Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (4): 461-482    DOI: 10.1007/s40333-024-0010-6
Research article     
Assessment of runoff changes in the sub-basin of the upper reaches of the Yangtze River basin, China based on multiple methods
WANG Xingbo1, ZHANG Shuanghu1,*(), TIAN Yiman1,2
1Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Download: HTML     PDF(3663KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

Quantitative assessment of the impact of climate variability and human activities on runoff plays a pivotal role in water resource management and maintaining ecosystem integrity. This study considered six sub-basins in the upper reaches of the Yangtze River basin, China, to reveal the trend of the runoff evolution and clarify the driving factors of the changes during 1956-2020. Linear regression, Mann-Kendall test, and sliding t-test were used to study the trend of the hydrometeorological elements, while cumulative distance level and ordered clustering methods were applied to identify mutation points. The contributions of climate change and human disturbance to runoff changes were quantitatively assessed using three methods, i.e., the rainfall-runoff relationship method, slope variation method, and variable infiltration capacity (Budyko) hypothesis method. Then, the availability and stability of the three methods were compared. The results showed that the runoff in the upper reaches of the Yangtze River basin exhibited a decreasing trend from 1956 to 2020, with an abrupt change in 1985. For attribution analysis, the runoff series could be divided into two phases, i.e., 1961-1985 (baseline period) and 1986-2020 (changing period); and it was found that the rainfall-runoff relationship method with precipitation as the representative of climate factors had limited usability compared with the other two methods, while the slope variation and Budyko hypothesis methods had highly consistent results. Different factors showed different effects in the sub-basins of the upper reaches of the Yangtze River basin. Moreover, human disturbance was the main factor that contributed to the runoff changes, accounting for 53.0%-82.0%; and the contribution of climate factors to the runoff change was 17.0%-47.0%, making it the secondary factor, in which precipitation was the most representative climate factor. These results provide insights into how climate and anthropogenic changes synergistically influence the runoff of the upper reaches of the Yangtze River basin.

Key words： economic belt      runoff change      influencing assessment      climate      human activities     
Received: 07 October 2023      Published: 30 April 2024
Corresponding Authors: *ZHANG Shuanghu (E-mail: zhangshh@iwhr.com)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
WANG Xingbo
ZHANG Shuanghu
TIAN Yiman
Cite this article:

WANG Xingbo, ZHANG Shuanghu, TIAN Yiman. Assessment of runoff changes in the sub-basin of the upper reaches of the Yangtze River basin, China based on multiple methods. Journal of Arid Land, 2024, 16(4): 461-482.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0010-6     OR     http://jal.xjegi.com/Y2024/V16/I4/461

Fig. 1 Location and hydrological and meteorological stations of the study area
Fig. 2 Annual runoff trends of eight hydrological stations and surface precipitation in six sub-basins of the upper reaches of the Yangtze River basin. (a), UJR (upstream of the Jinsha River); (b), DJR (downstream of the Jinsha River); (c), MTR (Mintuo River); (d), JLR (Jialing River); (e), WR (Wujiang River); (f), main stream of the upper reaches of the Yangtze River basin. SG, Shigu; PS, Pingshan; GC, Gaochang; FS, Fushun; BB, Beibei; WL, Wulong; YC, Yichang; CT, Cuntan. The abbreviations are the same in the following figures.
Station Location Area (km2) Runoff Precipitation
Rate |Z| Rate |Z|
SG UJR 215,840 0.675 1.551 0.375 1.148
PS DJR 256,328 0.223 0.724 0.123 0.498
GC MTR 135,378 -0.935 1.563 0.561 0.191
FS 19,613 -0.212 1.303
BB JLR 160,927 -1.495 1.313 -0.443 0.638
WL WR 87,731 -0.290 0.250 -1.880 2.347*
CT Main stream 100,837 -2.073 0.645 -1.317 1.887
YC -1.430 0.408
Table 1 Mann-Kendall test results of runoff and precipitation
Fig. 3 Mutation test results of runoff. (a), cumulative anomalies of eight hydrological stations; (b), results of the Mann-Kendall test, the red points are intersection points of UF and UB (two statistic series curves); (c), ordered clustering method for the BB (Beibei) station, and the red point is the minimum Sn (total sum of squared deviation).
