SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Interpolation of missing precipitation data using kernel estimations for hydrologic modeling 
Authors:Lee, H. and K. Kang 
Journal:Advances in Meteorology 
Article ID:935868 
URL (non-DOI journals): 
Broad Application Category:hydrologic only 
Primary Application Category:climate data effects 
Secondary Application Category:hydrologic assessment 
Watershed Description:1,361 km^2 Imha River, a tributary of the Nakdong River, located in east central South Korea 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data.This study also presents an assessment that compares estimation of missing precipitation data through 𝐾th nearest neighborhood (𝐾NN) regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT) hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the 𝐾NN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.