5. Conclusions
Given the frequent shortage of hydrologic and water quality monitoring data, the distributed models, in some cases cannot be applied to the challenge of diffuse pollution control. Based on the preliminary analysis from this study, the strength of spatial correlation analysis lies in its simplicity and in the fact that it has the advantage of assessing diffuse N loading. Spatial relationships were grouped into sub-basins of rice paddies and uplands, a distinction based on the crops produced. The spatial correlations of eight soil parameters with diffuse N loading at two depths proved that the assumption made was valid and provided new insights into diffuse pollution assessment. The differences observed among the metal and non-metal variables demonstrat- ed that the principles for diffuse N are different. The PLS analysis results indicated that the parameters for metal content (Fe, Mn, Cd, and Pb) in the top surface layer were the principal parameters for loading prediction in the upland sub-basins. The studies also showed that the TN in the top surface layer of the paddy rice sub- basins had distinct patterns with respect to the pollution loading. In this case study, the approach demonstrated that diffuse N loading can be quantified even when the available monitoring data are insufficient for use with a complicated model.