分布式降雨量估算模型与方法研究
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摘要
降雨量作为水文模型的重要输入参数，也作为防洪减灾、径流预报、农田灌溉等问题的重要考虑因素，一直以来都是水文水资源领域学者研究的重点。随着对这些研究对象的空间尺度范围越来越小和时间尺度范围越来越精细的要求，传统的通过布设雨量观测站点来获取区域内平均面雨量的方法手段已经难以满足实际工作的要求。而随着空间信息技术自身的迅猛发展并与传统水文水资源学科的交叉融合，以及更加丰富完善的非线性建模方法，为解决这类新问题提供了新的思路。目前迫切需要解决的难题就是如何综合应用遥感技术和地理信息系统，构建能够对流域内不同空间尺度范围的降雨量分别进行估算的理论模型，为流域分布式降雨量的空间插值估算和遥感反演估算提供科学理论依据以及方法和技术支持。本文基于空间插值理论和遥感反演理论，对流域的分布式降雨量估算模型的构建进行了深入探讨与研究。主要研究内容如下：
1、研究了数字地面模型(DTM)以及衍生的数字高程模型(DEM)，结合本文的研究对象，采用数字降雨模型(DRM)来具体表示流域内的降雨量分布情况，它和DEM一样描述了某种空间属性的特征，适合在地理信息系统中进行存储管理和分析。
2、结合具体的实例，将湖北省区域范围根据不同空间尺度大小划分为湖北全区域、十堰地区、宜昌地区、恩施地区等几个研究区域，并将各个区域内的降雨量根据不同的雨量强度划分为几个等级，采用距离反比加权法、克里格法以及BP神经网络法和遗传算法优化的BP神经网络分别建立了对应的空间插值估算模型。
3、基于云降雨机理，从MODIS遥感影像数据中，获取到有关云产品的气象资料，提取和降雨密切相关的气象因子，并与地面雨量站点的实测降雨量资料相结合，应用BP神经网络法和遗传算法优化的BP神经网络的非线性智能建模方法分别建立遥感反演估算模型，得到了较好的估算效果。也证明了MODIS高光谱分辨率的数据产品应用在流域分布式降雨量估算中具有实际操作意义。
4、对于BP神经网络的隐含层节点数难以确定的问题，根据已有的隐含层节点数确定的经验公式，提出了一种改进公式，在实际应用中证明了改进公式的实用性和有效性。另外针对BP神经网络的初始联接权值和阈值确定的随机性以及容易陷入局部极小点的固有缺陷，采用了具有全局优化搜索能力的遗传算法，两者进行优势互补，并将这两种模型分别用在空间插值估算模型和遥感反演估算模型的构建中。
通过对湖北省流域的具体实例进行分布式降雨量估算研究，实验结果表明：
1、在流域分布式降雨量的空间插值估算建模中，传统的插值估算方法各有其局限性和适用对象。对于湖北省流域范围不同空间尺度范围的插值估算结果也表明，在小区域范围或雨量密度分布均衡的情况下，插值估算的效果要好于对大区域范围或雨量密度分布不均匀进行插值估算的情况。另外从插值模型方法角度分析，基于非线性智能遗传算法优化的BP神经网络的空间插值模型，其估算的误差精度要好于基于统计概率的传统空间插值模型。并且空间插值模型的估算误差精度受到空间尺度范围和地形地势影响以及雨量强度大小的影响很大。不同的条件下估算的误差精度各不相同，平均相对误差范围大致在30％-80％之间。
2、利用气象卫星资料遥感反演估算流域分布式降雨量的研究结果表明，BP神经网络构建的反演估算模型和利用遗传算法优化的BP神经网络模型反演估算流域内的分布式降雨量，估算效果都比较好，模型的估算精度只与所选取的气象因子有关，与雨量站点的分布情况、研究区域的空间尺度范围以及雨量强度大小等因素相关性并不是很大。本文从可以获取到的MOIDS遥感影像云产品数据中提取了七个与降雨关系密切的气象因子参数，对于湖北省流域范围内的不同空间尺度范围和不同雨量强度大小的情况分别建立了反演估算模型，其估算的平均相对误差在20％-25％之间。
Rainfall has always been a focus of academic research in the field of hydrology and water resources because it is not only an important input parameter in hydrological model, but also an important consideration for the issue of flood control, disaster mitigation, runoff forecast, irrigation, etc. With the increasingly requirements of fine spatial scale and time scale in the research of distributed rainfall estimation, the average rainfall that obtained from the discrete rainfall observation sites in the region is difficult to meet the requirements of practical work effectively. However, the rapid development of spatial information technical methods and the non-linear modeling methods are combined with the traditional science of hydrology and water resources, a new way of solving these new problems is brought forward. How to apply the remote sensing (RS) and geography information system(GIS) to build a theoretical model is an problem need to be solved urgently , which is able to estimate the distributed rainfall in different spatial scales of the river basin. And it can provide scientific theories and technical support to spatial interpolation and remote sensing retrieval of distributed rainfall estimation. In this paper, distributed rainfall estimation model is discussed and studied based on the spatial interpolation theory and remote sensing retrieval theory. And the main contents of this paper are as follows:
