Accuracy and Uncertainty Assessment of Spatial Interpolation Methods for Reservoir Bathymetric Modelling

Accurate estimates of reservoir storage and long-term capacity loss are essential for reliable operation, sediment-management planning, and risk-informed decision making. However, bathymetric point surveys are irregularly distributed, and the reconstruction of the submerged surface introduces interpolation uncertainty that propagates into stage–storage curves and capacity-loss estimates. This study investigates the influence of spatial interpolation uncertainty on reservoir capacity-loss estimates and evaluates long-term sedimentation by comparing reservoir-bed conditions in 1987 and 2023 using Kamashi Reservoir (Uzbekistan) as a case study. Historical reservoir-bed conditions were reconstructed from digitized and georeferenced engineering topographic maps prepared in 1987, while the 2023 reservoir bed was modelled using bathymetric survey data. Five surface modelling approaches Inverse Distance Weighting (IDW), Radial Basis Functions (RBF), Ordinary Kriging (OK), Universal Kriging (UK), and Empirical Bayesia

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