The effect of climate change on the Fariman Dam watershed health using VOR model

Document Type : Research/Original/Regular Article

Authors

1 M.Sc. Student/ Range and Watershed Management Department, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran

2 Assistant Professor/ Range and Watershed Management Department, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran

3 Professor/ Range and Watershed Management Department, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction
Ecosystem health is the ability of ecosystems to maintain structure and function in the face of external pressures over time. The knowledge of watershed health with a systemic approach seeks to conserve the natural ecosystem by protecting healthy watersheds and preventing changes in them. Assessing watershed health and prioritizing sub-watersheds is essential for effective watershed management and will help in proper management and optimal allocation of resources. Considering that watersheds are dynamic systems, the hydrological function and health of watersheds are constantly changing under the influence of land use changes, climate change, and human interventions. The emission of greenhouse gases in recent decades has caused global warming, followed by changes in the hydrological regime and function of watersheds, which can threaten the health of the watersheds. In order to evaluate the health status of the ecosystem, various methods such as pressure-state-response (PSR), vigor-organization-resilience (VOR), reliability-resilience-vulnerability (RRV), and watershed health index (WHI) have been presented which determine the watershed health using several indicators. The aim of this research is to evaluate the health of the Fariman dam watershed in Khorasan Razavi province under current and future climate using the VOR model and hydrological simulation.
 
Materials and Methods
In order to achieve the research objectives, the hydrology of the watershed was simulated using the SWAT model. For this purpose, parameters sensitivity analysis, calibration, and validation of the model were performed using the SUFI-2 algorithm in SWAT-CUP software using daily discharge and suspended sediment yield data for the period of 2008-2014 and 2016-2019. Then, using the VOR model, the health of the watershed was calculated for the historical period of 1985-2014. In the VOR model, the indicators of landscape, soil erosion, and water loss were used to determine the components of the vigor, organization, and resilience of the watershed. The landscape indicators were determined using the watershed land use map in FRAGSTATS 4.2.1 software and indicators related to watershed hydrology (sediment yield and runoff) achieved from the output of the SWAT model. To assess the effect of climate change on watershed hydrology, precipitation and temperature data from CMCC-ESM2, GFDL-ESM4, and MRI-ESM2-0 climate models of IPCC sixth assessment report for three SSP1-2.6, SSP2-4.5 and SSP5-8.5 emission scenarios for two future time period (2030-2059 and 2070-2099), were downloaded. Then, CMhyd software was used for bias correction and downscaling of climate data. In the end, the SWAT model was run and the health index was calculated for future periods and compared with the historical period.
 
Results and Discussion
Calibration results of the SWAT model showed that Nash-Sutcliffe criterion for discharge and monthly sediment in the calibration period was 0.66 and 0.65, respectively. Nash-Sutcliffe criteria values ​​for the validation period were 0.57 and 0.56 respectively for discharge and sediment. The results of watershed health by VOR model in the historical period showed that the average health index of the sub-watersheds for MRI-ESM2-0, GFDL-ESM4, and CMCC-ESM2 models is 0.545, 0.533, and 0.665, respectively. The average index of all three models is 0.581 which means the watershed health status is "Moderate". The presented results show that in the SSP1-2.6 scenario in the period of 2030-2059, the health index in three sub-watersheds 2, 8 and 9 has decreased by 16.1, 3.6, and 0.6% (average 7.6%) compared to the historical period (1985-2014). The health index has decreased in 4 sub-watersheds in the SSP2-4.5 scenario and in 6 sub-watersheds in the SSP5-8.5 scenario. The average reduction in the SSP2-4.5 scenario is 9.3 percent and in the SSP5-8.5 scenario, it is 10.6%. The health index of sub-watersheds 2 and 9 has decreased in all emission scenarios and the health index of sub-watersheds 5 has decreased only in the SSP5-8.5 by 10.7 %. As a result, watershed health in the future and under climate change indicated that in the period of 2030- 2059 with the increase of greenhouse gas emissions, the number of sub-watersheds with a decrease in watershed health index will increase from three sub-watersheds in the SSP1-2.6 to 4 and 6 sub-watersheds in the SSP2 -4.5 and SSP5-8.5. In other words, the watershed health index has decreased in 34.6 % of the watershed area in the SSP1-2.6, while in the SSP2-4.5, 51 % and in the SSP5-8.5, 5.65 % of the watershed area will experience a decrease in health. Also, The results for the period 2070-2099 show that in the SSP1-2.6, the health index has decreased in sub-watersheds 2, 3, 5, 6, and 9 with an average of 11.2%, in the SSP2-4.5 scenario, sub-watersheds 2, 5, 7, 8, and 9 with an average of 5.1% and in the SSP5-8.5 scenario, sub-watersheds 2, 4, 5, 6, 8 and 9 with an average of 7.5% had a more decreasing trend. Sub-watersheds 2, 5, and 9 had a decreasing trend in all three scenarios, and sub-watersheds 3, 4, and 7 only had a decrease only in SSP1-2.6, SSP5-8.5, and SSP2-4.5 scenarios. The results in the period of 2070-2099 indicate that the watershed health index in the SSP1-2.6 has decreased in 50.1% of the watershed area, while in the SSP2-4.5, it was 56.3%, and in the SSP5-8.5, 65.5% of the watershed area.
 
