Quantifying climate change impacts on hydropower production under CMIP6 multi-model ensemble projections using SWAT model

References

• Abbaspour, K.C., et al., 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333 (2–4), 413–430. doi:10.1016/j.jhydrol.2006.09.014.
   Web of Science ®Google Scholar
• Abbaspour, K.C., 2015a. SWAT-CUP2: SWAT calibration and uncertainty programs - a user manual. Duebendorf, Switzerland: Eawag - Swiss Federal Institute of Aquatic Science and Technology.
   Google Scholar
• Abbaspour, K.C., et al., 2015b. 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.
   Web of Science ®Google Scholar
• Abbaspour, K.C., Johnson, C.A., and van Genuchten, M.T., 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3 (4), 1340–1352. doi:10.2136/vzj2004.1340.
   Web of Science ®Google Scholar
• Adam, J.C., Hamlet, A.F., and Lettenmaier, D.P., 2009. Implications of global climate change for snowmelt hydrology in the twenty‐1st century. Hydrological Processes, 23 (7), 962–972. doi:10.1002/hyp.7201.
   Web of Science ®Google Scholar
• Ahmed, K., et al., 2019. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences, 23 (11), 4803–4824. doi:10.5194/hess-23-4803-2019.
   Web of Science ®Google Scholar
• Almeida, M.P., Perpiñán, O., and Narvarte, L., 2015. PV power forecast using a nonparametric PV model. Solar Energy, 115, 354–368. doi:10.1016/j.solener.2015.03.006
   Web of Science ®Google Scholar
• Arnold, J.G., et al., 2013. SWAT 2012 input/output documentation. Texas: Texas Water Resources Institute.
   Google Scholar
• Bağçaci, S.Ç., et al., 2021. Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: a Mediterranean hot spot case, Turkey. Atmospheric Research, 256, 105576. doi:10.1016/j.atmosres.2021.105576.
   Web of Science ®Google Scholar
• Barnett, T.P., Adam, J.C., and Lettenmaier, D.P., 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303–309. doi:10.1038/nature04141
   PubMed Web of Science ®Google Scholar
• Bentsen, M., et al., 2019a. NCC NorESM2-MM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.8040.
   Google Scholar
• Bentsen, M., et al., 2019b. NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.608.
   Google Scholar
• Boisrame, G., 2011. WGNmaker4.1.xlsm Microsoft Excel macro [online]. Available from: https://swat.tamu.edu/software/ [Accessed 2 July 2022].
   Google Scholar
• Boucher, O., et al., 2018. IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.5195.
   Google Scholar
• Boucher, O., et al., 2019. IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.1532.
   Google Scholar
• Bozkurt, D. and Sen, O.L., 2013. Climate change impacts in the Euphrates-Tigris Basin based on different model and scenario simulations. Journal of Hydrology, 480, 149–161. doi:10.1016/j.jhydrol.2012.12.021
   Web of Science ®Google Scholar
• Cao, J., 2019. NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2027.
   Google Scholar
• Cao, J. and Wang, B., 2019. NUIST NESMv3 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.8769.
   Google Scholar
• Chen, J., et al., 2013. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49 (7), 4187–4205. doi:10.1002/wrcr.20331.
   Web of Science ®Google Scholar
• Chen, W., Jiang, Z., and Li, L., 2011. Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate, 24 (17), 4741–4756. doi:10.1175/2011JCLI4102.1.
   Web of Science ®Google Scholar
• Chenoweth, J., et al., 2011. Impact of climate change on the water resources of the eastern Mediterranean and Middle East region: modeled 21st century changes and implications. Water Resources Research, 47 (6), W06506. doi:10.1029/2010WR010269.
   Google Scholar
• Cygwin, 2022. Cygwin user’s guide [online]. Available from: https://www.cygwin.com/cygwin-ug-net/cygwin-ug-net.pdf [Accessed 20 May 2022].
   Google Scholar
• Daggupati, P., et al., 2017. Spatial and temporal patterns of precipitation and stream flow variations in Tigris-Euphrates river basin. Environmental Monitoring and Assessment, 189 (2), 50. doi:10.1007/s10661-016-5752-y.
   PubMed Web of Science ®Google Scholar
• Dix, M., et al., 2019a. CSIRO-ARCCSSACCESS-CM2 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4271.
