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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (8): 1048-1063    DOI: 10.1007/s40333-025-0024-8    
Research article     
Combined application of variable infiltration capacity model and Budyko hypothesis for identification of runoff evolution in the Yellow River Basin, China
QIU Yuhao1, DUAN Limin1,2,3,*(), CHEN Siyi1, WANG Donghua1, ZHANG Wenrui1, GAO Ruizhong1,2,3, WANG Guoqiang4, LIU Tingxi1,2,3
1State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China
2Inner Mongolia Key Laboratory of Ecohydrology and High Efficient Utilization of Water Resources, Hohhot 010018, China
3Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China
4Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Abstract  

Climate change and human activities are primary drivers of runoff variations, significantly impacting the hydrological balance of river basins. In recent decades, the Yellow River Basin, China has experienced a marked decline in runoff, posing challenges to the sustainable development of regional water resources and ecosystem stability. To enhance the understanding of runoff dynamics in the basin, we selected the Dahei River Basin, a representative tributary in the upper reaches of the Yellow River Basin as the study area. A comprehensive analysis of runoff trends and contributing factors was conducted using the data on hydrology, meteorology, and water resource development and utilization. Abrupt change years of runoff series in the Dahei River Basin was identified by the Mann-Kendall and Pettitt tests: 1999 at Dianshang, Qixiaying, and Meidai hydrological stations and 1995 at Sanliang hydrological station. Through hydrological simulations based on the Variable Infiltration Capacity (VIC) model, we quantified the factors driving runoff evolution in the Dahei River Basin, with climate change contributing 9.92%-22.91% and human activities contributing 77.09%-90.08%. The Budyko hypothesis method provided similar results, with climate change contributing 13.06%-20.89% and human activities contributing 79.11%-86.94%. Both methods indicated that human activities, particularly water consumption, were dominant factors in the runoff variations of the Dahei River Basin. The integration of hydrological modeling with attribution analysis offers valuable insights into runoff evolution, facilitating adaptive strategies to mitigate water scarcity in arid and semi-arid areas.

Key words： attribution analysis      climate change      human activity      hydrological model      runoff simulation      Variable Infiltration Capacity (VIC)     
Received: 25 December 2024      Published: 31 August 2025
Corresponding Authors: *DUAN Limin (E-mail: duanlimin820116@163.com)
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QIU Yuhao
DUAN Limin
CHEN Siyi
WANG Donghua
ZHANG Wenrui
GAO Ruizhong
WANG Guoqiang
LIU Tingxi
Cite this article:

QIU Yuhao, DUAN Limin, CHEN Siyi, WANG Donghua, ZHANG Wenrui, GAO Ruizhong, WANG Guoqiang, LIU Tingxi. Combined application of variable infiltration capacity model and Budyko hypothesis for identification of runoff evolution in the Yellow River Basin, China. Journal of Arid Land, 2025, 17(8): 1048-1063.

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http://jal.xjegi.com/10.1007/s40333-025-0024-8     OR     http://jal.xjegi.com/Y2025/V17/I8/1048

