# How have atmospheric components of the local water cycle changed around the abrupt climatic shift over France?

Léa Laurent, Albin Ullmann, Thierry Castel

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Léa Laurent, Albin Ullmann, Thierry Castel. How have atmospheric components of the local water cycle changed around the abrupt climatic shift over France?. Journal of Hydrology, 2024, 645 (Part A), pp.132211. 10.1016/j.jhydrol.2024.132211. hal-04956040

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# How have atmospheric components of the local water cycle changed around the abrupt climatic shift over France?

Léa Laurent<sup>a,b,*</sup>, Albin Ullmann<sup>a</sup>, Thierry Castel<sup>a</sup>

## ARTICLE INFO

This manuscript was handled by Marco Borga, Editor-in-Chief, with the assistance of Christian Massari, Associate Editor

**Keywords:**

- Local water cycle

- Abrupt warming

- Climate hazard

- Water constraint

- Water demand

## ABSTRACT

The significant increase in surface air temperature experienced by Western Europe over the last few decades has resulted in an abrupt warming in France, around 1987/1988 years. This climatic shift impacted hydrological cycle, particularly by reducing runoff in spring and summer. Evapotranspiration and precipitation have been identified as the main drivers of climatic water balance. But the impact of the 1987/1988 climatic shift on local water cycle over France has not been quantified yet. This study tries to assess the consequences of this rapid warming on the main climatic components of local water cycle. Climate variables linked to the water cycle extracted from a reanalysed observed climate database are analysed using robust Bayesian change points detection and mean comparison techniques. After the abrupt rise in surface air temperature and surface solar radiation, water demand increases significantly on almost the entire French territory in spring, summer and autumn. Our results show that, from March to May, the vegetation cover is able to respond to this increase by drawing from the soil water reservoirs. But in summer, most of the territory is facing a significant rise in water constraint (i.e. difference between potential and actual evapotranspiration), extending on the last decade over autumn. In spring and summer, the increase in potential evapotranspiration is the main driver of the intensification of water constraint. In the beginning of autumn, longer dry spell also plays a major role in the lengthening of periods of water constraint. This innovative study highlights the specific impact of a climatic shift on the main components of the atmospheric water balance at regional and local scale. As the observed changes in climate hazard linked to water cycle affect growth cycle of the majority of the vegetation covers and crops, this could lead to a worsening of hydric stress events. As such climatic shifts are expected to happen again in the future, their impacts on the local water cycle represent a major issue for natural terrestrial ecosystems as well as for agriculture.

## 1. Introduction

One of the main issues about global warming is the availability of water resources for natural or low anthropized ecosystems and for human activities, especially for agriculture (Alcamo et al., 2007; Fitton et al., 2019). The impact of climate change on hydrological cycle has been widely studied all over the world, and has various consequences depending on the region (Mandal et al., 2021; Middelkoop et al., 2001; Piao et al., 2010; Tolentino et al., 2016). Global warming is altering means and extremes of precipitation, evapotranspiration and runoff, compromising the assumption of stationarity of the water cycle (Milly et al., 2008). Over Europe, widespread areas show a decreasing trend in runoff in spring and summer months, despite positive precipitation

patterns in June and July (Stahl et al., 2010). Such observations suggest that during vegetative period evapotranspiration is one of the main drivers of runoff. The important increase of temperature in spring and summer results to higher potential evapotranspiration, leading to higher actual evapotranspiration, which reduces streamflow (van Lanen & Tallaksen, 2004).

Over the last few decades, Western Europe has seen an important increase in mean surface air temperature, especially in spring and summer (Dong et al., 2017; van Oldenborgh et al., 2009). This rapid warming is faster than expected from anthropogenic greenhouse gases increase (Philipona et al., 2009; Ruckstuhl et al., 2008). In addition to the warming trend, numerous natural regime shifts have been detected over the Northern Hemisphere, related to changes in atmospheric

<footnote><sup>a</sup> Biogéosciences, UMR 6282 CNRS, université de Bourgogne, 6 boulevard Gabriel, 21000 Dijon, France</footnote>

<footnote><sup>b</sup> Domaine Assurance Récolte, Groupama Rhône-Alpes Auvergne, 24 rue Charles Durand, 18020 Bourges Cedex, France</footnote>

<footnote>* Corresponding author at: Biogéosciences, UMR 6282 CNRS, université de Bourgogne, 6 boulevard Gabriel, 21000 Dijon, France.
E-mail address: lea.laurent@u-bourgogne.fr (L. Laurent).</footnote>
circulation (Hare & Mantua, 2000; Yasunaka & Hanawa, 2002). It is expected that these natural regime shifts may modulate climate change signal at regional scale. A major abrupt shift has been identified in the late 1980s over western part of Europe, along with a sharp increase of surface air temperature (Reid et al., 2016). This abrupt warming is likely to impact directly or indirectly several biophysical ecosystems (Reid et al., 2001; Woolway et al., 2017).