Fig. S1 Mann-Kendall mutation test results of runoff for eight hydrological stations. (a), SG (Shigu); (b), PS (Pingshan); (c), GC (Gaochang); (d), FS (Fushun); (e), BB (Beibei); (f), WL (Wulong); (g), CT (Cuntan); (h), YC (Yichang). The abbreviations are the same in the following figure.
Fig. S2 Runoff ordered clustering test results for eight hydrological stations. (a), SG; (b), PS;(c), GC; (d), FS; (e), BB; (f), WL; (g), CT; (h), YC. Sn is the total sum of squared deviation.
Station Cumulative anomaly method Mann-Kendall test Ordered clustering method
SG 1986#, 1997 1957, 1961, 1967, 1985# 1986#, 1988, 1997, 1960
PS 1997 1957, 1962, 1968, 1984#, 2012 2005, 1997, 1985#
GC 1993, 1968 1972, 1986#, 1992, 1993 2017, 1968
FS 1968 1959, 1966 1966, 2017
BB 1985# 1960, 1971, 1983# 1985#, 1990, 1993, 1968, 2018
WL 2002 2004, 2009, 2013, 2019 2019, 2002, 1985#, 1962
CT 1968, 2017 1957, 1961, 1969, 1986#, 1990, 2018 1968, 2017
YC 2000, 1968, 1993 1994, 1996, 2000, 2018 2019, 1968
Table 2 Summary of the three mutation test methods
Fig. 4 Morlet wavelet coefficients of runoff of eight hydrological stations. (a), PS; (b), SG; (c), GC; (d), FS; (e), BB; (f), WL; (g), CT; (h), YC. Red dotted box shows the first main period, and black dotted box shows the second main period.
Fig. 5 Wavelet variance curves and wavelet coefficient curves of precipitation in different main periods of UJR (a), DJR (b), MTR (c), JLR (d), WR (e), and main stream (f) of the upper reaches the Yangtze River basin. Red line is the real part curve of the first main period, and blue line corresponds to the second main period.
Fig. 6 Double mass curve between cumulative annual precipitation and annual runoff in JLR
Sub-basin Period Measured value
(×108 m3)		 Estimated value (×108 m3) Variation (×108 m3) Contribution (%)
Climate
factor		 Human activity Climate factor Human activity
UJR 1961-1985 414.70 - - - - -
1986-2020 518.86 530.97 116.27 -12.11 111.60 -11.60
DJR 1961-1985 1409.50 - - - - -
1986-2020 1445.93 1427.90 18.49 17.95 50.80 49.30
MTR 1961-1985 996.20 - - - - -
1986-2020 943.40 972.10 -24.10 -28.70 45.60 54.40
JLR 1961-1985 722.00 - - - - -
1986-2020 614.84 656.23 -65.77 -41.38 61.40 38.60
WR 1961-1985 512.20 - - - - -
1986-2020 471.38 483.62 -28.58 -12.24 70.00 30.00
Main stream 1961-1985 4404.60 - - - - -
1986-2020 4240.43 4053.37 -351.23 187.06 213.90 -113.90
Table 3 Contributions of climate factors and human activities to runoff
Sub-basin RSR (%) RSP (%) RSE (%) CP (%) CE (%) CC (%) CH (%)
UJR 0.09 0.06 0.02 70.92 -17.08 53.84 46.16
DJR 0.05 0.02 -0.01 46.76 21.84 68.60 31.40
MTR -0.05 -0.01 0.01 15.64 21.73 37.37 62.63
JLR -0.14 -0.04 0.03 26.60 19.76 46.36 53.64
WR -0.09 -0.03 -0.00 38.20 -1.44 36.76 63.24
Main stream -0.03 -0.01 -0.01 32.78 -15.10 17.68 82.32
Table 4 Slopes in R (runoff), P (precipitation), and E (evaporation), and the results of SCRCQ (slope changing ratio of cumulative quantity)
Sub-basin Aridity index εP εE ΔQc (m3) CC (%) CH (%)
Baseline period Changing period Baseline period Changing period Baseline period Changing period
