A comprehensive review of the existing river flow and sediment measurement data of South Korea shows specific sediment yield values ranging from 5 to 1,500 tons/km²·year. The watershed characteristics decisively affect the shape of mass curves for water and sediment, as well as the flow duration and sediment rating curves. In terms of flow regime, small watersheds have flashy hydrographs and high sediment concentrations at a given flow discharge. The sediment rating curve coefficients decrease from 1 to 0.02 as the watershed area increases from 100 to 20,000 km², while the exponents of these curves remain relatively constant between 1.5 and 2.0. The sediment transport in small watersheds depends on large floods, and the half‐yield discharge decreases with watershed area and typically ranges from 5 to 40 times the mean discharge. This study defines equations to calculate the mean annual flow discharge, flow duration curves, sediment yield, and cumulative water and sediment distribution curves validated with field measurements in South Korea. Additionally, the sediment regime is also presented in a continental perspective.

This study evaluates a novel biopolymer-based material reinforcement method. A real-scale experiment minimizing flood disasters and economic losses incurred by the collapse of river levees due to overtopping was conducted. At the Andong River Experiment Center, lateral overflow was reproduced to induce levee collapse using sand, reinforced novel materials, and vegetation levees represented as cases 1, 2, and 3, respectively. The flow in the upstream and downstream areas was measured, and fluctuations in the lateral overflow discharge were calculated using an acoustic Doppler current profiler. To quantitatively verify the performance of this method, the collapse delay effect based on the surface loss rate of the levee slope was analyzed using image pixel analysis and three-dimensional point cloud modeling. Comparing the collapse delay effect of the new-material levee with that of the non-reinforced levees, we found a time delay of approximately 2.7–7 times from the occurrence of overtopping via the lateral flow to the end of the test. These results indicate that we can secure time for emergency repairs and operations by reinforcing the levee surface using the material proposed in this study. These research findings are expected to provide the basis for the proper design and construction of river levees.

This study aimed to experimentally understand the seepage mechanism in levees and evaluate the applicability of an optical-fiber distributed temperature system based on Raman-scattered light as a levee stability monitoring method. To this end, a concrete box capable of accommodating two levees was built, and experiments were conducted by supplying water evenly to both levees through a system equipped with a butterfly valve. Water-level and water-pressure changes were monitored every minute using 14 pressure sensors, while temperature changes were monitored using distributed optical-fiber cables. Levee 1, composed of thicker particles, experienced a faster water pressure change, and a corresponding temperature change was observed due to seepage. While the temperature change inside the levees was relatively smaller than external temperature changes, measurement fluctuations were significant. Additionally, the influence of external temperature and the dependence of temperature measurements on the levee position made intuitive interpretation challenging. Therefore, five smoothing techniques with different time intervals were examined and compared to determine their effectiveness in reducing outliers, elucidating temperature change trends and enabling the comparison of temperature changes at different positions. Overall, this study confirmed that the optical-fiber distributed temperature system combined with appropriate data-processing techniques can be more efficient than existing methods for understanding and monitoring levee seepage.

This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at measuring points in the main stream of the Nakdong River located where the tributaries of the Geumho, Hwang, and Nam Rivers join. Surveys yielded estimations of water velocity, discharge, and suspended sediment concentrations were measured. In contrast with models based on the general watershed characteristics factors, some models based on hydraulic explanatory flow variables demonstrated an excellent predictability. This is because the selected submodels for validation, which provided excellent prediction results, were based on a large number of calibration data. It indicates that a sufficient number of reliable data is required in developing a sediment yield estimation model using data mining. For practical applications of data mining to extant sediment yield estimation models, comprehensive considerations are required, including the purpose and background of model development, and data range. Furthermore, the existing models should be periodically updated with the consideration of temporal and spatial lumping problems.

Sediment measurement data are essential for sediment transport analysis and therefore highly important in overall river planning. Extant sediment measurement methods consume considerable manpower and time and are limited by factors including economic reasons and worker risks. This study primarily aimed to predict the changes in SSC (Suspended Sediment Concentration) and turbidity by examining the change in color in underwater images. While maintaining a constant flow in a channel, the turbidity and concentration were measured under different SSC. Multiple regression models were developed using turbidity measurement results, and they exhibited high explanatory powers (adjusted R² > 0.91). Furthermore, upon verification using the verification dataset of the experimental results, an excellent predictive power (RMSE ≈ 0.4 NTU) was demonstrated. The model with the highest predictive power, which was inclusive of red and green bands and showed no underlying multicollinearity was used to predict turbidity. Finally, the turbidity and suspended sediment concentration relationship determined from the experimental results was used to estimate the sediment concentration from the color changes in the underwater images. The concentrations that were predicted by the model showed satisfactory results, compared to the measurements (RMSE ≈ 21 ppm). This study indicated the feasibility of continuous SSC monitoring using underwater images as a new measurement method.

Specific degradation (SD) is defined as the ratio of the sediment yield divided by the watershed area to compare sediment yield at the basin scale. The SD from 35 watersheds was calculated from field measurements of discharge and sediment concentrations. The watershed characteristics for each watershed were analyzed using GIS tools. All sediment gauging stations are located in alluvial river reaches and the estimated specific degradation typically ranges between 100 and 1,000 tons/km²·yr. Six regression models based on the watershed characteristics are proposed to estimate the mean annual sediment yield. The most useful relationship is function of the drainage area and mean annual precipitation. The proposed models were tested and validated with 15 additional river stations. The root mean square errors (RMSE) of the predictions are approximately 100 tons/km²·yr which is found to be satisfactory. The proposed models should be useful to estimate the sediment yield from ungauged watershed in South Korea.