HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-2015-2017The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over FranceFairbairnDavidBarbuAlina LaviniaNapolyAdrienAlbergelClémenthttps://orcid.org/0000-0003-1095-2702MahfoufJean-FrançoisCalvetJean-Christophejean-christophe.calvet@meteo.frhttps://orcid.org/0000-0001-6425-6492CNRM, UMR 3589 (Météo-France, CNRS), Toulouse, FranceJean-Christophe Calvet (jean-christophe.calvet@meteo.fr)13April20172142015203326April20169May201620March201722March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/2015/2017/hess-21-2015-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/2015/2017/hess-21-2015-2017.pdf
This study evaluates the impact of assimilating surface soil moisture (SSM)
and leaf area index (LAI) observations into a land surface model using the
SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages:
(1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the
three-layer ISBA land surface model, which then provides drainage and runoff
inputs to (3) the MODCOU hydro-geological model. The drainage and runoff
outputs from ISBA are validated by comparing the simulated river discharge
from MODCOU with over 500 river-gauge observations over France and with a
subset of stations with low-anthropogenic influence, over several years.
This study makes use of the A-gs version of ISBA that allows for
physiological processes. The atmospheric forcing for the ISBA-A-gs model
underestimates direct shortwave and long-wave radiation by approximately
5% averaged over France. The ISBA-A-gs model also substantially
underestimates the grassland LAI compared with satellite retrievals during
winter dormancy. These differences result in an underestimation
(overestimation) of evapotranspiration (drainage and runoff). The excess
runoff flowing into the rivers and aquifers contributes to an overestimation
of the SIM river discharge. Two experiments attempted to resolve these
problems: (i) a correction of the minimum LAI model parameter for grasslands
and (ii) a bias-correction of the model radiative forcing.
Two data assimilation experiments were also performed, which are designed to correct
random errors in the initial conditions: (iii) the assimilation of LAI
observations and (iv) the assimilation of SSM and LAI observations. The data
assimilation for (iii) and (iv) was done with a simplified extended Kalman
filter (SEKF), which uses finite differences in the observation operator
Jacobians to relate the observations to the model variables. Experiments
(i) and (ii) improved the median SIM Nash scores by about 9% and
18% respectively. Experiment (iii) reduced the LAI phase errors in
ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In
contrast, experiment (iv) resulted in spurious increases in drainage and
runoff, which degraded the median discharge Nash efficiency by about
7%. The poor performance of the SEKF originates from the observation
operator Jacobians. These Jacobians are dampened when the soil is saturated
and when the vegetation is dormant, which leads to positive biases in
drainage and/or runoff and to insufficient corrections during winter, respectively.
Possible ways to improve the model are discussed, including a new multi-layer
diffusion model and a more realistic response of photosynthesis to
temperature in mountainous regions. The data assimilation should be advanced
by accounting for model and forcing uncertainties.
Introduction
Soil moisture influences the flow of water to rivers and aquifers on weekly
to monthly timescales, which makes it an important factor in hydrological
models. In the last two decades there have been considerable advances in soil
moisture data assimilation (DA) using remotely sensed near-surface soil
moisture . The
estimation of global-scale soil moisture states has benefited considerably
from a huge expansion of the satellite coverage, namely the Advanced
Scatterometer (ASCAT) instrument on board the METOP satellites
, the Soil Moisture and Ocean Salinity (SMOS) Mission
and the Soil Moisture Active Passive (SMAP) Mission
, amongst others. However, these instruments can only
indirectly observe the top 1–3 cm of soil moisture and the data are subject
to retrieval errors. There are also spatial and temporal gaps in the
observation coverage. The vegetation influences the soil moisture state
through evapotranspiration and the vegetation coverage can be estimated by
the leaf area index (LAI). This is a dimensionless quantity that represents
the one-sided green leaf area per unit ground surface area
. The LAI can be derived from satellite measurements in
the visible range. However, over France it is available from polar-orbiting
satellites at a relatively low temporal frequency (on average every 10 days)
compared with soil moisture satellite observations (about every 3 days) due
to cloud cover. The aim of DA methods is to combine these observations with a
model forecast from the previous analysis (the background state) to provide
an improved estimate of the state of the system (the analysis). DA methods
are necessary to account for the errors in the observations and the model,
and to spread the information through space and time.