UJR 1.99 1.70 0.14 0.20 -0.08 -0.11 8.58 42.08 57.92
DJR 1.12 1.06 0.47 0.51 -0.26 -0.28 24.43 67.11 32.89
MTR 0.79 0.83 0.79 0.74 -0.45 -0.42 -22.57 42.74 57.26
JLR 0.78 0.89 0.80 0.68 -0.46 -0.38 -41.16 38.40 61.60
WR 0.67 0.68 0.96 0.95 -0.56 -0.55 -14.65 35.91 64.09
Main stream 0.88 0.89 0.69 0.68 -0.39 -0.38 -69.16 25.67 74.33
Table 5 Parameter and result of Budyko hypothesis method
Fig. 7 Parameter sensitivity in Budyko hypothesis method of UJR (I), DJR (II), MTR (III), JLR (IV), WR (V), and main stream (VI) of the upper reaches of the Yangtze River basin. CC is the contribution of climate changes to runoff, and ω is the plant-available water coefficient.
Sub-basin Precipitation-runoff relationship SCRCQ Budyko hypothesis method
CC (%) CH (%) CC (%) CH (%) CC (%) CH (%)
UJR 111.60 -11.60 53.84 46.16 42.08 57.92
DJR 50.80 49.30 68.12 31.88 67.11 32.89
MTR 45.60 54.40 37.37 62.63 42.74 57.26
JLR 61.40 38.60 46.36 53.64 38.40 61.60
WR 70.00 30.00 36.76 63.24 35.91 64.09
Main stream 213.90 113.90 17.68 82.32 25.67 74.33
Table 6 Summary of the results of the three methods
Fig. 8 Boxplot graph of adaptation of slope variation method (a) and Budyko hypothesis method (b). CC is the contribution of climate factors to runoff. Boxes indicate the IQR (interquartile range, 75th to 25th of the data). The median value is shown as a line within the box; square is shown as mean; and black diamond is outlier. Whiskers extend to the most extreme value within 1.5×IQR.
Fig. 9 Relationship between precipitation and runoff in different periods. 1961-1985, baseline period; 1886-2020, changing period.
Fig. S3 Relationship between precipitation and runoff in different periods for five sub-basins. (a), UJR (upstream of the Jinsha River); (b), DJR (downstream of the Jinsha River); (c), MTR (Mintuo River); (d), WR, Wujiang River; (e), main stream of the upper reaches of the Yangtze River basin. 1961-1985, baseline period; 1886-2020, changing period.
[1]   Bao Z X, Zhang J Y, Wang G Q, et al. 2012. Attribution for decreasing streamflow of the Haihe River basin, northern China: Climate variability or human activities? Journal of Hydrology, 460-461: 117-129.
[2]   Cao H C, Yan D D, Ju Y L. 2021. Drought and flood characteristics in the farming-pastoral ecotone of northern China based on the standardized precipitation index. Journal of Arid Land, 13(12): 1244-1259.
[3]   Chien H C, Yeh P J F, Knouft J H. 2013. Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States. Journal of Hydrology, 491: 73-88.
[4]   Du J, He F, Zhang Z, et al. 2011. Precipitation change and human impacts on hydrologic variables in Zhengshui River Basin, China. Stochastic Environmental Research and Risk Assessment, 25: 1013-1025.
[5]   Feng A Q, Li Y Z, Gao J B, et al. 2017. The determinants of streamflow variability and variation in Three-River Source of China: Climate change or ecological restoration? Environmental Earth Sciences, 76(20): 696, doi: 10.1007/s12665-017-7026-6.
