Sampling strategies for high quality time-series of climatic variables in forest resource assessment

Many ecological studies require long-term time series of high quality. Missing data may represent a serious problem since they can affect the reliability of measured variables in specific locations. To which extent and according to which methodology a gap in time series should be filled is a major research challenge. In this study, the time-series of meteorological data relative to 13 monitoring sites from the ICP-Forest network in Italy were analysed with the aim to define the minimum number of site-specific observations, which can be considered adequate for further analysis on forest resource management. Three main climatic variables were taken into account in the analysis: air temperature, relative humidity and total precipitation. By using an increasing proportion of available data, descriptive and inferential statistic methods were applied to evaluate the amount of variability along the period of analysis (1998-2013) and associated error of estimation at seasonal level. The relative importance of each factor accounted in our analysis (season, year, variable, plot, sampling proportion) was investigated fitting a Random Forest model on the results of the bootstrapping procedure. Air temperature was the variable with a marked seasonal profile and the easiest to be represented at monthly level on a specific time period. Humidity and precipitation were more stable across the analysed time period. Trends in precipitation showed that a high amount of variability could be detected only when > 80% of valid observations were available. Humidity showed an intermediate pattern, with an exponential increase in the amount of explained variability when using an increased proportion of sampled observations. Random Forest Regression models indicated sampling proportion (i.e., number of available observations) as an important factor for trend analysis of relative air humidity and precipitation. We conclude that monthly or seasonal statistics can be proficiently estimated for both air temperature and relative humidity with a proportion of missing values higher than 50%. Conversely, a reliable analysis of intra-seasonal or intra-monthly precipitation variability requires a much higher amount of observations. In the latter case gap filling represents the only feasible solution.