• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Korean Geographical Society
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• v.47 no.2
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• pp.208-225
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• 2012

The spatial characteristics of changes in extreme temperature indices for 2070-2099 relative to 1971-2000 in the Republic of Korea were investigated using daily maximum (Tmax) and minimum (Tmin) temperature data from a regional climate model (HadGEM3-RA) based on the IPCC RCP4.5/8.5 at 12.5km grid spacing and observations. Six temperature-based indices were selected to consider the frequency and intensity of extreme temperature events. For validation during the reference period (1971-2000), the simulated Tmax and Tmin distributions reasonably reproduce annual and seasonal characteristics not only for the relative probability but also the variation range. In the future (2070-2099), the occurrence of summer days (SD) and tropical nights (TR) is projected to be more frequent in the entire region while the occurrence of ice days (ID) and frost days (FD) is likely to decrease. The increase of averaged Tmax above 95th percentile (TX95) and Tmin below 5th percentile (TN5) is also projected. These changes are more pronounced under RCP8.5 scenario than RCP4.5. The changes in extreme temperature indices except for FD show significant correlations with altitude, and the changes in ID, TR, and TN5 also show significant correlations with latitude. The mountainous regions are projected to be more influenced by an increase of low extreme temperature than low altitude while the southern coast is likely to be more influenced by an increase of tropical nights.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.169-177
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• 2011

The impacts of climate change on paddy irrigation water demands in Korea have been analyzed. High-resolution ($27{\times}27\;km$) climate data for the SRES A2 scenario produced by the Korean Meteorological Research Institute (METRI) and the observed baseline climatology dataset were used. The outputs from the ECHO-G GCM model were dynamically downscaled using the MM5 regional model by the METRI. The Geographic information system (GIS) was used to produce maps showing the spatial changes in irrigation water requirements for rice paddies. The results showed that the growing season mean temperature for future scenarios was projected to increase by $1.5^{\circ}C$ (2020s), $3.3^{\circ}C$ (2050s) and $5.3^{\circ}C$ (2080s) as compared with the baseline value (1971~2000). The growing season rainfall for future scenarios was projected to increase by 0.1% (2020s), 4.9% (2050s) and 19.3% (2080s). Assuming cropping area and farming practices remain unchanged, the total volumetric irrigation demand was projected to increase by 2.8% (2020s), 4.9% (2050s) and 4.5% (2080s). These projections are contrary to the previous study that used HadCM3 outputs and projected decreasing irrigation demand. The main reason for this discrepancy is the difference with the projected climate of the GCMs used. The temporal and spatial variations were large and should be considered in the irrigation water resource planning and management in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.15-26
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• 2012

For stable and sustainable crop production, understanding the effects of climate changes on agricultural water resources is necessary to minimize the negative effects which might occur due to shifting weather conditions. Although various studies have been carried out in Korea concerning changes in evapotranspiration and irrigation water requirement, the findings are still difficult to utilize fordesigning the demand and unit duty of water, which are the design criteria of irrigation systems. In this study, the impact analysis of climate changes on the paddy water demand and unit duty of water was analyzed based on the high resolution climate change scenarios (specifically under the A1B scenario) provided by the Korea Meteorological Administration. The result of the study indicated that average changes in the paddy water demand in eight irrigation districts were estimated as -2.4 % (2025s), -0.2 % (2055s), and 3.2 % (2085s). The unit duty of water was estimated to increase on an average within 2 % during paddy transplanting season and within 5 % during growing season after transplanting. This result could be utilized for irrigation system design, agricultural water resource development, and rice paddy cultivation policy-making in South Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.268-279
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• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• Korean Journal of Ecology and Environment
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• v.44 no.2
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• pp.187-194
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• 2011

This study analyzed the A1B climate change scenario provided by National Institute of Meteorological Research (NIMR), Korea, to investigate potential climate changes in watersheds of 15 multi-purpose dams in South Korea. The A1B climate change scenario is produced by Regional Climate Model (RCM) with 27 km horizontal grid spacings using a one-way nesting technique with Global Climate Model (GCM). Relative to present climate conditions (1971~ 2000), the modeled 10-year averaged daily temperatures at the watersheds of the 15 multi-purpose dams continuously increased to year 2100, whereas precipitation changes were varied regionally (north, central, and south regions of South Korea). At two watersheds located in Gangwon-province (north region), the modeled temporal variations of precipitation rapidly increased in the 2090's after a slow decrease that had occurred since the 2050's. At seven watersheds in the central region, including Gyeongsangbuk-province to Jeollanam-province, the modeled temporal variations of precipitation increase showed 10-year periodic changes. At six watersheds in the south region, the modeled temporal variations of precipitation increased since the 2070's after a rapid decrease in the 2060's. Compared to the climate conditions of the late of 20th century (1971~2000), the number of rainy days and precipitation intensity increased (3% and 6~12%, respectively) in the late 21st century (2071~2100). The frequency of precipitation events tended to increase with precipitation intensity in all regions. The frequency of heavy precipitation events (>50 mm $d^{-1}$) increased with >100% in the north region, 60~100% in the central region, and 20~60% in the south region.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.