Assessing the Risks of Climate Change on Wetland Ecosystems Using the GCM Model (Case Study: Alagol, Ajigol and Almagol Wetlands, Golestan Province)

جغرافیا و مخاطرات محیطی

Volume 14, Issue 2 - Serial Number 54, June 2025, Pages 88-121   

Document Type: Research Article

DOI: 10.22067/geoeh.2025.90342.1526

Authors

Saleh Arekhi^{* 1}; Abolghasem Mamshli²; Abdulhafiz Panahi³

¹Golestan University, Gorgan , Iran

²Department of Civil Engineering, Non-Profit-Non-Governmental Institute, Lampi Gorgani Institute, Gorgan, Iran

³General Department of Meteorology of Golestan Province, Gorgan, Iran

Abstract

The present research based on the nature of the problem and subject under investigation, adopts a descriptive-analytical approach with the aim of identifying ecological stressors caused by climate change in wetlands. In the climate change section, simulation data of average rainfall from the HadCM3 model using LARS-WG under the SRES A1B scenario for the period 2021–2050 were utilized. The AHP method and Expert Choice software were employed to weight risk factors and assess the indicators influencing risk levels. According to the modeling results, the rainfall series at the Inche-Brun station shows a decreasing trend. Additionally, both maximum and minimum temperatures exhibit an increasing trend. The primary risks threatening the wetland include drought, water shortage, increased evaporation and transpiration, sedimentation and infilling, and habitat loss. Since the most significant risks, as identified based on the ALARP principle, are classified as high or medium, it is essential to control, eliminate, or reduce these risks. Given the ongoing degradation of wetlands, it is crucial to incorporate these processes and their consequences into future wetland management plans. As drought has been identified as the most critical risk, establishing a drought monitoring network and calculating and allocating environmental water rights for wetlands are the most important management strategies to prevent a drought crisis.
Extended Abstract
Introduction
Wetlands should be considered as biological reservoirs, and international programs for the reasonable and rational protection and exploitation must be prepared and implemented in order to ensure the survival of wetlands and their biodiversity. Environmental risk assessment is a step beyond human risk assessment, and in it, in addition to examining and analyzing various aspects of risk, a full understanding of the environment of the affected area, its sensitivity, and its special ecological values are also analyzed and evaluated.
Climate change refers to changes in the climatic behavior of a region compared to historical or expected patterns over a long period of time. This phenomenon is one of the most important challenges in sustainable development, with significant negative effects on land and sea ecosystems. Until recently, climate change was considered to be caused by natural factors only, but in recent years, human activity has also contributed to the climate change process. Climate change and loss of biodiversity are among the global challenges that may lead to an increased risk of losing ecosystem services.
Various studies have been conducted on the evaluation of the vulnerability of wetlands worldwide, focusing on the impacts of climate change. Using climate scenarios, models, and change analysis, it was determined that about 10% of wetland functions will be affected in the future due to climate change.
Jafari-Azer et al. (2017), in their article, evaluated and analyzed the environmental risks of Khorkhoran International Wetland using multi-criteria decision-making methods. They identified wetland risk indicators using the Delphi method and ranked and prioritized threats using AHP and TOPSIS methods. The results indicate that, according to the degree of closeness (CL+), among the environmental criteria, oil pollution (0.88), illegal fishing (0.87), and fuel smuggling (0.71), and among the natural criteria, drought and climate change (0.72), are the top risks.
Panahi et al. (2023), in an article titled "Investigation of climate change and transformation of natural components with emphasis on floods" (case study: Gorganrood watershed), used LARS-WG and HadCM3 model under the SRA1B scenario between 2011 and 2045. The results showed that human intervention in main streams, urban and rural expansion, dam construction, pasture degradation, and forest destruction are among the primary causes of sudden flooding in the region. This integrated approach highlighted the necessity of continuous evaluation and validation of hydrological and hydraulic models in the Gorganrood basin and its ephemeral streams.
