Boosik Kang

Distributed Hydrologic Prediction: Sensitivity to Accuracy of Initial Soil Moisture Conditions and Radar Rainfall Input

The aim of this study is to evaluate prediction accuracy and sensitivity of a distributed hydrologic model. Accurate predictions of runoff are needed where reservoir operations are used to control flooding and to manage water resources. The study area consists of watershed areas that are influent to reservoirs in the 967 km2 Yongdam basin, and the 2,293 km2 Namgang basin located on the Korean Peninsula. For these basins with complex terrain, a physics-based distributed hydrologic model is set up with geospatial data, calibrated, and used to test sensitivity to accuracy of radar and rain gauge input and initial conditions. The events studied range in magnitude from 86 to over 249 mm and include two typhoons and two heavy rainfall events. Radar reflectivity is converted to rainfall rates using Z-R relationships, and then corrected for bias using a spatially variable correction derived from the rain gauge networks that cover both basins. Adjustment of assumed model parameters for the Namgang and Yongdam wate...

Impact of Climate Change on Runoff in Namgang Dam Watershed

Climate change can impact hydrologic processes of a watershed system. The integrated modeling systems need to be built to predict and analyze the possible impacts of climate change on water environment for the optimal water resource operation and management. In this study, Namgang Dam watershed in the Nakdong River basin was selected as a study area. To evaluate the vulnerability of Namgang Dam watershed caused by climate change, the change in hydrologic runoff were predicted using the watershed model, SWAT. The RCM scenario was analyzed and downscaled using the artificial neural network and the dynamic quantile mapping. The results of this study will be utilized for suggesting an effective counterplan for climate change, and finally to propose the optimal water resource management method.

Multifractal properties of the peak flow distribution on stochastic drainage networks

In this paper, we examined the peak flow distribution on a realization of networks obtained with stochastic network models. Three network models including the uniform model, the Scheidegger model, and the Gibbsian model were utilized to generate networks. The network efficiency in terms of drainage time is highest on the Scheidegger model, whereas it is lowest on the uniform model. The Gibbsian model covers both depending on the parameter value of β. The magnitude of the peak flow at the outlet itself is higher on the Scheidegger model compared to the uniform model. However, the results indicate that the maximum peak flows can be observed not just at the outlet but also other parts of the mainstream. The results show that the peak flow distribution on each stochastic model has a common multifractal spectrum. The minimum value of α, which is obtained in the limit of a sufficiently large q, is equal to the fractal dimension of a single river. The multifractal properties clearly show the difference among three stochastic network models and how they are related. Moreover, the results imply that the multifractal properties can be utilized to estimate the value of β for a given drainage network.

Bias Correction for GCM Long-term Prediction using Nonstationary Quantile Mapping

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea

Prediction of hydrological water balances in dam reservoir water storage on long-term continuous rainfall–runoff estimation is highly important for accurate operational decision making related to sustainable water management planning, flood control and supply planning, etc. The physically based engineering model has been used conventionally, but requires a vast amount of hydrological and geographical data set for model construction. However, the data-driven model requires relatively low computational burden and shows reasonable accuracy depending on the feasible design of the predictor set. In this study, the dam inflow prediction using physically based model and data-driven model was compared. The antecedent soil moisture conditions were utilized effectively for training artificial neural network of continuous reservoir inflow modeling. The results indicated that the data-driven and physically based models had R2 values of 0.61 and 0.68, and Nash–Sutcliffe efficiency values of 0.60 and 0.66, respectively, which shows reasonable performance. Even though the dam inflow prediction results using the physically based model showed a relative superiority than the data-driven model, the difference was not high enough to diminish the advantages of the data-driven model. The data-driven model could be an effective alternative in areas of limited availability in hydrologic observations and geospatial data sets.

Parameter Sensitivity Analysis for Spatial and Temporal Temperature Simulation in the Hapcheon Dam Reservoir

다목적댐 저수지는 여름철에 발생하는 성층현상이 발생하고 가을철에는 성층화된 저수지의밀도 차로 인한 전도현상이 일어나게 된다. 이러한 현상은 저수지의 시공간적 수온분포의 변화에 의하여 발생하며, 이를 정확히 모의하고 예측하기 위해서는 수온에 작용하는 관련 매개변수의 작용특성을 명확히 파악할 필요가 있다....

