This paper describes the impact of the various changes
made to the Safran–Isba–Modcou (SIM) hydrometeorological system and
demonstrates that the new version of the model performs better than the
previous one by making comparisons with observations of daily river flows
and snow depths. SIM was developed and put into operational service at
Météo-France in the early 2000s. The SIM application is dedicated to
the monitoring of water resources and can therefore help in drought
monitoring or flood risk forecasting on French territory. This complex
system combines three models: SAFRAN, which analyses meteorological variables
close to the surface, the ISBA land surface model, which aims to calculate
surface fluxes at the interface with the atmosphere and ground variables,
and finally MODCOU, a hydrogeological model which calculates river flows and
changes in groundwater levels. The SIM model has been improved first by
reducing the infrared radiation bias of SAFRAN and then by using the more
advanced ISBA multi-layer surface diffusion scheme to have a more physical
representation of surface and ground processes. In addition, more accurate
and recent databases of vegetation, soil texture, and orography were used.
Finally, in mountainous areas, a sub-grid orography representation using
elevation bands was adopted, as was the possibility of adding a
reservoir to represent the effect of aquifers in mountainous areas. The
numerical simulations carried out with the SIM model covered the period from
1958 to 2018, thereby providing an extensive historical analysis of the
water resources over France.
Introduction
The coupling of hydrogeological models and land surface models (LSMs) aims
to represent the water cycle by considering as many physical processes as
possible. Thus, in LSMs, precipitation that reaches the ground contributes
to water storage, evaporation, surface runoff, and infiltration into the
soil. In addition to the water balance, LSMs simulate the surface energy
balance, which is closely related to the water balance in terms of
evaporation. In such a coupled system, surface runoff is collected by the
surface river system, while deep infiltration of the soil contributes to
aquifer recharge. Such systems have been used for decades to study water
resources and predict their evolution. LSMs, whether
coupled or not to hydrological models, have been the subject of numerous
studies that have improved them over time and have led to a better
description and understanding of the key processes governing exchanges at
the interface between the surface and the atmosphere and the surface and the
subsurface. These studies, which include international measurement campaigns
or more regional and even local initiatives, have made it possible to
evaluate surface models and even certain parameterizations by comparing
simulation results with different types of observations such as in situ
measurements, reanalyses, or satellite products. Simulations were carried out
offline, i.e. decoupled from the atmosphere, to limit the impact of
potential atmospheric biases in the surface schemes by constraining
atmospheric forcing through observations when possible. The first
international model intercomparison projects were the Project of
Intercomparison of Land surface Parametrization Schemes (PILPS), described
in Henderson-Sellers et al. (1996), which began with forcing from
atmospheric simulations (Pitman et al., 1993) and, in a second stage,
forcing from local observations (Chen et al., 1997). The successive phases
also focused on different issues, such as snow and frost parameterization
(Schlosser et al., 2000) and river flow assessment (Wood et al., 1998; Bowling
et al., 2003). In the spirit of PILPS, GSWP (Global Soil Wetness Project;
Dirmeyer, 2011) was initiated with global-scale simulations. The results of
this project are the first global offline multi-model simulations of LSMs.
Other more specific intercomparison projects have been carried out such as
SnowMIP (Etchevers et al., 2004) to study snow-related processes, ALMIP
(Boone et al., 2009), focusing on critical surface processes in West Africa
at the regional scale, and Rhône-AGGrégation (Boone et al., 2004) to
study coupling with hydrology. More recently, the PLUMBER project (Best et
al., 2015) has attempted to identify how LSMs behave in relation to certain
benchmarks and to define performance criteria that LSMs should be able to
achieve according to the information available in atmospheric forcing, thus
avoiding direct comparison with observations.
In many of these intercomparison studies, the surface models were validated
at the local scale and used average parameters that were known fairly
accurately. However, these models sometimes have strongly non-linear
components, such as the link between root zone moisture and transpiration
when the soil dries out (Sellers et al., 1997), so it is necessary to
develop sub-grid parameterizations to compensate for the lack of
representativeness of the mean parameters. Overgaard et al. (2006) conducted
a review of surface models based on energy balance that are used for hydrological
purposes. They stressed the need to validate the models at the local scale,
but also showed the interest of using remote sensing data to evaluate the
models. Indeed, the validation of LSMs using river flows alone does not
prove that surface fluxes, for example, are well simulated by the model and
that there is no error compensation. Furthermore, estimating surface fluxes
by remote sensing is not straightforward and requires certain assumptions
that are not always valid, and inversion models are used to translate the
remote sensing measurement into a model variable equivalent. However, using
surface fluxes to validate surface models is also subject to questioning
since the energy balance measured at the surface is generally not closed
(Foken, 2008), whereas it is an imposed constraint in surface models. International measurement networks such as FLUXNET (El Mayaar et al.,
2008; Napoly et al., 2017) are also widely used to evaluate surface models at
the point scale. Remote sensing provides a means of observing hydrological
state variables over large areas (Schmugge et al., 2002) and can be useful
in the case of LSMs coupled to hydrological models, in particular in order
to assess evaporation (Kalma et al., 2008; Long et al., 2014; Wang et al.,
2015) or soil moisture (Goward et al., 2000; Albergel et al., 2012; Fang et
al., 2016). It should be noted that these remote sensing data can be
assimilated to correct the model state variables at the initial time and during the hindcast (Albergel et al., 2017).
In addition, climate models have been evaluated at both global and regional
scales through hydrology. Indeed, the coupling between their land surface
model and hydrology allows a quantitative assessment to be made through
comparisons to variables such as river flow, groundwater levels, and snow
depth. This is the case of river flows simulated by hydrogeological models,
which can be compared with in situ measurements from gauging stations
(Habets et al., 2008; Decharme et al., 2013; Alkama et al., 2010; Barthel
and Banzhaf, 2015; Decharme et al., 2019). The coupling between LSMs and
hydrogeological models in water resource studies is an appropriate tool for
answering scientific questions such as the importance of climate change for
these resources (Vidal et al., 2010; Dayon et al., 2018; Bonnet et al.,
2017) or how human activity influences these resources (Martin et al., 2016;
Biancamaria et al., 2019). Recent initiatives to study the impact of
anthropization on water availability, such as those supported by the Global
Energy and Water Exchanges (GEWEX) project (Harding et al., 2015) wherein the
contribution of LSMs to modelling appears to be important, show that
irrigation needs to be considered in the models (Boone et al., 2019).
