10.57647/j.jap.2024.0802.23

A study on climate change impacts using lumped versus distributed hydrological models in a semi-arid basin

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  • Farahnaz Baharvand1,
  • Ali Reza Massah Bavani2,
  • Mohammad Mahdavi3,
  • Massoud Goodarzi*4,
  1. Department of Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
  2. Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Pakdasht, Tehran, Iran
  3. Department of Fisheries, North Tehran Branch, Islamic Azad University, Tehran, Iran
  4. Department of Drought and Climate Change, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

Received: 2024-07-24

Revised: 2024-09-01

Accepted: 2024-09-08

Published 2024-12-15

Copyright (c) 2025 Farahnaz Baharvand, Ali Reza Massah Bavani, Mohammad Mahdavi, Massoud Goodarzi (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Baharvand, F., Massah Bavani, A. R., Mahdavi, M., & Goodarzi, M. (2024). A study on climate change impacts using lumped versus distributed hydrological models in a semi-arid basin. Anthropogenic Pollution, 8(2), 1-16. https://doi.org/10.57647/j.jap.2024.0802.23
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Abstract

This paper, aims to evaluate the impacts of potential climate change on the stream flow of a semi-arid catchment (called Merek) in western Iran using Distributed Catchment Scale Model (DiCaSM) and IHACRES lumped model and compare their ability in simulation of the future stream flow in this area. The joint probability plot was used to generate seasonal climatic change factors (% change in rainfall and change in temperature °C) to apply as an input to the DiCaSM model. A suite of 15 Atmosphere-Ocean Global Circulation Models (AOGCMs) from the Coupled Model Inter-comparison Project (CMIP) with monthly rainfall and temperature data for the baseline period were evaluated. By analyzing the models, finally, the three best models, including GFDL-CM3, CNRM-CM5 and NorESM1-M models, which reproduce the climatic behavior of monthly temperature and precipitation values, were selected. To study the impact of future climatic change on water supply, this study applied the RCP Scenarios. It proved an acceptable performance in reproducing of the historical observations three Representative Concentration Pathways (RCP 2.6, RCP4.5, and RCP8.5) scenarios for the future period 2040-2069. Results indicated that both hydrological models were able to simulate the observed stream flow successfully in the study catchment. The projections of three AOGCMs showed that the future temperature would be increased in the area, while there was no agreement between the models in simulation of future rainfall. Changes in stream flow simulated by DiCaSM model were ranged from -5.2% to 6.2% for the period 2040-2069, while for IHACRES model, the changes ranged from -37.7% to 10.1%. Overally the model performed extremely well for both the calibration and validation years. It is recommended to use these hydrological models for a general evaluation of climate change impact in water resources studies.

Keywords

  • Climate change,
  • DiCaSM,
  • IHACRES,
  • RCP scenarios,
  • Stream flow
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References

