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.     

An integrated hydrological model for rain‐induced landslide prediction

This paper describes an extension to the Combined Hydrology And Stability Model (CHASM) to fully include the effects of vegetation and slope plan topography on slope stability. The resultant physically based numerical model is designed to be applied to site‐specific slopes in which a detailed assessment of unsaturated and saturated hydrology is required in relation to vegetation, topography and slope stability. Applications are made to the Hawke's Bay region in New Zealand where shallow‐seated instability is strongly associated with spatial and temporal trends in vegetation cover types, and the Mid‐Levels region in Hong Kong, an area subject to a variety of landslide mechanisms, some of which may be subject to strong topographic control. An improved understanding of process mechanism, afforded by the model, is critical for reliable and appropriate design of slope stabilization and remedial measures. Copyright © 2002 John Wiley & Sons, Ltd.     

基于广义Kопытов模型及乙型水驱曲线的综合预测法

将广义Kопытов预测模型和乙型水驱曲线方法有机的结合起来，得到了油田开发中后期——递减时期的一种预测水驱开发油田的含水率、产油量、产水量及其相应的累积产量随开发时间变化的方法，此方法克服了在水驱油田预测开发指标中二者所存在的局限性。     

Momentum transport of wave zero during March: A possible predictor for the Indian summer monsoon

Analysis of monthly momentum transport of zonal waves at 850 hPa for the period 1979 to 1993, between ‡S and ‡N for January to April, using zonal (u) and meridional (v) components of wind taken from the ECMWF reanalysis field, shows a positive correlation (.1% level of significance) between the Indian summer monsoon rainfall (June through September) and the momentum transport of wave zero TM(0) over latitudinal belt between 25‡S and 5‡N (LB) during March. Northward (Southward) TM(0) observed in March over LB subsequently leads to a good (drought) monsoon season over India which is found to be true even when the year is marked with the El-Nino event. Similarly a strong westerly zone in the Indian Ocean during March, indicates a good monsoon season for the country, even if the year is marked with El-Nino. The study thus suggests two predictors, TM(0) over LB and the strength of westerly zone in the Indian Ocean during March.     

疏勒河流域昌马灌区泉水量预测分析

疏勒河流域农业灌溉主要采用地表水灌溉 ,泉水为该区农业灌溉的主要水源之一 ,该文利用 2 0世纪 90年代的测流资料 ,采用有限差分方法对疏勒河中游泉水量的变化趋势进行预测分析 ,结果显示 ,随着时间的延长 ,泉流量呈逐渐减小的趋势 ,但下降梯度逐渐变小 ,最终趋于稳定     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

山西省主要河流流域面雨量预报业务流程

以T213、HLAFS模式、MM5中尺度模式输出的格点资料以及日本降水量格点资料为基础，将影响山西降水的天气动力模型归纳为诊断模型，从中引出多个能够全面反映降水模型特征的综合物理因子；根据各种数值模式输出的降水量预报性能和质量优劣特点，依据数值模式的形势场预报优于要素场预报的现实，构造在不同环流形势背景下，启动不同预报方程的面雨量预报业务流程，有效地遏止了在环流形势调整时预报输出不能快速响应的弱点，提高了点和面雨量预报的准确度。     

TRMM卫星微波成像仪分级产品及其反演降水算法

文章叙述了获取定量降水信息的意义，简要介绍了对热带测雨卫星TRMM(Tropical Rainfall Measurement Mission)的仪器、美国国家宇航局提供的微波成像仪TMI(TRMM Microwave Imager)分级产品。对比了物理方法和经验方法反演降水的特点，并对一些经验方法以及倾斜对流系统对反演降水的影响、动态聚类分析、神经网络反演方法的研究成果进行了介绍。     

数值预报产品在夏季持续高温预报中的释用

利用 1999～ 2 0 0 2年 6～ 8月 96～ 192h日本数值预报产品 85 0hPa气温与吉林省的日平均气温、阶段性高温及极端最高气温 (长春市 )资料 ,通过线性分析 ,找出了日本数值预报产品的 85 0hPa气温与吉林省地面日平均气温、阶段性高温及极端最高气温的对应关系 :吉林省 6～ 8月日平均气温在 96～ 192h的 85 0hPa日本数值预报长春站日平均值上加 6 6～ 4 1℃ ,6～ 8月的极端最高气温在相应的日本数值预报产品上加 9 3～ 13 0℃。