ارزیابی پیامد‌های درونی‌سازی اثرات جانبی آلودگی آب بر مدیریت کمی و کیفی حوضه آبریز زاینده‌رود

نوع مقاله : مقالات پژوهشی

نویسندگان

• حدیث کاوند ¹
• سامان ضیایی ²
• مصطفی مردانی نجف ‎آبادی ³

¹ دانشگاه زابل

² دانشگاه شیراز- دانشگاه زابل

³ کشاورزی و منابع طبیعی رامین خوزستان

https://doi.org/10.22067/jead2.vi0.86889

چکیده

بروز چالش‌های اخیر در وضعیت منابع آبی حوضه آبریز زاینده‌رود، منجر به آن گردیده است که زاینده‌رود نیز از آلودگی آب در امان نماند و تامین آب با کیفیت مناسب به عنوان یک چالش‌ اساسی در این حوضه محسوب گردد. از این‌رو ارائه یک الگوی کشت هدفمند از طریق کاهش اثرات جانبی آلودگی مصرف آب ناشی از فعالیت‌های کشاورزی برای حوضه آبریز رودخانه زاینده‌رود می‌تواند نقش موثری در مدیریت کمی و کیفی منابع آب حوضه ایفا نماید. برای این منظور مدل شبیه‌سازی هیدرولوژیکی (مدل WEAP) با مدل بهینه‌یابی اقتصادی تلفیق و در مرحله‌ی بعد، اثرات جانبی آلودگی آب با استفاده از مدل SWAT شبیه‌سازی و به‌عنوان ورودی و یک محدودیت زیست‌محیطی به مدل یکپارچه سطح حوضه اضافه شده است. داده‌های مورد نیاز این الگو به سه شیوه تحقیق پیمایشی، مطالعات و گزارشات اسنادی و استفاده از نظرات کارشناسان و خبرگان طی سال‌های آماری 91-1390 جمع‌آوری شد. نتایج پارامترهای هیدرولوژیکی در الگوی بهینه اقتصادی نشان داد که می‌توان با بکارگیری سیاست‌های حفاظت منابع آب، اثرات تغییر اقلیم در منطقه را تعدیل بخشید. همچنین مقایسه الگوی بهینه اقتصادی و اقتصادی-زیستی نشان داد که می‌توان ضمن بهبود بازده برنامه‌ای به میزان 12 میلیون ریال، میزان تلفات نیترات کمتر از حد مجاز در سطح حوضه را تحقق بخشید.

کلیدواژه‌ها

• آلودگی آب
• الگوی بهینه کشت
• شبیه‌سازی هیدرولوژیکی
• مدل یکپارچه اقتصادی-زیستی

عنوان مقاله [English]

Assessing the Consequences of Internalization of the Side Effects of Water Pollution on the Quantitative and Qualitative Management of Zayandehroud Basin

نویسندگان [English]

• H. Kavand ¹
• S. Ziaee ²
• M. Mardani ³

¹ University of Zabol

² Zabol university

³ Khozestan

چکیده [English]

