An analytical representation of non-gyrotropic distributions and related space applications

Clear signature of non-gyrotropic energetic electron distributions was found by ISEE 1 and ISEE 2 spacecrafts just upstream of the Earth's bow shock and recently detected by in situ observations of the WIND plasma experiment. On the other hand, the appearance of non-gyrotropic ion velocity distributions is well established in the magnetotail providing evidence of magnetic reconnection processes. Motivated by these findings we introduce an analytical representation of non-Maxwellian/non-gyrotropic distribution functions, accurately fitting the characteristics of observations, where Maxwellians are recovered as special case of these highly general velocity space distributions. In particular, the analytical distribution function model can serve as basis of detailed wave-particle interaction analysis and of studies of the physical background of the evolution of both, non-gyrotropic electron and ion distributions, which is discussed for various space plasma environments.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.     

THE CHARACTERISTIC OF MARINE ENVIRONMENT IN LINGDINGYANG ESTUARY

1 HYDROLOGIC FEATURES Lingdingyang Estuary, located at the middle south of Guangdong Province, is a bell-shaped estuary with a north-south direction. Its area is about 2100km2. The north of Qi′ao Island and Inner-Lingding Island, and the south of Humen are grouped as Neilingdingyang Estuary, having an area of 1041km2. Affected by topography, runoff and tide, its dynamic condition is very complicated. Different water areas have different hydrologic features. The topography under …     

HYDROLOGIC SERIES CHARACTERISTICSANALYSIS OF THE MAJOR RIVERS AROUNDTHE TAKLIMAKAN DESERT

I.IN~crIOXLocatedinthecoddlepatofTallmBasin,withanareaof33.76X104klnZ,theTaldirnakan~isinthehinterlandofEurasia.Blockedbythehighmountainsaround,vapourofoceancanhardlyreachthedesert.APartfromthis,asthedeSertisinthesinkingcompensationareaoftheascensionalaircurrentaamstheQinghal-XIZangPlateau,itSprecipitationisrareandtheevaporationcapacityisintensealltheyearround.Allthesecontributetotheformationsofthetypifydrydesertclimate.Theacidityindexofthedesertandthearoundregionisashighasmorethan50,w…     

遥感技术在地质找矿中的应用及发展前景

遥感技术作为矿产勘查的一种手段应用于找矿,并取得了一定成就.遥感技术的直接应用是蚀变遥感信息的提取,遥感技术的间接应用包括地质构造信息、植被的光谱特征及矿床改造信息等方面.遥感找矿具有很大的发展前景的领域主要有:高光谱数据、数据融合技术、3S的紧密结合、计算机技术的发展.     

水文信息处理系统研究

水文信息处理系统是在Windows环境下、用Visual Basic 6．0高级语言研制开发的智能水文资料整编软件，引用数据库存储信息，调用Excel输出整编成果。该软件实现了水位、流量、泥沙等要素关系线的自动生成和人工辅助定线，并完成全部整编计算工作，结束了我国水文资料电算整编工作中依靠手工定线整编的历史，解决了计算机定线、推流和成果输出一次完成的技术难题，真正实现了水文资料整编计算机化。     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

面向对象的时空概念建模方法

在面向对象技术的基础上，把地理信息的时空特征与对象模型融合到一起，产生了语义丰富的时空扩展对象模型，以支持包含时空信息的系统需求分析和设计。     

多功能水文频率分析计算软件的研制和应用

介绍了多功能水文频率分析计算软件的设计及功能扩充，尤其是增加了采用鼠标拉动曲线直接屏幕适线的功能。本软件在生产实践中，获得了较好的应用效果，不仅功能多，而且实用方便。     

A patch hierarchy approach to modeling surface and subsurface hydrology in complex flood‐plain environments

Geomorphology interacts with surface‐ and ground‐water hydrology across multiple spatial scales. Nonetheless, hydrologic and hydrogeologic models are most commonly implemented at a single spatial scale. Using an existing hydrogeologic computer model, we implemented a simple hierarchical approach to modeling surface‐ and ground‐water hydrology in a complex geomorphic setting. We parameterized the model to simulate ground‐ and surface‐water ?ow patterns through a hierarchical, three‐dimensional, quantitative representation of an anabranched montane alluvial ?ood plain (the Nyack Flood Plain, Middle Fork Flathead River, Montana, USA). Comparison of model results to ?eld data showed that the model provided reasonable representations of spatial patterns of aquifer recharge and discharge, temporal patterns of ?ood‐water storage on the ?ood plain, and rates of ground‐water movement from the main river channel into a large lateral spring channel on the ?ood plain, and water table elevation in the alluvial aquifer. These results suggest that a hierarchical approach to modeling ground‐ and surface‐water hydrology can reproduce realistic patterns of surface‐ and ground‐water ?ux on alluvial ?ood plains, and therefore should provide an excellent ‘quantitative laboratory’ for studying complex interactions between geomorphology and hydrology at and across multiple spatial scales. Copyright © 2004 John Wiley & Sons, Ltd.