Abstract
Global warming has led to a high incidence of extreme hydrometeorological events. Predicting the spatial and temporal response of water resources to future climate change is necessary for the construction of regional water facilities. In this study, Global Circulation Models (GCMs) and the Soil and Water Assessment Tool (SWAT) were combined with the Delta downscaling method in the Dongyu River Basin (DRB), China. Three different GCMs under two Shared Socioeconomic Pathways (SSPs) 2–4.5 and 3–7.0 scenarios were considered. Relative to the baseline (2008–2018), streamflow under two scenarios was assessed from 2025 to 2064. The results show that the mean annual precipitation would rise by 12.3% and 12.8% under SSP2-4.5 and SSP3-7.0 scenarios, respectively. The mean monthly maximum temperature and minimum temperature would increase in summer but decrease in winter under two scenarios. Future climate change was estimated to increase the mean annual streamflow by 9.00% under SSP2-4.5 scenario and by 6.92% under SSP3-7.0 scenario. The wet season was forecast to arrive earlier and the frequency of extreme events would likely increase in the future. A high positive correlation was projected between the annual precipitation and the annual streamflow, with an average correlation coefficient of 0.76 (p < 0.05). The dominant climate variables would be different on the monthly scale. Streamflow in the dry season would be more susceptible to temperature changes. The findings of this study show how climate change affects streamflow in the DRB and give adaptive management a scientific foundation.
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References
Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Kløve 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: https://doi.org/10.1016/j.jhydrol.2015.03.027
Abdo KS, Fiseha BM, Rientjes T, Gieske A, Haile AT (2009) Assessment of climate change impacts on the hydrology of gilgel abay catchment in lake tana basin, ethiopia. Hydrological Processes 23(26):3661–3669, DOI: https://doi.org/10.1002/hyp.7363
Abou Rafee SA, Uvo CB, Martins JA, Domingues LM, Rudke AP, Fujita T, Freitas ED (2019) Large-scale hydrological modelling of the upper paraná river basin. Water 11(5):882, DOI: https://doi.org/10.3390/w11050882
Anand J, Gosain AK, Khosa R (2018) Prediction of land use changes based on land change modeler and attribution of changes in the water balance of ganga basin to land use change using the swat model. Science of the Total Environment 644:503–519, DOI: https://doi.org/10.1016/j.scitotenv.2018.07.017
Arnell NW (1999) Climate change and global water resources. Global Environmental Change-Human and Policy Dimensions 9:S31–S49, DOI: https://doi.org/10.1016/S0959-3780(99)00017-5
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologie modeling and assessment Part I: Model development. JAWRA Journal of the American Water Resources Association 34(1):73–89, DOI: https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
Baker TJ, Miller SN (2013) Using the soil and water assessment tool (swat) to assess land use impact on water resources in an east afriean watershed. Journal of Hydrology 486:100–111, DOI: https://doi.org/10.1016/j.jhydrol.2013.01.041
Bhatta B, Shrestha S, Shrestha PK, Talehabhadel R (2019) Evaluation and application of a swat model to assess the climate change impact on the hydrology of the himalayan river basin. Catena 181:104082, DOI: https://doi.org/10.1016/j.catena.2019.104082
Cohen JL, Furtado JC, Barlow MA, Alexeev VA, Cherry JE (2012) Arctic warming, increasing snow cover and widespread boreal winter cooling. Environmental Research Letters 7(1):14007, DOI: https://doi.org/10.1088/1748-9326/7/1/014007
Dahal V, Shakya NM, Bhattarai R (2016) Estimating the impact of climate change on water availability in bagmati basin, nepal. Environmental Processes 3(1):1–17, DOI: https://doi.org/10.1007/s40710-016-0127-5
Gosain AK, Rao S, Basuray D (2006) Climate change impact assessment on hydrology of indian river basins. Current Science 90(3):346–353, https://www.jstor.org/stable/24091868
Hosseini R Newlands NK, Dean CB, Takemura A (2015) Statistical modeling of soil moisture, integrating satellite remote-sensing (sar) and ground-based data. Remote Sensing 7(3):2752–2780, DOI: https://doi.org/10.3390/rs70302752
Ji L, Duan K (2019) What is the main driving force of hydrological cycle variations in the semiarid and semi-humid weihe river basin, china? Science of the Total Environment 684:254–264, DOI: https://doi.org/10.1016/j.scitotenv.2019.05.333
