arXiv subjects

Hoshin Vijai Gupta

Publications and source records attributed to Hoshin Vijai Gupta.

2 recordsLinked to original sources

arXiv2026

Structure-Function Coherent Coarsening for Cross-Resolution Ecohydrological Modeling

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: (i) DEMs coarsened with a Hydro-Aware approach that better preserves drainage topology; (ii) land cover and soil type datasets coarsened with function-preserving methods (Auto-Weight and Auto-Reassign) that retain small but process-dominant classes; and (iii) 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.

physics.geo-ph↗

arXiv2025

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems

Simulating ecohydrological processes is essential for understanding complex environmental systems and guiding sustainable management amid accelerating climate change and human pressures. Process-based models provide physical realism but can suffer from structural rigidity, high computational costs, and complex calibration, while machine learning (ML) methods are efficient and flexible yet often lack interpretability and transferability. We propose a unified three-phase framework that integrates process-based models with ML and progressively embeds them into artificial intelligence (AI) through knowledge distillation. Phase I, behavioral distillation, enhances process models via surrogate learning and model simplification to capture key dynamics at lower computational cost. Phase II, structural distillation, reformulates process equations as modular components within a graph neural network (GNN), enabling multiscale representation and seamless integration with ML models. Phase III, cognitive distillation, embeds expert reasoning and adaptive decision-making into intelligent modeling agents using the Eyes-Brain-Hands-Mouth architecture. Demonstrations for the Samish watershed highlight the framework's applicability to ecohydrological modeling, showing that it can reproduce process-based model outputs, improve predictive accuracy, and support scenario-based decision-making. The framework offers a scalable and transferable pathway toward next-generation intelligent ecohydrological modeling systems, with the potential extension to other process-based domains.

cs.LG↗