Diagnosing Anthropogenic and Climatic Drivers of Lake Urmia Desiccation Using a Probabilistic Neural ODE Digital Twin

Masoudi, Babak; Bazzi, Safoora

doi:10.22077/jdcr.2026.11222.1228

Diagnosing Anthropogenic and Climatic Drivers of Lake Urmia Desiccation Using a Probabilistic Neural ODE Digital Twin

نوع مقاله : مقاله پژوهشی

نویسندگان

Babak Masoudi ¹

Safoora Bazzi ²

¹ Department of Information Technology, Payame Noor University (PNU),Tehran, Iran.

² Department of Biology, Payame Noor University (PNU), Tehran, Iran.

https://doi.org/10.22077/jdcr.2026.11222.1228

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

Drought

Endorheic systems

Hydrological modeling

Water resource management

Runoff interception

موضوعات

خشکسالی

Adombi, A. V. D. P. (2025). Scientific machine learning in hydrology: a unified perspective. Earth Science Informatics, 18(4), 522. https://doi.org/10.1007/s12145-025-02021-6

AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., Nasrollahi, N., Farahmand, A., Mehran, A. & Hasanzadeh, E. (2015). Aral Sea syndrome desiccates Lake Urmia: Call for action. Journal of Great Lakes Research, 41(1), 307-311. https://doi.org/10.1016/j.jglr.2014.12.007

Akbari-Alashti, H., Soncini, A., Dinpashoh, Y., Fakheri-Fard, A., Talatahari, S. & Bocchiola, D. (2018). Operation of two major reservoirs of Iran under IPCC scenarios during the XXI century. Hydrological Processes, 32(21), 3254-3271. https://doi.org/10.1002/hyp.13254

Alizadeh-Choobari, O., Ahmadi-Givi, F., Mirzaei, N. & Owlad, E. (2016). Climate change and anthropogenic impacts on the rapid shrinkage of Lake Urmia. International Journal of Climatology, 36(13), 4276-4286. https://doi.org/10.1002/joc.4630

Alkaabi, K., Sarfraz, U. & Al Darmaki, S. (2025). A Deep Learning Framework for Flash-Flood-Runoff Prediction: Integrating CNN-RNN with Neural Ordinary Differential Equations (ODEs). Water, 17(9), 1283. https://doi.org/10.3390/w17091283

Chen, R. T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural ordinary differential equations Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada.

Danesh-Yazdi, M., Bayati, M., Tajrishy, M. & Chehrenegar, B. (2021). Revisiting bathymetry dynamics in Lake Urmia using extensive field data and high-resolution satellite imagery. Journal of Hydrology, 603, 126987. https://doi.org/10.1016/j.jhydrol.2021.126987

Delju, A. H., Ceylan, A., Piguet, E. & Rebetez, M. (2013). Observed climate variability and change in Urmia Lake Basin, Iran. Theoretical and Applied Climatology, 111(1), 285-296. http://doi.org/10.1007/s00704-012-0651-9

Esmaeili, S., Bahrami, J. & Kamali, B. (2024). The contributions of natural and anthropogenic climate change on water resources reduction in Zarrinehroud basin of Lake Urmia. Advances in Civil Engineering and Environmental Science, 1(1), 1-14. http://doi.org/10.22034/acees.2024.195339

Fatehifar, A., Goodarzi, M. R., Talebi, A., Sušnik, J. & van der Zaag, P. (2026). Breaking the persisting supply–demand cycle: a critical review of water development and conflict in the Zayandeh-Rud basin. Water Policy, 28(2), 273-292. https://doi.org/10.2166/wp.2026.137

Huynh, N. N. T., Garambois, P. A., Colleoni, F. & Monnier, J. (2025). Hybrid Physics-AI and Neural ODE Approaches for Spatially Distributed Hydrological Modeling. EGUsphere, 2025, 1-24. https://doi.org/10.5194/egusphere-2025-2797

Jing, X., Yang, X., Luo, J. & Zuo, G. (2026). A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl. Environmental Modelling & Software, 197, 106802. https://doi.org/10.1016/j.envsoft.2025.106802

Kratzert, F., Klotz, D., Brenner, C., Schulz, K. & Herrnegger, M. (2018). Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005-6022. http://doi.org/10.5194/hess-22-6005-2018

