SVM-GAM Downscaling Framework for Quantifying Ecological Losses in Data-Limited Estuarine Dredging Areas

Accurate quantification of ecological losses in estuarine environments is often hindered by the mismatch between coarse-resolution biological surveys and fine-scale physical disturbances from engineering activities. While numerical models can simulate high-resolution environmental shifts, the inherent sparsity of ecological monitoring points limits the precision of spatial impact assessments. This study develops an integrated spatial-downscaling framework to transform sparse monitoring data into a high-resolution spatial continuum. A three-tiered modeling approach was used: first, the estuarine domain was partitioned into five eco-hydrodynamic zones using an entropy-weighted Support Vector Machine (SVM); second, localized chained Generalized Additive Models (GAMs) were established within each zone using MIKE-simulated hydrodynamic and water-quality data as proxy drivers; and third, these localized response functions were propagated across the study area to quantify multi-trophic biomass and economic losses. The framework revealed substantial spatial non-stationarity. Dredging operations locally altered the estuarine hydrodynamic regime. In northern channels, decreases in flow velocity were statistically associated with phytoplankton biomass to decline by 5.0% to 23.42%. Conversely, southern velocity increases enhanced water exchange and plankton growth. Using silt curtains as a mitigation strategy reduced the loss of phytoplankton by 11.4% and zooplankton by 9.6%. As a result, the total economic loss decreased from 26.54 million CNY to 25.34 million CNY, equivalent to a 4.5% reduction in economic loss. These results indicate that the proposed downscaling method can generate spatially explicit biological estimates. By offering a systematic pathway for impact evaluation and compensation in data-limited coastal regions, this framework supports more ecologically sustainable dredging operations. Nevertheless, the framework remains dependent on the representativeness of sparse monitoring stations, and future applications should integrate cross-estuary validation to improve transferability and uncertainty control.