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 descriptor-to-parameter neural network for regionalization. smash features an efficient, differentiable Fortran solver using Tapenade to automatically derive the adjoint model that supports CPU forward–inverse parallel computing and spatially distributed optimization of large parameter vectors thanks to an accurate cost gradient, interfaced in Python using f90wrap. This article presents smash algorithms and 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. The provided Python tool allows application to any other catchment from globally available datasets. Using CAMELS, as per recent articles, a median Kling–Gupta efficiency (KGE) > 0.8 is obtained in local spatially distributed calibration for daily Génie Rural (GR)like and variable infiltration capacity (VIC)-like model structures at dx = 103000 (∼ 3 km) and KGE > 0.6 in spatiotemporal 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 a Nash–Sutcliffe efficiency (NSE) > 0.6 in spatiotemporal validation. The proposed differentiable and regionalizable spatially distributed modeling framework is designed for gradient-based variational data assimilation, applicable to initial state (not shown) and parameter estimation at multiple timescales, 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.