A Multi-Dimensional Hydro-Climatic Similarity and Classification Framework Based on Budyko Theory for Continental-Scale Applications in China

Abstract

Our knowledge of the similarities and differences in ecological systems is vital to understanding the co-evolution of ecological factors. This study proposes a multi-dimensional hydro-climatic similarity and classification framework based on Budyko theory. The framework employs the dryness index (DI), evaporative index (EI), and an empirical parameter (ω) to further sub-divide four climatic zones (humid, semi-humid, semi-arid, and arid zones) in terms of DI. A criterion that define the similarities between stations is proposed to verify the classification to obtain optimal results. This method is applied to Mainland China, and 637 stations are adopted for continental-scale classification experiments. The point cloud of the Budyko curve for all the stations in Mainland China is plotted. We find that the hydrothermal conditions of the vertically distributed stations on the Budyko curve can be quite different in the same climatic zone when DI < 4.0. The higher the vertical locations of the stations on the Budyko curve are, the drier and colder the climates and corresponding natural landscapes. Under the proposed hydro-climatic classification framework, the four climatic zones are further divided into 17 sub-regions, and the hydrothermal conditions for each sub-region are discussed. The results suggest that regional differences of long-term water balance are resulted by not only mean annual hydrothermal factors and catchment forms but also annual distribution of hydrothermal factors. Our framework can provide hydrologically-based classification across continental scale and, thus, provide a profound understanding of hydrothermal conditions of continental-scale hydrological cycles.

Theoretical and empirical evidence against the Budyko catchment trajectory conjecture

“…In particular, the idea that the catchment-specific parameter is an effective empirical parameter related to biophysical features (i.e., interpretation 1) has been widely embraced by the catchment hydrology community, which has identified and grouped relevant biophysical features into three categories (Donohue et al, 2012;Harman and Troch, 2014): (1) climate variability, (2) catchment physical processes, and (3) vegetation structure and function. While it is generally well acknowledged that certain climatic variables (e.g., precipitation variability or the fraction of precipitation falling as snow) can influence the catchment-specific parameter (e.g., Roderick and Farquhar, 2011;Berghuijs and Woods, 2016), in practice, many studies effectively neglect this, instead focusing primarily on the role of landscape features or vegetation functioning (C. Zhang et al, 2004Zhang et al, , 2018Yang et al, 2008Yang et al, , 2016Greve et al, 2015;Xu et al, 2013;Donohue et al, 2012;Knighton et al, 2020;Gao et al, 2020;Chen et al, 2020;Wu et al, 2019;Qiu et al, 2019;J. Liu et al, 2019b;Guo et al, 2019).…”

Reinterpreting the Budyko Framework

“…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.…”

“…(2) catchment physical processes; and (3) vegetation structure and function. While it is generally well acknowledged that certain climatic variables (e.g., rainfall variability or the fraction of precipitation falling as snow) can influence the catchment-specific parameter (e.g., (Roderick and Farquhar, 2011;Berghuijs and Woods, 2016)), in practice, many studies effectively neglect this, instead focusing primarily on the role of landscape features or vegetation functioning (Wang et al, 2016a;Zhang et al, 2018;Yang et al, 2016;Greve et al, 2015;Xu et al, 2013;Yang et al, 2008;Donohue et al, 2012;Zhang et al, 2004;Knighton et al, 2020;Gao et al, 2020;Wu et al, 2019;Qiu et al, 2019;Liu et al, 2019b;Guo et al, 2019).…”

“…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;…”

Examining evaporative demand and water availability in recent past for sustainable agricultural water management in India at sub-basin scale

“…EI and DI corresponding to each sub-basin) should follow the theoretical Budyko curve (Creed et al, 2014). Any deviation of EI and DI from the theoretical Budyko curve suggest a change in the hydro-climatology of the river subbasin, one reason behind this could be climate change (Liu et al, 2019). Sinha et al (2018) applied Budyko framework to assess the impact of climate variability and anthropogenic activities on hydrologic resilience in Peninsular India and found that many subbasins of the Southern India were not enough resilient.…”