Bedforms-ATM (Bed forms analysis toolkit for multiscale modeling) is a software designed to hierarchize and quantify the dimensionality of natural bed forms fields. It comprises four modular applications, namely: (1) wavelet analysis, (2) Hovmöller analysis, (3) multiscale discrimination, and (4) three-dimensionality analysis. Bedforms-ATM also provides insights on bed form systems dynamics and their interrelationship with the surrounding hydrodynamic characteristics. The software structure encourages its expandability via the collaboration from the community of users. Both fluvial and synthetic bed form data accompany Bedforms-ATM.

This study presents a Bayesian calibration framework for 2D hydraulic models using convolutional neural networks (CNNs) as surrogate emulators of TELEMAC-2D. Applied to the Lower Piura River Basin in northern Peru, the method estimates spatially distributed Manning's roughness coefficients while accounting for structural model error. A CNN trained on a simulation ensemble predicts flood depth under varying roughness scenarios, enabling substantial computational savings. The emulator is embedded in a Bayesian inference scheme with a Gaussian Process discrepancy model to capture systematic deviations. Validation with synthetic scenarios demonstrates accurate roughness retrieval in hydraulically sensitive areas. Additionally, a real-case validation was performed using PeruSAT-1, a high-resolution Earth observation satellite operated by the Peruvian Space Agency (CONIDA), acquired during the...