未来气候变化情景下阜平流域径流变化分析

ROMAINE Ingabire; CAO Bo; CAO Jiansheng; ZHANG Xiaolong; LIU Xia; SHEN Yanjun

doi:10.12357/cjea.20210725

未来气候变化情景下阜平流域径流变化分析

doi: 10.12357/cjea.20210725

ROMAINEIngabire^{1, 2,},
曹博^{1, 2},
曹建生¹,
张晓龙¹,
刘夏¹,
沈彦军^{1, 2, ,}

1.
中国科学院遗传与发育生物学研究所农业资源研究中心/中国科学院农业水资源重点实验室/河北省节水农业重点实验室　石家庄　050022
2.
中国科学院大学　北京　100049

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中图分类号: P333; P467
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出版历程
- 收稿日期: 2021-10-29
- 录用日期: 2022-03-09
- 网络出版日期: 2022-03-18
- 刊出日期: 2022-05-18

Runoff conditions in the Fuping Basin under an ensemble of climate change scenarios

ROMAINE Ingabire^{1, 2
,},
CAO Bo^{1, 2},
CAO Jiansheng¹,
ZHANG Xiaolong¹,
LIU Xia¹,
SHEN Yanjun^{1, 2
, ,}

1.
Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences / Key Laboratory of Agricultural Water Resources, Chinese Academy of Sciences / Hebei Key Laboratory of Water-saving Agriculture, Shijiazhuang 050022, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: This research was supported by the Foundation for Innovative Research Groups of the Natural Science Foundation of Hebei Province (D2021503001) and the National Natural Science Foundation of China (41877170, 41807177).

More Information

Author Bio:
ROMAINE Ingabire, E-mail: ingabireromaine@gmail.com

Corresponding author: E-mail: shenyanjun@sjziam.ac.cn

摘要

摘要: 径流变化对于水资源管理至关重要, 然而, 未来气候变化对阜平流域径流的影响仍未知。本文基于实测数据及4个区域气候模式(RCMs)的集合数据, 使用MIKE11-NAM模型模拟了阜平流域(大清河流域上游的子流域)当前(2008—2017年)及在SSP1-2.6、SSP2-4.5和SSP5-8.5 3种情景下的未来(2025—2054年)径流变化情况。结果表明, MIKE11-NAM模型在日径流模拟中表现良好, R²和NSE在校准期分别为0.82和0.81, 在验证期分别为0.87和0.87。偏差校正后, 观测数据和RCM数据间的相关性提高。与基准期(1985—2014年)相比, 未来的降水量和气温均呈现增加趋势。在SSP5-8.5和SSP2-4.5情景下, 年均温和降水量将分别增加2.45 ℃和124 mm。预计夏季降水量增加幅度较大, 特别是在7—8月; 而各季节的气温将上升, 其中冬季气温上升幅度最大。在SSP2-4.5情景下, 预计年径流量将增加3.5 mm; 而在SSP1-2.6和SSP5-8.5情景下, 预计年径流量将分别减少12.0 mm和11.0 mm。季节尺度上, 在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下, 未来春季径流量将分别减少2.3 mm、1.2 mm和1.9 mm, 夏季径流量将分别减少9.0 mm、7.1 mm和12.9 mm。研究结果可为该地区水资源综合管理和规划提供科学参考。
- MIKE11-NAM /
- 大清河流域 /
- 模拟 /
- 径流 /
- 气候变化
Abstract: Changes in runoff are of great significance for water resources management, especially under the changing climate. In the Fuping Basin, one of the basins in the upper reaches of the Daqinghe Basin, the water resources are facing changes which show great importance of further studies on runoff conditions in the future in this basin. Hence, in this paper, MIKE11-NAM model was applied to simulate daily runoff (2008−2017) and future runoff conditions under a changing climate in the near future (2025−2054) in the Fuping Basin. After bias correction, an ensemble of four regional climate models (RCMs) was used to develop future climate data under three shared socio-economic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios. The obtained results showed a good performance of the MIKE11-NAM model in simulating daily runoff. R² and Nash-Sutcliffe efficiency coefficient (NSE) were 0.82 and 0.81 for calibration, 0.87 and 0.87 for validation, respectively. Although uncertainties remain, the correlation between observed and simulated RCM data was improved after bias correction for all models. Precipitation and temperature were projected to increase under all scenarios compared to the baseline period (1985−2014). Annual temperature and precipitation will increase by 2.45 ℃ and 124 mm under the SSP5-8.5 and SSP2-4.5 scenarios, respectively. However, precipitation is expected to mainly increase in summer while temperature will increase in all the seasons. The projected annual runoff will increase under SSP2-4.5 while decreasing under SSP1-2.6 and SSP5-8.5 scenarios. Seasonally, the future runoff will decrease during spring and summer under all the scenarios. Generally, the changes in runoff conditions will be more obvious in the future. Our findings can be important for integrated water resources management and planning in this region.
- MIKE11-NAM /
- Daqinghe Basin /
- Simulation /
- Runoff /
- Climate change

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Figure 1. Location and elevation of, and spatial distribution of meteorological and hydrological stations in the Fuping Basin

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Figure 2. MIKE11-NAM structure (DHI, 2009)

QOF: overland flow; QIF: interflow; GWPUMP: groundwater pumping; GWL: maximum groundwater depth; CAFLUX: capillar flux; Sy: specific yield; BF: baseflow.