   Google Scholar
• Dix, M., et al., 2019b. CSIRO-ARCCSSACCESS-CM2 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2285.
   Google Scholar
• DSI (General Directorate of State Hydraulic Works), 2022. Electrical power resources survey and development administration (EIE) - flow gauging yearbooks (1935-2011). Ankara: General Directorate of State Hydraulic Works.
   Google Scholar
• Duratorre, T., et al., 2020. Hydropower potential in the Alps under climate change scenarios: the Chavonne Plant, Val D’Aosta. Water, 12 (7), 2011. doi:10.3390/w12072011.
   Web of Science ®Google Scholar
• EC-Earth (EC-Earth Consortium), 2019a. EC-Earth-ConsortiumEC-Earth3 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4700.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2019b. EC-Earth-ConsortiumEC-Earth3 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.251.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2019c. EC-Earth-ConsortiumEC-Earth3-Veg model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4706.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2019d. EC-Earth-ConsortiumEC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.727.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2020a. EC-Earth-ConsortiumEC-Earth3-Veg-LR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4707.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2020b. EC-Earth-ConsortiumEC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.728.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2021a. EC-Earth-ConsortiumEC-Earth-3-CC model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4702.
   Google Scholar
• EC-Earth (EC-Earth Consortium), 2021b. EC-Earth-ConsortiumEC-Earth-3-CC model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.15327.
   Google Scholar
• EC-JRC (European Commission - Joint Research Centre), 2006. The Global Land Cover 2000 (GLC2000) products [online]. Available from: https://forobs.jrc.ec.europa.eu/products/glc2000/products.php [Accessed 28 June 2022].
   Google Scholar
• Ehsani, N., et al., 2017. Reservoir operations under climate change: storage capacity options to mitigate risk. Journal of Hydrology, 555, 435–446. doi:10.1016/j.jhydrol.2017.09.008.
   Web of Science ®Google Scholar
• EN-SU (EN-SU Engineering and Consultancy Limited Company), 2008. The Dipni Dam and HEPP project feasibility report. Ankara: EN-SU Engineering and Consultancy Limited Company.
   Google Scholar
• ESGF (Earth System Grid Federation), 2022. WCRP coupled model intercomparison project (Phase 6) [online]. Available from: https://esgf-node.llnl.gov/projects/cmip6/ [Accessed 15 May 2022].
   Google Scholar
• FAO (Food and Agriculture Organization of the United Nations), 2007. Digital Soil Map of the World (DSMW) [online]. Available from: https://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 [Accessed 28 June 2022].
   Google Scholar
• Farsani, I.F., et al., 2019. Assessment of the impact of climate change on spatiotemporal variability of blue and green water resources under CMIP3 and CMIP5 models in a highly mountainous watershed. Theoretical and Applied Climatology, 136, 169–184. doi:10.1007/s00704-018-2474-9.
   Web of Science ®Google Scholar
• FPGA (Euphrates Planning Group Authority), 1968. The Tigris Basin reconnaissance report. Ankara: General Directorate of State Hydraulic Works - Euphrates Planning Group Authority.
   Google Scholar
• Giorgi, F., 2006. Climate change hot-spots. Geophysical Research Letters, 33 (8), L08707. doi:10.1029/2006GL025734.
   Web of Science ®Google Scholar
• Guo, H., et al., 2018a. NOAA-GFDLGFDL-CM4 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.8594.
   Google Scholar
• Guo, H., et al., 2018b. NOAA-GFDLGFDL-CM4 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.9242.
   Google Scholar
• Gupta, H.V., et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. Journal of Hydrology, 377 (1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003.
   Web of Science ®Google Scholar
• Hasson, S.U., 2016. Future water availability from Hindukush-Karakoram-Himalaya upper Indus Basin under conflicting climate change scenarios. Climate, 4 (3), 40. doi:10.3390/cli4030040.
   Google Scholar
• IPCC (Intergovernmental Panel on Climate Change), 2021. Summary for policymakers. In: V. Masson-Delmotte, et al., eds. Climate change 2021: the physical science basis. Contribution of working group I to the IPCC sixth assessment report. Cambridge and New York: Cambridge University Press, 3–32.
   Google Scholar
• John, J.G., et al., 2018. NOAA-GFDLGFDL-ESM4 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.1414.
