Assessing the effects of climate change and human activities on runoff variations from a seasonal perspective

Li, Ziyan; Huang, Shengzhi; Liu, Dengfeng; Leng, Guoyong; Zhou, Shuai; Huang, Qiang

doi:10.1007/s00477-020-01785-1

Cited by 32 publications

(10 citation statements)

References 92 publications

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“…CC BY 4.0 License. Xiangyu et al, 2020;Song et al, 2020;Sinha et al, 2020;Li et al, 2020d;Li et al, 2020a;Deng et al, 2020;Zhang et al, 2019a;Young et al, 2019;Xin et al, 2019;Wang et al, 2019;Lv et al, 2019;Liu et al, 2019c;Lee and Yeh, 2019;Kazemi et al, 2019;He et al, 2019c;He et al, 2019b;He et al, 2019a;Wang et al, 2018;Xu et al, 2014). We argue and demonstrate herein that the two widely accepted parametric Budyko equations are non-unique, meaning they are only two of many possible single-parameter Budyko equations.…”

Section: Introductionmentioning

confidence: 59%

“…et al, 2018b; Li et al, 2018;Xiangyu et al, 2020) and multiple methods for decomposing anthropogenic and climatic impacts on rainfall partitioning (Wang and Hejazi, 2011;Xing et al, 2018b;Jaramillo et al, 2018;Mo et al, 2018;Sun et al, 2014;Jiang et al, 2015;Liang et al, 2015;Huang et al, 2016;Zhang et al, 2020;Yeh and Tsao, 2020;Xiangyu et al, 2020;Song et al, 2020;Sinha et al, 2020;Li et al, 2020d;Li et al, 2020a;Deng et al, 2020;Zhang et al, 2019a;Young et al, 2019;Xin et al, 2019;Wang et al, 2019;Lv et al, 2019;Liu et al, 2019c;Lee and Yeh, 2019;Kazemi et al, 2019;He et al, 2019c;He et al, 2019b;He et al, 2019a;Wang et al, 2018;Xu et al, 2014). Additionally, these interpretations have led numerous studies to pursue predictive relationships for the catchment-specific parameter based on various biophysical features ( Table S1 in the Supplemental Information) (Yang et al, 2007;Donohue et al, 2012;Yang et al, 2009;Shao et al, 2012;Li et al, 2013;Xu et al, 2013;Cong et al, 2015;Yang et al, 2016;…”

Section: Current Interpretations Of Explicit Budyko Curves and The Pamentioning

confidence: 99%

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Reinterpreting the Budyko Framework

Reaver¹,

Kaplan²,

Klammler³

et al. 2020

Preprint

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Abstract. The Budyko framework posits that a catchment's long-term mean evapotranspiration (E) is primarily governed by the availabilities of water and energy, represented by long-term mean precipitation (P) and potential evapotranspiration (E0), respectively. This assertion is supported by the distinctive clustering pattern that catchments take in Budyko space. Several semi-empirical, non-parametric curves have been shown to generally represent this clustering pattern but cannot explain deviations from the central tendency. Parametric Budyko equations attempt to generalize the non-parametric framework, through the introduction of a catchment-specific parameter (n or w). Prevailing interpretations of Budyko curves suggest that the explicit functional forms represent trajectories through Budyko space for individual catchments undergoing changes in aridity index, (E0/P), while n and w values represent catchment biophysical features; however, neither of these interpretations arise from the derivation of the Budyko equations. In this study, we re-examine, reinterpret, and test these two key components of the current Budyko framework both theoretically and empirically. In our theoretical test, we use a biophysical model for E to demonstrate that n and w values can change without invoking changes in landscape biophysical features and that catchments are not required to follow Budyko curve trajectories. Our empirical test uses data from 728 reference catchments in the United Kingdom and United States to illustrate that catchments rarely follow Budyko curve trajectories and that n and w are not transferable between catchments or across time for individual catchments. This non-transferability implies n and w are proxy variables for E/P, rendering the parametric Budyko equations under-determined and lacking of predictive ability. Finally, we show that the parametric Budyko equations are non-unique, suggesting their physical interpretations are unfounded. Overall, we conclude that, while the shape of Budyko curves generally captures the global behavior of multiple catchments, their specific functional forms are arbitrary and not reflective of the dynamic behavior of individual catchments.

