• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.802-809
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• 2010

An automatic calibration tool of Hydrological Simulation Program-Fortran (HSPF), Parameter Estimation (PEST) program, was applied at the Imha lake watershed to get optimal hydrological parameters of HSPF. Calibration of HSPF parameters was performed during 2004 ~ 2008 by PEST and validation was carried out to examine the model's ability by using another data set of 1999 ~ 2003. The calibrated HSPF parameters had tendencies to minimize water loss to soil layer by infiltration and deep percolation and to atmosphere by evapotranspiration and maximize runoff rate. The results of calibration indicated that the PEST program could calibrate the hydrological parameters of HSPF with showing 0.83 and 0.97 Nash-Sutcliffe coefficient (NS) for daily and monthly stream flow and -3% of relative error for yearly stream flow. The validation results also represented high model efficiency with showing 0.88 and 0.95, -10% relative error for daily, monthly, and yearly stream flow. These statistical values of daily, monthly, and yearly stream flow for calibration and validation show a 'very good' agreement between observed and simulated values. Overall, the PEST program was useful for automatic calibration of HSPF, and reduced numerous time and effort for model calibration, and improved model setup.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• Journal of Environmental Science International
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• v.2 no.3
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• pp.193-199
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• 1993

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Dong Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas. Key Words : runoff, effective rainfall, SCS method, clark watershed iou상ng, hydrological parameters, parameter estimation, least square method, correlation function method, input-output model, typhoon gladys.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.13-20
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• 2013

The purpose of this study was to evaluate the applicability of the HSPEXP expert system for the calibration of the Hydrological Simulation Program - Fortran (HSPF) for the study watershed. HSPEXP offers advice to the modeler, suggesting parameter changes that might result in better representation of a river basin and provides explanations supporting the recommended parameter changes. The study watershed, Sancheong, is located within the Nakdong River Basin and having the size of $1,072.4km^2$. Input data for the HSPF model were obtained from the landuse map, digital elevation map, meteorological data and others. Water flow data from 2006 to 2008 were used for calibration and from 2009 to 2010 were for validation. Using the HSPEXP expert system, hydrological parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. For the calibration and validation period, all the HSPEXP model performance criteria were satisfied.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.453-456
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• 2002

In this study, the effect of DEM resolution (15m, 30m, 50m, 70m, 100m, 200m, 300m) on the hydrological simulation was examined using BASINS (Better Assessment Science Integrating point and Nonpoint Source) for Heukcheon watershed (303.3km2) data from 1998 to 1999. Generally, as the cell size of DEM increased, topographical changes were observed as the original range of elevation decreased. The processing time of watershed delineation and river network needed more time and effort on smaller cell size of DEM. The larger DEM demonstrated had some errors in the junction of river network which might effects on the simulation of water quantity and quality. The area weighted average watershed slope became lower but the length weighted average channel slope became higher as the DEM size increased. DEM resolution affected substantially on the topographical parameter but less on the hydrological simulation. Considering processing time and accuracy on hydrological simulation DEM mesh size of 100m is recommended for this watershed.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.7
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• pp.25-35
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• 2002

In this study, the effect of DEM (Digital Elevation Model) resolution (15m, 30m, 50m, 70m, 100m, 200m, 300m) on the hydrological simulation was examined using the BASINS (Better Assessment Science Integrating point and Nonpoint Source) for the Heukcheon watershed (303.3 ㎢) data from 1998 to 1999. Generally, as the cell size of DEM increased, topographical changes were observed as the original range of elevation decreased. The processing time of watershed delineation and river network needed more time and effort on smaller cell size of DEM. The larger DEM demonstrated had some errors in the junction of river network which might affect on the simulation of water quantity and quality. The area weighted average watershed slope became milder but the length weighted average channel slope became steeper as the DEM size increased. DEM resolution affected substantially on the topographical parameter but less on the hydrological simulation. Considering processing time and accuracy on hydrological simulation, DEM grid size of 100m is recommended for this range of watershed size.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.1-8
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• 2019

In this study, the multi-objective optimization method is attemped to optimize the hydrological model to estimate the runoff through two hydrological processes. HL-RDHM, a distributed hydrological model that can simultaneously estimate the amount of snowfall and runoff, was used as the distributed hydrological model. The Durango River basin in Colorado, USA, was selected as the watershed. MOSCEM was used as a multi-objective optimization method and parameter calibration and hydrologic model optimization were tried by selecting 5 parameters related to snow melting and 13 parameters related to runoff. Data from 2004 to 2005 were used to optimize the model and verified using data from 2001 to 2004. By optimizing both the amount of snow and the amount of runoff, the RMSE error can be reduced from 7% to 40% of the simulation value based on the initial solution at three SNOTEL points based on the RMSE. The USGS observation point of the outflow is improved about 40%.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.489-499
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• 2020

Effective science-based management of the basin water resources requires an understanding of the characteristics of the streams, such as the baseflow discharge. In this study, the base flow was estimated in the two watersheds with the least artificial factors among the Nakdong River watersheds, as determined using the chemical hydrograph separation technique. The 16-year (2004-2019) discontinuous observed stream flow and electrical conductivity data in the Total Maximum Daily Load (TMDL) monitoring network were extended to continuous daily data using the TANK model and the 7-parameter log-linear model combined with the minimum variance unbiased estimator. The annual base flows at the upper Namgang Dam basin and the upper Nakdong River basin were both analyzed to be about 56% of the total annual flow. The monthly base flow ratio showed a high monthly deviation, as it was found to be higher than 0.9 in the dry season and about 0.46 in the rainy season. This is in line with the prevailing common sense notion that in winter, most of the stream flow is base flow, due to the characteristics of the dry season winter in Korea. It is expected that the chemical-based hydrological separation technique involving TANK and the 7-parameter log-linear models used in this study can help quantify the base flow required for systematic watershed water environment management.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.