多区域气候模式集合对中国径流深的模拟评估和未来变化预估

doi:10.12006/j.issn.1673-1719.2021.165

摘要/Abstract

摘要：

对5组区域气候模式集合模拟的中国径流深进行评估,并且预估了温室气体高排放情景RCP8.5下的未来变化。结果表明：多区域气候模式集合结果能够基本模拟出径流深的观测特征,对年径流深的空间分布特征模拟较好,但量值存在一定的系统偏差,特别是黄河中游、海河和松辽河存在明显的正偏差,且对全国9个流域片中东南、西南和西北诸河的年内分配总体模拟效果相对较差。未来到21世纪末,全国平均年径流深在各个时段都以增加为主,增加幅度多在5%以内。未来变化存在明显的空间差异,大致表现为“北增南减”的分布特征,但不会改变中国水资源南多北少的空间格局;其中,黄河、西南和西北诸河流域片呈显著的增加趋势,淮河、长江和东南诸河流域片呈现显著的减少趋势,海河、松辽和珠江流域的变化趋势不显著。21世纪末期各地的变化多在±30%以内,且多模式预估的正负变化一致性较高。到21世纪末期,各流域片平均的径流深季节分配总体特征没有明显变化,径流深的最大月份基本维持不变,分配比例的数值有±2%以内的变化,且各季节的增减变化存在明显流域间差异。

关键词: 区域气候模式, 径流深, 评估, 预估

Abstract:

The evaluation on simulated runoff over China from the five regional climate model ensemble simulations was conducted. Based on these simulations, future changes in runoff under the high emission scenario RCP8.5 were also projected. The results show that the ensemble mean simulations can well capture the observed features of runoff. The simulation on spatial pattern of annual runoff performs well, while certain biases exist, especially the positive biases over the middle reach of Yellow River basin, the Haihe River basin, and the Song-Liao River basin. The simulations on the monthly contributions perform relatively poor over Southeastern, Southwestern, and Northwestern rivers basins among nine basins of China. From now to the end of 21st century, national mean annual runoff will mostly increase, with the magnitude of less than 5%. There are spatial differences in annual runoff changes, with roughly “increase in north, decrease in south”. However, the climatic pattern of runoff ranging from wet south to dry north will not change. The area-averaged trends are significant positive over the Yellow River, Southwestern and Northwestern rivers basins, significant negative over the Huaihe River, Yangtze River, and Southeastern rivers basins, and there are no significant trends over the Haihe River, Song-Liao River and Zhujiang River basins. At the end of 21st century, the changes are mostly within ±30% across China, and the agreements on change sign are high. At the end of 21st century, the overall characteristics of monthly contributions over each basin will change little, with the peak months mostly unchanged. The changes in monthly contributions will be within ±2%, and there are large differences among nine basins in increases or decreases at certain month or season.

Key words: Regional climate model, Runoff, Evaluation, Projection

韩振宇, 徐影, 吴佳, 石英. 多区域气候模式集合对中国径流深的模拟评估和未来变化预估[J]. 气候变化研究进展, 2022, 18(3): 305-318.

HAN Zhen-Yu, XU Ying, WU Jia, SHI Ying. Evaluation on the simulated runoff in China and future change projection based on multiple regional climate models[J]. Climate Change Research, 2022, 18(3): 305-318.

图/表 8

表1 5套径流深格点化观测数据的概况

Table 1 Basic information on five gridded runoff observation data

图1 不同观测数据(a~e)和集合模拟(f)的1986—1995年平均年径流深空间分布,以及数据间的空间相关系数 (SCOR,g)、RMSE (h)和泰勒评分(i)

Fig. 1 Annual runoff averaged over 1986-1995 in different observational data (a-e) and ensemble mean (f) of simulation results, and the SCOR (g), RMSE (h), and Taylor score (i) among different data

