Evaluation of climate changes in wetland ecosystems using GCM model, Case Study: Alagol, Ajigol, and Almagol wetlands, Golestan province
Subject Areas : natural geography
saleh arekhi
1
,
Abolghasem Mamashli
2
,
Adolhafez Panahi
3
,
Somaye Emadoddin
4
1 - Associated Professor, Department of Geography, Faculty of Human Sciences, Golestan University, Gorgan, Iran
2 - Master's degree in Geographical Information System, Department of Civil Engineering, Non-Profit-Non-Governmental Institute, Gorgani Lamai, Gorgan, Iran
3 - PhD in Meteorology - General Department of Meteorology of Golestan Province, Golestan, Iran
4 - Assistant Professor, Department of Geography, Faculty of Human Sciences, Golestan University, Gorgan, Iran
Keywords: Climate Change, Modeling, LARS-WG Model, Alagol Wetland, Almagol Wetland, Ajigol Wetland,
Abstract :
Research over recent years has shown that climate change exerts varying effects on climatic and hydrological components. Scientific findings indicate that the impacts of climate change will become more pronounced in the future; therefore, analyzing and predicting these changes is of significant importance. This study, given its subject and nature, is a descriptive-analytical and applied research with an emphasis on quantitative methods, conducted using the General Circulation Model (GCM). To achieve the study objectives—including modeling climate change in the Alagol, Ajigol, and Almagol wetlands—data from the Incheh-Borun meteorological station were used. This station provides 20 years of monthly and annual average precipitation and temperature data (2001–2020). Forecasting of precipitation and temperature was carried out using outputs from the HadCM3 model in this study. The simulated average precipitation data, obtained from HadCM3 and downscaled in LARS-WG under the SRA1B scenario, were used to generate synthetic data for the future period (2021–2050) based on three IPCC-approved scenarios: B1 (optimistic), A2 (pessimistic), and A1B (moderate). To determine the best interpolation method, statistical indicators such as RMSE, ME, and MSE were applied. One of the most effective strategies in data weighting is the application of quantitative and mathematical techniques. According to the study results, the homogeneity tests demonstrated a high level of consistency. When trend analysis indicated a statistically significant trend in the time series, the homogeneity tests confirmed the non-homogeneity of those series. The precipitation series at Incheh-Borun station showed a generally decreasing trend, while both maximum and minimum temperatures exhibited increasing trends.