Assessment of the Water Resources Carrying Capacity of Anzali Wetland Using a Combined AHP-Entropy Weighting Method and Forward cloud model

Document Type : Research/Original/Regular Article

Authors

1 Former M.Sc. Student, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

2 Assistant Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

3 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

Introduction

For the sustainable development of the socio-economic economy, water resources are not only an important limiting factor but also play an irreplaceable "carrier" role. "Carrying capacity" is a term derived from ecology that refers to the limitation of the maximum number of individuals in specific environmental conditions. Water Resources Carrying Capacity (WRCC) is the support capacity of water resources for the livelihood of the region's population and socio-economic development. Due to the differences in water resource systems, socio-economic conditions, and ecological environmental settings in various regions and natural conditions, the assessment of WRCC does not follow a consistent pattern across different geographical areas, making the evaluation of WRCC a complex issue. The aim of assessing and analyzing WRCC is to determine the relationship between limited water resources and population, the environment, and economic development.

Wetlands play a pivotal role in sustainable development as a vital resource. With increasing population and economic growth, the pressure on water resources is rising. The Water Resource Carrying Capacity is defined as the ability of a water system to support human activities. In this study, considering the significance of Anzali wetland, which is being destroyed alongside the advancement of urbanization processes as one of the natural ecosystems, the water resources carrying capacity of Anzali wetland is being evaluated.

Materials and Methods

To evaluate the water resources carrying capacity, initially, the water resource system of the region was first modeled by using the system dynamics method and VENSIM software. Based on the modeling results, eight evaluation indicators were defined to assess the WRCC of Anzali Wetland, considering three subsystems: population, water resources, economy, and environment. Subsequently, based on the modeling results, indices were defined for each subsystem with the aim of assessing the water resources carrying capacity of Anzali wetland, with a total of 8 indices considered for evaluation. The weighting of the indices was done using the combined AHP-Entropy method .In order to accurately assess the carrying capacity of water resources, aligning the results with the actual situation of the region, and considering the water resources of Anzali Wetland, the current water resources and consumption in the Fumanat study area (located upstream of Anzali Wetland), the economic and environmental conditions of the region, and the use of water resources in the area, the evaluation indices were divided into four levels based on the standards for grading water resource carrying capacity indices in previous research. These levels are: I (loadable), II (weak), III (critical), and IV (extremely critical). Finally, using a fuzzy model and determining the membership degrees of the indices at each evaluation level and for each year, the water resources carrying capacity index of Anzali wetland was calculated for different evaluation levels over a 10-year period.

Results and Discussion

To determine the initial weights of the evaluation indices, expert opinions were utilized, and acceptable results were obtained using the Analytic Hierarchy Process (AHP) method and Consistency Ratio (CR). Subsequently, composite weights were calculated by combining the AHP weights with entropy, with the highest weight attributed to the indicator of water supply and demand ratio of the wetland (C7), followed by indicators such as the quality of input water resources and the impact of agricultural gross production. The results of this section demonstrate that the final composite weights are more realistic as the entropy method enhances the shortcomings of the AHP method, thereby improving the accuracy of the evaluation.

The results of the Water Resources Carrying Capacity (WRCC) assessment indicate a decreasing trend in the water resources carrying capacity of Anzali wetland between the years 1391 to 1400, shifting its capacity from level II (Weak) to level IV (Super Critical). The highest capacity is associated with the year 1391, while the lowest capacities are allocated to the years 1393, 1394, and 1400. Examination of the obstacle factors revealed that surface and groundwater resources significantly impact the water resources carrying capacity of the wetland, with several indicators from the water resources subsystem identified as the main obstacle factors in this regard. These constraints pose challenges to improving the wetland's capacity, and the increase or decrease of these resources, especially under environmental and economic conditions, significantly influences the wetland's status

Conclusion

The results showed that the WRCC of the wetland had a decreasing trend during the study period and had reached a supercritical state. The most important factors affecting the reduction in carrying capacity were identified as the shortage of surface and groundwater resources and the poor quality of water entering Anzali Wetland.The overall findings of the investigations indicate that Anzali wetland has been in critical and super-critical conditions in recent years, highlighting the necessity for planning to improve its situation. Considering the obstacle factors identified in this study, efforts can be directed towards planning and managing the wetland to move towards a more desirable state.