[6]   Francisco F, Malheiro A, Chaminé H I. 2022. Natural hazards and hydrological risks: Climate change-water-sustainable society nexus. SN Applied Sciences, 5: 36, doi: 10.1007/S42452-022-05214-6.
[7]   Fu J J, Wang W, Hunter P D, et al. 2023. Trends in normalized difference vegetation index time series in differently regulated cascade reservoirs in Wujiang Catchment, China . Ecological Indicators, 146: 109831, doi: 10.1016/j.ecolind.2022.109831.
[8]   Guo W X, Zhou H T, Zhang L, et al. 2022. Analysis of runoff changes and causes in Minjiang River basin. Water Resources and Power, 40(4): 32-36. (in Chinese)
[9]   He G H, Zhao Y, Wang J H, et al. 2019a. Attribution analysis based on Budyko hypothesis for land evapotranspiration change in the Loess Plateau, China. Journal of Arid Land, 11(6): 939-953.
[10]   He Y, Jiang X, Wang N, et al. 2019b. Changes in mountainous runoff in three inland river basins in the arid Hexi Corridor, China, and its influencing factors. Sustainable Cities and Society, 50: 101703, doi: 10.1016/j.scs.2019.101703.
[11]   Hiwasaki L, Luna E, Syamsidik, et al. 2014. Process for integrating local and indigenous knowledge with science for hydro-meteorological disaster risk reduction and climate change adaptation in coastal and small island communities. International Journal of Disaster Risk Reduction, 10: 15-27.
[12]   Hu C H, Zhang S H. 2022. Research on water security and ecological restoration strategy of the Yangtze River Economic Belt. China Engineering Science, 24(1): 166-175. (in Chinese)
[13]   Hu J F, Zhao G J, Mu X M, et al. 2019. Quantifying the impacts of human activities on runoff and sediment load changes in a Loess Plateau catchment. China Journal of Soils and Sediments, 19(11): 3866-3880. (in Chinese)
[14]   Kong D X, Miao C Y, Wu J W, et al. 2016. Impact assessment of climate change and human activities on net runoff in the Yellow River Basin from 1951 to 2012. Ecological Engineering, 91: 566-573.
[15]   Li L J, Zhang L, Wang H, et al. 2007. Assessing the impact of climate variability and human activities on streamflow from the Wuding River basin in China. Hydrological Processes, 21: 3485-3491.
[16]   Li Z W, Xu X L, Yu B F, et al. 2016. Quantifying the impacts of climate and human activities on water and sediment discharge in a karst region of Southwest China. Journal of Hydrology, 542: 836-849.
[17]   Liu J J, Chen J, Xu Y, et al. 2019. Attribution of runoff variation in the headwaters of the Yangtze River based on the Budyko hypothesis. International Journal of Environmental Research and Public Health, 16(14): 2506, doi: 10.3390/ijerph16142506.
[18]   Liu L L, Du J J. 2017. Documented changes in annual runoff and attribution since the 1950s within selected rivers in China. Advances in Climate Change Research, 8(1): 37-47.
[19]   Liu R, Xia J. 2013. Influence analysis of climate change and human activities on runoff of the upper Huaihe River. Yellow River, 35(9): 30-33. (in Chinese)
[20]   Liu X W, Peng D Z, Xu Z X. 2017. Identification of the impacts of climate changes and human activities on runoff in the Jinsha River Basin, China. Advances in Meteorology, 2017: 4631831, doi: 10.1155/2017/4631831.
[21]   Mankin J S, Seager R, Smerdon J E, et al. 2019. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nature Geoscience, 12: 983-988.
[22]   Millan M M. 2014. Extreme hydro meteorological events and climate change predictions in Europe. Journal of Hydrology, 518: 206-224.
[23]   Milliman J D, Farnsworth K L, Jones P D, et al. 2008. Climatic and anthropogenic factors affecting river discharge to the global ocean, 1951-2000. Global and Planetary Change, 62(3-4): 187-194.
