Integrated Watershed Management

Integrated Watershed Management

Simulation of climate change scenarios using the CMIP6 models (Case study: Taleqan Watershed)

Document Type : Original Article

Authors
Parvin Mohammadi 1
Arash Malekian 1
Ali Salajegheh 1
Mojtaba Nouri 2
Hamed Rafiei 3
1 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resource, University of Tehran, Karaj, Iran
2 Iran Water Resources Management Company, Tehran, Iran
3 Department of Agricultural Economics, Faculty of Agriculture, University of Tehran, Karaj, Iran
10.22034/iwm.2024.2022313.1132
Abstract
Extended Abstract
Introduction: The industrialization of communities has led to an increase in greenhouse gases over recent decades. This increase has caused the warming of the earth's atmosphere, and it affected other components of the climate system and led to climate change. The evidence confirms that the global average temperature of the Earth is increasing, especially in recent years. Moreover, precipitation intensity has changed over time. It is expected that changes in temperature and precipitation will cause a series of climatic extreme events. Therefore, it is an undeniable fact that the intensification of global climate change has affected the development and survival of mankind. The purpose of this research is to investigate and predict these changes in the future decades in the Taleqan watershed, Iran.
Materials and methods: The study area, Taleqan watershed with a mountainous topography, is located in the northwest of Alborz province, Iran. The annual precipitation and temperature of the region are 485.8mm and 11.4℃, respectively. To estimate and generate data for the future period (2021-2040), we used daily data from regional stations, including precipitation data from the base period (1979-2014) and average temperature data from the base period (2003-2014). We also utilized output data from the General Circulation Model (CanESM5) under climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). CanESM5 is a global model developed for simulating future climate change and developing seasonal and decadal forecasts. CanESM5 is usually used for large-scale projections, therefore, SDSM is chosen to downscale climate data. The changes of average precipitation and temperature parameters for three future periods (2021-2040, 2041-2060, and 2081-2100) were evaluated and RMSE, MAD and R were used to evaluate model accuracy.
Results and Discussion: The greatest increase in precipitation in Armot station is in March, May and November and the greatest decrease in precipitation in September and October is predicted under the SSP scenarios for the periods 2021-2040, 2041-2060 and 2081-2100. In Sakranchal station, the highest increase in precipitation is in three periods of March, February and May, and the highest decrease is in September and October under the SSP1-2.6, SSP2-4.5 and SSP5-8.5. The highest increase in precipitation in the Zidasht station is in March, May and November, April and December under the SSP scenarios. Also, the biggest decrease is in September under the SSP scenarios for the three forecast periods. In Gateh Deh station, the highest increase in precipitation during the periods of 2021-2040 and 2081-2100 is related to March, May and November, and for the period of 2041-2060, it is related to the months of March, February and May under the SSP5-8.5. During the periods of 2021-2040 and 2041-2060, the greatest decrease will be in September under the SSP scenarios, also in the period of 2081-2100, the greatest decrease will be in July under the SSP scenarios and in October, under the SSP5-8.5. During three periods, the greatest increase in Jovestan station precipitation is predicted in February, March, May and November. Also, the greatest decrease in the period of 2021-2040 will be in September and August under the SSP5-8.5 and in the periods of 2041-2060 and 2081-2100 in October and July under the SSP1-2.6. Based on the results of the temperature forecast in Zidasht station during three periods, the average and average maximum temperature in January and February under the SSP scenarios have a decreasing trend and other months show an increasing trend.
Conclusion: The results show that precipitation has a decreasing trend in some months and some increasing trend. The obtained results indicate that the precipitation and temperature variables in the periods of the 2021-2040, 2041-2060 and 2081-2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 will experience an increasing trend compared to the base period. The highest increase in temperature and precipitation is in the period of 2021-2040 under scenarios SSP2-4.5 and SSP1-2.6, respectively. This study demonstrates that Taleqan watershed will be vulnerable to future climate change. An increase in temperature can cause snowmelt and reduce snow storage. The stability time of water reserves in the watershed will be reduced. Precipitation changes in the region can alter the precipitation pattern from snow to rain. This reduces surface and underground water, and can affect crop yield.
Keywords
Climate change
Downscaling
Taleqan
SSP scenarios
CMIP6
SDSM

