**Research article**
18 Mar 2022

**Research article** | 18 Mar 2022

# Theoretical and empirical evidence against the Budyko catchment trajectory conjecture

Nathan G. F. Reaver David A. Kaplan Harald Klammler and James W. Jawitz

^{1,2},

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^{4}

**Nathan G. F. Reaver et al.**Nathan G. F. Reaver David A. Kaplan Harald Klammler and James W. Jawitz

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^{1}Water Institute, University of Florida, Gainesville, Florida, USA^{2}Engineering School of Sustainable Infrastructure and Environment (ESSIE), University of Florida, Gainesville, Florida, USA^{3}Department of Geosciences, Federal University of Bahia, Salvador, Bahia, Brazil^{4}Soil and Water Sciences Department, University of Florida, Gainesville, Florida, USA

^{1}Water Institute, University of Florida, Gainesville, Florida, USA^{2}Engineering School of Sustainable Infrastructure and Environment (ESSIE), University of Florida, Gainesville, Florida, USA^{3}Department of Geosciences, Federal University of Bahia, Salvador, Bahia, Brazil^{4}Soil and Water Sciences Department, University of Florida, Gainesville, Florida, USA

**Correspondence**: Nathan G. F. Reaver (nreaver@ufl.edu)

**Correspondence**: Nathan G. F. Reaver (nreaver@ufl.edu)

Received: 10 Nov 2020 – Discussion started: 13 Nov 2020 – Revised: 10 Jun 2021 – Accepted: 07 Jan 2022 – Published: 18 Mar 2022

The Budyko framework posits that a catchment's long-term
mean evapotranspiration (ET) is primarily governed by
the availabilities of water and energy, represented by long-term mean
precipitation (*P*) and potential evapotranspiration
(PET), respectively. This assertion is supported by
the distinctive clustering pattern that catchments take in Budyko space.
Several semi-empirical, nonparametric 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
nonparametric 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 the aridity index, $\left(\frac{\text{PET}}{P}\right)$, while the *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
reexamine, reinterpret, and test these two key assumptions of the current
Budyko framework both theoretically and empirically. In our theoretical
test, we use a biophysical model for ET 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 (UK) and United States (US) 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
nontransferability implies that *n* and *w* are proxy variables for
$\frac{\text{ET}}{P}$, rendering the parametric Budyko equations
underdetermined and lacking predictive ability. Finally, we show that the
parametric Budyko equations are nonunique, 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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The Budyko framework represents a catchment's long-term mean
evapotranspiration (ET) as a function of the aridity index (*ϕ*),
which is defined as the ratio of mean precipitation depth (*P*) to
mean potential evapotranspiration (PET). Current understanding of
the Budyko framework is the result of hydrological research over more than a
century. The approach has seen a resurgence within catchment hydrology in
recent years, partially due to its simplicity, analytical elegance, and
potential for studying and predicting landscape precipitation partitioning
under changing climate and land use (C. Wang et al.,
2016; Mianabadi et al., 2020). Early investigators proposed equations for
semi-empirical curves to describe the aggregate behavior of ET as a
function of *P* and PET for large numbers of catchments
(Schreiber, 1904; Ol'Dekop, 1911; Budyko, 1974). Since then, efforts to
extend the utility of the Budyko framework have both retained and emphasized
the concept of explicit curves (i.e., specific mathematical relationships),
leading to the development of parametric Budyko equations. 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 *P* and PET,
on ET (C. Wang et al., 2016). This interpretation has
motivated profound efforts to understand the relationship between
biophysical features and catchment-specific parameters
(Yang et al.,
2007, 2009, 2016; Donohue et al., 2012; Shao et al., 2012; Li et al.,
2013; S. Li et al., 2020; T. Li et al., 2020; Xu et al., 2013; Cong et al., 2015; Zhang et al.,
2018, 2019b; Abatzoglou and Ficklin, 2017; Xing et al., 2018a; Zhao et al., 2020; Ning
et al., 2019, 2020a, b; Bai et al., 2019).
Numerous studies have also
focused on determining the sensitivity of precipitation partitioning to
climatic and/or land use changes (Roderick and Farquhar, 2011; Wang and
Hejazi, 2011; Yang and Yang, 2011; W. Wang et al., 2016; Zhou et al., 2016; Shen
et al., 2017; Zhang et al., 2016, 2020; Yeh and Tsao, 2020; Sinha
et al., 2020; Ning et al., 2020b; Liu et al., 2020; Li et al., 2018, 2019; H. Li et al., 2020; Z. Li et al., 2020a; J. Liu et al., 2019a; Yang et al., 2018; Xing et al.,
2018b; Xiangyu et al., 2020) as well as on deriving causal
attribution to changes in this 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., 2019a, 2020; Yeh
and Tsao, 2020; Xiangyu et al., 2020; Song et al., 2020; Sinha et al., 2020; H. Li et al., 2020; Z. Li et al., 2020b; Deng et al., 2020; Young
et al., 2019; Xin et al., 2019; Wang et al., 2018, 2019; Lv et al., 2019; N. Liu et al.,
2019; Lee and Yeh, 2019; Kazemi et al., 2019; G. He et al., 2019; Y. He et al., 2019b; Xu et al., 2014).

Despite this widespread application, several doubts have been raised about the robustness of the assumptions and interpretations that underpin this vast and growing literature, particularly with respect to the parametric Budyko equations. For example, Gentine et al. (2012) suggested that the aggregate Budyko curve behavior already reflects the interdependence among vegetation, soil, and climate; therefore, they proposed that the inclusion of the catchment-specific parameter into the Budyko framework is unnecessary. However, this interpretation was partially based on catchment data with limited scatter in Budyko space. Additionally, Greve et al. (2015) highlighted that the catchment-specific parameter has no a priori physical meaning, cannot be estimated for ungauged catchments, and its specific dependence on biophysical features can vary substantially between catchments. Furthermore, Padrón et al. (2017) undertook a comprehensive overview of the wide variety of biophysical features proposed to control the catchment-specific parameter, finding that most proposed features did not actually correlate with the parameter and the types of features that were correlated varied substantially between climatic regions. Finally, Sposito (2017a, b) suggested that the interpretation of the catchment-specific parameter as representing biophysical features does not arise from physical reasoning; thus, identified statistical relationships between the parameter and biophysical features may be spurious and premature.

Given the recent resurgence of the Budyko framework and its importance to catchment hydrology, we build upon these previous critical observations, presenting a retrospective review of the framework's assumptions and development, with the overarching goals of harmonizing historical and current interpretations as well as understanding their implications. Specifically, we critically reinterpret two key and interrelated assumptions of the current framework: (1) the concept that explicit curves represent trajectories of individual catchments through climate space, and (2) the parametric forms of the Budyko equation themselves. We contend that many current interpretations of these assumptions are unsupported by the underlying framework, potentially leading researchers to spurious conclusions about catchment hydrology. However, we stress that the aim of this reinterpretation is not to discard the voluminous efforts put forth using current interpretations of the Budyko framework but rather to recontextualize the conclusions obtained from them. Additionally, we emphasize that the Budyko framework based on the curve-like clustering pattern observed across multiple catchments is a powerful and useful concept when used appropriately and within the proper context.