At Météo-France, the Safran–Isba–Modcou (SIM) system was first designed to study the
water cycle in major French basins such as the Rhône basin (Etchevers et
al., 2000), the Adour basin (Habets et al., 1998), the Garonne basin (Voirin
et al., 2001), and finally all of France (Habets et al., 2008). This system
has been shown to be very useful for many applications. For example, since
2003, the SIM system has been used operationally at Météo-France for
drought monitoring; this is done using hindcast simulations in addition to
near-real-time applications. These applications in France were based on an
LSM using the force–restore approach (Noilhan and Planton, 1989; Noilhan and
Mahfouf, 1996) for heat and water transfer in the soil. However, this method
has some limitations in terms of the realism of certain physical
parameterizations, which are detailed in Sect. 2.2. These limitations
concern the representation of snow, the interactions between snow and ground
freezing, which are not always well represented, the description of the
vertical profile of roots in the soil, and the composite approach to
represent vegetation, which mixes different types of vegetation into one with
aggregated characteristics. Although this method has proven, over the last
decades, to be suitable for addressing scientific issues related to water
resources, a more physical approach, based on the diffusion of heat and
moisture in the soil (Decharme et al., 2011), has been developed to consider
more sophisticated numerical schemes and improve system performance.
The objective of this paper is to show how the development of new
parameterizations and better atmospheric forcing prescriptions have improved
the performance of the system. The current study, based on numerical
simulations covering the period 1958–2018, shows how improvements in
atmospheric forcing, land surface model physics, and sub-grid orography and
hydrology improve the modelled river flow and snow depth of the SIM system.
It also aims to describe how the model results are affected by each change
separately and finally to demonstrate that the new model configuration
performs better than the previous one in terms of river flow extremes, as well as
when simulated snow depth or average river flow is compared to observed
data.
Section 2 describes the original SIM configuration and its recent updates.
Section 3 presents climate data and evaluation datasets, as well as the offline
experiments used to demonstrate the advantages of the new SIM system. In
Sect. 4 the results of the new system are presented, and finally they are
discussed in the last section.
SIM hydrometeorological modelOverview of the 2008 version of the model
The SIM hydrometeorological model (Habets et al., 2008) combines the three
models: SAFRAN, ISBA, and MODCOU. SAFRAN (Durand et al., 1993; Quintana Seguí
et al., 2008) performs a 6-hourly analysis of near-surface meteorological
variables such as temperature and relative humidity at 2 m, wind speed,
cloud cover, and a daily analysis of 24 h accumulated precipitation. The
analysis is carried out over geographical areas covering a few hundred
square kilometres (Le Moigne, 2002), and the analysed fields are
interpolated to hourly time steps. Direct and diffuse solar radiation and
infrared radiation are calculated from the analysis of cloud, temperature,
and humidity profiles using a radiative transfer model (Ritter and Geleyn,
1992). A spatial interpolation is then performed on a regular horizontal 8 km grid to provide the ISBA land surface model (Noilhan and Planton, 1989;
Noilhan and Mahfouf, 1996) with the necessary climate information. The grid
is composed of 9892 cells (Fig. 1a) covering France and is
extended beyond the borders to include the upstream part of the catchment
basins.
Height of the topography of the 9892 cells of the SIM grid (a)
and the 3878 cells of the mountain SIM grid (b). The cells of the
mountain grid correspond to the 1044 points having an altitude greater than
500 m and described vertically by several layers. Zones in yellow correspond
to the Seine and Rhône aquifers. The dotted line delimits the Alps.
The ISBA model uses SAFRAN analysis as input and calculates the surface
energy and water budgets over the vegetated areas. The water budget in ISBA
ensures that soil moisture results from the balance between water input from
incoming precipitation and water losses due to surface evaporation, surface
runoff, and infiltration into the soil. These last two components are fed
into the hydrogeological model MODCOU (Ledoux et al., 1989; Habets et al.,
1998) in order to calculate the temporal evolution of river flows for a
given set of gauging stations and groundwater heads where aquifers are
simulated, i.e. on the Seine and Rhône basins only (delimited by the
yellow zones in Fig. 1b). The original SIM system differs in
many respects from the version described in this document.
Improvements to the land surface model in SURFEX v8.0
In the original SIM system, heat and water transfers in the soil were based
on the force–restore method (Noilhan and Planton, 1989; Noilhan and Mahfouf,
1996; Decharme et al., 2011), which has been widely used in research for
decades and is still operationally used in the French global numerical
weather prediction model ARPEGE (Courtier and Geleyn, 1988) and the
mesoscale model AROME (Seity et al., 2011). In the force–restore method, the
soil is divided into two layers for temperature and three layers for
moisture (Boone et al., 1999). However, such a method has shown some
limitations in the representation of surface and soil processes such as the
interaction between snow and soil freezing (Luo et al., 2003) due to
vertical discretization and the inability to correctly represent the vertical
profile of roots in the soil (Braud et al., 2005) and thus the vertical
transfers of moisture and heat. The alternative to using the force–restore
method was developed by Boone et al. (1999) and revisited by Decharme et al. (2011), who proposed using diffusive equations to solve both heat and water
transfer equations in the soil based on Fourier and Darcy laws,
respectively. Such a method proposes a discretization of the soil into 14
layers, resulting in a total depth of 12 m, with a fine description of
the subsurface layers to capture the diurnal cycle. The vertical
discretization (bottom depth of each layer in metres) is as follows: 0.01,
0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and
12 m, as described in Decharme et al. (2013). Heat transfer is resolved over
the total depth, while moisture transfer is resolved only over the depth of
the roots, which depends on the type of vegetation and its geographical
location: a maximum of 1.5 m for type C3 crops and 2.5 m for forests in
France. In such a model, soil temperatures and soil moisture are calculated
at the same nodes, which is necessary to correctly represent soil freezing,
for example. Another notable improvement concerns snow modelling. The
original three-layer snow scheme developed by Boone and Etchevers (2001)
aimed to represent the physical processes in the snow realistically with a
simple model, and for this purpose some processes had been adapted from the
Crocus snow model (Vionnet et al., 2012) for snow avalanche forecasting. The
main new features recently developed and introduced into the ISBA snow model
are described in detail in Decharme et al. (2016) and concern (i) snow
stratification with an increase in the number of layers close to the surface
in contact with the air, but also with the ground to better represent the
diurnal cycle and heat transfer at the interface with the air and ground,
respectively, (ii) snow compaction due to changes in viscosity (Brun et al.,
1989) and wind-driven densification at the surface (Brun et al., 1997), and
(iii) snow absorption of solar radiation as a function of three-band spectral
albedo.
Then, the representation of vegetation in the model has also evolved from
the original version, wherein vegetation types within a grid cell were
aggregated with averaged surface parameters (Noilhan and Lacarrere, 1995),
whereas the new system uses 12 separate vegetation types, each with its own
set of parameters (Masson et al., 2003; Faroux et al., 2013). The
classification distinguishes three non-vegetated types (rocks, bare soil, and
permanent snow and ice) and nine vegetated types: temperate deciduous
forest, boreal conifers, tropical conifers, C3 crops, C4 crops, irrigated
crops, grasslands, tropical meadows, and peatlands, parks, and gardens.
