Impacts of climate change and LULC change on runoff in the Jinsha River Basin

doi:10.1007/s11442-020-1716-9

Abstract

Abstract:

The climate change and Land Use/Land Cover (LULC) change both have an important impact on the rainfall-runoff processes. How to quantitatively distinguish and predict the impacts of the above two factors has been a hot spot and frontier issue in the field of hydrology and water resources. In this research, the SWAT (Soil and Water Assessment Tool) model was established for the Jinsha River Basin, and the method of scenarios simulation was used to study the runoff response to climate change and LULC change. Furthermore, the climate variables exported from 7 typical General Circulation Models (GCMs) under RCP4.5 and RCP8.5 emission scenarios were bias corrected and input into the SWAT model to predict runoff in 2017-2050. Results showed that: (1) During the past 57 years, the annual average precipitation and temperature in the Jinsha River Basin both increased significantly while the rising trend of runoff was far from obvious. (2) Compared with the significant increase of temperature in the Jinsha River Basin, the LULC change was very small. (3) During the historical period, the LULC change had little effect on the hydrological processes in the basin, and climate change was one of the main factors affecting runoff. (4) In the context of global climate change, the precipitation, temperature and runoff in the Jinsha River Basin will rise in 2017-2050 compared with the historical period. This study provides significant references to the planning and management of large-scale hydroelectric bases at the source of the Yangtze River.

Key words: Jinsha River Basin, SWAT model, climate change, LULC change, scenario simulation, GCM

CHEN Qihui, CHEN Hua, ZHANG Jun, HOU Yukun, SHEN Mingxi, CHEN Jie, XU Chongyu. Impacts of climate change and LULC change on runoff in the Jinsha River Basin[J].Journal of Geographical Sciences, 2020, 30(1): 85-102.

