成因分析的人工神经网络方法为最佳。
合理的物理成因分析是提高预报精度的基础。本文在选定采用人工神经网进行预报因子与预报项目相关关系识别的基础上,进一步分析流域径流在不同季节、不同预报因子对径流的影响程度,逐步锁定关键预报因子,构建更具合理性和预报精度更高的流域中长期径流预报模型。
本文还对流域径流异常大旱大涝的主要影响因素、作用机理和演变规律进行了初步分析,采用可公度方法对嫩江流域大旱大涝的灾变节点年及表现周期进行了识别。流域径流异常的大旱大涝预报属于水文气候预测范畴,其主要研究如何利用流域的月、季或年降水总量和水文特征值的时间序列演变规律和物理因子相关关系来估算未来的旱涝趋势和径流丰枯水平。水文气候概念的内涵不同于研究流域径流月季变化的中长期径流预报,而更加关注径流异常,即大旱与大涝两种极端情况。
通过预报方法的选择、预报因子的进一步精选,以及预报模型的不断改进,最终确定出一套适用于嫩江流域的中长期径流预报模型和大旱大涝灾变节点年份预测的可公度网络图。
China is located in the East Asian monsoon region, where the climatical environment changes drasticly. The unique monsoon circulation in this area (or watershed) determines the complexity and variability of the hydrological cycle, which shown as the inhomogenous spatiotemporal distributing of precipitation. Compared with the other countries or regions, as a non-engineering measure which can resolve the conflict between water incoming and using, the mid- to long-term stream discharge forecasting can resolve the conflict between flood controlling and drought relief, water storing and discarding. Through overall planning, the mid- to long-term stream discharge forecasting really can ease the contradiction between water supply and demand, provide services for the flood recycling and the unified management of water resources, as well as achieve the economical and reasonable running of the hydropower engineering. Therefore, the study of the mid- to long-term stream discharge forecasting in our country has an important practical meaning, especially with the outstanding contradiction between water supply and demand, and it has paied more and more attention by the water resources administrative department in recent years.
Selecting a reasonable prediction method is the premise of making accurate forecast. In this paper, with the long series hydrological and meteorological datas in the Nenjiang River Basin, we conduct the research by citing the sensitivy analysis results which the runoff in the Nenjiang River basin to the main influences. Firstly, we compare simulative accuracy and the forcasting inspective accuracy of time series analysis and regression analysis, therefore to evaluate the mathematical statistics method and the physical cause analysis method. Then, we compare the simulative accuracy and the forcasting inspective accuracy of multiple regression analysis and artificial neural network method, what's more, we also compare the the strengths and weaknesses of linear and nonlinear analysis methods in the correlative identification between dependent and independent variables based on the physical cause analysis. Through these comparative studies, the artificial neural network method based on physical causes is preliminarily determined as the best.analysis method
A reasonable physical cause analysis is a the base to improve the forecast accuracy. In this paper, artificial neural networks in selected to identify the relationship between predictive factors and project, then, after the further analysis of the influence degree of runoff in different seasons and with different forecasting factors, we gradually identify the key predictors, thus to build a middle and long term runoff predictive model with more rational and high accuracy.
In addition, this paper primarily analyzes the main factors, mechanism and evolution rules of abnormal large droughts and floods, moreover, with the commensurable method, it also identifies the performed period and cataclysm nodal years of the abnormal large droughts and floods in the Nenjiang River Basin. Abnormal runoff forecasting such as droughts and floods belongs to hydrological and climatical forecasting, which mainly studies how to use month, quarter or year total precipitation in the basin, and the relationship between physical factors and evolution rule of temporal sequences of the hydrological characteristics to estimate drought and flood trends and the runoff dry and wet levels in the future. The connotation of hydrological climate is different from the middle and long term runoff forecasting which studies mounthly and seasonally changes of runoff, and it pays more attention to the abnormal runoff, namely large droughts and floods, the two extremes.
By selecting forecasting methods, the furtherly selecting the predictors and continuous improving the prediction model, finally, a set of middle and long term forecasting model for the Nenjiang River Basin and a commensurable figure which can identify the cataclysm nodal years ot the large droughts and floods.