Conclusion
The results showed that the overall watershed health index in the study area based on the VOR model is “moderate”, but with the increase in the amount of greenhouse gas emissions and the increase in temperature, the watershed health index decreases in a larger number of sub-watersheds, as in the SSP1 -2.6, the watershed health index has decreased in 34.6 % of the watershed, while in the SSP2-4.5, 51 % and in the SSP5-8.5 scenario, 65.5 % of the watershed area has been associated with a decrease in health. Overall, the results of the research showed that climate change can affect the watershed health index, and these effects are different in various sub-watersheds.

Keywords

Main Subjects

References

References
 
Abbaspour, K.C. (2009). User manual for SWAT-CUP2, SWAT calibration and uncertainty analysis programs. Swis Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland, 95 pages.
Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Klve, B. (2015). A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752. doi:10.1016/j.jhydrol.2015.03.027
Ahn, S.R., & Kim, S.J. (2019). Assessment of watershed health, vulnerability and resilience for determining protection and restoration Priorities. Environmental Modelling & Software, 122, 1-19. doi:10.1016/j.envsoft.2017.03.014
Alilou, H., Rahmati, O.P., Singhc, V., Choubin, B., Pradhan, B., Keesstra, S., Ghiasi, S.S., & Sadeghi, S.H.R. (2019). Evaluation of watershed health using Fuzzy-ANP approach considering geoenvironmental and topo-hydrological criteria. Journal of Environmental Management, 232, 22-36. doi:10.1016/j.jenvman.2018.11.019
Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., & Kannan, N. (2012). SWAT: model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.
Babaian, A., Modirian, R., Khazanedari, L., Kohi, M., Kozegran, S., Flamerzi, Y., Karimian, M., & Malboosi, Sh. (2021). Outlook of rainfall and temperature of iran in the 21st century using SSP socio-economic scenarios. Final report of research project, Climatology Research Institute. [In Persian]
Briak, H., Moussadek, R., Aboumaria, K., & Mrabet, R. (2016). Assessing sediment yield in Kalaya gauged watershed (Northern Morocco) using GIS and SWAT model. International Soil and Water Conservation Research, 4(3), 177-185. doi:10.1016/j.iswcr.2016.08.002
Costanza, R. (1992). Toward an operational definition of health. Pp. 239–256, In: Ecosystem Health—New Goals for Environmental Management, Norton BD (editors), Washington, DC: Inland Press.
Costanza, R. (2012). Ecosystem health and ecological engineering. Ecological Engineering, 45, 24–29. doi:10.1016/j.ecoleng.2012.03.023
Ding, Y., Wang, W., Chang, X., & Zhao, S. (2008). Ecosystem health assessment in Inner Mongolia region based on remote sensing and GIS. The international archives of the photogrammetry, remote sensing and spatial information sciences, XXXVII, Part B1, 1029-1034.
Duan, Z., Song, X., & Liu, J. (2009). Application of SWAT for sediment yield estimation in a mountainous agricultural basin. In Geoinformatics, 17th International Conference on (1-5).
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., & Rummukainen, M. (2014). Evaluation of climate models. Pp. 741-866, In: Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor,S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.), Climate Change 2013, The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Fallah-Ghalhari, G., Shakeri, F., & DadashiRoudbari, A. (2019). Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology, 138(3-4), 1539-1562. doi:10.1007/s00704-019-02906-9