Although this approach is more computationally time-intensive because the
model must be run for each vegetation type, the realism of ISBA simulations
is increased because the parameters better characterize the contrasting
surface properties. In addition, the explicit use of 12 vegetation types is
mandatory when using ISBA-A-gs, the simplified photosynthesis module of ISBA
(Calvet et al., 1998) aimed at representing a realistic photosynthesis of
the different biomes. In the new version, the drought avoidance or drought
tolerance response is adopted (Calvet et al., 2004).
Hydrological processes are obviously important in a system for calculating
the water budget of natural surfaces and simulating river flows. The old
parameterization of drainage, developed by Mahfouf and Noilhan (1996) for
the force–restore scheme, has been replaced by a method of diffusing water
into the soil. In ISBA, surface runoff occurs over saturated areas (Dunne
and Black, 1970). Habets et al. (1998) proposed sub-grid parameterization to
generate surface runoff over grids of several square kilometres before the
entire grid is saturated, in order to consider some regional heterogeneities
in infiltration arising from orographic variability or precipitation spatial
inhomogeneity. In this approach based on the sub-grid variability of topography used in the VIC model (Liang et al., 1994; Dümenil and Todini,
1992), the fraction of the saturated zone varies as a function of the water
content of the soil and a curvature term b that must be calibrated. In the
original system b is equal to 0.5, a very high value compared to other
studies at the watershed scale. Indeed, a more realistic value should be
around 0.2 (Lohmann et al., 1998; Ducharne et al., 1998). However, the
force–restore scheme is known to be too dry in terms of soil moisture
(Decharme et al., 2011, 2019), and a steep slope (therefore a fairly large
curvature term) in the grid mesh is required to generate sufficient runoff
in certain regions. The use of the diffusion scheme has removed this
constraint of a high b factor, and in the new SIM application, a value of 0.25
is now used on zones without aquifers for which it is set at 0.01 corresponding
to the absence of sub-grid runoff. Dümenil and Todini (1992) have
parameterized the fraction of saturated zone A as a function of soil moisture
Aw2=1-1-w2wsatb/(b+1), where
w2 is the volumetric water content of the soil in the rooting zone and
wsat its value at saturation. For a loamy zone
(wsat=0.45 m3 m-3) of wet soil (w2=0.4 m3 m-3), the presence of an aquifer (b=0.01) is characterized by
a small area of saturated fraction of about 2 %.
Use of more precise parameters for the land surface
In addition to the changes in model physics described above, the land cover
and topography databases have been updated to improve the realism of the
external parameters of the ISBA model. The hydrogeological database
representing the aquifer and the routing network was unchanged. In addition,
the soil texture database for France is unchanged. In the former SIM system,
the soil texture was based on a soil map provided by the Institut National
de Recherches Agronomiques (INRA – King et al., 1995) at a resolution of 1
km. In the new SIM system, texture is defined by the Harmonized World Soil
Database (HWSD – Nachtergaele et al., 2012), which is a soil map at 1 km
resolution that combines several datasets available worldwide. In
particular for France, the INRA soil map mentioned above has been integrated
into the HWSD dataset (used in other applications of SURFEX outside
France), so this change does not affect the SIM simulations.
The topography, derived from 30 arcsec global elevation data
(GTOPO30, http://eros.usgs.gov/{#}/Find_Data/Products_and_Data_Available/gtopo30_info, last access: August 2020), has been replaced by that of the
Shuttle Radar Topography Mission (SRTM90,
https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/, last access: October 2011)
at a 90 m resolution (Fig. 1a). Note that in practice the
impact of using SRTM90 is rather limited because the target grid resolution
for SIM applications over France is 8 km, which implies that small-scale
differences between the orography data are averaged at such a resolution
(thus the SIM topography is similar, whether GTOPO30 or SRTM90 is used).
The last modification of the input database is the vegetation map, which
provides the fraction of each ecosystem. The global 1 km resolution map
ECOCLIMAP1 (Masson et al., 2003) was originally used in the SIM application
for France. Subsequently, a new classification algorithm was developed over
Europe, the ECOCLIMAP2 land use map (Faroux et al., 2013), in order to use
more accurate and recent satellite information as input for a longer
period. Among the differences to note, ECOCLIMAP1 used, for example, AVHRR
satellite data for 1992–1993, whereas ECOCLIMAP2 uses SPOT/VEGETATION data
for 1999–2005. The impacts of modifying the vegetation fraction input to
the ISBA model are multiple and will not be described here in detail (for a
detailed comparison, see Faroux et al., 2013). ECOCLIMAP2 has definite
advantages, the effects of which are directly reflected in the ISBA model.
For example, ECOCLIMAP2 covers a longer time period than the previous
version and therefore allows a better representation of the variability of
surface parameters. Also, it distinguishes different types of crops that can
be modelled separately, and therefore more accurately, with ISBA. The
sensor onboard satellites have better accuracy and the uncertainty of the
measurement is reduced. The vegetation fraction in particular is improved
and with it the roughness length of the vegetation, which impacts the surface
wind by the obstacle effect on near-surface flows. The leaf area index is
also improved, and its increase leads to a better description of the
evaporative fraction, which is key for the energy partitioning in the model.
The more realistic surface albedo developed by Carrer et al. (2014) was also
used, as Decharme et al. (2013) showed that it improved results at the
global scale.
Evolution of downward infrared radiation
SAFRAN radiation has been corrected to compensate for a radiation deficit
already identified in several studies (Le Moigne et al., 2002; Carrer et
al., 2012; Decharme et al., 2013), although observations of this variable are
very rare. Radiation in SAFRAN is simulated (Ritter and Geleyn, 1992) from
an analysis of cloud cover based on analyses of temperature and humidity
profiles from the French global atmospheric model ARPEGE. Le Moigne (2002)
and Carrer et al. (2012) showed that SAFRAN's infrared radiation was weakly
biased, and Decharme et al. (2013) increased overall infrared radiation over
France by 5 % in their offline simulations. The bias is likely due to a
problem in the analysis and in the radiative transfer (RT) model. The cloud
cover analysis is computed using temperature and humidity profiles from a
large-scale atmospheric model that contains biases. Moreover, the model used
to solve the RT is an old model with a rather low vertical resolution and
is therefore probably suboptimal, but it was state of the art in the 1990s.
For example, Le Moigne (2002) showed that infrared and solar radiation were
too low at the Col de Porte site in the Alps and proposed a correction for
cloud cover and altitude which was successfully applied to the Rhône
basin in the Rhône-AGG intercomparison experiment (Boone et al., 2004).
In this study, only the infrared correction is considered and applied over
the whole French territory. The infrared correction, described in Appendix
A, was established by comparing the SAFRAN analysis and the infrared
measurements of two meteorological stations, Carpentras and Col de Porte,
which are reference stations for infrared measurements monitored by
Météo-France located in the south-east of France and in the
Alps, respectively. Carpentras is located in the plains, while Col de
Porte, an experimental measurement site of the Centre d'Etudes de la Neige,
is located in the French Alps at an altitude of 1340 m (Morin et al., 2012;
Lejeune et al., 2018). The correction is only applied below 1340 m, as a
positive bias is found at the Saint-Sorlin site (Quéno et al., 2020). Figure
2 shows the annual average over the 60-year period for initial infrared
radiation (panel a) and the amount of energy supplied when the correction
is applied (panel b).
