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# Identifying and Quantifying the Impact of Climatic and Non-Climatic Drivers on River Discharge in Europe

Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-segui

► To cite this version:

Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-segui. Identifying and Quantifying the Impact of Climatic and Non-Climatic Drivers on River Discharge in Europe. Water Resources Research, 2025, 61 (4), pp.e2024WR038220. 10.1029/2024wr038220. hal-05040076

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Submitted on 19 Apr 2025

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Water Resources Research®

RESEARCH ARTICLE

10.1029/2024WR038220

**Key Points:**

* The Budyko framework as a diagnostic tool for interpreting observations and physically based models

* Attributing the magnitude of climate change effect on hydrological dynamic changes compared to other sources of change

* Strong effect of non-climatic drivers on discharge changes in Europe, especially over the Mediterranean area

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

J. Collignan,
julie.collignan@lmd.ipsl.fr

**Citation:**

Collignan, J., Polcher, J., Bastin, S., & Quintana-Segui, P. (2025). Identifying and quantifying the impact of climatic and non-climatic drivers on river discharge in Europe. *Water Resources Research, 61*, e2024WR038220. https://doi.org/10.1029/2024WR038220

Received 2 JUL 2024
Accepted 26 FEB 2025

**Author Contributions:**

**Conceptualization:** Julie Collignan,
Jan Polcher

**Formal analysis:** Julie Collignan

**Funding acquisition:** Jan Polcher

**Methodology:** Julie Collignan

**Resources:** Pere Quintana-Segui

**Supervision:** Jan Polcher, Sophie Bastin

**Writing – original draft:** Julie Collignan

**Writing – review & editing:** Jan Polcher,
Sophie Bastin, Pere Quintana-Segui

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

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# Identifying and Quantifying the Impact of Climatic and Non-Climatic Drivers on River Discharge in Europe

Julie Collignan¹ ID, Jan Polcher¹, Sophie Bastin², and Pere Quintana-Segui³

¹Laboratoire de Météorologie Dynamique/IPSL - Ecole Polytechnique/CNRS, Paris, France, ²Laboratoire Atmosphères, Observations Spatiales/IPSL - CNRS, Paris, France, ³Observatori de l'Ebre (Universitat Ramon Llull—CSIC), Roquetes, Spain

**Abstract** Our water resources have changed over the last century through a combination of water management evolution and climate change. Understanding and decomposing the drivers of discharge changes is essential to preparing and planning adaptive strategies. To separate the response of catchment dynamics between climate change-related and other factors in discharge observations, we propose a methodology to compare discharge observations to discharge from a physically based model. The novelty lies in the fact that, to keep the comparison pertinent despite systematic biases in physically based model outputs, we compare both systems using a common framework of interpretation, a parsimonious model, which allows us to isolate trends in catchment dynamics from trends due to average changes in annual climate variables. The modeled system stands as the reference to reproduce changes only due to evolving climate dynamics. Comparing it to the interpretation framework applied to the observation system highlights the effect of the non-modeled factors on catchment dynamics and discharge, such as human intervention in rivers and water uptakes. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage. These results show the importance of including non-climatic factors in physically based models to understand the main drivers of discharge better and accurately project future changes.

**Plain Language Summary** Water is an essential resource. Its access and management are key challenges in the context of climate change. Changes in precipitation distribution and intensity and other climate effects lead to a change in the water availability and in the discharge of rivers. On top of that, humans intervene to uptake water from rivers and change streamflow dynamics. To better assess management practices and prepare for future climate conditions, it is important to understand which part of discharge evolution is due to climate and which part is due to human intervention. In this article, we present an innovative methodology to do so. We show that over most of Europe, water management practices are the main cause of discharge changes, while discharge in the North is mostly impacted by the evolution of climate. This is especially true in the case of drying discharge trends in rivers of Southern Europe. Therefore, the evolution of management practices must be particularly of interest when constructing adaptation pathways to future climate conditions.

## 1. Introduction

Water is an essential resource for both ecosystems and human needs. Floods or water scarcity can lead to environmental catastrophes, conflicts and economic hardships. Understanding the evolution of water availability is a key challenge in the context of climate change and a highly managed continental water cycle. Directly related to freshwater available to humans and ecosystems (Dai, 2016), a key variable to study the evolution of water resources is streamflow. For these same reasons, it has been a focus of water management over the last millennia and of observations over the last century. From a geophysical perspective, streamflow provides a comprehensive overview of the water dynamics of catchments as it is the result of the catchment-integrated balance between precipitation, evapotranspiration and water storage (Milly et al., 2005; Rottler et al., 2020). These first two fluxes relate to climate processes and thus are driven by atmospheric variability and trends (Christidis & Stott, 2022; García-Ruiz et al., 2011). The latter flux and evapotranspiration are dependent on surface dynamics, relationships with landscape characteristics and land surface processes. It is on these dynamics that human intervene, to optimize their benefits from surface water use, through the management of water storage (i.e., reservoirs or
groundwater pumping) and evaporation (i.e., land use and irrigation) (Riedel & Weber, 2020; Schneider et al., 2013).

All of these processes have confounding effects on river discharge, which makes it difficult to detect and attribute trends in water resources (Ficklin et al., 2018; Rottler et al., 2020). Both climate change and anthropogenic activities influence catchment dynamics and equilibrium. On the climate part, precipitation distribution, frequency, intensity are changing, along with an increase in atmospheric water demand due to increased available energy at the surface (Christidis & Stott, 2022; Dezsi et al., 2018; Douville et al., 2021; García-Ruiz et al., 2011; Ribes et al., 2019). In turn, human activities and management, through abstractions (for irrigation, domestic uses among others) and regulations (i.e., dams, reservoirs) and surface sealing due to urbanization (Oudin et al., 2018), directly impact the partitioning of water between runoff and evapotranspiration along with flow seasonality, due to additional water uptakes and controlled water releases (Ficklin et al., 2018; García-Ruiz et al., 2011; Rottler et al., 2020). To project future streamflow changes and adapt water management strategies to climate change, it is essential first to understand the relative weight of these different drivers in streamflow dynamics. Being able to attribute past changes in river discharge to either climatic factors or human intervention on the land surface processes provides invaluable information to water managers in an evolving water cycle.