Several hypotheses have been put forward to explain this late increase in temperature over Western Europe. Atlantic Ocean influence is suggested as one of the key drivers of these changes in climate in recent decades: the recent pattern of anomalies in European climate coincided with substantial warming of the North Atlantic Ocean (Sutton & Dong, 2012). Those multidecadal variations in North Atlantic Sea surface temperatures (SST) are known as the Atlantic Multidecadal Oscillation (AMO), a mode of observed climate variability alternating warm and cool phases over large parts of Northern Hemisphere (Knight et al., 2006). This rapid warming in SST – warm phase of AMO – over North Atlantic Ocean followed a prolonged positive phase of the North Atlantic Oscillation (NAO+) and can be inferred to a delayed response to this long NAO+ sequence (Robson et al., 2012a). Along with the late impact of NAO positive phase on the rapid ocean warming, multiple other drivers of North Atlantic Multidecadal variability have been identified (Terray, 2012). Aerosol emissions and periods of volcanic activity are particularly pointed out to be one of the main causes of long-term changes in sea surface temperature and solar radiation (Booth et al., 2012; Evan et al., 2009). Thus, anthropogenic factors are very likely responsible for long term trends in Atlantic Ocean warming (Mann & Emanuel, 2006).

As a result of this warming tendency, an abrupt shift in meteorological regimes is observed over Western Europe at the end of 1980s (Keevallik, 2011; de Laat & Crok, 2013). The causes of the delay between North Atlantic Ocean SST warming and this abrupt shift over Western Europe are still poorly documented, as well as the source of such a rapid and strong warming. Several consequences to the 1980s climate regime shift on aquatic and terrestrial ecosystems have been still highlighted (Kangur et al., 2020). As such pattern of rapid surface air temperature increase is likely to replicate in the context of climate change (Desbruyères et al., 2021; Robson et al., 2012b), the issue of its possible impacts on biophysical ecosystems and more especially on water balance elements is of primary concern.

In France – as in the rest of Western Europe –, mean temperature shows higher increase than over the entire planet during the period 1901–2000 (Moisselin et al., 2002). In the latter study, a strong warming signal is detected, driven by a significant increase in both minimum and maximum temperature on every homogenized series used. Since 1980s, the warming trend intensifies strongly, consistent with climate simulations including the anthropogenic forcing (Terray & Boé, 2013). In Brulebois et al. (2015), this abrupt shift is detected in both maximum and minimum surface air temperature recorded by Météo-France weather stations, between 1969–1987 and 1988–2009 periods. Along with this abrupt warming, the latest study reveals a significant decrease in observed runoff from January to July, with the most important variation occurring in June. Liquid precipitation shows significant decrease during this period, but to a lesser extent than for runoff, and in August and September, runoff and liquid precipitation have opposite trends. Variations in runoff response suggest a “soil memory effect”: negative soil moisture anomalies in spring lead to drier soils in summer, inducing lower runoff at the end of summer (Boé & Habets, 2014). Here, evapotranspiration is clearly identified as the primary cause in hydrological cycle modification, but the process remains unclear. In Southern France, warming trend is also identified as one of the main drivers of the evolution of surface water resources (Labrousse et al., 2020).

Of all the variables related to the water cycle, during the vegetative period, evapotranspiration plays a significant role. The process of soil evaporation and plant transpiration is complex to understand because of various interactions between soil, biosphere and atmosphere. To study

the effect of temperature rise on the evolution of vegetation water demand, several biophysical indices linked to evapotranspiration can be computed. First, potential evapotranspiration (PET) is defined by the Food and Agriculture Organisation (FAO) as “the rate of evapotranspiration from an extensive surface of 8 to 15 cm tall, green grass cover of uniform height, actively growing, completely shading the ground and not short of water” (Doorenbos & Pruitt, 1977). This variable is considered as a climatic measurement, introduced to study the evaporative demand of the atmosphere only affected by climatic parameters (Allen et al., 1998). Vegetation cover shows various structural and functional configurations that strongly modulate the amount of water required to fully compensate the evapotranspiration loss. Maximum evapotranspiration (MET) is defined as the evapotranspiration from a specific vegetation, under optimal water resources and environmental conditions (Allen et al., 1998). For cultivated crops in fields, the agro-nomic management and environmental conditions can differ from standard conditions, leading to an actual evapotranspiration (AET) that may vary from MET. AET is computed by using a water stress coefficient Ks in addition to the crop coefficient Kc (Allen et al., 1998), allowing to take into account stress induced by environmental conditions.

1987/1988 shift offers a strong opportunity to study how abrupt warming and solar radiation increase affect amplitude as well as spatial and temporal main climatic components of the local water cycle. This groundbreaking research work focuses on climatic water balance elements which are key points for all natural terrestrial ecosystems and for crops. What is the amplitude of PET changes after 1987/1988 shift? How does soil moisture follow to this change? What are the spatial and seasonal patterns of changes in climatic water balance components?

Local water cycle is the result of a subtle dynamic balance between incoming precipitation and outgoing evaporation, runoff and percolation. Local day-to-day variability of the two main water cycle components – precipitation and evapotranspiration – is jointly climate, soil and land-use/land-cover dependent. Due to the forcing of climate warming, the assessment of its evolution is needed. This is of primary importance to quantify impacts, design adaptation strategies and implement actions. However, there is an ongoing question on how precipitation and/or evapotranspiration did or did not simultaneously change around the abrupt surface air temperature shift detected over France. The large precipitation interannual variability with more or less pronounced geographical pattern hinders the ability to assess how and where the local balance is affected.

