地球物理信号能量(密度)多维分形及应用

地球物理信号代表的地质地球物理过程在多种尺度上和尺度之间表现为自相似性（self-affinity)或尺度无关性（Scale Invariant)，称为地球物理信号的分形性质，多个分形地球物理信号叠加在一起表现为多维分形特征，研究多维分形地球物理信号的能量或能量密度特征，可以进行时间或空间地球物理信号的校正、奇异性研究分析，或进行不同地球物理动力学过程的分解，本文描述了地球物理时间（空间）信号的多维分形过程和功率谱密度（能量密度）与波数以及重磁场能谱密度及面积（能量）与能谱密度的多维分形关系，并用地球物理测井与重磁资料作了试算。     

地球物理和地球化学异常的多重分形分析与分解

地球物理和地球化学异常是找矿的重要依据,异常的空间结构性包括奇异性和自相似性.奇异性反映了地球化学元素在岩石等介质中的局部富集和贫化规律,根据不同的自相似性特征可以分离地球物理和地球化学异常的背景场和异常场,有利于进一步评价异常与矿化的关系.近年来出现了基于空间域、付立叶域、特征值空间、沃尔什域的C-A、C-D、S-A、MSDV、W-A等异常的分解和分析方法,并成功应用于对地球物理和地球化学异常的解释中.本文对这些方法进行了概括和总结,探讨了小波域进行多重分形分析的方法在地球化学异常的分析和分解中的应用.并以山东乳山市葛口-石城测区的Au为例,以小波变换下的多重分形方法分析了该地区金成矿的可能前景,与实际情况较为吻合.     

ArcGeophysics 1.0:GIS环境下综合处理、解释地球物理与地质、地球化学、遥感等资料的组件(COM)包与应用实例

地球物理资料综合处理与解释在油气、煤田、矿物勘探\[5\]、水文分析、水文工程与环境评价中有重要应用.然而,目前的(位场)地球物理处理与解释可视化程度低,无法利用其它资料有效的约束、关联分析与综合快速解释.虽然地理信息系统(GIS)是目前综合多种资料,进行空间处理、分析,提供决策支持的强有力工具,但是,GIS是为一般地理问题分析定制的,缺少综合地球物理处理解释的工具.本文给出了解决上述问题的,用组件对象模型(COM)实现的软件包－ArcGeophysics. 在ArcGeophysics中,实现了勘探数据的有效管理,空间统计分析,地球物理（正演、反演）,地球化学与遥感处理、分析、解释与可视化,信息集成与勘探靶区预测等功能.特别需要指出的是：(1) ArcGeophysics 无缝地结合了ArcObject (ArcGIS, ESRI),也就继承了当前国际上占主导地位的GIS软件包中所有的GIS功能;(2) ArcGeophysics是建立在COM基础之上,任何支持COM的应用环境(如MS Office),编程环境(如MS Visual Studio (.Net), Borland Delphi等都可以调用这些功能(相当于通用开发工具包); (3)ArcGeophysics对地球物理资料的处理解释结合了非线性系统、多尺度分析与多维分形理论.本文不仅给出了各向异性奇异性的应用实例,而且还给出了位场在Fourier空间处理分数维导数,以及在余弦变换空间多维分形模型(C-SA).本文应用加拿大Nova Scotia省南半部的地球物理、地球化学、地质与成矿资料进行了试算,得到了理想的结果,文中图２～图５均直接出之ArcGeophysics的输出.     

GIS环境下地球物理信号的奇异值分解、多维分形特征与应用

将布格异常作为二维实矩阵对其进行了奇异值分解。用其左特征向量矩阵与右特征向量矩阵的立积构造了一个二维完备(特征空间)正交基。布格异常投影到该正交基上的系数是布格异常矩阵的特征值(奇异值的平方)。奇异值代表了布格异常在其特征空间的一种功率密度。对比了密度分布面数、密度分布面数的变化率、密度分布面数的积分能量后，定义了奇异值谱半径量度下的能量测度。能量测度与能量谱半径符合(简单分形)指数或(多维分形)分段指数变化。利用教优分段方法得到这些分段点，利用这些分段点在特征空间中对地球物理场进行了重建、滤波。编制了与GIS结合的程序。用该方法分析和处理加拿大Nova Scotin的地球物理资料，并将结果与巳知的地质、金矿点进行了对比。结果表明，可以很好地提取地球物理场中的背景、异常场，该结果与岩性、构造、巳知矿点关联，可进行矿产资源评价和靶区预测。该方法还可用于各种地球物理信号的分离、图像处理、图像压缩等。作者开发的结合GIS的应用程序，使得这些分析能快速完成。     

