• Journal of Radiation Protection and Research
• /
• v.45 no.1
• /
• pp.16-25
• /
• 2020

Background: To investigate radiological effects on biota, it is necessary to assess radiation dose for flora and fauna living in a terrestrial ecosystem. This paper presents a dynamic model to assess radioactivity concentration and radiation dose of terrestrial flora and fauna after a nuclear accident. Materials and Methods: Litter, organic soil, mineral soil, trees, wild crops, herbivores, omnivores, and carnivores are considered the major components of a terrestrial ecosystem. The model considers the physicochemical and biological processes of interception, weathering, decomposition of litter, percolation, root uptake, leaching, radioactive decay, and biological loss of animals. The predictive capability of the model was investigated by comparison of its predictions with field data for biota measured in the Fukushima forest area after the Fukushima nuclear accident. Results and Discussion: The predicted radioactive cesium inventories for trees agreed well with those for evergreens and deciduous trees sampled in the Fukushima area. The predicted temporal radioactivity concentrations for animals were within the range of the measured radioactivity concentrations of deer, wild boars, and black bears. The radiation dose for the animals were, for the whole simulation time, estimated to be much smaller than the lower limit (0.1 mGy·d^-1) of the derived consideration reference level given by the International Commission on Radiological Protection for terrestrial flora and fauna. This suggested that the radiation effect of the accident on the biota in the Fukushima forest would be insignificant. Conclusion: The present dynamic model can be used effectively to investigate the radiological risk to terrestrial ecosystems following a nuclear accident.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.587-607
• /
• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.118-118
• /
• 2020

This study investigates the change of flow characteristics over 10 Asian river basins in the past 30 years (1976-2005). The variation is estimated from The Soil and Water Assessment Tool (SWAT) model outputs based on reanalysis data which was bias-corrected for Asian monsoon reagion. The model was firstly calibrated and validated using observed data for daily streamflow. Four statistical criteria were applied to evaluate the model performance, including Coefficient of determination (R2), Nash - Sutcliffe model efficiency coeffi cient (NSE), Root mean square error-observations standard deviation ratio (RSR), and Percentage Bias (PBIAS). Then parameters of the model were applied for the historical period 1976-2005. The estimates show a temporal non-considerable increasing rate of daily streamflow in most of the basins over the past 30 years. The difference of monthly discharge becomes more significant during the months in the wet season (June to September) in all basins. The seasonal runoff shows significant difference in Summer and Autumn, when the rainfall intensity is higher. The line showing averaged runoff/rainfall ratio in all basins is sharp, presenting high variation of seasonal runoff/rainfall ratio from season to season.

• Korean Journal of Agricultural Science
• /
• v.14 no.1
• /
• pp.68-80
• /
• 1987

This study was carried out to investigate the evapotranspiration and variations of soil moisture contents for soybeans. The relationship between actual evapotranspiration obtained by the water balance equation and estimated evapotranspiration obtained by the soil moisture model was analyzed. The results obtained were summarized as follows; 1. The total amount of actual evapotranspiration of soybeans during growing season was 405.7mm. The total amount of reference crop evapotranspiration of soybeans that was estimated by Pan evaporation and Hargreaves method were 547.8 mm and 586.8 mm, respectively. Crop coefficient during growing season were shown on Table 1. 2. Measured actual evapotranspiration of soybean during growing season was 405.7 mm and estimated actual evapotranspiration by pan evaporation and Hargreaves method were 424.7 mm, and 426.1mm, r3 respectively. 3. The variations of soil moisture content for soybeans were high at 10cm layer, as compared with those at 30cm and 50cm layers. Because discrepancy between the variations of soil moisture content predicted by model and observed by soil moisture meter was still great, it is required to study the consumptive types of soil moisture at each root depth.

• Structural Monitoring and Maintenance
• /
• v.5 no.2
• /
• pp.231-242
• /
• 2018

Subway tunnel structure has been rapidly developed in many cities for its strong transport capacity. The model-based damage detection of subway tunnel structure is usually difficult due to the complex modeling of soil-structure interaction, the indetermination of boundary and so on. This paper proposes a new data-based method for the damage detection of subway tunnel structure. The root mean square acceleration and cross correlation function are used to derive a statistical pattern recognition algorithm for damage detection. A damage sensitive feature is proposed based on the root mean square deviations of the cross correlation functions. X-bar control charts are utilized to monitor the variation of the damage sensitive features before and after damage. The proposed algorithm is validated by the experiment of a full-scale two-rings subway tunnel lining, and damages are simulated by loosening the connection bolts of the rings. The results verify that root mean square deviation is sensitive to bolt loosening in the tunnel lining and X-bar control charts are feasible to be used in damage detection. The proposed data-based damage detection method is applicable to the online structural health monitoring system of subway tunnel lining.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.5
• /
• pp.595-610
• /
• 2003

