Interaction of vegetation, climate and topography on evapotranspiration modelling at different time scales within the Budyko framework

Ning, Tingting; Zhou, Sha; Chang, Feiyang; Shen, Hong; Li, Zhi; Liu, Wenzhao

doi:10.1016/j.agrformet.2019.05.001

Cited by 81 publications

(73 citation statements)

References 70 publications

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“…As such, the explanatory variables should be independent of each other. The interactions between these factors have been detected in the Loess Plateau of China (Ning et al, ), Australian (Shao et al, ) and US catchments (Gentine et al, ; Troch et al, ). In particular, vegetation is highly related to climate seasonality, soil quality and catchment topography on a long‐term scale.…”

Section: Discussionmentioning

confidence: 99%

“…In these analytical equations, vegetation, topography and climate seasonality have been identified as the main factors related to Budyko controlling parameters (Ning et al, ; Xu et al, ; Yang et al, ). However, vegetation is correlated with other factors and its change can alter other factors (Donohue et al, ; Li et al, ; Ning et al, ; Wei et al, ). As such, vegetation dynamics play a key role in influencing the variations in controlling parameters.…”

Section: Introductionmentioning

confidence: 99%

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Effects of forest cover change on catchment evapotranspiration variation in China

Ning

Feng

et al. 2020

Hydrological Processes

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Several large-scale revegetation programs in China have resulted in significant changes in forest cover (ΔF) during the past three decades. As forests have important effects on catchment hydrology, evaluating the effects of ΔF on hydrology is essential. Using data from 44 catchments across China, this study derived a rational analytical equation to link changes in actual evapotranspiration (ΔET) with those in F within the Budyko framework, and further quantified the effects of ΔF on ET variation during 1976-2015. The elasticity of ET to ΔF was found to be the greatest in the dry catchments in northwest China, followed by the humid catchments in south China, and was the smallest in the subhumid and semiarid catchments in north China. F averaged across all the catchments has increased, and has further led to the increase in ET. The F-ET relationship has become more prominent in the recent decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), with 68.0% of the catchments showing an average increase in F of 4.5% and a resultant average increase in ET of 8.2% compared with the baseline period (1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985). These results are helpful for quantitative assessment of hydrological responses to afforestation, especially in water-limited regions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of forest cover change on catchment evapotranspiration variation in China

Ning

Feng

et al. 2020

Hydrological Processes

Self Cite

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“…(5), (6), (9), and (10) directly contradict each other Of the previously proposed parametric Budyko equations, Eq. (5) and (6) have been the most widely used (e.g., (Donohue et al, 2012;Yang et al, 2007;Yang et al, 2009;Shao et al, 2012;Li et al, 2013;Xu et al, 2013;Cong et al, 2015;Yang et al, 2016;Zhang et al, 2018;Abatzoglou and Ficklin, 2017;Xing et al, 2018a;Zhao et al, 2020;Ning et al, 2020b;Ning et al, 2020a;Li et al, 2020c;Li et al, 2020b;Zhang et al, 2019b;Ning et al, 2019;Bai et al, 2019)). Any of these studies could have justifiably used Eq.…”

Section: Non-uniqueness Of the Parametric Budyko Equationsmentioning

confidence: 99%

“…The parameters of these equations are typically referred to as "catchment-specific parameters" and are generally interpreted as representing the influence of all catchment biophysical features, other than ̅ and 0 ̅̅̅ , on ̅ (Wang et al, 2016a). This interpretation has motivated profound efforts to understand the relationship between biophysical features and catchment-specific parameters (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;Zhang et al, 2018;Abatzoglou and Ficklin, 2017;Xing et al, 2018a;Zhao et al, 2020;Ning et al, 2020b;Li et al, 2020c;Li et al, 2020b;Zhang et al, 2019b;Ning et al, 2019;Bai et al, 2019). Numerous studies have also focused on determining the sensitivity of rainfall partitioning to climatic and/or land use changes (Roderick and Farquhar, 2011;Wang and Hejazi, 2011;Yang and Yang, 2011;Wang et al, 2016b;Zhou et al, 2016;Shen et al, 2017;Zhang et al, 2016;Yeh and Tsao, 2020;Zhang et al, 2020;Sinha et al, 2020;Ning et al, 2020b;Liu et al, 2020;Li et al, 2020e;Li et al, 2020a;Liu et al, 2019a;Li et al, 2019;Yang et al, 2018;Xing et al, 2018b;Li et al, 2018;…”

Section: Introductionmentioning

confidence: 99%

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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“…CC BY 4.0 License. al., 2013;Xu et al, 2013;Cong et al, 2015;Yang et al, 2016;Zhang et al, 2018;Abatzoglou and Ficklin, 2017;Xing et al, 2018;Zhao et al, 2020;Ning et al, 2020b;Ning et al, 2020a;Li et al, 2020b;Li et al, 2020a;Zhang et al, 2019;Ning et al, 2019;Bai et al, 2019;Ning et al, 2017). In all such studies, known values of ̅ , 0 ̅̅̅ , and ̅ , estimated empirically or via modelling, are used to numerically invert the parametric Budyko equations to obtain values of the catchment-specific parameter, which are then regressed against various biophysical features.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Analytical Inversion of the Parametric Budyko Equations

Reaver

Kaplan

Klammler

et al. 2020

Preprint

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Abstract. The non-parametric Budyko framework provides empirical relationships between a catchment's long-term mean evapotranspiration (E) and the aridity index, defined as the ratio of mean rainfall depth (P) to mean potential evapotranspiration (E0). The parametric Budyko equations attempt to generalize this framework by introducing a catchment-specific parameter (n or w), intended to represent differences in catchment climate and landscape features. Many studies have developed complex regression relationships for the catchment-specific parameter in terms of biophysical features, all of which use known values of P, E0, and E to numerically invert the parametric Budyko equations to obtain values of n or w. In this study, we analytically invert both forms of the parametric Budyko equations, producing expressions for n and w only in terms of P, E0, and E. These expressions allow for n and w to be explicitly expressed in terms of biophysical features through the dependence of P, E0, and E on those same features.

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Interaction of vegetation, climate and topography on evapotranspiration modelling at different time scales within the Budyko framework

Cited by 81 publications

References 70 publications

Effects of forest cover change on catchment evapotranspiration variation in China

Effects of forest cover change on catchment evapotranspiration variation in China

Reinterpreting the Budyko Framework

Technical Note: Analytical Inversion of the Parametric Budyko Equations

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