• Journal of Environmental Policy
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• v.7 no.4
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• pp.35-55
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• 2008

Assessing vulnerability to climate change is the first step to take when setting up appropriate adaptation strategies. Adaptive capacity to climate change is the important factor comprising vulnerability. An adaptive capacity index in agricultural water management system was developed considering agricultural water supply and demand for rice production in Jeolla-do, Korea. The agricultural water supply was assumed to be equal to the amount of water stored in the major agricultural reservoirs, while data on the agricultural water demand was obtained from the dynamic simulation results by Korea Agriculture Corporation(KAC). The spatial unit for analysis was conducted at the county(Si, Gun, Gu) level and temporal scale was based on every month from 1991-2003. Adaptive capacity for drought stress index(ACDS index) was calculated as the percentage of data points where the irrigated water supply was greater than the crop water demand. The ACDS index was compared with SWSCI(Standard Water Storage Capacity Index) and the relationship showed high degree of fit($R^2$=0.84) using the exponential function, indicating that the developed ACDS index is useful for evaluating the status of the balance between agricultural water supply and demand, especially for the small sized agricultural reservoirs. This study provided the methodological basis for developing climate change vulnerability index in agricultural water system which is projected to be more frequently exposed to drought condition in the future due to climate change. Further research should be extended to the study on the water demand of the crops other than rice and to the projection of the change in ACDS index in the future.

• Korean Journal of Environmental Biology
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• v.35 no.4
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• pp.549-556
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• 2017

Rivers continuously transport terrestrial organic carbon matter to the estuary and the ocean, and they play a critical role in productivity and biodiversity in the marine ecosystem as well as the global carbon cycle. The amount of terrestrial organic carbon transporting from the rivers to ocean is an essential piece of information, not only for the marine ecosystem management but also the carbon budget within catchment. However, this phenomenon is still not well understood. Most large rivers in Korea have a well-established national monitoring system of the river flow and the TOC (Total Organic Carbon) concentration from the mountain to the river mouth, which are fundamental for estimating the amount of the TOC flux. We estimated the flux of the total terrestrial organic carbon of five large rivers which flow out to the Yellow Sea, using the data of the national monitoring system (the monthly mean TOC concentration and the monthly runoff of river flow). We quantified the annual TOC flux of the five rivers, showing their results in the following order: the Han River ($18.0{\times}10^9gC\;yr^{-1}$)>>Geum River ($5.9{\times}10^9gC\;yr^{-1}$)>Yeongsan River ($2.6{\times}10^9gC\;yr^{-1}$)>Sumjin River ($2.0{\times}10^9gC\;yr^{-1}$)>>Tamjin River ($0.2{\times}10^9gC\;yr^{-1}$). The amount of the Han River, which is the highest in the Korean rivers, corresponds to be 4% of the annual total TOC flux of in the Yellow River, and moreover, to be 0.6% of Yangtze River.

• Journal of Wetlands Research
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• v.23 no.4
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• pp.287-295
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• 2021

As facilities such as dam reservoir wetlands and agricultural irrigation reservoir wetlands are built, sedimentation occurs over time through erosion, sedimentation transport, and sediment deposition. Sedimentation issues are very important for the maintenance of reservoir wetlands because long-term sedimentation of sediments affects flood and drought control functions. However, research on resignation has been estimated mainly by empirical formulas due to the lack of available data. The purpose of this study was to calculate and compare the sediment deposition rate by developing a multiple regression model along with actual data and empirical formulas. In addition, it was attempted to identify potential causes of collapse by applying it to 64 reservoir wetlands that suffered flood damage due to the long rainy season in 2020 due to reservoir wetland sedimentation and aging. For the target reservoir, 10 locations including the GaGog reservoir located in Miryang city, Gyeongsangnam province in South Korea, where there is actual survey information, were selected. A multiple regression model was developed in consideration of physical and climatic characteristics, and a total of four empirical formulas and sediment deposition rate were calculated. Using this, the error of the sediment deposition rate was compared. As a result of calculating the sediment deposition rate using the multiple regression model, the error was the lowest from 0.21(m³km²/yr) to 2.13(m³km²/yr). Therefore, based on the sediment deposition rate estimated by the multi-regression model, the change in the available capacity of reservoir wetlands was analyzed, and the effective storage capacity was found to have decreased from 0.21(%) to 16.56(%). In addition, the sediment deposition rate of the reservoir where the overflow damage occurred was relatively higher than that of the reservoir where the piping damage occurred. In other words, accumulating sediment deposition rate at the bottom of the reservoir would result in a lack of acceptable effective water capacity and reduced reservoir flood and drought control capabilities, resulting in reservoir collapse damage.