We first reexamine interpretations of Budyko curves that ascribe physical
meaning to the functional form of the curve, thus implying that explicit
curves govern catchment evapotranspiration (e.g., C. Wang et al., 2016; Wang
and Hejazi, 2011; Jiang et al., 2015; Liang et al., 2015; Jaramillo et al.,
2018; Zhang et al., 2004, 2018). This concept is typically
articulated through the suggestion that an individual catchment undergoing
only changes in the aridity index will follow an explicit Budyko curve
trajectory (“the catchment trajectory conjecture”). However, we note that
it is mathematically impossible for the aridity index to vary independently
of other climate variables that impact PET or *P*, meaning
that the catchment trajectory conjecture, as typically stated, is ill posed
and untestable. Given the stated conjecture's mathematical impossibility, in
practice, it is generalized implicitly (or unintentionally) to a well-posed
and testable form, which suggests that individual catchments with stable basin
characteristics that undergo changes in the aridity index will follow an
explicit Budyko curve trajectory. Here, we examine the support for the
well-posed conjecture and test it, the results of which suggest that
specific functional forms of Budyko curves do not have intrinsic physical
meaning but are instead semi-empirical conceptual tools that describe the
general aggregate behavior of multiple catchments – but do not predict the
specific behavior of individual catchments.

Second, we revisit the parametric Budyko equations that are currently
interpreted by most authors to represent more generalized forms of the
nonparametric Budyko equations (Budyko, 1974), and which can thus be
used to separate the effects of changes in the average climate (i.e.,
changes in the aridity index *ϕ*) on ET from the effects of all other
biophysical features (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., 2019a, 2020; Yeh and Tsao,
2020; Xiangyu et al., 2020; Song et al., 2020; Sinha et al., 2020; H. Li et al.,
2020; Z. Li et al., 2020a; Deng et al., 2020; Young et al.,
2019; Xin et al., 2019; Wang et al., 2019; Lv et al., 2019; N. Liu et al.,
2019; Lee and Yeh, 2019; Kazemi et al., 2019; G. He et al., 2019; Y. He et al., 2019a, b; Wang et al., 2018; Xu et al., 2014). We argue and
demonstrate herein that the two widely accepted parametric Budyko equations
(i.e., those derived in Zhang et al., 2004 and Yang et al., 2008) are nonunique, meaning they are only two of many possible
single-parameter Budyko equations. Importantly, under the catchment
trajectory conjecture, the various versions of the parametric Budyko
equations are contradictory, which casts doubt on their current
interpretations.

Additionally, while the catchment-specific parameters in the parametric
Budyko equations are typically regarded as empirical, “effective”
parameters analogous to, for example, Manning's roughness coefficient in
open channel flow or hydraulic conductivity in groundwater flow, we
demonstrate that this is not the case, as their values are not transferable
between catchments or across time for individual catchments. For an
empirical parameter to be transferable, the specific functional form of the
mathematical relationship in which it is contained must be empirically
valid. In such cases (e.g., Manning's formula and Darcy's Law), the
validated functional form contains information about the physics of its
respective system, allowing for the empirical parameter to be consistently
and independently related to physical properties of the system (e.g.,
channel surface roughness for Manning's roughness coefficient and soil pore
size for hydraulic conductivity). In these cases, the effective empirical
parameters can be estimated a priori, allowing their respective empirical
relationships to be used for making quantitative predictions under future
conditions (e.g., different hydraulic gradients). We test the empirical
validity of the parametric Budyko equations, with results suggesting that
the catchment-specific parameter is nontransferable. Thus, the value of
the catchment-specific parameter cannot be determined without first obtaining
estimates of *P*, PET, and (most importantly) ET,
effectively rendering it a proxy variable for $\frac{\text{ET}}{P}$ that
has no additional physical meaning and precluding the use of the parametric
Budyko equations in predictive pursuits.

Our reinterpretation is demonstrated theoretically using a stochastic soil moisture model (Porporato et al., 2004) as well as empirically using data from 728 reference catchments in the United Kingdom (UK) and United States (US). To provide context for these analyses, we first provide a brief background of the Budyko framework, describe its current dominant interpretations in the literature, and recall Budyko's own interpretation of explicit curves.

## 2.1 Overview of the Budyko hypothesis and equations

In its foundation, the Budyko framework is an expression of the water
balance for a catchment. Over long time periods, it is reasonable to assume
that positive and negative short-term changes in catchment storage average
to negligibly small values (Δ*S*≈0) with respect to the
cumulative long-term volumes involved in the water balance. Thus, with
Δ*S*=0, the long-term average precipitation, *P*, is partitioned
into either evapotranspiration, ET, or discharge, *Q*, from the catchment,
yielding

Budyko (1974), among others
(e.g., Ol'Dekop, 1911; Thornthwaite, 1948), recognized that available
water (i.e., *P*) and energy (i.e., PET) are the primary drivers of
long-term average catchment evapotranspiration; therefore, it has been suggested that
ET is a function of *P* and PET:

Several explicit functional forms of *f*_{0} have been proposed based on
their ability to match the central tendency of observed ET for
multiple catchments across a wide range of climates (Ol'Dekop,
1911; Schreiber, 1904; Bagrov, 1953), with Budyko (1974) putting forth the following expression:

However, Eq. (3) and other forms of Eq. (2) are unable to explain
differences in ET or the evaporative ratio between catchments with
identical *P* and PET or aridity indices, respectively.

Given this limitation, the original Budyko hypothesis has been modified in an attempt to explain deviations of individual catchments from the explicit Budyko curves by invoking a function that is implicit in ET (Yang et al., 2008):

where for a given *P* and PET, there may be more than one
value of ET that satisfies Eq. (4). Using the hypothesized
relationship given by Eq. (4) and applying constraints for purely water- and
energy-limited conditions, it is possible to analytically derive explicit
forms of *f*_{1}. It has been shown that there are at least two possible
analytical solutions to Eq. (4). The functional form of the first of these
solutions was proposed prior (Turc, 1953; Choudhury, 1999; Mezentsev, 1955)
to its formal derivation from Eq. (4) by Yang et al. (2008)
and is given by

where *n* is a parameter specific to each catchment. With slightly different
assumptions about the structure and boundary conditions of *f*_{1}, a
different parametric form of the Budyko equation can also be derived
(Fu, 1981; Zhang et al., 2004):

where *w* is also a catchment-specific parameter. This equation was also
proposed prior to its formal derivation (Tixeront, 1964; Berkaloff and
Tixeront, 1958). Equations (5) and (6) express the evaporative index $\left(\frac{\text{ET}}{P}\right)$ as a function of the aridity index $\left(\mathit{\varphi}=\frac{\text{PET}}{P}\right)$; however, equivalent forms for
both equations exist that express the R Index $\left(\frac{\text{ET}}{\text{PET}}\right)$ (Yao, 1974) as a function of
the humidity index $\left(\frac{\mathrm{1}}{\mathit{\varphi}}=\frac{P}{\text{PET}}\right)$ (Hulme et al., 1992). We refer to all of these expressions
as the “parametric Budyko equations”.

