Runoff variation trend of Ganjiang River basin under SSP “Double Carbon” path

doi:10.12006/j.issn.1673-1719.2021.104

Abstract

Abstract:

According to the target and the peak time of carbon emissions under SSPs scenarios, SSPs were divided into “double carbon” pathway (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-3.4, SSP4-6.0) and “high carbon” pathway (SSP3-7.0, SSP5-8.5). The runoff evolution characteristics of Ganjiang River basin were analyzed in two periods of peak carbon dioxide emissions (2028-2032) and carbon neutral (2058-2062) based on the SWAT hydrological model. The results are as follows. (1) Annual mean temperature in the Ganjiang River basin from 1961 to 2017 showed a significant upward trend with a rate of 0.17℃/(10 a), while the precipitation increase slightly with a rate of 17 mm/(10 a). Under the “double carbon” and “high carbon” pathways, the Ganjiang River basin will be in a warm and wet state from 2021 to 2100, with temperature rising and precipitation increasing. (2) In the periods of peak carbon dioxide emissions and carbon neutral, annual runoff shows increasing tendency during “double carbon” pathway. In “double carbon” pathway, monthly runoff shows increasing tendency in all scenarios in flood season, while increasing tendency in SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP4-3.4 and decreasing tendency in SSP4-6.0 in dry season. The intensity of extreme hydrological events under the “double carbon” pathway will be less than under the “high carbon” pathways.

Key words: Shared Socioeconomic Pathways (SSPs), Runoff change, The Ganjiang River basin

YANG Chen-Hui, WANG Yan-Jun, SU Bu-Da, PU Yang, WANG Yuan, JIANG Tong. Runoff variation trend of Ganjiang River basin under SSP “Double Carbon” path[J]. Climate Change Research, 2022, 18(2): 177-187.

Figures/Tables 11

Fig. 1 Spatial distribution of Waizhou hydrological station and meteorological stations in Ganjiang River basin

Table 1 Basic information of 5 CMIP6 global climate models (GCMs)

Table 2 Basic information of SSPs

Fig. 2 The plan of “double carbon” pathway in China. (The gray shaded areas represent the period of peak carbon dioxide emissions (2028-2032) and the period of carbon neutral (2058-2062), respectively)

Table 3 Evaluation of SWAT model in calibration (1964-1980) and validation (1981-2000) periods

Fig. 3 Comparison of simulated and observed monthly runoff during calibration period (1964-1980) and validation period (1981-2000)

Fig. 4 Comparison of monthly mean temperature (a) and monthly precipitation (b) by multi-model ensemble mean and observation in 1961-2014. (The upper and lower limits represent the maximum and minimum values of multiple models)

Fig. 5 The mean of multi-model annual temperature (a) and precipitation (b) change under different SSPs scenarios from 1995 to 2100 in Ganjiang River basin. (Solid lines represent the multi-model mean, shadows represent the range of multiple models)

Fig. 6 The change rate of annual runoff in the period of peak carbon dioxide emissions and carbon neutral relative to the base period (1995-2014)

Fig. 7 The change rate of monthly runoff in the period of peak carbon dioxide emissions (a, c) and carbon neutral (b, d)relative to the base period

Table 4 The change rate of Q10 and Q90 in the period of peak carbon dioxide emissions and carbon neutral relative to the base period. (The maximum and minimum value of change rate is in brackets, and the average value is outside brackets) %