Some studies try to do so from discharge observation by isolating catchments considered as "natural" and relatively untouched by anthropogenic water management (Beguería et al., 2003; Coch & Mediero, 2016; Ficklin et al., 2018; Stahl et al., 2010). However, these studies are limited, especially in spatial extent due to the limited catchments corresponding to such a definition (Stahl et al., 2010; Vicente-Serrano et al., 2019). Other studies have correlated changes in streamflow with different factors and direct data on human intervention, such as the area covered by irrigated lands (Vicente-Serrano et al., 2019), agriculture, mining, reservoir operations and inter-basin water transfers (Rodell et al., 2018) and satellite imagery linked to vegetation cover changes (Rodell et al., 2018; Vicente-Serrano et al., 2019). However, the previous examples are correlations and no attribution and only work if the factors considered in the analysis dominate the variability. Therefore, models are another important approach to solving the problem and can be used to better understand and attribute the role of climate fluctuations and human activities, land surface, and land use changes on discharge.

Physically based land surface models (LSMs) and global hydrological models (GHMs) have been developed to understand streamflow dynamics, reproduce land surface processes and predict the evolution of the water cycle using different scenarios for the future (Nazemi & Wheater, 2015; W. Zhao & Li, 2015). They represent the current understanding of surface/atmosphere interactions, vegetation dynamics and hydrological processes under the control of climate (Quintana-Seguí et al., 2020; Stephens et al., 2023; Tafasca et al., 2020). These models are very useful to study patterns of change and trends and link them to specific processes (Alkama et al., 2010; Do et al., 2020; Douville et al., 2021; Zanardo et al., 2012). There are different types of complex physically based models, developed with different goals in mind, such as LSMs, which were developed to provide surface boundary conditions to climate models and focus on the interactions of the surface with the atmosphere. Other models are GHMs which focused on the surface and underground hydrological processes. These models have grown more complex over time and include more and more processes (Do et al., 2020; Nazemi & Wheater, 2015; Telteu et al., 2021; W. Zhao & Li, 2015). For instance, LSMs now include representations of plant growth and vegetation dynamics and start representing hydrological surface exchanges. However, to this day, all physically based models lack some processes, depending on their original goals and designs. For instance, considering LSMs, they currently fail to effectively include most anthropogenic water usage and management, even if progress is being made in that direction (Decharme et al., 2019; Nazemi & Wheater, 2015; Schrapffer et al., 2020; F. Wang et al., 2018), while GHMs tend to include them more and more but have a simplified representation of surface/atmosphere interactions based on more general expressions of potential evapotranspiration (Nazemi & Wheater, 2015; Telteu et al., 2021).

In view of the complexity of land surface processes and the lack of data, another class of models has also been developed: parsimonious or calibrated models. Based on the perceived functioning of the surface hydrology (Beven & Chappell, 2021), relations and parameters are selected and then adjusted over a period to represent, at best, actual streamflow characteristics. These models have demonstrated their value for operational short-term predictions and to represent and detect current trends in discharge with a simplified interpretation tool (Andréassian et al., 2016; Jiang et al., 2015; Perrin et al., 2003). However, they are limited in their ability to predict changes associated with specific drivers due to the difficulty of physical interpretation of the adjusted
parameters and the undetermined sensitivity of these parameters to the drivers (Andréassian et al., 2016; Coron et al., 2014; Nicolle et al., 2021; Zheng et al., 2018). Still they have been used to try and separate the effect of anthropogenic activities from climatic drivers, often comparing a reference “untouched” period or area to a post-change period or to a similar but highly anthropized area (Ahn & Merwade, 2014; Ficklin et al., 2018; Luo et al., 2020; Palmer et al., 2008; W. Wang et al., 2020; J. Zhao et al., 2018; Zheng et al., 2018). However, these methods all rely on the debatable assumption that the adjusted parameters are independent of climate variability (Andréassian et al., 2016; Coron et al., 2014; Reaver et al., 2022). Indeed, by construction, these models are valid over specific space and time scale (over which they were developed). Their fit and calibration are not entirely independent of climate conditions (Nicolle et al., 2021) and they do not account for potential changes to climate-runoff relationship (Teng et al., 2012), so the evolution of the calibrated parameters could change with an evolution of this relationship in non-stationary conditions (Milly et al., 2008). Therefore, considering these parameters climate-independent could lead to an underestimation of the effect of climate change on streamflow in the long term.

In this paper, we propose using the combination of both types of models to analyze observed annual river discharge and decompose observed trends into climate-driven changes and those caused by human intervention on the continental water cycle. The parsimonious model is used as a framework of interpretation. More specifically, we use the Budyko space and the one-parameter parsimonious model proposed by Fu's equation (L. Zhang et al., 2004). This parsimonious model introduces a single calibrated parameter standing for the catchment evaporation efficiency $\omega$ and two main average climate variables precipitation (P) and potential evapotranspiration (PET). Here, we fit this parameter $\omega$ over successive time windows to get an evolution of the evaporation efficiency of catchments. Other studies have used a time-varying $\omega$. Some have constructed a function expressing it as dependent on several factors of the surface characteristics (Jiang et al., 2015; Simons et al., 2020; Tian et al., 2018; Xing et al., 2018; X. Zhang et al., 2019), but such methods have been contested since $\omega$ has no clear physical meaning (Berghuijs et al., 2020; Reaver et al., 2020, 2022). Others have fitted it either with a time-moving window (Jiang et al., 2015; J. Zhao et al., 2018), as we do here, or over different periods, a reference period and a post-change period (Jiang et al., 2015; Luo et al., 2020; W. Wang et al., 2020). Most of these studies, however, make the simplifying assumption that there is no climate component in the change of $\omega$, while, by construction, it cannot be fully independent of climate variable (Berghuijs et al., 2020; Coron et al., 2014; Padrón et al., 2017; Reaver et al., 2022). We have shown in a previous paper (Collignan et al., 2023) that the Budyko framework can be used without expressing $\omega$ as a physical function, when applied to a Land Surface Model (LSM), to identify the effect of climate change on the evolution of the evaporation efficiency parameter $\omega$ and compare its effect on discharge to the effect of the average of precipitation (P) and potential evapotranspiration (PET). We found that climate influences on $\omega$ are mostly due to the effect of seasonality and intra-annual precipitation trends.

