• Journal of Korea Water Resources Association
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• v.37 no.8
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• pp.631-641
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• 2004

The objective of this study is to test the applicability of SLURP (Semi-distributed Land Use-based Runoff Process) on Soyanggang-dam watershed. SLURP model is a conceptual semi-distributed form model that can be used to examine irrigation plan and the effects of proposed changes in water management within a basin or to see what effects external factors such as climate change or changing land cover might have on various water users. Topographical parameters were derived from DEM using TOPAZ and SLURPAZ. Monthly NDVIs were calculated from multi-temporal NOAA/AVHRR images during four years (1998 ∼ 2001). Weather elements (dew-point temperature, solar radiation, maximum/minimum temperature and relative humidify) were obtained from five meteorological stations within and near the study area. To simulate daily hydrograph during 1998 ∼ 2001, the model parameters of each land cover class were optimized by sensitivity analysis and SCE-UA method. Test result of SLURP was summarized by various statistics method (WMO volume error, Nash-Sutcliffe efficiency, mean error and coefficient of variation).

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1293-1307
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• 2012

This study is to evaluate the RHESSys (Regional Hydro-Ecological Simulation System) simulated streamflow (Q), evapotranspiration (ET), soil moisture (SM), gross primary productivity (GPP) and photosynthetic productivity (PSNnet) with the measured data. The RHESSys is a hydro-ecological model designed to simulate integrated water, carbon, and nutrient cycling and transport over spatially variable terrain. A 8.5 $km^2$ Seolma-cheon catchment located in the northwest of South Korea was adopted. The catchment covers 90.0% forest and the dominant soil is sandy loam. The model was calibrated with 2 years (2007-2008) daily Q at the watershed outlet and MODIS (Moderate Resolution Imaging Spectroradiometer) GPP, PSNnet and 3 year (2007～2009) daily ET data measured at flux tower using the eddy-covariance technique. The coefficient of determination ($R^2$) and the Nash-Sutcliffe model efficiency (ME) for Q were 0.74 and 0.63, and the average $R^2$ for ET and GPP were 0.54 and 0.93 respectively. The model was validated with 1 year (2009) Q and GPP. The $R^2$ and the ME for Q were 0.92 and 0.84, the $R^2$ for GPP were 0.93.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.341-341
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• 2019

물 순환과정의 구성요소 중 증발산(Evapotranspiration)은 수자원개발을 위한 계획의 수립과 수자원 시스템 운영적 측면에서 대단히 중요한 부분이다. 증발산량을 산정하기 위해서는 온도, 바람, 상대습도, 대기압, 수질 및 수표면의 성질과 형상 등을 산정하여야 하는데 이러한 기상자료들을 확보하기란 매우 어려운 실정이다. 본 연구에서는 기온자료만을 이용하여 기준증발산량을 산정할 수 있는 Hargreaves 공식의 경험적 매개변수 및 온도 매개변수를 수정하여 경안천유역의 기준증발산량을 산정하였다. 수정된 공식의 성능평가를 위해 현재 널리 사용되고 있는 Penman-Monteith 방법을 이용하여 산정된 기준증발산량을 정해로 가정하여 Root Mean Square Error와 Nash Sutcliffe Model Efficiency Coefficient분석을 수행하여 검증하였다. 또한 기온 및 Hargreaves 경험적 매개변수와의 상관관계를 이용한 회귀식에 대한 검증을 수행함으로써 본 연구에서 제안한 수정된 공식의 적용가능성을 확인하였으며, 향후 수자원 시스템 운영 측면에 도움이 될 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.803-812
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• 2018

The purpose of this study is to develop Particle Swarm Optimization (PSO) automatic calibration algorithm with multi-objective functions by Python, and to evaluate the applicability by applying the algorithm to the Soil and Water Assessment Tool (SWAT) watershed modeling. The study area is the upstream watershed of Gongdo observation station of Anseongcheon watershed ($364.8km^2$) and the daily observed streamflow data from 2000 to 2015 were used. The PSO automatic algorithm calibrated SWAT streamflow by coefficient of determination ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency ($NSE_Q$), and especially including $NSE_{INQ}$ (Inverse Q) for lateral, base flow calibration. The results between automatic and manual calibration showed $R^2$ of 0.64 and 0.55, RMSE of 0.59 and 0.58, $NSE_Q$ of 0.78 and 0.75, and $NSE_{INQ}$ of 0.45 and 0.09, respectively. The PSO automatic calibration algorithm showed an improvement especially the streamflow recession phase and remedied the limitation of manual calibration by including new parameter (RCHRG_DP) and considering parameters range.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.289-300
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• 2024

