Regional Flood Frequency Analysis for a Poorly Gauged Basin Using the Simulated Flood Data and L-Moment Method

Abstract

The design of hydraulic structures and the assessment of flood control measures require the estimation of flood quantiles. Since observed flood data are rarely available at the specific location, flood estimation in un-gauged or poorly gauged basins is a common problem in engineering hydrology. We investigated the flood estimation method in a poorly gauged basin. The flood estimation method applied the combination of rainfall-runoff model simulation and regional flood frequency analysis (RFFA). The L-moment based index flood method was performed using the annual maximum flood (AMF) data simulated by the rainfall-runoff model. The regional flood frequency distribution with 90% error bounds was derived in the Chungju dam basin of Korea, which has a drainage area of 6648 km2. The flood quantile estimates based on the simulated AMF data were consistent with the flood quantile estimates based on the observed AMF data. The widths of error bounds of regional flood frequency distribution increased sharply as the return period increased. The results suggest that the flood estimation approach applied in this study has the potential to estimate flood quantiles when the hourly rainfall measurements during major storms are widely available and the observed flood data are limited.

Regional Flood Frequency Analysis using Dimensionless Index Flood Method

“…In Table 3, the homogeneity statistic was estimated as 1.175. Although this is higher than the values of 0.39 obtained in a previous study by Lee and Kim [23] in an entirely different region of Chungju basin, Korea, the value of = 1.175 obtained in this study is less than 2. Hence, according to Sine and Ayalew (2004) [18], the six river sub-basins of Anambra-Imo river can be declared homogeneous as = 1.175 < 2.…”

“…Also, the percentage errors were relatively high, decreasing with increase in the return period (Table 6). Although, the relative errors of the mean annual flood for this study were higher than those of the previous study by Kim et al [26], similar model performance were observed by Lee and Kim [23] where the uncertainty in model performance increased with data ambiguity. Notwithstanding the poor model performance adjudged by the high relative errors, it is believed that better outcomes are possible by addressing input variable limitations…”

“…However, the model performance is rated lower than those obtained by Lee and Kim [23] and Dubey [15] in similar studies in which R 2 = 0.99 and 0.945 respectively. To buttress the validity of the model, the deficient model performance is linked to fewer number of small gauged catchments (A<1000km 2 ) used in this study due to scarce gauging sites in the region, in contrast to the 16 and 20 large gauging sites used in previous studies [15,23]. This is further confirmed in Figure 5 which presents the linear fit for the index flood-basin area scatter plot.…”

“…Equation 8 shows that the exponent and the coefficient of the watershed area obtained were 0.06676 and 0.495 respectively. In comparison, Lee and Kim [23], Kim et al [26] and Dubey [15] reported the following pairs of values for the exponent and the coefficient of watershed area as (0.907, 2.987), (0.873, 3.895) and (0.698, 8.87) respectively. Evidently, the values obtained in this study fall within the range of values obtained in previous studies which serve as a confirmation of the model acceptability.…”

“…RFFA estimates seem to overestimate the flood magnitude for all return periods. Lee and Kim [23] asserted that the relatively large differences seen between the RFFC and at-site FFC estimates are partially due to the model structure, which was constrained to index flood-basin area relation. Improved estimates are therefore possible by incorporating other catchment features such as basin physiography and climatic characteristics.…”