Volume 8, Issue 3 (2020)                   ECOPERSIA 2020, 8(3): 169-180 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Norouzi Nazar M, Asgari E, Baaghideh M, Lotfi S. Quantifying the Long-Term Flood Regulation Ecosystem Service under Climate Change Using SWAT Model. ECOPERSIA. 2020; 8 (3) :169-180
URL: http://ecopersia.modares.ac.ir/article-24-36672-en.html
1- Civil Engineering Department, Engineering Faculty, University of Sistan and Baluchestan, Zahedan, Iran
2- Climatology & Geomorphology Department, Geography & Environmental Sciences Faculty, Hakim Sabzevari University, Sabzevar, Iran
3- Climatology & Geomorphology Department, Geography & Environmental Sciences Faculty, Hakim Sabzevari University, Sabzevar, Iran , m.baaghideh @hsu.ac.ir
4- School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract:   (285 Views)
Aims: In recent years, interest in quantifying ecosystem services (ESs) has dramatically grown among the scientific society. By increasing global environmental crises as a result of population growth, it is becoming increasingly essential to quantify the impacts that human activities have on ESs. Soil and water assessment tool (SWAT) is a process-based distributed hydrological model that has been widely recommended to quantify the ESs. The purpose of the present study is to employ the SWAT model for quantifying the flood regulation ecosystem service in one of the highest flood prone watersheds in the west of Iran.
Materials & Methods: In this study, after calibration and validation of daily and monthly discharge using SUFI-2 algorithm, the flood regulation index (FRI) was calculated for each year of simulation period (1989-2017).
Findings: The results show that climate variables such as precipitation could severely affect the quantities of FRI in different years. According to middle of 95PPU, the FRI varies from 0.22 in the wettest year of 1994 to 0.72 in the driest year of 2017 with precipitation values of 1080 and 380mm, respectively. The results also indicate that lower, middle, and upper limits of FRI 95PPU show the correlation coefficient of 28, 66, and 72% with the precipitation values in different years.
Conclusion: The available knowledge on the application of SWAT model in addressing ESs can be similarly used in the regions with corresponding environmental challenges of the low delivery level of regulation ESs.
Full-Text [PDF 2207 kb]   (35 Downloads)    
Article Type: Original Research | Subject: Terrestrial Ecosystems
Received: 2019/09/26 | Accepted: 2020/01/9 | Published: 2020/09/20
* Corresponding Author Address: Room 127, Geography & Environmental Sciences Faculty, Hakim Sabzevari University, Tohidshahr, Sabzevar, Khorasan Razavi Province, Iran. Postal code: 9617976487