Lan, T., Zhang, J., Cheng, W., Wang, X., Zhang, H., Gong, X., Xie, X., Chen, Y. D. & Xu, C. Y. (2026). A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling. Hydrology and Earth System Sciences, 30(8), 2455-2471. https://doi.org/10.5194/hess-30-2455-2026

Mahdizadeh, M., Eslamian, S. & Sabzevari, Y. (2025). Chapter 16 - Analysis of Surface Flows of Urmia Lake Basin: a Review. In S. Eslamian & F. Eslamian (Eds.), Hydrosystem Restoration Handbook (pp. 217-241). Elsevier. https://doi.org/10.1016/B978-0-443-29802-8.00016-9

McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. http://doi.org/10.1080/01431169608948714

Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), 13603. http://doi.org/10.1038/ncomms13603

Meydani, A., Dehghanipour, A., Schoups, G. & Tajrishy, M. (2022). Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: Application to Urmia Lake basin, Iran. Journal of Hydrology: Regional Studies, 44, 101228. https://doi.org/10.1016/j.ejrh.2022.101228

Miri, M., Zarei, M. & Allen, D. M. (2026). A comparative study of key factors influencing groundwater in a temperate and a semi-arid regions. Groundwater for Sustainable Development, 32, 101562. https://doi.org/10.1016/j.gsd.2025.101562

Mohammadi, M. R., Delgarm, R. T., Farahmand, H., Nikraftar, Z., Badiezadeh, S., López-Carr, D. & Tajrishy, M. (2025). Interplay of Climate Change, Policy, and Human Behavior in Lake Basins: Identifying Key Factors and Future Climate Hotspots (Case Study: Urmia Lake Basin, Iran). Journal of Hydrology, 663, 134197. https://doi.org/10.1016/j.jhydrol.2025.134197

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C. & Thépaut, J. N. (2021). ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349-4383. http://doi.org/10.5194/essd-13-4349-2021

Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422. http://doi.org/10.1038/nature20584

Raheli, B., Talebbeydokhti, N., Saadat, S. & Nourani, V. (2024). Uncertainty Assessment of Surface Water Salinity Using Standalone, Ensemble, and Deep Machine Learning Methods: A Case Study of Lake Urmia. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 48(2), 1029-1047. http://doi.org/10.1007/s40996-024-01374-0

Sadeghfam, S., Fahmfam, N., Khatibi, R., Crookston, B. M., Vadiati, M. & Moazamnia, M. (2025). Introducing reservoir sustainability indexing to investigate reservoir operations and piloting it at the basin of Lake Urmia with sparse data. Environmental and Sustainability Indicators, 25, 100577. https://doi.org/10.1016/j.indic.2024.100577

Schoups, G. & Nasseri, M. (2021). GRACEfully Closing the Water Balance: A Data-Driven Probabilistic Approach Applied to River Basins in Iran. Water Resources Research, 57(6), e2020WR029071. https://doi.org/10.1029/2020WR029071

Schröder, T., Hassanzadeh, E., Darehshouri, S., Tajrishy, M. & Schulz, S. (2022). Satellite based lake bed elevation model of Lake Urmia using time series of Landsat imagery. Journal of Great Lakes Research, 48(6), 1710-1717. https://doi.org/10.1016/j.jglr.2022.08.016

Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M. & Schüth, C. (2020). Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia. Scientific Reports, 10(1), 236. http://doi.org/10.1038/s41598-019-57150-y

Sharifi, A., Shah-Hosseini, M., Pourmand, A., Esfahaninejad, M. & Haeri-Ardakani, O. (2023). The Vanishing of Urmia Lake: A Geolimnological Perspective on the Hydrological Imbalance of the World’s Second Largest Hypersaline Lake. In P. Ghaffari & E. V. Yakushev (Eds.), Lake Urmia: A Hypersaline Waterbody in a Drying Climate (pp. 41-78). Springer International Publishing. http://doi.org/10.1007/698_2018_359

Shirmohammadi, B., Rostami, M., Varamesh, S., Jaafari, A. & Taie Semiromi, M. (2023). Future climate-driven drought events across Lake Urmia, Iran. Environmental Monitoring and Assessment, 196(1), 24. http://doi.org/10.1007/s10661-023-12181-x

Wurtsbaugh, W. A., Miller, C., Null, S. E., DeRose, R. J., Wilcock, P., Hahnenberger, M., Howe, F. & Moore, J. (2017). Decline of the world's saline lakes. Nature Geoscience, 10(11), 816-821. https://doi.org/10.1038/ngeo3052