Annual average of uncorrected (a) and corrected (b)
downward longwave infrared radiation from SAFRAN analysis.
Altitudinal sub-grid variability in mountainous areas
In SAFRAN, the analysis is performed on homogeneous zones of several hundred
square kilometres, and the vertical component is explicitly considered with
a 300 m slicing along the vertical. For each grid cell i, the analysed
variables Xa(i) are then interpolated on an 8 km horizontal grid,
considering the average altitude of each grid cell. The analysed variables
are then used as input to the ISBA surface model. At this resolution, the
9892 grids cover all of France and some border areas for hydrological
purposes. However, this resolution is still too coarse to accurately capture
the variability of certain variables, particularly in the mountains.
Lafaysse et al. (2011) demonstrated in the Durance basin that the use of
altitude bands was an efficient method to better describe the spatial
variability of the snow cover and its impacts on river flows at a numerical
cost much lower than increasing the horizontal resolution. A similar
approach was therefore defined for the entire French territory and can be
summarized as follows: for a given mesh i, the SAFRAN analysis is performed
every 300 m and Xai,k represents the k sets of analysed
variables corresponding to each of the k altitude bands. For each i, if the
vertical sub-grid variability is sufficiently large, a complementary set of
k′ elevation bands is defined for different elevations in order to represent
this variability. Vertical interpolation is then performed on the
atmospheric forcing at each k′ band. For each k′ band, ISBA simulates surface
runoff and soil infiltration, which are used to calculate the total surface
runoff and soil infiltration for grid point i. Of the 9892 grid points, 1044
are above 500 m and have a high variability in sub-grid topography. Using a
vertical discretization of 300 m at each grid point to represent topographic
variability was ideal but too costly. A solution based on the distribution
of elevations in each grid cell into five bands represented by the quintiles
q20, q40, q60, and q80 was adopted. For each of the 1044 grid points, the
vertical discretization varies spatially, and the irregular vertical mesh
ranges from 23 m in medium mountains to 986 m in high mountains. Figure 1
(panel b) shows the elevation of the 1044 grid points at which the
elevation band method is applied.
In addition, to compensate for the inability of the SIM system to simulate
low flows when aquifers are not explicitly considered, sub-grid drainage
parameterization was used in the original SIM system. This sub-grid drainage
is controlled by a parameter calibrated for both lowland and mountain areas,
but such a calibration does not work very well because the water used to
support low flows is taken from the rooting zone and not from the aquifer.
In the new system, this parameterization is removed, and a parameterization
has been added to mimic the behaviour of a deep reservoir to support low
flows and to limit peak flooding due to snowmelt (Lafaysse et al., 2011).
Retaining water due to snowmelt and releasing it during the dry season made
it possible to simulate peak flooding, but summer low flows are still
underestimated.
Design of experiments and datasetsOffline simulations
The SIM system is an offline application whereby the ISBA land surface model
is driven by climate data and there is no feedback from the surface to the
atmosphere. Different SIM configurations were designed to highlight the
improvements achieved, with each simulation being equilibrated using a
2-year spin-up.
The first configuration refers to the old SIM system (SIM_REF
below, as described in Sect. 2.1), i.e. before any changes described above. An
additional reference simulation (SIM_REF2 below) is based on
SIM_REF wherein sub-grid drainage is removed. The first
experiment (SIM_PHY hereafter) consists of modifying the
physics and input databases. SIM_PHY uses the diffusion
scheme with 14 layers in the soil, the improved snow scheme with 12 layers,
a tile approach based on 12 vegetation types, and a runoff parameterization
wherein the high constraint on the coefficient b (b=0.5 in the runoff
parameterization in SIM_REF) has been lowered to 0.25. Also,
in SIM_PHY, updated databases are used for a better
representation of soil texture, orography, and vegetation. The correction of
SAFRAN infrared radiation according to cloud cover is then introduced in the
SIM_FRC experiment (based on SIM_PHY). Then
SIM_TOP (based on SIM_FRC) uses the
representation of sub-grid orography in mountains, and finally
SIM_NEW (based on SIM_TOP) considers a
drainage reservoir in mountains. Table 1 summarizes the main characteristics
of the experiments.
Main characteristics and differences in experiments.
In the SIM system, climatic data are provided by the SAFRAN analysis. In
this study, SAFRAN covers a 60-year period, from 1 August 1958 to 31 July
2018. In SAFRAN, the guess of the analysis used is ERA-40 until 2002 and the
ECMWF operational analysis thereafter. In France, the density of the
observation network is very high because a network dedicated to climatology
completes the less-dense synoptic network. There are therefore practically
no regions with poor coverage, especially for precipitation, which is
essential for hydrology, and the coarse resolution of the analysis first
guess is not an issue. The analysed variables are then interpolated every
hour on the SIM grid at a resolution of 8 km, and this complete set of
near-surface variables is then used to conduct offline simulations. The
averages of the fields analysed or reconstructed by SAFRAN over the entire
period over France, used as input data for offline experiments, are shown
in Fig. 3, while the annual averages of these quantities are shown in
Fig. 4.
Maps of the annual average of the SAFRAN analysis for the period
1958–2018 of (a) air temperature at 2 m, (b) specific air humidity at 2 m, (c) wind speed at 10 m, (d) total annual precipitation, (e)
direct solar radiation, and (f) diffuse solar radiation.
Annual average of the SAFRAN analysis of (a) air temperature at 2 m, (b) specific air humidity at 2 m, (c) wind speed at 10 m,
(d) direct solar radiation, (e) diffuse solar radiation, (f) infrared
radiation, and (g) total precipitation rate.
Datasets and validation tools
Various datasets were used to evaluate the performance of the SIM model
throughout the validation process to ensure that an improvement in the input
climate data or physics simultaneously improved the surface or ground
variables and river flows.
The Land Surface Analysis Satellite Applications Facility (LSAF)
disseminates products based on data from the Meteosat second-generation
geostationary satellites, in particular downwelling infrared radiation
(LSA SAF; Trigo et al., 2011; http://lsa-saf.eumetsat.int, last access: September 2018). LSAF data
covering the period from 1 August 2010 to 31 July 2015 are used here to
assess the quality of SAFRAN's infrared radiation.
In addition to the infrared radiation data from Carpentras and Col de Porte
already mentioned in Sect. 2.4, in situ data from the French GLACIOCLIM
observation service (https://glacioclim.osug.fr, last access: January 2018) stations at Saint-Sorlin
(2620 m) and Argentière (1900 m) were also used to assess SAFRAN's
infrared radiation at altitude. The period covered runs from December 2005
to December 2015.