1. Kumar P, editor. The economics of ecosystems and biodiversity: Ecological and economic foundations. Nairobi: UNEP; 2010. [Link]
2. Tang Z, Sun G, Zhang N, He J, Wu N. Impacts of land-use and climate change on ecosystem service in Eastern Tibetan Plateau, China. Sustainability. 2018;10(2):467. [Link] [DOI:10.3390/su10020467]
3. Gathenya M, Mwangi H, Coe R, Sang J. Climate-and land use-induced risks to watershed services in the Nyando River Basin, Kenya. Exp Agric. 2011;47(2):339-56. [Link] [DOI:10.1017/S001447971100007X]
4. Logsdon RA, Chaubey I. A quantitative approach to evaluating ecosystem services. Ecol Model. 2013;257:57-65. [Link] [DOI:10.1016/j.ecolmodel.2013.02.009]
5. Francesconi W, Srinivasan R, Pérez-Miñana E, Willcock SP, Quintero M. Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review. J Hydrol. 2016;535:625-36. [Link] [DOI:10.1016/j.jhydrol.2016.01.034]
6. Asadolahi Z, Salmanmahiny A, Sakieh Y. Hyrcanian forests conservation based on ecosystem services approach. Environ Earth Sci. 2017;76(10):365. [Link] [DOI:10.1007/s12665-017-6702-x]
7. Boumans R, Roman J, Altman I, Kaufman L. The Multiscale Integrated Model of Ecosystem Services (MIMES): Simulating the interactions of coupled human and natural systems. Ecosyst Serv. 2015;12:30-41. [Link] [DOI:10.1016/j.ecoser.2015.01.004]
8. Mulligan M. Trading off agriculture with nature's other benefits, spatially. In: Zolin CA, Rodrigues R, editors. Impact of climate change on water resources in agriculture. Boca Raton: CRC Press; 2015. pp. 184-204. [Link] [DOI:10.1201/b18652-10]
9. Gómez-Baggethun E, Martín-López B, Barton D, Braat L, Kelemen E, Garcia-Llorente M, et al. State-of-the-art report on integrated valuation of ecosystem services [Report]. Bellatera: EU FP7 OpenNESS Project; 2014 July. Report No.: D 4.1. Contract N.: 308428. [Link]
10. Vigerstol KL, Aukema JE. A comparison of tools for modeling freshwater ecosystem services. J Environ Manag. 2011;92(10):2403-9. [Link] [DOI:10.1016/j.jenvman.2011.06.040]
11. Arnold JG, Srinivasan R, Muttiah RS, Williams JR. Large area hydrologic modeling and assessment part I: Model development 1. J Am Water Resour Assoc. 1998;34(1):73-89. [Link] [DOI:10.1111/j.1752-1688.1998.tb05961.x]
12. Jung IW, Bae DH, Lee BJ. Possible change in Korean streamflow seasonality based on multi‐model climate projections. Hydrol Process. 2013;27(7):1033-45. [Link] [DOI:10.1002/hyp.9215]
13. Newton A, Brito AC, Icely JD, Derolez V, Clara I, Angus S, et al. Assessing, quantifying and valuing the ecosystem services of coastal lagoons. J Nat Conserv. 2018;44:50-65. [Link] [DOI:10.1016/j.jnc.2018.02.009]
14. Hejazizadeh Z, Fatahi E, Massah Bavani A, Naserzadeh M. Investigating the impacts of climate changes on floods using the atmospheric circulation model (AOGCM). Geography. 2012;10(34):5-24. [Persian] [Link]
15. Kayhanpanah M, Zare Bidaki R, Bazrafshan J. Flow modelling in great Karun sub-basins in terms of future climate. Echo Hydrol. 2018;4(4):1033-47. [Persian] [Link]
16. Mockus V. Design hydrographs. In: United States. Soil Conservation Service. National engineering handbook hydrology. Washington D.C.: USDA; 1972. pp. 2-12. [Link]
17. Heber Green W, Ampt GA. Studies on soil physics, part 1, the flow of air and water through soils. J Agric Sci. 1911;4(1):1-24. [Link] [DOI:10.1017/S0021859600001441]
18. Ritchie JT. Model for predicting evaporation from a row crop with incomplete cover. Water Resour Res. 1972;8(5):1204-13. [Link] [DOI:10.1029/WR008i005p01204]
19. Hargreaves GH, Samani ZA. Reference crop evapotranspiration from temperature. Appl Eng Agric. 1985;1(2):96-9. [Link] [DOI:10.13031/2013.26773]
20. Priestley CH, Taylor RJ. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev. 1972;100(2):81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 [Link] [DOI:10.1175/1520-0493(1972)1002.3.CO;2]
21. Monteith JL. Evaporation and the environment. Symp Soc Exp Biol. 1965;19:205-34. [Link]
22. Gassman PW, Reyes MR, Green CH, Arnold JG. The soil and water assessment tool: Historical development, applications, and future research directions. Trans ASABE. 2007;50(4):1211-50. [Link] [DOI:10.13031/2013.23637]
23. earthexplorer.usgs.gov [Internet]. Reston: United States geological survey agency; 1879 [cited 2011 March 15]. Available from: https://earthexplorer.usgs.gov/ [Link]
24. Schuol J, Abbaspour KC, Srinivasan R, Yang H. Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model. J Hydrol. 2008;352(1-2):30-49. [Link] [DOI:10.1016/j.jhydrol.2007.12.025]
25. Tejaswini V, Sathian KK. Calibration and validation of swat model for Kunthipuzha basin using SUFI-2 algorithm. Int J Curr Microbiol Appl Sci. 2018;7(1):2162-72. [Link] [DOI:10.20546/ijcmas.2018.701.260]
26. Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, et al. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol. 2007;333(2-4):413-30. [Link] [DOI:10.1016/j.jhydrol.2006.09.014]
27. Abbaspour KC, Rouholahnejad E, Vaghefi SR, Srinivasan R, Yang H, Kløve B. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol. 2015;524:733-52. [Link] [DOI:10.1016/j.jhydrol.2015.03.027]
28. Legates DR, McCabe Jr GJ. Evaluating the use of "goodness‐of‐fit" measures in hydrologic and hydroclimatic model validation. Water Resour Res. 1999;35(1):233-41. [Link] [DOI:10.1029/1998WR900018]
29. Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I-A discussion of principles. J Hydrol. 1970;10(3):282-90. [Link] [DOI:10.1016/0022-1694(70)90255-6]
30. Gupta HV, Sorooshian S, Yapo PO. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J Hydrol Eng. 1999;4(2):135-43. [Link] [DOI:10.1061/(ASCE)1084-0699(1999)4:2(135)]
31. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE. 2007;50(3):885-900. [Link] [DOI:10.13031/2013.23153]
32. Akhavan S, Abedi-Koupai J, Mousavi SF, Afyuni M, Eslamian SS, Abbaspour KC. Application of SWAT model to investigate nitrate leaching in Hamadan-Bahar Watershed, Iran. Agric Ecosyst Environ. 2010;139(4):675-88. [Link] [DOI:10.1016/j.agee.2010.10.015]
33. Amini MA, Torkan G, Eslamian E, Zareian MJ, Besalatpour AA. Assessment of SWAT hydrological model in catchments' water balance simulation located in semi-arid regions (case study: Zayandeh-Rud River Basin). J Water Soil. 2018;32(5):849-63. [Persian] [Link]
34. Mahzari S, Kiani F, Azimi M, Khormali F. Using SWAT model to determine runoff, sediment yield and nitrate loss in gorganrood watershed, Iran. ECOPERSIA. 2016;4(2):1359-77. [Link] [DOI:10.18869/modares.ecopersia.4.2.1359]
35. Hosseini M, Ghafouri M, Tabatabaei M, Ebrahimi N, Zare Garizi A. Estimation of hydrologic budget for Gharasou Watershed, Iran. ECOPERSIA. 2016;4(3):1455-69. [Link] [DOI:10.18869/modares.ecopersia.4.3.1455]

Add your comments about this article : Your username or Email:

Send email to the article author