1.黑龙江河湖长学院,黑龙江 哈尔滨 150080
2.黑龙江大学水利电力学院,黑龙江 哈尔滨 150080
3.黑龙江大学中俄寒区水文和水利工程联合实验室(国际合作),黑龙江 哈尔滨 150080
4.哈尔滨市生态与农业气象中心,黑龙江 哈尔滨 150028
龚骁(1999—),男,硕士研究生,主要从事水文水资源等工作。E-mail:1060500622@qq.com
戴长雷(1975—),男,博士,教授,从事水文学及水资源的教学科研工作。E-mail:hd_daichanglei@126.com
收稿:2025-05-13,
修回:2025-06-10,
录用:2025-06-12,
网络出版:2025-06-26,
纸质出版:2025-11-25
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龚骁,戴长雷,李树岭,等.基于CMIP6气候情景的松花江流域典型支流水文模拟及预测:以蚂蚁河流域为例[J].人民珠江,2025,46(11):65-74.
GONG Xiao,DAI Changlei,LI Shuling,et al.Hydrological Simulation and Prediction of Typical Tributaries in Songhua River Basin Based on CMIP6 Climate Scenarios: A Case Study of Ant River Basin[J].PEARL RIVER,2025,46(11):65-74.
龚骁,戴长雷,李树岭,等.基于CMIP6气候情景的松花江流域典型支流水文模拟及预测:以蚂蚁河流域为例[J].人民珠江,2025,46(11):65-74. DOI: 10.3969/j.issn.1001-9235.2025.11.008.
GONG Xiao,DAI Changlei,LI Shuling,et al.Hydrological Simulation and Prediction of Typical Tributaries in Songhua River Basin Based on CMIP6 Climate Scenarios: A Case Study of Ant River Basin[J].PEARL RIVER,2025,46(11):65-74. DOI: 10.3969/j.issn.1001-9235.2025.11.008.
评估未来气候变化对蚂蚁河流域径流过程的影响,可为区域水资源管理与防灾减灾提供科学依据。基于蚂蚁河流域1971—2021年水文气象数据构建
SWAT模型,使用CMIP6的11种气候模式的3个情景数据驱动SWAT模型,分析未来(2020—2100)气候变化下蚂蚁河流域径流演变特征。结果表明:①蚂蚁河流域莲花(二)水文站月径流率定期和验证期决定系数(
R
2
)和纳什系数(
E
NS
)均大于0.7,偏差百分比(
P
BIAS
)小于±5,SWAT模型在该流域具有良好的使用性;②未来蚂蚁河流域最高气温和最低气温均呈上升趋势,3种排放情景下最高气温年均增长率分别为0.036、0.039、0.050 ℃/a,最低气温分别为0.046、0.050、0.061 ℃/a,降水量呈上升趋势,增长率分别为1.146、1.160、1.260 mm/a,蒸散发在SSP1-2.6情景下呈不显著下降,SSP2-4.5和SSP5-8.5情景下显著上升,增长率分别为0.992、1.756 mm/a;③加权多模式集合方法能够有效提升模拟精度,并减少单一气候模式的不确定性;④蚂蚁河流域径流量在SSP1-2.6情景下显著增加,变化率为0.309 m
3
/(a·s),在SSP2-4.5情景下显著减少,变化率为-0.044 m
3
/(a·s),在SSP5-8.5情景下非显著增加,变化率为0.034 m
3
/(a·s)。
Assessing the impacts of future climate change on runoff processes in the Ant River Basin can provide a scientific basis for regional water resource management and disaster prevention and mitigation. The SWAT model was constructed based on the hydrometeorological data of the Ant River Basin from 1971 to 2021
and the model was driven by data from three scenarios of 11 climate models in CMIP6 to analyze the characteristics of runoff evolution in the Ant River Basin under future (2020—2100) climate change. The results showed that ① the coefficient of determination (
R
2
) and the Nash coefficient (
E
NS
) for the regular and validation periods of the monthly runoff rate at Lotus (Ⅱ) Hydrological Station in the Ant River Basin are both greater than 0.7
and the percentage of bias (
P
BIAS
) is less than ±5; the SWAT model is fully used in this basin. ② The maximum and minimum temperatures in the Ant River Basin in the future show an increasing trend
with average annual growth rates of 0.036
0.039
and 0.050 ℃/a for the maximum temperature and 0.046
0.050
and 0.061 ℃/a for the minimum temperature under the three emission scenarios
respectively; the precipitation shows an increasing trend
with growth rates of 1.146
1.160
and 1.260 mm/a
resp
ectively
and the evapotranspiration shows a non-significant decrease under the SSP1-2.6 scenario and a significant increase under the SSP2-4.5 and SSP5-8.5 scenarios
with growth rates of 0.992 and 1.756 mm/a
respectively. ③ The weighted multi-model ensemble approach can effectively improve the simulation accuracy and reduce the uncertainty of a single climate model. ④ The runoff in the Ant River Basin increases significantly under SSP1-2.6 scenario
with a rate of change of 0.309 m
3
/(a·s); it decreases significantly under SSP2-4.5 scenario
with a rate of change of -0.044 m
3
/(a·s) and increases non-significantly under SSP5-8.5 scenario
with a rate of change of 0.034 m
3
/(a·s).
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