To this aim we use an up-to-date daily re-analysis surface climate data covering the entire French territory with an 8km-resolution. The use of this gridded dataset allows to avoid spatial and temporal inhomogeneities in observational data. Starting in the end of the 1950s, the dataset offers two periods of similar and relevant time length both sides of 1987/1988. In addition to climate data, it includes actual evapotranspiration, offering the opportunity to study green water (Falkenmark & Rockström, 2006), which is a key component of water resources availability reflecting the trade-offs among various usages (crops, human and natural terrestrial ecosystems).

## 2. Material and methods

### 2.1. Study area and data

The initial dataset used for this study comes from the Safran-Isba-Modcou (SIM) model developed by Meteo France (Habets et al. 2008; Soubeyroux et al., 2008). This climate reanalysis chain combines the Safran meteorological analysis system (Durand et al., 2009), the Isba land surface model (Noilhan & Planton, 1989) and the Modcou hydrogeological model (Ledoux et al., 1989). This gridded daily dataset at an 8 km spatial resolution provides a homogeneous climate from 1959 onwards, that is relevant for regional climate change studies. In this work, the climatic variables linked to water cycle are extracted from the SIM chain on each grid point and analysed using robust statistical tests to
assess the impact of the abrupt warming on local water cycle.

The analysis system of the atmospheric forcing, Safran, is based on a robust vertical interpolation method for the main meteorological parameters (Durand et al., 1993). The French territory is divided in more than 600 climatically homogeneous zones, taking explicitly into account the elevation parameter. The model relies on both the observation network and the analysis of the atmospheric models (Arpège or CEPMMT), and solar radiation terms (visible and infrared) are estimated using a radiative transfer model. A total of 8 meteorological parameters are analysed at an hourly time scale (10m wind speed, surface air temperature, 2m relative humidity, cloudiness, visible and infrared solar radiation, solid and liquid precipitation) on more than 9000 grid points (Soubeyroux et al., 2008). This allows to have a continuous and uniform spatial information covering the entire French territory, in real time and in delayed time. A validation of the Safran module confirmed the relevance of the meteorological analysis, especially for the precipitation and the air temperature fields showing good correlations with respectively daily and hourly observations on several meteorological stations (means of the correlation coefficient for all the meteorological stations of 0.97 for temperature and 0.88 for precipitation) (Quintana-Seguí et al., 2008; Vidal et al., 2010).

Isba is a land surface model developed to be included in mesoscale and large-scale meteorological models (Noilhan & Planton, 1989). It has been designed for the best trade-off possible between accurate description of the main physical processes and restricting the number of input parameters. The latter are divided into two types: the primary parameters, i.e. dominant types of soil and vegetation within each grid cell, and the secondary parameters, i.e. thermal and hydraulic properties of the soil, and morphological and physiological properties of the vegetation. The description of the vegetation is as simple as possible: the transpiration is controlled by a surface resistance, the plants intercept precipitation and dew which evaporate at the potential rate, and the magnitude of the soil heat flux is modulated by the fraction of surface covered by vegetation. Soil heat and water transfers depend on soil texture and water content. The water changes are computed for an upper thin layer and a deeper one using a method derived from Deardorff's (1977) force restore method, including gravity effects and a calibration of the coefficients upon the types and wetness of soils. Isba model has been improved with time, especially concerning the evaporation from bare soil, the inclusion of gravitational drainage, a continuous formulation of soil transfer coefficients for heat and moisture, and a modification of the surface drag coefficients (Noilhan & Mahfouf, 1996). In its current version integrated into the SIM chain, the land surface model Isba consists of 3 layers (superficial, root and deep layers) (Boone et al., 1999), and includes surface runoff and drainage schemes (Habets et al., 1999). When compared to case studies observational data, results from the 3-layer version of Isba gives general improvement in modeling results, and values for key parameters that relate evapotranspiration to soil moisture are more consistent (Boone et al., 1999). Soil and vegetation parameters come from the Ecoclimap database at a resolution of 1-km over France (Masson et al., 2003). In the framework of the SIM chain, Isba computes 8-km resolution surface energy balances while sub-mesh surface runoff and drainage are computed using Topmodel (Beven & Kirkby, 1979). Potential EvapoTranspiration (PET) and Actual EvapoTranspiration (AET), analysed in this work, are computed by the Isba model using the Penman-Monteith equation as defined by the FAO (Allen et al., 1998). PET is thus influenced by several climatological variables, such as solar radiation, wind speed, vapor pressure and mostly air temperature (see the detailed equations in Noilhan & Planton, 1989; Noilhan & Mahfouf, 1996). Isba is then driven by Safran incoming solar radiation, liquid and solid precipitation, atmospheric pressure, surface air temperature and humidity, and wind speed at a reference level.

Hydrological transfers are simulated by the hydrogeological model Modcou, using surface runoff and infiltration data from Isba (Ledoux et al., 1989). In the SIM chain, runoff is computed with a 3-hour time step, and the evolution of groundwater tables level at a daily time step.

The results of this part of the SIM chain are not used in this study.

A validation of the whole SIM chain shows that the model is quite robust both in space and time, and gives a good estimation of the water fluxes (Habets et al., 2008). The comparison between the observed and simulated runoff, groundwater tables level and snow depth over France on a 10-year period, depicts an error of less than 4% at the yearly scale. Note that the precipitation is interpolated hourly using daily rain gauges (based on the evolution of relative air humidity) and considered as snowfall if the air temperature is below 0.5°C. The same error is identified for the evaporation fluxes, through the comparison of observed and simulated runoff associated with some hypotheses. SIM system is able to represent well the main local climate components driving water balance and runoff dynamic.

