To compensate for the intrinsic coarse spatial resolution of groundwater storage (GWS) anomalies (GWSA) from the Gravity Recovery and Climate Experiment (GRACE) satellites and make better use of current dense in situ groundwater-level data in some regions, a new statistical downscaling method was proposed to derive high-resolution GRACE GWS changes. A ground-based scaling factor (SFGB) method was proposed to downscale GRACE GWS changes that were corrected using gridded scaling factors estimated from ground-based GWS changes through forward modeling. The proposed method was applied in the North China Plain (NCP), where many observation wells and consistently measured specific yield are available. Importantly, the sensitivity of the proposed method was explored considering the uncertainties of in situ GWS changes due to variable specific yield and/or number of observation wells. Independent validation shows that SFGB can effectively recover GRACE GWSA at the 0.5º grid scale (r = 0.81, root mean square error = 40.51 mm/yr). The SFGB-corrected GWSA in the NCP was -32.60{plus minus}0.99 mm/yr (-4.6{plus minus}0.14 km3/yr) during 2004-2015, showing contrasting GWS trends in the piedmont west (loss) and the coastal east (gains). Uncertainties in SFGB-corrected GWSA arising from specific yield, groundwater-level, and both can be reduced by 90%, 65%, and 84%, respectively relative to ground-based GWSA. This study highlights the potential value of jointly using GRACE and in situ observation data to improve the accuracy of GRACE-derived GWSA at smaller scales. The new downscaling method and the improved groundwater storage change estimates would facilitate better groundwater management.

Abstract: Heterogeneity is crucial for predication of flow and contaminant transport in subsurface formations. To characterize the heterogeneous architecture, the relationship between multimodal correlation of hydraulic conductivity (K) and plume dispersion is investigated through integration of experimental, theoretical, and numerical simulation approaches. The spatial correlation structure of K in a heterogeneous sedimentary column is investigated by analyzing the covariance components and transition probability structures. The detailed sedimentary facies data of the column ensures the accuracy of heterogeneous sediment characterization. Lagrangian-based transport models were developed to estimate solute dispersion in non-reactive tracer injection experiments. The results show that the model successively predict the solute transport when the spatial correlation structure is well-defined. Dispersivity estimated by the Lagrangian-based model slightly larger than those obtained from the measurements of tracer experiments. Further, the upscaled dispersivity that derived from transition probability is dominated determined by the cross-transition probability structure, while the contribution of auto-transition terms are quite small. The contribution of the cross-transition terms increases with the increasing contrast in mean permeability between different facies. Numerical modeling results confirm that upscaled dispersivity values well capture solute breakthrough behavior along the heterogeneous sediment column. Keywords: Solute transport; Dispersion; Dispersivity; heterogeneity; porous media; Sedimentary structure; Column experiment; Lagrangian-based model.