Structure–Function Coherent Coarsening for Cross‐Resolution Ecohydrological Modeling

Abstract

Abstract Ecohydrological models are increasingly applied in regional scenario‐based studies, yet their cross‐resolution application remains constrained by high computational costs of fine‐resolution simulations and by structural inconsistencies introduced during scale transfer. We propose a Structure–Function Coherent Coarsening (SFCC) framework that improves the preservation of hydrological structure and functional heterogeneity during model input coarsening. Using the VELMA model in 24 subbasins of the Salish Sea Basin, USA, we evaluate coarsening strategies for three input types: (a) DEMs coarsened with a Hydro‐Aware approach that better preserves drainage topology; (b) land cover and soil type data sets coarsened with function‐preserving methods ( Auto‐Weight and Auto‐Reassign ) that retain small but process‐dominant classes; and (c) initial conditions coarsened with Hydro‐Aware , Landcover‐Aware , and Soil‐Aware strategies to improve temporal stability. Results show that the Hydro‐Aware method better preserves watershed morphology and yields more consistent runoff and nitrate loss predictions across resolutions than mean‐based coarsening. For categorical inputs, the function‐preserving methods can reduce the dominant‐class bias of majority aggregation, particularly in basins where small, high‐impact patches drive nitrogen export. Long‐term simulations show that hydrological variables stabilize quickly, whereas biogeochemical variables stabilize more slowly. In both cases, differences caused by coarsening decrease over time and eventually level off. This suggests that preserving both structural consistency and functional heterogeneity can help maintain stable model behavior over time. By operating directly at the data‐input level, SFCC improves integration of multi‐source data sets while retaining high‐resolution information. It supports cross‐scale simulation, calibration, and model integration, and provides a scalable foundation for regional scenario analysis.