Groundwater-level time series often have a substantial number of missing values which should be taken into consideration before using them for further analysis, particularly for numerical groundwater flow modelling applications. This study aims to comprehensively compare two data-driven models, singular spectrum analysis (SSA) and multichannel spectrum analysis (MSSA), to reconstruct groundwater-level time series and impute the missing values for 25 piezometric stations in Ardabil Plain, northwest Iran. The reconstructed groundwater-level time series are assessed against the complete observed groundwater time series, while the imputed values are appraised against the artificially created gap values. The results show that both SSA and MSSA demonstrate a solid competency in imputation and reconstruction of groundwater-level data. However, depending on the spatial correlation between the piezometers, and the most suitable probability distribution function (pdf) fitted to the time series of each piezometer, the performance may vary from piezometer to piezometer.

Projections of potential impacts of climate change and groundwater abstraction on gaining and losing streams, particularly in ephemeral river basins exhibiting sporadic and intricate flux exchanges, have remained largely unexplored. To fill this gap, we propose a promising modeling scheme based on the new fully integrated hydrological model SWAT‐MODFLOW‐NWT, calibrated and validated for 1978–2012, to quantify the intertwined surface‐groundwater interactions under a conjuncture of three climatic emission scenarios (RCP 2.6, 4.5 and 8.5) and two groundwater pumping variants: “pumping” (extending current groundwater utilization into the future) and “nonpumping” (assuming a complete cease of pumping in the future). By forcing the integrated model with future downscaled climate predictors of CanESM2 under the aforementioned RCPs for three time slices up to year 2100, projections of various water resources components for the Gharehsoo River Basin (GRB), in northwestern Iran were made. Results demonstrate that because of a general decrease of future precipitation, though with ups and downs across the total projection period, most of the surface and ‐subsurface budget quantities and fluxes are substantially affected. In particular, future groundwater discharge (baseflow) to the gaining streams will be more influenced by the “pumping” variant (increasing and decreasing for “nonpumping” and “pumping”, respectively) than the concentrated groundwater recharge from the losing streams (decreasing and increasing for “nonpumping” and “pumping”, respectively). Future water yield and groundwater storage will also diminish and, surprisingly, this cannot be alleviated by future “nonpumping”, indicating the groundwater overutilization is the compelling reason for the future water scarcity in the GRB, rather than climate change alone.

Dental and skeletal fluorosis caused by consuming high-fluoride groundwater has been reported over several decades globally. Prediction maps to estimate the fluoride contaminated area rely on interpolation methods. This study presents a comparison of the accuracy of nine spatial interpolation methods in predicting the fluoride in groundwater. Leave-one-out cross-validation (LOOCV), hold-out validation and validation with an independent dataset were used to assess the precision of the interpolation methods. This is the first study on fluoride with a large dataset (N = 13,585) applied at the regional level in India. Our findings showed that the inverse distance weighted (IDW) algorithm outperformed other methods in terms of less discrepancy between measured and predicted fluoride. IDW and local polynomial interpolation (LPI) were the only methods to predict contaminated areas (fluoride > 1.5 mg/L). However, the area estimated by the typical assessment of the percentage of unsuitable samples was much higher (6.1%) compared to that estimated by IDW (0.2%) and LPI (0.2%). LOOCV provided viable results than the other two validation methods. Interpolation methods are accompanied with uncertainty which are regulated by the sample size, sample density, sample distribution, minimum and maximum measured concentrations, smoothing and border effects. Drawing a comparison among variegated interpolation methods capturing a wide range of prediction uncertainty is suggested rather than relying on one method exclusively. The high-fluoride areas identified in this study can be used by the Government in planning remediation actions.