Linking hydro-meteorological factors to the assessment of nutrient loadings to streams from large-plotted paddy rice fields

Excessive nutrient loadings from rice paddy fields has been a great concern in Korea as rice paddy area spans over 1,153,000 ha, which covers approximately 60% of the total agricultural land area in Korea. The principal tasks of this study included undertaking work to better identifying the scope of the nutrient loadings from paddy fields to assess their adverse effects. Hydro-meteorological factors, rainfall and surface discharge, were considered as the major driving forces of nutrients into the water. A Generalized Regression Neural Network (GRNN) model was applied and its capability evaluated to predict the nutrient loading into the neighboring water. The 15 ha paddy fields surrounded by drainage and irrigation channels were chosen as a study area. Field data, such as rainfall, quantities of irrigation and discharge water, and nutrient contents (total nitrogen (T-N) and total phosphorus (T-P)) from two different water sources, were obtained throughout the study period. Simulation results showed that surface discharge had a positive correlation with rainfall (R = 0.84). In addition, the resulting predictions for nutrient concentrations corresponding to surface discharge were varied (R = 0.72 and 0.40 in total nitrogen and total phosphorus, respectively). This study found that both natural and artificial variations of nutrient contents in irrigation streams were significantly influenced the model results of nutrient predictions. Therefore, the nutrient loadings into the neighboring water can be accurately described with a more comprehensive and sufficient representation of both environmental inputs and hydrological processes.