Simulating and Predicting Climate Change in Mangrove Forests of ‎ Hara Biosphere Reserve

Document Type : Original Article

Authors

1 Postdoctoral student, Department of Environmental Science, Natural Resources Faculty, University of Tehran, Karaj, Iran

2 Professor, Department of Environmental Science, Natural Resources Faculty, University of Tehran, Karaj, Iran.

‎10.22052/deej.2024.253945.1034

Abstract

Introduction: Global warming has disrupted the climate balance in recent decades, causing extensive changes in different regions worldwide. In other words, climate change has influenced all biological processes by changing the temperature, precipitation, and other climatic variables’ patterns, leading to alterations in the ecosystem’s function and biodiversity loss, especially in dry and semi-arid regions. On the other hand, climate change is regarded as a main threat to mangrove forests, bringing about consequences such as increased sea levels, the occurrence of sea storms, alterations in precipitation patterns, increased temperature, and decreased coverage area of mangrove habitats. Therefore, simulating and predicting the prospective changes in the climate of mangrove forests can offer valuable suggestions for controlling the adverse effects of climate change and reducing the vulnerability of such natural ecosystems.
 
Materials and methods: This study sought to simulate precipitation, and minimum, maximum, and average temperature rates in the mangrove forests’ biosphere reserve throughout the observation period (1996-2022) and the future period (2022-2050) using the data collected from the HadCM3 General Circulation Model (GCM) and the SDSM under RCP2.6 and RCP8.5 scenarios. In addition, the De Martonne method was used to identify the climate of the study area during the observation and future periods.
 
Results: A comparison of the results obtained for the observation and the baseline periods (1996-2022) indicated that the obtained values of the investigated climatic parameters enjoyed great accuracy, with the highest and lowest levels of model validation belonging to the average and maximum temperature rates, respectively. Furthermore, the predictions made for temperature changes for the 2022-2050 period, as compared to the 1996-2022 period, suggested that the average temperature would rise under both the RCP2.6 and RCP8.5 scenarios. Additionally, the results indicated that the area’s maximum and minimum temperature rates would experience the highest increase, particularly in July. On the other hand, the results showed that the average precipitation would decrease throughout the 2022-2050 period in the Hara biosphere reserve under RCP2.6 and RCP8.5 scenarios, with the highest and lowest decreasing trends of precipitation belonging to January and July under the RCP2.6 scenario and the RCP8.5 scenarios, respectively. Furthermore, according to the results obtained from the De Martonne aridity index, the climate type of the region for observation and future periods (under the investigated scenarios studied) was classified under the dry category, with no changes being observed for the future period.
 
Discussion and conclusion: Considering the growth conditions dominating the Iranian mangroves’ habitats in hot and dry regions, the results of the study indicated that compared to other human-induced phenomena, climate change had turned into a serious degradation threat to such habitats. In this regard, the study found that the average temperature would increase in the future, especially during the warm seasons of the year, which in turn may affect the biological and ecological conditions of the mangrove forests of the region. Therefore, a high correlation was found between the changes in mangroves and plant communities of the Khamir and Qeshm region and the changes in drought intensity and temperature increase, indicating that the area of the region’s mangrove plant communities has decreased in recent decades due to continued drought. Accordingly, it could be argued that the prospective drought periods would exert considerable influence on the structure of the habitats and the spatial distribution of the mangrove forests. The results of the study also suggested that climate change was considered one of the major known threats to the mangrove forests’ Hara biosphere reserve. Therefore, simulating and predicting such changes can provide useful knowledge concerning the trend of temperature and precipitation changes (as the main climatic parameters) in these natural stands, helping to set appropriate measures for preserving and restoring such invaluable biological reserves. Furthermore, in the management plans of this region, climate change can be considered in the region’s management plans to reduce the destructive effects of those changes.