The purpose of this study is to evaluate the applicability of the GRM (Grid based rainfall-Runoff Model) to the continuous simulation by simulating the dam inflow. The GRM was previously developed for the simulation of rainfall-runoff events but has recently been improved to enable continuous simulation. The target watersheds are Chungju dam, Andong dam, Yongdam dam, and Sumjingang dam basins, and runoff models were constructed with the spatial resolution of 500 m × 500 m. The simulation period is 21 years (2001 to 2021). The simulation results were evaluated over the 17 year period (2005 to 2021), and were divided into three data periods: total duration, wet season (June to September), and dry season (October to May), and compared with the observed daily inflow of each dam. Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), correlation coefficient (CC), and total volume error (VE) were used to evaluate the fitness of the simulation results. As a result of evaluating the simulated dam inflow, the observed data could be well reproduced in the total duration and wet season, and the dry season also showed good simulation results considering the uncertainty of low-flow data. As a result of the study, it was found that the continuous simulation technique of the GRM model was properly implemented and the model was sufficiently applicable to the simulation of dam inflow in this study.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.999-1006
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• 2019

Recently, the quantitative management of agricultural water supply, which is the main source for water consumption in Korea, has become more important due to the effective water management organization of the Korean government. In this study, the estimation method for irrigation supply based on agricultural reservoir storage data was improved compared to previous research, in which drought year selection was unclear, and the outlier data for the rainfall-irrigation supply were not eliminated in the regression analysis. In this study, the drought year was selected by the ratio of annual precipitation to mean annual precipitation and the storage rate observed before the start of irrigation. The outlier data for the rainfall-irrigation supply were eliminated by the Grubbs & Beck test. The proposed method was applied to nine agricultural reservoirs for validation. As a result, the ratio of annual precipitation to mean annual precipitation is less than 53% and the storage rate observed before the start of irrigation is less than 55% it was judged to be the drought year. In addition, the drought supply factor, K, was found to be 0.70 on average, showing closer results to the observed reservoir rates. This shows that water management at the real is appling drought year practice. It was shown that the performance of the proposed method was satisfactory with NSE (Nash-Sutcliffe model efficiency coefficient) and R² (coefficient of determiniation) except for a few cases.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.21-31
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• 2015

Various crop models have been extensively used for estimation of the crop yields. Compared to the other models, the EPIC model uses a unified approach to simulate more than 100 types of crops. It has been successfully applied in simulating crop yields for various combinations of weather conditions, soil properties, crops, and management schemes in many countries. The objective of this study was to estimate the rice and maize yield in South Korea using the EPIC model. The input datasets for the 30 types in the 11 categories were created for the EPIC model. The EPIC model simulated rice and maize yields. The performance of the EPIC model was evaluated with the goodness-of-fit measures including Root Mean Square Error (RMSE), Relative Error (RE), Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE), and Pearson Correelation Coefficient (r). The rice yield showed to more high accuracy than maize yield on four type of method without NSEC. Theses results showed that the EPIC model better simulated rice yields than maize yields. The results suggest that the EPIC crop model can be useful to estimate crop yield in South Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.160-174
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• 2014

The purpose of this paper is to develop a meso-scale grid-based continuous hydrological model and apply to assess the future watershed hydrology by climate change. The model divides the watershed into rectangular cells, and the cell profile is divided into three layered flow components: a surface layer, a subsurface unsaturated layer, and a saturated layer. Soil water balance is calculated for each grid cell of the watershed, and updated daily time step. Evapotranspiration(ET) is calculated by Penman-Monteith method and the surface and subsurface flow adopts lag coefficients for multiple days contribution and recession curve slope for stream discharge. The model was calibrated and verified using 9 years(2001-2009) dam inflow data of two watersheds(Chungju Dam and Soyanggang Dam) with 1km spatial resolution. The average Nash-Sutcliffe model efficiency was 0.57 and 0.71, and the average determination coefficient was 0.65 and 0.72 respectively. For the whole Han river basin, the model was applied to assess the future climate change impact on the river bsain. Five IPCC SRES A1B scenarios of CSIRO MK3, GFDL CM2_1, CONS ECHO-G, MRI CGCM2_3_2, UKMO HADGEMI) showed the results of 7.0%~27.1 increase of runoff and the increase of evapotranspiration with both integrated and distributed model outputs.