• Journal of Korea Water Resources Association
• /
• v.35 no.1
• /
• pp.25-36
• /
• 2002

Window Probability Matching Method(WPMM) is achieved by matching identical probability density of rain intensities and radar reflectivities taken only from small window centered about the gage. The equation of $Z_{e}-R$ relationship is obtained and compared with data between a DWSR-88C radar and high density rain gage networks within 150km from radar site in summer season, 1998. The probability density of radar effective reflectivity is distributed with high frequency near 15dBZ. The frequency distribution of rain intensities shows that rain intensity is lower than 10mm/hr in most part of radar coverage area. As the result of $Z_{e}-R$ relationship using WPMM, curved line has shown to the log scale spatially and it can be explained more flexible than any straight-line power laws at the transformation to the rainfall amount from $Z_e$ value. During 3 months, total radar cumulative rainfall amount estimated by $Z=200R^{1.6}$ and WPMM relationships are 44 and 80 percentages of total raingage amount, respectively. Therefore, $Z_{e}-R$ relationships by WPMM may be widely needed a statistical method for the computation of accumulated precipitation.

• Journal of the Korean GEO-environmental Society
• /
• v.14 no.8
• /
• pp.11-21
• /
• 2013

In-situ test to find the change of $Fe^{2+}$ and $Mn^{2+}$ concentrations and ion contents in groundwater was conducted during two pumping tests at the riverbank filtration site, where is the riverine area of the Nakdong River in Changnyeong-Gun. Groundwater was sampled at one pumping well and 10 monitoring wells during a 5 steps drawdown pumping test with the rates from $500m^3/day$ to $900m^3/day$ and a constant pumping test with $800m^3/day$. The change in ion concentration of groundwater was more remarkable during a step drawdown pumping test than a constant pumping test. Especially, the decrease in $Fe^{2+}$ and $Mn^{2+}$ concentrations was distinct in a step drawdown pumping test and it happens predominantly along the direction that the radius of pumping influence was small due to a good aquifer connectivity to a pumping position. The precipitation and the oxidation of iron and manganese were caused by an air inflow and a disturbance in groundwater flow due to an abrupt change in pumping rate. The pumping rate and spatial distribution of an aquifer around a pumping well need to be considered as an important factor for the development of in-situ iron and manganese treatment technology.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.11 no.4
• /
• pp.103-112
• /
• 1991

In this study, a numerical model was established and applied to simulate the steady-state groundwater and heat flow in an isotropic, heterogeneous, three dimensional aquifer system with uniform thermal properties and no change of state. This model was developed as an aid in screening large groundwater-flow systems as prospects for underground waste storage. Driving forces on the system are external hydrologic conditions of recharge from precipitation and fixed hydraulic head boundaries. Heat flux includes geothermal heat-flow, conduction to the land surface, advection from recharge, and advection to or from fixed-head boundaries. The model uses an iterative procedure that alternately solves the groundwater-flow and heat-flow equations, updating advective flux after solution of the groundwater-flow equation, and updating hydraulic conductivity after solution of the heat-flow equation. Dierect solution is used for each equation. Travel time is determined by particle tracking through the modeled space. Velocities within blocks are linear interpolations of velocities at block faces. Applying this model to the groundwater-flow system located in Jigyung-ri. Songla-myun, Youngil-gun. Kyungsangbuk-do, the groundwater-flow system including distribution of head, temperature and travel time and flow line, is analyzed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.1
• /
• pp.79-87
• /
• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.6B
• /
• pp.599-606
• /
• 2010

Due to the short period of precipitation data in Korea, the uncertainty of drought analysis is inevitable from a point frequency analysis. So it is desired to introduce a regional drought frequency analysis. This study first extracted drought characteristics from 3-month and 12-month moving average rainfalls which represent short and long-term droughts, respectively. Then, the homogeneous regions were distinguished by performing a principal component analysis and cluster analysis. The Korean peninsula was classified into five regions based on drought characteristics. Finally, this study applied the bivariate frequency analysis using a kernel density function to quantify the regionalized drought characteristics. Based on the bivariate drought frequency curves, the drought severities of five regions were evaluated for durations of 2, 5, 10, and 20 months, and return periods of 5, 10, 20, 50, and 100 years. As a result, the largest severity of drought was occurred in the Lower Geum River basin, in the Youngsan River basin, and over in the southern coast of Korea.

