• Journal of Korea Water Resources Association
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• v.31 no.4
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• pp.503-512
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• 1998

When various elements of water balance are displayed at several points of a river network, the runoff amounts at an estuary especially tidal influenced are affected from the elements. This problem can be solved by a model that can generalize and formulate the elements and simulate daily runoff and water requirement. The WWASS model was built using DIROM for the simulation of daily runoff and water requirement, and the water balance elements were modeled to be balanced at the each control point of river network. The model was calibrated, verified and applied to the watershed for the Saemankeum tidal land reclamation development project. It showed that the results from the streamflow simulation at the Mankyung and Dongjin estuary were acceptable for the design of the Saemankeum estuary reservoir.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.209-220
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• 2011

Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.

• Water Engineering Research
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• v.5 no.3
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• pp.133-146
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• 2004

Soil and water assessment tool (SWAT) was simulated based on the default parameters and a priori soil parameter estimation method in Bocheong watershed of Korea. The performance of the model was tested against the measured daily runoff data for 5 years between 1993 and 1997. The sensitivity analysis of SWAT model parameters was conducted to identify the most sensitive model parameters affecting the model output. The results of SWAT simulation indicate that the overall performance of SWAT in calculating daily runoff is reasonably acceptable. However, there is a problem in estimating the low flow components of streamflow since the low flow components simulated by SWAT are significantly different from the measured low flow. The sensitivity analysis with SWAT points out that soil related parameters are the most sensitive parameters affecting surface and ground water balance components and groundwater flow related parameters exhibit negligible sensitivity.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.19-27
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• 2017

Recently, the hydrological model Soil Water Assessment Tool (SWAT) has been applied in many watersheds in South Korea. This study estimated parameters in SWAT for calibrating streamflow in long-term drought periods. Therefore, we focused on the continuous severe drought periods 2014~2015, and understand the model calibrated parameters. The SWAT was applied to a $366.5km^2$ Gongdo watershed by using 14 years (2002~2015) daily observed streamflow (Q) including two years extreme drought period of 2014~2015. The 9 parameters of CN2, CANMX, ESCO, SOL_K, SLSOIL, LAT_TIME, GW_DELAY, GWQMN, ALPHA_BF were selected for model calibration. The SWAT result by focusing on 5 normal years (2002~2006) calibration showed the 14 years average Nash-Sutcliffe model efficiency (NSE) for Q and 1/Q with 0.78 and 0.58 respectively. On the other hand, the 14 years average NSEs of Q and 1/Q by focusing on 2 drought years (2014~2015) calibration were 0.86 and 0.76 respectively. Thus, we could infer that the SWAT calibration trial by focusing on drought periods data can be a good approach to calibrate both high flow and low flow by controlling the 9 drought affected parameters.

• Journal of Korea Water Resources Association
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• v.32 no.3
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• pp.265-279
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• 1999

In this study, one of the techniques on the extension of low flow series has been developed, in which the daily streamflows were simulated by the Tank model with the input of extended daily rainfall series which were stochastically generated by the Markov chain model. The annual lowest flow serried for each of the given durations were formulated form the simulated daily streamflow sequences. The frequency of the estimated annual lowest flow series was analyzed. The distribution types to be used for the frequency analysis were two-parameter and three-parameter log-normal distribution, two-parameter and three-parameter Gamma distribution, three-parameter log-Gamma distribution, Gumbel distribution, and Weibull distribution, of which parameters were estimated by the moment method and the maximum likelihood method. The goodness-of-fit test for probability distribution is evaluated by the Kolmogorov-Sminrov test. The fitted distribution function for each duration series is applied to frequency analysis for developing duration-low flow-frequency curves at Yongdam Dam station. It was shown that the purposed technique in this study is available to generate the daily streamflow series with fair accuracy and useful to determine the probabilistic low flow in the watersheds having the poor historic records of low flow series.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.1
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• pp.33-40
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• 1992

Up to now, monthly water balance analysis has been dominantly used for the water resources planning. But, it is more reasonable to explain the variation of spatial and temporal distribution of water by the daily water balance model with daily streamflow data. Since we are recently facing the problems of regional unbalance of water quantity, and of multiuse of irrigation water, and of deterioration of water quality, it is urgently needed to develop the daily water balance model to solve those problems and establish the rational plannings of agricultural water resources. In the circumstances, Daily water Balance(DAWABA) model for irrigation reservoirs was developed and the operation rule of irrigation resorvoir during drought season was established.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.87-94
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• 2012

This study is to evaluate flood inundation and to recommend measures of damage reduction on sediment by concentrated torrential rainfall at Gonjiamcheon Watershed (183.4 $km^2$). Firstly, the SWAT (Soil and Water Assessment Tool) was simulated streamflow and sediment at upstream. Then, we produced a map of floodplain boundary by using HEC-RAS (Hydrologic Engineering Centers River Analysis System) at downstream. The SWAT model was calibrated with 2 years (2008~2009) daily streamflow and validated for another years (2010~2011. 7. 31). The SWAT model was simulated with 3 years (2008~2010) by monthly water quality (Sediment) at Gonjiamcheon water quality station. The streamflow and sediment from SWAT model were input as boundary conditions to HEC-RAS. The results of HEC-RAS indicated that mapping of floodplain boundary was Jiwol and Jiwol 2 district. Additionally, inundation area and depth were assessed and applied BMPs scenario for managing the sediment yield.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.