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  1. Aghashahi, M., Ardestani, M., Nik Sokhan, M. H., & Tahmasebi, B., 2012. Introduction and comparison of LARS-WG and SDSM models for microscaling of environmental parameters in climate change studies, 6th National Conference and Specialized Exhibition of Environmental Engineering, Tehran.
  2. Arab Solghar, A. A., Porhemmat, J., & Goodarzi, M., 2022. Prediction of Climate Change using General Circulation Models and SDSM and LARS-WG Downscaling Models under RCP Scenarios in Dez Watershed. Physical Geography Quarterly, 15(55), 129-149.
  3. Dastranj, A., & Rostami, M., 2020. Assessment and prediction of climate change in the next decade, by downscaling General Circulation Models (GCMs). Geography and Human Relationships, 3(1), 252-268.
  4. Davar, L., Griggs, G., Danehkar, A., Salmanmahiny, A., Azarnivand, H., & Naimi, B. 2021. A spatial integrated SLR adaptive management plan framework (SISAMP) toward sustainable coasts. Water, 13(16), 2263.
  5. Delfan, N., & Ghodrati Shojaei, M., 2021. A Review of the Impacts of Climate Change on Mangrove Ecosystems. Iranian Journal of Biology, 5(10), 111-116.
  6. Emadodin, I., Reinsch, T., & Taube, F., 2019. Drought and desertification in Iran. Hydrology, 6(3), 66.
  7. Etemadi, H., & Delshab, H., 2020. Potential expected climate change impact on Persian Gulf Coastal Mangrove Ecosystems based on temperature and precipitation variables. Journal of Environmental Science and Technology, 22(2), 1-13.
  8. Etemadi, H., 2021. Mangroves Responses to Projected Sea-Level Rise Due to Impact of Climate Change. Human & Environment, 19(4), 173-187.
  9. Etemadi, H., Sharifikia, M., Samadi, S. Z., Sari, E., & Danekar, A., 2015. Simulation of the Future Climatic Changes in Jask Area and Its Impact on Hara Forests. Geography and Development, 13(41), 87-104.
  10. Etemadi, H., Smoak, J. M., & Abbasi, E., 2021. Spatiotemporal pattern of degradation in arid mangrove forests of the Northern Persian Gulf. Oceanologia, 63(1), 99-114.
  11. Friess, D. A., Adame, M. F., Adams, J. B., & Lovelock, C. E., 2022. Mangrove forests under climate change in a 2 C world. Wiley Interdisciplinary Reviews: Climate Change, 13(4), e792.
  12. Gillis, L. G., Hortua, D. A., Zimmer, M., Jennerjahn, T. C., & Herbeck, L. S., 2019. Interactive effects of temperature and nutrients on mangrove seedling growth and implications for establishment. Marine environmental research, 151, 104750.
  13. Goodarzi, M., Mahdian, M. H., & Qermezcheshmeh, B., 2021. Assessment of climate change using SDSM downscaling Model (A case study: West of Iran). Water Harvesting Research, 4(1), 29-39.
  14. Hana, Y., Yanga, J., Dasb, L. C. (2023). Evaluation of SDSM Models for Climate Predictions in Bangladesh. International Journal of Big Data Mining for Global Warming, 5(01), 2350003.
  15. Hernanz, A., Correa, C., García-Valero, J. A., Domínguez, M., Rodríguez-Guisado, E., & Rodríguez-Camino, E., 2023. pyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies. Climate Services, 32, 100408.
  16. Intergovernmental Panel on Climate Change (IPCC)., 2021. Climate Change 2021 the Physical Science Basis Summary for Policymakers Technical Summary Frequently Asked Questions Glossary. Part of the Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
  17. Jennerjahn, T. C., Gilman, E., Krauss, K. W., Lacerda, L. D., Nordhaus, I., & Wolanski, E., 2017. Mangrove ecosystems under climate change. Mangrove Ecosystems: A Global Biogeographic Perspective: Structure, Function, and Services, 211-244.
  18. Jetz, W., Tuanmu, M. N., Melton, F. S., Parmentier, B., Amatulli, G., & Guzman, A. (2016). Remote-sensing supported monitoring of global biodiversity change. In AGU Fall Meeting Abstracts. pp. GC13I-02.
  19. Kabir, M., Habiba, U. E., Khan, W., Shah, A., Rahim, S., Farooqi, Z. U. R., & Shafiq, M., 2023. Climate change due to increasing concentration of carbon dioxide and its impacts on environment in 21st century; A mini review. Journal of King Saud University-Science, 35 (5), 102693.
  20. Mafi Gholami, D., Baharlouii, M., & Mahmoudi, B., 2018. Investigation of climate change consequences on mangroves and saltmarshes of Hara (Avicennia Marina) biosphere reserve of Gheshm Island. Environmental Researches, 9(17), 207-220.
  21. Mansfield, L. A., Nowack, P. J., Kasoar, M., Everitt, R. G., Collins, W. J., & Voulgarakis, A., 2020. Predicting global patterns of long-term climate change from short-term simulations using machine learning. npj Climate and Atmospheric Science, 3 (1), 44.
