• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• Journal of Korean Society on Water Environment
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• v.32 no.1
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• pp.70-79
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• 2016

Based on the soil moisture data assimilation suggested in the first paper (I), we estimated root zone soil moisture and evaluated drought severity using remotely sensed (RS) data. We tested the impacts of various spatial resolutions on soil moisture variations, and the model outputs showed that resolutions of more than 2-3 km resulted in over-/under-estimation of soil moisture values. Thus, we derived the 2 km resolution-scaled soil moisture dynamics and assessed the drought severity at the study sites (Chungmi-cheon sites 1 and 2) based on the estimated soil/root parameters and weather forcings. The drought indices at the sites were affected mainly by precipitation during the spring season, while both the precipitation and land surface characteristics influence the spatial distribution of drought during the rainy season. Also, the drought severity showed a periodic cycle, but additional research on drought cycles should be conducted using long-term historical data. Our proposed approach enabled estimation of daily root zone soil moisture dynamics and evaluation of drought severity at various spatial scales using MODIS data. Thus, this approach will facilitate efficient management of water resources.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.662-676
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• 2008

This paper presents a video copy detection algorithm that blocks online transfer of illegally copied DTV broadcast programs. Particularly, the proposed algorithm establishes a set of keyframes by detecting abrupt changes of luminance, and then exploits the spatio-temporal features of keyframes. Comparing with the preregistered features stored in the database of DTV broadcast programs, the proposed scheme performs a function of video filtering in order to distinguish whether an uploaded video is illegally copied or not. Note that we analyze only a set of keyframes instead of an entire video frame. Thus, it is highly efficient to identify illegal copied video when we deal with a vast size of broadcast programs. Also, we confirm that the proposed technique is robust to a variety of video edit-effects that are often applied by online video redistribution, such as apsect-ratio change, logo insertion, caption insertion, visual quality degradation, and resolution change (downscaling). In addition, we perform a benchmark test in which the proposed scheme outperforms previous techniques.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.19-30
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• 2013

This study is to evaluate the use of dual-polarization radar data for storm runoff modeling in Namgang dam (2,293 $km^2$) watershed using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were obtained from Han River Flood Control Office. Even the radar data were overall less than the ground data in areal average, the spatio-temporal pattern between the two data was good showing the coefficient of determination ($R^2$) and bias with 0.97 and 0.84 respectively. For the case of heavy rain, the radar data caught the rain passing through the ground stations. The KIMSTORM was set to $500{\times}500$ m resolution and a total of 21,372 cells (156 rows${\times}$137 columns) for the watershed. Using 28 ground rainfall data, the model was calibrated using discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI) with 0.85, 0.78 and 1.09 respectively. The calibration results by radar rainfall showed $R^2$, ME and VCI were 0.85, 0.79, and 1.04 respectively. The VCI by radar data was enhanced by 5 %.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.561-569
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• 2010

In this study, the impact of climate change on extreme drought events is investigated by comparing drought severity-area-duration curves under present and future climate. The depth-area-duration analysis for characterizing an extreme precipitation event provides a basis for analysing drought events when storm depth is replaced by an appropriate measure of drought severity. In our climate-change impact experiments, the future monthly precipitation time series is based on a KMA regional climate model which has a $27km{\times}27km$ spatial resolution, and the drought severity is computed using the standardized precipitation index. As a result, agricultural drought risk is likely to increase especially in short duration, while hydrologic drought risk will greatly increase in all durations. Such results indicate that a climate change vulnerability assessment for present water resources supply system is urgent.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.219-233
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• 2013

In this study, spatio-temporal variations of Land Surface Emissivity (LSE) of the three LSE data sets in the Asian-Oceanian regions were addressed. The MODerate Resolution Imaging Spectroradiometer (MODIS) LSE, Cooperative Institute for Meteorological Satellite Studies (CIMSS) LSE, and Kongju National Univ. (KNU) LSE data sets were used. The three data sets showed very similar emissivity in the Tibetan Plateau, desert in the Middle East and Australia, and low latitude regions irrespective of season. The emissivity of $12{\mu}m$ was systematically greater than that of $11{\mu}m$, in particular, in the Tibetan Plateau, desert over Middle East and Australia. In general, they showed a weak seasonal variation in the low latitude regions although the emissivity was different among them. However, the three data sets showed quite different spatial and temporal variations in the other regions of Asian-Oceanian regions. The KNU LSE showed a systematic seasonal variation with a high emissivity during summer and low emissivity during winter but the other two LSE data sets showed irregular seasonal variations without regard to the regions. And the annual mean correlations of $11{\mu}m$ and $12{\mu}m$ between KNU LSE and MODIS LSE (KNU LSE and CIMSS LSE; MODIS LSE and CIMSS LSE) were 0.423 and 0.399 (0.330, 0.101; 0.541, 0.154), respectively. The relatively low correlations and strong inter-month variations, in particular, in $12{\mu}m$, indicated that consistency in spatial variation was very low. The comparison results showed that caution should be given before operational use of the LSE data sets in these regions.

• Journal of Korea Water Resources Association
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• v.46 no.11
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• pp.1129-1140
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• 2013

Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.129-143
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• 2014

The purpose of this study is to evaluate the availability of dual-polarization radar rain for flood routing in Chungju Dam watershed($6,625.8km^2$) using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Sobaek dual-polarization radar data for 1 heavy rain and 3 typhoon(Khanun, Bolaven, and Sanba) events in 2012 were obtained from Han River Flood Control Office. The spatio-temporal patterns between the two data were similar showing the ratio of radar rain to ground rain with 0.97. The KIMSTORM was set to $500{\times}500m$ resolution and a total of 45,738 cells(198 rows${\times}$231 columns) for the watershed. For radar rain and 41 ground rains, the model was independently calibrated using discharge data at 3 streamflow gauging stations(YW1, YC, and CJD) with coefficient of determination($R^2$), Nash and Sutcliffe Model Efficiency(ME), and Volume Conservation Index(VCI). The $R^2$, ME, and VCI 0.80, 0.62 and 1.08 for radar rain and 0.83, 0.68 and 1.10 for ground rain respectively.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.141-153
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• 2016

Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.