Assessment of a Stream Gauge Network Using Upstream and Downstream Runoff Characteristics and Entropy

Abstract

A method for constructing a stream gauge network that reflects upstream and downstream runoff characteristics is assessed. For the construction of an optimal stream gauge network, we develop representative unit hydrographs that reflect such characteristics based on actual rainfall–runoff analysis. Then, the unit hydrographs are converted to probability density functions for application to entropy theory. This allows a comparison between two cases: one that considers the upstream and downstream runoff characteristics of a core dam area in South Korea, and another that uses empirical formula, which is an approach that has been widely used for constructing the stream gauge network. The result suggests that the case of a stream gauge network that considers upstream and downstream runoff characteristics provides more information to deliver, although the number of selected stream gauge stations of this case is less than that of the case that uses the empirical formula. This is probably because the information delivered from the constructed stream gauge network well represents the runoff characteristics of the upstream and downstream stations. The study area, the Chungju Dam basin, requires 12 stream gauge stations out of the current total of 18 stations for an optimal network that reflects both upstream and downstream runoff characteristics.

Optimal Stream Gauge Network Design Using Entropy Theory and Importance of Stream Gauge Stations

“…After a certain time point, however, the amount of information that can be obtained from the selected stations decreases due to the amount of overlapping information between stations. Therefore, the optimal stream gauge network is the combination of stream gauge stations that can maximize the amount of information about the basin [7].…”

“…For the concentration, time, and storage constant parameters, empirical formulas are used, or the values are calculated by analyzing observational data. Joo et al [7] compared unit hydrographs when empirical formulas and measurement data were employed and mentioned that the unit hydrographs derived using measurement data have the advantage of reflecting upstream and downstream runoff. Therefore, in this study, unit hydrographs were derived using measurement data to reflect the upstream and downstream runoff characteristics of each stream gauge station.…”

“…Most hydrological phenomena, however, occur continuously and, thus, continuous distribution types are mainly used for the probabilistic analysis of such phenomena. Among the continuous distributions, normal, log-normal, Gamma, log-Pearson, and generalized extreme value distributions are the most frequently used for hydrological analysis [7,33,34]. Entropy equations that are known to follow probability density functions include the normal, log-normal, and Gamma distributions.…”

“…The concept of evaluating the design and construction of stream gauge networks was established since the 1980s. Stream gauge stations are usually evaluated using entropy theory, principal component regression analysis, and correlation analysis, although the existing stream gauge networks are mainly evaluated using entropy theory [2][3][4][5][6][7]. Al-Zahrani and Husain [2] applied the entropy concept to the optimal number of stream gauge stations in a dense network and to the expansion of a stream gauge network with a low density.…”

“…Yang and Burn [6] subsequently attempted to design an optimal stream gauge network using the entropy method. Moreover, Joo et al [7] derived unit hydrographs using empirical formulas and actual runoff data and showed that it is appropriate to use actual runoff data that can reflect stream connectivity when applying entropy theory.…”