Sewer overflow simulation evaluation of urban runoff model according to detailed terrain scale

Frequently torrential rain is occurred by climate change and urbanization. Urban is formed with road, residential and underground area. Without detailed topographic flooded analysis consideration can take a result which are wrong flooded depth and flooded area. Especially, flood analysis error of population and assets in dense downtown is causing a big problem for establishments and disaster response of flood measures. It can lead to casualties and property damage. Urban flood analysis is divided into sewer flow analysis and surface inundation analysis. Accuracy is very important point of these analysis. In this study, to confirm the effects of the elevation data precision in the process of flooded analysis were studied using 10m DEM, LiDAR data and 1:1,000 digital map. Study area is Dorim-stream basin in the Darim drainage basin, Sinrim 3 drainage basin, Sinrim 4 drainage basin. Flooding simulation through 2010s heavy rain by using XP-SWMM. Result, from 10m DEM, shows wrong flood depth which is more than 1m. In particular, some of the overflow manhole is not seen occurrence. Accordingly, detailed surface data is very important factor and it should be very careful when using the 10m DEM.

Sensitive analysis of river geometry under various flow conditions in South Han River using GSTARS model

Flow input from the basin will not remain the same as before due to climate changes. Since the predictions on river discharge due to climate change is given by scenarios, various discharge scenarios were prepared in this study. For a long term and reach prediction, semi-two dimensional sediment transport model, GSTARS, was used. The flood water surface elevations predicted by GSTARS model were analysed statistically and it was concluded that the model is applicable for the South Han River. Three stream tubes is the most suitable to simulate two dimensional river geometric change River geometric changes. For sediment load computation, Ackers and White equation and Yang equation were resonable. River will become narrower regardless of discharge variation, more discharge results in deeper channel.

Hydrologic Safety Evaluation of Small Scale Reservoir by Simplified Assesment Method

Based on the statistical annual report, there are 17,649 reservoirs are operating for the purpose of agricultural water supply in Korea. 58 % of entire agricultural reservoirs had been constructed before 1948 which indicate the termination of required service life and rest of those reservoirs have also exposed to the dam break risk by extreme flood event caused by current ongoing climate change. To prevent damages from dam failure accident of these risky small size dams, it is necessary to evaluate and manage the structural and hydrological safety of the reservoirs. In this study, a simplified evaluation method for hydrologic safety of dam is suggested by using Rational and Creager formula. Hydrologic safety of small scale dams has evaluated by calculating flood discharge capacity of the spillway and compares the results with design frequency of each reservoir. Applicability and stability of suggested simplified method have examined and reviewd by comparing the results from rainfall-runoff modeling with dam break simulation using HEC-HMS. Application results of developed methodology for three sample reservoirs show that simplified assessment method tends to calculate greater inflow to the reservoirs then HEC-HMS model which lead lowered hydrologic safety of reservoirs. Based on the results of application, it is expected that the developed methodology can be adapted as useful tool for small scale reservoir`s hydrologic safety evaluation.

Assessment of Climate and Vegetation Canopy Change Impacts on Water Resources using SWAT Model

The objective of this study is to evaluate the future potential climate and vegetation canopy change impact on a dam watershed hydrology. A dam watershed, the part of Han-river basin which has the watershed outlet at Chungju dam was selected. The SWAT model was calibrated and verified using 9 year and another 7 year daily dam inflow data. The Nash-Sutcliffe model efficiency ranged from 0.43 to 0.91. The Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model3 (CGCM3) data based on Intergovernmental Panel on Climate Change (IPCC) SRES (Special Report Emission Scenarios) B1 scenario was adopted for future climate condition and the data were downscaled by artificial neural network method. The future vegetation canopy condition was predicted by using nonlinear regression between monthly LAI (Leaf Area Index) of each land cover from MODIS satellite image and monthly mean temperature was accomplished. The future watershed mean temperatures of 2100 increased by , and the precipitation increased by 20.4 % based on 2001 data. The vegetation canopy prediction results showed that the 2100 year LAI of deciduous, evergreen and mixed on April increased 57.1 %, 15.5 %, and 62.5% respectively. The 2100 evapotranspiration, dam inflow, soil moisture content and groundwater recharge increased 10.2 %, 38.1 %, 16.6 %, and 118.9 % respectively. The consideration of future vegetation canopy affected up to 3.0%, 1.3%, 4.2%, and 3.6% respectively for each component.