مدل‌سازی جامع ظرفیت‌برد منابع آب تالاب انزلی با استفاده از روش وزندهی ترکیبی AHP-Entropy- CRITIC و مدل TOPSIS-GRA

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران.

2 گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، گیلان.

3 گروه مهندسی آب، دانشکده علوم کشاورزی، دانشگاه گیلان، رشت، ایران

چکیده

تالاب‌های شهری به دلیل فرایند شهرنشینی و دفع فاضلاب با سرعت بالایی در حال نابودی هستند که تهدیدی برای کیفیت آب و منابع زندگی انسان‌هاست. ارزیابی ظرفیت‌برد منابع آب (WRCC) در این تالاب‌ها ضروری است تا دست‌یابی به توسعه پایدار و هم‌افزایی بین توسعه اقتصادی و حفاظت از منابع آب تسهیل شود. این مطالعه با ارزیابی ظرفیت‌برد منابع تالاب انزلی به بررسی وضعیت این تالاب که در محدوده مطالعاتی فومنات استان گیلان واقع شده است، در یک دوره‌ زمانی 10 ساله (1400-1391) پرداخته است. در این مطالعه، ابتدا با توجه به داده‌ها و اطلاعات موجود، 8 شاخص ارزیابی با درنظر گرفتن 3 زیرسیستم منابع آب، اقتصاد و محیط زیست تعریف شد. بر اساس داده‌ها و اطلاعات در دسترس، هر شاخص در 4 سطح I (قابل بارگیری)، سطح II (ضعیف)، سطح III (بحرانی) و سطح IV (فوق بحرانی) طبقه‌بندی شدند. سپس وزن هر شاخص با سه روش AHP، Entropy  و CRITIC محاسبه شد و وزن‌های حاصل از سه روش با روش میانگین هندسی ترکیب و تعیین شد. در ادامه، مقادیر سالانه هر یک از شاخص‌ها به همراه وزن‌های مربوطه و در 4 سطح ارزیابی به مدلی که ترکیبی از تحلیل رابطه خاکستری (GRA) و روش شباهت به گزینه ایده‌آل (TOPSIS) است، اعمال شد. در نهایت بر اساس نتایج ظرفیت‌برد سالانه، عوامل مانع WRCC شناسایی شدند. ارزیابی WRCC حاکی از وضعیت IV (فوق بحرانی) تالاب از سال 1392 به بعد است. زیرسیستم منابع آب در شرایط بحرانی و فوق بحرانی قرار دارد و موجب کاهش ظرفیت برد کلی می‌شود، در حالی که زیرسیستم اقتصادی به این ظرفیت کمک می‌کند. بر اساس نتایج مدل درجه مانع، چهار عامل اصلی تأثیرگذار بر ظرفیت‌برد شامل کیفیت آب ورودی، درصد تامین آب مورد نیاز، درصد تامین آب نواحی بالادست و نسبت کل آب در دسترس به جمعیت هستند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Comprehensive Assessment of Water Resources Carrying Capacity in Anzali Wetland Using AHP-Entropy-CRITIC Combined Weighting Method and TOPSIS-GRA Model

نویسندگان [English]

  • Maedeh Keyvanfar 1
  • Somaye Janatrostami 2
  • Afshin Ashrafzadeh 3
1 Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
2 Department of Water Engineering, College of Agriculture, University of Guilan, Rasht, Guilan.
3 Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
چکیده [English]

Urban wetlands are rapidly deteriorating due to urbanization and wastewater disposal, posing a threat to water quality and human livelihoods. Assessing the water resources carrying capacity (WRCC) of these wetlands is crucial to facilitate sustainable development and synergy between economic growth and water resource conservation. This study evaluated the WRCC of Anzali Wetland, located in the Fumanat region of Gilan Province, over a 10-year period (2011-2021). eight evaluation indicators were defined based on available data and information, considering three subsystems: water resources, economy, and environment. Each indicator was classified into four levels: I (loadable), II (weak), III (critical), and IV(Overload). The weight of each indicator was calculated using three methods: AHP, Entropy, and CRITIC. The weights obtained from the three methods were combined using the geometric mean. Subsequently, the annual values of each indicator, along with their corresponding weights and four evaluation levels, were applied to a model combining Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, based on the annual WRCC results, the obstacle factors were identified. The WRCC assessment indicated that the wetland has been in a overloading condition (IV) since 2013. The water resources subsystem is in a critical or overloading condition, reducing the overall carrying capacity, while the economic subsystem contributes to this capacity. According to the obstacle degree model results, the four main factors affecting the WRCC include the quality of incoming water, the percentage of water supply required, the percentage of water supply for upstream areas, and the ratio of total available water to the population.