• Korean Journal of Ecology and Environment
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• v.54 no.1
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• pp.49-60
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• 2021

We examined the flow characteristics by direct runoff and base flow in a headwater stream during observed 59 rainfall events of flood season (June~September) from 2017 to 2020 yrs. Total precipitation ranged from 5.0 to 400.8 mm, total runoff ranged from 0.1 to 176.5 mm, and runoff ratio ranged from 0.1 to 242.9% during the rainfall events. From hydrograph separation, flow duration in base flow (139.3 days) was tended to be longer than direct runoff (78.3 days), while the contribution of direct runoff in total runoff (54.2%) was greater than base flow (45.8%). The total amount and peak flow of direct runoff and base flow had the highest correlation (p<0.05) with total precipitation and duration of rain among rainfall and soil moisture conditions. Dominant rainfall events for the total amount and peak flow of base flow were generated under 5.0~200.4 and 10.5~110.5 mm in total precipitation. However, when direct runoff occurred as dominant rainfall events, total amount and peak flow were increased by 267.4~400.8 and 169.0~400.8 mm in total precipitation. Therefore, the unique aspects of our study design permitted us to draw inferences about flow characteristic analysis with the contribution of base flow and/or direct runoff in the total runoff in a headwater stream. Furthermore, it will be useful for the long-term strategy of effective water management for integrated surface-groundwater in the forested headwater stream.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.43-54
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• 2023

Due to climate change, drought and flood occurrences have been increasing. Accurate projections of watershed discharges are imperative to effectively manage natural disasters caused by climate change. However, climate change and hydrological model uncertainty can lead to imprecise analysis. To address this issues, we used two lumped models, IHACRES and GR4J, to compare and analyze the changes in discharges under climate stress scenarios. The Hapcheon and Seomjingang dam basins were the study site, and the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) were used for parameter optimizations. Twenty years of discharge, precipitation, and temperature (1995-2014) data were used and divided into training and testing data sets with a 70/30 split. The accuracies of the modeled results were relatively high during the training and testing periods (NSE>0.74, KGE>0.75), indicating that both models could reproduce the previously observed discharges. To explore the impacts of climate change on modeled discharges, we developed climate stress scenarios by changing precipitation from -50 % to +50 % by 1 % and temperature from 0 ℃ to 8 ℃ by 0.1 ℃ based on two decades of weather data, which resulted in 8,181 climate stress scenarios. We analyzed the yearly maximum, abundant, and ordinary discharges projected by the two lumped models. We found that the trends of the maximum and abundant discharges modeled by IHACRES and GR4J became pronounced as changes in precipitation and temperature increased. The opposite was true for the case of ordinary water levels. Our study demonstrated that the quantitative evaluations of the model uncertainty were important to reduce the impacts of climate change on water resources.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.229-241
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• 2024

With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.69-78
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• 2011