   Google Scholar
• Jones, P.W., 1999. First- and second-order conservative remapping schemes for grids in spherical coordinates. Monthly Weather Review, 127 (9), 2204–2210. doi:10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.
   Web of Science ®Google Scholar
• Jungclaus, J., et al., 2019. MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.6594.
   Google Scholar
• Kim, J., Ivanov, V.Y., and Fatichi, S., 2016. Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stochastic Environmental Research and Risk Assessment, 30, 923–944. doi:10.1007/s00477-015-1097-2
   Web of Science ®Google Scholar
• Kim, Y.H., et al., 2019a. KIOST KIOST-ESM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.5296.
   Google Scholar
• Kim, Y.H., et al., 2019b. KIOST KIOST-ESM model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.11241.
   Google Scholar
• Kitoh, A., Yatagai, A., and Alpert, P., 2008. First super-high-resolution model projection that the ancient “Fertile Crescent” will disappear in this century. Hydrological Research Letters, 2, 1–4. doi:10.3178/hrl.2.1.
   Google Scholar
• Krasting, J.P., et al., 2018. NOAA-GFDLGFDL-ESM4 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.8597.
   Google Scholar
• Lee, W.-L. and Liang, H.-C., 2020a. AS-RCEC TaiESM1.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.9755.
   Google Scholar
• Lee, W.-L. and Liang, H.-C., 2020b. AS-RCEC TaiESM1.0 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.9688.
   Google Scholar
• Legates, D.R. and McCabe, G.J., 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35 (1), 233–241. doi:10.1029/1998WR900018.
   Web of Science ®Google Scholar
• Lelieveld, J., et al., 2012. Climate change and impacts in the Eastern Mediterranean and the Middle East. Climatic Change, 114 (3–4), 667–687. doi:10.1007/s10584-012-0418-4.
   PubMed Web of Science ®Google Scholar
• Lemann, T., Roth, V., and Zeleke, G., 2017. Impact of precipitation and temperature changes on hydrological responses of small-scale catchments in the Ethiopian Highlands. Hydrological Sciences Journal, 62 (2), 270–282. doi:10.1080/02626667.2016.1217415.
   Web of Science ®Google Scholar
• Li, L., 2019a. CAS FGOALS-g3 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.3356.
   Google Scholar
• Li, L., 2019b. CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2056.
   Google Scholar
• Liersch, S., 2003. Dewpoint estimation programs: dew.exe and dew02.exe [online]. Available from: https://swat.tamu.edu/software/ [Accessed 2 July 2022].
   Google Scholar
• Lovato, T., Peano, D., and Butenschön, M., 2021a. CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.13195.
   Google Scholar
• Lovato, T., Peano, D., and Butenschön, M., 2021b. CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.13168.
   Google Scholar
• MGM (Turkish State Meteorological Service), 2022a. Annual maximum precipitation records in standard times for the Bingol meteorological station. Ankara: Turkish State Meteorological Service.
   Google Scholar
• MGM (Turkish State Meteorological Service), 2022b. Daily precipitation, maximum and minimum air temperature, solar radiation, wind speed, and relative humidity records of the Bingol meteorological station. Ankara: Turkish State Meteorological Service.
   Google Scholar
• MGM (Turkish State Meteorological Service), 2022c. Long-term all parameters bulletin for the Bingol meteorological station. Ankara: Turkish State Meteorological Service.
   Google Scholar
• Miao, C., et al., 2012. On the applicability of temperature and precipitation data from CMIP3 for China. PLoS ONE, 7 (9), e44659. doi:10.1371/journal.pone.0044659.
   PubMed Web of Science ®Google Scholar
• Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50 (3), 885–900. doi:10.13031/2013.23153.
   Web of Science ®Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I - a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6.
   Google Scholar
• Nazeer, A., et al., 2022. Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections. Scientific Reports, 12, 21442. doi:10.1038/s41598-022-25673-6.
   PubMedGoogle Scholar
• Neitsch, S.L., et al., 2011. Soil and water assessment tool theoretical documentation version 2009. Texas: Texas Water Resources Institute.
   Google Scholar
• Nilsson, C., et al., 2005. Fragmentation and flow regulation of the world’s large river systems. Science, 308 (5720), 405–408. doi:10.1126/science.1107887.
