HYDROLOGIC UNCERTAINTY MEASURE AND NETWORK DESIGN¹

Abstract

This paper presents a simple methodology, using the entropy concept, to estimate regional hydro logic uncertainty and information at both gaged and ungaged grids in a basin. The methodology described in this paper is applicable for (a) the selection of the optimum station from a dense network, using maximization of information transmission criteria, and (b) expansion of a network using data from an existing sparse network by means of the information interpolation concept and identification of the zones from minimum hydrologic information. The computation of single and joint entropy terms used in the above two cases depends upon single and multivariable probability density functions. In this paper, these terms are derived for the gamma distribution. The derived formulation for optimum hydrologic network design was tested using the data from a network of 29 rain gages on Sleeper River Experimental Watershed. For the purpose of network reduction, the watershed was divided into three subregions, and the optimum stations and their locations in each subregion were identified. To apply the network expansion methodology, only the network consisting of 13 stations was used, and feasible triangular elements were formed by joining the stations. Hydrologic information was calculated at various points on the line segments, and critical information zones were identified by plotting information contours. The entropy concept used in this paper, although derived for single and bivaviate gamma distribution, is general in type and can easily be modified for other distributions by a simple variable transformation criterion.

Optimal estuarine sediment monitoring network design with simulated annealing

“…by ordinary kriging. Other promising methods have been proposed for optimising the monitoring network design, in particular those based on information theory, as in articles such as those by Amorocho and Espildora (1973), Caselton and Husain (1980), Caselton and Zidek (1984), Harmancioglu and Yevjevich (1987), Husain (1989), and Harmancioglu and Alspaslan (1992). Despite the elegance of these methods, they are limited by the need to assume a probability distribution for the variables, which may be unknown or difficult to determine.…”

Principle of maximum entropy in the estimation of suspended sediment concentration

“…Several works have been developed applying the theory of entropy. In the area of water resources (SINGH, 1997;HUSAIN, 1989), in the application in hydrology (WANG;ZHU, 2001;SINGH, 1998), in historical series of precipitation and flow, mainly. In the prediction of hydrological variables (CONTE, 2005;WEIJS et al, 2010), in the evaluation of the prediction and stability of river flows (MUKHOPADHYAY; KHAN, 2015), in the estimation of the sediment concentration (SINGH; CUI, 2015;CUI;SINGH, 2014;GAN et al, 2014;LIEN;TSAI, 2003;CHIU et al, 2000;LUO;SINGH, 2011;GOMEZ;PHILLIPS, 1999;SING et al, 1988;CHIU, 1988;CHAO-LIN CHIU, 1987;KRSTANOVIC, 1987), in the estimation of the precipitation ratio X flow (SINGH, 2012;CONTE, 2005;SONUGA, 1976), in river processes (XU; ZHAO, 2013;DESHPANDE;KUMAR, 2013), among other applications.…”

Determination of Optimal Water Quality Monitoring Points in Sewer Systems using Entropy Theory

“…Therefore, many studies across diverse fields have sought to determine the combinations of assessment points that maximize the multivariate trans-information. Examples include the design of a precipitation monitoring network [16,17] and the assessment of a water quality monitoring network in river streams [18,19]. These studies however used the inefficient trial and error method in searching for the combinations that maximize the information transfer.…”