Karami et al. (2023) assessed the vulnerability of ecosystem services of the Hamoon International Wetland to climate change. The results showed that climate change, drought, water diversion in the upstream basin, and livestock and vehicle traffic are the main threats. Diplomatic efforts to secure water supplies are the most important management strategy to reduce vulnerability.
This research provides a framework for managing climate change-related risks in wetlands. Its main purpose is to investigate temperature, precipitation, and climate indicators in the Alagol, Ajigol, and Almagol wetlands. Using statistical data and satellite images, climate changes over different time periods are modeled. Risks are then calculated based on severity and likelihood. A multi-criteria decision-making process is used to prioritize these risks. Finally, suitable management solutions are presented to reduce unacceptable risks.
Material and Methods
Alagol, Ajigol, and Almagol international wetlands are located in Golestan Province, near the Iran–Turkmenistan border, at relatively close distances from one another in the Dashli-Borun region. In this research, the assessment of climate change risks on wetland ecosystems was conducted using the GCM model.
To implement the research objectives—including modeling climate change, forecasting ecological conditions, and identifying and prioritizing ecological risks—20 years of data (2001–2020) on monthly and annual average precipitation and temperature from local meteorological stations were used.
Precipitation and temperature forecasts were generated using outputs from the HadCM3 model, a coupled atmosphere–ocean general circulation model (AOGCM). ArcGIS 10.8 was used for initial data preparation, ENVI 5.3 for radiometric and atmospheric corrections, and IDRISI TerrSet for image classification, pre-processing, change analysis, and model validation. Simulated rainfall data from HadCM3 in LARS-WG under the SRA1B scenario (2021–2050) were analyzed. Statistical indicators such as ME, RMSE, and MSE were used to select the best interpolation method.
Results and Discussion
The region's temperature between 2021 and 2050 is expected to increase by 0.5 to 1.5°C compared to the base period. The results indicate temperature increases across all months and climate scenarios. Scenario A2 shows the highest increase, B1 the lowest (optimistic), and A1B a moderate rise.
Rainfall percentage changes under the three scenarios indicate a decline in most months. Annual rainfall will decrease by 6–8% compared to the base period. Scenario A2 shows the greatest reduction, particularly in March (up to 75%), while B1 shows the smallest, particularly in February (up to 55%). In a few months, slight increases in rainfall are observed, though these are not significant in dry months.
To compare the ecological risks of natural origin with those caused by human activity, the AHP method and Expert Choice software were used. The analysis shows high importance for natural-origin risks. Ecological and human-origin risks each make up nearly half of total risks.
Drought has the highest risk weight at 0.19. Natural-origin risks make up 51%, and human-origin risks 49%. Ten key risks were identified based on ecological conditions, field visits, expert interviews, and previous studies. All identified risks are present and will intensify under future climate conditions.
The most significant climate-related threats to the wetlands include: drought and water shortage, increased evaporation and transpiration, sedimentation and infilling, and habitat loss. According to the ALARP principle, high and medium-level risks must be controlled, reduced, or eliminated. Risks with higher occurrence probability, severity, and extent require prioritized control measures. However, this does not imply that other risks are unimportant, as the final scores were derived by integrating all three risk indicators.
Conclusion
The prioritization results show that the key threats to Alagol, Ajigol, and Almagol wetlands are drought/dehydration, increased evaporation and transpiration, sedimentation and filling, and habitat loss.
By 2035, the area of Alagol, Almagol, and Ajigol wetlands is expected to reach 1747.50 ha, 102.30 ha, and 33.90 ha, respectively. This reflects reductions in Almagol and Ajigol, although Alagol shows a slight increase—still indicating an overall decline in extent over the study period.
Predicted land use maps for 2035 show saline land occupying 13,346.55 ha (63%), vegetation 5,601.78 ha (26.4%), and water surface 2,265.75 ha (10.6%). This reveals decreased water levels and increased salt marshes, signifying drought and reduced rainfall.
The effectiveness of the modeling methods and climate scenarios (HadCM3, A2, A1B1, B2, and LARS-WG) is consistent with the findings of Arkhi et al. (2024), Panahi et al. (2023), and Karami et al. (2022). Additionally, the environmental risk evaluation aligns with the multi-criteria methods used by Jafar-Azer et al. (2019) and Jafari-Azer et al. (2017).
 