我国位于气候环境激烈变化的东亚季风区,独特的季风环流决定了区域(流域)水文循环的复杂性和多变性,具体表现为降水时空分布的不均匀。相对其他国家和地区而言,中长期径流预报作为解决来水和用水矛盾的非工程性措施,能够解决防洪与抗旱、蓄水与弃水等矛盾,通过统筹规划,可以缓解水资源供需矛盾、服务于洪水资源化和流域水资源统一调度的现实需求,以及实现水利水电工程经济合理地运行,在我国进行这项研究具有重要的现实意义,尤其在水资源供需矛盾日益突出的情况下,近年来越来越受到水资源管理部门的重视。
选择合理的预报方法是做出准确预报的前提。本文结合嫩江长系列水文气象资料,并引用嫩江流域径流对于主要影响因素的敏感性分析结果,首先,比较了时间序列分析方法和回归分析方法模拟精度和预报检验精度,以评价数理统计方法和物理成因分析方法的优劣；进而在物理成因分析基础上,比较多元回归分析和人工神经网络方法的模拟精度和预报检验精度,以评价线性分析和非线性分析方法在因变量和自变量相关关系识别中的优劣。通过这些比较研究,初步确定基于物理成因分析的人工神经网络方法为最佳。
合理的物理成因分析是提高预报精度的基础。本文在选定采用人工神经网进行预报因子与预报项目相关关系识别的基础上,进一步分析流域径流在不同季节、不同预报因子对径流的影响程度,逐步锁定关键预报因子,构建更具合理性和预报精度更高的流域中长期径流预报模型。
本文还对流域径流异常大旱大涝的主要影响因素、作用机理和演变规律进行了初步分析,采用可公度方法对嫩江流域大旱大涝的灾变节点年及表现周期进行了识别。流域径流异常的大旱大涝预报属于水文气候预测范畴,其主要研究如何利用流域的月、季或年降水总量和水文特征值的时间序列演变规律和物理因子相关关系来估算未来的旱涝趋势和径流丰枯水平。水文气候概念的内涵不同于研究流域径流月季变化的中长期径流预报,而更加关注径流异常,即大旱与大涝两种极端情况。
通过预报方法的选择、预报因子的进一步精选,以及预报模型的不断改进,最终确定出一套适用于嫩江流域的中长期径流预报模型和大旱大涝灾变节点年份预测的可公度网络图。
China is located in the East Asian monsoon region, where the climatical environment changes drasticly. The unique monsoon circulation in this area (or watershed) determines the complexity and variability of the hydrological cycle, which shown as the inhomogenous spatiotemporal distributing of precipitation. Compared with the other countries or regions, as a non-engineering measure which can resolve the conflict between water incoming and using, the mid- to long-term stream discharge forecasting can resolve the conflict between flood controlling and drought relief, water storing and discarding. Through overall planning, the mid- to long-term stream discharge forecasting really can ease the contradiction between water supply and demand, provide services for the flood recycling and the unified management of water resources, as well as achieve the economical and reasonable running of the hydropower engineering. Therefore, the study of the mid- to long-term stream discharge forecasting in our country has an important practical meaning, especially with the outstanding contradiction between water supply and demand, and it has paied more and more attention by the water resources administrative department in recent years.
Selecting a reasonable prediction method is the premise of making accurate forecast. In this paper, with the long series hydrological and meteorological datas in the Nenjiang River Basin, we conduct the research by citing the sensitivy analysis results which the runoff in the Nenjiang River basin to the main influences. Firstly, we compare simulative accuracy and the forcasting inspective accuracy of time series analysis and regression analysis, therefore to evaluate the mathematical statistics method and the physical cause analysis method. Then, we compare the simulative accuracy and the forcasting inspective accuracy of multiple regression analysis and artificial neural network method, what's more, we also compare the the strengths and weaknesses of linear and nonlinear analysis methods in the correlative identification between dependent and independent variables based on the physical cause analysis. Through these comparative studies, the artificial neural network method based on physical causes is preliminarily determined as the best.analysis method
A reasonable physical cause analysis is a the base to improve the forecast accuracy. In this paper, artificial neural networks in selected to identify the relationship between predictive factors and project, then, after the further analysis of the influence degree of runoff in different seasons and with different forecasting factors, we gradually identify the key predictors, thus to build a middle and long term runoff predictive model with more rational and high accuracy.
In addition, this paper primarily analyzes the main factors, mechanism and evolution rules of abnormal large droughts and floods, moreover, with the commensurable method, it also identifies the performed period and cataclysm nodal years of the abnormal large droughts and floods in the Nenjiang River Basin. Abnormal runoff forecasting such as droughts and floods belongs to hydrological and climatical forecasting, which mainly studies how to use month, quarter or year total precipitation in the basin, and the relationship between physical factors and evolution rule of temporal sequences of the hydrological characteristics to estimate drought and flood trends and the runoff dry and wet levels in the future. The connotation of hydrological climate is different from the middle and long term runoff forecasting which studies mounthly and seasonally changes of runoff, and it pays more attention to the abnormal runoff, namely large droughts and floods, the two extremes.
By selecting forecasting methods, the furtherly selecting the predictors and continuous improving the prediction model, finally, a set of middle and long term forecasting model for the Nenjiang River Basin and a commensurable figure which can identify the cataclysm nodal years ot the large droughts and floods.
引文
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