A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes

M Jawad; HV Gupta; MA Farmani; A Behrangi; GY Niu

cs.LGOct 10, 2026

A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes

Authors: M Jawad, HV Gupta, MA Farmani, A Behrangi, GY Niu

Organizations: Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA, 85721.

Abstract

Recent advances in machine learning have improved rainfall-runoff prediction, yet most data-driven models remain difficult to interpret and do not explicitly enforce physical conservation laws. We present the Gated Catchment Hydrology Model (GCHM), a mass-conserving architecture based on the Mass-Conserving Perceptron (MCP). GCHM uses five storage states representing canopy interception, snow, soil moisture, groundwater, and streamflow generation. Constrained gates govern precipitation partitioning, evapotranspiration, infiltration, seepage, runoff, and baseflow while learning context-dependent process relationships. We evaluate GCHM across 50 CAMELS catchments spanning snowmelt-dominated, recent-rainfall, historical-rainfall, and arid regimes, using NOAA AORC meteorological forcing and remotely sensed vegetation and land-surface data. Performance is assessed using the Jawad-Kling-Gupta Efficiency (JKGE) metric, which accounts for stationary and non-stationary hydrologic behavior. We compare the full architecture with ablated variants and recurrent neural network benchmarks. GCHM reproduces streamflow dynamics across diverse hydroclimatic regimes, including arid catchments, while maintaining interpretable states and fluxes. Ablation experiments show that simplified architectures may perform adequately under wet conditions but struggle to represent flow persistence and recession during dry periods, when groundwater storage sustains low flows. Learned gates recover hydrologically plausible relationships governing snowmelt, runoff, seepage, and evapotranspiration. GCHM combines competitive predictive performance with mass conservation and interpretability, providing a foundation for hydrologic modeling and counterfactual analysis.

Hydrology

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Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mar 26, 2026Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3Interpretable ML Physics-Informed ML
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA
From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations
Jul 29, 2026Yuan-Heng Wang, Hoshin V. GuptaNeural Network Interpretability Hydrology
Department of Hydrology and Atmospheric Science, University of Arizona, Tucson, AZ
Topology enables learning-based hydrodynamic prediction of the global river system
Feb 25, 2026Hancheng Ren, Gang Zhao, Shuo Wang +12Hydrology Streamflow Forecasting
College of Water Sciences, Beijing Normal University, Beijing, China. · School of Geography and the Environment, University of Oxford, Oxford, UK. · Department of Transdisciplinary Science and Engineering, Institute of Science Tokyo, Tokyo, Japan. +6

Mar 26, 2026J/K move · Enter open · S save

Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints

Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi, Muhammad Jawad+2

Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA

Interpretable ML Physics-Informed ML Hydrology

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Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The Mass-Conserving Perceptron (MCP) provides a physics-aware artificial intelligence (AI) framework that enforces conservation principles while allowing hydrological process relationships to be learned from data. In this study, we investigate how progressively embedding physically meaningful representations of hydrological processes within a single MCP storage unit improves predictive skill and interpretability in rainfall-runoff modeling. Starting from a minimal MCP formulation, we sequentially introduce bounded soil storage, state-dependent conductivity, variable porosity, infiltration capacity, surface ponding, vertical drainage, and nonlinear water-table dynamics. The resulting hierarchy of process-aware MCP models is evaluated across 15 catchments spanning five hydroclimatic regions of the continental United States using daily streamflow prediction as the target. Results show that progressively augmenting the internal physical structure of the MCP unit generally improves predictive performance. The influence of these process representations is strongly hydroclimate dependent: vertical drainage substantially improves model skill in arid and snow-dominated basins but reduces performance in rainfall-dominated regions, while surface ponding has comparatively small effects. The best-performing MCP configurations approach the predictive skill of a Long Short-Term Memory benchmark while maintaining explicit physical interpretability. These results demonstrate that embedding hydrological process constraints within AI architectures provides a promising pathway toward interpretable and process-aware rainfall-runoff modeling.

Abstract

Hydrology

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