The ecohydrological effects of climate and landscape interactions within the Budyko framework under non-steady state conditions

Huang, Peng; Song, Jinxi; Wu, Qiong; Sun, Haotian; Mao, Ruichen; Cheng, Dandong; Zhang, Jiaxiong; Shi, Yuna

doi:10.1016/j.catena.2022.106481

Cited by 15 publications

(9 citation statements)

References 66 publications

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“…where ω 1 is the parameter representing catchment characteristics, which is found to be correlated with vegetation cover, slope, the relative soil water storage, and the relative infiltration capacity (Chen et al, 2013;Huang et al, 2022;Li et al, 2013;Wu et al, 2017;Zhou et al, 2016).…”

Section: 𝑃𝑃mentioning

confidence: 99%

Controlling Factors of Evapotranspiration Predictability Under Diverse Climates With the Effects of Water Storage Change in the Budyko Framework

Wu,

Yeh,

Zhou

et al. 2024

Water Resources Research

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The Budyko models (BM) have been extended in previous studies by incorporating water storage change (ΔS) (subtracting ΔS from precipitation) to estimate evapotranspiration (ET) under non‐steady state conditions at scales finer than the climatological mean scale. However, a systematic assessment of the interannual ET predictability of the extended BM is still lacking, hence its validity and controlling factors of improvement (over the original BM) under globally diverse climates is not yet well understood. Based on a long‐term (1984–2008) gridded water budget data set, we present a comparative analysis of annual ET predictability between the original BM (ET1) and the extended BM considering ΔS (ET2) in 32 global river basins to explore the sensitivity of climate factors and catchment hydrologic responses in determining ET predictability. Results show that the difference between ET1 and ET2 increases linearly with ΔS, with ET2 < ET1 (ET2 > ET1) when ΔS > 0 (ΔS < 0). When both ET1 and ET2 overestimate (underestimate) observed ET, the error in ET2 is smaller than ET1 when ΔS > 0 (ΔS < 0) for all 32 basins considered. When the error signs of ET2 and ET1 differ, however, the difference in the absolute magnitude of ET2 and ET1 errors (REdiff) under extremely humid climates is determined by the difference between potential ET and ET, leading to comparable accuracy between ET2 and ET1. In contrast, under extremely arid climates, REdiff is controlled by the combined influences of ΔS and R, resulting in more accurate ET2 than ET1 under the condition of the in‐phase, positive‐correlated relationship between ΔS and R.

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Section: 𝑃𝑃mentioning

confidence: 99%

Controlling Factors of Evapotranspiration Predictability Under Diverse Climates With the Effects of Water Storage Change in the Budyko Framework

Wu,

Yeh,

Zhou

et al. 2024

Water Resources Research

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“…For one thing, it is possibly due to slow growth and recovery rates of arid woody species, when severe damages (e.g., tree mortality) occurred before (Bonet, 2004). For another, the upstream vegetation largely reduces runoff to downstream by increasing evapotranspiration (i.e., transpiration), especially in dry conditions (Huang et al, 2022). So, we may have to wait for several years to see the positive consequence of ecological water conveyance at such a distance.…”

Section: Anthropogenic Versus Natural Changes In Water Stress For Ndv...mentioning

confidence: 99%

Spatial–temporal variations in vegetation with changing water stress in the Kongqi River basin, China