The statistical indexes such as RMSE (Root Mean Square Error), Mean Bias error, and IOA (Index of agreement) are used to evaluate 3 Dimensional wind and temperature fields predicted by operational meteorological model RAMS (Regional Atmospheric Meteorological System) implemented in CARIS (Chemical Accident Response Information System) for the dispersion forecast of hazardous chemicals in case of the chemical accidents in Korea. The operational atmospheric model, RAMS in CARIS are designed to use GDAPS, GTS, and AWS meteorological data obtained from KMA (Korean Meteorological Administration) for the generation of 3-dimensional initial meteorological fields. The predicted meteorological variables such as wind speed, wind direction, temperature, and precipitation amount, during 19 ∼ 23, August 2002, are extracted at the nearest grid point to the meteorological monitoring sites, and validated against the observations located over the Korean peninsula. The results show that Mean bias and Root Mean Square Error are 0.9 (m/s), 1.85 (m/s) for wind speed at 10 m above the ground, respectively, and 1.45 ($^{\circ}C$), 2.82 ($^{\circ}C$) for surface temperature. Of particular interest is the distribution of forecasting error predicted by RAMS with respect to the altitude; relatively smaller error is found in the near-surface atmosphere for wind and temperature fields, while it grows larger as the altitude increases. Overall, some of the overpredictions in comparisons with the observations are detected for wind and temperature fields, whereas relatively small errors are found in the near-surface atmosphere. This discrepancies are partly attributed to the oversimplified spacing of soil, soil contents and initial temperature fields, suggesting some improvement could probably be gained if the sub-grid scale nature of moisture and temperature fields was taken into account. However, IOA values for the wind field (0.62) as well as temperature field (0.78) is greater than the 'good' value criteria (> 0.5) implied by other studies. The good value of IOA along with relatively small wind field error in the near surface atmosphere implies that, on the basis of current meteorological data for initial fields, RAMS has good potentials to be used as a operational meteorological model in predicting the urban or local scale 3-dimensional wind fields for the dispersion forecast in association with hazardous chemical releases in Korea.

• Korean Journal of Soil Science and Fertilizer
• /
• v.50 no.5
• /
• pp.422-433
• /
• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.51 no.1
• /
• pp.1-9
• /
• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• Journal of Environmental Impact Assessment
• /
• v.18 no.1
• /
• pp.51-57
• /
• 2009

Corylopsis coreana Uyeki is endemic species in the Korean peninsula and is designated a Category Endangered Plant Species by the Wildlife Protection Act of South Korea. We developed the plan and cooperative model within the local community for the species conservation. In order to carry out this plan we first investigated the ecological characteristics of the species. The species shows patterns of discontinuous distribution and is coupled with the unusual feature of only growing on northern exposed slopes. Although Corylopsis coreana is cut the stem every year, many new sprouts are still grown from the root. Natural germination of the seed occurs only on north-facing slopes, but not on south-facing slopes at spring. That is, the species is highly influenced by soil moisture until the seedling stage has been reached. This factor limits the distribution of the species. When saplings are planted on south-facing slopes, they grow well. The information we gathered greatly helped with efforts to draw up conservation plans. In addition, when the information was shared with the local community, builders and residents showed great interest and displayed a will to help with conservation efforts. Therefore, a cooperative model within the local community was drawn up for the conservation of the species. Accordingly this model could be applied at mitigation measure at environment impact assessment.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.22 no.4
• /
• pp.340-345
• /
• 2020

In this study, we developed the quality control and assurance method of measurement data of SPAR (Soil-Plant-Atmosphere-Research) system, a climate change research facility, for the first time. It was found that the precise processing of CO2 flux data among many observations were sig nificantly important to increase the accuracy of canopy photosynthesis measurements in the SPAR system. The collected raw CO2 flux data should first be removed error and missing data and then replaced with estimated data according to photosynthetic lig ht response curve model. Comparing the correlation between cumulative net assimilation and soybean biomass, the quality control and assurance of the raw CO2 flux data showed an improved effect on canopy photosynthesis evaluation by increasing the coefficient of determination (R2) and lowering the root mean square error (RMSE). These data processing methods are expected to be usefully applied to the development of crop growth model using SPAR system.