The SIM chain dataset starts from the 1st of august 1958 and is updated periodically until present time. In this work, 1959–2019 period is selected. This offers a 60-year period, allowing us to distinguish two climates based on 1987/1988 shift in surface air temperature warming. As a first approximation, the different variables of interest for the two ~30-year climate periods before and after the shift can be considered as independently stationary.

## 2.2. Agro-climatic indices

Three climate variables among all SIM outputs – maximum temperature (Tmax), liquid precipitation (PRELIQ) and PET – are considered here, along with actual evapotranspiration (AET) linking climate, water and vegetation characteristics. Surface solar radiation (SSR) is also examined in the supplementary materials. These daily variables are aggregated to monthly, seasonal and yearly scale and are used to compute several types of indices providing information on how climate warming affects regime and cycle of the local water cycle components.

Along with PET and AET, two indices depicting the evolution of the local water cycle are computed. The difference between PET and AET informs on the hydric constraint on vegetation. When PET – AET is close to 0, evapotranspiration under actual environmental and climatic conditions is getting close to PET under optimal soil water content. The vegetation is able to respond to climatic water demand, and does not suffer from water shortage. On the contrary, the increase of PET – AET suggests a hydric constraint increase. The difficulty to reach the amount of PET reveals a lack of water in soil reservoirs which impacts vegetation growth and may lead to severe consequences up to its death. The second index computed is the ratio AET/PET, which depicts the efficiency of the evapotranspiration process. The more this ratio is close to 1, the more the water status of the soil is good, and the plant is able to respond to the climatic evaporative demand. The study of the evolution of those two indices allows to assess the impact of climate warming on the evolution of local water cycle and vegetation cover water constraint.

## 2.3. Statistical analysis

Climate change over France can be apprehended in many ways. The assessment of a stationarity break in surface air temperature in 1987/1988 is particularly interesting, as the non-stationarity of climate warming can have unexpected and severe impacts on ecosystems. Trying to estimate how climatic components of local water cycle have changed around the 1987/1988 abrupt warming by using change point detection and mean comparison techniques occurs to be necessary.

The five daily series of the variables of interest extracted from the SIM chain on each grid point are aggregated to monthly, seasonal (winter: December-January-February (DJF); spring: March-April-May (MAM); summer: June-July-August (JJA); autumn: September-October-November (SON)) and yearly time scale. Robust statistical tests such as change point detection and distribution comparison techniques are used to assess the evolution in precipitation, PET, AET and SSR due to temperature warming.

A Bayesian method allowing both the detection of change points – i.
e. shifts (Barry & Hartigan, 1993) – and the computation of the posterior mean (Erdman & Emerson, 2008) is applied to the aggregated climate time series variables for each grid point. This method is relevant for climate data series (Ruggieri, 2013) and has been successfully applied to weather station records of temperature and liquid precipitation (Brulebois et al., 2015). The ability to jointly detect change points and compute posterior mean is useful to identify isolated extreme events, as for instance 1976 severe drought conditions, from long term climate change trends.

Finally, to address differences in mean values of each period before and after 1987/1988 temperature shift for liquid precipitation, PET, AET and SSR, a robust Bayesian test is used (Kruschke, 2013). This estimation is based on the Bayesian posterior probability distribution, evaluating whether the probability of a difference is high enough to matter. By using the values of the 95% Highest Density Interval (HDI) to define the confidence interval, the Bayesian method provides information about the magnitude and significance of the difference between two distributions.

Statistical analysis is conducted over the 9892 grid cells covering the entire French metropolitan territory using R software (R Core Team, 2021). Change points detection techniques and Bayesian differences test come respectively from the bcp and the BEST packages, described in Erdman & Emerson (2008) and Kruschke (2013) respectively.

Geographical evolution of the AET/PET index over the 1959–2019 period is also considered to study the impact of the 1987/1988 abrupt warming on water constraint over the entire French territory. Anomalies for each SIM grid points are computed using the following equation:

$$anomaly = \frac{mean_{1988-2019} - mean_{1959-1987}}{mean_{1959-1987}}$$

Using Bayesian differences test results, each SIM grid point on which the difference between pre- and post-shift periods is non-significant is covered-up on the map with a transparent mask.

# 3. Results

## 3.1. Detection of shift in temporal evolution of variables linked to the local water cycle

Temporal analysis of interannual evapotranspiration depicts a significant shift in yearly PET amount at the end of the 1980s, following the evolution of the yearly mean of maximum temperature (Fig. 1a). The mean of the PET amount on the 1988–2019 period is around +100mm higher than the mean of the PET amount on the 1959–1987 period. Consequently to the abrupt increase in maximum surface air temperature in 1987/1988 over France, the evaporative demand increases.

Surface solar radiation (SSR) also depicts a strong and abrupt increase at the end of the 1980s, coinciding with the abrupt warming (Supporting Information Fig. 1). As for temperatures, spring displays the most intense and widespread increase in surface solar radiation on the 1988–2019 period compared to the 1959–1987 period (Supporting Information Table 1). Solar radiation might also play a significant role in the evolution of potential evapotranspiration over the country on the recent decades. Geographical evolutions of maximum temperature and SSR at yearly and seasonal time scales around 1987/1988 coincide to varying degrees with changes in PET depending on the region studied (Supporting Information Figs. 2 and 3). At yearly time scale, temperature seems to play an important role in PET changes over extreme north and northeast of France and over the Massif Central. Over the Morvan mountain, the Parisian Basin and the Cévennes mountain and plateau, SSR might be the main driving factor behind changes in evaporative demand. Seasonal time step analyses also show varying degrees of impact of SSR and temperature on PET trends, depending on the region studied. Changes in PET therefore depend in various ways on the direct impacts of temperature and SSR, and on the indirect impact of SSR (via

its impacts on diurnal temperature). By affecting atmospheric water demand, this climatic shift around 1987/1988 leads to changes in actual evapotranspiration and soil water content.