Here, we go one step further, comparing a physical model-based and an observation-based system to identify the effect of non-climatic factors through this common framework of interpretation. The observation-based system is influenced both by climate changes and by changes in surface dynamics due to other evolving factors (water management...). The use of the framework of interpretation allows to isolate the effect of average changes in annual climate variables from changes in evaporation efficiency of catchments. However, as previously identified, the influence of climate persists in the changes in surface dynamics (due to seasonality evolution for instance). Therefore, we compare the observation-based system outputs to the outputs of the framework applied to LSM outputs. In this model-based system, the effect of climate is isolated because it is the only source of change. We chose an LSM with fixed land cover as the climatic reference, as it represents the behavior of catchments and land surface dynamics responding to changes in climate variables, the only source of change present in the LSM simulation. Using a parsimonious model as a common framework of interpretation for both systems allows for an interesting comparison. Compared to an observation-based study alone, it allows us to isolate the effects of climate change from the rest over the entire set of catchments. Compared to a model-based study, where the incomplete representation of the complex land surface processes and the lack of representation of human water management makes the direct validation of the predicted river discharge to observation difficult (Hagemann & Dümenil, 1997), the proposed analysis allows for an indirect interpretation and highlights the effect of the processes missing in the modeled system. This results in a good and relatively simple diagnostic tool to identify the main drivers of discharge and their relative magnitude, therefore highlighting the gaps in the current complex models to reproduce streamflow trends and orientating future research.
This paper is structured as follows: we first present the method used with more details, describing the data and the
LSM used, how the Budyko framework is applied, and the robustness tests carried out. Then we show the results
obtained over Europe, followed by a discussion to reflect on the perspectives of these results.

## 2. Method

The proposed method allows the comparison of discharge observations and physically based model outputs through a common framework of interpretation. The framework decomposes the direct effect of average climate variables changes from the effect of evaporation efficiency changes. The latter changes are linked to the evolution of land surface dynamics, which can be related to climate change (changes in annual cycle, synchronicity between P and PET for instance) but also to other sources of change such as anthropogenic water management. The observation-based system includes all these sources of change. We compare it to the model-based system, where the only source of change is the climate forcing. Comparing both allows to compare the magnitude of discharge changes when non-direct climatic factors are included or not. In this section, we introduce the physically based model, the climate forcing used, the set of discharge observations and further details on the Budyko framework used as a common space of interpretation of the two systems.

### 2.1. Data

#### 2.1.1. The Land Surface Model ORCHIDEE

The LSM used in this study is the Organizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) (Krinner et al., 2005) from the Institut Pierre Simon Laplace (IPSL). The current version of the model simulates the global carbon cycle and quantifies terrestrial water and energy balance through biophysical and natural biogeochemical processes. It represents vegetation intra-annual dynamics (growth and LAI evolution) (Druel et al., 2019). Aquifer representation is still conceptual in the model, which, however, includes lateral movement of the water through a validated routing system (Polcher et al., 2022; Schrapffer et al., 2023). It can include some anthropogenic interference such as inter-annual land cover changes, forest and grassland management (Lawrence et al., 2016) or irrigation (Guimberteau et al., 2012). Here the model is used without these options, as only the climatic dependences of hydrology are sought. We did on further run adding changes in land cover types as introduced in Lawrence et al. (2016). When used in offline conditions, atmospheric conditions are forced by a given data set, which defines the resolution at which the LSM runs.

The model has been used and validated on its ability to reproduce many aspects of the land surface processes (hydrology, vegetation, and carbon cycle processes) (Guion et al., 2022; Nguyen-Quang et al., 2018; Polcher et al., 2022; Tafasca et al., 2020; F. Wang et al., 2018). It is challenging to compare the LSM outputs directly to observations since processes are certainly missing from the model, notably direct human water management (F. Wang et al., 2018). Based on previous literature, we assume that the model simulate the mechanisms under actual evapotranspiration (Barella-Ortiz et al., 2013) and therefore is an adequate replicate of the “natural” response of catchments with time-invariant physical characteristics to the past climate conditions over Europe, as used for our method. Therefore we assume that the LSM accurately reproduces the response of an idealized natural catchment to changes in the climatic conditions (Polcher et al., 2022) even if it might have biases in the absolute discharge values. The LSM is never calibrated. The parameters are fixed based on the physical characteristics of catchments and assumed time-invariant. This introduces systematic biases but allows for the only source of change to be the physical response to climate. Therefore, we only compare trends later on.

#### 2.1.2. Forcing Data Sets

Three different climatic data sets are used to drive the LSM. These data sets are used as input to the off-line LSM and provide the climatic variables needed in the Budyko framework. The main one is the forcing data set GSWP3 (Hyungjun, 2017), covering the 1901–2012 period at a 3-hourly resolution with a geographic resolution of 0.5° × 0.5°. It is a dynamical downscaling of twentieth Century Reanalysis using a Global Spectral Model. It is bias-corrected using Global Precipitation Climatology Center (Rudolf et al., 2005) and Climate Research Unit observational data (Harris et al., 2020). The results presented later on are obtained with this forcing data set, therefore at the resolution of 0.5° × 0.5°.
We also use two other forcings, WFDEI-GPCC (Weedon et al., 2014) and E2OFD (Beck et al., 2017), both covering the 1979 to 2014 period. Testing the methodology with different independent climate data sets allows to verify the robustness of our results comparing the two systems and their sensitivity to the choice of climate forcing used (see Supporting Information S1).

### 2.1.3. Watersheds and Discharge Observation Data Sets

The river discharge observations collected by the Global Runoff Data Center (GRDC) from gauging stations all over Europe (Global Runoff Data Centre (GRDC), 2022) are the base of the current study. They were supplemented over Spain with data obtained from the Geoportal of Spain Ministerio (Ministerio para la Transición Ecológica y el Reto Demográfico, 2020) and over France from HYDROFrance national website (Ministère de l'écologie, du développement durable et de l'énergie, 2021). From the Spanish database, we also got data on dams' location and water storage volume (Ministère de l'écologie, du développement durable et de l'énergie, 2021). In the final analysis, only 814 stations were kept with at least 50 years of observations and for which we were able to satisfyingly reproduce the upstream catchment in the hydrological routing of the LSM (Nguyen-Quang et al., 2018; Polcher et al., 2022), based on the data set HydroSHEDS (Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales) (Lehner et al., 2008).

### 2.2. The Budyko Framework Applied to LSM Outputs and Observations

The Budyko framework is a relatively simple empirical framework that relies on balancing the water and energy fluxes through only a few variables (precipitation $P$ and potential evapotranspiration $PET$) to express the partitioning of water between evapotranspiration $E$ and runoff. Compared to other simple empirical models, such as linear regression models, it accounts for physical boundaries: the water and energy limits on the system. For the framework to work, it needs to be applied to a closed system where the boundaries can be defined, such as a watershed at an equilibrium state (water storage variations within the catchment are supposed to be small). It is simple enough to apply to a wide variety of observed catchments, as only basic variables are needed. In this study, we used the parametric equation of Fu-Tixeront (L. Zhang et al., 2004, 2008; Zheng et al., 2018). It reduces evaporation efficiency for each catchment to a specific parameter $\omega$ (Equation 1), fitted over hydrological year averages, in a given period. For the same climatic conditions $P$, $PET$, a catchment with a higher $\omega$ will evaporate more than another one with a smaller $\omega$. Here we consider $P$ from the climate data set used and $PET$ from the LSM used, forced with that same forcing.

$$ \frac{E}{P} = 1 + \frac{PET}{P} - \left( 1 + \left( \frac{PET}{P} \right)^{\omega} \right)^{\frac{1}{\omega}} \quad (1) $$

In the original framework, the parameter $\omega$ is assumed to be constant since the watershed is considered stationary and only driven by climate. $E$ is the actual evaporation at the scale of the catchment. With the same assumption of a closed system and no water storage change, the water continuity yields for discharge $Q = P - E$.