图2 1986—1995年平均9个流域片内的月径流深贡献率以及数据间的时间相关系数（TCOR）和S评分注：图例中黑色实心圆圈在曲线图中表示ISLSCP,而在下方的点图中表示ensR与ISLSCP比较得到的统计值,类似图例同;红色点表示观测中最大月径流深出现的月份;蓝色阴影表示多模式模拟的不确定性范围。

Fig. 2 Climatic mean of monthly contributions to annual runoff over 9 basins and the TCOR and S scores among different datasets.(Red markers indicate maximum monthly runoff, blue shadings indicate the uncertainty ranges)

图3 RCP8.5情景下2021—2098年全国平均年径流深的变化注：相对于基准期1986—2005年,阴影表示多模式预估的不确定性范围。

Fig. 3 Future change in national averaged runoff (relative to 1986-2005) under RCP8.5 during 2021-2098.(Shading indicates the uncertainty ranges)

图4 年径流深在2021—2098年间变化的线性趋势(a),以及在2079—2098年相对基准期的变化值(b) 注：(a)图中竖线表示线性趋势通过0.05的显著性检验;(b)图中竖线表示集合成员中超过80%的预估未来变化符号一致。

Fig. 4 Linear trends (a) in annual runoff in 2021-2098, and the changes in the end of the 21st century (b). (Hatched areas in (a) indicate that the linear trends are significant, and those in (b) indicate that 80% or more of ensemble members agree on the sign of change)

图5 2021—2098年9个流域片平均年径流深的变化注：相对于基准期1986—2005年,阴影表示多模式预估的不确定性范围。

Fig. 5 Future changes in runoff over 9 basins. (Relative to 1986-2005; shading indicates the uncertainty ranges)

表2 全国9个流域片年径流深在21世纪末相对于基准期的变化及其不确定性范围以及在2021—2098年间的线性趋势值

Table 2 Changes in annual runoff over 9 basins in the end of the 21st century and their uncertainty ranges, and linear trends during 2021-2098

图6 历史基准期（1986—2005年）和21世纪末期（2079—2098年）平均的各个流域的月径流深贡献率模拟值

Fig. 6 Simulated climatic mean of monthly contributions on runoff averaged over each basin during historical reference period (1986-2005) and the end of 21st century (2079-2098)

