• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.823-832
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• 2006

Precipitation is the most important component to the study of water and energy cycle in hydrology. In this study we investigate rainfall retrieval uncertainty from different sources of remotely sensed precipitation field and then probable error propagation in the simulation of hydrologic variables especially, runoff on different vegetation cover. Two remotely sensed rainfall retrievals (space-borne IR-only and ground radar rainfall) are explored and compared visually and statistically. Then, an offline Community Land Model (CLM) is forced with in situ meteorological data to simulate the amount of runoff and determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes. Results show there are big differences between different sources of precipitation fields in terms of the magnitude and temporal variability. The study provides some intuitions on the uncertainty of hydrologic prediction via the interaction between the land surface and near atmosphere fluxes in the modelling approach. Eventually it will contribute to the understanding of water resources redistribution to the climate change in Korean Peninsula.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.57-66
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• 2001

This study used four theoretical models, such as two-point linear model, linear regression model, quadratic regression model and cubic regression model which are presented from The Ministry of Science and Technology, for extraction of urban surface temperature from Landsat TM band 6 image. Through correlation and regression analysis between result of four models and AWS(automatic weather station) observation data, this study could verify spatial distribution characteristic of urban surface temperature using GIS spatial analysis method. The result of analysis for surface temperature by landcover showed that the urban and the barren land belonged to the highest surface temperature class. And there was also -0.85 correlation in the result of correlation analysis between surface temperature and NDVI. In this result, the meteorological environmental characteristics wuld be regarded as one of the important factor in urban planning.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.583-594
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• 2003

Ozone is an important atmospheric pollutant that is occurred in tropospheric chemical process and it also affects the human health and plants. For a correct application of abatement strategies for ozone, it is necessary to understand the factors that control atmospheric ozone removal by dry deposition processes. The present study investigates the numerical simulation of the dry deposition velocity (V$^{d}$ ) obtained from PNU/DEM (Pusan National University Deposition Model). PNU/DEM includes seasonal categories, meteorological factors, surface properties and land-use types and proposes for an accurate numerical computation. And, this study examines the ability of the PNU/DEM to compute V$_{d}$ of ozone over water surfaces and evaluates PNU/DEM by comparing its estimated V$_{d}$ to past observed V$_{d}$ over water. The parametrization was found to yield V$_{d}$ values generally in good agreement with the observations for the deciduous forest and the coniferous forest. Ozone is removed slowly at wet surface or water due to its low water solubility. Therefore V$_{d}$ values over water were lower than Vd values over the other surfaces. Comparison of PNU/DEM simulated V d values to observations of ozone V$_{d}$ that have been reported in the literature implies that PNU/DEM produces realistic results.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.51-60
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• 2020

The purpose of this study is to determine the spatial and temporal resolutions that can represent land surface characteristics comprised of various land use using Landsat/AMSR2-based soil moisture data. We estimated the Landsat (30 m×30 m)-based soil moisture values using the soil moisture regression model. Then, the Landsat (30 m×30 m)-based soil moisture (reference values) were resampled to the relatively coarse resolutions from 1 km to 4 km, respectively. Comparing the reference values to the resampled soil moisture values, we confirmed that uncertainties were increased with the spatial resolutions of 2 km~4 km indicating that the spatial resolution of 1 km×1 km is required to represent the complicated land surface. Also, the AMSR2 soil moisture values have less uncertainties compared to SMAP data with the temporal resolution of 1~2 days. Thus, our findings can be useful for various areas such as agriculture, hydrology, forest, etc.

• Atmosphere
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• v.33 no.4
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.79-90
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• 2006

The partitioning level of a catchment becomes an issue if the calculated results from different levels show the same performance regardless of the levels. This study aims to identify the proper processing level of spatial resolution for the SWMM model application in an urban area. Using GIS overlaying technique, the division of subcatchments as a hydrologic similarity unit (HSU) is achieved with a comprehensive consideration of surface slope conditions, flow directions of storm sewers, and current land cover situation. Three surface-sewer alternatives are made on the basis of three different levels of surface divisions as well as the number of sewer connections and used as runoff simulation fields for the application of SWMM. As the result, it is found that the effect of a spatial resolution on the surface runoff results is not significant. On the other hand, the accumulated pollution load from an unit subcatchment, which is built by aggregation of several unit subcatchments consisting of various land cover conditions is reduced through the deterioration of surface spatial resolution. Although overall runoff pattern and accumulated runoff are little affected by spatial resolution, the simulated runoff from sewer outlet shows slight difference at the peak appearance time. The gap between surface pollution load accumulated and it discharged from the sewer outlet in a surface-sewer alternative during runoff period is monitored but the level of error is less than 5-10% except the lowest spatial resolution case.

• Journal of Korea Water Resources Association
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• v.36 no.5
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• pp.799-810
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• 2003

Hydrologic models generally require land surface analysis to different topographic parameters defined as direct or indirect input variables to the model. Specially GIS supply the these parameters from digital data set of land surface The sensitivity analysis to DEM(Digital Elevation Model) resolution and the threshold area are of GIS extracted digital data set applied GIUH(Geomorphological Instantaneous Unit Hydrograph)model is investigated. Also it is compared the responses of GIUH model as input data of stream networks from digital data set(blue line) of NGIS and those extracted from DEM of various grid sizes. The results shows that the GIUH model is significantly affected by the DEM resolution and threshold area. According to the results, DEM grid size is suitable from 25m to 50m. Also threshold area is in the range of 30%∼50% for exceedance probability.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.463-471
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• 2008

The quality of orthoimages mainly depends on the elevation information and exterior orientation (EO) parameters. Since LiDAR data directly provides the elevation information over the earth's surface including buildings and trees, the concept of true orthorectification has been rapidly developed and implemented. If a LiDAR-driven digital surface model (DSM) is used for orthorectification, the displacements caused by trees and buildings are effectively removed when compared with the conventional orthoimages processed with a digital elevation model (DEM). This study utilized LiDAR data to generate orthorectified digital aerial images. Experimental orthoimages were produced using digital terrain model (DTM) and DSM. For the preparation of orthorectification, EO components, one of the inputs for orthorectification, were adjusted with the ground control points (GCPs) collected from the LiDAR point data, and the ground points were extracted by a filtering method used in a previous research. The orthoimage generated by DSM corresponded more closely to non-ground LiDAR points than the orthoimage produced by DTM.

• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.667-681
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• 2011

The objective of this study is to evaluate the applicability of a Land Surface Model (LSM) for drought analysis in Korea. For evaluating the applicability of the model, the model was calibrated on several upper dam site watersheds and the hydrological components (runoff and soil moisture) were simulated over the whole South Korea at grid basis. After converting daily series of runoff and soil moisture data to accumulated time series (3, 6, 12 months), drought indices such as SRI and SSI are calculated through frequency analysis and standardization of accumulated probability. For evaluating the drought indices, past drought events are investigated and drought indices including SPI and PDSI are used for comparative analysis. Temporal and spatial analysis of the drought indices in addition to hydrologic component analysis are performed to evaluate the reproducibility of drought severity as well as relieving of drought. It can be concluded that the proposed indices obtained from the LSM model show good performance to reflect the historical drought events for both spatially and temporally. From this point of view, the LSM can be useful for drought management. It leads to the conclusion that these indices are applicable to domestic drought and water management.