Here, we consider only that the system is piece-wise stationary and that the parameter can be assumed to be constant over a short period (11 years) (Han et al., 2020). This introduces a time dependence in the parameter $\omega$ by successive fits over an 11-year time-moving window. We, therefore, capture the long-term effects of climate change and anthropogenic activities, both influencing catchment responses and the evaporation efficiency $\omega$. Again, $\omega$ has no clear physical meaning and is never expressed as a function of defined physical factors. It regroups everything influencing catchments' evaporation efficiency (Berghuijs et al., 2017; Reaver et al., 2020). As a result, both the annual mean of $P$ and $PET$ and the evaporation efficiency $\omega$ are time-dependent. This allows the construction of a framework of interpretation with a simple decomposition of discharge trends $Q$. We can isolate the trends due to long-term changes in average climate variable $P$ and $PET$ by fixing $\omega$ to an average value $\bar{\omega}$ or isolate the trend due to changes in evaporation efficiency by randomizing the climate and only keeping the trend in the time-series $\omega$ (Figure 1). More details for this methodology are given in Collignan et al. (2023). For notation purposes only, the pair of annual average climate variables $P$ and $PET$ have been noted $C$ to lighten the equation, since these two variables are always treated and interpreted together.
<figure><img src="image_5.png" /><figcaption>Figure 1.</figcaption></figure>
The two systems considered are (Figure 1):

*   **The observation-based system, referred to as actual system:** the framework is used to decompose the observed discharge changes. We successively fit the framework to discharge observations, getting a time series of the evaporation efficiency parameter $\omega_a$. This provides the following information:

- $dQ_{actual}(C, \omega_a)/dt$: overall trend in $Q$ in the actual system.

- $dQ_a(C, \bar{\omega}_a)/dt$: partial trend due to changes in $P$ and $PET$ (paired in the notation $C$), where $\bar{\omega}_a$ is the average evaporation efficiency over the entire period.

- $dQ_a(\bar{C}, \omega_a)/dt$: partial trend due to climatic impact on evaporation efficiency $\omega_a$, where $\bar{C}$ is a random series of average annual pair ($P$, $PET$), so the series has no trends. By randomizing the average annual climate, the partial trend due to the evolution of evaporation efficiency $\omega_a$ can be estimated. Since it is an observation-based system, all changes in the catchment responses (anthropogenic as well as climate-induced) are included in the $\omega_a$ time series.

*   **A climate-driven system, referred to as climatic system:** An LSM is used to estimate the climate-induced changes in the evaporation efficiency ($\omega_c$). The LSM stands as our climatic reference. Once the Budyko framework is fitted to this system, it provides us with the following information:

- $dQ_{climate}(C, \omega_c)/dt$: defined as the climate driven discharge trends.

- $dQ_c(C, \bar{\omega}_c)/dt$: partial trend due to fluctuations in annual averages of $P$ and $PET$ (paired in the notation $C$), where $\bar{\omega}_c$ is the average evaporation efficiency over the entire period.

- $dQ_c(\bar{C}, \omega_c)/dt$: partial trend due to climatic impact on evaporation efficiency $\omega_c$, where $\bar{C}$ is a random average annual climate with no trends. As it was designed for this model-based system, the $\omega_c$ time series only includes the response of the catchment dynamics to climate, such as the effect of changes in annual partitioning of $P$ and $PET$.

By comparing both systems, we can compare the magnitude of the trends due to $\omega$ linked to climate ($\omega_c$, for the modeled system) or linked to all factors combined ($\omega_a$, for the observation-based system) relative to the trends due to changes in average climate factors ($P$ and $PET$, in both systems). We have made the assumption that the feedback of land and water use changes on the climate is integrated into the forcing data (GSWP3) we have used and, therefore, included in the effect of climate change later on in our analysis. In any case, the impact of land and water use in Western Europe is believed to be of second order compared to the impact of increased greenhouse gases. In this framework, we only account for anthropogenic water management and water usage influences on the catchment responses and its evaporation efficiency, and not on the climate variables.

As previously explained, we assume that the LSM accurately represents surface/atmosphere dynamics and can have systematic biases so we only look at and compare trends. All trends are computed using the Mann-Kendall non-parametric test, associated with the Thiel-Sen slope estimator (Xiong et al., 2020), with a 0.05 p-value threshold for significance. We illustrate the differences in both systems over a given catchment, with a figure of $Q_{climate}$, $Q_{actual}$, $\omega_c$ and $\omega_a$, for the station of Castejon, upstream of the Ebro river in Spain (in Figure S2 in Supporting Information S1). The time series in total and partial discharges at the catchment levels for both systems are presented in Figure 4, for two different catchments.

## 2.3. Robustness of the Method

### 2.3.1. Adequacy of the Framework

The method aims to compare the trends in river discharge $Q_{LSM}$ from the model, which represents the climatic conditions only, and $Q_{obs}$ deduced from observations, which represent all conditions at once, the actual conditions. They are not compared directly but through their substitutes $Q_{climate}$ and $Q_{actual}$ determined with the Budyko

**Figure 1.** Method to decompose the effect of climate drivers and non-climatic factors on the river discharge. The full methodology is applied twice with the same climate variables: once to discharge output of a land surface model ($Q_{LSM}$) and once to discharge observations ($Q_{obs}$) over the same watersheds. To compare these two systems, we use a common framework: Fu's equation. The equation is fitted once over the full period and once successively with an 11-year sliding window to get the evaporation efficiency time variation. This framework allows to calculate discharge and to decompose the effect of the annual mean of climate variables and the effect of changes in the evaporation efficiency by estimating the partial derivatives corresponding to each (Collignan et al., 2023). The notation $C$ is used to simplify the writing of $P$ and $PET$ into a common variable for notation purposes only. The partial response to changes in the evaporation efficiency $\omega$ only is estimated by randomly drawing the annual mean of the climate variables (a) and the partial derivative due to changes in the annual mean of climate variables $C$ is estimated by fixing the evaporation efficiency parameter over the full period (b).
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Using Nash-Sutcliffe coefficient (NSC) and absolute Percent bias (PBIAS) to compare river discharge modeled *Q*<sub>LSM</sub> or observed *Q*<sub>obs</sub> to river discharge *Q*<sub>climate</sub> and *Q*<sub>actual</sub> calculated with Fu's equation, to attest the quality of the Budyko framework. Colors from yellow to blue and from orange to green are considered as satisfactory for the NSC and the PBIAS respectively.</figcaption></figure>

framework, which facilitates the interpretation of partial trends. We, therefore, need to attest to the quality of the Budyko framework to reproduce *Q*<sub>LSM</sub> and *Q*<sub>obs</sub> through their parametric representation.

