• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.59-68
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• 2005

The rainfall-runoff potential of Jangseong reservoir watershed was studied based on SCS (Soil Conservation Service, which is now the NRCS, Natural Resources Conservation Service, USDA) runoff curve number (CN) technique. Precipitation and reservoir operation data had been collected. The rainfall-runoff pairs from the watershed for ten years was estimated using reservoir water balance analysis using reservoir operation records. The maximum retention, S, for each storm event from rainfall-runoff pair was estimated for selected storm events. The estimated S values were arranged in descending order, then its probability distribution was determined as log-normal distribution, and associated CNs were found about probability levels of Pr=0.1, 0.5, and 0.9, respectively. A subwatershed that has the similar portions of land use categories to the whole watershed of Jangseong reservoir was selected and hydrologic monitoring was conducted. CNs for subwatershed were determined using observed data. CNs determined from observed rainfall-runoff data and reservoir water balance analysis were compared to the suggested CNs by the method of SCS-NEH4. The $CN_{II}$ measured and estimated from water balance analysis in this study were 78.0 and 78.1, respectively. However, the $CN_{II}$, which was determined based on hydrologic soil group, land use, was 67.2 indicating that actual runoff potential of Jangseong reservoir watershed is higher than that evaluated by SCS-NEH4 method. The results showed that watershed runoff potential for large scale agricultural reservoirs needs to be examined for efficient management of water resources and flood prevention.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.794-803
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• 2011

Self Organizing Map (SOM) was applied for pattern classification of hydrological and water quality data measured at Jangseong Reservoir on a monthly basis. The primary objective of the present study is to understand better data characteristics and relationship between the data. For the purpose, two SOMs were configured by a methodologically systematic approach with appropriate methods for data transformation, determination of map size and side lengths of the map. The SOMs constructed at the respective measurement stations for water quality data (JSD1 and JSD2) commonly classified the respective datasets into five clusters by Davies-Bouldin Index (DBI). The trained SOMs were fine-tuned by Ward's method of a hierarchical cluster analysis. On the one hand, the patterns with high values of standardized reference vectors for hydrological variables revealed the high possibility of eutrophication by TN or TP in the reservoir, in general. On the other hand, the clusters with low values of standardized reference vectors for hydrological variables showed the patterns with high COD concentration. In particular, Clsuter1 at JSD1 and Cluster5 at JSD2 represented the worst condition of water quality with high reference vectors for rainfall and storage in the reservoir. Consequently, SOM is applicable to identify the patterns of potential eutrophication in reservoirs according to the better understanding of data characteristics and their relationship.