Equation (4) has been interpreted as indirectly capturing unknown
catchment-specific factors impacting ET, other than *P* and
PET. The catchment-specific parameters in Eqs. (5) and (6) (*n* or
*w*) arise in part due to the implicit nature of Eq. (4). Two catchments
that experience the same *P* and PET but have a different
*n* or *w* will have different ET. Higher values of *n* and *w*
correspond to a higher fraction of *P* becoming ET, with
ET approaching PET in energy-limited systems and ET
approaching *P* in water-limited systems (i.e., as *n* or *w*
approaches ∞). The lower limits of *n* and *w* are 0 and 1,
respectively. Mathematically, the value of the catchment-specific parameter
describes a family of curves in Budyko space.

## 2.2 Current interpretations of explicit Budyko curves and the parametric framework

Most current interpretations of the functional forms of Budyko curves explicitly acknowledge their semi-empirical nature; however, many studies simultaneously ascribe specific physical meaning to the mathematical expressions. This interpretation suggests that the curves represent trajectories within Budyko space that a catchment will follow if its aridity index changes, which supposedly allows one to make predictions about ET under different climates (e.g., Roderick and Farquhar, 2011; Wang and Hejazi, 2011; Yang and Yang, 2011; W. Wang et al., 2016; Zhou et al., 2016; Shen et al., 2017; Zhang et al., 2016; Milly et al., 2018). Critically, this interpretation extends the concept of an explicit curve from its representation of an emergent global behavior of multiple catchments to the behavior of individual catchments, implying that the mathematical expressions describing Budyko curves represent fundamental catchment hydrological processes associated with the aridity index. The specific details of these catchment processes are considered to be unknown, but their integrated effects are represented in the functional form of the explicit curves.

Current interpretations of the catchment-specific parameter follow from the application of explicit curves to individual catchment behavior. Generally, these interpretations can be grouped into four distinct viewpoints. (1) The catchment-specific parameter is an effective empirical parameter related to biophysical features, and it is possible to discern and understand that relationship (e.g., C. Wang et al., 2016). (2) The parameter is related to biophysical features, but it may not be possible to determine an explicit relationship; therefore, it should be treated probabilistically (Gudmundsson et al., 2016; Greve et al., 2015; Singh and Kumar, 2015). (3) The catchment-specific parameter and parametric forms of the Budyko equation contradict the Budyko hypothesis (Sposito, 2017a, b; Gentine et al., 2012). (4) The parameter is an arbitrary empirical constant that is generated as a part of the solution to Eq. (4), but it has no a priori physical meaning (Greve et al., 2015; Sposito, 2017b; Daly et al., 2019a). 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. Wang et al., 2016; Zhang et al., 2004, 2018; Yang et al., 2008, 2016; Greve 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; Liu et al., 2020; Guo et al., 2019).

The widely held interpretations of explicit curves representing individual
catchment behavior and the catchment-specific parameter representing
biophysical/landscape features has led to the development of methods to
determine the sensitivity of precipitation partitioning to climate and/or
landscape changes for individual catchments (Roderick and Farquhar,
2011; Wang and Hejazi, 2011; Yang and Yang, 2011; W. Wang et al., 2016; Zhou et
al., 2016; Shen et al., 2017; Yeh and Tsao, 2020; Zhang et
al., 2016, 2020; Sinha et al., 2020; Ning et al., 2020b; J. Liu et al., 2019a; Liu et al., 2020; Li et al., 2018, 2019; Z. Li et al., 2020b; H. Li et al., 2020; Yang et al.,
2018; Xing et al., 2018b; Xiangyu et al., 2020) and multiple
methods for decomposing anthropogenic and climatic impacts on precipitation
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; Z. Li et al., 2020a; H. 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; N. Liu et al., 2019; Lee and Yeh,
2019; Kazemi et al., 2019; Y. He et al., 2019a, b; G. He et al.,
2019; 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; Zhang et al., 2018; Abatzoglou and
Ficklin, 2017; Xing et al., 2018a; Zhao et al., 2020; Ning et al., 2020b; Ning
et al., 2020a; T. Li et al., 2020; S. Li et al., 2020; Zhang et al., 2019b; Ning et
al., 2019; Bai et al., 2019; Ning et al., 2017). However, these relationships are
all statistical or derived from curve fitting, which makes it difficult to
develop a mechanistic understanding of causal relationships between the
catchment-specific parameter and relevant biophysical features.
Additionally, interpretations of these relationships implicitly assume that
the functional forms of either Eqs. (5) or (6) represent a physically
meaningful relationship between the aridity and evaporative indices, which has not been empirically validated as previously noted by
Berghuijs et al. (2020). An explicit derivation of *n* or *w* in
terms of biophysical features would substantially improve our understanding
of the catchment-specific parameter, as has been noted many times (Zhang et
al., 2004, 2018; Yang et al., 2008; Donohue et al., 2012; Xu et al., 2013; Greve et
al., 2015; C. Wang et al., 2016). Reaver et al. (2020)
fulfilled this literature-identified need by analytically inverting both
forms of the parametric Budyko equations, i.e., Eq. (5) and (6), resulting
in expressions for *n* and *w* only in terms of *P*, PET,
and ET. These expressions allow for *n* and *w* to be explicitly
expressed in terms of biophysical features through the dependence of
*P*, PET, and ET on those same features.

## 2.3 Budyko's interpretation of explicit curves

It is useful to recall that Budyko (1974) considered explicit curves to be
semi-empirical. While the physical basis for explicit curves is dictated by
the conservation of mass and energy (i.e., the curves could never cross the
water and energy limits in Budyko space) and the fact that the curves must
approach the energy and water limits for increasing humidity (i.e., *ϕ*→0) and aridity (i.e., *ϕ*→∞), respectively, their
empirical nature comes from the choice of functional form as they transition
between the energy and water limits. Any functional form that satisfies the
previous two conditions and provides a “good” fit to observed data could
thus be a reasonable choice. Indeed, Budyko's own explicit formulation (Eq. 3) was simply the geometric mean of the Schreiber and Ol'Dekop formulae,
which provided a slightly better fit to observed data (Budyko, 1974).
These interpretations are suggested by Budyko's own words (Budyko, 1974, p. 325–326):

“The choice of one or another interpolation function for the transition from the first of these conditions to the second is not very important, since, over most of the range of variation in the parameters of the relationship equation, the appropriate relation deviates little from one or the other boundary condition.”.

From this interpretation, it is clear that the explicit curves resulting from the original Budyko hypothesis, while constrained at their limits by fundamental physical laws, are empirical in nature and not derived from catchment hydrologic processes. It should also be noted that the explicit curve relationships were developed to describe the general behavior of multiple catchments over a wide range of aridity indices. This gives the nonparametric Budyko curves (e.g., Eq. 3) some predictive power, albeit in a probabilistic sense. Any given individual catchment would, on average, be expected to fall close to the explicit curves but, in principle, could fall anywhere in Budyko space. Therefore, predictions of ET using the original Budyko curves have a quantifiable uncertainty associated with them. Budyko and Zubenok (1961) showed that this mean error was approximately 10 %, which has been confirmed more recently (Gentine et al., 2012).

