Search

The Soil and Water Assessment Tool (SWAT) has gained increasing prominence in Moroccan water research, with a surge in applications over the past decade. This review analyzes 83 SWAT-based studies conducted in Moroccan watersheds, highlighting the model's effectiveness in assessing water resources, soil erosion, and the impacts of climate and land-use changes. While research has predominantly focused on the Rif region, emphasizing the need for broader geographical coverage, the review demonstrates SWAT's adaptability to diverse landscapes and hydrological regimes. However, data scarcity and the need for model adaptations to capture unique hydrological features remain key challenges. By exploring innovative solutions such as regionalization techniques, alternative data sources, and model modifications, we can unlock the full potential of SWAT to inform sustainable and resilient water management strategies in Morocco.

In the realm of oceanography, understanding the complex dynamics of Earth's oceans is crucial. One key aspect of this understanding is the classification of water masses based on their physical properties, such as temperature and salinity. Clustering analysis has emerged as a powerful method for automated water masses classification using the temperature-salinity (T-S) diagram. However, it faces limitations in regions with complex mixing processes. To address this, a novel approach using the K Nearest Neighbors (KNN) algorithm based on potential density and potential spicity (σ-π) diagrams was proposed. In this context, we introduce the classwms tool, a user-friendly MATLAB Graphical User Interface (GUI) that combines clustering analysis and KNN classification for efficient water mass classification.