参考文献 51

[1]	Wang G Q, Zhang J Y, Jin J L, et al. Assessing water resources in China using PRECIS projections and a VIC model[J]. Hydrology & Earth System Sciences, 2012, 16 (1): 231-240
[2]	Leng G, Tang Q, Huang M, et al. Projected changes in mean and interannual variability of surface water over continental China[J]. Science China Earth Sciences, 2015, 58 (5): 739-754 doi: 10.1007/s11430-014-4987-0 URL
[3]	Li J, Chen Y D, Zhang L, et al. Future changes in floods and water availability across China: linkage with changing climate and uncertainties[J]. Journal of Hydrometeorology, 2016, 17 (4): 1295-1314 doi: 10.1175/JHM-D-15-0074.1 URL
[4]	Zhai R, Tao F, Xu Z. Spatial-temporal changes in runoff and terrestrial ecosystem water retention under 1.5 and 2℃ warming scenarios across China[J]. Earth System Dynamics, 2018, 9 (2): 717-738 doi: 10.5194/esd-9-717-2018 URL
[5]	Zou J, Xie Z H, Qin P H, et al. Changes of terrestrial water storage in river basins of China projected by RegCM4[J]. Atmospheric and Oceanic Science Letters, 2013, 6 (3): 154-160 doi: 10.1080/16742834.2013.11447073 URL
[6]	Lee J W, Ham S, Hong S Y, et al. Future changes in surface runoff over Korea projected by a regional climate model under A1B scenario[J]. Advances in Meteorology, 2014, 753790: 1-8
[7]	Du E, Di Vittorio A, Collins W D. Evaluation of hydrologic components of community land model 4 and bias identification[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 48: 5-16 doi: 10.1016/j.jag.2015.03.013 URL
[8]	Yang H, Zhou F, Piao S, et al. Regional patterns of future runoff changes from Earth system models constrained by observation[J]. Geophysical Research Letters, 2017, 44 (11): 5540-5549 doi: 10.1002/2017GL073454 URL
[9]	Di Sante F, Coppola E, Giorgi F. Projections of river floods in Europe using EURO-CORDEX, CMIP5 and CMIP6 simulations[J]. International Journal of Climatology, 2021, 41 (5): 3203-3221 doi: 10.1002/joc.7014 URL
[10]	Dai A. Hydroclimatic trends during 1950-2018 over global land[J]. Climate Dynamics, 2021, 56 (11): 4027-4049 doi: 10.1007/s00382-021-05684-1 URL
[11]	Gao X, Giorgi F. Use of the RegCM system over East Asia: review and perspectives[J]. Engineering, 2017, 3 (5): 766-772 doi: 10.1016/J.ENG.2017.05.019 URL
[12]	吴佳, 高学杰. 一套格点化的中国区域逐日观测资料及与其它资料的对比[J]. 地球物理学报, 2013, 56 (4): 1102-1111.
	Wu J, Gao X J. A gridded daily observation dataset over China region and comparison with the other datasets[J]. Chinese Journal of Geophysics, 2013, 56 (4): 1102-1111 (in Chinese)
[13]	Schellekens J, Dutra E, Martínez-De La Torre A, et al. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset[J]. Earth System Science Data, 2017, 9 (2): 389-413 doi: 10.5194/essd-9-389-2017 URL
[14]	Warszawski L, Frieler K, Huber V, et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework[J]. Proceedings of The National Academy of Sciences, 2014, 111 (9): 3228-3232 doi: 10.1073/pnas.1312330110 URL
[15]	Fekete B M, Vörösmarty C J, Grabs W. High-resolution fields of global runoff combining observed river discharge and simulated water balances[J]. Global Biogeochem Cycles, 2002, 16 (3): 15-1-15-10
[16]	Fekete B, Vorosmarty C, Hall F, et al. ISLSCP II UNH/GRDC composite monthly runoff[DB/OL]. 2011 [2021-08-13]. https://doi.org/10.3334/ORNLDAAC/994
[17]	Ghiggi G, Humphrey V, Seneviratne S I, et al. GRUN: an observation-based global gridded runoff dataset from 1902 to 2014 [J]. Earth System Science Data, 2019, 11 (4): 1655-1674 doi: 10.5194/essd-11-1655-2019 URL
[18]	Gudmundsson L, Seneviratne S I. Observation-based gridded runoff estimates for Europe (E-RUN version 1.1)[J]. Earth System Science Data, 2016, 8 (2): 279-295 doi: 10.5194/essd-8-279-2016 URL
