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Abstract The wetland loss rate in Iran is faster than the global average. Comprehending the shrinkage rate in Iranian wetlands and identifying the underlying drivers of these changes is essential for safeguarding their ecosystems' health and services. This study proposes a novel gray‐box modeling framework to quantify the effects of climate change and anthropogenic activities on the wetlands, by combining process‐based and machine learning models. The developed model is utilized to project the Anzali coastal wetland shrinkage by simulating the complex interaction between the meteorological, hydrological, anthropogenic and sea water level characteristics, and the changes in wetland water surface area. Our framework aggregates Soil and Water Assessment Tool model, the 12 General Circulation Models of the Coupled Model Intercomparison Project Phase 6, Landsat imagery, and the Long Short‐Term Memory model to project the shrinkage of the wetland till 2100. A comprehensive range of climate and Land Use/Cover change scenarios are analyzed. The results show that wetland will seasonally desiccate in 2058, mainly due to increasing air temperature, reduction in precipitation and inflow, excessive sediment loading to the wetland, and decline in the Caspian Sea level. For optimistic scenarios, where no changes in the Caspian Sea level is considered, the wetland will gradually diminish and become a seasonal waterbody by 2100. The outcomes of this study highlight that the Anzali wetland desiccation has profound implications for the regional‐scale ecological balance, ecosystem health and function, public health, and local economy. Robust environmental interventions and sustainable development strategies are urgently needed to mitigate the detrimental impacts of climate and anthropogenic drivers on the wetland.
Mahdian et al. (Mon,) studied this question.