Given this background, it is important to recognize the difference between applying a semi-empirical curve to describe the general behavior of aggregated catchments and using a similar curve to represent the trajectory of an individual catchment undergoing changes in aridity. The original Budyko curve emerges from the ensemble characteristics of many catchments across a range of aridity indices. Suggesting that Budyko curve behavior applies to the trajectories of individual catchments may be a reasonable conjecture, but it requires either theoretical justification or empirical validation, both of which are currently lacking. In the following sections (Sect. 3.1.1, 3.1.2), we describe our methods for testing this assumption using both theoretical models and empirical data.

## 3.1 Reinterpreting explicit Budyko curves

### 3.1.1 Theoretically testing for Budyko curve trajectories

To test the catchment trajectory conjecture, we employed the biophysical
stochastic soil moisture model of Porporato et al. (2004). This model,
being physically based, has been used to lend support to Budyko curves and
in developing relationships between *n* or *w* and catchment biophysical
features (e.g., Donohue et al., 2012; Zhang et al., 2018; Cong et al.,
2015). Porporato et al. (2004) developed a model of the equilibrium
probability distribution of the “effective” relative soil moisture under
stationary stochastic precipitation in the form of a marked Poisson process,
from which ET can also be calculated. It is important to note that
this model accounts for the temporal dependence of precipitation but assumes
constant potential evaporation. While this limits some of the specific
conclusions that can be drawn from the model, it is adequate for testing the
Budyko curve catchment trajectory conjecture, as the conjecture cannot be
valid generally if it is not valid for catchments with time-invariant
potential evapotranspiration.

We first write the model of Porporato et al. (2004) in a form that can be plotted in Budyko space:

where$\phantom{\rule{0.125em}{0ex}}\stackrel{\mathrm{\u203e}}{x}$ is the mean of effective relative soil moisture
$x=\frac{\left(s-{s}_{\text{W}}\right)}{\left({s}_{\text{I}}-{s}_{\text{W}}\right)}$; *s* is the
relative soil moisture; *s*_{W} is the relative soil moisture at wilting
point; *s*_{I} is the well-watered condition threshold relative soil
moisture falling between saturation (i.e., *s*=1) and relative soil field
capacity; *ρ* is the soil porosity; *Z*_{r} is the effective rooting
depth; *α* and *λ* are the mean precipitation depth and event
frequency for marked Poisson process precipitation, respectively; and *γ*( , ) is the lower incomplete gamma function. The seven
parameters (*s*_{W}, *s*_{I}, *ρ*, *Z*_{r}, *α*, *λ*, and
PET), can be rewritten in terms of three effective parameters,
defined as ${Z}_{\mathrm{0}}=\left({s}_{\text{I}}-{s}_{\text{W}}\right)\mathit{\rho}{Z}_{\text{r}}$, $\mathit{\psi}=\frac{\mathrm{1}}{\mathit{\alpha}}$, and $\mathit{\eta}=\frac{\mathit{\lambda}}{\text{PET}}$. This
simplifies the expression of Eq. (7) to

which we refer to as the “Porporato model” hereafter. The four parameters
that correspond to landscape properties (*s*_{W}, *s*_{I}, *ρ*, and
*Z*_{r}) are combined into a single effective parameter, *Z*_{0}, which
represents maximum soil water storage available for evapotranspiration,
while the three parameters corresponding to the climate (*α*, *λ*, and PET) reduce to two effective parameters, *ψ* and *η*, defined above. The ratio of *ψ* to *η* is the aridity index,
$\mathit{\varphi}=\frac{\text{PET}}{P}=\frac{\text{PET}}{\mathit{\alpha}\mathit{\lambda}}=\frac{\mathit{\psi}}{\mathit{\eta}}$. Equation (8) could be further simplified into only
two effective parameters (Porporato et al., 2004; Harman et al.,
2011; Doulatyari et al., 2015), although doing so reduces the conceptual
clarity provided by *Z*_{0}, *ψ*, and *η*, which explicitly
distinguish climate and landscape parameters.

We tested the catchment trajectory conjecture by varying the model climatic
parameters while holding the landscape parameter constant. If the resulting
trajectories are not Budyko curves, the conjecture should be rejected.
Notably, there are five qualitatively distinct ways that *ψ* and *η*
can be varied to produce trajectories in Budyko space, giving five test
cases of the catchment trajectory conjecture: (1) varying *ψ* alone,
which we denote “variable storm size”; (2) varying *η* alone, which we
denote “variable storm frequency”; (3) varying *ψ* less than *η*,
which we denote “storm-frequency-dominated aridity”
(Trenberth, 2011; Fischer et al., 2014); (4) varying *ψ* more
than *η*, which we denote “storm-size-dominated aridity”
(Fischer et al., 2014); and (5) varying *ψ* equal to *η*,
which we denote “variable precipitation flashiness”. All of these test
cases can be expressed through a functional relationship between the two
variables, *η*=*ψ*^{c}, with *c*=0 for the variable storm size test
case, *c*→∞ for the variable storm frequency test case, *c*=2 for
the storm-frequency-dominated aridity test case, $c=\frac{\mathrm{1}}{\mathrm{2}}$ for the
storm-size-dominated aridity test case, and *c*=1 for the variable
precipitation flashiness test case. In all test cases, we set *Z*_{0}=2 *m*.

The effective climate and landscape parameters in the Porporato model appear exclusively in ratios, such that only the relative magnitude between parameters is important. Moreover, the same trajectories can be made from infinite parameter combinations. For our test cases, we chose parameter values to maintain illustrative simplicity and to produce visually informative trajectories not restricted to a small portion of Budyko space.

## 3.2 Empirically testing for Budyko curve trajectories

Our empirical test of the catchment trajectory conjecture involves tracking the actual trajectories of reference catchments in Budyko space over time and quantifying whether they follow Budyko curves. Reference catchments are defined based on long-term stability of land use. Therefore, any changes to precipitation partitioning over time in reference catchments must be attributed to climatic factors, and the catchment trajectory conjecture predicts that their expected trajectories through Budyko space must be Budyko curves (i.e., those described by Eqs. 5 or 6). This prediction can be tested by comparing actual Budyko space trajectories of reference catchments computed from empirical observations against the expectation from the catchment trajectory conjecture. If the observed reference catchment trajectories are distinct from the expected Budyko curve trajectories, the conjecture should be rejected.

For a given reference catchment, estimates of *P* and PET
were obtained from daily records of precipitation and potential evapotranspiration, while estimates of
ET were calculated from the catchment water balance,
$\text{ET}=P-Q$, which assumes that impacts from storage dynamics are
negligible (Δ*S*≈0). As *P*, PET and
ET represent temporal averages, and we were also interested in
temporal trajectories of those magnitudes, we computed time series of moving
averages for each of the three variables, similar to the method employed by
van der Velde et al. (2014). The temporal averaging window for which Δ*S*≈0 is typically unknown; however it has been shown to exhibit a
threshold behavior (i.e., above a certain averaging window size, Δ*S* is consistently negligible) (Han et al., 2020). The threshold
averaging window size can vary between catchments, but approximately 71 %
of global catchments reach the threshold with an averaging window of 10
years, and 94 % of catchments reach the threshold with an averaging window
of 30 years (Han et al., 2020). To address the uncertainty in the
threshold averaging window size, we computed different “realizations” of
the actual trajectories in terms of $\frac{\text{PET}}{P}$ and
$\frac{\text{ET}}{P}$ for each catchment for all possible
integer-year averaging windows in annual steps from 1 year to the full
length of record. The “conjectured” Budyko curve of Eq. (5) was fitted by
adjusting the value of *n* using the full length of record in each
catchment.