AET also increases, but to a lesser extent than PET (+50mm in average between the two periods). As a result, the difference between PET and AET is increasing sharply at the end of the 1980s, from an average of 210mm before the shift up to an average of 260mm after the shift. Such an evolution reveals that the vegetation cover and the soil are not able to respond to the increase of the evaporative demand at the yearly scale, suggesting the larger deficit of soil water content over the second period. As liquid precipitation remains quite stable during the studied period, the results suggest that temperature increase is the main driver of the local evolution of climate water cycle elements. The lower soil water content expected on the 1988–2019 period consequently increases hydric constraints.

The evolution of the local water cycle elements shows contrasted seasonal changes. In spring, AET follows the evolution pattern of PET, both increasing in a similar way by approximately 30mm in average between the two periods surrounding the shift at the end of the 1980s (Fig. 1b). This indicates that between March and May, soil water content is still sufficient for the soil and vegetation cover to respond to the evaporative demand. From June to August, PET increases strongly after 1990, from 320mm in average during the first period up to 360mm during the second period (Fig. 1c). This evaporative demand is yet not satisfied with AET remaining quite stable between 1959 and 2019. As a result, PET – AET index is rising strongly after 1987/1988, from 110mm to 140mm. In summer over the 1988–2019 period, the increase of this index reveals that soil water content becomes insufficient to supply vegetation water requirements. In autumn (Fig. 1d) and in winter (Supporting Information Figure S4), no abrupt changes are detected in PET or AET at the end of the 1980s, water balance remains stable during the studied period. Interestingly, recent decade in summer and autumn depicts an important rise of the PET – AET index. Water constraint increases even more significantly since 2014/2015 in those seasons, autumn being also impacted by soil water deficit caused by the important warming of surface air temperature and increase of water demand.

Taken together, these results suggest that the major climatic shift in 1987/1988 has a strong influence on the evolution of the local water cycle variables. The increasing gap between PET and AET since the 1987/1988 suggests that water balance decreases on the second half of the studied period, along with the increase of water demand and soil drying. Consequently, water constraint is increasing rapidly at the end of the 1990s, both yearly scale and seasonal scale, especially in summer and more recently in autumn. Seasonal analysis also highlights the link between spring, summer and autumn evolution of local water cycle. Even if the soil water content is still sufficient in spring for the vegetation cover to follow to the evaporative demand, the latter experienced a rapid significant increase. Vegetation cover drew more heavily in soil water reservoirs, accelerating the summer increase in water constraint. On the last decade, summer water constraint increase extends over autumn with the continuous rise of water demand. This is consistent with recent events of prolonged drought and dry spells in September and October over France that had several impacts on natural ecosystems (Schuldt et al., 2020; Valo, 2022).

## 3.2. 1987/1988 shift: A turning point

Regarding the previous results, it is interesting to investigate the evolution of the local water cycle variables before and after the 1987/1988 climatic shift. Tables 1, 2, 3 and Supporting Information Tables 1 and 2 summarize the number of Safran grid points recording a significant shift in 1987/1988 (according to Bayesian differences test) and the anomalies between the means of 1959–1987 and 1988–2019 periods for each of the variables of interest linked to water cycle. Although there are no abrupt changes in time series, PET and AET means both increase significantly on the second period 1988–2019 compared to the first
<figure><img src="image_3.png" /></figure>

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<figure><img src="image_6.png" /><figcaption>**Fig. 1.** (a) Yearly and (b, c, d) seasonal evolution of variables linked to water cycle between 1959 and 2019. PET: Potential EvapoTranspiration; AET: Actual EvapoTranspiration. Gray bars: SIM chains values; gray solid curves: Bayesian posterior mean values. Solid colored curves: Bayesian posterior mean values; dashed colored curves: SIM chains values. Vertical dashed line indicates 1987.</figcaption></figure>
<caption>**Table 1**</caption>

Percentage of grid points recording a significant shift in 1987/1988, anomalies (ΔPET, mm) and 95% confidence interval of the difference in the mean (95% HDI) over the entire French metropolitan territory at yearly and seasonal scales, for potential evapotranspiration (PET, mm). * means significant difference according to Bayesian difference test.

<table><thead><tr><th rowspan="2">Period</th><th colspan="3">Potential evapotranspiration (PET)</th></tr><tr><th>% of grid points</th><th>ΔPET (mm)</th><th>95 % HDI</th></tr></thead><tbody><tr><td>year</td><td>98</td><td>+90.3*</td><td>89.8 – 90.9</td></tr><tr><td>DJF</td><td>53</td><td>+5.73*</td><td>5.64 – 5.81</td></tr><tr><td>MAM</td><td>98</td><td>+32.5*</td><td>32.4 – 32.7</td></tr><tr><td>JJA</td><td>95</td><td>+38.5*</td><td>38.2 – 38.8</td></tr><tr><td>SON</td><td>74</td><td>+13.6*</td><td>13.5 – 13.8</td></tr></tbody></table>

period 1959–1987, yearly and seasonally (Tables 1 and 2). At the yearly scale, 98% of Safran grid points register a shift in PET in 1987/1988, against only 76% of grid points for AET. The shift in PET impacts a larger proportion of the French territory than the shift in AET at the yearly scale, highlighting the increase in evaporative demand and in hydric constraints after 1987/1988.