 
Introduction: The increase in water demand and the expansion of water pollution due to the development of agricultural, urban and industrial activities have led to a serious risk of water quality in many places. Therefore, its rational and logical management has become very difficult and complicated. In recent decades, concerns about water pollution from agricultural activities and its consequences have been growing. The existing regulations are not sufficient to limit the water pollution of the agricultural sector and to achieve the desired environmental consequences. Thus, economic tools have increasingly been proposed as an affordable way to limit pollution. Therefore, the side effects of water consumption in agriculture are vital issues for controlling and managing water pollution. The recent challenges in water resources of the Zayandehroud basin have led to the fact that this area has not been safe from water pollution and also the supply of high quality water is a major challenge in this basin. Therefore, providing a purposeful cropping pattern by reducing the side effects of water pollution caused by agricultural activities for the Zayandehroud basin can play an effective role in the quantitative and qualitative management of watershed resources.
Materials and Methods: In this study, the water resources management system of Zayandehroud basin has been modeled based on a multi-objective programming model. This model includes hydrological functions, land allocation, resource transfer and exploitation capacities, and the objective function is to maximize the net present value of the total benefits at the basin level. Also, the amount of water available in different sub-basins, the crop yield and net water requirements was simulated using the WEAP model for the 2040 horizon. This data was used as input in the economic model. In the next step, the side effects of water pollution are estimated and internalized in the economic model using permissible limit of water pollution in constraint and the cost of water pollution in the objective function. The mechanism designed to internalize the side effects of water pollution is simulated using the SWAT model and added to the integrated water management model of the basin as environmental constraint and cost of nitrate losses in objective function. Therefore, by comparing the results of these two models, it is possible to evaluate the internalization of the side effects of water pollution on farmers' livelihoods and the cropping pattern in the basin.
Results and Discussion: Cropping pattern under basic conditions was applied in WEAP software for different regions. Yield and net water requirements of products were simulated using MABIA tools. The results were extracted by each region and then estimated at basin level. The results of the optimal cropping pattern after simulating the hydrological parameters of the basin showed that the gross margin compared to the current conditions for Najafabad, North Mahyar, Lenjanat, Kuhpayeh-Segzi, Isfahan-Borkhar and Ben-Saman regions was 14, 5, 15,18, 15 and 20 million Rials per hectare, respectively. The increase in the share of irrigation technologies in the economic model compared to the current model for Najafabad, Lenjanat, Kuhpayeh-Segzi, Isfahan-Borkhar and Ben-Saman regions was 40, 57, 35, 45 and 91 percent, respectively.Therefore, it can be expected that by changing the cropping pattern and also increasing the use of new irrigation systems, it is possible to improve the livelihood of farmers in the basin according to the current and future hydrological conditions. But these changes have increased the side effects of pollution on the basin. Therefore, it is necessary to provide a model that, in addition to improving the benefit, also reduces the cost of nitrate losses. The results of the optimal bioeconomic model indicate that with the application of this model, the rate of gross margin and the cost of nitrate losses have been obtained at 58 and 28 million Rials per hectare, respectively. Comparison of this model and the optimal economic model shows that farmers' gross margin and nitrate losses have decreased about 3 and 2 million Rials per hectare, respectively. Comparison of the current and optimal bioeconomic model also shows that while improving the gross margin by 12 million Rials, the rate of nitrate losses below the allowable level in the basin can be realized.
Conclusion: Zayandehroud basin is one of the most important watersheds in the country, which is facing the challenge of quantitative and qualitative water shortages. The main purpose of this study is to quantitatively and qualitatively manage water resources and evaluate the consequences of internalizing the side effects of water pollution on this type of management. The results of the study showed that using the optimal economic cropping pattern will increase the farmers' gross margin and improve their livelihoods.  Also, comparing the results of the economic model with the multi-objective bioeconomic model showed that considering the side effects of water resources pollution in some areas is effective and in others is ineffective. Therefore, it is recommended that in future studies, considering the effective role of different policies in the field of water resources quality, the effects of different scenarios of climate change, drought, population growth, etc. be examined and analyzed.

کلیدواژه‌ها [English]

• Hydrological simulation
• Integrated bioeconomic model
• Optimal cropping pattern
• Water pollution

مراجع

1- Akbari F., Taheri Borojeni G., Moridi A and Khazaei pool A. 2018. Investigating the effect of climate change on runoff the Aras River basin using the model SWAT. Journal of Environmental Science and Technology, 16:75-91.(in Persian)

2- Akhavan S.J., Abedi-Koupai S.F. Mousavi M. Afyuni S.S. Eslamian and K.C. Abbaspour. 2010. Application of SWAT model to investigate nitrate leaching in Hamadan-Bahar watershed, Iran. J. Agric. Ecosystem and Environ. 139(4): 675- 688.