The conjecture was tested for each reference catchment by comparing all realizations of actual trajectories to the conjectured Budyko curve trajectory using the nonparametric sign test (Holander and Wolfe, 1973). This is a distribution-free test for consistent over- or underestimation between paired observations (see also Sect. S2). If the catchment trajectory conjecture is correct, then the frequency at which actual and expected Budyko space trajectories are found to be statistically indistinguishable will be higher than what is expected due to random chance (see also Sect. S2). Moreover, we calculated the maximum deviations of the actual trajectories (using the 10-year averaging window) from the expected Budyko curve trajectory for all reference catchments. These values represent the largest magnitudes of climate-induced changes in precipitation partitioning that would be misinterpreted as land-use-induced changes when subscribing to the catchment trajectory conjecture. Finally, we estimated the magnitude of the largest errors in the evaporative index that occurred when using the well-established nonparametric Budyko curve instead of the parametric form. This was done by calculating the maximum deviations between Eq. (3) and the actual trajectories (10-year averaging window) for all reference catchments.

Our empirical tests were based on 728 UK and US reference catchments
identified from well-accepted peer-reviewed datasets. These datasets were
produced using standardized methodologies with well-documented quality
control standards. The 68 UK catchments (Fig. S1a) were from the Catchment
Attributes and MEteorology for Large-sample Studies for Great Britain
(CAMELS-GB) dataset (Coxon et al., 2020a), which also had membership
in the UK Benchmark Network (UKBN2) dataset (Harrigan et al.,
2018) and had the highest data-quality metric (a benchmark score of 6).
UKBN2 reference catchments have been identified as “near-natural” and are
intended to be used for the investigation of climate-driven changes in river
flow. The CAMELS-GB dataset contains daily time series of discharge, precipitation, and
potential evapotranspiration for each catchment with contiguous record lengths of between 12 and 45 years. The 660 US reference catchments (Fig. S1b) were from the original
CAMELS dataset (Addor et al., 2017; Newman et al., 2015). All catchments
in the CAMELS dataset are considered reference catchments, with minimal
land use changes or disturbances and minimal human water withdrawals
(Newman et al., 2015). Daily times series of discharge, precipitation,
maximum temperature, and minimum temperature with contiguous lengths between 20 and 35 years were
available for each US reference catchment. Daily potential evapotranspiration time series were
computed from the daily *T*_{max} and *T*_{min} values using the Hargreaves
potential evaporation equation (Hargreaves and Allen, 2003; Lu et al.,
2005; Allen et al., 1998), although we note that our empirical test methodology is
insensitive to the specific choice of potential evapotranspiration method (see Sect. S2.6).

The aridity indices of the 728 UK and US reference catchments span from 0.13
to 5.93 (300 are arid, *ϕ*>1, and 428 are humid, *ϕ*<1) and, thus, provide excellent coverage of Budyko space.
Additionally, the reference catchments span a wide range of climates,
capturing 4 of the 5 main Köppen–Geiger climate classification groups
(arid, warm temperate, boreal, and polar) and 16 of the 31
subclassifications (hot desert, cold desert, hot semiarid, cold semiarid,
humid subtropical, temperate oceanic, subpolar oceanic, hot-summer
Mediterranean, warm-summer Mediterranean, cold-summer Mediterranean,
hot-summer humid continental, warm-summer humid continental, subarctic,
Mediterranean-influenced warm-summer humid continental,
Mediterranean-influenced subarctic climate, and tundra) (Kottek et al.,
2006; Rubel et al., 2017; McCurley Pisarello and Jawitz, 2020). With this
broad and inclusive range of climatic conditions, robust and general
conclusions about the Budyko framework and catchment trajectory conjecture
can be drawn from this set of reference catchments.

## 3.3 Reinterpreting the parametric Budyko framework

### 3.3.1 Catchment-specific parameters as proxy variables for the evaporative index

To understand the limitations of the catchment-specific parameters within
the parametric Budyko framework, it is illuminating to first review their
origin. In the derivations of both forms of the parametric Budyko equations
(Eqs. 5 and 6), *n* and *w* arise as arbitrary constants from
mathematical necessity rather than being introduced in relation to any
physically relevant quantities (Zhang et al., 2004; Yang et
al., 2008). Specifically, they arise as “separation constants” that are
used when solving partial differential equations by the method of separation
of variables. Therefore, the most basic interpretation of the catchment-specific
parameter is that it is an arbitrary constant required for the
solutions of Eq. (4) to satisfy the boundary conditions (i.e., the water and
energy balances) while allowing catchments to have different values of
ET for a given *P* and PET. This is contrary to the
prevailing interpretation of the catchment-specific parameter as an
empirical effective parameter related to biophysical features (Sects. 1,
2.2). The association of the catchment-specific parameter to biophysical
features seems to have first arisen as conjecture that was subsequently
bolstered by statistical and curve-fitting relationships (Table S1), rather
than being motivated by specific physical processes.

Empirical relationships with effective parameters are common and useful in hydrology (e.g., Manning's formula and Darcy's Law). The usefulness of such relationships comes from their transferability either between similar physical systems or within the same system at different times. For example, Darcy's Law states that under certain constraints (i.e., small flow velocities and laminar flow) the flux of water through a porous medium will change linearly with changes in the hydraulic gradient. As long as the flow velocities within the given medium remain small, the slope of the relationship between the hydraulic gradient and flux (i.e., the hydraulic conductivity) will remain constant, meaning its value is transferable across time for that porous medium system. The linear gradient–flux relationship holds for a wide range of different porous media, which allows the slope of the relationship to be independently related to physical properties of the various systems (e.g., pore size distributions, Wang et al., 2017). Therefore, the hydraulic conductivity can be estimated a priori from information independent of the hydraulic gradient and flux, and its value can be consistently transferred between systems with similar properties (i.e., those with similar porous media). For the parametric Budyko equations to be useful empirical relationships analogous to Darcy's Law, the functional forms of Eqs. (5) and (6) must be empirically valid. Specifically, the formulae must be shown to describe how a catchment's evaporative index changes for a given change in the aridity index (i.e., the catchment trajectory conjecture would need to be shown to be valid).

We test the empirical validity of the parametric Budyko framework and the transferability of the catchment-specific parameter with our empirical test of the catchment trajectory conjecture using the 728 UK and US reference catchments (Sect. 3.1.2). This analysis tests the hypothesis that catchments' evaporative indices follow parametric Budyko curves through Budyko space when undergoing changes in the aridity indices. Our test of the transferability of the parametric Budyko curves is directly analogous to testing the linear gradient–flux relationship for Darcy's Law.

