Deep Learning-Based Projection of Caspian Sea Level Variations under Climate Change Scenarios: A Spatially-Explicit Non-Stationary Approach

The Caspian Sea, the world’s largest enclosed water body, exhibits significant level fluctuations driven by complex hydroclimatic processes across its vast watershed. Projecting future sea level variations remains challenging due to non-linear interactions, non-stationary climate dynamics, and the basin’s response to anthropogenic climate change. This study develops a novel spatially-explicit deep learning framework to project Caspian Sea level variations under multiple Shared Socioeconomic Pathway (SSP) scenarios.

Our methodology integrates gridded climate data from CMIP6 models with a hybrid CNN-Transformer architecture that explicitly accounts for: (1) spatial heterogeneity across major sub-basins (Volga, Kura, Ural, Terek watersheds), (2) temporal non-stationarity in evaporation, precipitation, and river discharge patterns, and (3) dynamic land-water boundaries in shallow coastal zones. The model employs multi-head attention mechanisms to capture long-range dependencies in climate teleconnections while maintaining physical consistency through a water balance constraint layer.

A critical innovation is our treatment of non-stationary processes where future evaporation rates may exceed historical ranges. We implement adaptive normalization and time-varying parameter modules that learn evolving climate patterns without relying solely on historical statistics. For regions projected to desiccate under extreme scenarios, we incorporate dynamic masking that temporally deactivates precipitation-evaporation fluxes in exposed grid cells.

Spatial analysis reveals differential impacts across sub-basins, with the northern shallow zones showing heightened sensitivity. The attention weights highlight the dominant role of Volga discharge variability and Caspian surface evaporation in controlling decadal-scale level changes.

This physics-informed deep learning approach provides computationally efficient, probabilistic projections while maintaining interpretability through attention visualization and uncertainty quantification. The framework is transferable to other enclosed basins facing similar non-stationary climate challenges.