Keywords

Main Subjects

References

منابع
اداره کل هواشناسی گیلان، (1396)،http://www.gilmet.ir
استانداری گیلان، (1396)، http://www.gilan.ir
اسلامی، زینب، جنت­رستمی، سمیه، اشرف­زاده، افشین. (1398). کاربرد مدلسازی در مدیریت رابطه پیوندی آب، غذا و انرژی. آب و توسعه پایدار، 6 (2)، 8-1. doi:10.22067/jwsd. v6i2.74126.
ایوبی­کیا، رضا، جنت­رستمی، سمیه، اشرف­زاده، افشین، شفیعی ثابت، بهنام (1397). بهینه سازی تخصیص منطقه ای منابع آب در حوضه آبریز سفیدرود با رویکرد عدالت اجتماعی. تحقیقات منابع آب ایران، 14(5)، 252-236.
زبردست، لعبت، و جعفری، حمیدرضا (1390). ارزیابی روند تغییرات تالاب انزلی با استفاده از سـنجش از دور و ارائة راه‌حل مدیریتی، محیط شناسی، 37(57)، 57-64.
 dor: 20.1001.1.10258620.1390.37.57.7.5
آوریده، حمیدرضا، صفری، عبدالرضا، همایونی، سعید، و خزایی، صفا (1393). برآورد عمق آب‌های ساحلی به کمک تصاویر سنجش از دور فراطیفی، مهندسی نقشه‌برداری و اطلاعات مکانی، 6(1)، 11-1.10.magiran.com/p1364335.
کیوانفر، مائده، جنت­رستمی، سمیه، و اشرف­زاده، افشین (1404). مدل‌سازی جامع ظرفیت برد منابع آب تالاب انزلی با استفاده از روش وزندهی ترکیبی AHP-Entropy- CRITIC و مدل TOPSIS-GRA. تحقیقات آب و خاک ایران. 56(1)، 105-126. doi:10.22059/ijswr.2024.384228.669817.
مدبری، هادی، و شکوهی لنگرودی، علیرضا (1399). تعیین نیاز آبی تالاب انزلی بر اساس شاخص‌های اکولوژیکی-گردشگری در چارچوب IWRM، تحقیقات آب و خاک ایران. 51(10)، 2517-2501.doi:10.22059/ijswr.2020.303554.668633
 
