Full-Flowfield Inversion of Debris-Flow Dynamics: A Global–Local Optimization Framework for Model Parameter Estimation from Final Accumulation Fields

Calibration of debris-flow models is important for reproducible hazard mapping, transparent scenario analysis, and risk-informed territorial planning. Reliable calibration of debris-flow models is limited by parameter uncertainty, non-uniqueness, and the loss of spatial information when observations are reduced to scalar targets. This study presents a global–local full-flowfield inversion framework for estimating effective Voellmy friction parameters from complete final-thickness rasters, thereby replacing subjective trial-and-error adjustment with an explicit forward–inverse workflow and a spatially distributed objective function. A two-dimensional finite-volume shallow-flow solver is coupled with a genetic algorithm for bounded global exploration and projected finite-difference gradient descent for local refinement. The composite objective combines thickness, wet footprint, signed boundary distance, and total volume mismatches. The framework was evaluated through controlled parameter recovery, objective profiling, ten-seed repeatability tests, observation perturbations, conditional identifiability analysis, and a controlled Morino–Rendinara back-analysis retrieval test based on a published real-event parameterization and the real terrain. Uniform reference coefficients of 0.10, 0.25, and 0.45 were recovered with a mean absolute error of 1.90×10−4 and exact wet footprint agreement. Across ten baseline inversions, the standard deviation of the estimate was 1.53×10−4. Random removal of up to 50% of observation cells had negligible influence, whereas thickness noise and systematic scaling produced larger deviations; at 20% noise, mean IoU remained 0.9861. Prescribing ξ between 250 and 1000 m s−2 shifted the estimated μ from 0.243038 to 0.254628 despite nearly identical final deposits, demonstrating conditional identifiability. In the two-zone experiment, the framework recovered μup=0.009991 and μdown=0.100184, with a held-out RMSE of 4.49×10−5 m and IoU equal to one. Full-flowfield inversion therefore provides accurate and reproducible event-specific parameter retrieval.