Abstract
Low-flow quantiles at ungauged locations are generally estimated based on hydrological methods, such as the drainage area ratio and frequency analysis methods. In practice, the drainage area ratio approach is a popular but simple linear model. When hydrologically nonlinear characteristics govern the runoff process, the linear approach leads to significant bias. This study was conducted to develop an improved nonlinear approach using a canonical correlation analysis and neural network (CCA-NN)-based regional frequency analysis (RFA) for low-flow estimation. The jackknife technique was utilized to validate the two methods. The approaches were applied to 33 river basins in South Korea. In this work, we focused on two-year and five-year return periods. For the two-year return period, the BIAS, RMSE, and R2 were 0.013, 0.511, and 0.408 with the RFA, respectively, and −0.042, 1.042, and 0.114 with the drainage area ratio method, respectively; whereas for the five-year return period, the respective indices were −0.018, 0.316, and 0.573 with RFA, respectively, and 0.166, 0.536, and 0.044 with the drainage area ratio method, respectively. RFA outperformed the drainage area ratio method based on its high prediction accuracy and ability to avoid the bias problem. This study indicates that machine learning-based nonlinear techniques have the potential for use in estimating reliable low-flows at ungauged sites.
Highlights
Reliable low-flow estimates are necessary to provide information for water supply planning, reservoir storage design, water quantity and quality preservation, irrigation, hydropower production, and pollution load dispersion [1,2,3,4]
We used with low-flow quantiles of a tworeturn period and a five-year return period and in the second method, we used AREA, mean basin slope (MBS), AMT, return period and with a five-year period and in the second method, we used AREA, MBS, AMT,year
Based on the optimal number of hidden neurons for the ensemble artificial neural networks (ANNs) models based on the canonical correlation analysis (CCA), we obtained root mean squared error (RMSE), BIAS, and R2 values for the results of the regional frequency analysis (RFA) using the two-year and five-year quantiles and compared these values with the results of the drainage area ratio method
Summary
Reliable low-flow estimates are necessary to provide information for water supply planning, reservoir storage design, water quantity and quality preservation, irrigation, hydropower production, and pollution load dispersion [1,2,3,4]. In the case of insufficient or no streamflow records, several approaches can be used to obtain low-flow estimates. Several studies with nonlinear models have been conducted to provide more reliable low-flow estimates [5,6,7]. The drainage area ratio method is a linear model between the drainage area and discharge and has been popular for the estimation of low-flow with a 10/365 non-exceedance probability [8]. A number of studies have applied the drainage area ratio method. Wiche et al [9] examined historic streamflow data by focusing on the James River in North Dakota and South Dakota, USA and performed record
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