Fig. 1 Dahei River watershed, and hydrological and meteorological stations
Hydrological station Latitude Longitude Controlled watershed area (km2) Period
Dianshang 40°49′12″N 111°19′12″E 1334.28 1959-2022
Qixiaying 40°58′12″N 112°07′12″E 2912.58 1952-2022
Meidai 40°48′00″N 111°58′12″E 4279.80 1951-2022
Sanliang 40°36′00″N 111°22′12″E 6970.79 1953-2022
Table 1 Information of the four typical hydrological stations in the Dahei River Basin
Fig. 2 Trends in annual runoff at the four hydrological stations of the Dahei River Basin. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang.
Fig. 3 Abrupt change detection of annual average runoff based on the Mann-Kendall test. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang. UF, forward sequence statistics; UB, backward sequence statistics.
Fig. 4 Abrupt change detection of annual average runoff based on the Pettitt test. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang. Year with dashed red line denotes runoff change point.
Hydrological station Change year Period division
Mann-Kendall method Pettitt method Final
determination		 Reference period Change period
Dianshang 1999-2000 1999 1999 1960-1999 2000-2022
Qixiaying 1998-1999 1999 1999 1960-1999 2000-2022
Meidai 1998-1999 1999 1999 1960-1999 2000-2022
Sanliang 1986-1995 1995 1995 1960-1995 1996-2022
Table 2 Abrupt change year and period division of annual average runoff at the four hydrological stations of the Dahei River Basin
Fig. 5 Comparison of observed and simulated monthly average runoff during baseline period at the four hydrological stations of the Dahei River Basin. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang. NSE, Nash-Sutcliffe efficiency.
Fig. 6 Comparison of observed and simulated monthly average runoff during change period at the four hydrological stations of the Dahei River Basin. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang.
Hydrological station B Ds Dsmax Ws d1 (cm) d2 (cm) d3 (cm)
Dianshang 0.016 0.005 2.40 0.2 0.1 1.4 7.0
Qixiaying 0.010 0.001 2.50 0.3 0.1 1.0 0.3
Meidai 0.004 0.008 0.16 0.1 0.1 0.9 8.0
Sanliang 0.009 0.001 0.16 0.1 0.1 1.1 8.0
Table 3 VIC (Variable Infiltration Capacity) model parameter calibration results for each hydrological station
Hydrological station Period Measured multi-year average runoff (m3/s) Simulated multi-year average runoff (m3/s) Total increase in runoff (m3/s) Contribution value (m3/s) Contribution rate (%)
Climate change Human activities Climate change Human activities
Dianshang Baseline 1.122 - - - - - -
Change 0.638 1.074 -0.484 -0.048 -0.436 9.92 90.08
Qixiaying Baseline 2.763 - - - - - -
Change 1.197 2.525 -1.566 -0.238 -1.328 15.20 84.80
Meidai Baseline 1.588 - - - - - -
Change 0.455 1.463 -1.133 -0.125 -1.008 11.03 88.97
Sanliang Baseline 4.181 - - - - - -
Change 1.505 3.568 -2.676 -0.613 -2.063 22.91 77.09
Table 4 Effects of climate change and human activities on runoff changes in the Dahei River Basin
Hydrological station Measured change in runoff depth (mm) Climate change Human activities
Precipitation Potential evapotranspiration Water consumption by human activities Underlying surface conditions
Change amount (mm) Change in runoff depth (mm) Change amount (mm) Change in runoff depth (mm) Change amount (mm) Change in runoff depth (mm) Change amount (mm) Change in runoff depth (mm)
Dianshang -11.45 -37.33 -7.45 34.07 -1.66 -8.52 -8.52 -0.18 6.18
Qixiaying -16.96 -12.73 -2.69 10.55 -0.62 -10.36 -10.36 0.10 -3.29
Meidai -8.42 -7.78 -0.85 7.22 -0.25 -6.32 -6.32 0.08 -1.00
Sanliang -12.11 -14.49 -2.08 10.98 -0.45 -7.80 -7.80 0.09 -1.78
Table 5 Changes in observed runoff and influencing factors during change period compared with baseline period in the Dahei River Basin
Hydrological station Climate change Human activities
Precipitation (%) Potential evapotranspiration (%) Total (%) Water consumption by humans (%) Underlying surface conditions (%) Total (%)
Dianshang 12.47 2.78 15.25 74.41 10.34 84.75
Qixiaying 15.86 3.66 19.52 61.08 19.40 80.48
Meidai 10.09 2.97 13.06 75.06 11.88 86.94
Sanliang 17.17 3.72 20.89 64.41 14.70 79.11
Table 6 Contribution rates of climate change and human activities to runoff changes in the Dahei River Basin
Fig. 7 Sankey diagram of land use change in watersheds controlled by the four hydrological stations. (a), Dianshang; (b), Qixiaying; (c), Meidai; (d), Sanliang.
Hydrological station Period Long-term average precipitation (mm) Long-term average potential evapotranspiration (mm)
Dianshang Baseline period 384.50 877.47
Change period 347.17 901.23
Qixiaying Baseline period 379.04 844.35
Change period 366.31 854.90
Meidai Baseline period 383.55 858.87
Change period 375.77 866.09
Sanliang Baseline period 380.17 875.91
Change period 365.68 886.89
Table 7 Statistical results of precipitation and potential evapotranspiration for baseline and change periods in watersheds controlled by each hydrological station
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