arXiv subjects

Morgan Thornwell

Publications and source records attributed to Morgan Thornwell.

3 recordsLinked to original sources

arXiv2026

Assessing global drivers of forest transpiration using clustered machine learning models

Understanding the environmental drivers of forest transpiration is critical for improving global predictions of water availability and ecosystem health. Due to many competing controls on plant water stress and ecosystem transpiration, however, these drivers may vary widely across tree species which have adapted hydraulically to local climate conditions. Here, clustered machine learning models were used to analyze global drivers of forest transpiration rates using the SAPFLUXNET database. Sap flux data from a total of ninety-five sites spanning seven biomes were grouped using two clustering strategies: by biome and by plant functional type. Two supervised machine learning algorithms, a random forest algorithm and a neural network algorithm, were used to predict rates of sap flux for each cluster. The performance and feature importance in each model were analyzed and compared to evaluate the environmental variables that control each cluster's performance. By defining site clusters, these models are able to predict transpiration and its environmental drivers across a wide variety of geographical sites and tree species. Unlike models trained on the entire dataset, high-performing clustered models achieved R$^2$ values to measurement data in the range of 0.74 to 0.90, with the highest performance being achieved in mid-sized clusters of up to thirty-six sites. There was high variance in feature importance between clusters, indicating that key predictors of transpiration varied strongly across both plant functional type and biome. Overall, water-limited climates tended to be more controlled by soil moisture, whereas climates with high mean annual temperature tended to be more controlled by solar radiation and less dependent on air temperature. These findings provide insights into how forest transpiration responds to environmental factors across a wide range of climate types and tree species.

q-bio.QM↗

arXiv2025

Structure-Function Coherent Coarsening for Cross-Resolution Ecohydrological Modeling

Ecohydrological models are increasingly applied across multiple scenarios, yet their application remains constrained by high computational costs of fine-resolution simulations and structural inconsistencies in cross-scale modeling. This study develops a Structure-Function Coherent Coarsening (SFCC) framework that preserves both hydrological connectivity and functional heterogeneity during model input coarsening. We apply the VELMA model to 24 subbasins in the Salish Sea Basin, U.S. and examine three types of inputs: (i) DEM coarsened with a Hydro-aware approach that preserves drainage topology; (ii) land-use 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 hydrology-, land-cover-, and soil-aware strategies to enhance temporal stability. Results show that the Hydro-aware method effectively preserves watershed morphology and yields more consistent runoff and nitrate predictions than mean-based coarsening across scales. For categorical inputs, the function-preserving methods alleviate the dominant-class bias of majority aggregation, particularly in basins where small high-impact patches drive nitrogen export. Long-term simulations further show that although hydrological variables equilibrate rapidly and biogeochemical processes adjust more gradually, deviations in both decrease over time and converge toward a steady state. These demonstrate that structural consistency and functional preservation together maintain dynamic stability through spatiotemporal feedback. Compared with existing work, the proposed SFCC framework operates directly at the data-input level, enabling more coherent integration of multi-source datasets and maximizing the retention of high-resolution information.

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↗