• Journal of Korea Water Resources Association
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• v.43 no.5
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• pp.483-493
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• 2010

In this study, for the purpose of analyzing variability and periodicity of Korean hydrologic weather parameters, 5 hydrologic weather parameters data such as annual precipitation, annual rainy days, annual average temperature, annual average relative humidity, annual duration of sunshine are collected from 63 domestic meteorological stations that has the hydrologic weather parameters records more than 30 years. And in this study the variability and periodicity using the statistical methods like Wald-Wolfowitz test, Mann-Whitney test, and Wavelet Transform about hydrologic weather parameters is analyzed. The results of statistical analysis of variability and periodicity can be summarized as follows: 1) Variability commonly appeared in annual average temperature and annual average relative humidity. 2) Annual precipitation, annual rainy days and annual duration of sunshine showed different results according to area. 3) Periodicity appeared in annual precipitation and annual rainy days but did not appeard in annual average temperature, annual average relative humidity and annual duration of sunshine.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.409-419
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• 2010

In this study, for the purpose of analyzing the characteristics of Korean hydrologic weather parameters, 9 hydrologic weather parameters data such as annual precipitation, annual rainy days, annual average relative humidity, annual average temperature, annual duration of sunshine, annual evaporation, annual duration of precipitation, annual snowy days and annual new snowy days are collected from 63 domestic meteorological stations that has the hydrologic weather parameters records more than 30 years. And the basic characteristics of hydrologic weather parameters through basic statistics, moving average and linear regression analysis are perceived. Also trend using the statistical methods like Hotelling-Pabst test and Mann-Kendall test about hydrologic weather parameters is analyzed. Through results of basic analysis, moving average and linear regression analysis it is shown that precipitation is concentrated in summer and deviation of precipitation for each season showed significant difference in accordance with Korean climate characteristics, besides the increase in annual precipitation and annual average temperature, annual average relative humidity and annual duration of sunshine reduction and annual rainy days is said to increase or decrease. The results of statistical analysis of trend are summarized as trend commonly appeared in annual average relative humidity and annual average temperature. and annual precipitation, annual rainy days and annual duration of sunshine showed different results according to area.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.439-439
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• 2015

The temporal and spatial relationship of the weather elements such as rainfall and temperature is closely linked to the streamflow simulation, especially, to the flood forecasting problems. For the study area, Imjin river basin, which has the specific characteristics in geography with river cross operation between North and South Korea, the meteorological information in the northern area is totally deficiency, lead to the inaccuracy of streamflow estimation. In the paper, this problem is solved by using the combination of global (such as soil moisture content, land use) and local hydrologic components data such as weather data (precipitation, evapotranspiration, humidity, etc.) for the model-driven runoff (surface flow, lateral flow and groundwater flow) data in each subbasin. To compute the streamflow in Imjin river basin, this study is applied the hydrologic model SURR (Sejong Univ. Rainfall-Runoff) which is the continuous rainfall-runoff model used physical foundations, originally based on Storage Function Model (SFM) to simulate the intercourse of the soil properties, weather factors and flow value. The result indicates the spatial variation in the runoff response of the different subbasins influenced by the input data. The dependancy of runoff simulation accuracy depending on the qualities of input data and model parameters is suggested in this study. The southern region with the dense of gauges and the adequate data shows the good results of the simulated discharge. Eventually, the application of SURR model in Imjin riverbasin gives the accurate consequence in simulation, and become the subsequent runoff for prediction in the future process.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.143-148
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• 2005

The main purpose of this study is to suggest and evaluate an operational method for assessing the potential impact of climate change on hydrologic components and water resources of regional scale river basins. The method, which uses large scale climate change information provided by a state of the art general circulation model(GCM) comprises a statistical downscaling approach and a spatially distributed hydrological model applied to a river basin located in Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The values are used to modify the parameters of the stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of $2CO_2$. This approach is applied to the Yongdam dam basin in southern part of Korea. The results show that under the condition of $2CO_2$, about $7.6\% of annual mean streamflow is reduced when it is compared with the observed one. And while Seasonal streamflows in the winter and autumn are increased, a streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern and the analysis of the duration cure shows the mean of averaged low flow is increased while the averaged wet and normal flow are decreased for the climate change.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.1-9
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• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.1
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• pp.89-97
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• 2007

