SMASH v1.0: A Differentiable and Regionalizable High-Resolution Hydrological Modeling and Data Assimilation Framework

Abstract. The smash software is a differentiable and regionalizable framework enabling modular high-resolution hydrological modeling and data assimilation, from catchment to regional and country scales, for water research and operational applications. smash combines various process-based conceptual operators for vertical and lateral flows, which can be hybridized with a descriptors-to-parameters neural network for regionalization. smash features an efficient, differentiable Fortran solver using Tapenade that supports CPU parallel computing and spatially distributed optimization of large parameter vectors, interfaced in Python using f90wrap. This article presents smash algorithms, their open-source code, documentation and tutorials. It highlights foundational research, benchmarking on state-of-the-art datasets, and readiness for scientific and operational use. To ensure reproducibility, open-source datasets are used to demonstrate the main functionalities of smash, including parallel computation performances and the application of multiple spatially distributed conceptual model structures over a large catchment sample. These functionalities include uniform or spatially distributed calibration and regionalization by learning the relation between descriptors and parameters. Provided Python tool allows application to any other catchment from globally available datasets. Using CAMELS, as per recent articles, median KGE > 0.8 are obtained in local spatially distributed calibration for daily GR-like and VIC-like model structures at dx = 1′30′′ (∼ 3 km), and KGE > 0.6 in spatio-temporal validation in a regionalization context. The regionalization of a high resolution hourly GR-like model structure at dx = 500 m over a difficult mediterranean flash-flood prone case results in NSE > 0.6 in spatio-temporal validation. The proposed differentiable and regionalizable spatially distributed modeling framework is designed for variational data assimilation and is intended for collaborative research and operational applications. Additionally, smash supports the implementation of other differentiable hydrological and hydraulic models, as well as hybrid physics-AI models, further enhancing its versatility and applicability.