The effects of climate and catchment characteristic change on streamflow in a typical tributary of the Yellow River

Lv, Xizhi; Zuo, Zhiyan; Ni, Yongxin; Sun, Junyi; Wang, Henian

doi:10.1038/s41598-019-51115-x

Cited by 57 publications

(26 citation statements)

References 31 publications

(30 reference statements)

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“…They are often used to estimate the long-term hydrological impacts of climate change (e.g. Arora, 2002;Jiang et al, 2015;Lv et al, 2019;Ning et al, 2018;Renner and Bernhofer, 2012;Shen et al, 2017;Teng et al, 2012;Wang et al, 2016), as catchment scale (lumped) models. Based on these frameworks, the elasticity of climate change impacts has also been tested in numerous studies (Andréassian et al, 2016;Gao et al, 2016;Konapala and Mishra, 2016;Liang et al, 2015;Tian et al, 2018;Wang and Hejazi, 2011;Zhang et al, 2016;Zhou et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-model climatic water balance prediction in the Zala River Basin (Hungary) based on a modified Budyko framework

Csáki

Gyimóthy

Kalicz

et al. 2020

Journal of Hydrology and Hydromechanics

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AbstractProviding information on the impacts of climate change on hydrological processes is becoming ever more critical. Modelling and evaluating the expected changes of the water resources over different spatial and time scales can be useful in several fields, e.g. agriculture, forestry and water management. Previously a Budyko-type spatially distributed long-term climate-runoff model was developed for Hungary. This research includes the validation of the model using historical precipitation and streamflow measurements for three nested sub-catchments of the Zala River Basin (Hungary), an essential runoff contributing region to Lake Balaton (the largest shallow lake in Central Europe). The differences between the calculated (from water balance) and the estimated (by the model) mean annual evapotranspiration varied between 0.4% and 3.6% in the validation periods in the sub-catchments examined. Predictions of the main components of the water balance (evapotranspiration and runoff) for the Zala Basin are also presented in this study using precipitation and temperature results of 12 regional climate model simulations (A1B scenario) as input data. According to the projections, the mean annual temperature will be higher from period to period (2011–2040, 2041–2070, 2071–2100), while the change of the annual precipitation sum is not significant. The mean annual evapotranspiration rate is expected to increase slightly during the 21st century, while for runoff a substantial decrease can be anticipated which may exceed 40% by 2071–2100 relative to the reference period (1981–2010). As a result of this predicted reduction, the runoff from the Zala Basin may not be enough to balance the increased evaporation rate of Lake Balaton, transforming it into a closed lake without outflow.

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Section: Introductionmentioning

confidence: 99%

Multi-model climatic water balance prediction in the Zala River Basin (Hungary) based on a modified Budyko framework

Csáki

Gyimóthy

Kalicz

et al. 2020

Journal of Hydrology and Hydromechanics

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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%

“…(4). The functional form of the first of these solutions was proposed prior (Turc, 1953;Choudhury, 1999;Mezentsev, 1955) to its formal analytical derivation from Eq. (4) by Yang et al (2008) and is given by…”

Section: Overview Of the Budyko Hypothesis And Equationsmentioning

confidence: 99%

“…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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“…It has been used to address fundamental questions in hydrology, such as water availability and management, runoff prediction, and climate change studies. 17,[30][31][32][33][34][35][36][37][38][39][40][41][42][43] It has also been used at various spatial and temporal scales to perform diagnostic analyses of the long-term mean annual water balances in catchments and to study the interactions between hydroclimate, soil, vegetation, and topography. [44][45][46][47][48][49][50] This study aims to: (1) estimate the closure of the surface water balance across subcatchments of the ARB comparing different datasets of P, E, and E p ; (2) employ Budyko's framework using Roderick and Farquhar's approach 35 to assess the sensitivity of runoff (R) in the ARB, given changes in climatic variables (P) and potential evapotranspiration (E p ), as well as in other properties that affect the partitioning of P, represented by the parameter n, by estimating the sensitivity coefficients that analytically predict the weight of each variable (P, E p , and n) in the relative change of R across the ARB and its six major subbasins.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of runoff sensitivity to climate change in the Amazon River basin

Carmona

Renner

Kleidon

et al. 2020

Annals of the New York Academy of Sciences

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We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (E p), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and E p within the Amazon River basin to quantify the uncertainty of the hydrologic response at the subcatchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Óbidos), a 10% increase in P would increase R on average 16%, while a 10% increase in E p would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and E p are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (subcatchment and subbasin) scale in Amazonia.

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The effects of climate and catchment characteristic change on streamflow in a typical tributary of the Yellow River

Cited by 57 publications

References 31 publications

Multi-model climatic water balance prediction in the Zala River Basin (Hungary) based on a modified Budyko framework

Multi-model climatic water balance prediction in the Zala River Basin (Hungary) based on a modified Budyko framework

Reinterpreting the Budyko Framework

Uncertainty of runoff sensitivity to climate change in the Amazon River basin

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