We use the Nash-Sutcliffe coefficient (NSC) and the Percent bias (PBIAS) (Figure 2). The Budyko framework is able to reproduce correctly the annual mean of observed river discharge over all European basins with a very good PBIAS (<10% for all river basins) (Figure 2d) and a good NSC > 0.5 for 569 stations out of 849, except for north-eastern Europe, where we locate the majority of stations which fail this test (Figure 2c). This second test is more demanding and attests to the quality of Budyko framework to reproduce the inter-annual variations of discharge. It is also efficient to reproduce the climatic river discharge from the model (Figures 2a and 2b) with NSC > 0.5 and PBIAS ≤ 15% except for a few basins and still an under-performance for NSC over Eastern Europe. Therefore, the Budyko framework can be an adequate parametric representation of annual mean discharge in both systems, with an NSC < 0.5 for 569 stations, adequately reproducing both *Q*<sub>LSM</sub> and *Q*<sub>obs</sub>. Filtering out and keeping only these stations, we can use *Q*<sub>climate</sub> and *Q*<sub>actual</sub> derived from this framework to compare the climatic behavior of the
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Comparison of the coefficient of variation $\sigma/\mu$ ($\sigma$: standard deviation, $\mu$ average of the time series) of the $\omega$ time series with the different forcing tested (GSWP3, WFDEIGPCC, E2OFD), for each system ($\omega_a$ for the “actual” system vs. $\omega_c$ for the “climatic” system). Each boxplot shows the statistical distribution of the coefficient of variations of $\omega$ series over all catchments, for each pairing forcing/system.</figcaption></figure>

watershed and its actual behavior, without the analysis being tinted by the ability of the Budyko framework to effectively reproduce $Q_{LSM}$ and $Q_{obs}$ respectively.

### 2.3.2. Robustness to Climate Data

We also tested the method's robustness and its sensitivity to data driving the LSM by comparing its application with different forcing data sets. Three independent atmospheric data sets are available over the 1979–2010 period. Over such a short period, trends are mostly non-significant and cannot be appropriately statistically compared. However, for all forcings considered, the patterns are very similar. Here, we focus on the efficiency parameters $\omega_c$ and $\omega_a$ correlation and coefficient of variation for each forcing. We analyze the impact of the forcing choice on how our method which attributes variations of $\omega$ to climatic behavior with our LSM.

The coefficients of variation (standard deviation $\sigma$ normalized by the average $\mu$ of the time series) have a similar order of magnitude for each $\omega$ no matter the forcing used to calculate it, consistently producing larger values for $\omega_a$ than for $\omega_c$ with all forcings (Figure 3). We also look at the correlation between $\omega$ time series calculated with different forcings (Table 1). The poorest correlation is between E2OFD and GSWP3 (the forcings most different from each other) and mostly for $\omega_c$, which has the smallest average coefficient of variation. Therefore, it will impact our results less when comparing trends. However, the absolute values of $\omega$ are significantly different depending on the forcing used, reinforcing the idea that this method can only be used to assess and compare trends. The forcing data sets are not fully independent given the limited number of observations. For instance, GSWP3 and

WFDEI use the same precipitation product to bias correct the re-analyses on which they are based. E2OFD and WFDEI use the same re-analysis but interpolated to different resolutions and corrected with two distinct observational precipitation estimates. E2OFD has a finer resolution. Given that the results are closer for GSWP3 and WFDEI, we can hypothesize that the method is more sensitive to the precipitation data used than to the other variables.

These results show globally that the method is robust since it is not very sensitive to the forcing used. The differences in coefficients of variation between forcings are smaller than those between $\omega_a$ and $\omega_c$ for all tested forcing (Figure 3). Results presented in the article are obtained with the forcing data set GSWP3, which covers the longest time period 1901–2012 and is thus most relevant for evaluating the driver of river discharge trends.

<table><caption>**Table 1**

Comparison of the Evaporation Efficiencies Time-Series Calculated With the Different Forcings for Each System Over the Period 1979–2010: $\omega_c$ for the Climatic System and $\omega_a$ for the Actual System</caption><caption>Correlations: % of stations with average correlation > 0.6 and median correlation between all catchments</caption><thead><tr><th></th><th></th><th>ω<sub>c</sub></th><th></th><th>ω<sub>a</sub></th></tr></thead><tbody><tr><td>E2OFD/GSWP3</td><td>38%</td><td>0.50</td><td>53%</td><td>0.65</td></tr><tr><td>WFDEI/GSWP3</td><td>73%</td><td>0.75</td><td>77%</td><td>0.99</td></tr><tr><td>E2OFD/WFDEI</td><td>64%</td><td>0.70</td><td>59%</td><td>0.73</td></tr></tbody></table>
## 3. Results

### 3.1. Specific Analysis for Two Catchments With Different Behaviors

Our methodology allows for each catchment, and for both systems, to decompose discharge as a partial discharge when only changes in average annual climate variables are considered and a partial discharge when only changes in evaporation efficiency are considered. Here we show these different time series for two example catchments with different behaviors (Figure 4). As previously explained, we will not compare the magnitude of streamflow between both systems, as we know the modeled system may have a systematic bias. Later on, when comparing the two systems, we will only look at the relative trends.

For both catchments, for the model-based system (Figure 4c), we can observe that the variations in total discharge $Q_{climate}$ mostly follow the variations of partial discharge when the changes in average climate variables are considered $Q_c(C, \bar{\omega}_c)$. The partial discharge $Q_c(\bar{C}, \omega_c)$ when only variations in evaporation efficiency are considered shows very little variations and seems to explain very little of the total changes in $Q_{climate}$.

