• Journal of Soil and Groundwater Environment
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• v.28 no.6
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• pp.71-89
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• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Advances in nano research
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• v.16 no.4
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• pp.325-340
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• 2024

There are several novel uses for dispersing many nanoparticles into a conventional fluid, including dynamic sealing, damping, heat dissipation, microfluidics, and more. Therefore, melting heat and mass transfer characteristics of a 3-D MHD Hybrid Nanofluid flow over a rotating disc with presenting dufour and soret effects are assessed numerically in this study. In this instance, we investigated both ferric sulfate and molybdenum disulfide as nanoparticles suspended within base fluid water. The governing partial differential equations are transformed into linked higher-order non-linear ordinary differential equations by the local similarity transformation. The collection of these deduced equations is then resolved using a Chebyshev spectral collocation-based algorithm built into the Mathematica software. To demonstrate how different instances of hybrid/ nanofluid are impacted by changes in temperature, velocity, and the distribution of nanoparticle concentration, examples of graphical and numerical data are given. For many values of the material parameters, the computational findings are shown. Simulations conducted for different physical parameters in the model show that adding hybrid nanoparticle to the fluid mixture increases heat transfer in comparison to simple nanofluids. It has been identified that hybrid nanoparticles, as opposed to single-type nanoparticles, need to be taken into consideration to create an effective thermal system. Furthermore, porosity lowers the velocities of simple and hybrid nanofluids in both cases. Additionally, results show that the drag force from skin friction causes the nanoparticle fluid to travel more slowly than the hybrid nanoparticle fluid. The findings also demonstrate that suction factors like magnetic and porosity parameters, as well as nanoparticles, raise the skin friction coefficient. Furthermore, It indicates that the outcomes from different flow scenarios correlate and are in strong agreement with the findings from the published literature. Bar chart depictions are altered by changes in flow rates. Moreover, the results confirm doctors' views to prescribe hybrid nanoparticle and particle nanoparticle contents for achalasia patients and also those who suffer from esophageal stricture and tumors. The results of this study can also be applied to the energy generated by the melting disc surface, which has a variety of industrial uses. These include, but are not limited to, the preparation of semiconductor materials, the solidification of magma, the melting of permafrost, and the refreezing of frozen land.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.2
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• pp.47-61
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• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.157-184
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• 2010

Global climate change is destroying the water circulation balance by changing rates of precipitation, recharge and discharge, and evapotranspiration. The Intergovernmental Panel on Climate Change (IPCC 2007) makes "changes in rainfall pattern due to climate system changes and consequent shortage of available water resource" a high priority as the weakest part among the effects of human environment caused by future climate changes. Groundwater, which occupies a considerable portion of the world's water resources, is related to climate change via surface water such as rivers, lakes, and marshes, and "direct" interactions, being indirectly affected through recharge. Therefore, in order to quantify the effects of climate change on groundwater resources, it is necessary to not only predict the main variables of climate change but to also accurately predict the underground rainfall recharge quantity. In this paper, the authors selected a relevant climate change scenario, In this context, the authors selected A1B from the Special Report on Emission Scenario (SRES) which is distributed at Korea Meteorological Administration. By using data on temperature, rainfall, soil, and land use, the groundwater recharge rate for the research area was estimated by period and embodied as geographic information system (GIS). In order to calculate the groundwater recharge quantity, Visual HELP3 was used as main model for groundwater recharge, and the physical properties of weather, temperature, and soil layers were used as main input data. General changes to water circulation due to climate change have already been predicted. In order to systematically solve problems associated with how the groundwater resource circulation system should be reflected in future policies pertaining to groundwater resources, it may be urgent to recalculate the groundwater recharge quantity and consequent quantity for using via prediction of climate change in Korea in the future and then reflection of the results. The space-time calculation of changes to the groundwater recharge quantity in the study area may serve as a foundation to present additional measures for the improved management of domestic groundwater resources.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.16-34
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• 2015