Ghafari, H., & Gorji, M. (2021). Evaluation of soil erosion effects on rainfed wheat (Triticum aestivum) yield using SWAT model. Water and Soil Management and Modeling, 1(3), 53-66. doi:10.22098/mmws.2021.9267.1029 [In Persian]
Hazbavi, Z., Parchami, N., Alaei, N., & Babaei, L. (2020). Assessment and Analysis of the KoozehTopraghi Watershed health status, Ardabil Province, Iran, Journal of Water and Soil Resources Conservation, 9(3), 121-142. dor:20.1001.1.22517480.1399.9.3.8.0 [In Persian]
Hazbavi, Z., & Sadeghi, S.H.R. (2017). Watershed health characterization using reliability–resilience–vulnerability conceptual framework based on hydrological responses. Land Degradation & Development, 28(5), 1528-1537. doi:10.1002/ldr.2680
Hazbavi, Z., & Sadeghi, S.H.R. (2017). Watershed health (part three): vigor, organization and resilience conceptual model. Promotion and Development of Watershed Management, 5(16), 373-393. [In Persian]
Hazbavi, Z., Sadeghi, S.H.R., Gholamalifard, M., & Davudirad, A.A. (2020). Watershed health assessment using the pressure–state–response (PSR) framework. Land Degradation & Development, 31(1), 3-19. doi:10.1002/ldr.3420
Hijioka, Y., Lin, E., Pereira, J.J., Corlett, R., Cui, X., Insarov, G., Surjan, A., Field, C., Barros, V., & Mach, K. (2014). Asia Climate Change 2014: Impacts, Adaptation, and Vulnerability, Pp. 351-365, In: IPCC Working Group II Contribution to AR5, Cambridge, U. Press, Cambridge UK and New York, USA, 139(3).
Holling, C.S. (1986). The resilience of terrestrial ecosystems; local surprise and global change. Pp.  292-320, In: Clark, W.C. and Munn, R.E., Sustainable Development of the Biosphere.
IPCC, (2007). Summary for policymakers. in climate change 2007: impacts, adaptation and vulnerability. Pp. 7-22, In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., and Hanson, C.E. Eds., Contribution of working group ii to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press.
Jafari, A., Keivan-behjou, F., & Mostafazadeh, R. (2017). Comparing the conditions of different Ecosystem Health components in Iiril watershed, Ardabil Province. Desert Ecosystem Engineering Journal, 6(16), 81-92. doi:10.22052/6.16.81 [In Persian]
Jahandari, J., Hejazi, R., Jozi, S.A., & Moradi, A. (2022). Impacts of urban expansion on spatio-temporal patterns of carbon storage ecosystem service in Bandar Abbas Watershed using InVEST software. Water and Soil Management and Modelling, 2(4), 91-106. doi:10.22098/mmws.2022.11069.1097 [In Persian]
Khorooshi, S., Mostafazadeh, R., Esmali Ouri, A., & Raoof, M. (2017). ‏Spatiotemporal assessment of the hydrologic river health index variations in Ardabil Province Watersheds. Iranian journal of Ecohydrology, 4(2), 379-393. doi:10.22059/ije.2017.61475 [In Persian]
Mageau, M.T., Costanza, R., & Ulanowicz, R.E. (1998). Quantifyingthe trends associated with developing ecosystems. Ecological Modeling, 1-22. doi:10.1016/s0304-3800(98)00092-1
MEA, (2005). Ecosystems and Human Well-Being. Washington DC: Island Press, 155 pages.
Moriasi, D.N., Gitau, M.W., Pai, N., & Daggupati, P. (2015). Hydrologic and water quality models: performance measures and evaluation criteria. Transactions of the ASABE, 58(6), 1763-1785. doi:10.13031/trans.58.10715