For the observation-based “actual” system (Figure 4b), we start to see significant differences in both catchment behaviors. First, for both, we can still see that the partial discharge $Q_a(\bar{C}, \omega_a)$ due to changes in evaporation efficiency shows a lot less inter-annual variability than the one due to changes in annual average climate variables $Q_a(C, \bar{\omega}_a)$. Therefore, average climate variable changes still account for most of the total discharge inter-annual variability. However, for both catchments, the changes due to evaporation efficiency $Q_a(\bar{C}, \omega_a)$ exhibit clear decreasing trends. In these catchments, the changes in evaporation efficiency in the “actual” system are very different than in the modeled “climatic” system, showing that the non-climatic effects are dominant on evaporation efficiency changes. For the first catchment in Sweden (Figure 4b, left graph), we see that the total discharge $Q_{actual}$ still mostly follows the variations of the partial discharge due to changes in average climate variables. However, and that is where we capture the main difference of behaviors between these two catchments, for the one in Spain (Figure 4b, right graph), we see that the total discharge $Q_{actual}$ detaches itself from $Q_a(C, \bar{\omega}_a)$ by the end of the period, following the decreasing trend exhibited by $Q_a(\bar{C}, \omega_a)$. In this catchment, the non-climatic changes in evaporation efficiency dominate the underlying trend in total “actual” discharge.

In the following part, we will generalize that analysis to all the studied catchments, focusing on how much each partial trend in $Q(\bar{C}, \omega)$ and $Q(C, \bar{\omega})$ accounts for the total discharge trend in both systems $Q_{climate}$ and $Q_{actual}$.

### 3.2. Geographical Distribution of Discharge Trend Characteristics

The spatial distribution of the significant relative trends (Figure 5) is spatially coherent, which also attests to the method's robustness. When some specific catchments other than the ones from the previous section are referred to, the geographic location of these catchments is illustrated in supplementary material (Figure S1 in Supporting Information S1).

In the modeled “climatic” system (Figure 5a), basins in eastern Europe and Spain are getting dryer with trends in discharge between -0.2%/yr and -0.5%/yr over the past century. In central and northern Europe, the climatic discharge is increasing with trends of +0.2%/yr to 0.5%/yr over the past century. Similarly to the results for the example catchments, we observe that in this system, the trends in discharge $Q_{climate}$ (Figure 5a) are mostly driven by changes in average climate variables C (Figure 5c) and not to changes in evaporation efficiency $\omega_c$ (Figure 5e). These partial trends due to changes in the evaporation efficiency $\omega_c$ (Figure 5e) are small (between -0.2%/yr to +0.2%/yr) and are mostly positive. This effect is, however, negligible when looking at the total trends in discharge (Figure 5a). It can however amplify the partial trend due to changes in the annual average of climate variables C. This effect is illustrated in the Duero basin in north-western Spain, where both partial trends concur to a decrease in $Q_{climate}$. They can also cancel each other out, for instance, in the Tiber River in Italy, where the decrease in $Q_{climate}$ is not significant, while the partial trend due to changes in C (Figure 5c) was.

For the actual system, our results show on the contrary that, over Europe, the discharge trends (Figure 5b) are mostly explained by changes in the evaporation efficiencies (Figure 5f), as for the catchment in Spain used in specific example (Figure 4b, left graph). Remember that here, the changes in the evaporation efficiency $\omega_a$ encompass all changes in the catchment's evaporative behaviors, those induced by climate and those by changing
<figure><img src="image_8.png" /><figcaption>(a) Example of watersheds: the gauging station Motala Strom in Sweden and the gauging station Castejon on the Ebro river.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b) Discharge for the observation-based system, referred as actual system. We show the total discharge Q<sub>actual</sub> (orange line), the discharge Q<sub>a</sub>(C, ω̄<sub>a</sub>) (purple dotted line) only showing the fluctuations due to changes in annual averages of climate variables (C is a notation for the pairing of P and PET), and Q<sub>a</sub>(C̄, ω<sub>a</sub>) (dashed light orange line) showing the influence of ω<sub>a</sub> variations on discharge. In the actual system, ω<sub>a</sub> is influenced by all factors impacting catchment's characteristics (effects of both climatic and non-climatic factors such as land use changes).</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(c) Discharge for the modeled climate-driven system, referred as climatic system. We show the total discharge Q<sub>climate</sub> (blue line), the discharge Q<sub>c</sub>(C, ω̄<sub>c</sub>) (purple dotted line) only showing the fluctuations due to changes in annual averages of climate variables, and Q<sub>c</sub>(C̄, ω<sub>c</sub>) (dashed lightblue line) showing the influence of ω<sub>c</sub> variations on discharge. In the model-based system, ω<sub>c</sub> is only influenced by climate changes, for instance changes in seasonality of climate variables.</figcaption></figure>

<caption>Figure 4.</caption>
water usage. Very high North, as for the catchment in Sweden illustrated in Figure 4b (right graph), this is less true. There the partial increasing trend due to changes in average climate variables is still dominating.

Similarly to results from other studies (Vicente-Serrano et al., 2019) over Western Europe, we find the highest negative trends are over Southern Spain and are mostly driven by non-climatic factors. To facilitate the comparison, scales of Figure 5 are fixed thus for Figures 5d and 5f they saturate, not showing that the trends are a lot higher in Spain, with up to -4.4%/yr change over the past century in *Q*<sub>actual</sub>. Over the rest of Europe, trends are lower and less significant, with positive trends generally in northern Europe, Great Britain and Sweden and negative trends over central Europe. The south of Spain corresponds to an area where both climate changes and changes related to human activities led to a significant decrease in river discharge over the past century. There, mainly, the changes in evaporation efficiency result in decreasing trends in *Q*<sub>actual</sub> (Figure 5f). This is coherent with increasing irrigation water uptakes. However, the Guadiana River stands out in our results. It seems that over that specific catchment, the overall effect of human water management and land use changes tend to increase *Q*<sub>actual</sub>, contrary to the rest of Spain. As discussed later, this could be linked to unsustainable groundwater pumping (Holtz & Pahl-Wostl, 2012), which invalidates the hypothesis of no water storage change and, therefore, results in a lower apparent evaporation efficiency and an artificially underestimated evapotranspiration, increasing the resulting discharge. More generally, there are several basins over Europe where the trends induced by changes in the evaporation efficiency lose their significance when the climate variability is considered in the reconstructed discharge. See, for instance, western France, northern Germany, Serbia. There, the high inter-annual variability due to changes in average annual climate variables hid the partial trends due to changes in evaporation efficiency in total discharge *Q*<sub>actual</sub>.