[19]	Abatzoglou J T, Dobrowski S Z, Parks S A, et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015[J]. Scientific Data, 2018, 5 (1): 1-12 doi: 10.1038/s41597-018-0002-5 URL
[20]	Beck H E, De Roo A, Van Dijk A I. Global maps of streamflow characteristics based on observations from several thousand catchments[J]. Journal of Hydrometeorology, 2015, 16 (4): 1478-1501 doi: 10.1175/JHM-D-14-0155.1 URL
[21]	Murray S, Foster P, Prentice I. Evaluation of global continental hydrology as simulated by the Land-surface Processes and eXchanges Dynamic Global Vegetation Model[J]. Hydrology and Earth System Sciences, 2011, 15 (1): 91-105 doi: 10.5194/hess-15-91-2011 URL
[22]	Davie J, Falloon P, Kahana R, et al. Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP[J]. Earth System Dynamics, 2013, 4 (2): 359-374 doi: 10.5194/esd-4-359-2013 URL
[23]	Wang D, Wang G, Parr D T, et al. Incorporating remote sensing: based ET estimates into the Community Land Model version 4.5[J]. Hydrology and Earth System Sciences, 2017, 21 (7): 3557-3577 doi: 10.5194/hess-21-3557-2017 URL
[24]	Umair M, Kim D, Choi M. Impacts of land use/land cover on runoff and energy budgets in an East Asia ecosystem from remotely sensed data in a community land model[J]. Science of The Total Environment, 2019, 684: 641-656 doi: 10.1016/j.scitotenv.2019.05.244 URL
[25]	Yan J, Jia S, Lv A, et al. Water resources assessment of China’s transboundary river basins using a machine learning approach[J]. Water Resources Research, 2019, 55 (1): 632-655 doi: 10.1029/2018WR023044 URL
[26]	Miao Y, Wang A. Evaluation of routed-runoff from land surface models and reanalysis using observed streamflow in Chinese river basins[J]. Journal of Meteorological Research, 2020, 34 (1): 73-87 doi: 10.1007/s13351-020-9120-z URL
[27]	Qin Y, Abatzoglou J T, Siebert S, et al. Agricultural risks from changing snowmelt[J]. Nature Climate Change, 2020, 10 (5): 459-465 doi: 10.1038/s41558-020-0746-8 URL
[28]	Giorgi F, Coppola E, Solmon F, et al. RegCM4: model description and preliminary tests over multiple CORDEX domains[J]. Climate Research, 2012, 52: 7-29 doi: 10.3354/cr01018 URL
[29]	Gao X J, Wu J, Shi Y, et al. Future changes in thermal comfort conditions over China based on multi-RegCM4 simulations[J]. Atmospheric and Oceanic Science Letters, 2018, 11 (4): 291-299 doi: 10.1080/16742834.2018.1471578 URL
[30]	韩振宇, 高学杰, 石英, 等. 中国高精度土地覆盖数据在RegCM4/CLM模式中的引入及其对区域气候模拟影响的分析[J]. 冰川冻土, 2015, 37 (4): 857-866.
	Han Z Y, Gao X J, Shi Y, et al. Development of Chinese high resolution land cover data for the RegCM4/CLM and its impact on regional climate simulation[J]. Journal of Glaciology and Geocryology, 2015, 37 (4): 857-866 (in Chinese)
[31]	Gao X, Shi Y, Giorgi F. Comparison of convective parameterizations in RegCM4 experiments over China with CLM as the land surface model[J]. Atmospheric and Oceanic Science Letters, 2016, 9 (4): 246-254 doi: 10.1080/16742834.2016.1172938 URL
[32]	Gao X, Shi Y, Han Z, et al. Performance of RegCM4 over major river basins in China[J]. Advances in Atmospheric Sciences, 2017, 34 (4): 441-455 doi: 10.1007/s00376-016-6179-7 URL
[33]	IPCC. Climate change 2013: the physical science basis[M]. Cambridge: Cambridge University Press, 2013
[34]	Shi Y, Wang G, Gao X. Role of resolution in regional climate change projections over China[J]. Climate Dynamics, 2018, 51 (5): 2375-2396 doi: 10.1007/s00382-017-4018-x URL
[35]	Han Z, Shi Y, Wu J, et al. Combined dynamical and statistical downscaling for high-resolution projections of multiple climate variables in the Beijing-Tianjin-Hebei region of China[J]. Journal of Applied Meteorological Science, 2019, 58 (11): 2387-2403
