Runoff changes in the major river basins of China and their responses to potential driving forces

Yang, Lin; Zhao, Guangju; Tian, Peng; Mu, Xingmin; Tian, Xiaojing; Feng, Jiahao; Bai, Yunpeng

doi:10.1016/j.jhydrol.2022.127536

Cited by 105 publications

(28 citation statements)

References 67 publications

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“…By drawing the statistical sequence curve, M-K test can analyze whether there is a mutation point and the occurrence time of the mutation point at 0.05 significance level. As shown in Fig 5 , the abrupt change of runoff series occurred around 1959, and the runoff has shown a downward trend since 1974 (from the Ufk curve), when it was the time to carry out industrialization reform and Reform and Open policy to promotes the construction of water conservancy projects [ 4 ]. Therefore, this study selects the runoff series data after 1960 as the sample data of the model.…”

Section: Resultsmentioning

confidence: 99%

“…However, due to the nonlinearity and complexity of runoff series [ 3 ], accurate and reliable prediction of runoff is a very challenging task. In addition, the frequent extreme climate events make the runoff series show greater volatility [ 4 ], which brings great challenges to the runoff prediction. Therefore, advanced models were needed to provide higher accuracy and more stable runoff prediction [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the frequent extreme climate events make the runoff series show greater volatility [ 4 ], which brings great challenges to the runoff prediction. Therefore, advanced models were needed to provide higher accuracy and more stable runoff prediction [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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“…Combined with equations (Xue et al, 2021;Yang et al, 2022), the average annual runoff of the basin can be expressed as:…”

Section: Budyko Frameworkmentioning

confidence: 99%

“…With global climatic and drastic changes in land use or vegetational cover in most areas, river runoff in many river basins in China has changed considerably (Xue et al, 2021;Gao et al, 2022;Yang et al, 2022). Since 1999, the Chinese government has implemented large-scale projects, such as returning farmland to forest and ecological restoration, which have intensified human activities in the Loess Plateau and indirectly affected the region's underlying surface properties Li H. et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Attribution analysis of runoff evolution in Kuye River Basin based on the time-varying budyko framework

Huang

Wang

et al. 2023

Front. Earth Sci.

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The underlying surface parameters in the Budyko framework (such as parameter n in the Choudhury–Yang equation) are crucial for studying the relationship between precipitation, evapotranspiration, and runoff. It is important to accurately quantify the influence of climate and human activities on the evolution of underlying surface characteristic parameters. However, due to the spatiotemporal heterogeneity of underlying surface parameters, it is often difficult to accurately quantify these relationships. In this study, taking the Kuye River Basin located in the northern Loess Plateau as the research object, we first used trend analysis and non-linear regression methods to estimate the evolution characteristics of runoff and underlying surface parameter n. We then determined the contribution of runoff changes by using the elasticity coefficient method under the 9-year moving average window. The results showed that: 1) the Kuye River Basin runoff underwent a sudden change in 1997, and the complex human activities are the main reasons for the sharp runoff decrease. 2) In addition to precipitation and potential evapotranspiration, temperature changes will alter the basin’s underlying surface parameters, ultimately changing the runoff. Moreover, climate change first inhibited and then promoted the runoff reduction trend. 3) Human activities, represented by changes in vegetation coverage and coal mining, considerably influenced runoff evolution in Kuye River Basin. More importantly, the change of runoff in the Kuye River Basin caused by coal mining is approximately four times that of the normalized vegetation index. This study can improve the applicability of the Budyko framework in the Loess Plateau sub-basin and provide scientific guidance for water resource management.

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Evolution and driving mechanism of runoff in the Han River basin from 1980 to 2020 under changing environments

Luo,

Liu,

Wang

et al. 2024

Ecohydrology

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Recent studies in ecohydrology have focussed on the impact of climate and vegetation changes on runoff. To reveal the hydrological response mechanism under changing environments, this investigation explored the evolution and driving mechanism of Q (the mean annual runoff) in the Han River basin from 1980 to 2020 by combining the Budyko–Porporato–Guswa coupling model and the Soil and Water Assessment Tool (SWAT) model, with scenario analysis and statistical analysis. The results showed that (1) Q was 719.38 mm and showed a fluctuating trend from 1980 to 2020. The spatial distribution was characterised by the largest in the upper Mei River, the second in the Ting River, and the smallest in the lower Mei River and the upper‐middle Han River. (2) Q increased with obvious spatial heterogeneity under the impact of climate change and the integrated impact of climate and vegetation changes. (3) Q had the strongest sensitivity to the change in E0, followed by P, and the weakest was vegetation. Q and E0 showed a strong correlation. (4) The principal driver for the change in Q was climate change, and the impact of P was greater than E0. The impact of vegetation variability was the smallest. This paper can serve as a reference for the research of hydrological response to changing environments and an academic foundation for the scheduling of water resources.

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Runoff changes in the major river basins of China and their responses to potential driving forces

Cited by 105 publications

References 67 publications

Improved runoff forecasting based on time-varying model averaging method and deep learning

Improved runoff forecasting based on time-varying model averaging method and deep learning

Attribution analysis of runoff evolution in Kuye River Basin based on the time-varying budyko framework

Evolution and driving mechanism of runoff in the Han River basin from 1980 to 2020 under changing environments

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