Analysis of Runoff Variation Characteristics and Influencing Factors in the Wujiang River Basin in the Past 30 Years

Guo, Wenjuan; Hu, Jianwen; Wang, Hongxiang

doi:10.3390/ijerph19010372

Cited by 14 publications

(4 citation statements)

References 39 publications

(39 reference statements)

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“…Pettitt's test was employed to identify the occurrence of mutation points in the Q series (Figure 3 and Table 3). Results reveal that Q in MJR, WJR, JLR, JSR, and UYR experienced an abrupt change in 1993, 2004, 1993, 1997, and 2000, respectively, which is consistent with previous studies (Gao et al., 2010; W. Guo et al., 2021, 2022; N. Li et al., 2016; Ye et al., 2020). All the identified mutation points occurred after 1990, which is primarily attributed to the ecological restoration activities carried out in UYR (G. Xu et al., 2018).…”

Section: Resultssupporting

confidence: 91%

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Reduced Runoff in the Upper Yangtze River Due To Comparable Contribution of Anthropogenic and Climate Changes

Zeng,

He,

et al. 2024

Earth's Future

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The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics.

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Section: Resultssupporting

confidence: 91%

“…Results reveal that Q in MJR, WJR, JLR, JSR, and UYR experienced an abrupt change in 1993, 2004, 1993, 1997, and 2000, respectively, which is consistent with previous studies (Gao et al, 2010;W. Guo et al, 2021W.…”

Section: Changes In Hydrometeorological Variablessupporting

confidence: 91%

Reduced Runoff in the Upper Yangtze River Due To Comparable Contribution of Anthropogenic and Climate Changes

Zeng,

He,

et al. 2024

Earth's Future

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“…From the perspective of the runoff generation mechanism, it is known that the formation process of runoff is mainly influenced by climate factors and underlying surface conditions in the watershed. Among these factors, climate characteristics, particularly precipitation and evaporation, are the most important factors that affect long-term runoff changes [76]. Precipitation is the source of runoffs, and its spatial distribution and amount directly affect the formation of runoffs.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis

Tian,

Gao,

Tian

et al. 2023

Water

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The Long Short-Term Memory (LSTM) neural network model is an effective deep learning approach for predicting streamflow, and the investigation of the interpretability of deep learning models in streamflow prediction is of great significance for model transfer and improvement. In this study, four key hydrological stations in the Xijiang River Basin (XJB) in South China are taken as examples, and the performance of the LSTM model and its variant models in runoff prediction were evaluated under the same foresight period, and the impacts of different foresight periods on the prediction results were investigated based on the SHapley Additive exPlanations (SHAP) method to explore the interpretability of the LSTM model in runoff prediction. The results showed that (1) LSTM was the optimal model among the four models in the XJB; (2) the predicted results of the LSTM model decreased with the increase in foresight period, with the Nash–Sutcliffe efficiency coefficient (NSE) decreasing by 4.7% when the foresight period increased from one month to two months, and decreasing by 3.9% when the foresight period increased from two months to three months; (3) historical runoff had the greatest impact on streamflow prediction, followed by precipitation, evaporation, and the North Pacific Index (NPI); except evaporation, all the others were positively correlated. The results can provide a reference for monthly runoff prediction in the XJB.

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“…The trend shifts in WS, WC, and SC in 2005 were mainly owing to the influence of the natural climate. Precipitation in the WJRB decreased from 2000 to 2005 and increased from 2005 to 2020 [37]. Precipitation has a direct influence on WS and WC [30,38], and the level of precipitation significantly affects vegetation cover and SC [39].…”

Section: Es Spatial-temporal Characteristicsmentioning

confidence: 99%

Spatial and Temporal Patterns of Ecosystem Services and Trade-Offs/Synergies in Wujiang River Basin, China

Yang,

Li,

et al. 2023

Remote Sensing

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Analysis of the relationships among ecosystem services (ESs) can help ensure that benefits from ecosystems are consistent over time. This study explored the spatial and temporal patterns of water supply (WS), grain supply (GS), carbon storage (CS), water conservation (WC), soil conservation (SC), and habitat quality (HQ) in the Wujiang River Basin (WJRB) from 2000 to 2020 and the trade-off/synergy relationships of ESs. The ESs in the WJRB are downstream > midstream > upstream in space, with the greatest increase and decrease in the upstream and midstream temporal dimensions, respectively. The WS, WC, and SC underwent a trend shift in 2005 due to climatic influences, whereas GS and HQ underwent a trend shift in 2010 due to human social development. GS formed a trade-off with other ESs in the spatial pattern, whereas WS formed a trade-off with CS and WC in the temporal dimension. Adjusting the GS spatial pattern reduces the trade-off between ESs in the spatial pattern, allowing for focusing on monitoring soil and water erosion-prone areas to prevent extensive soil erosion during heavy precipitation years; this reduces the trade-off between ESs in the time dimension in the WJRB. This provides a theoretical basis for achieving high-quality WJRB development.

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Analysis of Runoff Variation Characteristics and Influencing Factors in the Wujiang River Basin in the Past 30 Years

Cited by 14 publications

References 39 publications

Reduced Runoff in the Upper Yangtze River Due To Comparable Contribution of Anthropogenic and Climate Changes

Reduced Runoff in the Upper Yangtze River Due To Comparable Contribution of Anthropogenic and Climate Changes

Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis

Spatial and Temporal Patterns of Ecosystem Services and Trade-Offs/Synergies in Wujiang River Basin, China

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