Search

There has been an increase for the landslide areas and restoration expenses due, in large part, to the increased locally heavy rains caused by recent climate change as well as the reckless development. This study carried out a slope stability analysis by the application of distributed wetness index, using the GIS-based infinite slope stability model, which took the root cohesion effect into consideration, for part of Mt. Umyeon in Seoul, where landslide occurred in July 2011, in order to compensate the defects of existing analysis method, and subsequently compared its result with the case on the exploitation of lumped wetness index. In addition, this study estimated the distributed wetness index by methodology, applying three methods of specific catchment area calculation: single flow direction (SFD), multiple flow direction (MFD), and infinity flow direction (IFD), for catchment area, one of the variables of distributed wetness indices, and finally implemented a series of comparative analysis for slope stability by methodology. The simulation results showed that most unstable areas within the study site were dominantly located in cutting-area surroundings along with the residential area and the mountaintop and unstable areas of IFD and lumped wetness index method were similar while SFD and MFD provided smaller unstable areas than the two former methods.

This paper presents the effect of spatially-distributed rainfall on both rainfall-sediment-runoff and erosion or deposition in the experimental Cheoncheon catchment: upstream of Yongdam dam basin. The rainfall fields were generated by three rainfall interpolation techniques (Thiessen polygon: TP, Inverse Distance Weighting: IDW, Kriging) based only on ground gauges and two radar rainfall synthetic techniques (Gauge-Radar ratio: GR, Conditional Merging: CM). Each rainfall field was then assessed in terms of spatial feature and quantity and also used for rainfall-sediment-runoff and erosion-deposition simulation due to the spatial difference of rainfall fields. The results showed that all the interpolation methods based on ground gauges provided very similar hydrologic responses in spite of different spatial pattern of erosion and deposition while raw radar and GR rainfall fields led to underestimated and overestimated simulation results, respectively. The CM technique was acceptable to improve the accuracy of raw radar rainfall for hydrologic simulation even though it is more time consuming to generate spatially-distributed rainfall.

In hydrologic modeling, prediction uncertainty generally stems from various uncertainty sources associated with model structure, data, and parameters, etc. This study aims to assess the parameter uncertainty effect on hydrologic prediction results. For this objective, a distributed rainfall-sediment yield-runoff model, which consists of rainfall-runoff module for simulation of surface and subsurface flows and sediment yield module based on unit stream power theory, was applied to the mesoscale mountainous area (Cheoncheon catchment; 289.9 ). For parameter uncertainty evaluation, the model was calibrated by a multi-objective optimization algorithm (MOSCEM) with two different objective functions (RMSE and HMLE) and Pareto optimal solutions of each case were then estimated. In Case I, the rainfall-runoff module was calibrated to investigate the effect of parameter uncertainty on hydrograph reproduction whereas in Case II, sediment yield module was calibrated to show the propagation of parameter uncertainty into sedigraph estimation. Additionally, in Case III, all parameters of both modules were simultaneously calibrated in order to take account of prediction uncertainty in rainfall-sediment yield-runoff modeling. The results showed that hydrograph prediction uncertainty of Case I was observed over the low-flow periods while the sedigraph of high-flow periods was sensitive to uncertainty of the sediment yield module parameters in Case II. In Case III, prediction uncertainty ranges of both hydrograph and sedigraph were larger than the other cases. Furthermore, prediction uncertainty in terms of spatial distribution of erosion and deposition drastically varied with the applied model parameters for all cases.

Accelerated soil erosion due to extreme climate change, such as increased rainfall intensity, and human-induced environmental changes, is a widely recognized problem. Existing soil erosion models are generally based on the gross erosion concept to compute annual upland soil loss in tons per acre per year. However, such models are not suitable for event-based simulations of erosion and deposition in time and space. Recent advances in computer geographic information system (GIS) technologies have allowed hydrologists to develop physically based models, and the trend in erosion prediction is towards process-based models, instead of conceptually lumped models. This study aims to propose an effective and robust distributed rainfall-sediment yield-runoff model consisting of basic element modules: a rainfall-runoff module based on the kinematic wave method for subsurface and surface flow, and a runoff-sediment yield-runoff model based on the unit stream power method. The model was tested on the Cheoncheon catchment, upstream of the Yongdam dam using hydrological data for three extreme flood events due to typhoons. The model provided acceptable simulation results with respect to both discharge and sediment discharge even though the simulated sedigraphs were underestimated, compared to observations. The spatial distribution of erosion and deposition demonstrated that eroded sediment loads were deposited in the cells along the channel network, which have a short overland flow length and a gentle local slope while the erosion rate increased as rainfall became larger. Additionally, spatially heterogeneous rainfall intensity, dependant on Thiessen polygons, led to spatially-distinct erosion and deposition patterns.