MorSnowAI v1.0 : An Open-Source Python Package for Empowering Artificial Intelligence in Snow Hydrology - A Comprehensive Toolbox

Data-driven methods, such as machine learning (ML) and deep learning (DL), play a pivotal role in advancing the field of snow hydrology. These techniques harness the power of algorithms to analyze and interpret vast datasets, allowing researchers to uncover intricate patterns and relationships within the complex processes of snow dynamics. In snow hydrology, where traditional models may struggle to capture the nonlinear and dynamic nature of snow-related phenomena, data-driven methods provide a valuable alternative. Using data-driven methods (ML and DL) requires advanced skills in various fields, such as programming and hydrological modeling. In response to these challenges, we have developed an open-source Python package named MorSnowAI that streamlines the process of building, training and testing artificial intelligence models based on machine learning and deep learning methods. MorSnowAI not only automates the building, training, and testing of artificial intelligence models but also significantly simplifies the collection of data from various sources and formats, such as reanalyzing datasets (ERA5-Land) from Copernicus Climate Data and remote sensing data from Modis, Landsat, and Sentinel datasets to calculate Normalized Difference Snow Index (NDSI). It can also utilize local datasets as inputs for the model. Among other features available in the MorSnowAI package, it provides pre-processing and post-processing methods that users can choose, along with visualization and analysis of the available time series. The scripts developed in the MorSnowAI package have already undergone evaluation and testing in various snow hydrology applications. For instance, these applications include predicting snow depth, streamflow, snow cover, snow water equivalent, and groundwater levels in mountainous areas of Morocco. The automated processes within MorSnowAI contribute to advancing the field, enabling researchers to focus on refining model inputs, interpreting results, and improving the overall understanding of complex hydrological systems. By bridging the gap between hydrology and advanced data-driven techniques, MorSnowAI fosters advancements in research, offering valuable insights for resource management in regions heavily influenced by snow dynamics.