### 3.3.2 Nonuniqueness of the parametric Budyko equations

Equations (5) and (6) are the most widely accepted and frequently used
single-parameter Budyko equations. The following properties of these
equations are used either as foundational constraints in their derivation or
to justify their validity in describing Budyko space: (1) they describe a
family of concave down, nonintersecting curves; (2) these curves satisfy
conservation of mass and energy; (3) every point within Budyko space belongs
to only one curve (i.e., the uniqueness requirement); (4) the values and
first derivatives of all curves approach 0 and 1, respectively, in the humid
limit (i.e., *ϕ*→0); (5) the values and first derivatives of all curves
are 1 and 0, respectively, in the arid limit (i.e., *ϕ*→∞); (6) the curves asymptotically approach the energy and water limits as the
parameter approaches infinity; and (7) the curves asymptotically approach
0 as the parameter approaches its lower bound. Many previously proposed
single-parameter equations violate at least one of these properties (e.g.,
Zhang et al., 2001, violates property 2; Wang and Tang, 2014,
violates property 3; and Milly, 1993, violates property 5).
However, any other single-parameter equation that has these properties is as
equally valid as Eqs. (5) and (6). In this sense, neither Eqs. (5) and (6)
nor any other possible single-parameter Budyko equation has a particular
claim of being the “correct” equation for representing Budyko space.

Commonly held interpretations about the parametric Budyko equations, such as the catchment trajectory conjecture, explicitly or implicitly ascribe physical meaning to the specific mathematical functions (e.g., Eqs. 5 and 6) that describe single-parameter curves (e.g., individual catchment trajectories). However, different (but valid) single-parameter Budyko curves described by nonequivalent functions will produce contradictory results when used in hydrological applications (e.g., causal attributions and sensitivity analyses). Such results suggest that the physical interpretations attributed to specific functional expressions of Budyko curves are unfounded.

To illustrate the contradictory nature of the parametric Budyko equations, we compare behaviors of Eqs. (5) and (6) to those of two new relationships that also conform to all of the properties of Eqs. (5) and (6) and have analogous parameters:

and

where *q*_{n} and *q*_{w} are the catchment-specific parameters and Γ() is the gamma function. The parameter *q*_{n} is
analogous to *n* of Eq. (5), taking values ranging between 0 and
∞, and *q*_{w} is analogous to *w* in Eq. (6), taking
values ranging between 1 and ∞ (Eqs. 9 and 10 are
developed fully in Sect. S3).

## 4.1 Reinterpreting explicit Budyko curves

### 4.1.1 Theoretically testing for Budyko curve trajectories

The theoretical test of the catchment trajectory conjecture for cases 1
(variable storm size) and 2 (variable storm frequency) generally resemble
Budyko curves in that they are monotonically increasing, concave down, and
approach the energy and water limits as *ϕ* approaches 0 and ∞,
respectively (Fig. 1a). While the trajectories of these two cases appear
“Budyko-like”, they have nonidentical shapes (i.e., they follow two
distinct paths), contrary to what would be expected from the catchment
trajectory conjecture. For test cases 3 (storm-frequency-dominated aridity)
and 4 (storm-size-dominated aridity), neither theoretical catchment
trajectory can be described as a Budyko-like curve (Fig. 1b). When using
the relationship *η*=*ψ*^{2} (storm-frequency-dominated aridity), the
trajectory is not even monotonically increasing and actually moves away from the
water limit with increasing aridity. The main conclusion of this theoretical
test is that a catchment undergoing changes in the aridity index does not have
to follow a Budyko curve, contrary to the catchment trajectory conjecture.

The Budyko space trajectory for test case 5 (variable precipitation
flashiness) is a vertical line at *ϕ*=1, with
$\frac{\text{ET}}{P}\to \mathrm{0}$ as *ψ*→0 and
$\frac{\text{ET}}{P}\to \mathrm{1}$ as *ψ*→∞ (Fig. 1c), which is
clearly not a Budyko-like curve. Additionally, this trajectory shows
that the catchment-specific parameter is not independent of climate and
that changes in climate alone (i.e., changing only *ψ* and *η* in
the Porporato model) can result in arbitrary values of the
catchment-specific parameters. The main conclusion to be taken from this
test is that the catchment-specific parameter can be highly dependent on
climate. While this climate dependency is acknowledged in many current
interpretations of *n* and *w* (Sect. 2.2), it is contrary to how the
catchment-specific parameter is typically used in practice, namely as purely
representative of landscape features alone. In combination, our five
theoretical tests illustrate that catchments undergoing changes in climate
alone can follow Budyko-like curves but are not required to do so.

### 4.1.2 Empirically testing for Budyko curve trajectories

The empirical test of the Budyko curve catchment trajectory conjecture evaluated whether real-world reference catchments not subjected to significant land use change actually follow Budyko curve trajectories over time. The catchments investigated span a wide range of aridity indices, climate zones, latitudes, longitudes, and vegetation types, and the global behavior of their long-term mean water balances is in agreement with the nonparametric Budyko curve (Fig. 2). However, individual catchments do not generally follow parametric Budyko curve trajectories (Figs. 2, 3a), implying significant errors in the prediction of precipitation partitioning sensitivity based on the catchment trajectory conjecture (Fig. 3b). In addition to the theoretical test of the conjecture (Sects. 3.1.1, 4.1.1), the qualitative and quantitative results of this empirical test provide further evidence against the conjecture.

The data for the 728 UK and US reference catchments are shown in Budyko space with their corresponding expected and actual Budyko curve trajectories in Fig. 2. The data generally cluster in a manner reflective of the well-known nonparametric Budyko curve behavior (blue solid curve). Additionally, the aggregate behavior of the actual trajectories (red solid curves) also generally follows the nonparametric Budyko curve. However, there are significant discrepancies between the shape of the overall ensemble cloud of catchments and their actual trajectories versus the corresponding conjectured trajectories (gray dashed curves) for most individual catchments. Many of the curves that would be expected based on the catchment trajectory conjecture span regions of “unpopulated” Budyko space where actual catchments are rarely observed.

Nonparametric sign tests showed that none of the reference catchments
consistently followed the Budyko curves that would be expected based on the
catchment trajectory conjecture (i.e., for multiple realizations of actual
trajectories using different averaging window sizes). From the total of
24 501 actual trajectory realizations, 23 231 (95 %) were found to have
consistent differences (*p* < 0.05) from their expected
trajectories (i.e., they did not follow Budyko curves), whereas only 1270
(5 %) were found to be statistically indistinguishable (*p* > 0.05). We note that the 5 % of actual trajectory realizations
for which Budyko curve trajectories could not be rejected is consistent with
the expected 5 % that would be accepted due to random chance at a
significance level of 95 %.

Figure 3a gives examples of actual trajectory realizations (10-year average) that are statistically distinguishable (red curves) and indistinguishable (blue curves) from their expected trajectories (black dotted curves). The maximum deviation between the actual evaporative index (10-year average) and those determined from expected trajectories shown in Fig. 3a is 0.14, corresponding to an absolute relative error of 212 %. Figure 3b gives a histogram of the maximum absolute relative errors in the evaporative index between the 10-year average actual trajectory realizations and expected trajectories for all 728 reference catchments, truncated to a maximum value of 225 %. The locations of the errors associated with the example trajectories in Fig. 3a are given by arrows in Fig. 3b, with their colors (red or blue) corresponding to the trajectory's statistical distinguishability. The full range of evaporative index errors spanned from 0.4 % to 1991 %, with a mean of 26 %. The mean error closely agrees with the value (27.9 %) found by Berghuijs and Woods (2016) in a comparable test of the catchment trajectory conjecture using Eq. (6) and 420 catchments from the MOPEX (Model Parameter Estimation Project) dataset (Schaake et al., 2006). Importantly, the average relative error for the parametric Budyko framework (26 %) is actually larger than that for Eq. (3) (23 %), which suggests that the nonparametric Budyko curve is in better agreement with the global behavior of catchments than the ensemble of parametric curves specifically fit to the individual catchments.