Interestingly, when we draw similar maps for sub-periods of 10 years (Figures S5-1 and S5-2 in Supporting Information S1), the impact of evaporation efficiency changes on discharge is not dominant anymore. At the decadal scale, the climatic variability is even higher. This climatic noise covers the effect of changes in the catchment's evaporation efficiency in discharge trends. At the scale of the century, the signal-to-noise ratio is higher, bringing to light the long-term role of changes in the catchment's evaporation efficiency and catchment's behavior on discharge.

## 3.3. Decomposing the Trends in River Discharge Over the Past Century

In this part, we generalize even more, looking at the dominant behaviors over all the catchments studied. We group all catchments in one figure for each system (Figure 6). Each figure shows the separation of trends in river discharge (*Q*) into the part explained by the evolution of climate *C* (x-axis) and a partial trend due to changes in the catchment affecting evaporation efficiency (*ω*) (y-axis). The colored points correspond to catchments for which the total trend in discharge is significant. Figure 6a illustrates the relative importance of both components of the trends as estimated with the methodology presented above. Positioning the results for one catchment in Figure 6a allows to illustrate the magnitudes of the partial trends due to each component and whether they are concurrent or opposite and if they tend to increase or decrease discharge. Two different systems are projected on this framework for comparison (see Method): the climatic system (*Q*<sub>climate</sub>) based on the LSM outputs and the actual system (*Q*<sub>actual</sub>) based on observed records.

Our results show that in the case of the climatic system (Figure 6b), the changes in annual mean climate variables (x-axis) have about a four times larger impact than the changes in evaporation efficiency (y-axis) on relative annual mean discharge trends. Overall, almost all catchments where *Q*<sub>climate</sub> has significantly changed (catchments with significant trends correspond to colored points) have concurrent trends in both components. A high covariance between these two components allows to better detect the trends. More generally, there is a dominance of the trends due to annual mean climate variables *P*, *PET*, amplified by the response of evaporation efficiency of the catchment induced by climate change. These cases correspond to catchments where an increase in *P* and/or a decrease in *PET* tends to increase annual mean discharge or, inversely, for a decrease. For instance, if an increase

**Figure 4.** Description of the discharge time series for two example stations, the Ebro river in Spain and Motala Ström in Sweden, for each system studied: the first system is based on the observations, the second one is based on LSM outputs. Once our method is applied to each system, we get the total discharge, the partial discharge when only climate fluctuations of average *P* and *PET* (paired in the notation *C*) are considered, and the partial discharge when only evaporation efficiency changes are considered. From these times series, we can then compare the total and partial trends and the two systems. For each system, the left graph shows the result for the Ebro river, and the right the Motala Ström.
<figure><img src="image_11.png" /><figcaption>(a) Relative changes in $Q_{climate}$</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>(b) Relative changes in $Q_{actual}$</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(c) Relative trend in $Q_c(C, \bar{\omega}_c)$: trends in relative $Q_{climate}$ changes due to climate variables C.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(d) Relative trend in $Q_a(C, \bar{\omega}_a)$: trends in relative $Q_{actual}$ changes due to climate variables C.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(e) Relative trends in $Q_c(\bar{C}, \omega_c(t))$: trends in relative $Q_{climate}$ changes due to changes in the evaporation efficiency $\omega_c$.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>(f) Relative trends in $Q_a(\bar{C}, \omega_a(t))$: trends in relative $Q_{actual}$ changes due to changes in the evaporation efficiency $\omega_a$.</figcaption></figure>

**Figure 5.** Significant trends in the relative river discharge $Q/\bar{Q}$ over the time period 1901–2012 (%/yr over the century). The scales have been forced to be the same for all maps for comparison purposes but some extrema can go higher or lower.
<figure><img src="image_17.png" /></figure>

(a) Interpretation scheme: comparing significant trends due to climate variables or due to ω. The graphs have four quadrants: the top right and the bottom left ones correspond to area of the graph where the climatic trend and the area due to ω are complementary/ going into the same direction. The top left one contains the basins for which the trends due to ω are positive and the trends due to climate variables are negative and the bottom right quadrant the opposite.

<figure><img src="image_18.png" /></figure>

(b) Climatic system *Q*<sub>climate</sub>: relative trends (%/yr over the century) due to changes in ω<sub>c</sub> versus relative trends due to changes in climate variables *C*

<figure><img src="image_19.png" /></figure>

(c) Actual system *Q*<sub>actual</sub>: relative trends (%/yr over the century) due to changes in ω<sub>a</sub> versus relative trends due to changes in climate variables *C*

**Figure 6.** Comparing the relative trends ($\frac{dQ}{dt}/Q$) due to a change in climate variables or due to a change in evaporation efficiency $\omega$ in the evolution of discharge, for both system $Q_{\text{climate}}$ and $Q_{\text{actual}}$. One point corresponds to a basin with at least 50 years of river discharge observations over Europe. The scale of trends due to $\omega_a$ in the actual system is 10 times larger than the one for trends due to $\omega_c$ in the climatic system. The green line is the line $y = x$. The color scale represents the significance of the trend in Q when all factors are considered. The markers indicate whether the partial trends are significant due to changes in C (x-axis), in $\omega$ (y-axis), or both.
in *P* is inhomogeneous, with an even stronger increase in winter precipitation, the partitioning toward runoff is usually higher, which translates into a decreased evaporation efficiency and thus an even stronger increase of *Q*<sub>climate</sub> (top right quadrant, Figure 6b). Therefore in this example, the increase in the annual mean *Q*<sub>climate</sub> is not only due to an increase in annual mean *P* but is amplified by the more contrasted seasonality and its impact on evaporation efficiency. More generally, there are fewer catchments where the changes in the evaporation efficiency tend to decrease discharge in the climatic system, except when they concur with a high decrease in relative discharge due to a decrease in *P* and/or an increase in *PET*. This is coherent with the increasing intensity and contrasted seasonality of precipitation events observed over Europe (Christidis & Stott, 2022; Douville et al., 2021; Ribes et al., 2019; Riedel & Weber, 2020; Zveryaev, 2004), which would logically tend to decrease evaporation efficiency by increasing local runoff, and therefore lead to a positive partial trend in discharge.

In the case of the observed system (Figure 6c), the relationship between the two partial trends looks very different. The one linked to changes in evaporation efficiency (y-axis) is larger than the partial trends linked to the annual mean in *P* and *PET* by a factor of 3. More generally, the total trends in *Q*<sub>actual</sub> (catchments with significant trends correspond to colored points) follow the partial trends due to changes in the catchments' evaporation efficiency. Therefore, in the actual system, the trends in discharge are mainly due to changes in catchment behavior due to non-climatic factors. Contrary to when only climate change is considered for modeled catchments, land use changes and human water management tend to increase the evaporation efficiency of catchments and, therefore, decrease *Q*<sub>actual</sub>. This is coherent with activities such as irrigation or agriculture in general, which aim to optimize evapotranspiration over catchments and with the previously analyzed geographical results.