1. Digital terrain model (DTM) of basin and digital elevation model (DEM) derived from DTM is studied, and combined with the study object of this paper, digital rainfall model (DRM) is used to represent the distribution of rainfall in a river basin, and which is suitable for applying in geographic information systems analysis and management.
2. The traditional spatial interpolation methods are studied and is combined with concrete examples, the scope of the Hubei Province is divided into several study areas based on different size of spatial scales, and then the rainfall in the study areas depending on rainfall intensity is divided into several levels. In this paper, Inverse Distance Weighted Method(IDWM), Kriging method, Back Propagation Neural Network(BPNN) model and BPNN model optimized by genetic algorithm are used to establish spatial interpolation estimation model respectively.
3. The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery, and these meteorological parameters are combined with the actual observed rainfall data which is obtained from ground-based rainfall site correspondingly. The remote sensing retrieval model is established respectively based on the BP neural network and GA-BP neural network, and a better effect of error precision estimation is obtained. It's also proved that the high spectral resolution of MODIS data products used in distributed rainfall estimation of river basin is very practical significant.
4. Aiming at the problem of the hidden layer nodes of BP neural network is difficult to determine, in this paper, an improved algorithm formula used to confirm the hidden layer nodes is brought forward. It is proved that the improved formula is very practicality and effectiveness. In addition, BP neural network has inherent defects that the initial connection weights and thresholds is determined randomly and is prone to local convergence easily, Due to the genetic algorithm (GA) has good performance in global search and optimize, so GA is used in this paper to optimize the connection weights and thresholds of BPANN. And these two models are used to construct the spatial interpolation model and retrieval model of rainfall estimation respectively.
Through the research about estimating missing rainfall on River Basin in Hubei Province, the experimental results show that:
1. In some spatial interpolation precipitation models, the traditional methods of estimation interpolation have their own limitations and applied range. The results of estimation on the study about Hubei Province for the different spatial scales of interpolation also show that in small regional or balanced density distribution of rainfall area, the interpolation is better to estimate in the effect of large area or uneven distribution of rainfall area. And from another point of view in interpolation model, Comparing with other traditional spatial interpolation based on statistical model, BP neural network model based on nonlinear optimization is better in the estimated accuracy. At the same time, the estimation error of spatial interpolation model largely affect by the scope of spatial scale, topography and the precipitation. Under different conditions, the error precision estimates vary, and the mean relative error in the range of roughly between 30% -80%.