Subjects

Chai, T. and Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), pp.1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
Cheng, T.F., Lu, M. and Dai, L., 2019. The zonal oscillation and the driving mechanisms of the extreme western North Pacific subtropical high and its impacts on East Asian summer precipitation. Journal of Climate, 32(10), 3025-3050. https://doi.org/10.1175/JCLI-D-18-0076.1
Croitoru, A.E., Piticar, A. and Burada, D.C., 2016. Changes in precipitation extremes in Romania. Quaternary International, 415, 325-335. https://doi.org/10.1016/j.quaint.2015.07.028
Dao, P.U., Heuzard, A.G., Le, T.X.H., Zhao, J., Yin, R., Shang, C. and Fan, C., 2023. The impacts of climate change on groundwater quality: A review. Science of The Total Environment, p.169241. https://doi.org/10.1016/j.scitotenv.2023.169241
Drobinski, P., Silva, N.D., Panthou, G., Bastin, S., Muller, C., Ahrens, B., Borga, M., Conte, D., Fosser, G., Giorgi, F. and Güttler, I., 2018. Scaling precipitation extremes with temperature in the Mediterranean: past climate assessment and projection in anthropogenic scenarios. Climate dynamics, 51, pp.1237-1257. https://doi.org/10.1007/s00382-016-3083-x
Duan, R., Huang, G., Li, Y., Zhou, X., Ren, J. and Tian, C., 2021. Stepwise clustering future meteorological drought projection and multi-level factorial analysis under climate change: A case study of the Pearl River Basin, China. Environmental Research, 196, 110368. https://doi.org/10.1016/j.envres.2020.110368
Ershadfath, F., Raeini Sarjaz, M., Shahnazari, A., Eivind Olesen, J., 2022. Application of Linear Scaling Post-Processing Method for Bias Correction of Climate Models Retrieved from CMIP6. Iran-Water Resources Research, 18(3), 131-144. https://doi.org/20.1001.1.17352347.1401.18.3.9.1  (in Persian)
Feng, J. and Hu, D., 2014. How much does heat content of the western tropical Pacific Ocean modulate the South China Sea summer monsoon onset in the last four decades?. Journal of Geophysical Research: Oceans, 119(7), 4029-4044. https://doi.org/10.1002/2013JC009683
Funk, C., Shukla, S., Thiaw, W.M., Rowland, J., Hoell, A., McNally, A., Husak, G., Novella, N., Budde, M., Peters-Lidard, C., Adoum, A., 2019. Recognizing the famine early warning systems network: over 30 years of drought early warning science advances and partnerships promoting global food security. Bulletin of the American Meteorological Society, 100(6), 1011-1027. https://doi.org/10.1175/BAMS-D-17-0233.1
Goodarzi, M.R., Abedi, M.J., Pour, M.H., 2022. Climate change and trend analysis of precipitation and temperature: A case study of Gilan, Iran. In Current Directions in Water Scarcity Research 7, 561-587. Elsevier. https://doi.org/10.1016/B978-0-323-91910-4.00032-7
Goodarzi, M.R., Fatehifar, A., Moradi, A., 2020. Predicting future flood frequency under climate change using Copula function. Water and Environment Journal, 34, 710-727. https://doi.org/10.1111/wej.12572
Guptha, G.C., Swain, S., Al-Ansari, N., Taloor, A.K. and Dayal, D., 2021. Evaluation of an urban drainage system and its resilience using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 23, 100601. https://doi.org/10.1016/j.rsase.2021.100601
Guptha, G.C., Swain, S., Al-Ansari, N., Taloor, A.K. and Dayal, D., 2022. Assessing the role of SuDS in resilience enhancement of urban drainage system: A case study of Gurugram City, India. Urban Climate, 41, p.101075. https://doi.org/10.1016/j.uclim.2021.101075
Hanafi, 2023. Climate change and water crisis in West Asia. Amad and Defense Technology Quarterly, 6(3), pp.153-178. (in Persian(
Hassani, M.R., Niksokhan, M.H., Ardestani, M., Mousavi Janbehsarayi, S.F., 2023. Evaluating the effects of climate change on urban runoff based on CMIP6 models (case study: district 10 of Tehran municipality). Water and Soil Management and Modeling 3(2), 269-285. https://doi.org/10.22098/MMWS.2022.11849.1176 (in Persian)