Jiang J, Zhou T, Chen X, Zhang L (2020) Future changes in precipitation over central asia based on cmip6 projections. Environmental Research Letters 15(5):54009, DOI: https://doi.org/10.1088/1748-9326/ab7d03
Johnston R, Smakhtin V (2014) Hydrological modeling of large river basins: How much is enough? Water Resources Management 28(10): 2695–2730, DOI: https://doi.org/10.1007/s11269-014-0637-8
Langen PL, Alexeev VA (2007) Polar amplification as a preferred response in an idealized aquaplanet gcm. Climate Dynamics 29(2):305–317, DOI: https://doi.org/10.1007/s00382-006-0221-x
Li C, Fang H (2021) Assessment of climate change impacts on the streamflow for the mun river in the mekong basin, southeast asia: Using swat model. Catena 201:105199, DOI: https://doi.org/10.1016/j.catena.2021.105199
Li H, Shi C, Sun P, Zhang Y, Collins AL (2021) Attribution of runoff changes in the main tributaries of the middle yellow river, China, based on the budyko model with a time-varying parameter. Catena 206:105557, DOI: https://doi.org/10.1016/j.catena.2021.105557
Li S (2012) Simulation of non-point source pollution of nitrogen, phosphorus using swat model and response of lacustrine deposit in nansihu basin. Dissertation, Shandong Normal University, Jinan, China (in Chinese)
Li Z, Fang H (2017) Modeling the impact of climate change on watershed discharge and sediment yield in the black soil region, northeastern china. Geomorphology 293:255–271, DOI: https://doi.org/10.1016/j.geomorph.2017.06.005
Liu W, Bailey RT, Andersen HE, Jeppesen E, Nielsen A, Peng K, Molina-Navarro E, Park S, Thodsen H, Trolle D (2020) Quantifying the effects of climate change on hydrological regime and stream biota in a groundwater-dominated catchment: A modelling approach combining swat-modflow with flow-biota empirical models. Science of the Total Environment 745:140933, DOI: https://doi.org/10.1016/j.scitotenv.2020.140933
Lu XC (2019) Research on future climate change in the nansi lake basin and its impact on the runoff of typical lake-entering rivers. Dissertation, Shandong University, Jinan, China (in Chinese)
Maeda EE, Pellikka PKE, Siljander M, Clark BJF (2010) Potential impacts of agricultural expansion and climate change on soil erosion in the eastern arc mountains of kenya. Geomorphology 123(3):279–289, DOI: https://doi.org/10.1016/j.geomorph.2010.07.019
Malago A, Bouraoui F, Vigiak O, Grizzetti B, Pastori M (2017) Modelling water and nutrient fluxes in the danube river basin with swat. Science of the Total Environment 603:196–218, DOI: https://doi.org/10.1016/j.scitotenv.2017.05.242
Meng X, Wang H (2017) Significance of the china meteorological assimilation driving datasets for the swat model (cmads) of east asia. Water 9(10):765, DOI: https://doi.org/10.3390/w9100765
Mengistu D, Bewket W, Dosio A, Panitz H (2021) Climate change impacts on water resources in the upper blue nile (abay) river basin, ethiopia. Journal of Hydrology 592:125614, DOI: https://doi.org/10.1016/j.jhydrol.2020.125614
Min SK, Zhang XB, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334):378–381, DOI: https://doi.org/10.1038/nature09763
Mosier TM, Hill DF, Sharp KV (2014) 30-arcsecond monthly climate surfaces with global land coverage. International Journal of Climatology 34(7):2175–2188, DOI: https://doi.org/10.1002/joc.3829
Murty PS, Pandey A, Suryavanshi S (2014) Application of semi-distributed hydrological model for basin level water balance of the ken basin of central india. Hydrological Processes 28(13):4119–4129, DOI: https://doi.org/10.1002/hyp.9950
Nilawar AP, Waikar ML (2019) Impacts of climate change on streamflow and sediment concentration under rcp 4.5 and 8.5: A case study in purna river basin, india. Science of the Total Environment 650:2685–2696, DOI: https://doi.org/10.1016/j.scitotenv.2018.09.334
O’Neill BC, Tebaldi C, van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque JF, Lowe J, Meehl GA, Moss R, Riahi K, Sanderson BM (2016) The scenario model intercomparison project (scenariomip) for cmip6. Geoscientific Model Development 9(9):3461–3482, DOI: https://doi.org/10.5194/gmd-9-3461-2016
Ouyang F, Zhu Y, Fu G, Lü H, Zhang A, Yu Z, Chen X (2015) Impacts of climate change under cmip5 rcp scenarios on streamflow in the huangnizhuang catchment. Stochastic Environmental Research and Risk Assessment 29(7):1781–1795, DOI: https://doi.org/10.1007/s00477-014-1018-9
Pan S, Liu D, Wang Z, Zhao Q, Zou H, Hou Y, Liu P, Xiong L (2017) Runoff responses to climate and land use/cover changes under future scenarios. Water 9(7):475, DOI: https://doi.org/10.3390/w9070475
Pan Z, Liu P, Gao S, Xia J, Chen J, Cheng L (2019) Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical bayesian regression framework. Hydrology and Earth System Sciences 23(8):3405–3421, DOI: https://doi.org/10.5194/hess-23-3405-2019