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References 52

1	Abbaspour K C, Yang J, Maximov I et al., 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333(2-4):413-430.
2	Arnold J G, Srinivasan R, Muttiah R S et al., 1998. Large area hydrologic modeling and assessment (Part 1): Model development. Journal of the American Water Resources Association, 34(1):73-89.
3	Awan U K, Liaqat U W, Choi M et al., 2016. A SWAT modeling approach to assess the impact of climate change on consumptive water use in Lower Chenab Canal area of Indus basin. Hydrology Research, 47(5):1025-1037.
4	Chen J, Brissette F P, Chaumont D et al., 2013. Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. Journal of Hydrology, 479(5):200-214.
5	Chen J, Brissette F P, Leconte R , 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401(3/4):190-202.
6	Chen L, Chang J, Wang Y et al., 2018. Assessing runoff sensitivities to precipitation and temperature changes under global climate-change scenarios. Hydrology Research, 50(1):24-42. doi: 10.2166/nh.2018.192.
7	da Silva R M, Dantas J C, Beltrão J D A et al., 2018. Hydrological simulation in a tropical humid basin in the Cerrado biome using the SWAT model. Hydrology Research, 49(3):908-923.
8	Du J, Shi C, Zhang C , 2013. Modeling and analysis of effects of precipitation and vegetation coverage on runoff and sediment yield in Jinsha River Basin. Water Science and Engineering, 6(1):44-58.
9	Gao C, Ruan T , 2018. The influence of climate change and human activities on runoff in the middle reaches of the Huaihe River Basin, China. Journal of Geographical Sciences, 28(1):79-92.
10	Gao C, Zhang Z, Zhai J et al., 2015. Research on meteorological thresholds of drought and flood disaster: A case study in the Huai River Basin, China. Stochastic Environmental Research and Risk Assessment, 29(1):157-167.
11	Gassman P W, Reyes M R, Green C H et al., 2007. The soil and water assessment tool: Historical development, applications, and future research directions. Transactions of the ASABE, 50(4):1211-1250.
12	Guo J, Zhang Z, Wang S et al., 2012. Effects of climate and land use changes on stream flow and sediment yield in Chaohe river basin. Transactions of the Chinese Society of Agricultural Engineering, 28(14):236-243. (in Chinese)
13	Guo J, Zhang Z, Wang S et al., 2014. Appling SWAT model to explore the impact of changes in land use and climate on the streamflow in a watershed of northern China. Acta Ecologica Sinica, 34(6):1559-1567. (in Chinese)
14	Hamed K H , 2008. Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis. Journal of Hydrology, 349(3/4):350-363.
15	Hao F, Chen L, Liu C et al., 2010. Impact of land use change on runoff and sediment yield. Journal of Soil Water Conservation, 18(3):5-8. (in Chinese)
16	Immerzeel W W, Pellicciotti F, Bierkens M F P , 2013. Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds. Nature Geoscience, 6(9):742-745.
17	Kendall M G , 1975. Rank Correlation Methods. London: Griffin.
18	Li D, Wu Y, Liu C , 2005. Runoff simulation with physical-based distributed hydrological model. Scientia Geographica Sinica, 25(3):299-304. (in Chinese)
19	Li J, Zhang X, Yang Y , 2012. SWAT model of runoff study under different land use land cover scenarios in source region of the Yangtze River. Research of Soil & Water Conservation, 19(3):119-129. (in Chinese)
20	Li Y, Chang J, Luo L et al., 2018. Spatiotemporal impacts of land use land cover changes on hydrology from the mechanism perspective using SWAT model with time-varying parameters. Hydrology Research, 50(1):244-261. doi: 10.2166/nh.2018.006.
21	Li Z, Liu W, Zhang X et al., 2009. Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 377(1/2):35-42.
22	Liang Z, Tang T, Li B et al., 2017. Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: Case study of Danjiangkou Reservoir. Hydrology Research, 49(5):1513-1527. doi: 10.2166/nh.2017.085.
23	Liu C, Li D, Tian Y et al., 2003. An application study of DEM based distributed hydrological model on macroscale watershed. Progress in Geography, 22(5):437-445. (in Chinese)
24	Liu X, Li X, Shi X et al., 2008. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211(1/2):169-181.
25	Liu X, Peng D, Xu Z , 2017. Identification of the impacts of climate changes and human activities on runoff in the Jinsha River Basin, China. Advances in Meteorology, 2017: 1-9.
26	Liu Z, Yao Z, Huang H et al., 2014. Land use and climate changes and their impacts on runoff in the Yarlung Zangbo River Basin, China. Land Degradation & Development, 25(3):203-215.
27	Lu J, Cui X, Chen X et al., 2016. Evaluation of hydrological response to extreme climate variability using SWAT model: Application to the Fuhe basin of Poyang Lake Watershed, China. Hydrology Research, 48(6):1730-1744.
28	Luo Q, Wang K, Wang Q , 2011. Using SWAT to simulate runoff under different land use scenarios in Xiangjiang River Basin. Chinese Journal of Eco-Agriculture, 19(6):1431-1436. (in Chinese)
29	Mann H B , 1945. Nonparametric tests against trend. Econometrica, 13(3):245-259.
30	Mu X, Wang F, Li J, , et al. 2004. Review of evaluation method of impact of soil and water conservation practices on river flows. Bulletin of Soil & Water Conservation, (3):73-78. (in Chinese)
31	Ozturk M, Copty N K, Saysel A K , 2013. Modeling the impact of land use change on the hydrology of a rural watershed. Journal of Hydrology, 497:97-109.
32	Pan S, Liu D, Wang Z et al., 2017. Runoff responses to climate and land use/cover changes under future scenarios. Water, 9(7):475. doi: 10.3390/w9070475.
33	Pan Z, Ruan X, Qian M et al., 2018. Spatio-temporal variability of streamflow in the Huaihe River Basin, China: Climate variability or human activities? Hydrology Research, 49(1):177-193.
34	Piao S, Ciais P, Huang Y et al., 2010. The impacts of climate change on water resources and agriculture in China. Nature, 467(7311):43-51.
35	Singh H V, Kalin L, Morrison A et al., 2015. Post-validation of SWAT model in a coastal watershed for predicting land use/cover change impacts. Hydrology Research, 46(6):837-853.
36	Song M, Li T, Chen J , 2012. Preliminary analysis of precipitation runoff features in the Jinsha River Basin. Procedia Engineering, 28(10):688-695.
37	Taylor K E, Stouffer R J, Meehl G A , 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4):485-498.
38	Vuuren D P V, Edmonds J, Kainuma M et al., 2011. The representative concentration pathways: An overview. Climatic Change, 109(1/2):5-31.
39	Wang F, Ge Q, Yu Q et al., 2017a. Impacts of land-use and land-cover changes on river runoff in Yellow River basin for period of 1956-2012. Chinese Geographical Science, 27(1):13-24.
40	Wang H, Sun F, Xia J et al., 2017b. Impact of LUCC on streamflow based on the SWAT model over the Wei River basin on the Loess Plateau in China. Hydrology and Earth System Sciences, 21(4):1929-1945.
41	Wang S, Zhang X , 2012. Long-term trend analysis for temperature in the Jinsha River Basin in China. Theoretical and Applied Climatology, 109(3/4):591-603.
42	Xia J, Wang M , 2008. Runoff changes and distributed hydrologic simulation in the upper reaches of Yangtze River. Resources Science, 30(7):962-967. (in Chinese)
43	Yang P, Xia J, Zhan C et al., 2018. Separating the impacts of climate change and human activities on actual evapotranspiration in Aksu River Basin ecosystems, Northwest China. Hydrology Research, 49(6):1740-1752. doi: 10.2166/nh.2018.136.
44	Yang Y, Lu G, Wu Z et al., 2012. Variation characteristics analysis of hydrological cycle factors in upper reaches of Jinshajiang Basin. Water Resources & Power, 30(3):8-10. (in Chinese)
45	Yin J, He F, Xiong Y et al., 2017. Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China. Hydrology and Earth System Sciences, 21(1):183-196.
46	Yuan Y, Zhang Z, Meng J , 2015. Impact of changes in land use and climate on the runoff in Liuxihe Watershed based on SWAT model. Chinese Journal of Applied Ecology, 26(4):989-998. (in Chinese)
47	Yue S, Pilon P, Cavadias G , 2002a. Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259(1):254-271.
48	Yue S, Pilon P, Phinney B et al., 2002b. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9):1807-1829.
49	Zhang A, Zhang C, Fu G et al., 2012. Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China. Water Resources Management, 26(8):2199-2217.
50	Zhang L, Pang B, Xu Z , et al. 2014. Impacts of climate change and LUCC on hydrological processes in the Gulang River Basin. South-to-North Water Transfers and Water Science & Technology, 12(1):42-46. (in Chinese)
51	Zhao Y, Yu X, Zheng J et al., 2012. Quantitative effects of climate variations and land-use changes on annual streamflow in Chaobai river basin. Transactions of the Chinese Society of Agricultural Engineering, 28(22):252-260. (in Chinese)
52	Zhuo G, Jian J, Bianbaciren , 2011. Runoff of the Jinsha River: Characteristics and its response to climate change. Journal of Glaciology & Geocryology, 33(2):405-415. (in Chinese)

