Influence of spatial interpolation methods for climate variables on the simulation of discharge and nitrate fate with SWAT

Abstract

For ecohydrological modeling climate variables are needed on subbasin basis. Since they usually originate from point measurements spatial interpolation is required during preprocessing. Different interpolation methods yield data of varying quality, which can strongly influence modeling results. Four interpolation methods to be compared were selected: nearest neighbour, inverse distance, ordinary kriging, and kriging with external drift (Goovaerts, 1997). This study presents three strategies to evaluate the influence of the interpolation method on the modeling results of discharge and nitrate load in the river in a mesoscale river catchment (∼1000 km2) using the Soil and Water Assessment Tool (SWAT, Neitsch et al., 2005) model: <br><br> I. Automated calibration of the model with a mixed climate data set and consecutive application of the four interpolated data sets. <br><br> II. Consecutive automated calibration of the model with each of the four climate data sets. <br><br> III. Random generation of 1000 model parameter sets and consecutive application of the four interpolated climate data sets on each of the 1000 realisations, evaluating the number of realisations above a certain quality criterion threshold. <br><br> Results show that strategies I and II are not suitable for evaluation of the quality of the interpolated data. Strategy III however proves a significant influence of the interpolation method on nitrate modeling. A rank order from the simplest to the most sophisticated method is visible, with kriging with external drift (KED) outperforming all others. Responsible for this behaviour is the variable temperature, which benefits most from more sophisticated methods and at the same time is the main driving force for the nitrate cycle. The missing influence of the interpolation methods on discharge modeling is explained by a much higher measuring network density for precipitation than for all other climate variables.