River flow observations are taken from the French national database Banque
Hydro (http://hydro.eaufrance.fr/, last access: October 2019) from 1958 to 2018. Daily and monthly flow
data from 470 selected gauging stations were used to evaluate river flows
simulated by the MODCOU hydrogeological model. Only gauging stations with
observations for at least half of the days over the total period were kept.
The Nash–Sutcliffe efficiency (NSE; Nash and Sutcliffe, 1970) was used to
evaluate the performance of the model, and the flow ratio
between SIM simulations and observations was calculated to assess the bias
of the system. The complementary cumulative distribution function (CCDF,
below) of the NSE, which calculates the probability that the NSE is greater
than a threshold averaged over the number of gauging stations in France, is
also used as a measure to evaluate the NSE.
Observed snow depth is another independent dataset (i.e. not assimilated in
the reanalysis process) used to evaluate the system. Measurements from 185
stations in the Alps, the Pyrenees, and Corsica at altitudes between 600 and
3000 m above sea level are used. They include 26 ultrasonic sensors (located
mainly in high-altitude areas: the Nivose network) and 161 stations operated
by Météo-France partners, mainly at ski resorts, which are manual
measurements using snow sticks. The daily total snow depth is used to
calculate the bias and root mean square errors for the SIM_REF and SIM_NEW simulations over the period 1984–2016 between
October and June. Note that most stations do not provide complete data for
the entire period. The length of the measurement series and the number of
seasons that stations are open are sources of variability in the scores.
However, since very few series are complete, the choice was made to evaluate
the performance of the model by considering as many stations as possible
rather than trying to homogenize the length of the series.
The SAFRAN analysis is performed on homogeneous zones in terms of horizontal
gradients, and the analysed fields are spatially interpolated to a regular 8 km grid taking altitude into account. Thus, the comparison of infrared
radiation (IR) is made between the SAFRAN analysis interpolated at 8 km and
the local observation. The horizontal variability of IR radiation at 8 km is
small enough to allow a direct comparison with in situ observations.
Moreover, the ISBA model outputs of ground temperature and snow depth
profiles are relatively sparse, and only a direct comparison between the
model outputs and the observations is possible. Finally, with respect to
river flows, the MODCOU model grid varies in the range of 8 to 1 km near
the riverbed, and the comparison between the model output and the observed
flow is made by considering the flow at the river outlet and the
corresponding model grid point in the 1 km hydrological network grid. This
way of locally validating models by comparing the observation to the
corresponding model grid point is not new and has been used in many studies
in France and elsewhere (Habets et al., 2008; Decharme et al., 2013;
Lafaysse et al., 2011; Vergnes et al., 2014).
ResultsDescription of climate data
Figure 4 shows the annual averages of atmospheric forcing from 1958 to 2018.
The 2 m air temperature (Fig. 4a) and specific humidity (Fig. 4b) show
natural interannual variability and a tendency to increase over time by
about 1.4 K and 0.6 g kg-1 (linear regression of the time series of
annual means), respectively. The abrupt change in temperature in 1987–1988,
referred to by Brulebois et al. (2015), is not so obvious to explain. The
10 m wind speed (Fig. 4c) at the beginning and end of the analysis
period is of the same order, with an amplitude of 2.8 m s-1 but with a
significant decrease of 0.5 m s-1 between 1983 and 1995, followed by a
steady increase until 2018. The interannual variability is greater for
precipitation than for the other variables, but shows no trend on average.
Incident radiation also shows a remarkable change around 1988 with about
+15 W m-2 for direct solar radiation and +5 W m-2 for infrared
radiation between the periods before and after 1988. At the same time,
diffuse solar radiation decreases by 10 W m-2 from 1988 onwards. On
average, the total amount of solar and infrared energy received by the
surface increases by about 10 W m-2. This behaviour is consistent with
the discussion of Brulebois et al. (2015) and the analysis of Boé (2016)
and may be caused by several factors. It can be argued that a decrease in
aerosols and the increase in greenhouse gases in the atmosphere have
significantly increased incident radiation as shown by climate studies (Wild,
2012). In addition to this physical reason, more technical reasons such as
changes over time in the density of assimilated observations or changes in
the ECMWF operational system may have affected the ERA-40 reanalysis.
Although the model used in the reanalysis is a frozen version, the
reanalysis system includes input observations whose density varies
significantly over time (Uppala et al., 2005). In addition, during the
production of the ERA-40 reanalysis, the ECMWF operational data assimilation
system has evolved considerably and switched to a 4D-Var variational method
(1997) compared to the 3D-Var method previously used. As a consequence, the
calculation of the error covariances of the observations and the guess were
revised in the 4D-Var, but also the 3D-Var, and directly impacted the ERA-40
reanalysis. The comparison in terms of bias and root mean square error
(RMSE) at the four weather stations measuring infrared radiation is
summarized in Table 2. With the exception of the Carpentras station, where
the LSAF IR radiation is almost unbiased and the error is the smallest
compared to SAFRAN, the scores are better for the high-altitude stations
with SAFRAN when the correction is applied. Due to their high altitude, no
correction was applied at Argentière or Saint-Sorlin. At the
Argentière station, the bias and root mean square error are lower with
SAFRAN than with LSAF. At Saint-Sorlin, the bias is higher with SAFRAN but
the RMSE is of the same order of magnitude as LSAF.
Annual mean bias and RMSE of LSAF, SAFRAN, and corrected SAFRAN
infrared radiations at Carpentras (95 m), Col de Porte (1340 m),
Argentière (1900 m), and Saint-Sorlin (2620 m).
LSAF IR radiation SAFRAN IR radiation SAFRAN IR radiation W m-2without correction W m-2with correction W m-2BiasRMSEBiasRMSEBiasRMSECarpentras1.310.2-8.421.53.120.3Col de Porte-10.320.2-14.420.4-9.317.5Argentière-18.832.6-3.618.5-3.618.5Saint-Sorlin0.127.810.525.310.525.3Impact of new model configurations
The first comparison concerns the SIM_REF2 experiment in which
river flow is slightly underestimated compared to SIM_REF
(not shown), and the underestimation is corrected by calibrating the sub-grid
drainage term. In the SIM_REF simulation, the ratio of
simulated to observed flow is centred around 1, and the daily efficiency
range (NSE below) characterized by its CCDF is larger for all stations.
SIM_PHY does not consider any parameterization of the sub-grid
drainage and is therefore closer to the SIM_REF2 simulation
in terms of sub-grid hydrology. Figure 5 shows the comparison of each SIM
simulation with the observed river flow for the 470 gauging stations.
SIM_PHY tends to overestimate flows, as indicated by the
average ratio between simulated and observed flows. SIM_PHY
shows slightly poorer results for NSE, ranging from 0.5 to 0 (about 40 %
of the stations), but in this case both models do not perform very well.