Impervious covers(IC) are artificial structures, such as driveways, sidewalks, building's roofs, and parking lots, through which water cannot infiltrate into the soil. IC is an environmental concern because the pavement materials seal the soil surface, decreasing rainwater infiltration and natural groundwater recharge, and consequently disturb the hydrological cycle in a watershed. Increase of IC in a watershed can cause more frequent flooding, higher flood peaks, groundwater drawdown, dry river, and decline of water quality and ecosystem health. There has been an increased public interest in the institutional adoption of LID(Low Impact Development) and GI(Green Infrastructure) techniques to address the adverse impact of IC. The objectives of this study were to construct the modeling site for a samll urban watershed with the Storm Water Management Model(SWMM), and to evaluate the effect of various LID techniques on the control of rainfall runoff processes and non-point pollutant load. The model was calibrated and validated using the field data collected during two flood events on July 17 and August 11, 2009, respectively, and applied to a complex area, where is consist of apartments, school, roads, park, etc. The LID techniques applied to the impervious area were decentralized rainwater management measures such as pervious cover and green roof. The results showed that the increase of perviousness land cover through LID applications decreases the runoff volume and pollutants loading during flood events. In particular, applications of pervious pavement for parking lots and sidewalk, green roof, and their combinations reduced the total volume of runoff by 15~61 % and non-point pollutant loads by TSS 22~72 %, BOD 23~71 %, COD 22~71 %, TN 15~79 %, TP 9~64 % in the study site.

• Journal of the Korean Society for Marine Environment & Energy
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• v.7 no.1
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• pp.1-12
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• 2004

The summer distribution of $Cha-{alpha}$ and physical processes for simulating outbreak region of red tide were estimated by the Eco-Hydrodynamic model in Chinhae Bay. As a result of simulation of surface residual currents, the southward flow come in contact with the northward flow at the inlet and western part of bay in case of windlessness and below wind velocity 2 m/sec. As wind velocity increases, the velocity and direction of currents were fairly shifted. The predicted concentration of $Cha-{alpha}$ exceeded 20 mg/㎥ in Masan and Haengam Bays, and most regions were over 10 mg/㎥, which meant the possibility of red tide outbreak. From the results of the contributed physical processes to $Cha-{alpha}$, accumulation sites were distributed at the northern part of Kadok channel, around the Chilcheon island, the western part of Kajo island and some area of Chindong Bay. On the other hand, inner parts of the study area such as Masan Bay were estimated as the sites of strong algal activities. Masan and Haengam Bay are considered as the initial outbreak region of red tide by the modeling and observed data, and then red tide expanded to other areas such as physical accumulation region and western inner bay, as depending on environmental variation. The increase of wind velocity led to decrease of $Cha-{alpha}$ and enlargement of accumulation region. The variation of intensity of radiation and sunshine duration caused to rapidly fluctuation of $Cha-{alpha}$: however, it was not largely affected by the variation of pollutant loads from the land only.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1635-1650
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• 2023

Under global warming, the steadily increasing sea surface temperature (SST) severely impacts marine ecosystems,such as the productivity decrease and change in marine species distribution. Recently, the catch of Todarodes Pacificus, one of South Korea's primary marine resources, has dramatically decreased. In this study, we analyze the marine environment that affects the formation of fishing grounds of Todarodes Pacificus and develop seasonal habitat suitability index (HSI) models based on various satellite data including Geostationary Ocean Color Imager (GOCI) data to continuously manage fisheries resources over Korean exclusive economic zone. About 83% of catches are found within the range of SST of 14.11-26.16℃,sea level height of 0.56-0.82 m, chlorophyll-a concentration of 0.31-1.52 mg m^-3, and primary production of 580.96-1574.13 mg C m^-2 day^-1. The seasonal HSI models are developed using the Arithmetic Mean Model, which showed the best performance. Comparing the developed HSI value with the 2019 catch data, it is confirmed that the HSI model is valid because the fishing grounds are formed in different sea regions by season (East Sea in winter and Yellow Sea in summer) and the high HSI (> 0.6) concurrences to areas with the high catch. In addition, we identified the significant increasing trend in SST over study regions, which is highly related to the formation of fishing grounds of Todarodes Pacificus. We can expect the fishing grounds will be changed by accelerating ocean warming in the future. Continuous HSI monitoring is necessary to manage fisheries' spatial and temporal distribution.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.