From these results, we can conclude that individual catchments do not generally or consistently follow Budyko curve trajectories as posited by the catchment trajectory conjecture. As such, the use of this conjecture in hydrological analyses (e.g., precipitation partitioning sensitivity and causal attribution to anthropogenic and climatic impacts) will likely introduce significant errors and may lead to spurious conclusions.

## 4.2 Reinterpreting the parametric Budyko framework

## 4.3 Catchment-specific parameters as proxy variables for the evaporative index

The results of our empirical test of the Budyko curve catchment trajectory
conjecture (Sects. 3.1.2, 4.1.2) strongly suggest that the parametric
Budyko equations do not describe the long-term evaporative behavior of
individual catchments (i.e., they are not empirically valid). This further
suggests that their specific functional forms are not physically meaningful,
and the catchment-specific parameter cannot be independently related to
physical properties. Thus, *n* and *w* are not transferrable either between
catchments or between different time points in a single catchment. Without
empirical tethers to physical reality, the functional forms of Eqs. (5) and
(6) do not carry information independent of *P*, PET, and
ET, leaving the parametric Budyko equations underdetermined and
establishing that the catchment-specific parameter and
$\frac{\text{ET}}{P}$ are proxies for each other.

Due to this nontransferability and proxy relationship, it is not possible
to solve for *n* or *w* without first obtaining $\frac{\text{ET}}{P}$,
making it impossible to obtain the value of *n* and *w* for a catchment a priori.
When *P* and PET are known, the values of *n* or *w* and
$\frac{\text{ET}}{P}$ are inextricably intertwined because they can only
be “measured” by inverting Eqs. (5) and (6) using the same evaporative
fluxes that are to be eventually estimated. This reliance makes their use in
predictive pursuits purely circular. Thus, the parametric Budyko equations
are underdetermined, as each equation will always contain two unknowns
(i.e., $\frac{\text{ET}}{P}$ and either *n* or *w*). This means that for
any given *ϕ*, there are infinite valid combinations of the
catchment-specific parameter and $\frac{\text{ET}}{P}$ (Sect. S4,
Fig. S2).

The inability to estimate *n* and *w* without $\frac{\text{ET}}{P}$ has
also been noted previously in the literature (e.g.,
Zhang et al., 2004; Greve et al., 2015). This fact is
made even more evident by examining the processes used to develop the
proposed *n* and *w* relationships summarized in Table S1. In every case,
$\frac{\text{ET}}{P}$ is first estimated empirically from discharge and
precipitation data or from a biophysical model prior to being used to
calculate a value for *n* or *w*, which is subsequently used to develop the
statistical curve-fitting relationships. The apparent dependence of the
catchment-specific parameter on biophysical features is, thus, directly
derived from the dependence of $\frac{\text{ET}}{P}$ on those same
features (Reaver et al., 2020). In all of these cases, transforming
$\frac{\text{ET}}{P}$ to *n* or *w* adds no new information given our
empirical finding that Eqs. (5) and (6) do not represent
trajectories of individual catchments (Sect. 4.1.2). For the relationships
in Table S1, the parametric Budyko equations essentially act as
(unnecessary) coordinate transformations from Budyko space, with coordinates
$\left(\frac{\text{PET}}{P},\frac{\text{ET}}{P}\right)$, to
“Budyko curve space”, with coordinates $\left(\frac{\text{PET}}{P},n\right)$ or $\left(\frac{\text{PET}}{P},w\right)$. Practically, *n* and *w* could be
eliminated from each of these studies by fitting the proposed models to the
estimated values of $\frac{\text{ET}}{P}$ directly, bypassing the
parametric Budyko framework altogether. The resulting models would likely be
easier to interpret, as they would relate catchment biophysical features to
a real quantity, either $\frac{\text{ET}}{P}$ or ET, rather than
to an ambiguous parameter. In short, using the parametric Budyko equations
to estimate ET from *P* and PET always requires one
to first estimate ET; the same is true for estimating changes in ET
from changes in *P* and PET. This severely limits the practical
applicability of the parametric Budyko framework.

In principle, with an appropriate interpretation of the catchment-specific
parameter, use of the parametric Budyko framework in landscape hydrology is
benign, if unnecessary. However, in practice, even with an appropriate
interpretation of *n* and *w*, the use of Eqs. (5) and (6) in a hydrological
analysis will likely have deleterious effects on both the quantitative
values of results and their interpretations. The reason for this is that the
catchment-specific parameter is a poor proxy variable for
$\frac{\text{ET}}{P}$, as it maps the finite space of
$\frac{\text{ET}}{P}$ (i.e., 0 to 1) to the infinite spaces of *n*
(i.e., 0 to ∞) and *w* (i.e., 1 to ∞). Therefore, as a
catchment approaches the water and energy limits in Budyko space,
infinitesimal changes in $\frac{\text{ET}}{P}$ result in infinitely
large changes in the catchment-specific parameter, allowing for small
numerical errors to be dramatically amplified and further confounding
relationships based on these transformations (e.g., the relationships in
Table S1).

While the acknowledgment of the proxy nature of the catchment-specific
parameter and $\frac{\text{ET}}{P}$ casts doubt on the specific
conclusions of previous parametric Budyko-based research, we note that both
the intent and much of the effort of many such studies can be preserved. For
example, studies that related *n* or *w* to catchment biophysical features
using various analytical tools could employ the same methods to relate
ET or $\frac{\text{ET}}{P}$ to biophysical features directly.
Doing so would preserve most of the analyses of such studies (i.e., near-identical methods) as well as their intent (i.e., understanding the
relationship between ET and catchment biophysical features).

### 4.3.1 Nonuniqueness of the parametric Budyko equations

If the family of curves described by parametric Budyko equations are interpreted as trajectories for catchments undergoing changes in aridity, then each possible parametric Budyko equation contradicts all others, as each gives specific but nonequivalent functional forms for the trajectories. Even Eqs. (5) and (6), which are generally regarded as essentially interchangeable when using the approximate relationship, $w\approx n+\mathrm{0.72}$ (Yang et al., 2008; Andréassian and Sari, 2019), give nonequivalent trajectories, particularly for small values of the catchment-specific parameter. The contradiction between Eqs. (5) and (6) alone should cast doubt on current commonly held interpretations of parametric Budyko equations, particularly regarding the physical meaning of explicit curves and the provenance and meaning of the catchment-specific parameter. However, our introduction of Eqs. (9) and (10) further illustrates the irreconcilable contradictions between competing parametric Budyko equations.