Many studies have investigated the assimilation of surface soil moisture
(SSM) and streamflow observations into hydrological models in order to
improve streamflow predictions and hydrological parameters
. For
example, used the best linear unbiased estimate (BLUE)
method to assimilate streamflow observations into the MODCOU hydrogeological
model over France, which they used to update soil moisture in the ISBA land
surface model (LSM).
LSMs simulate water and energy fluxes between the soil and atmosphere. Unlike hydrological models,
layer-based LSMs such as the ISBA model are typically point-wise (there is no
horizontal interaction between the grid points), which greatly reduces the
computational expense. A 1-D Kalman filtering approach (where observations
are used to update collocated grid points only) is also implemented in this
study, which is commonly applied to 1-D LSMs
.
In large-scale land surface DA, it is common to assimilate satellite-derived
SSM observations and screen-level temperature and humidity observations into
a LSM, in order to improve soil moisture and screen-level variables.
Typically, the root-zone soil moisture (hereafter referred to as WG2) (1–3 m deep) is of more
interest than SSM as it has a much larger water capacity and a long memory
(from weeks to months). Land surface DA is often performed using an ensemble
Kalman filter (EnKF) or a simplified extended Kalman filter (SEKF).
There has been increasing interest in ensemble DA for LSMs over the last two
decades
,
partly because these methods can estimate the “errors of the day” in the
background-error covariance. The operational EnKF at Environment Canada is
also motivated by coupling land surface DA with ensemble weather forecasting
. On the other hand, the SEKF simplifies the extended Kalman filter (EKF) by using
fixed and uncorrelated background errors at the start of each cycle.
Importantly, the SEKF generates flow-dependence and implicit background-error
covariances from additional model integrations in the observation operator
Jacobian calculations. found the flow-dependence from a
24 h assimilation window was sufficient to enable the SEKF to perform
similarly to an EKF (which cycles the background-error covariance). Likewise,
and found that the SEKF and EnKF performed
similarly, in spite of different linear assumptions.
Historically, the SEKF originated from a simplified 2D-Var (theoretically
equivalent to an SEKF) scheme for the assimilation of screen-level
temperature and humidity at the German Weather Service (DWD: Deutscher Wetterdienst)
. An SEKF has been developed for research purposes to
assimilate satellite-derived soil moisture at Météo-France
and the UK Met Office , amongst other
variables. The European Centre for Medium-Range Weather Forecast (ECMWF)
model assimilates screen-level temperature and humidity operationally with an
SEKF and since more recently has assimilated ASCAT-derived SSM
observations .
In our study, we use an SEKF to assimilate LAI and SSM observations to update
LAI and WG2 in the ISBA LSM within the SAFRAN–ISBA–MODCOU (SIM) hydrological
suite. This study makes use of the A-gs version of ISBA that allows for
physiological processes. SIM is operational at Météo-France and its
streamflow and soil moisture outputs are used as a tool by the French
National flood alert services . SIM consists of three
stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces
(2) the ISBA-A-gs land surface model, which then provides drainage and runoff
inputs to (3) the MODCOU distributed hydrogeological model. The drainage and
runoff outputs from ISBA-A-gs are validated by comparing the simulated
streamflow from MODCOU with observations. This study is relevant to the land
surface DA community because several operational centres assimilate SSM
observations using an SEKF to update WG2. Many studies have demonstrated that
the force-restore dynamics of the ISBA three-layer model can effectively simulate
soil moisture and propagate the increments downwards from the surface to the
root zone . An integrated
validation using SIM has demonstrated that the ISBA three-layer model can
skilfully simulate drainage and runoff fluxes over France .