PET and AET evolutions show a strong seasonality. Spring shows the highest percentage of cells recording a significant shift in 1987/1988 for PET and AET, with respectively 98% and 95% of Safran grid points (Tables 1 and 2). This season also records the strongest and most brutal increase in surface air temperature and SSR, directly impacting the evolution of PET and AET (Brulebois et al., 2015). Summer displays the larger gap between PET and AET anomalies, rising from respectively 39mm and only 8mm (Tables 1 and 2). However, liquid precipitation slightly increases yearly and in summer and autumn in the second period, but always to a lesser extent than PET (Table 3). Consequently, after the important rise of evaporative demand in spring, soil water content drops quickly. Evaporative demand still rises in summer, resulting in an aggravation of hydric constraints between June and August, extending into autumn. Note that the general evolution of liquid precipitation described in this table can hide important geographical disparities.

Detailed annual cycles for both periods pre- and post-shift confirm the evolution of the variables linked to the local water cycle (Fig. 2). Mean annual cycle of maximum temperatures is always higher after the shift of 1987/1988 than before, especially from March to August (Fig. 2a). In contrast, mean annual cycle of liquid precipitation remains stable between the two periods (Fig. 2b). Mean annual cycle of PET is higher for the second period from February to October, in line with the evolution of maximum temperature cycle (Fig. 2c). The largest differences are observed from March to August, following the most important increase in surface air temperature. From March to June, AET mean annual cycle is also higher for the second period, confirming the ability of soil water content to respond to the rise of evaporative demand at this time of the year (Fig. 2d). Conversely, for summer months, AET remains stable between the two periods and does not follow the evolution of PET, suggesting clearly the lack of soil water content to meet the evaporative demand. The evolution of mean annual cycle of both indices PET – AET and AET/PET confirms the modification of the local water cycle from June to September after the 1987/1988 shift (Fig. 2e and 2f). At this time of the year during the second period, PET – AET is more important than during the first period, while AET/PET is lower, indicating more severe water constraints.

As shown in previous section, a new rapid modification in water constraints is identified around 2014/2015 years, especially in summer and autumn. Although the significance of this step change cannot be tested (due to the short time period after 2014/2015 of only 5 years), further analysis has been made by separating 1988–2019 period in 2 sub-periods: 1988–2014 and 2015–2019 (Fig. 2g and 2h). This allows to further investigate this new rapid change in hydric constraints. The increase between the end of June and mid-October of the mean PET – AET

for the 2015–2019 period compared to the mean for the 1988–2014 period confirms the accentuation of water constraints for the last decade (Fig. 2g and 2h). At the same time of the year, AET/PET mean is lower for the 2015–2019 period than for the 1988–2014 period, the vegetation cover struggles to respond to the important evaporative demand, soil water content declines faster in the last decade. These results also corroborate the lengthening of the increase in water constraint in autumn on the last decade, PET – AET being higher over the 2015–2019 period than over the 1988–2014 period and AET/PET being lower over the 2015–2019 period than over the 1988–2014 period.

As for liquid precipitation, the evolution of the variables linked to the local water cycle outlined in these results may hide geographical disparities.

### 3.3. Geographical response of local water cycle variables to temperature shift

Anomalies between the means of the two periods pre- and post-shift compared to the mean of the first period depict spatial diversities in their evolution, for all the studied variables (Fig. 3 and Supporting Information Figs. 2 and 3). PET is rising everywhere after the climatic shift, confirming the strong influence of the abrupt warming and the important increase in SSR on the evolution of water demand (Supporting Information Fig. 2a, 3a and 3b). Northeast of France (Vosges, Jura, Morvan mountains), Rhône valley, west of the Côte d'Azur, center and southwest of France experience the strongest increase in evaporative demand after the 1987/1988 shift, the mean of the 1988–2019 period being around 30% above the mean of the 1959–1987 period. AET increases with the same order of magnitude in the Vosges, the Jura, the Morvan and the Massif Central (Supporting Information Fig. 2b). Liquid precipitation displays more contrasted evolutions, south of 44/45°N depicting a downward trend while north of this line showing a rising trend (Fig. 3a). Some territorial specifities yet appears: north of France and Finistère depict a significant rise of almost 20% whereas some grid points in the south-central and south-western regions of the territory show a significant liquid precipitation decrease of almost 20% for the second period.

Consequently, means of the AET/PET index present contrasted territorial evolutions (Fig. 3b). On the southwest of France, the Cévennes, in Picardie and in Basse-Normandie, and along the Mediterranean coast, evaporative demand PET experiences a more important increase than AET, leading to a decrease in AET/PET about 10% to 20% on the second period compared to the 1959–1987 period. On those regions, evaporative demand increases considerably after the climatic shift and soil dries up, intensifying vegetation water constraint.