Keywords

Climate Change; Ecological Risk Assessment; Climate Change Modeling; Wetland Vulnerability; Alagol– Ajigol –Almagol Wetlands

References

Aghashahi, M., Ardestani, M., & Fahmi, H. (2013). Climate Change in Lake Urmia. Master's thesis, University of Tehran, Faculty of Environment. [In Persian]

Alizadeh, A. (2010). Principles of Applied Hydrology (7th ed.). Mashhad: Astan Quds Razavi Publications. [In Persian]

Arekhi, S., Gerkaz, S., & Emadadin, S. (2024). Zoning of flood risk under the influence of climate change using the SWAT hydrological model in a GIS environment (Case study: Gharesu watershed, Golestan province). Climate Change Research, 4(14), 1-26. [In Persian] https://doi.org/10.30488/ccr.2023.394308.1127

Brooks, R. T. (2009). Potential impacts of global climate change on the hydrology and ecology of ephemeral freshwater systems of the forests of the northeastern United States. Climate Change, 95(3), 469–483. http://dx.doi.org/10.1007/s10584-008-9531-9

Bussi, G., Whitehead, P., Thomas, A., Masante, D., Jones, L., Cosby, J., … & Prosser, H. (2017). Climate and land-use change impact on faecal indicator bacteria in a temperate maritime catchment (the River Conwy, Wales). Journal of Hydrology, 553, 248-261. https://doi.org/10.1016/j.jhydrol.2017.08.011  

Chen, H., Xu, C. Y., & Guo, S. (2012). Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, 434, 36-45. https://doi.org/10.1016/j.jhydrol.2012.02.040

Golestan Province Meteorological Organization. (2019). Statistics and data from meteorological stations. [In Persian]

Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., ... & Wood, R. A. (2000). The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics, 16, 147-168. https://doi.org/10.1007/s003820050010

Griggs, D. J., & Noguer, M. (2002). Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Weather, 57(8), 267-269. https://doi.org/10.1256/004316502320517344

Hajivandpaydari, S., Yazdanpanah, H., & Andarzian, S. B. (2022). Investigation of regional effects of climate change phenomenon in the north of Khuzestan province using HadCM3 model under LARS-WG exponential comparison in the statistical period of 2030-2010 and 2050-2030. Geography and Human Relationships, 5(1), 299-314. [In Persian] https://doi.org/10.22034/gahr.2022.330821.1669

Heller, N. E., & Zavaleta, E. S. (2009). Biodiversity Management in the Face of Climate Change: A Review of 22 Years of Recommendations. Biological Conservation, 142, 14-32.
https://doi.org/10.1016/j.biocon.2008.10.006

Jafariazar, S., Sabzghabaei, G. R., Tavakoly, M., & Dashti, S. (2019). Assessment and Management of environmental risk of Salty, Sweet and Minab Rivers International Wetlands on the basis of multi-criteria decision-making methods. Journal of Spatial Analysis Environmental Hazards, 5(4), 65-88. http://dx.doi.org/10.29252/jsaeh.5.4.65

 

Jafariazar, S., Sabzghabaei, G. R., Tavakoly, M., & Dashti, S. S. (2017). Assessment and Analysis of Khur-e-khuran International Wetland Environmental Risks Using Multi-Criteria Decision-Making Methods. Irrigation Sciences and Engineering, 40(3), 63-75. [In Persian] https://doi.org/10.22055/jise.2017.13262

Karami, R., Rezaei, H., Salman Mahini, A., & Ghorbani, K. (2022). Climate Change Assessment in the basin of Hamoon International Wetlands Using LARS-WG6 Model. Journal of Natural Environmental Hazards, 11(31), 107-122. [In Persian] https://doi.org/10.22111/jneh.2021.36069.1710

