GEOGRAPHY ON THE PLANETS: GIFT OF REMOTE SENSING*

Extraterrestrial geography has become a reality, as we move from Earth's moon to the planets. A broad-scale regional physiography is being established on twenty planets and satellites through remote sensing techniques. Spacecraft images yield most of the information on Solar System landscapes. Topographic measurements are extracted by monoscopic image-processing, stereophotogrammetry, and radar analysis. Invisible parts of the spectrum furnish non-topographic data.     

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…     

流域水文模型参数识别的现代优化方法研究进展

水文参数优化是水文模型发展中的一个重要部分,针对水循环过程模拟中的流域水文模型参数识别的复杂优化问题,回顾了水文模型优化算法的发展历程,对国内外水文模型参数识别算法的最新进展进行了阐述.介绍了模拟退火算法、遗传算法、SCE-UA、粒子群算法等常用算法的参数设定及一般流程,并对编码遗传算法、单纯形混合加速遗传算法和运用融合技术的几种遗传算法以及其它几种常用算法在新安江模型中的优化结果进行了比较,认为混合加速遗传算法是一种较好的方法.在系统研究现代优化算法与传统优化算法的基础上,建立各种优化算法的融合技术和法则可能是进一步提高参数优化算法性能的方向.     

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.     

Modelling of the OASIS Energy Flux Measurements Using Two Canopy Concepts

Two land surface schemes, SCAM and CSIRO9, were used to model the measured energy fluxes during the OASIS (Observations At Several Interacting Scales) field program. The measurements were taken at six sites along a 100 km rainfall gradient. Two types of simulations were conducted: (1) offline simulations forced with measured atmospheric input data at each of the six sites, and (2) regional simulations with the two land surface schemes coupled to the regional climate model DARLAM.The two land surface schemes employ two different canopy modelling concepts: in SCAM the vegetation is conceptually above the ground surface, while CSIRO9 employs the more commonly used `horizontally tiled' approach in which the vegetation cover is modelled by conceptually placing it beside bare ground. Both schemes utilize the same below-ground components (soil hydrological and thermal models) to reduce the comparison to canopy processes only. However, the ground heat flux, soil evaporation and evapotranspiration are parameterised by the two canopy treatments somewhat differently.Both canopy concepts reproduce the measured energy fluxes. SCAM has a slightly higher root-mean standard error in the model-measurement comparison for the ground heat flux. The mean surface radiative temperature simulated by SCAM is approximately 1K lower than in the CSIRO9 simulations. However, the soil and vegetation temperatures (which contribute to the radiative temperature) varied more in the CSIRO9 simulations. These larger variations are due to the absence of a representation of the aerodynamic interactions between vegetation and ground.