3- Amini A., Javan M., Eghbalzadeh A., and Ghasemi M.R. 2016. Evaluation of water resources management in Gamasiab basin of Kermanshah province using model WEAP. Journal of Water Resources Engineering, 10:13-18. (In Persian)

4- Arnold J.G., Allen P.M., Volk M., Williams J. R., and Bosch D.D. 2010a. Assessment of different representations of spatial variability on SWAT model performance. Trans. ASABE 53(5): 1433-1443.

5- Bagheri rahdaneh, M.R., Ghaemi Z., and Khajehzadeh A. 2018. The first conference of Soil and Water Management Tool (SWAT) in the management of water resources, Isfahan, Isfahan University of Technology.

6- Esteve P., Varela-Ortega C., Blanco-Gutierrez I., and Downing T. E. 2015. A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture. Ecological Economics. 120, 49-58.

7- Eliasson J., 2015. The rising pressure of global water shortages. Nature 517 (7532), 6.

8- Gassman P.W., Reyes M., Green C.H., and Arnold J.G. 2007. The Soil and Water Assessment Tool: Historical development, applications, and future directions. Trans. ASABE 50(4): 1211- 1250.

9- Gohar A. A., Amer S.A., and Ward F.A. 2015. Irrigation infrastructure and water appropriation rules for food security. Journal of Hydrology, 520:85-100.

10- Han Y., Huang Y.F., Wang G.Q. and Maqsood I. 2011. A multi-objective linear programming model with interval parameters for water resources allocation in Dalian city. Water Resource Management, 25:449–463.

11- Iran Water Resource Management Company. 2016. Reports of integrated water resource management of the zayandehroud basin.

12- Kemanian A., Julich R.S., Manoranjan V.S., and Arnold J.G. 2011. Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing. Ecol. Modelling 222(12): 1913-1921.

13- Knisel W.G., Foster G.R., and Leonard R.A. 1980. CREAMS: A system for evaluating management practice in Schaller. Agricultural Management and Water Quality. Lowa University Press. 178-199.

14- Leonard R.A., Knisel W.G., and Still D.A. 1987. GLEAMS: groundwater loading of agricultural management systems. Transactions of the ASAE, 30(5):1403-1418.

15- Li J., He L., Chen Y.Z., Song X.S., and Lu H.W., 2017b. A believe l groundwater management model with minimization of stochastic health risks at the leader level and remediation cost at the follower level. Stoch. Environ. Res. Risk Assess. 31 (10), 2547–2571.

16- Mimi Z., and Sawalhi B.I. 2003. A decision tool for allocating the waters of the Jordan River basin between all riparian parties. Water Resources Management, 17: 447–461.

17- Moriasi D.N., Arnold J.G., Van Liew M.W., Binger R.L., Harmel R.D., and Veith T. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed.

18- Nasabian S., Mohammadi H. and Kikha A.R. 2014. The effect of modification of cropping pattern on reducing pollution of agricultural activities. Journal of Environmental Science and Technology, 16:75-91.(in Persian)

19- Narula K.K, Gosain A.K. 2013. “Modeling hydrology, groundwater recharge and non-point nitrate loadings in the Himalayan Upper Yamuna basin”. Science of the Total Environment 468–469 102–116.

20- Pei Y., and Zhao Y. 2012. Risk evaluation of groundwater pollution by pesticides in China: a short review. Procardia Environmental Sciences, 13: 1739 – 1747.

21- Reidsma P., Ewert F., Lansil A., and Leemans R. 2010. Adaptation to climate change and climate variability in European agriculture. The importance of farm level responses. European Journal of Agronomy. 32: 91-102.

22- Ranga Prabodanie R.A., Raffensperger J.F., Granr Read E., and Milke W. 2014. LP models for pricing diffuse nitrate discharge permits. Journal of Operational Research, 220: 87-109

23- Regional Water Company of Isfahan. 2016. Available online at: http://www.esrw.ir/.site.