[24]   Peng T, Tian H, Qin Z X, et al. 2018. Study on the impacts of climate change and human activities on runoff sediment in the Yangtze River. Sediment Research, 43(6): 54-60. (in Chinese)
[25]   Pirnia A, Golshan M, Darabi H, et al. 2019. Using the Mann-Kendall test and double mass curve method to explore stream flow changes in response to climate and human activities. Journal of Water and Climate Change, 10(4): 725-742.
[26]   Poschlod B, Zscheischler J, Sillmann J, et al. 2020. Climate change effects on hydrometeorological compound events over southern Norway. Weather and Climate Extremes, 28: 100253, doi: 10.1016/j.wace.2020.100253.
[27]   Qian K Z, Wan L, Wang X S, et al. 2012. Periodical characteristics of baseflow in the source region of the Yangtze River. Journal of Arid Land, 4(2): 113-122.
[28]   Qin P C, Liu M, Du L M, et al. 2019. Predicted impacts of climate change on runoff in the upper reaches of the Yangtze River. Progress in Climate Change Research, 15(4): 405-415. (in Chinese)
[29]   Raz-Yaseef N, Rotenberg E, Yakir D. 2010. Effects of spatial variations in soil evaporation caused by tree shading on water flux partitioning in a semi-arid pine forest. Agricultural and Forest Meteorology, 150(3): 454-462.
[30]   Setti S R, Maheswaran D, Radha V, et al. 2020. Attribution of hydrologic changes in a tropical river basin to rainfall variability and land-use change: Case study from India. Journal of Hydrologic Engineering, 25(8): 0001937, doi: 10.1061/(ASCE)HE.1943-5584.0001937.
[31]   Shao Y T, He Y, Mu X M, et al. 2021. Contributions of climate change and human activities to runoff and sediment discharge reductions in the Jialing River, a main tributary of the upper Yangtze River, China. Theoretical and Applied Climatology, 145(3-4): 1437-1450.
[32]   Sheng Q. 2006. Changes of evaporation from evaporation dishes in China over the past 45 a and analysis of the causes. MSc Thesis.Nanjing: Nanjing University of Information Science and Technology. (in Chinese)
[33]   Tang Q, He X B, Bao Y H, et al. 2013. Determining the relative contributions of climate change and multiple human activities to variations of sediment regime in the Minjiang River, China. Hydrological Processes, 27(25): 3547-3559.
[34]   Wang G Q, Zhang J Y, Pagano T C, et al. 2016. Simulating the hydrological responses to climate change of the Xiang River basin, China. Theoretical and Applied Climatology, 124(3-4): 769-779.
[35]   Wang H W, Qi Y, Lian X H, et al. 2022. Effects of climate change and land use/cover change on the volume of the Qinghai Lake in China. Journal of Arid Land, 14(3): 245-261.
[36]   Wang J C, Peng T, Xiang Y H, et al. 2023a. Analysis of the characteristics of uneven spatio-temporal distribution in Wujiang River basin over the last 60 years. Atmosphere, 14(9): 1356, doi: org/10.3390/atmos14091356.
[37]   Wang J F, Li Y W, Wang S, et al. 2023b. Determining relative contributions of climate change and multiple human activities to runoff and sediment reduction in the eastern Loess Plateau, China. Catena, 232: 107376, doi: 10.1016/j.catena.2023.107376.
[38]   Wang S, Yan M, Yan Y, et al. 2012. Contributions of climate change and human activities to the changes in runoff increment in different sections of the Yellow River. Quaternary International, 282: 66-77.
[39]   Wang W G, Shao Q X, Yang T, et al. 2013a. Quantitative assessment of the impact of climate variability and human activities on runoff changes: A case study in four catchments of the Haihe River basin, China. Hydrological Processes, 27(8): 1158-1174.
[40]   Wang Y, Ding Y J, Ye B S, et al. 2013b. Contributions of climate and human activities to changes in runoff of the Yellow and Yangtze rivers from 1950 to 2008. Science China (Earth Sciences), 56(8): 1398-1412.
