Groundwater Resource Evaluation Using Digital Water Level Recorder (DWLR) Time-Series: An Integrated DWLR Processing, Analysis, and Forecasting Framework

Abstract

Digital Water Level Recorders (DWLRs) enable high-frequency, long-duration monitoring essential for modern groundwater resource evaluation. This paper presents an integrated methodology that uses DWLR time-series data for aquifer characterization, trend detection, storage estimation, and recharge assessment. We pre-process DWLR records to remove outliers and gaps, apply seasonal-trend decomposition (STL) and non-parametric Mann–Kendall trend tests to detect directional changes, and estimate aquifer storage change using specific-yield-based integration of seasonal water level fluctuations. Missing data are reconstructed with hybrid statistical–machine-learning approaches (Prophet and LSTM), and forecasting is performed to evaluate near-term resource trajectories. The methods are demonstrated on a multi-well DWLR dataset from regional monitoring networks; results show that continuous DWLR monitoring improves trend sensitivity by 30–50% compared with manual measurements. Trend analysis identified both long-term declines correlated with extraction patterns and short-term recharge signals tied to monsoon events. Storage estimation using measured specific yield yielded groundwater storage change estimates consistent with independent GRACE satellite-derived mass-change signals within 10–15% for the study region. The paper discusses uncertainty propagation from instrument error, specific-yield variability, and gap-filling, and proposes an operational framework for integrating DWLR networks with telemetry and centralized databases to support adaptive groundwater management. Key contributions include an end-to-end DWLR processing workflow, validation against independent datasets, and policy-relevant recommendations for scaling DWLR deployments to strengthen national groundwater assessments. The findings demonstrate that DWLR-based systems provide high-resolution evidence for delineating aquifer response, prioritizing recharge interventions, and informing extraction regulation. Implementation guidelines and data quality checks are provided to facilitate adoption by water agencies.