Abstract
BackgroundOver 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).MethodsA 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.ResultsANN 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.ConclusionsIt 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.
Highlights
Over the last decades interest has grown on how climate change impacts forest resources
Predictive capability of estimation approaches The artificial neural networks (ANN) and multiple linear regression (MLR) approaches produced the best results for most statistical criteria at both target stations 1 and 11, presenting a lower bias and higher precision compared to the other approaches (Table 2)
Estimation approaches showed a decrease in RSR and percent bias (PBIAS), as well as an increase in Nash-Sutcliffe efficiency (NSE), when they were applied to the higher elevation target station 1 compared to the lower target station 11 (Table 2)
Summary
Over the last decades interest has grown on how climate change impacts forest resources. Climate data are required for parameterizing process-based simulators of tree growth (Sands and Landsberg 2002) and for studying forest water balance (Huber and Trecaman 2002), phenology processes (Codesido et al 2005) and to carry out pest and disease research (Ahumada et al 2013). To perform these studies, complete and homogenous climate data that covers a sufficiently long period of time is required (Teegavarapu 2012; Khosravi et al 2015). Factors that might affect their precision have not been studied in detail
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