The dynamic vegetation model in ISBA-A-gs is also capable of modelling
seasonal changes in LAI . But
relatively few studies have assessed the SEKF performance using an integrated
validation of the drainage and runoff fluxes. To our knowledge, this is the
first article to consider this type of validation for LAI assimilation.
Furthermore, the validation is robust because it is performed using more than
500 river gauges over France over several years.
This work is partly motivated by the study of , who
investigated the influence of assimilating ASCAT-derived SSM with an SEKF on
SIM over France. They used a version of SIM with high-quality atmospheric
forcing to represent the “truth” and lower-quality atmospheric forcing for
the model. Although the SEKF seemed to improve the results in their study,
they acknowledged that this may have been related to a bias in the SEKF
rather than the assimilation accurately responding to the precipitation
errors. Despite the fact that SAFRAN can be considered as a high-quality
atmospheric forcing, studies by and
have found underestimations of about 5% in the direct shortwave and
long-wave radiative fluxes respectively, averaged over France. In addition to
these problems with radiative forcing, we demonstrate in this study that the
LSM substantially underestimates LAI for grasslands in winter (compared with
satellite retrievals). The specification of the LAI minimum in the model is
important because it prevents vegetation mortality and allows the regrowth of
vegetation in the spring period . We use SIM to validate
the impact of four experiments on the drainage and runoff fluxes:
Correcting the model-underestimated LAI minimum parameter;
Bias-correcting the SAFRAN radiative forcing;
Assimilating only LAI observations with an SEKF;
Assimilating SSM and LAI observations with an SEKF.
The first two experiments attempt to resolve systematic model issues, while
experiments (iii) and (iv) assimilate data in order to correct random errors
in the initial conditions.
Since already investigated the impact of assimilating SSM
in ISBA on river discharges with MODCOU, it was not necessary to perform an
experiment with the assimilation of SSM only. We validate the performance of
these experiments using observations from more than 500 river gauges over
France during the period July 2007 to August 2014. We include an additional
validation using a subset of 67 stations with low-anthropogenic influence
because the MODCOU hydrogeological model only accounts for natural features.
It should be noted that a bias in the forecast model invalidates the
assumption of bias-blind DA . We therefore
repeat experiments (iii) and (iv) after applying (i) and (ii) in to explore
whether the systematic model errors impact the SEKF performance.
The paper is structured as follows. The methods and materials are given in
Sect. , which includes a description of the LSM,
the assimilated observations, the DA methods, the experimental setup and the
SIM validation. The results are presented in Sect. ,
including the impact of the model simulations and DA on the model state
variables and the river discharge. A discussion in Sect.
considers potential solutions to the problems encountered in this study.
Finally, the conclusions are given in Sect. .
Methods and materialsISBA-A-gs land surface model
In our study, the ISBA-A-gs LSM was forced by the atmospheric variables
provided by the “Système d'Analyse Fournissant des Renseignements à la
Neige” (SAFRAN). The analyses of temperature, humidity, wind speed, and
cloudiness are originally performed every 6 h using the ARPEGE (Action
de Recherche Petite Echelle Grande Echelle)
NWP (Numerical Weather Prediction) model .
The original precipitation analysis is performed daily at 06:00 UTC, to include in the analysis
the numerous rain gauges that measure precipitation on a daily basis.
A linear interpolation converts these values to the hourly SAFRAN forcing values .
Instantaneous variables such as precipitation are assumed to be constant for each 15 min model time step during these hourly intervals,
while other variables are linearly interpolated.
The SAFRAN forcing
is assumed to be homogeneous over 615 specified climate zones.