The parametric Budyko equations described by Eqs. (5), (6), (9), and (10)
represent four equally valid families of curves (Fig. 4) in that they are
all monotonically increasing, concave down, and approach the energy and
water limits as *ϕ* approaches 0 and ∞, respectively. Curves
with constant parameters from each of the four parametric Budyko
formulations generally cross and diverge as the aridity index changes (Fig. 5). Traveling along a trajectory with a constant catchment-specific
parameter (i.e., *n*, *w*, *q*_{n}, or *q*_{w}) in one formulation of the
parametric Budyko equations means the parameters of the other three
formulations must continuously change. Thus, Eqs. (5), (6), (9), and (10)
directly contradict each other.

Of the previously proposed parametric Budyko equations, Eqs. (5) and (6) have been the most widely used (e.g., Donohue et al., 2012; Yang et al., 2007, 2009, 2016; Shao et al., 2012; Li et al., 2013; T. Li et al., 2020; S. Li et al., 2020; Xu et al., 2013; Cong et al., 2015; Zhang et al., 2018, 2019b; Abatzoglou and Ficklin, 2017; Xing et al., 2018a; Zhao et al., 2020; Ning et al., 2019, 2020a, b; Bai et al., 2019). Any of these studies could have justifiably used Eqs. (9) or (10) instead, as there is not an objective reason to choose any one over the others. However, each equation would lead to substantially different and potentially contradictory results. For example, methods for predicting the sensitivity of precipitation partitioning to changes in the aridity index or the catchment-specific parameter (Sect. 2.2) rely on the specified shape of the Budyko curve. The use of Eq. (5) to compute sensitivities would produce substantially different results compared with those produced from Eq. (10). Additionally, methods for attributing changes in precipitation partitioning to anthropogenic and climatic changes (Sect. 2.2) will produce contradictory conclusions when using one parametric Budyko formulation compared with using another.

It is important to note that Eqs. (5), (6), (9), and (10) are not the only potential parametric Budyko equations. In fact, the Porporato model (Eq. 8) can be manipulated into a single-parameter Budyko equation (e.g., Harman et al., 2011; Daly et al., 2019b). There are likely many more, all equally valid, versions with even starker differences in the shapes of the curves (leading to even larger discrepancies between formulations if the current interpretations of explicit Budyko curves and parametric Budyko equations are maintained). This “equifinality” and nonuniqueness of the parametric Budyko equations is incompatible with the overwhelming current interpretation of the parametric framework and lends support to our contention that the parametric Budyko formulations are better understood as arbitrary coordinate transformations between alternative representations of Budyko space.

The original Budyko hypothesis given in Eq. (2) and the resulting nonparametric curve (e.g., Eq. 3) provide an overarching framework for understanding catchment hydrology in terms of energy and water balances. As the development of the Budyko framework advanced over the past century, early conceptual tools, such as explicit functional curves, gained considerable influence, resulting in interpretations that are not actually supported by the framework and which may lead to spurious conclusions. In this study, we have revisited, summarized, and critically evaluated these interpretations, leading to a reinterpretation of explicit Budyko curves and the parametric Budyko equations.

It is apparent from the literature that the prevailing interpretation of
explicit Budyko curves ascribes undue physical meaning to the explicit
mathematical expression describing the curve. By returning to Budyko's own
interpretation of explicit curves, we saw that earlier conceptual frameworks
considered the specific choice of functional form to be arbitrary as long as
the curves suggested conservation of energy and mass in the humid and arid
limits and provided a good representation of the global behavior of multiple
catchments across a range of aridity conditions. We reinforce that the general global
Budyko curve behavior observed across multiple catchments is a valid, well-documented, and physically driven phenomenon. However, the attribution of
physical meaning to the specific functional forms of curves, as well as explicitly
interpreting them as trajectories for catchments undergoing changes in
aridity, is an unsupported conjecture. Our tests of this conjecture showed,
both theoretically and empirically, that conceptualizing Budyko curves as
trajectories is unjustified. Therefore, as an alternative to using explicit
Budyko curves to understand catchment trajectories, we reiterate the
long-standing suggestion (e.g., Eagleson, 1978; Milly, 1994; Daly and
Porporato, 2006; Rodriguez-Iturbe et al., 1999; Feng et al., 2015, etc.)
that process-based evapotranspiration models should be used. Additionally,
to be a valid representation of catchment evapotranspiration, process-based
models need to able to reproduce the empirically established, nonparametric
Budyko curve behavior when applied to multiple catchments across a range of
climates. Thus, the general Budyko curve behavior can serve as a global
constraint (i.e., calibration or validation) in the application of such
models (e.g., Greve et al. 2020). Furthermore, while the
parametric Budyko framework lacks predictive power, the nonparametric
framework allows for probabilistic predictions of ET and *Q* as
well as changes in ET and *Q* for ungauged basins. Within these
contexts, the nonparametric Budyko framework is a tremendously useful
conceptualization.

A literature review suggests that most current interpretations view the parametric Budyko equations as more general and versatile forms of the nonparametric Budyko equations. We illustrated that the parametric Budyko equations are underdetermined, lack predictive power, and are nonunique, merely serving as a coordinate transformation between Budyko space and Budyko curve space. Coupled to current interpretations of the parametric equations is the idea that the catchment-specific parameter is a lumped quantity that represents the influence of catchment biophysical features on $\frac{\text{ET}}{P}$, with many studies, in practice, treating it as only representing landscape features. We tested the climate independence of the catchment-specific parameter theoretically and demonstrated that its value can change due to climate alone. Given the underdetermined nature of the parametric Budyko equations, the catchment-specific parameter is best understood as an arbitrary constant that is effectively a proxy variable for $\frac{\text{ET}}{P}$. The collective results from our analyses suggest that current commonly held interpretations of Budyko curve trajectories and the parametric Budyko equations are unsupported. We propose that the catchment hydrology community look critically at the well-accepted but unjustified interpretations that are the current commonly held standard.

In closing, we recommend that improved understanding of ET should emerge from fundamental physical and biological controls, utilizing the empirically validated global Budyko curve behavior as a constraint, rather than ascribing undue meaning to arbitrary functional forms or ambiguous parameters. As with any empirical relationship, extrapolating the use of the Budyko curve beyond the regime for which is was developed is unjustified without additional evidence. By doing so we risk drawing spurious conclusions about the hydrologic functioning of landscapes. Empirical relationships, such as the Budyko curve, emerge from the underlying physics within a given context, but those relationships are susceptible to losing their physical foundations outside of that context.

The data used in this paper can be obtained from the following locations: the CAMELS-GB database (https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9, Coxon et al., 2020b); the UKBN2 database (https://nrfa.ceh.ac.uk/benchmark-network, Harrigan et al., 2018) and (http://nrfa.ceh.ac.uk/sites/default/files/UKBN_Station_List_vUKBN2.0_1.xlsx, Harrigan et al., 2018); and the CAMELS database (https://doi.org/10.5065/D6MW2F4D, Newman et al., 2014).

The supplement related to this article is available online at: https://doi.org/10.5194/hess-26-1507-2022-supplement.

NGFR conceived the study, compiled the data, performed the analyses, and drafted the paper. All authors contributed in the methodological design, interpretation of results, and paper preparation.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Nathan G. F. Reaver received support from a University of Florida graduate fellowship.

This paper was edited by Luis Samaniego and reviewed by two anonymous referees.

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