Regression Equations for Estimating the TANK Model Parameters

Abstract

The TANK model has been widely used in rainfall-runoff modeling due to its simplicity of concept and computation while achieving forecast accuracy. A major barrier to the model application is to determine parameter values for ungauged watersheds, leading to the need of a method for the parameter estimation. The objective of this study was to develop regression equations for estimating the 3th TANK model parameters considering their variations for the ungauged watersheds. Thirty watersheds of dam sites and stream stations were selected for this study. A genetic algorithm was used to optimize TANK model parameters. Watershed characteristics used in this study include land use percent, watershed area, watershed length, and watershed average slope. Generalized equations were derived by correlating to the optimized parameters and the watershed characteristics. The results showed that the TANK model, with the parameters determined by the developed regression equations, performed reasonably with 0.60 to 0.85 of Nash-Sutcliffe efficiency for daily runoff. The developed regression equations for the TANK model can be applied for the runoff simulation particularly for the ungauged watersheds, which is common for upstream of agricultural reservoirs in Korea.

Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

“…The Tank models have been widely employed as a tool to predict the streamflow of ungauged watersheds in Korea, Japan, and Germany [4,13,14,17,18,40,41] (Table 2). The model parameters are estimated from the relationship between parameter values and watershed characteristics, which are developed using observations made in gauged watersheds.…”

“…In Korea, a three-layer Tank model, modified with two side outlets on the top layer, has been widely employed, especially to study small-or medium-sized watersheds where flow travel time is short, and the recession limb of a streamflow hydrograph is steep [9,36]. Various regionalization approaches have been carried out in Korea to estimate ungauged streamflow [13,18,40,41]. Kim and Park [13] developed regression equations to estimate reservoir inflow in ungauged watersheds using only the information of drainage area and land-use composition.…”

“…Kim and Park [13] developed regression equations to estimate reservoir inflow in ungauged watersheds using only the information of drainage area and land-use composition. Similarly, Huh et al [40], Kim et al [41], and An et al [18] considered the topographical characteristics, such as stream length, slope, and form factor when developing the regression relationship.…”

“…The regression approach statistically links the calibrated parameter values to quantified watershed characteristics. The relationship is then applied to ungauged watersheds that have geohydrological features statistically similar to those of the gauged ones [4,5,13,18,19]. The quality of parameter regionalization is dependent on many factors, including model structure, the quality of observations used for regionalization, hydrological variables of interest, calibration techniques, and regression methods.…”

“…Various regional Tank models have been developed for water resources planning and management for ungauged watersheds [4,13,14,17,18,40,41]. However, there is substantial room for improvement of the regional models as methods and techniques for calibration and hydrological analyses are improving along with advanced computing resources.…”