نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی فناوری اطلاعات- دانشگاه پیام نور
2 گروه زیست شناسی دانشگاه پیام نور
چکیده
The environmental collapse of terminal lake basins necessitates a rigorous distinction between climatic variability and anthropogenic pressures to inform restoration policies. This study aims to diagnose the primary drivers of desiccation in the Lake Urmia Basin through a continuous-time modeling approach. A Probabilistic Neural Ordinary Differential Equation (Neural ODE) framework was developed as a "Digital Twin" of the basin, calibrated on the natural period (1981–1996) using multi-source satellite imagery and ERA5-Land reanalysis data. By projecting these learned natural dynamics through 2025, counterfactual simulations were employed to quantify the relative contributions of climate warming and water withdrawal. Results indicate that while climate warming (representing temperature and evaporation shifts) accounts for approximately 37% of the surface area decline, direct anthropogenic intervention is responsible for 63% (±5%) of the total deficit. Furthermore, seasonal decomposition reveals a critical water deficit during winter months, consistent with the interception of runoff by upstream infrastructure. Identifying this seasonal timing of water scarcity is highly critical for drought risk management and restoration policy, as it distinguishes baseline evaporative losses from direct infrastructure-driven runoff interception, thereby guiding targeted reservoir release strategies. These findings demonstrate that the basin retains significant hydro-climatic potential for recovery. The study concludes that restoration efforts should prioritize the reform of winter reservoir release policies and the enforcement of agricultural consumption caps rather than focusing solely on climate adaptation.
کلیدواژهها
موضوعات
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