[41]   Wu C S, Yang S L, Lei Y P, 2012. Quantifying the anthropogenic and climatic impacts on water discharge and sediment load in the Pearl River (Zhujiang), China (1954-2009). Journal of Hydrology, 452-453: 190-204.
[42]   Wu C X, Xu R R, Qiu D X, et al. 2022. Runoff characteristics and its sensitivity to climate factors in the Weihe River Basin from 2006 to 2018. Journal of Arid Land, 14(12): 1344-1360.
[43]   Wu L H, Wang S J, Bai X Y, et al. 2017. Quantitative assessment of the impacts of climate change and human activities on runoff change in a typical karst watershed, SW China. Science of the Total Environment, 601: 1449-1465.
[44]   Xiao Z W, Shi P, Jiang P, et al. 2018. The spatiotemporal variations of runoff in the Yangtze River basin under climate change. Advances in Meteorology, 2018: 5903451, doi: 10.1155/2018/5903451.
[45]   Xu X Y, Yang D W, Yang H B, et al. 2014. Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin. Journal of Hydrology, 510: 530-540.
[46]   Xu S, Zhang Y Y, Dou M, et al. 2017. Characteristics of spatial and temporal land use changes in the Yangtze River Basin and its runoff effects. Progress in Geography, 36(4): 426-436.
[47]   Xue F, Zhang X P, Zhang L, et al. 2022. Identification of water-sand change attribution based on Budyko's hypothesis and fractal theory-A case study of Beiluo River Basin. Journal of Geography, 77(1): 79-92. (in Chinese)
[48]   Yang Y H, Tian F. 2009. Abrupt change of runoff and its major driving factors in Haihe River Catchment, China. Journal of Hydrology, 374(3-4): 373-383.
[49]   Yazdandoost F, Morandian S. 2021. Climate change impacts on the streamflow of Zarrineh River, Iran. Journal of Arid Land, 13(9): 891-904.
[50]   Ye X C, Zhang Q, Liu J, et al. 2013. Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. Journal of Hydrology, 494(6): 83-95.
[51]   Yu Y B, Tang G P, Niu X Y, et al. 2022. Effect of land use and climate change on runoff in Liuxi River reservoir basin. Soil and Water Conservation Research, 30(6): 32-39, 48. (in Chinese)
[52]   Yuan Z, Yan D H, Yang Z Y, et al. 2018. Attribution assessment and projection of natural runoff change in the Yellow River Basin of China. Mitigation and Adaptation Strategies for Global Change, 23(1): 27-49.
[53]   Zeng X F, Zhao N, Sun H W, et al. 2015. Changes and relationships of climatic and hydrological droughts in the Jialing River Basin, China. PloS ONE, 10(11): e0141648, doi: 10.1371/journal.pone.0141648.
[54]   Zeng Z Z, Zhu Z C, Lian X, et al. 2016. Responses of land evapotranspiration to Earth's greening in CMIP 5 Earth System Models. Environmental Research Letters, 11(10): 104006, doi: 10.1088/1748-9326/11/10/104006.
[55]   Zhai R, Tao F L. 2017. Contributions of climate change and human activities to runoff change in seven typical catchments across China. Science of the Total Environment, 605(12): 219-229.
[56]   Zhang B Q, He C S. 2016. A modified water demand estimation method for drought identification over arid and semiarid regions. Agricultural and Forest Meteorology, 230-231: 58-66.
[57]   Zhang J, Zhang Y, Feng Y, et al. 2021. Impact and prospects of water conservation on fish habitat and advances of ecobiology operation in Yangtze River, China: A review. Journal of Environmental Biology, 42(5): 1201-1212.
[58]   Zhang L, Dawes W R, Walker G R. 2001. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research, 37(3): 701-708.
[59]   Zhang P P, Cai Y P, Xie Y L, et al. 2022. Effects of a cascade reservoir system on runoff and sediment yields in a river basin of southwestern China. Ecological Engineering, 179: 106616, doi: 10.1016/j.ecoleng.2022.106616.
