Submitted to: Computers in Agriculture
Publication Type: Abstract Only
Publication Acceptance Date: February 5, 2009
Publication Date: N/A
Missing meteorological data have to be estimated for agricultural and environmental modeling. The objective of this work was to develop a technique to reconstruct the missing daily precipitation data in the central part of the Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN). We also applied the two-step reconstruction method (RT+ANN) that employed ANN with inputs only from stations that were found to be influential in bootstrap applications of RT. In addition to characterizing the reconstruction accuracy using statistics and the reconstruction uncertainty using bootstrapping, we performed functional testing of the technique by evaluating the precipitation error propagation in streamflow simulations with the Soil and Water Assessment Tool (SWAT model). RT provided a transparent visual representation of the similarity between the stations in their daily precipitation time series. With the seven years of data from 39 weather stations, both RT and ANN provided reconstruction accuracy comparable to or better than published earlier results of ANN application to the precipitation reconstruction. The RT+ANN method proposed in this study significantly improved accuracy and was more robust compared with RT and ANN. This method was also more accurate and robust in SWAT streamflow predictions than with reconstructed precipitation.