• International Journal of Contents
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• v.12 no.1
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• KCID journal
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• v.14 no.2
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• pp.225-235
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• 2007

The shape of streamflow hydrograph during the early period of spring is very much controlled by the area and depth of snow cover especially in mountainous area. When we simulate the streamfolw of a watershed snowmelt, we need some information for snow cover extent and depth distribution as parameters and input data in the hydrological models. The purpose of this study is to suggest an extraction method of snow cover area and snow depth distribution using Terra MODIS image. Snow cover extent for South Korea was extracted for the period of December 2000 and April 2006. For the snow cover area, the snow depth was interpolated using the snow depth data from 69 meteorological observation stations. With these data, it is necessary to run a hydrological model considering the snow-related data and compare the simulated streamflow with the observed data and check the applicability for the snowmelt simulation.

• Water for future
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• v.18 no.3
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• pp.265-270
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• 1985

The prediction of a design-flood hydrograph at a particular site on a river may be based on the derivation of discharge or stage hydrograph at an upstream section, togeater with a method to route this hydrograph along the rest of river. On the other hand, flood routing methods provide a useful tool for the analysis of flooding in all but the smaller catchment, and these methods are largely stored into hydrological method and hydraulic method. Although the Muskingum Method as a hydrological method ignores dynamic effects on the flood wave, Muskingum-Cunge Method based on hydraulic method is possible to improve the method so that it gives a good approximation to the solution of the linear convective-diffusion equation. This is made on the basis of the finite diffeience equation for the Muskingum Method. In the study, the outflows predicted by Muskingum-Cunge Method are campared with the observed outflows of the Pyung Chang River.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1047-1051
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• 2004

The design storm is a crucial element in urban drainage design and hydrological modeling. The total rainfall depth of a design storm is usually estimated by hydrological frequency analysis using historic rainfall records. The different geostatistical approaches (ordinary kriging, universal kriging) have been used as estimators and their results are compared and discussed. Variogram parameters, the sill, nugget effect and influence range, are analysis. Kriging method was applied for developing contour maps of design storm depths In bocheong stream basin. Effect to utilize weather radar data and grid-based basin model on the spatial variation characteristics of storm requires further study.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1321-1325
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• 2004

The continuous monitoring of the runoff in the small-scaled urban watershed and easily accessible experiment catchment is necessary to investigate the overall status of the development in the urban catchment and the varying aspects of the discharge characteristics due to the urbanization. However, the research on the management and the characteristics of the small-scaled model basin for discharge tests has not been actively performed up to now. This study selects the Dong-Eui university basin, which locates at Gaya-dong in Busan, as the experiment catchment to monitor the discharge rate in the urban watershed. EMS(DEMS, DATA-PCS EMS, mini rain gage & AWS(AWS-DEU, DATA-PCS AWS) monitoring system installed for the collection of hydrological data such as the rainfall and the waterlevel. This experiment catchment is the typical urban catchment and is under development, and it is possible to analyze the varying aspects of the discharge rate during and after the development.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1441-1444
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• 2007

It is now widely acknowledged that climate variability modifies the frequency spectrum of hydrological extreme events. Traditional hydrological frequency analysis methodologies are not devised to account for nonstationarity that arises due to variation in exogenous factors of the causal structure. We use Hierarchical Bayesian Analysis to consider the exogenous factors that can influence on the frequency of extreme floods. The sea surface temperatures, predicted GCM precipitation, climate indices and snow pack are considered as potential predictors of flood risk. The parameters of the model are estimated using a Markov Chain Monte Carlo (MCMC) algorithm. The predictors are compared in terms of the resulting posterior distributions of the parameters associated with estimated flood frequency distributions.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.226-226
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• 2015