24- Rong Q., Cai Y., Chen B., Shen Z., Yang Z., Yue W., and Lin X. 2018. Field management of a drinking water reservoir basin based on the investigation of multiple agricultural nonpoint source pollution indicators in north China. Ecological Indicators, 92, 113-123.

25- Salehi S., Chizari M., Sadighi H., and Bijani M., 2017. Assessment of agricultural groundwater users in Iran: a cultural environmental bias. Hydrogeology Journal, 26(1): 285-295. (in Persian)

26- Sheikh Zeinodin A. 2016. Determining the management strategies of the agricultural system of the irrigation and drainage network of Dorodzan, biological economic approach. PhD Thesis in Agricultural Economics, Faculty of Agriculture, Shiraz University. (in Persian)

27- Shoushtarian A. 2018. Analysis of Agricultural and Environmental Economic Policies under the Abar Basin Basin Basin, an approach to agricultural sustainability. PhD Thesis in Agricultural Economics, Faculty of Agriculture, Shiraz University. (in Persian)

28- Smith M., Thomas Nichols E., Vidaurre D., Winkler A.M., Behrens T.E.J., Glasser M.F., Ugurbil K., Barch D.M., Van Essen D.V., and Miller K.L. 2015. A positive-negative mode of population covariation links brain connectivity, demographics and behavior, 18, 1565-1567.

29- Tanaka S. K., Zhu T., Lund J.R., Howitt R.E., Jenkins M.W., Pulido M.A., Tauber M., Ritzema R.S., and Ferreira I.C. 2006. Climate warming and water management adaptation for California. Climate Change, 76, 361-387.

30- Tuppad P., Douglas-Mankin K.R., Lee T, Srinivasan R., and Arnold J.G. 2011. Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: Extended capability and wider adoption. Trans. ASABE 54(5): 1677-1684.

31- Van R.J., Veeren H.M. and Lorenz C.M. 2002. Integrated economic–ecological analysis and evaluation ofmanagement strategies on nutrient abatement in the Rhine basin. Journal of Environmental Management, 66: 361–376.

32- Ward F.A. 2014. Economic impacts on irrigated agriculture of water conservation programs in drought. Journal of Hydrology, 508, 114-127.

33- Wang R., Fang L., and Kalin L. 2011. Modelling effects of land use/cover changes under limited data. Eco hydrology, 4: 265–276.

34- Williams J.R., Arnold J.G., Kiniry J.R., Gassman P.W., and Green, C.H. 2008. History of model development at Temple, Texas. Hydrological Science Journal, 53(5): 948–960.

35- Yang L., Bai X., Zheng Khanna N., Yi S., Hu Y., Denga J., Gao H., Tuo L., Xianga SH., and Zhoub N. 2018. Water evaluation and planning (WEAP) model application for exploring the water deficit at catchment level in Beijing. Desalination and Water Treatment, 118:12–25.

36- Yates D., Sieber J., Purkey D., and Huber-Lee A. 2005. WEAP21—A demand-, priority-, and preference-driven water planning model,”model. Water International, 30 (4): 487-500.

37- Yazdanpanah T., Khodashenas, K., and Gahraman, B. 2008. Water Resource Management of basin by Weap (Case Study: Azgand basin).Agriculture Sience of thecnology. 22(1):211-222. (in Persian)

38- Zeinodini S., Anoori S., and Zahmatkesh Z. 2018. Application of simulati optimization approache to assess the effect of climate and management scenarios on a water resource system. Iran-Wate Resources Research, 14(5): 295-310. (in Persian)

39- Zu B., Saleh A., Jaynes D.B., and Arnold J.G. 2006. Evaluation of SWAT in simulating nitrate nitrogen and atrazine fates in a watershed with tiles and potholes. Trans. ASABE 49(4): 949- 959.