Most of the stations affected by deterioration in the lower part of the NSE
CCDF have an NSE below 0.55 and represent about 57 % of the total number
of stations. Part of the explanation comes from the calibration of the
sub-grid drainage in SIM_REF, which is not done in
SIM_PHY. However, the NSE CCDF shows that SIM_PHY outperforms SIM_REF (and also SIM_REF2,
not shown) for NSEs greater than 0.56, which corresponds to half the
total number of stations and highlights the added value of physics
associated with a better description of vegetation types and the use of
other more accurate databases. Figure 5 shows how the scores are improved
for experiments with corrected infrared radiation (SIM_FRC),
sub-grid orography (SIM_TOP), and hydrology (SIM_NEW) in terms of both the NSE CCDF and flow ratio. The bias in river flow is
significantly reduced when infrared radiation is increased due to higher
total evaporation, resulting in less water available in rivers. However, a
positive bias remains, which is expected, since SIM simulates natural runoff
and river flow, i.e. without abstraction or diversion, while some basins are
influenced by human activity. In some basins, the human footprint on the
landscape is characterized by an increase in urban and agricultural areas
and the presence of dams. In the model, urban areas have been replaced by
rocks, a type of natural surface, to represent the presence of urban areas
that enhance surface runoff. However, the model does not explicitly
represent irrigation or the impact of the presence of dams on river flow.
The basins impacted by human activity are of great interest for the
evaluation as they allow for quantifying errors in the system and proposing
improvements. The SIM_FRC and SIM_TOP NSE
scores are very close and better than SIM_PHY for all
stations and SIM_REF for about 75 % of the stations with
NSE greater than 0.4. Finally, SIM_NEW and SIM_TOP tend to overestimate river flow, but their NSEs are significantly better
than SIM_REF for all NSE ranges.
Comparison of the NSE CCDF (a) and the simulated to
observed flow ratio (b) for SIM_REF (dashed blue
line), SIM_PHY (solid red line), SIM_FRC
(solid cyan line), SIM_TOP (solid green line), and
SIM_NEW (solid orange line).
Figure 6 presents a map of the differences in mean annual NSEs (for stations
with positive NSEs) between the different configurations. Over the entire
reanalysis period, in Fig. 6a, it is first confirmed that
SIM_PHY alone does not improve the flow simulations
everywhere in France but only for the gauging stations that were already
reasonably represented (with NSEs above 0.56). Second, the new IR forcing
improves the scores almost everywhere except in two isolated stations in the
Seine basin (Fig. 6b). As expected, SIM_TOP only has an
impact on mountains, especially over the Alps (Fig. 6c). Finally, the
comparison between SIM_NEW and SIM_TOP
highlights the advantages of using an underground mountain reservoir for
snow (Fig. 6d). It should be noted that the number of stations is reduced in
Fig. 6c and d because these experiments do not encompass the entire
territory. In Fig. 7, SIM_NEW is compared with
SIM_REF so that it reveals the advantages of all the changes.
The SIM_NEW NSE map indicates that the model explains a large
part of the flow variance at most stations (brown to green colours), but
some stations still have average (red) to low (blue) NSE values. In
particular, the gauging stations in northern France are not well
simulated, in addition to the Alpine region, which is known to have
significant anthropogenic influences on the flow regime.
Maps of the difference in mean NSE for NSE > 0 between
the following simulations: (a) SIM_PHY and SIM_REF, (b)
SIM_FRC and SIM-PHY, (c) SIM_TOP and
SIM_FRC, (d) SIM_NEW and SIM_TOP.
Map of the difference in mean NSE for NSE > 0 between
SIM_NEW and SIM_REF (a) and the
SIM_NEW NSE map (b).
Seasonal river flows
To complement the previous results and to demonstrate the successive
improvements in simulated flows, seasonal scores were displayed over the
60-year simulation period using Taylor plots, which have been recognized to
be a useful tool for graphically summarizing how a set of simulations
compares to observations (Taylor, 2001). A set of experiments can be
analysed in terms of correlation, centred root mean square difference (RMSD),
and the magnitude of their variation represented by the normalized standard
deviation. These scores are calculated from all daily observations and
simulations. Seasonal Taylor plots (DJF, MAM, JJA, and SON for winter, spring,
summer, and fall, respectively) of the different experiments are presented in
Fig. 8. As a result, regardless of the season, the SIM_NEW
simulation has the highest correlation and the lowest RMSD, except perhaps
for JJA, the season with the highest normalized standard deviation. For DJF,
the scores are very good with relatively little spread, while for JJA, the
scores are still tightly clustered but the RMSD is higher. MAM and SON
confirm the interest of using an underground reservoir to conserve water in
the mountains before releasing it in the spring.
Taylor diagrams of seasonal river flows for the different
experiments over the period 1958–2018.
Extreme river flows
The previous results showed how SIM_NEW behaved on average
over the 60-year simulation period. In order to assess the ability of the
new system to correctly simulate extreme river flows and thus to distinguish
between high and low flow periods, the deciles of daily river flows were
calculated, and special attention was paid to decile Q10 corresponding to low
flow states and decile Q90, the threshold above which a flow is considered
to be decadal (here defined as a flood).
As shown in Fig. 9, Q10 and Q90 first indicate that in very dry periods
(flows less than or equal to Q10), all the simulations except
SIM_NEW underestimate the amplitude of the variations.
Furthermore, for the SIM_NEW experiment, the correlation, the
RMSD, and the normalized standard deviation are the best. The variability in
terms of normalized standard deviation is reversed when considering floods
(Q90) versus dry periods (Q10). Again, SIM_NEW has the
smallest RMSD value and all simulation correlations are greater than 0.99.
Figure 10 compares the observed and simulated monthly flows of the Garonne
River at Lamagistère with SIM_NEW and confirms the
model's ability to simulate low flows during the summer seasons fairly
accurately and its tendency to overestimate flood peaks.
Taylor diagram of Q10 and Q90 deciles of river flows over the
period 1958–2018.
Comparison of monthly river flows with SIM_NEW for
the Garonne at Lamagistère over the period 1958–2018.
Snow height
To complement the previous results with respect to flows, a comparison of
the snow depths between SIM_REF and SIM_NEW
was carried out using the 185 stations described in Sect. 3.2. In Fig. 11,
the spatial variability of scores is presented as a function of elevation
with notched box plots in which the boxes represent the interquartile range, the
whiskers the 10th and 90th percentiles, and the notch the 90 %
confidence interval of the median estimated by a bootstrap sampling
technique among the available stations. The SIM_REF
simulation has a positive median bias at the lowest elevations and a
negative median bias between 2000 and 2400 m, while the SIM_NEW simulation is unbiased at any elevation. The variability of the bias
between stations is also reduced in the SIM_NEW simulation.
