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.     

Distribution of nonlinear wave crests

The distribution of nonlinear wave crests is examined on the basis of a theoretical probability density previously given elsewhere (J. Eng. Mech. 120 (1994) 1009). Certain errors contained in the original theoretical density are corrected, and the corresponding exceedance distribution is derived. The resulting theoretical forms of the probability density and exceedance distribution are then slightly simplified and compared with nonlinear wave data gathered under hurricane conditions. The results indicate that the proposed theoretical forms describe the observed distributions of large wave crests better than the Rayleigh law. However, the quantitative accuracy of the predictions is somewhat poor, as is typical of approximate theories based on Gram–Charlier-type expansions.     

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.     

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.     

Reliability based risk index for the design of reinforced earth structures

The design methods currently used for earth reinforcement are mostly based on deterministic properties of both the soil and the construction materials used. Nowadays, however, the general trend is designing at a specific degree of reliability. This is even more true where the raw data such as soil properties exhibit significant variation. Deterministic solutions, in this case, may not suffice. Therefore, this paper will attempt to use probabilistic formulations thereby modifying the existing design procedure of reinforced earth retaining walls to account for uncertainties and variabilities. Through a first order Taylor's series expansion about the mean, the mean and variance of the strip reinforcing components, namely width and length, are derived in terms of the variations in the soil properties. Design charts that enable estimation of both mean and variance are developed to avoid extensive partial differentiation involved in the computations. Using appropriate probability distributions along with the mean and variance, the final design outputs are determined for a selected failure probability by introducing what is refered to as 'risk index'. The results indicate that the risk index increases with an increase in the coefficient of variations and a decrease in failure probability. Furthermore, it is shown that in some cases, depending on the variabilities of the soil properties, the classical design technique produced a relatively high failure probability. This revised version was published online in July 2006 with corrections to the Cover Date.     

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。     

On the long-term seismic hazard analysis in the Zhangjiakou–Penglai seismotectonic zone, China

The Zhangjiakou–Penglai seismotectonic zone (ZPSZ) lies in the northern part of North China and extends along the Zhangjiakou–Beijing–Tianjin–Bohai Bay–Penglai–Yellow Sea. It is about 900 km long and some 250 km wide in a northwest direction. The great Sanhe-Pinggu (M_S=8.0) earthquake occurred on September 1679 and the Tangshan (M_S=7.8) earthquake on July 1976 caused serious economic and life losses. According to some differences in crust structure and regional tectonic stress field, the ZPSZ is divided into western and eastern segment by the 117°E line for study on long-term seismic hazard analysis. An analysis of Gutenberg–Richter's empirical relation of earthquake-frequency and time process of historic and recent earthquakes along the eastern and western segments shows that the earthquake activity obeys a Poisson process, and these calculations indicate that the earthquake occurrence probability of M_S=6.0–6.9 is 0.77–0.83 in the eastern segment and the earthquake occurrence probability of M_S=7.0–7.9 is 0.78–0.80 in the western segment of the ZPSZ during a period from 2005 to 2015.