References
Ayoubikia, R., Janatrostami, S., Ashrafzadeh, A., & Shafiei-Sabet, B. (2018). Optimization of regional water resources allocation in sefidroud river basin by social equity approach. Iranian Water Resources Research. 14(5), 236-252. [In Persian].
Avarideh, H. R., Safari, A. R., Homayouni, S., & Khazaei, S. (2015). Nearshore bathymetry using hyperspectral remotesensing. Geospatial Engineering, 6(1), 1-10. [In Persian]. doi: 10.magiran.com/p1364335.
Cui, Y., Feng, P., Jin, J., & Liu, L. (2018). Water resources carrying capacity evaluation and diagnosis based on set pair analysis and improved the entropy weight method. Entropy. 20 (5), 359. doi:10.3390/e20050359.
Deng, L., Yin, J., Tian, J., Li, Q., & Gua, S. (2021). Comprehensive evaluation of water resources carrying capacity in the Han River Basin. Water 13 (3), 249. doi:10.3390/w13030249
Duan, Q.C., Liu, C.M., Chen, X.N., Liu, W.H., & Zheng, H.X. )2010(. Preliminary research on regional water resources carrying capacity conception and method. Acta Geographica Sinica. 65, 82–90. doi:10.11821/xb201001009.
Fu, J., Zang, C., & Zhang, J. )2020(. Economic and resource and environmental carrying capacity trade-off analysis in the Haihe River basin in China. Journal of Cleaner Production. 270, 122271. doi:10.1016/j. jclepro.2020.122271.
Fu, Q., Meng, F.X., Li, T.X., Liu, D., Gong, F.L., Osman, A., & Li, Y.T. )2016(. Cloud modelbased analysis of regional sustainable water resource utilization schemes. International Journal of Agricaltural and Biological Engineering. 9, 67–75. doi:10.3965/j.ijabe.20160905.2529.
Eslami, Z., Janatrostami, S., Ashrafzadeh, A. (2019). Application of Modeling in Management of Water, Food and Energy Nexus. Journal of Water and Sustainable Development. 6(2), 1-8. [In Persian]. doi:10.22067/jwsd. v6i2.74126.
Gerlak, A.K., House-Peters, L., Varady, R.G., Albrecht, T., Zuniga-Teran, A., de Grenade, R.R., Cook, C., & Scott, C.A. (2018). Water security: A review of place-based research. Environmental Science & Policy 82, 79–89. doi: 10.1016/j.envsci. 2018.01.009.
He, L.i., Du, Y.u., Wu, S., & Zhang, Z. )2021(. Evaluation of the agricultural water resource carrying capacity and optimization of a planting-raising structure. Agricultural Water Management 243, 106456. doi: 10.1016/j.agwat.2020.106456.
Ji, J., Qu, X., Zhang, Q., & Tao, J. )2022(. Predictive analysis of water resource carrying capacity based on system dynamics and improved fuzzy comprehensive evaluation method in Henan Province. Environmental Monitoring and Assessment. 194 (7), 500. doi:10.1007/s10661-022-10131-7.
Keyvanfar, M., Janatrostami, S., & Ashrafzadeh, A. (2025). A Comprehensive Assessment of Water Resources Carrying Capacity in Anzali Wetland Using AHP-Entropy-CRITIC Combined Weighting Method and TOPSIS-GRA Model. Iranian Journal of Soil and Water Research. 56(1), 105-126. [In Persian]. doi:10.22059/ijswr.2024.384228.669817.
Kummu, M., Guillaume, J.H.A., de Moel, H., Eisner, S., Floerke, M., Porkka, M., Siebert, S., Veldkamp, T.I.E., & Ward, P.J. )2016(. The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Scientific Reports. UK 6, 38495. doi:10.1038/srep38495.Li, D., Liu, C., Du, Y., & Han, X. )2004(. Artificial intelligence with uncertainty. Journal of Software. (11), 1583-1594. doi:10.1201/9781315366951.
Li, M., Zheng, T.Y., Zhang, J., Fang, Y.H., Liu, J., Zheng, X.L., & Peng, H. )2019(. A new risk assessment system based on set pair analysis - variable fuzzy sets for underground reservoirs. Water Resources Management. 33, 4997–5014. doi:10.1007/s11269-019-02390-w.
Li, Y., & Chen, Y. )2021(. Variable precondition S-type cloud algorithm: Theory and application on water resources carrying capacity assessment. Ecological indicators. 121, 107209 doi: 10.1016/j.ecolind. 2020.107209.
Liu, Y., Gao, C., Ji, X., Zhang, Z., Zhang, Y., Liu, C., & Wang, Z. (2022). Simulation of water resources carrying capacity of the Hangbu River Basin based on system dynamics model and TOPSIS method. Front. Journal of Environmental Sciences. 2022, 10, 1045907. doi:10.3389/fenvs.2022.1045907.
Lu, L., Lei, Y., Wu, T., & Chen, K. )2022(. Evaluating water resources carrying capacity: the empirical analysis of Hubei Province,
 