• Analytical Science and Technology
• /
• v.22 no.5
• /
• pp.395-406
• /
• 2009

The determination of iodine in the spent nuclear fuel and the volatile behavior during its acid dissolution have been studied by NAA(neutron activation analysis) and electron probe microanalysis (EPMA). Simulated spent fuels (SIMFUELs) were dissolved in $HNO_3$(1+1) at $90^{\circ}C$ for 8 hours. The iodine remained in a dissolver solution after dissolution, and that condensed in dissolution apparatus and trapped in the adsorbent by volatilization during the dissolution were determined, respectively. The condensed iodine was recovered by the redistillation with $HNO_3$(1+1) after transfer of the dissolver solution. The iodines in the dissolver and redistilled solution were separated by solvent extraction followed by ion exchange or precipitation method and determined by RNAA (radiochemical neutron activation analysis). The ion exchange column and filtration kit used for the isolation of iodine, which were prepared with a polyethylene tube, were used as an insert in the pneumatic tube for neutron irradiation. The iodine volatilized during the dissolution of SIMFUELs was collected in a trapping tube containing Ag-silica gel (Ag-impregnated silica gel) adsorbent, and the distribution of iodine trapped in the adsorbents were determined by EPMA. The adsorbing characteristics shown with the SIMFUELs were compared with those shown with a real spent fuel from the nuclear power plant.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.2B
• /
• pp.155-163
• /
• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.35 no.3
• /
• pp.545-556
• /
• 2015

This study is to project the future snowfall and to assess heavy snowfall vulnerable area in South Korea using ground measured snowfall data and RCP climate change scenarios. To identify the present spatio-temporal heavy snowfall distribution pattern of South Korea, the 40 years (1971~2010) snowfall data from 92 weather stations were used. The heavy snowfall days above 20 cm and areas has increased especially since 2000. The future snowfall was projected by HadGEM3-RA RCP 4.5 and 8.5 scenarios using the bias-corrected temperature and snow-water equivalent precipitation of each weather station. The maximum snowfall in baseline period (1984~2013) was 122 cm and the future maximum snow depth was projected 186.1 cm, 172.5 mm and 172.5 cm in 2020s (2011~2040), 2050s (2041~2070) and 2080s (2071~2099) for RCP 4.5 scenario, and 254.4 cm, 161.6 cm and 194.8 cm for RCP 8.5 scenario respectively. To analyze the future heavy snowfall vulnerable area, the present snow load design criteria for greenhouse (cm), cattleshed ($kg/m^2$), and building structure ($kN/m^2$) of each administrative district was applied. The 3 facilities located in present heavy snowfall areas were about two times vulnerable in the future and the areas were also extended.

• Journal of Korea Water Resources Association
• /
• v.36 no.2
• /
• pp.195-209
• /
• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.

• Korean Journal of Ecology and Environment
• /
• v.52 no.3
• /
• pp.210-220
• /
• 2019

Population growth and the increase of energy consumption due to civilization caused global warming. Temperature on the Earth rose about $0.7^{\circ}C$ for the last 100 years, the rate is accelerated since 2000. Temperature is a factor, which determines physiological action, growth and development, survival, etc. of the plant together with light intensity and precipitation. Therefore, it is expected that global warming would affect broadly geographic distribution of the plant as well as structure and function ecosystem. In order to understand the effect of global warming on the ecosystem, a study about the effect of temperature rise on germination and growth in the plant is required necessarily. This study was carried out to investigate the effects of experimental warming on the germination and growth of two oak species(Quercus mongolica and Q. serrata) in temperature gradient chamber(TGC). This study was conducted in control, medium warming treatment($+1.7^{\circ}C$; Tm), and high warming treatment ($+3.2^{\circ}C$; Th) conditions. The final germination percentage, mean germination time and germination rate of two oak species increased by the warming treatment, and the increase in Q. serrata was higher than that in Q. mongolica. Root collar diameter, seedling height, leaf dry weight, stem dry weight, root dry weight, and total biomass were the highest in Tm treatment. Butthey were not significantly different in the Th treatment. In the Th treatment, Q. serrata had significantly higher H/D ratio, S/R ratio, and low root mass ratio (RMR) compared with control plot. Q. mongolica had lower RMR and higher S/R ratio in the Tm and Th treatments compared with control plot. Therefore, growth of Q. mongolica are expected to be more vulnerable to warming than that of Q. serrata. The main findings of this study, species-specific responses to experimental warming, could be applied to predict ecosystem changes from global warming. From the result of this study, we could deduce that temperature rise would increase germination of Q. serrata and Q. mongolica and consequently contribute to increase establishment rate in the early growth stage of the plants. But we have to consider diverse variables to understand properly the effects that global warming influences germination in natural condition. Treatment of global warming in the medium level increased the growth and the biomass of both Q. serrata and Q. mongolica. But the result of treatment in the high level showed different aspects. In particular, Q. mongolica, which grows in cooler zones of higher elevation on mountains or northward in latitude, responded more sensitively. Synthesized the results mentioned above, continuous global warming would function in stable establishment of both plants unfavorably. Compared the responses of both sample plants on temperature rise, Q. serrata increased germination rate more than Q. mongolica and Q. mongolica responded more sensitively than Q. serrata in biomass allocation with the increase of temperature. It was estimated that these results would due to a difference of microclimate originated from the spatial distribution of both plants.