Simple regression models for pollutants load estimation of paddy developed by the Ministry of Environment in 1995 were tested with the data (T-N, T-P, $COD_{Mn}$, and SS) collected from Yeongsan and Seomjin river watersheds, and improvement measures were suggested. Overall, the simulated values showed a great difference from the measured values except for T-P according to the statistical analyses (RMSE, root mean square error; RMAE, root mean absolute error; RB, relative bias; EI, efficiency index). Such difference was assumed due to the fact that the models use only hydrologic factors (quantity factor) associated with precipitation and run-off as input parameters, but do not consider other factors which are likely to affect pollutant concentration (quality factor) including days after fertilization. In addition, in terms of accessibility of the models, some parameters in the models such as run-off depth and run-off amount which can not be obtained from the weather database but should be collected by on-site measurements need to be replaced with other variables.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.172-172
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• 2011

The interaction between land surface and atmosphere is essentially affected by hydrometeorological variables including soil moisture. Accurate estimation of soil moisture at spatial and temporal scales is crucial to better understand its roles to the weather systems. The KLDAS(Korea Land Data Assimilation System) is a regional, specifically Korea peninsula land surface information systems. As other prior land data assimilation systems, this can provide initial soil field information which can be used in atmospheric simulations. For this study, as an enabling high-resolution tool, weather research and forecasting(WRF-ARW) model is applied to produce precipitation data using GFS(Global Forecast System) with GFS embedded and KLDAS soil moisture information as initialization data. WRF-ARW generates precipitation data for a specific region using different parameters in physics options. The produced precipitation data will be employed for simulations of Hydrological Models such as HEC(Hydrologic Engineering Center) - HMS(Hydrologic Modeling System) as predefined input data for selected regional water responses. The purpose of this study is to show the impact of a hydrometeorological variable such as soil moisture in KLDAS on hydrological consequences in Korea peninsula. The study region, Chongmi River Basin, is located in the center of Korea Peninsular. This has 60.8Km river length and 17.01% slope. This region mostly consists of farming field however the chosen study area placed in mountainous area. The length of river basin perimeter is 185Km and the average width of river is 9.53 meter with 676 meter highest elevation in this region. We have four different observation locations : Sulsung, Taepyung, Samjook, and Sangkeug observatoriesn, This watershed is selected as a tentative research location and continuously studied for getting hydrological effects from land surface information. Simulations for a real regional storm case(June 17~ June 25, 2006) are executed. WRF-ARW for this case study used WSM6 as a micro physics, Kain-Fritcsch Scheme for cumulus scheme, and YSU scheme for planetary boundary layer. The results of WRF simulations generate excellent precipitation data in terms of peak precipitation and date, and the pattern of daily precipitation for four locations. For Sankeug observatory, WRF overestimated precipitation approximately 100 mm/day on July 17, 2006. Taepyung and Samjook display that WRF produced either with KLDAS or with GFS embedded initial soil moisture data higher precipitation amounts compared to observation. Results and discussions in detail on accuracy of prediction using formerly mentioned manners are going to be presented in 2011 Annual Conference of the Korean Society of Hazard Mitigation.

• 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.

• Journal of Wetlands Research
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• v.22 no.1
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• pp.15-23
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• 2020

The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.707-718
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• 2004

This study is to analyze the Probable Maximum Flood(PMF) as a part of counterplan for the disaster prevention of hydraulic structures such as dams, according to recent unfavorable weather conditions. During the period of typhoon RUSA in August 2002, the rainfall recorded in Gang-loeng Province was 880mm a day and exceeded the scale of PMP made in 2001. Accordingly, the reconsideration of hydrologic criteria for dam design was inevitable. In the design of dams for flood controls, the design flood must be determined by introducing the concept of maximum values. When the duration of design rainfall is determined, it needs to use the critical duration which causes the maximum flood by the maximum runoff. In this study, we Investigate the variation of critical duration with hydrologic parameters used in three different synthetic unit hydrographs(Clark, Nakayasu and SCS methods). As a result, the total runoff calculated from 24-hour duration is larger than that calculated from the critical duration. We calculate also the hydrographs with three different time distribution models(Huff's 4-quartile, IDF curve and Mononobe) and compare those with measured hydrograph data. From this comparison, we propose that the Huff's 4-quartile model must be used to obtain the desirable data in the hydrologic design of dams.