   PubMed Web of Science ®Google Scholar
• Nohara, D., et al., 2006. Impact of climate change on river discharge projected by multimodel ensemble. Journal of Hydrometeorology, 7 (5), 1076–1089. doi:10.1175/JHM531.1.
   Web of Science ®Google Scholar
• O’Neill, B.C., et al., 2016. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9 (9), 3461–3482. doi:10.5194/gmd-9-3461-2016.
   Web of Science ®Google Scholar
• Özdoğan, M., 2011. Climate change impacts on snow water availability in the Euphrates-Tigris basin. Hydrology and Earth System Sciences, 15 (9), 2789–2803. doi:10.5194/hess-15-2789-2011.
   Web of Science ®Google Scholar
• Qin, P., et al., 2022. Projected impacts of climate change on major dams in the Upper Yangtze River Basin. Climatic Change, 170 (1–2), 8. doi:10.1007/s10584-021-03303-w.
   Web of Science ®Google Scholar
• Rathjens, H., et al., 2016. CMhyd user manual: documentation for preparing simulated climate change data for hydrologic impact studies [online]. Available from: https://swat.tamu.edu/media/115265/bias_cor_man.pdf [Accessed 25 May 2022].
   Google Scholar
• Roberts, N.M. and Lean, H.W., 2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Monthly Weather Review, 136 (1), 78–97. doi:10.1175/2007MWR2123.1.
   Web of Science ®Google Scholar
• Santhi, C., et al., 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. Journal of the American Water Resources Association, 37 (5), 1169–1188. doi:10.1111/j.1752-1688.2001.tb03630.x.
   Web of Science ®Google Scholar
• Schulzweida, U., 2021. CDO user guide version 2.0.5. Hamburg: Max Planck Institute for Meteorology.
   Google Scholar
• Schupfner, M., et al., 2019. DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2450.
   Google Scholar
• Seker, M. and Gumus, V., 2022. Projection of temperature and precipitation in the Mediterranean region through multi-model ensemble from CMIP6. Atmospheric Research, 280, 106440. doi:10.1016/j.atmosres.2022.106440
   Web of Science ®Google Scholar
• Seland, Ø., et al., 2019a. NCC NorESM2-LM model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.8036.
   Google Scholar
• Seland, Ø., et al., 2019b. NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.604.
   Google Scholar
• Şen, Z., 2019. Climate change expectations in the upper Tigris River basin, Turkey. Theoretical and Applied Climatology, 137, 1569–1585. doi:10.1007/s00704-018-2694-z.
   Web of Science ®Google Scholar
• Shiogama, H., Abe, M., and Tatebe, H., 2019. MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.898.
   Google Scholar
• Siqueira, P.P., et al., 2021. Effects of climate and land cover changes on water availability in a Brazilian Cerrado basin. Journal of Hydrology: Regional Studies, 37, 100931. doi:10.1016/j.ejrh.2021.100931.
   Google Scholar
• Stewart, I.T., 2009. Changes in snowpack and snowmelt runoff for key mountain regions. Hydrological Processes, 23 (1), 78–94. doi:10.1002/hyp.7128.
   Web of Science ®Google Scholar
• Sun, C., et al., 2022. CMIP6 model simulation of concurrent continental warming holes in Eurasia and North America since 1990 and their relation to the Indo-Pacific SST warming. Global and Planetary Change, 213, 103824. doi:10.1016/j.gloplacha.2022.103824.
   Web of Science ®Google Scholar
• Swart, N.C., et al., 2019a. CCCma CanESM5 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.3610.
   Google Scholar
• Swart, N.C., et al., 2019b. CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.1317.
   Google Scholar
• Tan, M.L., et al., 2020. Southeast Asia HydrO-meteorological droughT (SEA-HOT) framework: a case study in the Kelantan River Basin, Malaysia. Atmospheric Research, 246, 105155. doi:10.1016/j.atmosres.2020.105155.
   Web of Science ®Google Scholar
• Tatebe, H. and Watanabe, M., 2018. MIROC MIROC6 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.5603.
   Google Scholar
• Troin, M. and Caya, D., 2014. Evaluating the SWAT’s snow hydrology over a Northern Quebec watershed. Hydrological Processes, 28 (4), 1858–1873. doi:10.1002/hyp.9730.
