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GIS-Based Reach File Generation for Efficient TMDLs Implementation

C. Lee1, K. Kim1*, Y. Park1 and H. Lee2

  1. Department of Geoinformatics Engineering, Inha University, Incheon, Republic of Korea
  2. Water Quality Control Center, National Institute of Environmental Research , Incheon, Republic of Korea

*Corresponding author. Tel: +82-32-875-4413 Fax: +82-32-863-1506 Email:


The ability to generate stream network data is essential to conducting fast searches and accurate analyses to obtain various types of environmental information on individual streams required to implement a Total Maximum Daily Loads (TMDLs) program. This study presents a major concept, an algorithm, and a procedure to build such stream network data, the Korean Reach File (KRF), based on a geographic information system (GIS). For the generation of the KRF’s graphic data, a 1:25,000 scale nationwide river/stream map, a watershed map, and a polygon type administrative district map were utilized to delineate stream flow lines, split stream reaches, and assign unique identifiers. To generate the KRF’s attribute data, we designed attribute tables for all the graphic data. All the attributes were inputted to each item of graphic data according to designed attribute input rules and the KRF was generated in a shapefile format. To evaluate the accuracy of the graphic data, we compared the stream lengths of the KRF on national level streams with actually surveyed stream lengths. The study results showed the graphic and attribute data that were generated for the four major river basins with a total stream length of 21,163 km. The Mean Absolute Percentage Error (MAPE) was about 3.8%, and we confirmed that the KRF network data were generated with relatively high spatial accuracy. The KRF’s applicability for the TMDLs was also discussed with examples. In the future, a GIS-based management system using the KRF should be developed to support TMDLs more efficiently.

Keywords: geographic information system (GIS), Korean Reach File (KRF), Total Maximum Daily Loads (TMDLs), stream network analysis

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