  22. Maryanji, Z., Sotoudeh, F., & Toulabi nejad, M., 2022. Modeling and predicting the trend of temperature changes in Hamadan county. Journal of Applied researches in Geographical Sciences, Online.
  23. Mirshekaran, Y., Kakapour, V., & Zarey, A., 2021. Assess the effect of climate change on precipitation and temperature using AR4 models (Case Study: Gharasoo Basin of Kermanshah province). Climate Change Research, 2(8), 23-34.
  24. National Meteorological Organization., 2022. Annual meteorological reports.
  25. Pouralkhas Nokandeie, M., Esmali-Ouri, A., Mostafazadeh, R., Hazbavi, Z., & Sharari, M., 2022. Indicators and components of assessing variations and changes in climate change. Disaster Prevention and Management Knowledge (quarterly), 12(1), 85-98.
  26. Rostami, F., Attarod, P., Keshtkar, H., & Nazeri Tahroudi, M., 2022. Impact of climatic parameters on the extent of mangrove forests of southern Iran. Caspian Journal of Environmental Sciences, 20(4), 671-682.
  27. Segaran, T. C., Azra, M. N., Lananan, F., Burlakovs, J., Vincevica-Gaile, Z., Rudovica, V., & Satyanarayana, B., 2023. Mapping the Link between Climate Change and Mangrove Forest: A Global Overview of the Literature. Forests, 14 (2), 421.
  28. Sillmann, J., Thorarinsdottir, T., Keenlyside, N., Schaller, N., Alexander, L. V., Hegerl, G., & Zwiers, F. W. 2017. Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities. Weather and climate extremes, 18, 65-74.
  • Emadodin, I., Reinsch, T., & Taube, F., 2019. Drought and desertification in Iran. Hydrology, 6 (3), 66.
  1. Singh, M., Schwendenmann, L., Wang, G., Adame, M. F., & Mandlate, L. J. C., 2022. Changes in mangrove carbon stocks and exposure to sea level rise (SLR) under future climate scenarios. Sustainability, 14(7), 3873.
  2. Sobhani, P., & Esmaeilzadeh, H., 2020. The Impact of Climate Changes on Tourism in Protected Areas (Case Study: Alvand No-Hunting Area). Geography and Territorial Spatial Arrangement, 10(37), 65-90.
  3. Sobhani, P., & Danehkar, A., 2023c. Investigating tourism climate conditions in Iran's mangrove forests using Tourism Comfort Climate Index (TCI) and Holiday Climate Index (HCI). Journal of Natural Environment, 75(Special Issue Coastal and Marine Environment), 29-45.
  4. Sobhani, P., & Danehkar, A., 2023a. Natural features and management areas of‎ Khamir and Qeshm mangrove forests. Iran Nature, 8(4), 97-112.
  5. Sobhani, P., & Danehkar, A., 2023b. Spatial-temporal changes in mangrove Forests for Analyzing habitat Integrity: A case of hara biosphere Reserve, Iran. Environmental and Sustainability Indicators, 100293.
  6. Steward, P. R., Dougill, A. J., Thierfelder, C., Pittelkow, C. M., Stringer, L. C., Kudzala, M., & Shackelford, G. E., 2018. The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields. Agriculture, Ecosystems & Environment, 251, 194-202.
  7. Timbal, B., Fernandez, E., & Li, Z., 2009. Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environmental Modelling & Software, 24(3), 341-358.
  8. Wang, X., Fan, Y., Zhao, S., Xie, Y., & Von Storch, H., 2022. Future climate scenarios: Regional climate modelling and data analysis. Frontiers in Environmental Science, 10, 858153, 1-4.
  9. Ward, R. D., Friess, D. A., Day, R. H., & Mackenzie, R. A., 2016. Impacts of climate change on mangrove ecosystems: a region by region overview. Ecosystem Health and sustainability, 2(4), e01211.
  10. Wilby, R. L., Tomlinson, O, J., & Dawson, C. W., 2007. Multi-site simulation of precipitation by condition resampling. Journal of Climate Research, 23, 183-194.
  11. Xiukang, W., Zhanbin, L., & Yingying, X., 2015. Effects of mulching and nitrogen on soil temperature, water content, nitrate-N content and maize yield in the Loess Plateau of China. Agricultural Water Management, 161, 53-64.
  12. Yaghoubzadeh, M., Salmanmahiny, A., Mikaeili Tabrizi, A. R., & Danehkar, A., 2023. Forecasting inundation zone caused by climate change in mangrove forests. Journal of Marine Science and Technology, 22(2), 68-72.
  13. Yaqubzadeh, M., 2022. Management-based prediction of the effects of land use change and climate change on mangrove forests in Hormozgan province. PhD dissertation in the field of environment and land management, Faculty of Fisheries and Environment, Gorgan University of Agricultural Sciences and Natural Resources.
  14. Vicente-Serrano, S. M., Quiring, S. M., Peña-Gallardo, M., Yuan, S., & Domínguez-Castro, F., 2020. A review of environmental droughts: Increased risk under global warming?. Earth-Science Reviews, 201, 102953.