Temporal moments of a tracer pulse in a perfectly parallel flow system

被引:12
作者
Bardsley, WE [1 ]
机构
[1] Univ Waikato, Dept Earth Sci, Hamilton, New Zealand
关键词
stratified aquifer; moments; advection-dispersion; mixed distribution;
D O I
10.1016/S0309-1708(03)00047-2
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Perfectly parallel groundwater transport models partition water flow into isolated one-dimensional stream tubes which maintain total spatial correlation of all properties in the direction of flow. The case is considered of the temporal moments of a conservative tracer pulse released simultaneously into N stream tubes with arbitrarily different advective-dispersive transport and steady flow speeds in each of the stream tubes. No assumptions are made about the form of the individual stream tube arrival-time distributions or about the nature of the between-stream tube variation of hydraulic conductivity and flow speeds. The tracer arrival-time distribution g(t,x) is an N-component finite-mixture distribution, with the mean and variance of each component distribution increasing in proportion to tracer travel distance x. By utilising moment relations of finite mixture distributions, it is shown (to r = 4) that the rth central moment of g(t, x) is an rth order polynomial function of x or phi, where phi is mean arrival time. In particular, the variance of g(t, x) is a positive quadratic function of x or phi. This generalises the well-known quadratic variance increase for purely advective flow in parallel flow systems and allows a simple means of regression estimation of the large-distance coefficient of variation of g(t, x). The polynomial central moment relation extends to the purely advective transport case which arises as a large-distance limit of advective-dispersive transport in parallel flow models. The associated limit g(t, x) distributions are of N-modal form and maintain constant shapes independent of travel distance. The finite-mixture framework for moment evaluation is also a potentially useful device for forecasting g(t, x) distributions, which may include multimodal forms. A synthetic example illustrates g(t,x) forecasting using a mixture of normal distributions. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:599 / 607
页数:9
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