IPCC, C.C., 2014. IPCC Fifth Assessment Synthesis Report-Summary for Policymakers-an Assessment of Inter-Governmental Panel on Climate Change.
Iranshahi, M., Ebrahimi, B., Yousefi, H., Moridi, A., 2022. Investigating the Effects of Climate Change on Temperature and Precipitation Using Neural Network and CMIP6 (Case Study: Aleshtar and Khorramabad Stations). Journal of Water and Irrigation Management, 12 (4), 821-845. https://doi.org/10.22059/JWIM.2022.346796.1009 (in Persian)
Kamruzzaman, M., Wahid, S., Shahid, S., Alam, E., Mainuddin, M., Islam, H.T., Cho, J., Rahman, M.M., Biswas, J.C. and Thorp, K.R., 2023. Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs. Heliyon, 9(5). https://doi.org/10.1016/j.heliyon.2023.e16274
Karamouz, M., Nazif, S., Zahmatkesh, Z., 2013. Self-organizing Gaussian-based downscaling of climate data for simulation of urban drainage systems. Journal of Irrigation and Drainage Engineering, 139(2), 98-112. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000500
Kodinariya, T.M. and Makwana, P.R., 2013. Review on determining number of Cluster in K-Means Clustering. International Journal, 1(6), 90-95.
Legates, D.R., 2002. Limitations of climate models as predictors of climate change. Brief analysis, 396.
Liu, C., Yang, C., Yang, Q., Wang, J., 2021. Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China. Scientific Reports, 11(1), 1280. https://doi.org/10.1038/s41598-020-80527-3
Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L., Lamarque, J.F., Matsumoto, K., Montzka, S.A., Raper, S.C., Riahi, K. and Thomson, A.G.J.M.V., 2011. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, pp.213-241. https://doi.org/10.1007/s10584-011-0156-z
Mo, C., Tang, P., Huang, K., Lei, X., Lai, S., Deng, J., Bao, M., Sun, G., Xing, Z., 2023. Evolution of Drought Trends under Climate Change Scenarios in Karst Basin. Water, 15(10), 1934. https://doi.org/10.3390/w15101934
Modaresi, F., Araghinejad, S., Ebrahimi, K. and Kholghy, M., 2012. Assessment of climate change effects on the annual water yield of rivers: A case study of Gorganroud river, Iran. Water and Soil, 25(6). https://doi.org/10.22067/JSW.V0I0.12152 (in Persian)
Nandi, S. and Swain, S., 2023. Analysis of heatwave characteristics under climate change over three highly populated cities of South India: a CMIP6-based assessment. Environmental Science and Pollution Research, 1-13. https://doi.org/10.1007/s11356-022-22398-x
Niroumand fard, F., Khashei Sivaki, A., Hashemi, R., Ghorbani, Kh., 2022. Investigation of Climate Change Projection on Temperature and Precipitation Parameters Using CMIP6 Models (Case Study: Birjand Station). Iranian Journal of Soil and Water Research 53 (9), 2009-2026. https://doi.org/10.22059/IJSWR.2022.343936.669284 (in Persian)
O’Neill, B.C., Kriegler, E., Riahi, K., Ebi, K.L., Hallegatte, S., Carter, T.R., Mathur, R. and Van Vuuren, D.P., 2014. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change, 122, pp.387-400. https://doi.org/10.1007/s10584-013-0905-2
O'Neill, B.C., Tebaldi, C., Van Vuuren, D.P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.F., Lowe, J. and Meehl, G.A., 2016. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), pp.3461-3482. https://doi.org/10.5194/gmd-9-3461-2016
Patel, P., Thakur, P.K., Aggarwal, S.P., Garg, V., Dhote, P.R., Nikam, B.R., Swain, S. and Al-Ansari, N., 2022. Revisiting 2013 Uttarakhand flash floods through hydrological evaluation of precipitation data sources and morphometric prioritization. Geomatics, Natural Hazards and Risk, 13(1), 646-666. https://doi.org/10.1080/19475705.2022.2038696
Pervez, M.S. and Henebry, G.M., 2014. Projections of the Ganges–Brahmaputra precipitation—Downscaled from GCM predictors. Journal of Hydrology, 517, 120-134. https://doi.org/10.1016/j.jhydrol.2014.05.016