کلیدواژه‌ها [English]

  • Barrier Degree
  • Composite Weights
  • Evaluation Indicators
  • Water Resources Carrying Capacity

EXTENDED ABSTRACT

 

Introduction

Urban wetlands are facing severe threats due to urbanization and pollution from wastewater, which significantly impacts water quality and human livelihoods. Assessing Water Resources Carrying Capacity (WRCC) with a focus on balancing water supply for human needs and environmental preservation is crucial. Various approaches have been employed for this assessment, including comprehensive index modeling and complex systems analysis. These efforts help optimize water resource allocation and promote sustainable development, although challenges in data and precise modeling exist. Numerous studies have utilized diverse models for WRCC assessment, such as the "Pressure-State-Response" model, the "Socio-economic-ecological-water resources" model, and other region-specific models."

Objective

The objective of this study is to assess the water resources carrying capacity of Anzali Wetland using a comprehensive indicator modeling approach. Evaluation indicators were defined based on the long-term conditions of Anzali Wetland, and the AHP, entropy, and CRITIC methods were employed to calculate the weights of these indicators. The combined weight was computed using the geometric mean method. Subsequently, considering the various approaches for WRCC assessment, a model combining the Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods was utilized.

Methods

This study assesses the water resources carrying capacity of Anzali Wetland, which is under threat from urbanization. Initially, the influencing environmental factors were categorized into three subsystems: population, water resources, and socio-economic-environmental.: Indicators were defined for each subsystem, and their weights were determined using a combined AHP-Entropy-CRITIC method. Subsequently, the TOPSIS-GRA model was employed to calculate the water resources carrying capacity over a 10-year period. Finally, using an obstacle degree model, the critical factors influencing the improvement of the wetland's carrying capacity were identified, and recommendations for its enhancement were proposed.

Results

The results of the indicator weighting revealed significant differences in the weights obtained from various methods. The geometric mean can effectively reflect the importance of indicators from multiple perspectives. The results indicated that the highest weights were assigned to indices C8 and C7. The TOPSIS-GRA model results for assessing the WRCC of each year at different evaluation levels showed that the wetland was predominantly in a critical (IV) condition, particularly from 2013 onwards, while 2012 exhibited a better status. An examination of the three subsystems of water resources, economy, and ecology in assessing the status of Anzali Wetland's water resources indicated that the water resources subsystem was consistently in a critical or hypercritical condition throughout the period. Conversely, the economic subsystem was in a hypercritical condition in all years except 2012, and the ecological subsystem was in a similar state. Comparing the proximity degrees to evaluation levels in each subsystem revealed that the economic subsystem contributed to increasing the overall carrying capacity, whereas the water resources subsystem decreased it. The results of the obstacle degree model analysis of WRCC barriers demonstrated that the four primary factors influencing the current status of the wetland's water resources carrying capacity were the quality of water entering the Anzali Wetland, the percentage of water supply required for the wetland, the percentage of water supply demand for the upstream areas of Anzali Wetland, and the ratio of total available water to the population.

Conclusions

Based on the results of the obstacle degree analysis and the identification of the most significant factors affecting the WRCC of Anzali Wetland, it is recommended to take actions to improve the wetland's carrying capacity considering the region's specific conditions and culture. Whenever possible, it is advisable to implement these actions. For instance, measures could be planned in the wetland's upstream areas to improve the quality of incoming water and increase its volume.

Author Contributions

Conceptualization, Maedeh Keyvanfar and Somaye Janatrostami; methodology, Somaye Janatrostami and ; Maedeh Keyvanfar software, Maedeh Keyvanfar and Afshin Ashrafzadeh; validation, Somaye Janatrostami; formal analysis and investigation, Maedeh Keyvanfar, Somaye Janatrostami; resources, Maedeh Keyvanfar; data curation, Maedeh Keyvanfar and Afshin Ashrafzadeh; writing—original draft preparation, Maedeh Keyvanfar; writing—review and editing, Somaye Janatrostami; visualization, Somaye Janatrostami; supervision, Somaye Janatrostami; project administration, Somaye Janatrostami.

Data Availability Statement

Data available on request from the authors.

Acknowledgements

The authors would like to thank all participants of the present study.

