Attribution analysis of runoff variations in the Changma River Basin based on the Budyko hypothesis

doi:10.7522/j.issn.1000-694X.2024.00064

Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (6): 110-121.DOI: 10.7522/j.issn.1000-694X.2024.00064

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Attribution analysis of runoff variations in the Changma River Basin based on the Budyko hypothesis

Zhenliang Yin¹^,²(), Rui Zhu³, Chunshuang Fang³(), Huaqing Yang³, Zexia Chen¹

^1.Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands / National Cryosphere Desert Data Center，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^2.College of Safety and Environmental Engineering，Shandong University of Science and Technology，Qingdao 266000，Shandong，China
^3.Faculty of Geomatics / National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring / Gansu Provincial Engineering Laboratory for National Geographic State Monitoring，Lanzhou Jiaotong University，Lanzhou 730000，China

Received:2024-04-29 Revised:2024-05-22 Online:2024-11-20 Published:2024-12-06
Contact: Chunshuang Fang

Abstract

Abstract:

The high-quality economic development and the security of the oasis ecosystem in the middle and lower reaches of the Shule River Basin are intimately connected to the runoff emerging from the upper reaches. The Changma River， as the principal channel of the Shule River， exhibits extreme sensitivity to variations in climate and underlying surface changes. This study， focusing on the Changma River Basin and employing the Budyko hypothesis for the attribution analysis of runoff variations， arrives at the following primary conclusions：（1） Runoff， climate， and underlying surface factors all display pronounced characteristics of abrupt change， with the overall climate trend leaning towards warmer and more humid conditions. （2） Runoff exhibits the highest sensitivity to changes in precipitation， followed by underlying surface changes and potential evapotranspiration. In 1965-2015， the runoff in the Changma River demonstrated a significant increasing trend， with climate change （precipitation and potential evapotranspiration） and changes in the underlying surface contributing 37.31% and 62.79% to the runoff quantity changes， respectively. Changes in the underlying surface emerge as the predominant cause of runoff variations， with an increase in precipitation playing a secondary role. （3） The leaf area index is the factor most correlated with changes in runoff， followed by precipitation and soil water content. The change of underlying surface factors can explain the changes in runoff to a greater extent.

Key words: Changma River Basin, runoff change, climate change, Budyko hypothesis

CLC Number:

P333

Zhenliang Yin, Rui Zhu, Chunshuang Fang, Huaqing Yang, Zexia Chen. Attribution analysis of runoff variations in the Changma River Basin based on the Budyko hypothesis[J]. Journal of Desert Research, 2024, 44(6): 110-121.

Figures/Tables 13

Fig.1 Location of the study area

Fig.2 M-K mutation test of runoff and impact factors

Fig.3 Theil-Sen slope and Mann-Kendall test of runoff and impact factors

Fig.4 Continuous wavelet transforms of runoff and impact factors. Thick contours denote 5% significance levels against red noise； pale regions denote the cone of influence where edge effects might distort the results； the color denotes the strength of wavelet power

Table 1 Statistics in hydro-climatic variables and elasticity coefficient of streamflow to P， ET0， and ω in two periods

时期	$R ˉ$ /mm	$P ˉ$ /mm	$E T 0 ˉ$ /mm	$ω$	$R ˉ$ / $P ˉ$	$E T 0 ˉ$ / $P ˉ$	弹性系数（ $ε X$ ）
时期	$R ˉ$ /mm	$P ˉ$ /mm	$E T 0 ˉ$ /mm	$ω$	$R ˉ$ / $P ˉ$	$E T 0 ˉ$ / $P ˉ$	$ε p$	$ε E T 0$	$ε w$
基准期(1965—2000年)	78.4	283.4	833.4	0.95	0.28	2.94	1.69	-0.69	-1.59
变化期(2001—2015年)	113.5	309.6	858.0	0.80	0.367	2.78	1.53	-0.53	-1.33
差值	35.1	26.2	24.6	-0.15	0.087	-0.16	-0.16	0.16	0.26

Table 1 Statistics in hydro-climatic variables and elasticity coefficient of streamflow to P， ET0， and ω in two periods