  1. Al-Safi HIJ, Kazemi H, Sarukkalige R (2019) Comparative study of conceptual versus distributed
  2. hydrologic modelling to evaluate the impact of climate change on future runoff in unregulated
  3. catchments. J Water Clim Chan. J Water Clim Change. (In Press).
  4. Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401:190-202. DOI:10.1016/j.jhydrol.2011.02.020
  5. Cunderlik J (2003) Hydrologic model selection for the CFCAS Project: Assessment of water resources risk and vulnerability to changing climatic conditions, Project Report I. University of Western Ontario, Canada.
  6. D’Agostino DR, Trisorio LG, Lamaddalena N, Ragab R (2010) Assessing the results of scenarios of climate and land use changes on the hydrology of an Italian catchment: modelling study. Hydrol Process 24:2693-2704. DOI:10.1002/hyp.7765
  7. Dakhlaoui H, Ruelland D, Tramblay Y, Bargaoui Z (2017) Evaluating the robustness of conceptual rainfall- runoff models under climate variability in northern Tunisia. J Hydrol 550:201-217. https://doi.org/10.1016/j.jhydrol.2017.04.032
  8. Dams J, Nossent J, Senbeta TB, Willems P, Batelaan, O (2015) Multi-model approach to assess the impact of climate change on runoff. J Hydrol 529:1601-1616. https://doi.org/10.1016/j.jhydrol.2015.08.023
  9. Davoodi Memar Otagvar, L. , Fataei, E. , Tajiabadi, M. and Naeimi, B. (2024). Spatio-temporal assessment of changes in groundwater quality in Qazvin Plain. Journal of Range and Watershed Managment, 77(3), 353-369. doi: 10.22059/jrwm.2024.370962.1744
  10. Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the River Thames, United Kingdom. Clim Change 69:245-268. https://doi.org/10.1007/s10584-005-1157-6
  11. Faiz MA, Liu D, Fu Q, Li M, Baig F, Tahir AA, Khan MI, Li T, Cui S (2018). Performance evaluation of hydrological models using ensemble of General Circulation Models in the north eastern China. J Hydrol 565: 599-613. DOI:10.3390/w13030299
  12. Fisher AC, Rubio SJ (1997) Adjusting to climate change: Implica-tions of increased variability and asymmetric adjustment costs for investment in water reserves. Environ Econ Manag 34:207–227. https://doi.org/10.1006/jeem.1997.1011
  13. Gash JHC, Lloyd CR, Lachaud G (1995) Estimating sparse rainfall interception with an analytical model. J Hydrol 170: 79-86. https://doi.org/10.1016/0022-1694(95)02697-N
  14. Gosling SN, Taylor RG, Arnell NW, Todd MC (2011) A Comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol Earth Syst Sci 15:279-294. https://doi.org/10.5194/hess-15-279-2011
  15. Hlavcova K, Stefunkova Z,Valent P, Kohnova S,Vyleta R, Szolgay J (2016) Modelling the climate change impact on monthly runoff in central Slovakia. Procedia Eng 161:2127-2132.
  16. Jakeman AJ, Hornberger GM (1994) How much complexity is warranted in a rainfall-runoff model? Water Resour Res 29:2637-2649. https://doi.org/10.1029/94WR01804
  17. Jakeman AJ, Littlewood IG, Withehead PG (1990) Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. J Hydrol 117:275-300. https://doi.org/10.1016/0022-1694(90)90097-H
  18. Jones RN, Chiew FHS, Boughton WC, Zhang L (2006) Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models. Adv Water Resour 29:1419-1429. https://doi.org/10.1016/j.advwatres.2005.11.001
  19. Kahil MT, Dinar A, Albiac J (2015) Modeling water scarcity and drought for policy adaptation to climate change in arid and semiarid regions. J Hydrol 522:95-109. http://dx.doi.org/10.1016/j.jhydrol.2014.12.042
  20. Karlsson IB, Sonnenborg TO, Refsgaard JC, Trolle D, Børgesen CD, Olesen JE, Jeppesen E, Jensen KH (2016) Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change. J Hydrol 535:301-317. https://doi.org/10.1016/j.jhydrol.2016.01.069
  21. Khoramnejadian, S., & Fatemi, F. (2017). Determination of lead and cadmium in the water of the damavand river, Iran. Appl Ecol Environ Res, 15(1), 439-444.
  22. Li F, Zhang Y, Xu Z,Teng J, Liu C, Liu W, Mpelasoka F (2013) The impact of climate change on runoff in the southeastern Tibetan Plateau. J Hydrol 505:188-201. https://doi.org/10.1016/j.jhydrol.2013.09.052
  23. Littlewood IG (2003) Improved unit hydrograph identification for seven Welsh rivers: implications for estimating continuous streamflow at ungauged sites. Hydrol Sci J 48:743-762. https://doi.org/10.1623/hysj.48.5.743.51454
  24. Menzel L, Burger G (2002) Climate change scenarios and runoff response in the Mulde catchment (Southern Elbe, Germany). J Hydrol 267:53-64. DOI:10.1016/S0022-1694(02)00139-7
  25. Montenegro A, Ragab R (2010) Hydrological response of a Brazilian semi-arid catchment to different land use and climate change scenarios: a modelling study. Hydrol Process 24:2705-2723. http://dx.doi.org/10.1002/hyp.7825
  26. Montenegro S, Ragab R (2012) Impact of possible climate and land use changes in the semi arid regions: A case study from North Eastern Brazil. J Hydrol 434-435:55-68. https://doi.org/10.1016/j.jhydrol.2012.02.036
  27. Morris D, Flavin R (1994) Sub-set of the UK 50 m by 50 m hydro-logical digital terrain model grids. NERC, Institute of Hydrology, Wallingford