Consistently, a significant reduction in MSE is obtained at the lowest and
highest altitudes with SIM_NEW, as is a reduction in the
90th percentile MSE at all altitudes. These results are consistent with
improved altitudinal discretization in mountainous areas, which reduces the
altitude differences between the simulated grid cells and the observation
stations. Slight improvements in SIM_NEW scores could have
been obtained by linearly interpolating the simulated snow depths at the two
layers surrounding the observation. However, the point vertically closest to
the observation was chosen in order to use the same selection as in
SIM_REF. It should also be noted that improvements in the
snow parameterization, but also the use of more accurate vegetation maps,
can explain some of the improvement in scores (Decharme et al., 2016).
Bias and RMSE of daily total snow depth for SIM_NEW (blue) and SIM_REF (red) simulations as a function of
elevation. The scores are computed for 185 stations over the period
1984–2016 for months between October and June. The boxes represent the
interquartile interval, the whiskers the 10th and 90th
percentiles, and the notch the 90 % confidence interval
of the median estimated by a bootstrap sampling technique among the
available stations.
Changes in the simulated water and energy budgets
This section compares the climatology of the SIM system before and after the
changes made. The aim is to qualitatively identify the impact of the new
model on the distribution of energy fluxes, which is important for certain
hydrological or agriculture-related applications. Maps of the Bowen ratio
and the evaporation to precipitation ratio are shown in Fig. 12. The areas
with the highest Bowen ratio are located in the mountains where snowfall
limits evaporation, along the Mediterranean coast where annual precipitation
is lower in quantity and incident radiation rather strong, and in a large
area covering the Garonne basin and part of the Loire and Seine basins,
characterized by high vegetation fractions. The evaporation to precipitation
ratio is also highest in the lowland areas where the Bowen ratio is high. On
the mountains, heavy precipitation and limited evaporation due to snow lead
to the lowest evaporation to precipitation ratio. These results are
comparable to those obtained by Habets et al. (2008) for another period,
except that in SIM_NEW, the Landes forest (south-western
France on the Atlantic coast) has a higher Bowen ratio. The first reason
comes from the difference in the parameterization of photosynthesis, more
precisely the parameterization of leaf conductance used in
SIM_REF based on Jarvis (1976) and SIM_NEW
based on ISBA-A-gs (Calvet et al., 1998), which explicitly models
photosynthesis (thus the canopy resistance is more physically based) and
models plant stress in a more detailed manner; this considerably reduces
evaporation over vegetated areas. Thus, the surface energy budget tends to
increase the sensible heat flux. The second reason is related to the
increase in incoming infrared radiation; this increases the sensible heat
flux and decreases the latent heat flux, which generally occurs on dry soils
with low evaporation capacity. The interannual variability of the
evaporation to precipitation (E/P hereafter) ratio and the Bowen ratio are
presented in Fig. 13 for SIM_REF, SIM_PHY, and
SIM_NEW to first characterize the old system relative to the
new one and to highlight the impact of changes from SIM_PHY
to SIM_NEW on the energy budget. E/P is greater in
SIM_REF than in the other two simulations each year, and E/P
in SIM_NEW is closer to SIM_REF than in
SIM_PHY. Total precipitation is very similar but slightly
lower in SIM_REF and in SIM_PHY or
SIM_NEW due to the representation of sub-grid orography in
the mountains, enhanced by a higher resolution of the orography, which allows
for finer vertical discretization. Therefore, higher E/P corresponds to
higher total evaporation. In SIM_NEW, the ratio of simulated
to observed flow is in excess, whereas it is better simulated in
SIM_REF with a peak centred around 1. This result is
consistent with an evaporation deficit in SIM_NEW compared to
SIM_REF. The Bowen ratio is lowest for SIM_REF, increases in SIM_PHY, and is highest in
SIM_NEW, which already tends to evaporate more than
SIM_PHY. This result shows that the sensible heat flux in
SIM_NEW is much higher than in SIM_PHY, mainly
due to the increased incoming infrared radiation, which partially
compensates for the evaporation deficit.
Maps of the mean annual Bowen ratio (a) and evaporation to
precipitation ratio (b) for SIM_NEW on average over the period
1958–2018.
Mean annual evaporation to precipitation ratio (a) and Bowen
ratio (b) for experiments SIM_REF, SIM_PHY,
and SIM_NEW.
DiscussionClimatic data
As shown in Fig. 4, there is heterogeneity in the forcing data, particularly
with respect to radiation. There are two possible reasons for the break in
the time series; the first is due to the large-scale analysis used to
reconstruct temperature, humidity, and cloudiness profiles. As explained in
Sect. 4.1, the calculations of these profiles have varied over time as a
result of improvements in the global data assimilation systems used in the
ERA-40 reanalysis production. The second reason is the variation in the
observation density network over time. Indeed, from 1958 to the present,
substantial changes have been observed in the deployment of new weather
stations. The combination of these two changes means that the SAFRAN
reanalysis is not homogeneous over time, and it seems important to understand
how the optimal interpolation results are influenced by these changes when
analysing the simulation results. However, an abrupt change may also be due
to the darkening–lightening effect (Wild, 2012; Brulebois et al., 2015;
Boé, 2016).
As already mentioned, the uncertainty in SAFRAN's IR radiation is
significant. The ability to observe the IR in the plains and mountains
allowed for a fair comparison between LSAF and SAFRAN products without
correction (SIM_PHY) and with correction (SIM_FRC). The impact of this variable is very important, especially over snow
(Quéno et al., 2020; Sauter and Obleitner, 2015); therefore, an extension
of the in situ observation network would allow for a better understanding of its
spatial variability and the potential improvement of model simulations. The
extension of the correction to the entire French territory is debatable, but
this decision was guided by the positive bias of river flows and also by the
desire to have a more realistic energy input in mid-mountain areas (i.e.
below 1500 m) in order to better model the evolution of the snowpack.
We also compared the simulated soil temperatures to the observations made
over France. The IR correction on soil temperature has a positive impact and
significantly reduces biases and RMSEs (not shown). The results are
consistent with and of the same order of magnitude as those obtained by Decharme
et al. (2013).
River flows
The results show that SIM_REF simulates the correct ratio
between modelled and observed river flow (centred around 1), whereas in
SIM_PHY, this ratio indicates an overestimation. However,
good results in SIM_REF are due to error compensation since
despite a radiative deficit, river flow is rather well simulated. In
SIM_PHY, as explained in the model description, more
complexity has been added to the model based on a better representation of
physics. The calculations, performed on each of the vegetation types, use
the A-gs photosynthesis parameterization, which tends to produce less
evaporation on the vegetation, leading to more water available in the
rivers. On the other hand, it has already been mentioned that radiative
forcing is underestimated. The combination of more water available in the
soil and less radiative energy to evaporate leads to an overestimation of
river flows. By correcting for IR radiation, the SIM_FRC
simulation shows a clear improvement in river flow scores, with a peak of
the modelled to observed ratio closer to 1 and an improved daily efficiency
range in almost all cases, except perhaps for NSEs below 0.4, but in this
case the difference with SIM_REF is very small. The
implementation of the sub-grid topography with the use of elevation bands
(SIM_TOP) and the sub-grid hydrology with the inclusion of a
snow reservoir (SIM_NEW) essentially impact the hydrology in
the mountains and thus the snow and river flows that are affected by
snowmelt.