Ethical considerations

The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

The author declares no conflict of interest. 

مراجع

Ait-Aoudia, M.N., Berezowska-Azzag, E., (2016). Water resources carrying capacity assessment: The case of Algeria’s capital city. Habitat International 58, 51–58. https://doi.org/ 10.1016/j.habitatint.2016.09.006.
Avarideh, H. R., Safari, A. R.,  Homayouni, S., Khazaei, S. 2015. Nearshore bathymetry using hyperspectral remotesensing. Geospatial Engineering Journal, 6(1): 1-10. (In Persian)
Chen, Q, Y., Zhu, M, T., Zhang, C.J.. et al.,2023.The driving effect of the spatial-temporal difference of water resources carrying capacity in the Yellow River Basin. Journal of Cleaner Production ,388:135709. https://doi.org/10.1016/j.jclepro.2022.135709.
Chen, Y.-T., Sun, E.W., Lin, Y.-B., (2019). Coherent quality management for big data systems: a dynamic approach for stochastic time consistency. Annals of Operations Research,. 277, 3–32. https://doi.org/10.1007/s10479-018-2795-1.
Cheng, Q.Y.,(2010). Structure Entropy Weight Method to Confirm the Weight of Evaluating Index. systems engineering-theory & practice,30(07):1225-1228.
Gulishengmu, A., Yang, G., Tian, L., Pan, Y., Huang, Z., Xu, X., Gao, Y., Li, Y., (2023). Analysis of water resource Carrying capacity and obstacle factors based on GRATOPSIS evaluation method in Manas River basin. Water 15. https://doi.org/ 10.3390/w15020236
Hettiarachchi M., Morrison T. H., Wickramsinghe D., Mapa R., De Alwis A., McAlpine C. A., (2014). The eco-social transformation of urban wetlands: A case study of Colombo, Sri Lanka, Landscape and Urban Planning,132, 55–68. https://doi.org/10.1016/j.landurbplan.2014.08.006
Jia, Y., Wang, H., (2023). Study on water resource Carrying capacity of Zhengzhou City based on DPSIR model, 20 International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph20021394.
Jiang, H., He, G., (2023). Analysis of spatial and temporal evolution of regional water resources Carrying capacity and influencing factors-Anhui Province as an example. Sustainability, 15. https://doi.org/10.3390/su151411255.
Li, Q., Liu, Z., Yang, Y., Han, Y., Wang, X., (2023a). Evaluation of water resources carrying capacity in Tarim River basin under game theory combination weights, 154 Ecological Indicators. https://doi.org/10.1016/j.ecolind.2023.110609.
Li, W., Jiang, S., Zhao, Y., Li, H., Zhu, Y., Ling, M., Qi, T., He, G., Yao, Y., Wang, H., (2023b). Comprehensive evaluation and scenario simulation of water resources carrying capacity: a case study in xiong’an new area, China. Ecological Indicators. 150 https:// doi.org/10.1016/j.ecolind.2023.110253.
Liu, Y., Jia, R.X., Hou, X.L.,(2005). Evaluation of China’s regional sustainable utilization of water resources and its type classification. Environmental Science. 2005, 26, 42.
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. Frontiers in Environmental Science, (2022). 10, 1045907. https://doi.org/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 https://doi.org/10.1016/j.ecolind.2022.109454.
Lv, B., Liu, C., Li, T., Meng, F., Fu, Q., Ji, Y., Hou, R., (2023). Evaluation of the water resource carrying capacity in Heilongjiang, eastern China, based on the improved TOPSIS model, 150 Ecological Indicators. https://doi.org/10.1016/j.ecolind.2023.110208.
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. (2008). 23, 1448–1460. https://doi.org/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. Irainian Journal Of Soil And Water Research. DOI:// 668633ijswr10.22059/ (In Persian)
Morin T.H., Bohrer G., Naor-Azrieli L., Mesi S., Kenny W.T., Mitsch W.J., Schäfer K.V.R., (2014). The seasonal and diurnal dynamics of methane flux at a created urban wetland, Ecological Engineering, 72, 74–83. https://doi.org/10.1016/j.ecoleng.2014.02.002
Murgatroyd, A., & Hall, J. W. (2021). Selecting indicators and optimizing decision rules for long-term water resources planning. Water Resources Research, 57, e2020WR028117. https:// doi.org/10.1029/2020WR028117
Pavlacka, O., 2014. On various approaches to normalization of interval and fuzzy weights. Fuzzy Sets and Systems. 243, 110–130. https://doi.org/10.1016/j.fss.2013.07.026.
