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Over the last decades interest has grown on how climate change impacts forest resources. However, one of the main constraints is that meteorological stations are riddled with missing climatic data. This study compared five approaches for estimating monthly precipitation records: inverse distance weighting (IDW), a modification of IDW that includes elevation differences between target and neighboring stations (IDWm), correlation coefficient weighting (CCW), multiple linear regression (MLR) and artificial neural networks (ANN). A complete series of monthly precipitation records (1995–2012) from twenty meteorological stations located in central Chile were used. Two target stations were selected and their neighboring stations, located within a radius of 25 km (3 stations) and 50 km (9 stations), were identified. Cross-validation was used for evaluating the accuracy of the estimation approaches. The performance and predictive capability of the approaches were evaluated using the ratio of the root mean square error to the standard deviation of measured data (RSR), the percent bias (PBIAS), and the Nash-Sutcliffe efficiency (NSE). For testing the main and interactive effects of the radius of influence and estimation approaches, a two-level factorial design considering the target station as the blocking factor was used. ANN and MLR showed the best statistics for all the stations and radius of influence. However, these approaches were not significantly different with IDWm. Inclusion of elevation differences into IDW significantly improved IDWm estimates. In terms of precision, similar estimates were obtained when applying ANN, MLR or IDWm, and the radius of influence had a significant influence on their estimates, we conclude that estimates based on nine neighboring stations located within a radius of 50 km are needed for completing missing monthly precipitation data in regions with complex topography. It is concluded that approaches based on ANN, MLR and IDWm had the best performance in two sectors located in south-central Chile with a complex topography. A radius of influence of 50 km (9 neighboring stations) is recommended for completing monthly precipitation data.
Barrios et al. (Tue,) studied this question.