The forcing is interpolated from these zones to
a Lambert-projected grid with a horizontal resolution of
8 km. The delayed cut-off version of SAFRAN was employed,
which uses information from an additional 3000
climatological observing
stations (which report once a month) over France
after the real-time cut-off, which makes the resulting analyses more accurate.
Version 8.0 of SURFEX was used in the experiments, which contains
the “Interactions between Soil, Biosphere and
Atmosphere” (ISBA) LSM .
The model uses the same horizontal grid resolution as SAFRAN of 8 km.
The ISBA-A-gs version was used, which allows for the influence
of physiological processes, including photosynthesis .
Each grid cell is split into twelve vegetation types
(so-called “patches”). Soil and vegetation
parameters are derived from the ECOCLIMAP database .
The nitrogen dilution version (referred to as “NIT” hereafter)
of ISBA-A-gs was applied, which
dynamically simulates the LAI evolution .
The NIT version
allows for the effects of atmospheric conditions on the LAI, including the
carbon dioxide concentrations.
The three-layer version of ISBA was adopted for this study .
This includes the WG1 layer with depth 0–1 cm. The WG2 layer
includes WG1 and is 1–3 m deep, with the depth depending on the patch type.
A recharge zone
exists below the WG2 layer. The model water transfers are governed
by the force-restore method of . The surface and root-zone
layers are forced by the atmospheric variables and restored
towards an equilibrium value. The drainage and runoff outputs from ISBA-A-gs drive
the MODCOU hydrogeological model. The gravitational drainage is proportional
to the water amount exceeding the field capacity (the effective limit
where gravitational drainage ceases) .
It is driven by the hydraulic conductivity of the soil, which depends on its
texture.
A small residual drainage below field capacity was introduced
by to account for unresolved aquifers.
Runoff occurs when the soil moisture exceeds the saturation value.
Assimilated observations
The SSM observations were retrieved from ASCAT C-band spaceborne radar
observations, which observe at 5.255 GHz and a resolution of approximately
25 km. The radar is on-board EUMETSAT's Meteorological Operational (MetOP)
satellites. The assimilation of ASCAT data was chosen because it was
available throughout the analysis period. The original backscatter values
were converted into a surface degree of saturation (SDS, with values between
0 and 1) using a change detection technique, which was developed at the
Vienna University of Technology and is detailed in
and . The historically lowest and highest
backscatter coefficient values are assigned to dry and saturated soils
respectively. The Copernicus Global Land Service then calculates a soil
wetness index (SWI) by applying a recursive exponential filter to these SDS
values using a timescale that may vary between 1 and
100 days. The SWI represents the soil wetness over the soil profile and also
has values between 0 (dry) and 1 (saturated). The longer the timescale of
the exponential filter, the deeper the representative soil profile. The
SWI-001 version 2.0 product was used in this study, which has a 1 day
timescale and represents the SWI for a depth of up to 5 cm.
A surface-state flag is provided with the ASCAT product, which identifies
frozen conditions, the presence of snow cover or temporary melting ice or
water on the surface.
Observations are screened during frozen surface conditions or when snow cover
is present if the ASCAT flag is set to frozen. Additionally, observations
with a topographic complexity flag greater than 15% and/or a wetland
fraction greater than 5% (both provided with the ASCAT data) are
removed. More information about ASCAT quality flags can be found in
. After screening, the data were projected onto the 8 km
resolution model grid by averaging all the data within 0.15∘ of each
grid point . As in an additional
screening step was performed to remove observations whenever frozen
conditions were detected in the model using a threshold temperature of
0∘ C. In addition, observations with an altitude greater than
1500 m and with an urban fraction greater than 15% in the ECOCLIMAP
database were removed.
In order to remove biases between model and observations, a linear rescaling
to the SWI-001 data was conducted, which scales them such that the mean and standard
deviations match the WG1 layer climatology . We
found that it was necessary to rescale the SSM observations to match the SSM
model climatology, partly because differences in the representation of the
soil texture can cause very large systematic differences between the
observations and the model. These differences are illustrated in terms of
probability distribution in Fig. S4 of the Supplement. It shows the innovation
histogram and the Gaussian fitting curve of the SSM product before rescaling.