References 27

[1]	IPCC. Climate change 2013: the physical science basis[J]. Cambridge: Cambridge University Press, 2013
[2]	Santikarn M, Theuer S L, Eden A, et al. Emissions trading worldwide: status report 2019 [R]. Berlin: ICAP, 2019
[3]	Beaulieu E, Goddéris Y, Donnadieu Y, et al. High sensitivity of the continental-weathering carbon dioxide sink to future climate change[J]. Nature Climate Change, 2012, 2(5):346-349 doi: 10.1038/nclimate1419 URL
[4]	Griffin M T, Montz B E, Arrigo J S. Evaluating climate change induced water stress: a case study of the lower cape fear basin, NC[J]. Applied Geography, 2013, 40:115-128 doi: 10.1016/j.apgeog.2013.02.009 URL
[5]	刘璇, 郭家力, 张静文, 等. 气候变化影响下的赣江流域水资源变化趋势与幅度分析[J]. 水利水电技术, 2018, 49(6):39-46.
	Liu X, Guo J L, Zhang J W, et al. Analysis on variation trend and amplitude of water resources in Ganjiang River basin under impact of climate change[J]. Water Resources and Hydropower Engineering, 2018, 49(6):39-46 (in Chinese)
[6]	刘子豪, 陆建忠, 黄建武, 等. 基于CMIP5模式鄱阳湖流域未来参考作物蒸散量预估[J]. 湖泊科学, 2019, 31(6):1685-1697.
	Liu Z H, Lu J Z, Huang J W, et al. Prediction and trend of future reference crop evapotranspiration in the Poyang Lake basin based on CMIP5 models[J]. Journal of Lake Sciences, 2019, 31(6):1685-1697 (in Chinese)
[7]	赵梦霞, 苏布达, 王艳君, 等. 气候变化对东部季风区赣江和官厅流域径流的影响[J]. 气候变化研究进展, 2020, 16(6):679-689.
	Zhao M X, Su B D, Wang Y J, et al. Impacts of climate change on river runoff at the Ganjiang and Guanting River basins in the eastern monsoon region[J]. Climate Change Research, 2020, 16(6):679-689 (in Chinese)
[8]	周梦瑶, 袁飞, 张利敏, 等. 未来气候变化对赣江上游区极端径流影响预估[J]. 水电能源科学, 2020, 38(1):5-8.
	Zhou M Y, Yuan F, Zhang L M, et al. Projection of future climate change impacts on extreme runoff in the upper reaches of Ganjiang River basin[J]. Water Resources and Power, 2020, 38(1):5-8 (in Chinese)
[9]	O'neill B C, Tebaldi C, van Vuuren D P, et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6[J]. Geoscientific Model Development, 2016, 9(9):3461-3482 doi: 10.5194/gmd-9-3461-2016 URL
[10]	Riahi K, van Vuuren D P, Kriegler E, et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview[J]. Global Environmental Change, 2017, 42:153-168 doi: 10.1016/j.gloenvcha.2016.05.009 URL
[11]	姜彤, 王艳君, 苏布达, 等. 全球气候变化中的人类活动视角: 社会经济情景的演变[J]. 南京信息工程大学学报: 自然科学版, 2020, 12(1):68-80.
	Jiang T, Wang Y J, Su B D, et al. Perspectives of human activities in global climate change: evolution of socio-economic scenarios[J]. Journal of Nanjing University of Information Science & Technology: Natural Science Edition, 2020, 12(1):68-80 (in Chinese)
[12]	姜彤, 吕嫣冉, 黄金龙, 等. CMIP6模式新情景(SSP-RCP)概述及其在淮河流域的应用[J]. 气象科技进展, 2020, 10(5):102-109.
	Jiang T, Lǚ Y R, Huang J L, et al. New scenarios of CMIP6 model (SSP-RCP) and its application in the Huaihe River basin[J]. Advances in Meteorological Science and Technology, 2020, 10(5):102-109 (in Chinese)
[13]	Li H, Sheffield J, Wood E F. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching[J]. Journal of Geophysical Research, 2010, 115:D10101 doi: 10.1029/2009JD012882 URL
[14]	Wood A W, Leung L R, Sridhar V, et al. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J]. Climate Change, 2004, 62(1-3):189-216 doi: 10.1023/B:CLIM.0000013685.99609.9e URL
[15]	Rogelj J, Popp A, Calvin K V, et al. Scenarios towards limiting global mean temperature increase below 1.5℃[J]. Nature Climate Change, 2018, 8(4):325-332 doi: 10.1038/s41558-018-0091-3
[16]	Gidden M J, Riahi K, Smith S J, et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century[J]. Geoscientific Model Development, 2019, 12(4):1443-1475 doi: 10.5194/gmd-12-1443-2019 URL
[17]	van Marle M J E, Kloster S, Magi B I, et al. Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750-2015)[J]. Geoscientific Model Development, 2017, 10(9):3329-3357 doi: 10.5194/gmd-10-3329-2017 URL
[18]	Hoesly R M, Smith S J, Feng L, et al. Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)[J]. Geoscientific Model Development, 2018, 11(1):369-408 doi: 10.5194/gmd-11-369-2018 URL
[19]	王书霞, 张利平, 喻笑勇, 等. 遥感降水产品在澜沧江流域径流模拟中的适用性研究[J]. 长江流域资源与环境, 2019, 28(6):1365-1374.
	Wang S X, Zhang L P, Yu X Y, et al. Application of remote sensing precipitation products in runoff simulation over the Lancang River basin[J]. Resources and Environment in The Yangtze Basin, 2019, 28(6):1365-1374 (in Chinese)
[20]	Kundzewicz Z W, Huang S, Szcześniak M, et al. Projections of runoff in the Vistula and the Odra river basins with the help of the SWAT model[J]. Hydrology Research, 2018, 49(2):303-317 doi: 10.2166/nh.2017.280 URL
[21]	Worku T, Khare D, Tripathi S K. Modeling runoff-sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed[J]. Environmental Earth Sciences, 2017, 76(16):1-14 doi: 10.1007/s12665-016-6304-z URL
[22]	Bieger K, Hörmann G, Fohrer N. Detailed spatial analysis of SWAT-simulated surface runoff and sediment yield in a mountainous watershed in China[J]. Hydrological Sciences Journal, 2015, 60(5):784-800
[23]	Alavian V, Qaddumi H M, Dickson E, et al. Water and climate change: understanding the risks and making climate-smart investment decisions[M]. Washington, DC: World Bank, 2009
[24]	Guo H, Hu Q, Jiang T. Annual and seasonal streamflow responses to climate and land-cover changes in the Poyang Lake basin, China[J]. Journal of Hydrology, 2008, 355(1-4):106-122 doi: 10.1016/j.jhydrol.2008.03.020 URL
[25]	熊翰林. 赣江流域径流对气候变化的响应[D]. 南昌: 南昌工程学院, 2018: 29-33.
	Xiong H L. Runoff simulation in Ganjiang River basin and its response to climate change[D]. Nanchang: Nanchang Institute of Technology, 2018: 29-33 (in Chinese)
[26]	Su B, Huang J, Zeng X, et al. Impacts of climate change on streamflow in the upper Yangtze River basin[J]. Climatic Change, 2016, 141(3):533-546 doi: 10.1007/s10584-016-1852-5 URL
[27]	Wen S, Su B, Wang Y, et al. Comprehensive evaluation of hydrological models for climate change impact assessment in the upper Yangtze River basin, China[J]. Climatic Change, 2020, 163(3):1207-1226 doi: 10.1007/s10584-020-02929-6 URL