MSDT v1.0: An Open-source Multi-method Snow Drought Toolbox

Abstract. Snow drought, characterized by anomalously low snow water equivalent (SWE) relative to climatological conditions, has become an increasingly important cryospheric hazard with substantial environmental and socio-economic impacts. Although numerous snow drought identification methods have been developed, existing implementations are typically fragmented into independent scripts and region-specific workflows, hindering methodological consistency, reproducibility, and systematic intercomparison. Here we present the Multi-method Snow Drought Toolbox (MSDT), an open-source Python software package that provides an end-to-end workflow for snow drought research, encompassing SWE data acquisition, multi-method identification, data visualization, and comparative analysis through an intuitive graphical user interface. MSDT integrates seven widely adopted snow drought identification methods spanning yearly, monthly, and daily temporal scales, with flexible and adjustable parameters that enable users to tailor analyses to different climatic regions and research objectives. The toolbox streamlines data access by supporting automated downloading of state-of-the-art SWE datasets together with a curated catalog of commonly used products, and further supports interactive geospatial visualization for data exploration and methodological comparison. In addition, the toolbox adopts a memory- and performance-efficient architecture that supports large spatiotemporal datasets while maintaining responsive snow drought computing. Its modular architecture enables straightforward integration of additional datasets and identification algorithms. We demonstrate the capability of MSDT using ERA5-Land SWE data to reproduce global snow drought climatology, temporal variability, and representative historical events documented in previous studies. The results show that MSDT consistently reproduces published snow drought characteristics while substantially improving computational efficiency, workflow reproducibility, and methodological comparability. By providing a standardized, extensible, and user-friendly platform, MSDT lowers technical barriers and facilitates reproducible snow drought monitoring, model evaluation, and impact assessment.