Khalyani, A. H., Gould, W. A., Harmsen, E., Terando, A., Quinones, M., & Collazo, J. A. (2016). Climate change implications for tropical islands: Interpolating and interpreting statistically downscaled GCM projections for management and planning. Journal of Applied Meteorology and Climatology, 55(2), 265-282. https://doi.org/10.1175/JAMC-D-15-0182.1

Kim, J., Choi, J., Choi, C., & Park, S. (2013). Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Science of the Total Environment, 452, 181-195. https://doi.org/10.1016/j.scitotenv.2013.02.005

Kim, K. G., Lee, H., & Lee, D. H. (2011). Wetland restoration to enhance biodiversity in urban areas: a comparative analysis. Landscape and Ecological Engineering, 7, 27-32. https://doi.org/10.1007/s11355-010-0144-x

Lemly, A. D. (1997). Risk assessment as an environmental management tool: considerations for freshwater wetlands. Environmental Management, 21(3), 343-358. https://doi.org/10.1007/s002679900034

Malekmohammadi, B., & Blouchi, L. R. (2014). Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. Ecological Indicators, 41, 133-144. https://doi.org/10.1016/j.ecolind.2014.01.038

Malekmohammadi, B., Blouchi, L. R., Ghehi, N. K., & Shakib, F. J. (2015). Investigating the Effects of Climate Change on Wetlands Using Risk Assessment and Remote Sensing (Case Study: Choghakhor Wetland, Iran). Paper presented at the US-Iran Symposium on Climate Change: Impacts and Mitigations, California.

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Paper presented at the Proceedings of the 8th Conference on Applied Climatology (pp. 179-183).

Mehdinasab, M., & Bagherzadeh Karimi, M. (2020). Environmental Risk Assessment of Poldokhtar Wetlands Based on Model EFMEA. Environment and Interdisciplinary Development, 5(69), 27-36. [In Persian] https://doi.org/10.22034/envj.2020.181306

Panahi, A. P., Janbaz Ghobadi, G., Motavalli, S., & Khaledi, S. (2024). Investigating climate change and transformation of natural components with emphasis on floods (Case study: Gorganrood watershed). Journal of Geography and Environmental Hazards, 13(3), 184-213. [In Persian] https://doi.org/10.22067/geoeh.2023.84657.1419

Panahi, A., Janbaz Ghobadi, G., Motavalli, S., & Khaledi, S. (2023). Measuring and predicting flood potential under climate change conditions (case study: Gorganrood watershed). Geographical Studies of Coastal Areas Journal, 4(2), 45-61. [In Persian] https://doi.org/10.22124/gscaj.2023.22411.1172

Rahimi Blouchi, L., & Malekmohammadi, B. (2013). Environmental Risk Assessment of Shadegan International Wetland Based on Ecological Indicators. Journal of Environmental Studies, 39(1), 101-112. [In Persian] https://doi.org/10.22059/jes.2013.30394

Ramsar Convention Secretariat. (2007). Ramsar Handbooks for the Wise Use of Wetlands(3rd ed.). Ramsar Convention Secretariat: Gland, Switzerland. https://www.gwp.org/globalassets/global/toolbox/references/wise-use-of-wetlands-ramsar-2007.pdf

Semenov, M. A., Brooks, R. J., Barrow, E. M., & Richardson, C. W. (1998). Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research, 10(2), 95-107. http://dx.doi.org/10.3354/cr010095

UNDP. (2013). National Environment Council for Sustainable Development. Sustainable Wetland Management in the Face of Climate Risks in Niger: The Case of La Mare de Tabalak, International Institute for Sustainable Development (IISD).

Ziaee, N., Ownegh, M., Asgari, H. R., Massah Bavani, A. R., Salman Mahini, A., & Hoseinalizadeh, M. (2019). Forecasting future climate variables using HadCM3 model and climate scenarios, case study: selected synoptic stations of Hableh Roud Basin. Watershed Engineering and Management, 11(3), 771-783. [In Persian] https://doi.org/10.22092/ijwmse.2017.110512.1298