The complex climate system regarding human actions is well represented through global climate models (GCMs). The output from GCMs provides useful information about the rate and magnitude of future climate change. Especially, the temperature variable is most reliable among other GCM outputs. However, hydrological variables (e.g. precipitation) from GCM outputs for future climate change contain too high uncertainty to use in practice. Therefore, we propose a method that simulates temperature variable with increasing in a certain level (e.g. 0.5oC or 1.0oC increase) as a global warming scenario from observed data. In addition, a hydrometeorological variable can be simulated employing block-wise sampling technique associated with the temperature simulation. The proposed method was tested for assessing the future change of the seasonal precipitation in South Korea under global warming scenario. The results illustrate that the proposed method is a good alternative to levy the variation of hydrological variables under global warming condition.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.5
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• pp.37-48
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• 2013

The objective of this study is to assess the impact of potential climate change on the hydrological components, especially on the streamflow, evapotranspiration and snowmelt, by using the Soil Water Assessment Tool (SWAT) for 17 Hanriver middle watersheds of South Korea. For future assessment, the SWAT model was calibrated in multiple sites using 4 years (2006-2009) and validated by using 2 years (2010-2011) daily observed data. For the model validation, the Nash-Sutcliffe model efficiency (NSE) for streamflow were 0.30-0.75. By applying the future scenarios predicted five future time periods Baseline (1992-2011), 2040s (2021-2040), 2060s (2041-2060), 2080s (2061-2080) and 2100s (2081-2100) to SWAT model, the 17 middle watersheds hydrological components of evapotranspiration, streamflow and snowmelt were evaluated. For the future precipitation and temperature of RCP 4.5 scenario increased 41.7 mm (2100s), $+3^{\circ}C$ conditions, the future streamflow showed +32.5 % (2040s), +24.8 % (2060s), +50.5 % (2080s) and +55.0 % (2100s). For the precipitation and temperature of RCP 8.5 scenario increased 63.9 mm (2100s), $+5.8^{\circ}C$ conditions, the future streamflow showed +35.5 % (2040s), +68.9 % (2060s), +58.0 % (2080s) and +63.6 % (2100s). To determine the impact on snowmelt for Hanriver middle watersheds, snowmelt parameters of SWAT model were determined through evaluating observed streamflow data during snowmelt periods (November-April). The results showed that average SMR (snowmelt / runoff) of 17 Hanriver middle watersheds was 62.0 % (Baseline). The annual average SMR were 42.0 % (2040s), 39.8 % (2060s), 29.4 % (2080s) and 27.9 % (2100s) by applying RCP 4.5 scenario. Also, the annual average SMR by applying RCP 8.5 scenario were 40.1 % (2040s), 29.4 % (2060s), 18.3 % (2080s) and 12.7 % (2100s).

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.137-137
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• 2021

In this study we aimed to enhance streamflow prediction skill of a land-surface hydrological model, WRF-Hydro, over one of the snow dominated catchments lies in Himalayan mountainous range, Astore. To assess the response of the Himalayan river flows to climate change is complex due to multiple contributors: precipitation, snow, and glacier melt. WRF-Hydro model with default glacier module lacks generating streamflow in summer period but recently developed WRF-Hydro-CROCUS model overcomes this issue by melting snow/ice from the glaciers. We showed that by implementing WRF-Hydro-CROCUS model over Astore the results were significantly improved in comparison to WRF-Hydro with default glacier module. To constraint the model with the observed streamflow we chose 17 sensitive parameters of WRF-Hydro, which include groundwater parameters, surface runoff parameters, channel parameters, soil parameters, vegetation parameters and snowmelt parameters. We used Dynamically Dimensioned Search (DDS) method to calibrate the daily streamflow with the Nash-Sutcliffe efficiency (NSE) being greater than 0.7 both in calibration (2009-2010) and validation (2011-2013) period. Based on the number of iterations per parameter, we found that the parameters related to channel and runoff process are most sensitive to streamflow. The attempts to address the responses of the streamflows to climate change are still very weak and vague especially northwest Himalayan Part of Pakistan and this study is one of a few successful applications of process-based land-surface hydrologic model over this mountainous region of UIB that can be utilized to have an in-depth understanding of hydrological responses of climate change.