Paparrizos S, Mans F, Matzarakis A (2016) Integrated analysis of present and future responses of precipitation over selected greek areas with different climate conditions. Atmospheric Research 169:199–208, DOI: https://doi.org/10.1016/j.atmosres.2015.10.004
Salvacion AR, Magcale-Macandog DB, Cruz PCS, Saludes RB, Pangga IB, Cumagun CJR (2018) Evaluation and spatial downscaling of cru ts precipitation data in the philippines. Modeling Earth Systems and Environment 4(3):891–898, DOI: https://doi.org/10.1007/s40808-018-0477-2
Santos JYGD, Montenegro SMGL, Silva RMD, Santos CAG, Quinn NW, Dantas APX, Ribeiro Neto A (2021) Modeling the impacts of future lulc and climate change on runoff and sediment yield in a strategic basin in the caatinga/atlantic forest ecotone of brazil. Catena 203:105308, DOI: https://doi.org/10.1016/j.catena.2021.105308
Song YH, Chung E, Shahid S (2022) Differences in extremes and uncertainties in future runoff simulations using swat and lstm for ssp scenarios. Science of the Total Environment 156162, DOI: https://doi.org/10.1016/j.scitotenv.2022.156162
Tan ML, Ibrahim AL, Yusop Z, Chua VP, Chan NW (2017) Climate change impacts under cmip5 rcp scenarios on water resources of the kelantan river basin, malaysia. Atmospheric Research 189:1–10, DOI: https://doi.org/10.1016/j.atmosres.2017.01.008
Tuo Y, Duan Z, Disse M, Chiogna G (2016) Evaluation of precipitation input for swat modeling in alpine catchment: A case study in the adige river basin (italy). Science of the Total Environment 573:66–82, DOI: https://doi.org/10.1016/j.scitotenv.2016.08.034
Veettil AV, Mishra AK (2016) Water security assessment using blue and green water footprint concepts. Journal of Hydrology 542:589–602, DOI: https://doi.org/10.1016/j.jhydrol.2016.09.032
Wang Y, Bian J, Zhao Y, Tang J, Jia Z (2018) Assessment of future climate change impacts on nonpoint source pollution in snowmelt period for a cold area using swat. Scientific Reports 8(1), DOI: https://doi.org/10.1038/s41598-018-20818-y
Wang TL, Hamann A, Spittlehouse D, Carroll C (2016) Locally downscaled and spatially customizable climate data for historical and future periods for north america. Plos One 11(6), DOI: https://doi.org/10.1371/journal.pone.0156720
Wang GQ, Zhang JY, Xuan YQ, Liu JF, Jin JL, Bao ZX, He RM, Liu CS, Liu YL, Yan XL (2013) Simulating the impact of climate change on runoffin a typical river catchment of the loess plateau, china. Journal of Hydrometeorology 14(5):1553–1561, DOI: https://doi.org/10.1175/JHM-D-12-081.1
Yu XH (2016) Research on hydrological effects of land use changes in nansihu basin. Dissertation, China University of Mining and Technology (in Chinese)
Zaremehrjardy M, Victor J, Park S, Smerdon B, Alessi DS, Faramarzi M (2022) Assessment of snowmelt and groundwater-surface water dynamics in mountains, foothills, and plains regions in northern latitudes. Journal of Hydrology 606, DOI: https://doi.org/10.1016/j.jhydrol.2022.127449
Zhang YG, Nearing MA, Zhang XC, Xie Y, Wei H (2010) Projected rainfall erosivity changes under climate change from multimodel and multiscenario projections in northeast china. Journal of Hydrology 384(1):97–106, DOI: https://doi.org/10.1016/j.jhydrol.2010.01.013
Zhang X, Xu Y, Fu G (2014) Uncertainties in swat extreme flow simulation under climate change. Journal of Hydrology 515:205–222, DOI: https://doi.org/10.1016/j.jhydrol.2014.04.064
Zhang YQ, You QL, Chen CC, Ge J (2016) Impacts of climate change on streamflows under rcp scenarios: A case study in xin river basin, china. Atmospheric Research 178:521–534, DOI: https://doi.org/10.1016/j.atmosres.2016.04.018
Zhao GJ, Mu XM, Jiao JY, Gao P, Sun WY, Li EH, Wei YH, Huang JC (2018) Assessing response of sediment load variation to climate change and human activities with six different approaches. Science of the Total Environment 639:773–784, DOI: https://doi.org/10.1016/j.scitotenv.2018.05.154
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We would like to acknowledge the financial support from the Special Foundation of Shandong Engineering Research Center for Groundwater Environmental Protection and Remediation (Grant No.: 801KF2022-7).
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Yang, T., Yang, X., Jia, C. et al. Combining Downscaled Global Climate Model Data with SWAT to Assess Regional Climate Change Properties and Hydrological Responses. KSCE J Civ Eng 27, 2327–2338 (2023). https://doi.org/10.1007/s12205-023-2211-5
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DOI: https://doi.org/10.1007/s12205-023-2211-5