垂向归纳模型下鄱阳湖丰、枯水期初级生产力特征及与环境因子相关性分析

于2014年1月(枯水期)、7月(丰水期)对鄱阳湖湖水进行采集,测定相应的理化参数、叶绿素a浓度和光合有效辐射,结合初级生产力垂向归纳模型估算浮游植物初级生产力,分析湖区初级生产力特征及与环境因子的相关性.结果表明,鄱阳湖枯水期浮游植物初级生产力波动范围为83.50~355.43 mg C/(m~3·d),平均值为193.33 mg C/(m~3·d),初级生产力空间分布特征主要受水体类型的影响,枯水期初级生产力与氮、磷营养盐浓度呈负相关,其中与铵态氮浓度呈显著负相关,枯水期不会出现营养盐限制现象;丰水期浮游植物初级生产力波动范围为113.80~1134.06 mg C/(m~3·d),平均值为412.12 mg C/(m~3·d),初级生产力空间分布主要受河流注入的影响,丰水期浮游植物初级生产力与总磷及悬浮物浓度呈显著正相关,由于悬浮物对浮游植物生长的促进作用大于抑制作用,鄱阳湖丰水期会出现磷营养盐的限制;鄱阳湖整体平均流速约为0.28 m/s,易于浮游植物的生长,南鄱阳湖平均流速约为0.21 m/s,而北鄱阳湖平均流速约为0.35 m/s,所以南鄱阳湖比北鄱阳湖更容易发生水体富营养化并暴发水华.     

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.     

A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.     

How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance

Model diagnostic analyses help to improve the understanding of hydrological processes and their representation in hydrological models. A detailed temporal analysis detects periods of poor model performance and model components with potential for model improvements, which cannot be found by analysing the whole discharge time series. In this study, we aim to improve the understanding of hydrological processes by investigating the temporal dynamics of parameter sensitivity and of model performance for the Soil and Water Assessment Tool model applied to the Treene lowland catchment in Northern Germany. The temporal analysis shows that the parameter sensitivity varies temporally with high sensitivity for three groundwater parameters (groundwater time delay, baseflow recession constant and aquifer fraction coefficient) and one evaporation parameter (soil evaporation compensation factor). Whereas the soil evaporation compensation factor dominates in baseflow and resaturation periods, groundwater time delay, baseflow recession constant and aquifer fraction coefficient are dominant in the peak and recession phases. The temporal analysis of model performance identifies three clusters with different model performances, which can be related to different phases of the hydrograph. The lowest performance, when comparing six performance measures, is detected for the baseflow cluster. A spatially distributed analysis for six hydrological stations within the Treene catchment shows similar results for all stations. The linkage of periods with poor model performance to the dominant model components in these phases and with the related hydrological processes shows that the groundwater module has the highest potential for improvement. This temporal diagnostic analysis enhances the understanding of the Soil and Water Assessment Tool model and of the dominant hydrological processes in the lowland catchment. Copyright © 2013 John Wiley & Sons, Ltd.     

Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

In this study, uncertainty in model input data (precipitation) and parameters is propagated through a physically based, spatially distributed hydrological model based on the MIKE SHE code. Precipitation uncertainty is accounted for using an ensemble of daily rainfall fields that incorporate four different sources of uncertainty, whereas parameter uncertainty is considered using Latin hypercube sampling. Model predictive uncertainty is assessed for multiple simulated hydrological variables (discharge, groundwater head, evapotranspiration, and soil moisture). Utilizing an extensive set of observational data, effective observational uncertainties for each hydrological variable are assessed. Considering not only model predictive uncertainty but also effective observational uncertainty leads to a notable increase in the number of instances, for which model simulation and observations are in good agreement (e.g., 47% vs. 91% for discharge and 0% vs. 98% for soil moisture). Effective observational uncertainty is in several cases larger than model predictive uncertainty. We conclude that the use of precipitation uncertainty with a realistic spatio‐temporal correlation structure, analyses of multiple variables with different spatial support, and the consideration of observational uncertainty are crucial for adequately evaluating the performance of physically based, spatially distributed hydrological models.     

Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Abstract

Abstract After the destructive flood in 1998, the Chinese government planned to build national weather radar networks and to use radar data for real-time flood forecasting. Hence, coupling of weather radar rainfall data and a hydrological (Xinanjiang) model became an important issue. The present study reports on experience in such coupling at the Shiguanhe watershed. After having corrected the radar reflectivity and the attenuation data, the weather radar rainfall was estimated and then corrected in real time using a Kalman filter. In general, the precipitation estimated from weather radar is reasonably accurate in most of the catchment investigated, after corrections as above. Compared to the results simulated by raingauge data, the simulations based on the weather radar data are of similar accuracy. Present research results show that rainfall estimated from the weather radar, the radar data correction method, the method of coupling, and the Xinanjiang model lend themselves well to application in operational real-time flood forecasting.