et al. 2023

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Identifying dominant water stress on vegetation growth over the arid basins is essential to better understand ecosystems and water interactions, which are fundamental in managing regional water resources. The Kongqi River basin is an ecologically sensitive area with limited water resources in the northern part of Tarim Basin in northwest China. How is the spatial distribution of vegetation controlled by natural conditions and anthropogenic regulation of water resources is a concern for ecology system protection. This study explores the spatio–temporal variations in the remote‐sensing vegetation index across the basin and identifies water stress forces with the Mann–Kendall trend test and linear regression analysis. Results show that the controls of vegetation change are different among geomorphological units over the past 20 years. In the northern mountains, the increasing vegetation trend mainly responds to climate change, particularly to increased precipitation. In contrast, in the plain area, the vegetation change is controlled by changes in hydrologic and anthropogenic water stresses. A significant rise in surface water and groundwater utilization counteracts the negative effect of the increasing hydrologic water stress in the upstream oasis. However, the increasing anthropogenic water stress contributed to more than 50% of vegetation decay in the downstream area during 2004–2015. The ecological water conveyance beginning in 2016 has promoted vegetation restoration, but the effect decreased with distance to the upstream area and showed a delay in response. This study provides evidence for ecological consequence of anthropogenic water stress that is beneficial to improve water resources management and ecosystem restoration.

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“…Given that water storage changes are assumed to be non-negligible under non-steady state conditions, P turns out not to be the only source of available water for evaporation [28]. Consequently, several studies have adopted effective precipitation (Pe = P-∆S) to represent water availability in the Budyko framework [29][30][31], so that the extended Budyko framework under non-steady conditions is expressed as:…”

Section: The Extended Budyko Framework Under Non-steady Conditionsmentioning

confidence: 99%

“…The close relationship between vegetation indices and climatic seasonality suggests that when changes in CMS occur, vegetation will take adaptive strategies in response to shifting climate condition at the mean annual scale. Due to the coevolution of CMS and vegetation, the adaptability of vegetation will influence its relationship with the spatial variations in Budyko scatter [14,29]. Similarly, Padrón, et al [15] concluded that after accounting for the effect of climatic indices and catchment topography, vegetation indices generally have a low contribution for explaining the spatial variability of water balances.…”

Section: Budyko Scatter Analysis From Spatial Perspectivementioning

confidence: 99%

Association of catchment characteristics to Budyko hydrologic model’s uncertainty in humid catchments

Zhang,

Marshall,

Vrieling

et al. 2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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Accurate quantification of precipitation partitioning into evapotranspiration and runoff is important for global water balance estimation and water resources managements. The Budyko framework is a simple yet robust solution to parameterize precipitation partitioning and has been widely applied for studying catchment-level water and energy fluxes. However, substantial variations between the observed and Budyko-predicted evaporative indices have been observed. Many studies have attributed the scatter around the Budyko curve to catchment characteristics (e.g., vegetation and soil property), which are not directly accounted for in the Budyko framework. However, modified Budyko-type equations that consider catchment characteristics are not transferable between regions and the interannual catchment behaviours still fail to follow the adjusted Budyko trajectories. To explore if the pronounced Budyko scatter in humid catchments has a systematic pattern caused by measurable catchment properties, this study comprehensively investigated the relationship between Budyko scatter and multiple catchment biophysical features from both spatial and temporal perspectives. Results reveal that for humid catchments, topography and seasonal cumulative moisture surplus can explain the spatial distributions of Budyko scatter with r higher than 0.65, whereas soil properties and vegetation indices explained little of the variance (r≤0.30). Temporally, the interannual variability of Budyko scatter was negatively correlated with annual average vegetation indices, particularly for catchments with relatively low vegetation cover. Overall, this study provides valuable insights to the interpretation of Budyko framework and offers possible solutions to improve its performance to predict the spatio-temporal variability of water balances.

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The ecohydrological effects of climate and landscape interactions within the Budyko framework under non-steady state conditions

Cited by 15 publications

References 66 publications

Controlling Factors of Evapotranspiration Predictability Under Diverse Climates With the Effects of Water Storage Change in the Budyko Framework

Controlling Factors of Evapotranspiration Predictability Under Diverse Climates With the Effects of Water Storage Change in the Budyko Framework

Spatial–temporal variations in vegetation with changing water stress in the Kongqi River basin, China

Association of catchment characteristics to Budyko hydrologic model’s uncertainty in humid catchments

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