China 2008–2020[J]. Ecological indicators. 144, 109454 doi: 10.1016/j.ecolind.2022.109454.
Makropoulos, C., Natsis, K., Liu, S., Mittas, K., & Butler, D. (2008). Decision support for sustainable option selection in integrated urban water management. Environmental Modelling & Software. 12 (23), 1448–1460. doi: 10.1016/j.envsoft.2008.04.010
Modaberi, H., Shokoohi, A, (2019). Determining Water requirement of Anzali Wetland based on Eco-Tourism Indices within the Framework of IWRM. Soil And Water Research. [In Persian]. doi:10.22059/ijswr.2020.303554.668633
Mou, S., Yan, J., Sha, J., Deng, S., Gao, Z., Ke, W., & Li, S. (2020). A comprehensive evaluation model of regional water resource carrying capacity: model development and a case study in Baoding. China. Water-Sui. 12 (9), 2637. doi:10.3390/w12092637.
Murgatroyd, A., & Hall, J.W. (2021). Selecting indicators and optimizing decision rules for long-term water resources planning. Water Resources Research. 57(5), e2020WR02811. doi:10.1029/2020WR028117.
Peng, T., & Deng, H. (2020). Comprehensive evaluation on water resource carrying capacity in karst areas using cloud model with combination weighting method: a case study of Guiyang, southwest China. Environmental Science and Pollution Research. 27 (29), 37057–37073. doi:10.1007/s11356-020-09499-1.
Peng, T., Deng, H., Lin, Y., & Jin, Z. (2021). Assessment on water resources carrying capacity in karst areas by using an innovative DPESBRM concept model and cloud model. Science of The Total Environment.767, 144353. doi: 10.1016/j.scitotenv.2020.144353.
Sachs, J.D. (2012). From Millennium Development Goals to Sustainable Development Goals. The Lancet 379 (9832), 2206–2211. doi:10.1016/S0140-6736(12)60685-0.
Wang, G., Xiao, C., Qi, Z., Meng, F., & Liang, X. (2021). Development tendency analysis for the water resource carrying capacity based on system dynamics model and the improved fuzzy comprehensive evaluation method in the Changchun city, China. Ecological Indicators. 122, 107232 doi: 10.1016/j.ecolind.2020.107232.
Wang, X., Liu, L., Zhang, S., & Gao, C. (2022). Dynamic simulation and comprehensive evaluation of the water resources carrying capacity in Guangzhou city. China. Ecological Indicators. 135, 108528 doi: 10.1016/j.ecolind.2021.108528.
Wang, X.T., Yang, W.M., Xu, Z.H., Hu, J., Xue, Y.G., & Lin, P. (2019). A normal cloud modelbased method for water quality assessment of springs and its application in Jinan. Sustainability 11, 16. doi:10.3390/su11082248.
Wang., Hou, Y., & Xue, Y. (2017). Water resources carrying capacity of wetlands in Beijing: analysis of policy optimization for urban wetland water resources management. Journal of Cleaner Production. 161, 1180–1191. doi; 10.1016/j.jclepro.2017.03.204.
Wei, X., Wang, J., Wu, S., Xin, X., Wang, Z., & Liu, W. (2019). Comprehensive evaluation model for water environment carrying capacity based on VPOSRM framework: A case study in Wuhan. China. Sustainable Cities and Society. 50, 101640 doi:10.1016/j.scs.2019.101640.
Wu, C., Zhou, L., Jin, J., Ning, S., Zhang, Z., & Bai, L. (2019). Regional water resource carrying capacity evaluation based on multi-dimensional precondition cloud and risk matrix coupling model Science of the Total Environment. 710, 136324. doi: 10.1016/j.scitotenv.2019.136324
Wu, C., Zhou, L., Jin, J., Ning, S., Zhang, Z., & Bai, L. (2020). Regional water resource carrying capacity evaluation based on multi-dimensional precondition cloud and risk matrix coupling model. Science of the Total Environment. 710, 136324 doi:10.1016/j. scitotenv.2019.136324.
Wu, L., Su, X.L., Ma, X.Y., Kang, Y., & Jiang, Y.A. (2018). Integrated modeling framework for evaluating and predicting the water resources carrying capacity in a continental river basin of northwest china. Journal of Cleaner Production. 204, 366–379. doi: 10.1016/j.jclepro.2018.08.319
Xing, L., Xue, M., & Hu, M. (2019). Dynamic simulation and assessment of the coupling coordination degree of the economy–resource–environment system: case of Wuhan City in China[J]. Journal of Environmental Management. 230, 474–487. doi: 10.1016/j.jenvman.2018.09.065.
