Assessment of Scale Effects on Dynamics of Water Quality
and Quantity for Sustainable Paddy Field Agriculture

Kim, Minyoung; Kim, Minkyeong; Lee, Sangbong; Jeon, Jonggil

doi:2010.15.2.123

Environmental Engineering Research 2010;15(2): 123-126. DOI: https://doi.org/10.4491/eer.2010.15.2.123

Assessment of Scale Effects on Dynamics of Water Quality and Quantity for Sustainable Paddy Field Agriculture

Minyoung Kim, Minkyeong Kim^†, Sangbong Lee, and Jonggil Jeon

Department of Agricultural Engineering, National Academy of Agricultural Science, Rural Development Administration, Suwon 441-707, Korea

Corresponding Author: Minkyeong Kim ,Tel: +82-31-290-0223, Fax: +82-31-290-0206, Email: kimmk72@korea.kr

Received: December 15, 2009; Accepted: February 22, 2010.

ABSTRACT

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales (1.5 × 10⁵ and 1.62 × 10⁶ m²) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfallsurface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.