Efficient Emulation, Uncertainty Quantification, and Sensitivity Analysis for a Land Surface Model Using Evidential Deep Learning

Abstract

Land surface models (LSMs), such as the Community Land Model version 5 (CLM5), represent complex vegetation processes; however, systematic biases persist between modeled and observed leaf area index (LAI) because of parameter uncertainties and knowledge gaps. The high computational cost of CLM5 is a barrier to extensive sensitivity analyses and ensemble simulations at the global scale. This study addresses these limitations by training an evidential deep neural network (EDNN) emulator on a 500‐member CLM5 perturbed parameter ensemble generated via Latin hypercube sampling of 32 key plant physiological parameters. The EDNN employs cyclic temporal encoding to preserve seasonal periodicity and to predict LAI anomalies, thereby emphasizing variability. It also quantifies predictive uncertainty by jointly learning aleatoric and epistemic components in a single forward pass, yielding well‐calibrated probabilistic outputs without requiring computationally intensive ensembles. Across the contiguous United States, the EDNN reproduces CLM5‐simulated LAI with a median R2≈0.8 ${R}^{2}\approx 0.8$ on held‐out members and years while requiring substantially less computation. The EDNN emulator captures seasonal cycles, interannual variability in LAI, and uncertainties (aleatoric and epistemic) in a single pass. The sensitivity analysis highlights photosynthetic capacity and the leaf carbon‐to‐nitrogen ratio as dominant controls on LAI variability, with seasonal shifts in their influence reflecting phenological dynamics. These capabilities enable comprehensive parameter‐sensitivity studies and more efficient calibration and tuning of land surface models by supporting rapid probabilistic forecasting and adaptive model refinement, thereby paving the way for scalable Earth‐system modeling, robust parameter exploration, and uncertainty‐informed projections of land‐surface processes.