**4. Discussion**

Our method uses a parsimonious hydrological model to decompose the observed river discharge trends into climate-driven processes, as estimated with a state-of-the-art LSM, and non-climatic changes that can be attributed to human activities. It can be generalized to the use of any pairing of calibrated parsimonious models and physically based models and be an effective operational tool to estimate and illustrate the effect of non-climatic drivers and land surface changes not well accounted for in current models and which may have a strong direct impact on water resources.

In accordance with previous studies (Berghuijs et al., 2017; Gudmundsson et al., 2017; Liu et al., 2021; Vicente-Serrano et al., 2019), we show a significant decrease in discharge in southern Europe, with the dominant explanation being non-climatic factors. Trends in Western Europe are more difficult to analyze, and less significant. In some catchments of Northern Europe, climate change seems to be the dominating driver of change. There, the sensitivity to precipitation dominates trends (Berghuijs et al., 2017; Vicente-Serrano et al., 2019). Still, our results highlight the fact that not accounting for non-climatic trends leads to a high underestimation of discharge changes in the physically based model used and, therefore, high uncertainties in projections of future water resource trends, especially when looking at long-term trends.

With this methodology, we can only estimate the magnitude of non-climatic trends but not attribute them to specific processes. In some areas where a dominant process can be hypothesized, such as irrigation, correlation with indicators can allow to verify the plausibility of the assumed cause. For instance, over Spain, especially over the Ebro basin, the strong increase in evaporation efficiency and reduced discharge is correlated to the development of dams with a coefficient above 0.7 when correlating $\omega_a$ to reservoirs levels for 6 sub-basins in this catchment (Figure S3 in Supporting Information S1). Dams' water storage is an indicator of the human management of water resources, which impacts the evaporation efficiency of watersheds. More generally, we see that the changes in the evaporation efficiency intensified over the second part of the century, where areas equipped for irrigation have been developed (Angelakis et al., 2020; Siebert et al., 2015). However, the available data are insufficient to attest to a correlation with that latter factor or with the effective amount of water used for irrigation. Groundwater pumping and glacier melt can explain positive discharge trends due to additional water sources not accounted for in the climatic system, which lead to artificially low evaporation efficiencies in our framework. For the Guadiana River in Spain, the unsustainable groundwater pumping (Esteban & Albiac, 2012; Holtz & Pahl-Wostl, 2012; Llamas et al., 2015) can explain the positive trend. In a similar way, for the Po river in Italy, which is highly irrigated (Siebert et al., 2015), we would expect a strong decrease in discharge as in most of Spain, but glacier melt brings additional water to the system (Huss & Hock, 2018; Schaner et al., 2012; Vincent et al., 2017), explaining a reduced detected negative trend over the end of the century. Other phenomena, such as soil sealing and river management, would be expected
to have similar effects due to a decrease in evapotranspiration or to an artificial enhancement of runoff. Applying the same methodology, this time activating changes in land use in ORCHIDEE (Lawrence et al., 2016) has little effect on discharge trends over the studied period and area and therefore was not shown here, but it could have a more significant effect at finer scale over small catchments.

Quantifying the contribution of climatic and non-climatic factors to changes in river discharge is an important first step. But it should be followed by an attribution. The use of the LSM in its current state allows to attribute the changes due to climate. However, the non-climatic factors remain challenging to attribute to specific processes, especially since most factors have concurring and competing effects. Detection and attribution methods have been developed in climate studies to assess anthropogenic climate change. They have allowed to determine the role of different factors by reproducing them first in GCMs (Douville et al., 2021; Hegerl & Zwiers, 2011). Similarly, we would need to simulate water usage such as irrigation, dam management, groundwater pumping and other missing phenomena such as glacier melting in the LSMs so that their impact on the evaporation efficiency can be identified and their contribution to the non-climatic trend quantified. Other physically based models, GHMs, already include some of these processes. On the other hand, they rely on a simpler representation of surface/atmosphere interactions. A complementary study would be to apply the analysis presented here to other LSMs and GHMs in parallel. This would also offer an error estimate for the method we propose and test our attribution hypotheses.

Understanding and quantifying the contribution of various processes contributing to observed discharge changes over the past century is an essential step in developing adaptation strategies to face climate change. In the future, changes in climatic variables are expected to increase even further, with increase in intense precipitation events (Douville et al., 2021; Ribes et al., 2019), an increase in evaporative demand and especially a decrease in average precipitation leading to water scarcity over southern Mediterranean Europe (Alkama et al., 2013; Gudmundsson et al., 2017). Concurrently, in Europe, human water management is expected to evolve to adapt to climate change and other constraints, such as changes in water and energy demand and regulations (Arheimer et al., 2017). For instance, the extent of irrigated land in Europe peaked at the end of the twentieth century and the future irrigation evolution is expected to follow new goals and mostly rely on improved efficiency (Adeyeri et al., 2020). Therefore, the balance between the different terms influencing catchment evaporation efficiency and discharge may change. If non-climatic factors dominated over the past century to explain discharge trends, it may not be the same in the future. Attribution needs to be tested over the documented past to improve the representation of non-climatic processes and allow the effective projection of future evolutions and potential changes in this balance.

Data Availability Statement

The outputs of the LSM ORCHIDEE for each catchment used in this study with the forcing GSWP3 are gathered in a file freely available on Zenodo.org (Collignan et al., 2022). This file also contains the description of the stations used in the study: their location, the size of the upstream area used to position the station on the grid and annual averages of streamflow observations. These data were gathered from different data sources (Global Runoff Data Centre (GRDC), 2022; Ministerio para la Transición Ecológica y el Reto Demográfico, 2020; Ministère de l'écologie, du développement durable et de l'énergie, 2021) more or less easy to access, and we therefore grouped them here to ease their access. The software of the LMS ORCHIDEE is available on the IPSL website (IPSL, 2017).

Acknowledgments

We would like to acknowledge the support of the Agence Nationale de la Recherche under contract HLIAISE (ANR-19-CE01-0017-02). The lead author would like to thank Institut Polytechnique de Paris for the Gaspard Monge fellowship, which funded her PhD thesis. We would also like to acknowledge the ESPRI team from IPSL, which allowed us access to data storage space and computation servers. We would also like to thank Ross Woods from the University of Bristol for reviewing this work and whose insightful comments allowed us to improve this manuscript.

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