2.Using meteorological satellite data to estimate basin inversion of remote sensing research distributed rainfall results shows that BP neural network model to build estimates of the inversion and the BP neural network model optimized using of genetic algorithms, the results in estimating rainfall are better. The estimate of the accuracy of the model selected only with the relevant meteorological factors. The relevance with the distribution of rainfall stations、the range of research area and the rainfall are not too great. This article accessed seven meteorological factors closely related with rainfall intensity from the cloud MOEDS product. For the basin in Hubei province within different spatial scales and different rainfall intensity, this study built different models for each cases. And the estimated mean relative error was between 20% -25%.
1、研究了数字地面模型(DTM)以及衍生的数字高程模型(DEM)，结合本文的研究对象，采用数字降雨模型(DRM)来具体表示流域内的降雨量分布情况，它和DEM一样描述了某种空间属性的特征，适合在地理信息系统中进行存储管理和分析。
2、结合具体的实例，将湖北省区域范围根据不同空间尺度大小划分为湖北全区域、十堰地区、宜昌地区、恩施地区等几个研究区域，并将各个区域内的降雨量根据不同的雨量强度划分为几个等级，采用距离反比加权法、克里格法以及BP神经网络法和遗传算法优化的BP神经网络分别建立了对应的空间插值估算模型。
3、基于云降雨机理，从MODIS遥感影像数据中，获取到有关云产品的气象资料，提取和降雨密切相关的气象因子，并与地面雨量站点的实测降雨量资料相结合，应用BP神经网络法和遗传算法优化的BP神经网络的非线性智能建模方法分别建立遥感反演估算模型，得到了较好的估算效果。也证明了MODIS高光谱分辨率的数据产品应用在流域分布式降雨量估算中具有实际操作意义。
4、对于BP神经网络的隐含层节点数难以确定的问题，根据已有的隐含层节点数确定的经验公式，提出了一种改进公式，在实际应用中证明了改进公式的实用性和有效性。另外针对BP神经网络的初始联接权值和阈值确定的随机性以及容易陷入局部极小点的固有缺陷，采用了具有全局优化搜索能力的遗传算法，两者进行优势互补，并将这两种模型分别用在空间插值估算模型和遥感反演估算模型的构建中。
通过对湖北省流域的具体实例进行分布式降雨量估算研究，实验结果表明：
1、在流域分布式降雨量的空间插值估算建模中，传统的插值估算方法各有其局限性和适用对象。对于湖北省流域范围不同空间尺度范围的插值估算结果也表明，在小区域范围或雨量密度分布均衡的情况下，插值估算的效果要好于对大区域范围或雨量密度分布不均匀进行插值估算的情况。另外从插值模型方法角度分析，基于非线性智能遗传算法优化的BP神经网络的空间插值模型，其估算的误差精度要好于基于统计概率的传统空间插值模型。并且空间插值模型的估算误差精度受到空间尺度范围和地形地势影响以及雨量强度大小的影响很大。不同的条件下估算的误差精度各不相同，平均相对误差范围大致在30％-80％之间。
2、利用气象卫星资料遥感反演估算流域分布式降雨量的研究结果表明，BP神经网络构建的反演估算模型和利用遗传算法优化的BP神经网络模型反演估算流域内的分布式降雨量，估算效果都比较好，模型的估算精度只与所选取的气象因子有关，与雨量站点的分布情况、研究区域的空间尺度范围以及雨量强度大小等因素相关性并不是很大。本文从可以获取到的MOIDS遥感影像云产品数据中提取了七个与降雨关系密切的气象因子参数，对于湖北省流域范围内的不同空间尺度范围和不同雨量强度大小的情况分别建立了反演估算模型，其估算的平均相对误差在20％-25％之间。
Rainfall has always been a focus of academic research in the field of hydrology and water resources because it is not only an important input parameter in hydrological model, but also an important consideration for the issue of flood control, disaster mitigation, runoff forecast, irrigation, etc. With the increasingly requirements of fine spatial scale and time scale in the research of distributed rainfall estimation, the average rainfall that obtained from the discrete rainfall observation sites in the region is difficult to meet the requirements of practical work effectively. However, the rapid development of spatial information technical methods and the non-linear modeling methods are combined with the traditional science of hydrology and water resources, a new way of solving these new problems is brought forward. How to apply the remote sensing (RS) and geography information system(GIS) to build a theoretical model is an problem need to be solved urgently , which is able to estimate the distributed rainfall in different spatial scales of the river basin. And it can provide scientific theories and technical support to spatial interpolation and remote sensing retrieval of distributed rainfall estimation. In this paper, distributed rainfall estimation model is discussed and studied based on the spatial interpolation theory and remote sensing retrieval theory. And the main contents of this paper are as follows:
1. Digital terrain model (DTM) of basin and digital elevation model (DEM) derived from DTM is studied, and combined with the study object of this paper, digital rainfall model (DRM) is used to represent the distribution of rainfall in a river basin, and which is suitable for applying in geographic information systems analysis and management.