Seasonal evolutions of PET and AET depict various geographical changes, affecting the evolution of the local water cycle in different ways throughout the seasons (Fig. 3 and Supporting Information Fig. S2). In spring, southwest of France and Côte d'Azur start to experience high water constraints (Fig. 3d), affected by an increase in evaporative demand that vegetation cannot keep up (Supporting Information Fig. S2c and d) and no increase in liquid precipitation (Fig. 3c). Water constraint extends on almost all French territory in summer, particularly affecting north of Lorraine, Bassin Parisien, Normandie, Poitou-Charentes, southwest of France, Rhône valley and Côte d'Azur (Fig. 3f). AET/PET drops from 15 to 30% on those areas, following the increase of evaporative demand and the lack of soil water due to temperature warming and SSR increase (Supporting Information Fig. S2e, 2f, 3e and 3f). In autumn, a large part of France is still under higher water constraints on the second period, southwest revealing the most important decrease in AET/PET (Fig. 3h).

Most of the regions experiencing an increase in water constraint after the 1987/1988 climatic shift are large agricultural production areas. This climatic shift, affecting the water cycle over the entire French territory in various geographical and temporal ways, may have important negative impacts on agriculture and other ecosystems.
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Daily mean cycles of variables linked to the water cycle. (a, b, c, d, e, f) Black solid curves: mean cycle of SIM values from 1959 to 1987; red solid curves: mean cycle of SIM values from 1988 to 2019. (g, h) Black solid curves: mean cycle of SIM values from 1959 to 1987; red solid curves: mean cycle of SIM values from 1988 to 2014; purple solid curves: mean cycle of SIM values from 2015 to 2019. The solid curve shows the ensemble mean; the color shading extends to ± 1 standard deviation to show interannual variability.</figcaption></figure>

## 4. Discussion

Surface air temperature rise is one of the main drivers of hydrological response across the world (Dobler et al., 2010; Gebre and Ludwig, 2015; Khanal et al., 2021). It affects ice and snow cover in mountainous regions (Huss et al., 2017; Marty et al., 2017), biodiversity (Malcolm et al., 2006) and runoff (Gu et al., 2020) all over the world. At global scale, runoff has been shown to increase with the increase of temperature (Labat et al., 2004). This assumption hides seasonal and geographical disparities, as for example in France where runoff is expected to decrease

significantly from January to July (Brulebois et al., 2015). The present study aims at depicting the evolution of climate components of the local water cycle (liquid precipitation, evapotranspiration) over metropolitan French territory in the context of an abrupt warming. The use of reanalyzed gridded climate data enables to clearly assess the impact of the climatic shift on the water cycle in a continuous and homogeneous way, both spatially and temporally. Compared to previous studies over the same area (e.g. Brulebois et al., 2015), this innovative work aims at disentangling the contributions of evapotranspiration and liquid precipitation to changes in climatic water balance induced by the rapid
<figure><img src="image_8.png" /><figcaption>**Fig. 3.** Map of the anomalies of period 1988–2019 compared to period 1959–1987 for (a, c, e, g) liquid precipitation (mm) and (b, d, f, h) ratio AET/PET. (a, b) Year, (c, d) Spring, (e, f) Summer, (g, h) Autumn. Each SIM grid point on which the difference between 1988–2019 and 1959–1987 periods is non-significant according to the Bayesian differences test is covered-up with a transparent mask.</figcaption></figure>
<table><caption>**Table 2**
Same as in Table 1 but for actual evapotranspiration (AET, mm).</caption><thead><tr><th rowspan="2">Period</th><th colspan="3">Actual evapotranspiration (AET)</th></tr><tr><th>% of grid points</th><th>ΔAET (mm)</th><th>95 % HDI</th></tr></thead><tbody><tr><td>year</td><td>76</td><td>+45.7*</td><td>45.4 – 46.1</td></tr><tr><td>DJF</td><td>73</td><td>+8.29*</td><td>8.19 – 8.40</td></tr><tr><td>MAM</td><td>95</td><td>+23.2*</td><td>23.0 – 23.3</td></tr><tr><td>JJA</td><td>19</td><td>+7.69*</td><td>7.43 – 7.96</td></tr><tr><td>SON</td><td>29</td><td>+5.35*</td><td>5.25 – 5.46</td></tr></tbody></table>

<table><caption>**Table 3**
Same as in Table 1 but for liquid precipitation (PRELIQ, mm).</caption><thead><tr><th rowspan="2">Period</th><th colspan="3">Liquid precipitation (PRELIQ)</th></tr><tr><th>% of grid points</th><th>ΔPRELIQ (mm)</th><th>95 % HDI</th></tr></thead><tbody><tr><td>year</td><td>9</td><td>+23.2*</td><td>22.0 – 24.3</td></tr><tr><td>DJF</td><td>17</td><td>-3.07*</td><td>-3.60 – -2.56</td></tr><tr><td>MAM</td><td>0</td><td>+0.28</td><td>-0.12 – 0.69</td></tr><tr><td>JJA</td><td>2</td><td>+3.53*</td><td>3.08 – 3.99</td></tr><tr><td>SON</td><td>3</td><td>+10.4*</td><td>9.90 – 10.9</td></tr></tbody></table>

warming.

Evapotranspiration is affected by multiple factors, on both global and regional scales (Duethmann & Blöschl, 2018; Yang et al., 2023). Among all the climatic variables impacting the evaporative demand, solar radiation and maximum temperature seem to play the major role (Vicente-Serrano et al., 2014). This is confirmed in this study, where temporal and geographical evolutions of maximum temperature and surface solar radiation reveal their significant role in driving the potential evapotranspiration shift over the country on the recent decades.

