A novel framework for filling data gaps in groundwater level observations

Abstract Groundwater level knowledge is one of the crucial information for sustainable use of groundwater resources and efficient and effective conjunctive management of surface and groundwater resources. Despite the fact that there is an increase of water related datasets collected, still groundwater information remains one of the Achilles heel in terms of analysis and modeling. The present paper is introducing a novel framework for filling data gaps in groundwater time series in an efficient scheme with low computational cost. It utilizes direct (known measurements) and auxiliary (regional groundwater model) available information. The framework employs an exogenous seasonal autoregressive integrated moving average (SARIMAX) stochastic model to describe the simulated groundwater level fluctuation process of a regional physical groundwater model and the Ensemble Smoother (ES) for predicting the water table level. The framework was evaluated by conducting three numerical experiments for five groundwater wells in the South Platte alluvial aquifer. The results indicate that this framework could serve as a valuable tool for the enhancement of groundwater time series, for both intermittent missing data and of continuous gaps with short span, which could then lift hindrances in their analysis and use in groundwater models, and thus aid water management decisions.