Naseri, F., Azari, M., & Dastoorani, M.T. (2018). Simulation of stream flow and sediment yield in Fariman Dam Watershed using SWAT model and genetic algorithm. Water and Soil Journal (Agricultural Sciences and Industries), 32(3), 447-462. doi:10.22067/jsw.v32i3.68900 [In Persian]
Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., & Arnold, J.G. (2016). CMhyd user manual. Doc. Prep. Simulated Clim. Change Data Hydrol, Impact Study.
Ray, A., Pandey, V.P., & Thapa, B.R. (2022). An assessment of climate change impacts on water sufficiency: The case of Extended East Rapti watershed, Nepal. Environmental Research, 113434. doi:10.1016/j.envres.2022.113434
Redman, C.L. (1999). Human impact on ancient environments. University of Arizona Press, Tucson, AZ, 239 pages.
Ross, E.R. & Randhir, T.O. (2022). Effects of climate and land use changes on water quantity and quality of coastal watersheds of Narragansett Bay. Science of the total environment, 807, 151082. doi:10.1016/j.scitotenv.2021.151082
Sadeghi, S.H.R., & Hazbavi, Z. (2017). Spatiotemporal variation of watershed health propensity through reliability-resilience-vulnerability based drought index (case study: Shazand Watershed in Iran). Science of the Total Environment, 587, 168-176. doi:10.1016/j.scitotenv.2017.02.098
Sadeghi, S.H.R., Hazbavi, Z., & Gholamalifard, M. (2019). Zonation of health dynamism for the Shazand Watershed based on low and high flow discharges. Watershed Engineering and Management, 11(3), 589-608. doi:10.22092/ijwmse.2018.120288.1427 [In Persian]
Sharafati, A., Nabaei, S., & Shahid, S. (2020). Spatial assessment of meteorological drought features over different climate regions in Iran. International Journal of Climatology, 40(3), 1864-1884. doi:10.1002/joc.6307
Singh, R., Kayastha, S.P., & Pandey, V.P. (2022). Climate change and river health of the Marshyangdi Watershed, Nepal: An assessment using integrated approach. Environmental Research, 114104. doi:10.1016/j.envres.2022.114104
Suo, A.N., Xiong, Y.C., Wang, T.M., Yue, D.X., & Ge, J.P. (2008). Ecosystem health assessment of the Jinghe River watershed on the huangtu plateau. International Association for Ecology and Health, 5, 127–136. doi:10.1007/s10393-008-0167-z
Wohl, E., Angermeier, P.L., Bledsoe, B., Kondolf, G.M., MacDonnell, L., Merritt, D.M., Palmer, M.A., Poff, N.L., & Tarboton, D. (2005). River restoration. Water Resources Research, 41(10301), 1-12.
Xiao, R., Liu, Y., Fei, X., Yu, W., Zhang, Z., & Meng, Q. (2019). Ecosystem health assessment: A comprehensive and detailed analysis of the case study in coastal metropolitan region, eastern China. Ecological indicators, 98, 363-376. doi:10.1016/j.ecolind.2018.11.010
Xiaoyan, L., Yuanfeng, Z., & Jianzhong, Z. (2006). Healthy Yellow River’s essence and indicators. Journal of Geographical Sciences, 16(3), 259-270. doi:10.1007/s11442-006-0301-1
Xu, F.L., Jorgensen, S.E., & Shu, T. (1999). Ecological indicators for assessing freshwater ecosystem health. Ecological Modeling, 116, 77–106. doi:10.1016/s0304-3800(98)00160-4
Yang, J., Reicher, P., Abbaspour, K.C., Xia, J., & Yang, H. (2008). Comparing uncertainty analysis techniques for a SWAT application to the Chao he Basin in China. Journal of Hydrology, 358 (1–2), 1-23. doi:10.1016/j.jhydrol.2008.05.012
 
Water and Soil Management and Modelling
Volume 3, Issue 4
December 2023
Pages 107-121

Files

History

  • Receive Date: 17 October 2022
  • Revise Date: 07 November 2022
  • Accept Date: 08 November 2022

Share

How to cite

Statistics