Abstract The classic Dunne Diagram qualitatively illustrates how atmospheric conditions, soil, and topography shape runoff generation mechanisms under long-term average conditions. This study revisits the diagram using a novel theoretical framework that unifies infiltration excess and saturation excess runoff into a continuous scheme governed by dynamic soil moisture variations across catchments. The framework captures the spatial and temporal transitions between runoff mechanisms. Using observational data from 600 natural catchments across diverse US atmospheric conditions and landscapes, we develop a data-driven, quantitative reinterpretation of the Dunne Diagram based on multiannual catchment behavior, and further examine runoff generation mechanisms at seasonal timescale. At the mean annual scale, saturation excess runoff dominates the West Coast and Eastern United States, infiltration excess runoff prevails in central regions, and baseflow dominates the Western interior and Appalachian areas. In the western United States, both saturation- and infiltration-excess runoff exhibit strong seasonality, closely following the precipitation patterns along the West Coast. Individual catchments show seasonal switching and coexistence of dominant runoff generation mechanisms. The Unified Scheme for Modeling Saturation and Infiltration Excess Runoff (URSSIE) model broadly reproduces the general pattern of the Dunne Diagram and provides quantitative support for the Dunne Diagram. This approach enhances the understanding of runoff processes and improves hydrologic modeling at catchment scales, with important implications for flood forecasting and water resource management in complex or changing hydrologic regimes.
Modeling glacier evolution is critical for understanding ice-sheet dynamics and sea-level rise, but high-fidelity nu merical solvers such as the Ice-sheet and Sea-level System Model (ISSM) remain computationally prohibitive for large ensembles or high resolutions. We introduce a temporal consistency-enhanced neural operator that serves as a statistical deep-learning emulator of ISSM transient simulations. The model couples a dual-encoder input mod ule, which decouples low-dimensional drivers from static geophysical fields, with an input-shuffle strategy and a finite-difference consistency loss that constrains inter-step changes. Comprehensive experiments on Pine Island Glacier show that our approach outperforms state-of-the-art neural operators and graph-based emulators across all resolutions. Ablations reveal that temporal consistency is the main source of accuracy gains. The dual encoder provides modest improvements for thickness but delivers significant speed gains and reduces cross-resolution vari ance. Training on only 5% of the data already surpasses prior GNN-based emulators trained on the full dataset, and the relative advantage over vanilla FNO remains substantial as the training set is reduced, demonstrating high data efficiency. Overall, explicitly enforcing temporal structure and separating dynamic drivers from static fields enable accurate and robust long-horizon ice-sheet emulation.
Bio-oil obtained from biomass pyrolysis needs further hydrodeoxygenation (HDO) for its suitability in fuel applications. However, catalysts studied to date suffer from coke formation and low yields. In this study, Cu-based catalysts, prepared using the wet impregnation method, were characterized, and their performance in the twostep HDO (mild stabilization followed by severe treatment in a batch reactor) was investigated. HDO in the presence of a Cu/Al2O3 catalyst achieved a maximum 50.5 wt.% treated oil yield and 67.6% carbon conversion compared to Co-Mo/Al2O3 and Co/Al2O3 catalysts. The measured coke formation was less than 3% for the Cubased catalyst, which was attributed to controlled deoxygenation and hydrogenation. The high heating value of the treated bio-oil ranged from 31.4 to 34.6 MJ kg-1. Additionally, gas chromatography-mass spectrometry showed that the treated bio-oils were rich in alkyl phenols and hydrocarbons, with the concentration ranging from 4.6 to 55.3 and 15.9 to 32.0 wt.% of total, respectively. Direct deoxygenation, hydrogenation, and demethoxylation were the significant reactions leading to the formation of hydrocarbons. Based on the findings, Cu-based catalysts can be used for HDO of pyrolysis bio-oil to obtain valuable chemicals and fuels.
The s-step variants of GMRES reduce synchronization costs by computing multiple Krylov vectors per global reduction, but numerical instability limits the practical block size. This paper studies Newton-Leja s-step GMRES through the Gram matrix of the normalized accumulated basis. Under an explicit Gram-decay hypothesis with a sufficient margin, the condition number of the basis is bounded uniformly in the iteration, and a conditional backward-stability theorem follows within the Carson-Ma framework. A decay envelope is proved for the raw Newton-Leja factors. An exact coordinate identity yields a small-matrix monitor for the basis condition number; the check adds no global communication beyond the block orthogonalization. Experiments at constant s on two test problems are reported, together with the dimension at which the accumulated basis loses numerical rank and a comparison of the monitor against the measured condition numbers. An adaptive policy that holds conditioning in reserve converges on both recorded problems, including the Grcar case for which the greedy rule stops before convergence; there the restored convergence proceeds by single columns at nearly every step.
Wave energy has the potential to improve renewable complementarity in coastal microgrids; however, its technoeconomic competitiveness under moderate resource conditions remains uncertain. This study develops a two-stage stochastic mixed-integer linear programming (MILP) framework to evaluate the integration of wave energy converters (WECs) into hybrid offshore microgrids under joint resource and demand uncertainty. Renewable generation and demand are modeled using ARIMA-based forecasting, with interdependencies captured via a Student-t copula and reduced through k-mediods clustering. The framework optimally sizes wind, floating photovoltaics, battery energy storage, and WEC systems by minimizing the expected annual system cost. A case study in coastal New Jersey indicates that, under current cost assumptions, WEC deployment is not economically viable, despite the potential to reduce storage requirements through resource complementarity. Sensitivity analysis identifies capital cost reduction thresholds of approximately 70%–80%, depending on device type and operating policy, to achieve cost parity and system-level benefits. Demand-side management improves curtailment outcomes but provides limited economic gains without substantial WEC cost reductions. Results demonstrate that the capital cost, rather than resource variability, is the primary barrier to wave integration in moderate Atlantic wave climates. The proposed framework provides a generalizable tool for evaluating deployment thresholds of emerging marine technologies within multi-resource energy systems under uncertainty.
