Question: Is it possible to improve the general applicability and significance of empirical relationships between abiotic condi-tions and vegetation by harmonization of temporal data? Location: The Netherlands. Methods: Three datasets of vegetation, recorded after periods with different meteorological conditions, were used to analyze relationships between soil moisture regime (expressed by the mean spring groundwater level – MSL t calculated for differ-ent periods) and vegetation (expressed by the mean indicator value for moisture regime F m). For each relevé, measured groundwater levels were interpolated and extrapolated to daily values for the period 1970-2000 by means of an impulse-response model. Sigmoid regression lines between MSL t and F m were determined for each of the three datasets and for the combined dataset. Results: A measurement period of three years resulted in sig-nificantly different relationships between F m and MSL t for the three datasets (F-test, p < 0.05). The three regression lines only coincided for the mean spring groundwater level computed over the period 1970-2000 (MSL climate) and thus provided a general applicable relationship. Precipitation surplus prior to vegetation recordings strongly affected the relationships. Conclusions: Harmonization of time series data (1) eliminates biased measurements, (2) results in generally applicable rela-tionships between abiotic and vegetation characteristics and (3) increases the goodness of fit of these relationships. The pre-sented harmonization procedure can be used to optimize many relationships between soil and vegetation characteristics. Abbreviations: F m = mean indicator value for moisture re-gime; MSL = Mean spring groundwater level; RMSE = Root mean squared error.