Ramezanipour, M., 2018. Forecasting climate Oscillations for three decades; Case study: Mazandaran province. Physical Geography Quarterly, 11(39), pp.115-130.‎ (in Persian)
Riahi, K., van Vuuren, D.P., Kriegler, E., Edmonds, J., O’Neill, B.C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J.C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenoder, ¨ F., Da Silva, L.A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J.C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., Tavoni, M., 2017. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, pp.153-168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
Roshani, A., Hamidi, M., 2022. Forecasting the effects of climate change scenarios on temperature and precipitation based on CMIP6 models (Case study: Sari station). Jornal of Water and Irrigation Management 11(4), 781-795. https://doi.org/10.22059/JWIM.2022.330603.920 (in Persian)
Rummukainen, M., 2010. State‐of‐the‐art with regional climate models. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 82-96. https://doi.org/10.1002/wcc.8
Sahoo, S., Majumder, A., Swain, S., Gareema, Pateriya, B. and Al-Ansari, N., 2022. Analysis of decadal land use changes and its impacts on urban heat island (UHI) using remote sensing-based approach: a smart city perspective. Sustainability, 14(19), 11892. https://doi.org/10.3390/su141911892
Sarabi, M., Dastourani, M., Zarin, A., 2021. Investigating the impact of future climate changes on precipitation (Case study: Mashhad Dam Targ watershed). The 8th National Conference on Water Resources Management of Iran. (in Persian)
Sayad, Vahidah., 2023. Analysis of studies on the effect of climate change in the future of the world and strategies to reduce its harmful effects. Amad and Defense Technology Quarterly, 6(3), pp.127-152. (in Persian)
Shoja, F., Shamsipour, A., 2023. Projection of future Precipitation changes in Tehran's water supply watersheds. Journal of Natural Environmental Hazards 12(36), 151-180. https://doi.org/10.22111/JNEH.2022.42622.1908 (in Persian)
Singh, D., Gupta, R.D., Jain, S.K., 2015. Assessment of impact of climate change on water resources in a hilly river basin. Arabian Journal of Geosciences, 8, 10625-10646. https://doi.org/10.1007/s12517-015-1985-2
Song, Z., Xia, J., She, D., Li, L., Hu, C., Hong, S., 2021. Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China. Journal of Hydrology, 601,126643. https://doi.org/10.1016/j.jhydrol.2021.126643
Swart, N.C., Cole, J.N., Kharin, V.V., Lazare, M., Scinocca, J.F., Gillett, N.P., Anstey, J., Arora, V., Christian, J.R., Hanna, S. and Jiao, Y., 2019. The Canadian earth system model version 5 (CanESM5. 0.3). Geoscientific Model Development, 12(11), 4823-4873. https://doi.org/10.5194/gmd-12-4823-2019
Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American meteorological Society, 93(4), 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1
Wang, Z., Zhong, R., Lai, C., Zeng, Z., Lian, Y. and Bai, X., 2018. Climate change enhances the severity and variability of drought in the Pearl River Basin in South China in the 21st century. Agricultural and Forest Meteorology, 249, 149-162. https://doi.org/10.1016/j.agrformet.2017.12.077
Wilby, R.L. and Dawson, C.W., 2013. The statistical downscaling model: insights from one decade of application. International Journal of Climatology, 33(7), pp.1707-1719. https://doi.org/10.1002/joc.3544
Wilby, R.L., Dawson, C.W. and Barrow, E.M., 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2), pp.145-157. https://doi.org/10.1016/S1364-8152(01)00060-3
Zarrin, A., Dadashi-Rodbari, A., Salehabadi, N., 2021. Projected temperature anomalies and trends in different climate zones in Iran based on CMIP6. Iranian Journal of Geophysics, 15(1), 35-54. https://doi.org/10.30499/IJG.2020.249997.1292 (in Persian)

  • Receive Date 06 February 2024
  • Revise Date 29 March 2024
  • Accept Date 30 April 2024