   Web of Science ®Google Scholar
• Tumsa, B.C., 2022. Performance assessment of six bias correction methods using observed and RCM data at upper Awash basin, Oromia, Ethiopia. Journal of Water and Climate Change, 13 (2), 664–683. doi:10.2166/wcc.2021.181.
   Web of Science ®Google Scholar
• Turner, S.W.D., Ng, J.Y., and Galelli, S., 2017. Examining global electricity supply vulnerability to climate change using a high-fidelity hydropower dam model. Science of the Total Environment, 590-591, 663–675. doi:10.1016/j.scitotenv.2017.03.022.
   PubMed Web of Science ®Google Scholar
• USGS (United States Geological Survey), 2014. Shuttle Radar Topography Mission (SRTM): 1 arc-second global elevation database [online]. Available from: https://earthexplorer.usgs.gov/ [Accessed 28 June 2022].
   Google Scholar
• Usul, N., 2009. Engineering hydrology. Ankara: METU Press.
   Google Scholar
• van Vliet, M.T.H., et al., 2016. Power-generation system vulnerability and adaptation to changes in climate and water resources. Nature Climate Change, 6, 375–380. doi:10.1038/nclimate2903.
   Web of Science ®Google Scholar
• Volodin, E., et al., 2019a. INM INM-CM4-8 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.5069.
   Google Scholar
• Volodin, E., et al., 2019b. INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.12321.
   Google Scholar
• Volodin, E., et al., 2019c. INM INM-CM5-0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.5070.
   Google Scholar
• Volodin, E., et al., 2019d. INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.12322.
   Google Scholar
• Wasti, A., et al., 2022. Climate change and the hydropower sector: a global review. Wiley Interdisciplinary Reviews: Climate Change, 13 (2), e757. doi:10.1002/wcc.757.
   Web of Science ®Google Scholar
• Weigel, A.P., et al., 2010. Risks of model weighting in multimodel climate projections. Journal of Climate, 23 (15), 4175–4191. doi:10.1175/2010JCLI3594.1.
   Web of Science ®Google Scholar
• Wen, K., Gao, B., and Li, M., 2021. Quantifying the impact of future climate change on runoff in the Amur River Basin using a distributed hydrological model and CMIP6 GCM projections. Atmosphere, 12 (12), 1560. doi:10.3390/atmos12121560.
   Web of Science ®Google Scholar
• Wieners, K.-H., et al., 2019a. MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.6595.
   Google Scholar
• Wieners, K.-H., et al., 2019b. MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.793.
   Google Scholar
• Wu, T., et al., 2018. BCC BCC-CSM2MR model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2948.
   Google Scholar
• Xiang, Y., et al., 2022. Impact of climate change on the hydrological regime of the Yarkant River Basin, China: an assessment using three SSP scenarios of CMIP6 GCMs. Remote Sensing, 14 (1), 115. doi:10.3390/rs14010115.
   Web of Science ®Google Scholar
• Xin, X., et al., 2019. BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.1732.
   Google Scholar
• Yalcin, E., 2019. Estimation of irrigation return flow on monthly time resolution using SWAT model under limited data availability. Hydrological Sciences Journal, 64 (13), 1588–1604. doi:10.1080/02626667.2019.1662025.
   Web of Science ®Google Scholar
• Yalcin, E. and Tigrek, S., 2019. The Tigris hydropower system operations: the need for an integrated approach. International Journal of Water Resources Development, 35 (1), 110–125. doi:10.1080/07900627.2017.1369867.
   Web of Science ®Google Scholar
• Yukimoto, S., et al., 2019a. MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.6842.
   Google Scholar
• Yukimoto, S., et al., 2019b. MRI MRI-ESM2.0 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.638.
   Google Scholar
• Ziehn, T., et al., 2019a. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.4272.
   Google Scholar
• Ziehn, T., et al., 2019b. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp245 and ScenarioMIP ssp585. Earth System Grid Federation. doi:10.22033/ESGF/CMIP6.2291.
   Google Scholar
• Zittis, G., et al., 2022. Climate change and weather extremes in the Eastern Mediterranean and Middle East. Reviews of Geophysics, 60 (3), e2021RG000762. doi:10.1029/2021RG000762.
   Web of Science ®Google Scholar