Evapotranspiration is a key factor of runoff and drought evolutions, contributing to soil drying, water constraints and hydric stress (Teuling et al., 2013; Rossi et al., 2016). Our results allow to better understand the role of evapotranspiration evolution, driven by the abrupt increase in surface solar radiation and surface air temperature in 1987/1988, on the water cycle modification at local scale in France. 1987/1988 climatic shift strongly affects local water cycle and soil water balance. From the 1990s, water demand rises rapidly, contributing to the drying of soils. Spring depicts the most widespread shift in evaporative demand, leading to a depletion of soil water reservoirs. Water demand increases further in summer up to the end of September, enhancing and extending the drying of soils. Consequently, hydric constraint on vegetation increases both yearly and seasonally after 1988, summer and more recently autumn being particularly impacted. The results of this study assess and confirm that evapotranspiration is the main driver of the changes in local water cycle and soil moisture deficit, which is in line with other findings at larger scales (Seneviratne, 2012).

Temporal evolution of the water cycle is associated with strong spatial disparities. Water constraint increases yearly over almost all the French territory except mountainous regions (Alps, Massif Central, Jura, Morvan, Vosges and Ardennes). Southwest and extreme northeast of France, Picardie, Rhône valley and Côte d'Azur depict the most important increase in water constraint. In those areas, the abrupt shift in surface solar radiation and surface air temperature impacts water availability for vegetation, and may impact crop cycle and, by extension, yields (Zhou et al., 2020).

The increase in water demand in spring is expected to worsen in the future, with temperature warming. This is consistent with other findings on more limited areas in France (Chaouche et al., 2010). This could lead to a faster draining of soil water reservoirs, leading to situations of hydric constraint on vegetation earlier in the year. Our results also point out the lengthening of the period of water constraint towards autumn, driven mainly by September. Along with the increase in water demand, longer dry spells during this month could play a major role in the drying of soils (Raymond & Ullmann, 2021). Taken together, this study reveals

a lengthening of the period where climatic hazard linked to water cycle could lead to hydric stress on ecosystems after the climatic shift of 1987/1988. This intensification and lengthening of periods of water constraint is mainly driven by the increase in surface solar radiation, surface air temperature and water demand, but also by a decrease in liquid precipitation in late summer and beginning of autumn.

In addition to being closely linked to changes in evaporative demand, the local water cycle may also be affected by changes in precipitation in certain regions, such as the Mediterranean area (Hertig et al., 2013). The latter depicts a significant decrease in cumulative precipitation that could lead to modifications of soil water availability. On other parts of the French territory, non-significant changes in yearly cumulative precipitation hides dry spells frequency and length modifications (Raymond & Ullmann, 2021). Such intra-seasonal events also impact local water cycle and water constraints.

This study also depicts a new rapid increase in water constraint over France around the 2014/2015 years. This may be due to combined effects of temperature and precipitation evolutions. In fact, the entire French territory experienced a new rapid increase in air temperature during this period. The stagnation, or slight decrease in some regions, of precipitation – probably linked to the natural climate variability – exacerbates the effects of climate warming. The causes and consequences of this new abrupt warming still need to be explored, as such event may happen again in the future.

## 5. Conclusion

This work demonstrates the impact of the 1987/1988 climatic shift on local water cycle main components over France. The rapid warming, along with abrupt modification of surface solar radiation, affects water demand, which rises sharply on the 1988–2019 period compared to the 1959–1987 period. Evapotranspiration is identified as one of the main drivers of climatic water balance changes over the country on the studied period. This leads to modifications in hydric constraints, with spatial disparities over the territory.

As for other parts of the world, modifications of climate components of the local water cycle after the important climatic shift over France could causes major damages on ecosystems (Higgins & Vellinga, 2004; Creed et al., 2015). Changes in water availability and water cycle induced by air temperature warming (and other drivers) may directly and indirectly impact natural terrestrial ecosystems and human activities, especially agriculture (Yang et al., 2021). The extension of the period with high hydric constraint from spring to the beginning of autumn pointed out in this work is covering the growing season of the majority of vegetation covers and crops. Thus, the abrupt increase in surface air temperature is expected to modify crop water stress events, such as winter bread wheat for example (Laurent et al., 2023). Along with the intensification of global warming in the future, these impacts are expected to be more severe by the end of the century, accelerating the need for adaptation of natural and anthropized ecosystems (Boé et al., 2009; Quevauviller, 2011).

### CRediT authorship contribution statement

**Léa Laurent:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Albin Ullmann:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. **Thierry Castel:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.
# Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Acknowledgments

This study is part of a CIFRE PhD project, funded by Groupama Rhône-Alpes Auvergne french insurance company. We would like to thank Claude Perrot, company's head of crop insurance, for facilitating our research work. We are also grateful to Xavier Aubouy, data scientist in the actuarial department of Groupama Mutuelle d'Assurance (the national subsidiary of the company) and studying French crop insurance portfolio, for his advice. Both have been involved in the whole process of research work and made constructive feedback on the results and their interpretation. We would also like to thank Agnès Tamburini (Météo France) for giving us access to SIM dataset.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jhydrol.2024.132211.

# Data availability

Data will be made available on request.

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