Xu, Y., Ma, L., & Khan, N.M. (2020). Prediction and Maintenance of Water Resources Carrying Capacity in Mining Area-A Case Study in the Yu-Shen Mining Area. Sustainability 12 (18), 7782. doi:10.3390/su12187782.
Yang, G., Dong, Z., Feng, S., Li, B., Sun, Y., & Chen, M. (2021). Early warning of water resource carrying status in Nanjing City based on coordinated development index. Journal of Cleaner Production. 284, 124696  doi: 10.1016/j.jclepro.2020.124696.
Yang, Z., Song, J., Cheng, D., Xia, J., Li, Q., & Ahamad, M.I. (2019). Comprehensive evaluation and scenario simulation for the water resources carrying capacity in Xi’an city, China. Journal of Environmental management. 230, 221–233. doi:10.1016/j.jenvman.2018.09.085.
Yu, C., Li, Z., Yang, Z., Chen, X., & Su, M. (2020). A feedforward neural network based on normalization and error correction for predicting water resources carrying capacity of a city. Ecological Indicators. 118, 106724. doi: 10.1016/j.ecolind.2020.106724.
Zebardast, L., & Jafari, H. (2011). Use of remote sensing in monitoring the trend of changes of Anzali Wetland in Iran and proposing environmental management solution. Journal of Environmental Studies, 37(57), 1-8. [In Persian].
 dor: 20.1001.1.10258620.1390.37.57.7.5
Zhang, B., Chen, Z.M., Zeng, L., Qiao, H., & Chen, B. (2016). Demand-driven water withdrawals by Chinese industry: a multi-regional input-output analysis. Frontiers of Earth Science. 10, 13–28. doi:10.1007/s11707-015-0505-8.
Zhang, J., Zhang, C., Shi, W., & Fu, Y. (2019). Quantitative evaluation and optimized utilization of water resources-water environment carrying capacity based on naturebased solutions. Journal of Hydrology. 568, 96–107. doi: 10.1016/j.jhydrol.2018.10.059
Zhang, J.D., Fu, J.T., Liu, C.Y., Qu, Z.G., Li, Y.A., Li, F., Yang, Z.F., & Jiang, L.P. (2019). Evaluating water resource assets based on fuzzy comprehensive evaluation model: A case study of Wuhan city, china. Sustainability 11, 16. doi:10.3390/su11174627.
Zhang, S.H., Xiang, M.S., Xu, Z., Wang, L., & Zhang, C. (2020). Evaluation of water cycle health status based on a cloud model. Journal of Cleaner Production. 245,15. doi:10.1016/j.jclepro.2019.118850.
Zhao, J.; Jin, J.; Zhu, J.; Xu, J.; Hang, Q.; Chen, Y., & Han, D. (2016). Water resources risk assessment model based on the subjective and objective combination weighting methods. Water Resour. Managment. 2016, 30, 3027–3042. doi:10.1007/s11269-016-1328-4
Zhao, Y., Wang, Y., & Wang, Y. (2021). Comprehensive evaluation and influencing factors of urban agglomeration water resources carrying capacity. Journal of Cleaner Production 288, 125097. doi: 10.1016/j.jclepro.2020.125097.
Zheng, D., Lin, Z., & Wu, F. (2020). Measurement method of regional water resources carrying capacity based on ecological footprint. Desalination and Water Treatment. 187, 114–122.  doi:10.5004/dwt.2020.25308.
Zuo, Q., Guo, J., Ma, J., Cui, G., Yang, R., & Yu, L. (2021). Assessment of regional-scale water resources carrying capacity based on fuzzy multiple attribute decision-making and scenario simulation. Ecological Indicators. 130, 108034 doi:10.1016/j. ecolind.2021.108034.
Zuo, Q., Zhang, Z., & Wu, B. (2020). Evaluation of water resources carrying capacity of nine provinces in Yellow River basin based on combined weight TOPSIS model. Water Resour Prod. 36 (02), 1–7. doi:10.3390/w15244229
Zyoud, S.H., Kaufmann, L.G., Shaheen, H., Samhan, S., & Fuchs-Hanusch, D. (2016). A framework for water loss management in developing countries under fuzzy environment: integration of fuzzy AHP with fuzzy TOPSIS[J]. Expert Systems with Applications. 61, 86–105. doi: 10.1016/j.eswa.2016.05.016
Water and Soil Management and Modelling
Volume 5, Issue 2
June 2025
Pages 251-271

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History

  • Receive Date: 07 October 2024
  • Revise Date: 15 November 2024
  • Accept Date: 16 November 2024

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