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 https://doi.org/10.1016/j.ecolind.2020.107232.
Wang, Q., Zhao, Z., Shen, N., Liu, T., (2015). Have chinese cities achieved the win-win between environmental protection and economic development? from the perspective of environmental efficiency. Ecological Indicators. 51, 151–158. https://doi.org/10.1016/j. ecolind.2014.07.022.
Wang, S., Chakrabarty, A., Taha, A.F.F., (2023a). Data-driven identification of dynamic quality models in drinking water networks, 149 Journal of Water Resources Planning and Managment. https://doi.org/10.1061/jwrmd5.Wreng-5431.
Wang, S., Zhang, G., (2014). Study on Comparing the comprehensive Carrying capacity among Beijing-Tianjin-Hebei region based on the rank-sum ratio. Areal Research and Development. 33, 19–25.
Wang, X., Zhang, S., Tang, X., Gao, C., 2023c. Spatiotemporal heterogeneity and driving mechanisms of water resources carrying capacity for sustainable development of Guangdong Province in China. Journal of Cleaner Production. 412 https://doi.org/10.1016/j. jclepro.2023.137398.
Wang, Y., Wang, J., Duan, X., Wang, L., (2023b). Assessment and simulation of water environment Carrying capacity in a River Basin using system dynamics model. Polish Journal of Environmental Studies. 32, 2893–2907. https://doi.org/10.15244/pjoes/161326.
Wang, Y., Zhang, Y., Sun, W., Zhu, L., (2022). The impact of new urbanization and industrial structural changes on regional water stress based on water footprints. Sustainable Cities and Society. 79 https://doi.org/10.1016/j.scs.2022.103686.
Wu, C., Zhou, L., Jin, J., Ning, S., 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 https://doi.org/10.1016/j. scitotenv.2019.136324.
Xu, M., Chen, M., Li, Y., Jiang, Y., (2020). Analysis of water resources Carrying capacity of coastal cities along the Yangtze River based on PSR model. Journal of Coastal Research. 109 https://doi.org/10.2112/JCR-SI109-018.1.
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 https://doi.org/10.1016/j.jclepro.2020.124696.
Yu, L., (2021). Study on the essence of objective weighting method and its application in scientific and technological evaluation. Information Studies: Theory & Application 44, 50–56. https://doi.org/10.16353/j.cnki.1000-7490.2021.02.007.
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.. 20.1001.1.10258620.1390.37.57.7.5. (In Persian)
Zeng X.T., Huang G.H., Chen H.L., Li Y.P., Kong X.M., Fan Y.R., (2016). A simulation-based water-environment management model for regional sustainability in compound wetland ecosystem under multiple uncertainties, Ecological Modelling, 334, 60–77. https://doi.org/10.1016/j.ecolmodel.2016.04.021
Zhang, J.; Zhang, C.; Shi, W.; Fu, Y., (2019).  Quantitative evaluation and optimized utilization of water resources-water environment carrying capacity based on nature-based solutions. Journal of Hydrology. (2019), 568, 96–107. https://doi.org/10.1016/j.jhydrol.2018.10.059
Zhang, Y, Wei, H.B., (2012). Multi-attribute decision-making combination weighting method based on CRITIC. statistics and decision, (16):75-77. https://link.cnki.net/ doi/10.13546/j.cnki.tjyjc.2012.16.009 (in Chinese).
Zhang, Y., (2023). Quantitative evaluation of the Carrying capacity of county water resources in the context of “four determinations with water”. China Rural Water and Hydropower. 60–68+73. https://doi.org/10.12396/znsd.220812.
Zhou, F., Zhang, W., Jiang, A., Peng, H., Li, L., Deng, L., Sun, Y., Wang, H., (2023). Spatialtemporal variation characteristics and coupling coordination of the “water resources - water environment - water ecology” carrying capacity in the three gorges reservoir area. Ecological Indicators. 154 https://doi.org/10.1016/j.ecolind.2023.110874.
Zhou, K., (2022). Comprehensive evaluation of water resources carrying capacity based on improved AGA-AHP method. Applied Water Science. 12 (5). https://doi.org/10.1007/ S13201-022-01626-2.
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 in (Chinese).
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. Expert Systems with Applications. 61, 86–105. https://doi.org/10.1016/j.eswa.2016.05.016.
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فروردین 1404
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