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Figure 3. Trend analysis of annual precipitation, temperature and runoff in the Fuping Basin from 1980 to 2017

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Figure 4. Observed vs. simulated runoff for both calibration (a, 2009−2011) and validation (b, 2012−2017) periods of the Fuping Basin

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Figure 5. Future (2025−2054) monthly precipitation projection based on multi-model ensemble of four regional climate models (MME) compared to the baseline (1985−2014) precipitation

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Figure 6. Future (2025−2054) monthly temperature projection based on multi-model ensemble of four regional climate models (MME) compared to the baseline (1985−2014) temperature

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Figure 7. Projected future monthly runoff (2025−2054) in the Fuping Basin under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios compare to baseline (1985−2014)

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Table 1. Parameters used in the MIKE11-NAM model during simulation

Parameter	Description	Value range	Final value
U_max (mm)	Maximum water content in the surface storage	10−20	12.3
L_max (mm)	Maximum water content in root zone storage	50−300	50.5
CQOF	Overland flow runoff coefficient	0−1	0.894
CKIF (h)	Time constant for routing interflow	200−1000	449.4
CK1,2 (h)	Time constant for routing overland flow	3−48	40.5
TOF	Root zone threshold value for overland flow	0−0.99	0.685
TIF	Root zone threshold value for interflow	0−0.99	0.0121
TG	Root zone threshold value for groundwater recharge	0−0.99	0.154
CKBF (h)	Time constant for routing base flow	1−5000	2761

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Table 2. Trends of temperature, precipitation and runoff from 1980 to 2017 in the Fuping Basin based on the Mann-Kendall test

Time	Temperature		Precipitation		Runoff
Time	℃∙decade⁻¹	P-value	mm∙decade⁻¹	P-value	mm∙decade⁻¹	P-value
Annual	0.4	<0.001	8.0	0.33	−2.2	0.36
Spring	0.6	<0.001	−3.0	0.30	0.4	0.29
Summer	0.3	<0.05	−2.4	0.38	−3.0	0.19
Autumn	0.3	<0.05	17.5	<0.001	0.8	0.34
Winter	0.7	<0.001	1.0	0.09	−0.3	0.32

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Table 3. Bias correction performance of four regional climate models for monthly precipitation and temperature estimation compared to the observed data

Variable	Model	Before correction		After correction
Variable	Model	R²	RMSE	R²	RMSE
Temperature	ACCESS_ESM1_5	0.97	2.18 ℃	0.97	1.83
	Can_ESM5	0.95	4.30 ℃	0.95	2.40
	EC_EARTH3	0.95	2.69 ℃	0.96	2.27
	MPI_ESM1_2HR	0.96	2.17 ℃	0.97	2.05
Precipitation	ACCESS_ESM1_5	0.43	50.71 mm	0.43	49.00
	Can_ESM5	0.35	51.45 mm	0.45	49.73
	EC_EARTH3	0.46	50.15 mm	0.47	48.62
	MPI_ESM1_2HR	0.41	50.51 mm	0.47	49.85

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Table 4. Precipitation and temperature change comparison between historical observed data (1985−2014) and future data (2025−2054) based on four regional climate models (RCM) and multimodel ensemble under 3 scenarios

Variable	Scenario	Historical (1985−2014)	RCM model (2025−2054)				Multi-model ensemble (MME)
Variable	Scenario	Historical (1985−2014)	ACCESS-ESMI-5	Can-ESM5	EC-Earth3	MPI-ESMI-2HR	Multi-model ensemble (MME)
Temperature (℃)	SSP1-2.6	7.8	+1.96	+2.06	+2.54	+0.93	+1.87
	SSP2-4.5		+1.97	+2.43	+2.44	+0.83	+1.92
	SSP5-8.5		+2.52	+2.97	+2.92	+1.37	+2.45
Precipitation (mm)	SSP1-2.6	670	+72	+142	+104	+70	+97
	SSP2-4.5		+99	+187	+121	+89	+124
	SSP5-8.5		+23	+194	+129	+51	+99
“+” means the increase.

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