Snow depth
The snow depth simulation is of equivalent quality on the 9892 meshes in
SIM_FRC, SIM_TOP, and SIM_NEW
because the same IR correction is applied. On the other hand, the sub-grid
representation of the topography improves the realism of SIM_TOP and SIM_NEW in terms of snow depth but applies only to
the 1044 additional grid cells. However, for the evaluation of the snow
depth, the comparison can only be made on the 9892 cells that correspond
to the SIM_REF grid. In addition, in order not to
disadvantage SIM_REF and to assess the impact of changes in
physics and atmospheric fields, sub-grid processes in SIM_TOP
and SIM_NEW were not considered in the evaluation (the
additional vertical levels of the 1044 cells were not used). It was decided
to present the fairest comparison with SIM_REF by only
considering SIM_PHY. Under these conditions in which sub-grid
effects are not activated, SIM_PHY is quite close to the
other three simulations; the only difference is related to the change in IR
forcing, limited below 1340 m.
Sub-grid hydrology
This method showed that the hydrology of mountainous areas was improved
because the analysed precipitation rate and phase were better represented
for each altitude band than when averaged vertically, resulting, in the case
of the Durance River (Lafaysse et al., 2011), in a decrease in the
overestimated spring peak flow associated with a better phase between the
observed monthly flow and the simulated flow. However, summer and winter
peak flows were still significantly underestimated by the model. During long
periods of drought without precipitation or snowmelt, river flows are
controlled by subsurface drainage. In the framework of the Aqui-FR project
(http://www.geosciences.ens.fr/aqui-fr/, last access: December 2019) aimed at developing a
platform with multiple regionally specialized hydrogeological models over
France to simulate flows and water table heights, aquifers are explicitly
simulated, and the water flows of SURFEX (Masson et al., 2013) used as inputs
should not be impacted by an empirical representation of aquifers. Moreover,
in Aqui-FR, some hydrogeological applications have been calibrated using
SURFEX runoff and infiltration water flows as inputs (Vergnes et al., 2020).
Conclusions and outlook
This study illustrates how developments over the last 10 years are
improving the SIM hydrometeorological system. Several important changes have
been made, particularly in the soil physics of the ISBA model wherein the
force–restore method has been abandoned and replaced by the multi-layer soil
diffusion method. At the same time, as described in Sect. 2, the snow
model has been revised to improve vertical layering, snow compaction, and
solar energy transmission within the snowpack through the use of spectral
albedo, as is done in more advanced models. The model was run according
to the vegetation tiling approach, with each of the 12 vegetation types
characterized by its own set of parameters, in contrast to the single
vegetation type approach whereby the parameters are aggregated. Then, more
accurate databases for soil, orography, and land use were used. A more
precise infrared forcing significantly improved the results, as did the
use of a groundwater reservoir in mountains associated with a specific
vertical discretization of the massifs. The new configuration of the model,
including all the new or updated functionalities mentioned above, proved to
be more efficient than the old system and was therefore better adapted to
water resource studies. Comparisons with independent observations of daily
total snow depth and river flows were made and confirmed that the scores
were improved. In addition, the new SIM system better represented river flow
extremes for both low and high flow periods.
Some perspectives can be proposed to improve the SIM system. The first is to
improve the description of climate. It was found that SAFRAN worked well in
most cases, but some shortcomings remained. A new near-surface reanalysis
system is being developed at Météo-France to replace SAFRAN. It
includes a new surface analysis of air temperature, relative humidity at 2 m, and daily precipitation, and it uses high-resolution model outputs as
a first guess of the analysis. In addition, as part of the Copernicus programme,
a 5.5 km high-resolution reanalysis will be produced over Europe and will
be an interesting product to compare with SAFRAN over France.
The second is to improve the representation of surfaces in the model.
Indeed, the ecosystem database is representative of the 1999–2006 period.
For more recent simulations or quasi-real-time applications, it would be
interesting to study the contribution of new high-resolution satellite
products, such as the land cover product of the European Space Agency and
the Climate Change Initiative, or certain other parameters derived from
Copernicus products, such as albedo, which allow for a better
description of surface types.
The third concerns improving the physics of the model, more specifically
the use of the multi-energy balance (MEB) scheme (Boone et al., 2017; Napoly
et al., 2017) to enable the explicit calculation of the interactions of the
canopy with the air and the ground. The MEB model showed some modest gains
within the SIM_REF simulation owing to a better temporal
partitioning between bare soil evaporation and transpiration (Napoly, 2016).
Moreover, the MEB model demonstrated that the use of litter in forests
improved surface flux results.
Considering anthropization, in particular irrigation and the
presence of dams, could benefit the SIM system in improving its realism and
allowing for more accurate comparisons with gauging stations in anthropized basins.
Irrigation is currently being developed in the ISBA model, and the integration of
dams is a longer-term project. Finally, a better representation of
groundwater and its characteristics in France is another challenge to be
taken up.
The formula of the infrared correction
This correction was proposed to compensate for a deficit in longwave
radiation analysed by SAFRAN compared with infrared measurements from two
reference meteorological stations, Carpentras and Col de Porte, respectively located
in south-eastern France and the Alps. The correction is applied
below 1340 m. The comparison was made for measurements collected between
August 1993 and August 1994 every 3 h.
The correction is written as follows:
A1εσ=-5.42+1.14σ-0.11σ2×10-2,A2LWDcor=LWDref/1+εσ,
where σ is the cloudiness analysed in octas, LWDref the
SAFRAN longwave downward radiation, and LWDcor the longwave downward
radiation when the correction is applied, i.e. when it is divided by
1+εσ. Figure A1 shows the magnitude of the
correction as a function of cloudiness. The increase in radiation is highest
under clear-sky conditions, decreases with cloudiness up to 5 octas, and
increases again for cloudier skies.
Infrared correction as a function of cloudiness.
Code availability
The SURFEX v8.0 source code, including the ISBA code, used in this study
is available at 10.5281/zenodo.3685899 (Le Moigne, 2020), as is the SAFRAN code. The
post-processing codes, including the scores package from the open-source
snowtools project, are also available there.
Data availability
The results of all the models examined here and the R and Python programmes
for plotting the results are available at 10.5281/zenodo.3685899 (Le Moigne, 2020).
Author contributions
Model developments were performed by PLM and EM for the SAFRAN analysis on
France, SF for SURFEX, BD and AB for the diffusive version of
SURFEX–ISBA, and FH for MODCOU. PLM and FB designed the experiments and
carried them out. ML carried out the comparison of the results on snow. DL
provided valuable Python scripts for the figures. JB and ML first tested the
model for their own research. PE, FB, and FRR are responsible for the SIM
operational suite at Météo-France. PLM prepared the paper with
the help of all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Review statement
This paper was edited by Sophie Valcke and reviewed by two anonymous referees.
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