Data type	Description	Data source
DEM data	Spatial resolution of 200 m	Geospatial Data Cloud
Soil data	Spatial resolution of 1000 m	Food and Agriculture Organization of the United Nations (FAO)
Land use data	In year 1980, 1990, 2000, 2010, 2015 with spatial resolution of 1000 m	National Earth System Science Data Sharing Infrastructure
Climate data	Daily data from 31 weather stations, including precipitation, temperature, wind speed, solar radiation, humidity and evaporation (1960-2016)	China Meteorological Data Service Center (CMDC)
Hydrological data	Monthly runoff data from 7 hydrological stations (1960-2016)	Yangtze River Water Conservancy Commission

Periods	P1	P2	P3	P4
Climate data	1977-1986	1987-1996	1997-2006	2007-2016
Land use data	LU1980	LU1990	LU2000	LU2010

Scenarios	Climate data	Land use data	Scenarios	Climate data	Land use data
S1	1977-1986	LU1980	S9	1997-2006	LU1980
S2	1977-1986	LU1990	S10	1997-2006	LU1990
S3	1977-1986	LU2000	S11	1997-2006	LU2000
S4	1977-1986	LU2010	S12	1997-2006	LU2010
S5	1987-1996	LU1980	S13	2007-2016	LU1980
S6	1987-1996	LU1990	S14	2007-2016	LU1990
S7	1987-1996	LU2000	S15	2007-2016	LU2000
S8	1987-1996	LU2010	S16	2007-2016	LU2010

Periods	Annual average precipitation (mm)
Periods	Reg I	Reg II	Reg III	Reg IV	Basin
P1 (1977-1986)	321.5	682.9	556.6	875.3	604.9
P2 (1987-1996)	310.5	704.9	600.7	862.7	610.5
P3 (1997-2006)	345.3	711.0	622.3	930.7	645.7
P4 (2007-2016)	392.2	701.0	572.9	843.6	624.7
Historical (1960-2016)	338.7	691.4	583.6	873.6	616.2
Z values in M-K test	2.07	1.63	0.76	0.59	2.07

Temperature (℃)		P1	P2	P3	P4	1960-2016	Z values
Reg I	T_max	4.0	4.9	5.2	5.6	4.7	5.1
Reg I	T_min	-10.7	-10.3	-9.6	-8.6	-10.0	5.8
Reg II	T_max	13.5	13.8	14.3	15.0	14.0	4.6
Reg II	T_min	-1.1	-0.4	-0.1	0.5	-0.6	7.6
Reg III	T_max	16.0	16.0	16.4	17.1	16.3	3.7
Reg III	T_min	1.0	1.4	1.9	2.4	1.3	8.0
Reg IV	T_max	20.3	20.3	20.9	21.4	20.6	3.5
Reg IV	T_min	8.5	8.8	9.3	9.7	8.9	6.8
Basin	T_max	12.9	13.3	13.7	14.3	13.4	4.7
Basin	T_min	-1.0	-0.5	-0.0	0.6	-0.5	7.4