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Section: Introductionmentioning

confidence: 59%

Section: Current Interpretations Of Explicit Budyko Curves and The Pamentioning

confidence: 99%

Reinterpreting the Budyko Framework

Reaver¹,

Kaplan²,

Klammler³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The reliability of the GLDAS dataset in the study area was verified by earlier studies through a comparison with the in situ measurements (Feng et al 2016;Wu et al 2019). Furthermore, Bf data were calculated using the Lyne-Hollick filtering method (the relevant calculation formula can be obtained by referring to the paper by Zhang et al (2017), and the potential evapotranspiration (PET) data were calculated based on the Penman formula (Guo et al 2019;Zhou et al 2020) revised by the Food and Agricultural Organization (FAO). The geographical location of the study watershed and the spatial distribution of hydrometeorological stations are shown in Fig.…”

Section: B Datamentioning

confidence: 87%

“…The natural streamflow here is the observed streamflow plus the water use quantity, which eliminates the influence of water resource development and utilization (i.e., direct human activities) such as reservoir impoundment, irrigation, and industrial, domestic, and agricultural water consumption (Y. Li et al 2020). In the results of ''Revision on hydrological design results in the Yellow River basin,'' the calculation process of the natural streamflow and the verification of its accuracy and rationality were recorded in detail (H. Liu et al 2019).…”

Section: B Datamentioning

confidence: 99%

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Clarifying the propagation dynamics from meteorological to hydrological drought induced by climate change and direct human activities

Huang

Zhou

et al. 2021

Journal of Hydrometeorology

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An understanding of the propagation process from meteorological to hydrological drought contributes to accurate prediction hydrological drought. However, the comprehensive influence of direct human activities involved in drought propagation is not well understood. In this study, an identification framework for drought propagation time was constructed to quantify the effects of direct human activities (i.e., reservoir storage, irrigation, industrial, domestic and agricultural water consumption) on drought propagation. Subsequently, the effects of meteorological and underlying surface factors on the drought propagation process were clarified based on random forest method, and the driving effect of teleconnection factors was investigated from top to bottom. The Wei River Basin (WRB), the largest tributary of the Yellow River Basin, was selected as the case study. Results disclosed that the propagation time from meteorological to hydrological drought was short in summer (approximately 2 months) and autumn (approximately 3 months), while long in spring (approximately 3–5 months) and winter (approximately 3–8 months), exhibiting noticeable spatial variability. In a changing environment, the propagation time generally showed a decreasing trend in spring and winter, while increasing propagation time was observed in summer and autumn. The dynamic drought propagation time of each season was all jointly controlled by the different extent variation of meteorological and underlying surface conditions, and the basic flow is all relatively significant throughout the period. Direct human activities had an effect on the seasonal dynamics of drought propagation, especially during the winter of the non-flood season, which alleviated the severity of winter hydrological drought to some extent, thus delaying the transmission of meteorological signals to hydrological systems. Sunspots, the dominant direct teleconnection driving force in the WRB, could indirectly affect the local precipitation and base flow in spring, autumn, and winter and interferes with the drought propagation process. This study sheds new insights into the attribution of drought propagation dynamics in a changing environment.

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Identification of the interactions and feedbacks among watershed water-energy balance dynamics, hydro-meteorological factors, and underlying surface characteristics

Wei

Huang

et al. 2020

Stoch Environ Res Risk Assess

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Assessing the effects of climate change and human activities on runoff variations from a seasonal perspective

Cited by 32 publications

References 92 publications

Reinterpreting the Budyko Framework

Reinterpreting the Budyko Framework

Clarifying the propagation dynamics from meteorological to hydrological drought induced by climate change and direct human activities

Identification of the interactions and feedbacks among watershed water-energy balance dynamics, hydro-meteorological factors, and underlying surface characteristics

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