2. The traditional spatial interpolation methods are studied and is combined with concrete examples, the scope of the Hubei Province is divided into several study areas based on different size of spatial scales, and then the rainfall in the study areas depending on rainfall intensity is divided into several levels. In this paper, Inverse Distance Weighted Method(IDWM), Kriging method, Back Propagation Neural Network(BPNN) model and BPNN model optimized by genetic algorithm are used to establish spatial interpolation estimation model respectively.
3. The main meteorological parameters which influencing the rainfall can be distilled from the MODIS satellite cloud imagery, and these meteorological parameters are combined with the actual observed rainfall data which is obtained from ground-based rainfall site correspondingly. The remote sensing retrieval model is established respectively based on the BP neural network and GA-BP neural network, and a better effect of error precision estimation is obtained. It's also proved that the high spectral resolution of MODIS data products used in distributed rainfall estimation of river basin is very practical significant.
4. Aiming at the problem of the hidden layer nodes of BP neural network is difficult to determine, in this paper, an improved algorithm formula used to confirm the hidden layer nodes is brought forward. It is proved that the improved formula is very practicality and effectiveness. In addition, BP neural network has inherent defects that the initial connection weights and thresholds is determined randomly and is prone to local convergence easily, Due to the genetic algorithm (GA) has good performance in global search and optimize, so GA is used in this paper to optimize the connection weights and thresholds of BPANN. And these two models are used to construct the spatial interpolation model and retrieval model of rainfall estimation respectively.
Through the research about estimating missing rainfall on River Basin in Hubei Province, the experimental results show that:
1. In some spatial interpolation precipitation models, the traditional methods of estimation interpolation have their own limitations and applied range. The results of estimation on the study about Hubei Province for the different spatial scales of interpolation also show that in small regional or balanced density distribution of rainfall area, the interpolation is better to estimate in the effect of large area or uneven distribution of rainfall area. And from another point of view in interpolation model, Comparing with other traditional spatial interpolation based on statistical model, BP neural network model based on nonlinear optimization is better in the estimated accuracy. At the same time, the estimation error of spatial interpolation model largely affect by the scope of spatial scale, topography and the precipitation. Under different conditions, the error precision estimates vary, and the mean relative error in the range of roughly between 30% -80%.
2.Using meteorological satellite data to estimate basin inversion of remote sensing research distributed rainfall results shows that BP neural network model to build estimates of the inversion and the BP neural network model optimized using of genetic algorithms, the results in estimating rainfall are better. The estimate of the accuracy of the model selected only with the relevant meteorological factors. The relevance with the distribution of rainfall stations、the range of research area and the rainfall are not too great. This article accessed seven meteorological factors closely related with rainfall intensity from the cloud MOEDS product. For the basin in Hubei province within different spatial scales and different rainfall intensity, this study built different models for each cases. And the estimated mean relative error was between 20% -25%.
引文
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