时期	$R ˉ$ /mm	$P ˉ$ /mm	$E T 0 ˉ$ /mm	$ω$	$R ˉ$ / $P ˉ$	$E T 0 ˉ$ / $P ˉ$	弹性系数（ $ε X$ ）
时期	$R ˉ$ /mm	$P ˉ$ /mm	$E T 0 ˉ$ /mm	$ω$	$R ˉ$ / $P ˉ$	$E T 0 ˉ$ / $P ˉ$	$ε p$	$ε E T 0$	$ε w$
基准期(1965—2000年)	78.4	283.4	833.4	0.95	0.28	2.94	1.69	-0.69	-1.59
变化期(2001—2015年)	113.5	309.6	858.0	0.80	0.367	2.78	1.53	-0.53	-1.33
差值	35.1	26.2	24.6	-0.15	0.087	-0.16	-0.16	0.16	0.26

Table 2 Contribution of P， ET0， and ω to runoff change

基准期	变化期	ΔR_o/mm	ΔR_P /mm	Δ $R E T 0$ /mm	ΔR_ω /mm	ΔR_c/mm	贡献率/%
基准期	变化期	ΔR_o/mm	ΔR_P /mm	Δ $R E T 0$ /mm	ΔR_ω /mm	ΔR_c/mm	CON_p	$C O N E T 0$	CON_w
1965—2000年	2000—2015年	35.10	14.68	-1.72	27.66	40.62	33.31%	-3.90%	62.79%

Table 2 Contribution of P， ET0， and ω to runoff change

基准期	变化期	ΔR_o/mm	ΔR_P /mm	Δ $R E T 0$ /mm	ΔR_ω /mm	ΔR_c/mm	贡献率/%
基准期	变化期	ΔR_o/mm	ΔR_P /mm	Δ $R E T 0$ /mm	ΔR_ω /mm	ΔR_c/mm	CON_p	$C O N E T 0$	CON_w
1965—2000年	2000—2015年	35.10	14.68	-1.72	27.66	40.62	33.31%	-3.90%	62.79%

Fig.5 Land cover classification maps over the study area in 1980 （A）， 2000 （B） and 2020 （C）； Land cover classification proportions （D）； Land use change over the study area between 1980 and 2000 （E） and between 2000 and 2020 （F）

Table 3 Glacier change over the study area between 1970s and 2020

时期	冰川面积 /km²	冰川数量	冰储量/km³
FCGI(1960s—1970s)	494.46^[21]	505^[21]	30.58^[21]
1990年	481.07	503	28.07
1995年	456.28	506	26.64
2000年	437.39	505	25.51
2005年	421.24	505	24.57
SCGI(2005—2010年)	415.29^[21]	501^[21]	24.81^[21]
2010年	414.12	506	24.13
2015年	401.14	520	23.31
2020年	384.05	519	22.80

Fig.6 The interrelation between runoff and influencing factors.Lag0-Lag 11 denotes the impact of runoff lagging behind influencing factors by 0 to 11 months. Significance P0.05， marked as **； significance P0.1， marked as *

Table 4 Coherence between runoff and individual influencing factor

	PCP	ET₀	TMP	SW	LAI	SCE	SD
WTC	0.90	0.89	0.91	0.85	0.89	0.83	0.85
PASC	46.67%	29.99%	25.04%	41.82%	54.48%	15.85	18.05

Fig.7 Wavelet coherence between runoff and individual influencing factor. Thick contours denote 5% significance levels against red noise； pale regions denote the cone of influence where edge effects might distort the results； the color denotes the strength of coherence

Table 5 Coherence between runoff and multiple influencing factor

双变量	MWC2	PASC/%	三变量	MWC3	PASC/%
LAI-SW	0.95	60.65	LAI-SW-SCE	0.98	58.87
LAI-SCE	0.94	55.23	LAI-SW-PCP	0.98	58.03
LAI-PCP	0.95	53.40	LAI-SW-ET₀	0.98	55.55
LAI-ET₀	0.95	51.75	LAI-SCE-PCP	0.98	54.60
PCP-SW	0.95	51.24	LAI-SCE-ET₀	0.97	54.31
PCP-ET₀	0.95	51.19	LAI-SW-SD	0.97	54.29
LAI-SD	0.94	50.03	LAI-PCP-ET₀	0.98	53.11
LAI-TMP	0.95	47.23	LAI-SCE-TMP	0.97	51.84
PCP-SCE	0.96	44.40	LAI-PCP-SD	0.98	51.78
PCP-SD	0.95	40.47	LAI-SW-TMP	0.98	51.38
PCP-TMP	0.95	39.30	LAI-SCE-SD	0.97	49.20

Fig.8 Wavelet coherence between runoff and multiple influencing factors. Thick contours denote 5% significance levels against red noise； pale regions denote the cone of influence where edge effects might distort the results； the color denotes the strength of coherence

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Attribution analysis of runoff variations in the Changma River Basin based on the Budyko hypothesis

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