Network-Scale KNN Imputation for Monthly Groundwater Level Records with Gaps

Abstract Monthly groundwater level records in monitoring networks frequently include gaps that complicate drought assessment, pumping management, and groundwater-model calibration. This paper presents GWMonthlyKNN, an open-source workflow that imputes missing monthly water-surface-elevation (WSE) records using k -nearest neighbors (KNN) in a feature space encoding seasonality and short-term persistence, while limiting candidate donor wells to hydrologically plausible groups. The workflow was evaluated on a California Department of Water Resources dataset of 515 wells using blocked 12- and 24-month masked-gap experiments that emulate monitoring outages. Full-network tests identified eligible 12- and 24-month outage blocks for 461 and 410 wells, respectively. Under the default configuration, median coefficients of determination were 0.83 and 0.77, median RMSE values were 0.97 and 0.85 m, and median signed biases were + 0.20 and + 0.15 m . Sensitivity runs across neighborhood sizes showed similar median performance. GWMonthlyKNN outputs completed station series, diagnostics, and a provenance map for auditable groundwater data completion.