This rescaling is a linear approximation of the cumulative distribution
matching technique, which uses higher-order moments
. As in , we applied a
seasonal rescaling
using a 3-month moving average over the experiment period (2007–2014).
In the rescaling process the SWI-001 data
are converted into the same units as the model, expressed in volumetric soil moisture (m3 m-3).
The rescaled SSM observations were assimilated into the WG1 model layer.
The observations were assumed to
occur at the same time as the analysis at 09:00 UTC and had a temporal frequency of about
3 days.
This was a reasonable assumption since the satellite overpass is at 09:30 UTC and the atmospheric forcing
is assumed to be constant over hourly intervals for instantaneous measurements such as precipitation.
Therefore any discrepancies in SSM due to this 30 min time difference are small.
The GEOV1 LAI product is part of the European Copernicus Global Land Service.
The LAI observations were retrieved from the SPOT-VGT (August 2007 to June 2014) and
PROBA-V (June 2014 to July 2014) satellite data. The retrieval methodology is discussed
by .
Following , the 1 km resolution observations were
interpolated to the 8 km model grid points, provided that observations were
present for at least 32 of the observation grid points (just over half the
maximum amount). The observations were averaged over a 10-day period and
assimilated at 09:00 UTC. This assumption was reasonable given that LAI
evolves slowly. When considering removing systematic differences between the
model and the observations, a linear rescaling of the LAI observations to the
model climatology would be problematic because the model-observation bias is
linked to model deficiencies. On the other hand, for SSM, systematic errors
are related to the misspecification of physiographic parameters, such as the
wilting point and the field capacity. As mentioned by several authors (e.g.
), the information content of soil moisture
does not necessarily rely on its absolute magnitude but instead on its time
variations. For SSM, the systematic bias between the model and the data
consists mainly in their magnitude rather than their seasonal variability.
Therefore this justifies the common approach used in land surface DA studies for the SSM variable. We should be aware that biases in
soil moisture can show systematic variability, which may be due to model
deficiencies rather that to the misspecification of certain parameters. It is
not always possible to clearly determine which of the model features is to
blame for the bias.
Contrary to SSM, the LAI bias between the model and the data has two
components: one in magnitude and the other one in timing (see for example Fig. 6 in
). When compared with the satellite data, the LAI model
dynamics clearly show a shift in the seasonal cycle, mainly caused by model
errors. The remote sensing LAI measurements potentially encapsulate realistic
environmental features that are not represented or are incorrectly represented by the model.
Forcing the data to conform to the model climatology would result in a loss
of relevant information. Therefore, in this context, a rescaling of the LAI
data to the model climatology was not considered. Furthermore,
found that the assimilation without rescaling can cope with
these model errors.
Data assimilation
The SEKF simplifies the
EKF () by using a fixed estimate of the
background-error variances and zero covariances at the start of each cycle .
Implicit background-error covariances between the layers and the prognostic variables are generated
at the analysis time by the model integration in the observation operator Jacobians.
We used the same SEKF formulation as for the assimilation
of SSM and LAI observations over France. The prognostic variables are LAI
and WG2. The WG1 layer is not included in the analysis update
because it is a shallow layer (1 cm depth) that is driven by the atmospheric forcing rather than the initial conditions
.
The background state (xb) at time ti is a model propagation
of the previous analysis (xa(ti-1)) to the end of the 24 h assimilation window:
xb(ti)=Mi-1(xa(ti-1)),
where M is the (nonlinear) ISBA-A-gs model.
The observation was assimilated at the analysis time (09:00 UTC), at the end of
the 24 h assimilation window.