[60]   Zhang Q, Xu C Y, Becker S, et al. 2006. Sediment and runoff changes in the Yangtze River basin during past 50 years. Journal of Hydrology, 331(3-4): 511-523.
[61]   Zhang X F, Yan H C, Yue Y, et al. 2019. Quantifying natural and anthropogenic impacts on runoff and sediment load: An investigation on the middle and lower reaches of the Jinsha River Basin. Journal of Hydrology, 25: 100617, doi: 10.1016/j.ejrh.2019.100617.
[62]   Zhao X, Huang G. 2022. Exploring the impact of landscape changes on runoff under climate change and urban development: Implications for landscape ecological engineering in the Yangmei River Basin. Ecological Engineering, 184: 106794, doi: 10.1016/j.ecoleng.2022.106794.
[63]   Zhou P, Luukkanen O, Tokola T, et al. 2008. Vegetation dynamics and forest landscape restoration in the Upper Min River watershed, Sichuan, China. Restoration Ecology, 16(2): 348-358.
[1] YANG Zhiwei, CHEN Rensheng, LIU Zhangwen, ZHAO Yanni, LIU Yiwen, WU Wentong. Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China[J]. Journal of Arid Land, 2024, 16(4): 483-499.
[2] ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(3): 373-395.
[3] WANG Baoliang, WANG Hongxiang, JIAO Xuyang, HUANG Lintong, CHEN Hao, GUO Wenxian. Runoff change in the Yellow River Basin of China from 1960 to 2020 and its driving factors[J]. Journal of Arid Land, 2024, 16(2): 168-194.
[4] LIU Xinyu, LI Xuemei, ZHANG Zhengrong, ZHAO Kaixin, LI Lanhai. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains[J]. Journal of Arid Land, 2024, 16(2): 195-219.
[5] ZHAO Yaxuan, CAO Bo, SHA Linwei, CHENG Jinquan, ZHAO Xuanru, GUAN Weijin, PAN Baotian. Land use and cover change and influencing factor analysis in the Shiyang River Basin, China[J]. Journal of Arid Land, 2024, 16(2): 246-265.
[6] ZHAO Xuqin, LUO Min, MENG Fanhao, SA Chula, BAO Shanhu, BAO Yuhai. Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change[J]. Journal of Arid Land, 2024, 16(1): 46-70.
[7] Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia[J]. Journal of Arid Land, 2023, 15(9): 1023-1036.
[8] QIN Guoqiang, WU Bin, DONG Xinguang, DU Mingliang, WANG Bo. Evolution of groundwater recharge-discharge balance in the Turpan Basin of China during 1959-2021[J]. Journal of Arid Land, 2023, 15(9): 1037-1051.
[9] MA Jinpeng, PANG Danbo, HE Wenqiang, ZHANG Yaqi, WU Mengyao, LI Xuebin, CHEN Lin. Response of soil respiration to short-term changes in precipitation and nitrogen addition in a desert steppe[J]. Journal of Arid Land, 2023, 15(9): 1084-1106.
[10] ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China[J]. Journal of Arid Land, 2023, 15(7): 871-885.
[11] ZHANG Zhen, XU Yangyang, LIU Shiyin, DING Jing, ZHAO Jinbiao. Seasonal variations in glacier velocity in the High Mountain Asia region during 2015-2020[J]. Journal of Arid Land, 2023, 15(6): 637-648.
[12] GAO Xiang, WEN Ruiyang, Kevin LO, LI Jie, YAN An. Heterogeneity and non-linearity of ecosystem responses to climate change in the Qilian Mountains National Park, China[J]. Journal of Arid Land, 2023, 15(5): 508-522.
[13] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[14] Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset[J]. Journal of Arid Land, 2023, 15(4): 377-392.
[15] Mehri SHAMS GHAHFAROKHI, Sogol MORADIAN. Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?[J]. Journal of Arid Land, 2023, 15(4): 424-438.