[36]	韩振宇, 高学杰, 徐影. 多区域模式集合的东亚陆地区域的平均和极端降水未来预估[J]. 地球物理学报, 2021, 64 (6): 1869-1884.
	Han Z Y, Gao X J, Xu Y. Mean and extreme precipitation projection over land area of East Asia based on multiple regional climate models[J]. Chinese Journal of Geophysics, 2021, 64 (6): 1869-1884 (in Chinese)
[37]	Wang Y, Han Z, Gao R. Changes of extreme high temperature and heavy precipitation in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Geomatics, Natural Hazards and Risk, 2021, 12 (1): 1101-1126 doi: 10.1080/19475705.2021.1912834 URL
[38]	Oleson K, Niu G Y, Yang Z L, et al. Improvements to the Community Land Model and their impact on the hydrological cycle[J]. Journal of Geophysical Research: Biogeosciences, 2008, 113, G01021
[39]	Perkins S, Pitman A, Holbrook N, et al. Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions[J]. Journal of Climate, 2007, 20 (17): 4356-4376 doi: 10.1175/JCLI4253.1 URL
[40]	Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 2001, 106 (D7): 7183-7192 doi: 10.1029/2000JD900719 URL
[41]	Peng D, Zhou T, Zhang L, et al. Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5℃ less global warming[J]. Climate Dynamics, 2020, 54 (1): 543-560 doi: 10.1007/s00382-019-05014-6 URL
[42]	李博, 周天军. 基于IPCC A1B情景的中国未来气候变化预估: 多模式集合结果及其不确定性[J]. 气候变化研究进展, 2010, 6 (4): 270-276.
	Li B, Zhou T J. Projected climate change over China under SRES A1B scenario: multi-model ensemble and uncertainties[J]. Climate Change Research, 2010, 6 (4): 270-276 (in Chinese)
[43]	Tebaldi C, Arblaster J M, Knutti R. Mapping model agreement on future climate projections[J]. Geophysical Research Letters, 2011, 38 (23): L23701
[44]	Li H Y, Leung L R, Getirana A, et al. Evaluating global streamflow simulations by a physically based routing model coupled with the Community Land Model[J]. Journal of Hydrometeorology, 2015, 16 (2): 948-971 doi: 10.1175/JHM-D-14-0079.1 URL
[45]	Sheng M, Lei H, Jiao Y, et al. Evaluation of the runoff and river routing schemes in the Community Land Model of the Yellow River basin[J]. Journal of Advances in Modeling Earth Systems, 2017, 9 (8): 2993-3018 doi: 10.1002/2017MS001026 URL
[46]	陈仁升, 张世强, 阳勇, 等. 冰冻圈变化对中国西部寒区径流的影响[M]. 北京: 科学出版社, 2019.
	Chen R S, Zhang S Q, Yang Y, et al. The impact of cryospheric change on runoff of cold regions in Western China[M]. Beijing: Science Press, 2019 (in Chinese)
[47]	Xing W, Wang W, Zou S, et al. Projection of future runoff change using climate elasticity method derived from Budyko framework in major basins across China[J]. Global and Planetary Change, 2018, 162: 120-135 doi: 10.1016/j.gloplacha.2018.01.006 URL
[48]	Lehner F, Wood A W, Vano J A, et al. The potential to reduce uncertainty in regional runoff projections from climate models[J]. Nature Climate Change, 2019, 9 (12): 926-933 doi: 10.1038/s41558-019-0639-x URL
[49]	Zou J, Xie Z, Yu Y, et al. Climatic responses to anthropogenic groundwater exploitation: a case study of the Haihe River basin, Northern China[J]. Climate Dynamics, 2014, 42 (7-8): 2125-2145 doi: 10.1007/s00382-013-1995-2 URL
[50]	占车生, 宁理科, 邹靖, 等. 陆面水文-气候耦合模拟研究进展[J]. 地理学报, 2018, 73 (5): 893-905. doi: 10.11821/dlxb201805009
	Zhan C S, Ning L K, Zou J, et al. A review on the fully coupled atmosphere-hydrology simulations[J]. Acta Geographica Sinica, 2018, 73 (5): 893-905 (in Chinese)
[51]	Xie Z, Liu S, Zeng Y, et al. A high-resolution land model with groundwater lateral flow, water use, and soil freeze-thaw front dynamics and its applications in an endorheic basin[J]. Journal of Geophysical Research: Atmospheres, 2018, 123 (14): 7204-7222