The influence of the length of the calibration period and observation frequency on predictive uncertainty in time series modeling of groundwater dynamics

Abstract

The influence of the length of the calibration period and observation frequency on the predictive uncertainty in time series modeling of groundwater dynamics is investigated. Studied series are from deltaic regions with predominantly shallow groundwater tables in a temperate maritime climate where heads vary due to precipitation and evaporation. Response times vary over a wide range from ∼60 to ∼1200 days. A Transfer Function-Noise model is calibrated with the Markov Chain Monte Carlo method to both synthetic series and measured series of heads. The model fit and uncertainty are evaluated for various calibration periods and observation frequencies. It is often assumed that the required length of the calibration period is related to the response time of the system. In this study, no strong relationship was observed. Results indicate, however, that the required length of the calibration period is related to the decay time of the noise. Furthermore, the length of the calibration period was much more important than the total number of observations. For the measured series, the credible intervals could commonly be reduced to ∼10% of the measured head range and the prediction intervals to ∼50% of the measured head range with calibration periods of 20 years with approximately two observations per month.