  28. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I-A discussion of principles. J Hydrol 10:282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  29. Perra E, Piras M, Deidda R, Paniconi C, Mascaro G, Vivoni ER, Cau P, Marras PA, Ludwig R, Meyer S (2018) Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment. Hydrol Earth Syst Sci 22:4125-4143. https://doi.org/10.5194/hess-22-4125-2018, 2018.
  30. Ragab R, Bromley J (2010) IHMS-integrated hydrological modelling system. Part1: The hydrological processes and general structure. Hydrol Process 24:2663-2680. https://doi.org/10.1002/hyp.7681
  31. Ragab R, Bromley J, Dorflinger G, Katsikides S (2010) IHMS-integrated hydrological modelling system. Part 2.Application of linked unsaturated, DiCaSM and saturated zone, MODFLOW models on Kouris and Akrotiri catchment in Cyprus. Hydrol Process 24:2681-2692. https://doi.org/10.1002/hyp.7682
  32. Ragab R, Prudhomme C (2002) Climate change and water resources management in arid and semi-arid regions: Prospective and challenges for the 21st century. Biosyst Eng 81:3-34. https://doi.org/10.1006/bioe.2001.0013
  33. Rawls Wj, Brakensiek DL (1989) Estimation of soil water retention and hydraulic properties. In: Morel-Seytoux HJ (ed) Unsaturated Flow in Hydrologic Modeling: Theory and Practice. Kluwer Academic Publishers, Netherlands, pp. 275-300. https://doi.org/10.1007/978-94-009-2352-2_10
  34. Singh CR, Thompson JR, French JR, Kingston DG, Mackay AW (2010) Modelling the impact of prescribed global warming on runoff from headwater catchments of the Irrawaddy River and their implications for the water level regime of Loktak Lake, northeast India. Hydrol Earth Syst Sci 14:1745–1765 https://doi.org/10.5194/hess-14-1745-2010
  35. Solaymani HR, Gosain AK (2014) Assessment of climate change impacts in a semi-arid watershed in Iran using reginal climate models. J Water Clim Change 6:161-180. DOI:10.2166/wcc.2014.076
  36. Tan ML, Ibrahim AL, Yusop Z, Chua VP, Chan NW (2017) Climate change impact under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia. Atmos Res 189:1-10. https://doi.org/10.1016/j.atmosres.2017.01.008
  37. Tarboton DG (2003) Rainfall-Runoff processes, A workbook rainfall-runoff processes web module, chap 6: Simulation of runoff generation in hydrologic models. Technical Report, Utah State University, Logan, UT, USA.
  38. Teklesadik AD, Alemayehu T, van Griensven A, Kumar R, Liersch S, Eisner S et al (2017) Inter-model comparison of hydrological impacts of climate change on the Upper Blue Nile basin using ensemble of hydrological models and global climate models. Clim Change 141: 517–532. DOI: 10.1007/s10584-017-1913-4
  39. Vansteenkiste T, Tavakoli M, Ntegeka V, De Smedt F, Batelaan O, Pereira F, Willems P (2014) Intercomparison of hydrological model structures and calibration approaches in climate scenario impact projections. J Hydrol 519:743-755. https://doi.org/10.1016/j.jhydrol.2014.07.062
  40. Van Vuuren DP, Edmonds J, Kainuma M, Riahi K,Thomson A, Hibbard K et al. (2011) The representative concentration pathways: an overview. Clim Change 109:5-31. DOI: 10.1007/s10584-011-0148-z
  41. Venkataraman K, Tummuri S, Medina A, Perry J (2016) 21st century drought. Outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management. J Hydrol 534:300-316. https://doi.org/10.1016/j.jhydrol.2016.01.001
  42. Von Hoyningen-Huene J (1981) Die Interzeption des Niederschlags in Landwirtschaftlichen Pflanzenbestanden. Arbeitsbericht Deutscher Verband fur Wasserwirtschaft und Kulturbau, DVWK 57:1-53.
  43. Wade SD, Rance J, Reynard N (2013) The UK climate change risk assessment: assessing the impacts on water resources to inform policy makers. Water Resour Manage 27:1085–1109 DOI: 10.1007/s11269-012-0205-z
  44. Weyant J, Azar C, Kainuma M, Kejun J, Nakicenovic N, Shukla PR et al (2009) Report of 2.6 Versus 2.9 Watts/m2 RCPP Evaluation Panel. Intergovernmental Panel on Climate Change, Geneva, Switzerland. DOI:10.4081/gh.2016.421
  45. Wheater H, Sorooshian S, Sharma KD (2007) Hydrological Modelling in Arid and Semi-Arid Area. Cambridge University Press, Cambridge, UK. https://doi.org/10.1017/CBO9780511535734
  46. Wu J, Miao C, Zhang X,Yang T, Duan Q (2017) Detecting the quantitative hydrological response to changes in climate and human activities. Sci Total Environ 586:328-337. https://doi.org/10.3390/w14020257
  47. Yang W, Long D, Bai P (2019) Impact of future land cover and climate changes on runoff in the mostly afforested river basin in North China. J Hydrol 570:201-219. https://doi.org/10.1016/j.jhydrol.2018.12.055
  48. Zhang Y,You Q, Chen C, Ge J (2016) Impact of climate change on stream flows under RCP scenarios: A case study in Xin River Basin, China. Atmos Res 178:521-534. https://doi.org/10.1016/j.atmosres.2016.04.018
  49. Zhu D, Das S, Ren Q (2017) Hydrological appraisal of climate change impacts on the water resources of the Xijiang basin, South China. Water 9:793. https://doi.org/10.3390/w9100793