Rice is one of the world's staple foods. Paddy rice fields have unique biophysical characteristics that the rice is grown on flooded soils unlike other crops. Information on the spatial distribution of paddy fields and the timing of irrigation are of importance to determine hydrological balance and efficiency of water resource management. In this paper, we detected the timing of irrigation and spatial distribution of paddy fields using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Aqua satellite. The timing of irrigation was detected by the combined use of MODIS-based vegetation index and Land Surface Water Index (LSWI). The detected timing of irrigation showed good agreement with field observations from two flux sites in Korea and Japan. Based on the irrigation detection, a land cover map of paddy fields was generated with subsidiary information on seasonal patterns of MODIS enhanced vegetation index (EVI). When the MODISbased paddy field map was compared with a land cover map from the Ministry of Environment, Korea, it overestimated the regions with large paddies but underestimated those with small and fragmented paddies. Potential reasons for such spatial discrepancies may be attributed to coarse pixel resolution (500 m) of MODIS images, uncertainty in parameterization of threshold values for discarding forest and water pixels, and the application of LSWI threshold value developed for paddy fields in China. Nevertheless, this study showed that an improved utilization of seasonal patterns of MODIS vegetation and water-related indices could be applied in water resource management and enhanced estimation of evapotranspiration from paddy fields.

• Korean Journal of Environmental Biology
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• v.38 no.2
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• pp.299-307
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• 2020

This study reported on the phytoplankton community and seasonal changes in the Seoul passage section and downstream in the Han River in 2012. Field samples were collected monthly from the upper (Paldang), middle (Cheongdam), and downstream(Seongsan) areas of the Seoul passage section. Water temperature, DO, pH, and conductivity were measured at each station. All environmental factors measured were recorded similarly at the three stations. The water temperature ranged from 2 to 30℃ and the dissolved oxygen ranged from 4.8 to 9.1 mg L^-1, showing typical patterns of temperate regions. The phytoplankton cell concentrations ranged from 990 cells mL^-1 (Paldang, December) to 2.9×10⁴ cells mL^-1 (Seongsan, March), and the chlorophyll-a content showed similar patterns to the cell numbers. The phytoplankton community was comprised of 75 genera and 95 species, including 37 diatoms, 29 Chlorophyta, 11 cyanobacteria, and two dinoflagellates. The number of species that appeared seasonally varied greatly, from nine species (Paldang, May) to 35 species (Cheongdam, December). Diatoms were the most dominant in all stations and seasons, except in summer. In contrast, chlorophytes and cyanobacteria showed sporadic high numbers in the summer and fall seasons. Four diatoms Stephanodiscus hantzschii f. tenuis, S. hantzschii, Fragilaria sp., and Aulacoseira spp., a chlorophyte Actinastrum hantzschii, and a cyanobacterium Microcystis sp. were each present in proportions greater than 10%. This study provides fundamental data from phytoplankton communities and environmental factors in the Han River for understanding water quality for long-term environmental monitoring.

• Korean Journal of Ecology and Environment
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• v.48 no.4
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• pp.219-228
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• 2015

To secure water resources, dams are normally constructed on the upper - middle part of streams, and it generates physical disturbances such as habitat alteration and stream fragmentation. Such construction can restrict movement of aquatic organisms, especially for freshwater fish which is one of top predator in aquatic ecosystem, and cause genetic fragmentation and community change. In this study, to investigate impact of habitat alteration after dam construction on freshwater fish, we monitored fish community changes, and compared fish fauna between dam reservoir and inflows. Additionally, movement characteristics and habitat boundaries of four species were identified by radio telemetry method. The study was conducted in the Gunwi Dam which was constructed in December 2010. Radio telemetry was applied to Pungtungia herzi, Zacco platypus (living lotic and lentic), Silurus asotus (lentic preferred species) and Zacco koreanus (lotic preferred species). The number of species was remarkably decreased (4 family, 10 species) comparing with before the dam construction (7 family, 15 species). Specifically, Coreoleuciscus splendidus, Niwaella multifasciata, Liobagrus mediadiposalis, Coreoperca herzi and Odontobutis platycephala that inhabit in the lotic environment were not collected in the study area. A total of 8 species were caught in both the dam reservoir and tributaries except 2 species (C. auratus and S. asotus). Sorenson's similarity between the reservoir and its tributaries was high (0.842). All of the radio tagged species stayed in the reservoir except S. asotus which moved to the tributary. These species mainly utilized the shallow littoral zone as a habitat. These results could be useful as a baseline data for efficient management of fishes in lakes.