• Journal of Ecology and Environment
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• v.40 no.2
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• pp.89-106
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• 2016

Many plant species have two modes of reproduction: sexual and asexual. Both modes of reproduction have often been viewed as adaptations to temporally or spatially variable environments. The plant should adjust partitioning to match changes in the estimated success of the two reproductive modes. Perennial plants showed that favorable habitats in soil nutrients or water content tend to promote clonal growth over sexual reproduction. In contrast, under high light-quantity conditions, clonal plants tend to allocate more biomass to sexual reproduction and less to clonal propagation. On the other hand, plants with chasmogamous and cleistogamous flowers provides with a greater tendency of the opportunity to ensure some seed set in any stressful environmental conditions such as low light, low soil nutrients, or low soil moisture. It is considered that vegetative reproduction has high competitive ability and is the major means to expand established population of perennial plants, whereas cleistogamous reproduction is insurance to persist in stressful sites due to being strong. Chasmogamous reproduction mainly enhances established and new population. Therefore, the functions of sexual and asexual propagules of perennial or annual plants differ from each other. These traits of propagule thus determine its success at a particular region of any environmental gradients. Eventually, if environmental resources or stress levels change in either space or time, species composition will probably also change. The reason based on which the plants differ with respect to favored reproduction modes in each environmental condition, may be involved in their specific realized niche.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.151-158
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• 1999

Biogenic source emissions refer to naturally occuring emissions from vegetation, microbial activities in soil, lightening, and so on. Vegetation is especially known to emit a considerable amout of volatile organic compounds into the atmosphere. Therefore, biogenic source emissions are an important input to photochemical air quality models. since most biogenic source emissions are calculated at the county-level, they should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation for biogenic source emissions has been to use a "spatial surrogate indicator" such as a county area. In order to examine the applicability of such approximations, this study developed more detailed surrogate indicators to improve the spatial allocation method for biogenic source emissions. Due to the spatially variable nature of biogenic source emissions, Geographic Information Systems(GIS) were introduced as new tools to develop more detailed spatial surrogate indicators. Use of these newly developed spatial surrogate indicators for biogenic source emission allocation provides a better resolution than the standard spatial surrogate indicator.indicator.

• Journal of the Korean Society of Groundwater Environment
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• v.1 no.2
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• pp.90-99
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• 1994

Heterogeneous hydraulic conductivity in a flow domain is generated under the assumption that it is a random variable with a lognormal, spatially-correlated distribution. The hydraulic head and the conductivity in a groundwater flow system are represented as a stochastic process. The method of Monte Carlo Simulation (MCS) and the finite element method (FEM) are used to determine the statistics of the head and the logconductivity. The second moments of the head and the logconductivity indicate that the cross-covariance of the logconductivity with the head has characteristic distribution patterns depending on the properties of sources, boundary conditions, head gradients, and correlation scales. The negative cross-correlation outlines a weak-response zone where the flow system is weakly responding to a stress change in the flow domain. The stochastic approach has a potential to quantitatively delineate the zone of influence through computations of the cross-covariance distribution.

• Journal of the Korean Society of Groundwater Environment
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• v.5 no.2
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• pp.101-109
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• 1998

Hydraulic property of fissured aquifers often depends on geologic structure which acts main channel of groundwater flow. We treated theories of linear flow related to vertical geologic structure. Then, we analyzed the result of two pumping tests conducted in Okmyeong-ri area (Kyeongbook province) using fractal model and found hydraulic characteristic of the fissured aquifer in this area. According to the pump test analyses, groundwater flow around the holes (pumping well D9; observation wells C3 and D7) of test 1 is linear. and is controlled by vertical geologic structure with infinite length and infinitesimally small width. On the other hand, around the hole D10 (pumping well) of test 2, groundwater flow is pseudo-radial (n=1.9) or radial (n=2). Thus, the characteristic of fractured aquifer often shows variable groundwater flow spatially and temporally.

• 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.