The analysis was calculated from the generic Kalman filter equation:
xa(ti)=xb(ti)+Ki(yio-yi),
where yo is the assimilated observation and
yi=H(xb(ti)) is the model-predicted value of the
observation at the analysis time. The Kalman gain is defined as follows:
Ki=BiHiT(HiBiHiT+Ri)-1,
where H is the Jacobian matrix of the linearized observation operator,
B is the background-error covariance matrix and
R is the observation-error covariance matrix.
The observation operator Jacobians were calculated using finite differences
for observation k and model variable l:
Hikl=Hik(Mi-1(x(ti-1)+Δxi-1l))-Hik(Mi-1(x(ti-1)))Δxi-1l,
where Δxl is a model perturbation applied to model
variable l.
The WG2 and LAI perturbations were set to 1.0×10-4×(wfc-wwilt) and
1.0 ×10-3× LAI respectively. These were within the range of acceptable perturbation sizes
based on the experiments of
and .
Equation () requires a 24 h model simulation for each prognostic variable, which
implicitly propagates the background-error covariance from the start of the window to the time of the observations at the end of the window.
The linear assumptions in deriving the Jacobians are generally
acceptable for these perturbation sizes. However, occasionally the linear assumptions can break down, especially during
dry periods in summer . For this reason we set an upper bound on the
soil moisture Jacobians of 1.0. It is worth mentioning that in situations where the model and atmospheric forcing errors are
not properly taken into account, the SEKF analysis will be suboptimal even if the Jacobians are accurately computed.
The Jacobian matrix derived from Eq. () is defined as follows:
H=∂WG1∂WG2∂WG1∂LAI∂LAI∂WG2∂LAI∂LAI.
When assimilating just LAI, only the ∂LAI∂WG2
and ∂LAI∂LAI terms are included. The ∂WG1∂LAI
is generally small, since the LAI does not substantially influence the surface layer .
The ∂WG1∂WG2 Jacobian couples WG1 with WG2 .
The ∂LAI∂WG2 couples LAI with WG2 . The ∂LAI∂LAI
Jacobian was studied by and has a strong seasonal dependence.
As we will demonstrate in Sect. , the examination of these Jacobians
is essential in order to understand the performance of the SEKF.
SURFEX is implemented using the mosaic approach of ,
where each model grid box is split into 12 vegetation patches.
The SEKF analysis is calculated independently for each patch using the Jacobians for each individual patch but with
one mean observation per grid box.
The analysis for the grid point is calculated by aggregating the analyses over the 12 patches, which are
weighted according to their patch fractions (see , for further details).
Taking into account the grid heterogeneity has been the justification for including vegetation patches in the model and in the assimilation scheme.
The assimilation scheme
uses the hypothesis that the distribution of innovations is proportional to the cover area.
The analysis is adapted to plant functional types via the patch fractions and via the Jacobians.
Following , the WG2 background-error standard deviation was set to 0.2(wfc-wwilt),
where wfc is the field capacity and wwilt is the wilting point.
The scaling by (wfc-wwilt)
assumes that there is a linear relationship between the soil moisture errors and the dynamic range, which depends on
soil texture .
The SSM observation error
standard deviation was set to 0.65(wfc-wwilt), which is about 0.055 m3 m-3 averaged over France.
This value is slightly larger than the median ASCAT-derived SDS error of 0.05 m3 m-3 estimated by
because it also approximates the oversampling issue i.e. the same ASCAT observation covers several grid points.
This reduces the size of the analysis increments by approximately 10 %.
This value is comparable with observation errors expected for remotely
sensed SSM observations .
As in , the LAI background and observation error standard deviations were proportional to the LAI values
themselves and a value of 0.2× LAI was used for LAI values greater than 2 m2 m-2. For LAI
values below 2 m2 m-2 the LAI errors were fixed at 0.4 m2 m-2. Both the background-error and observation-error covariance
matrices of the SEKF are diagonal (zero covariances between layers), but implicit background-error covariances are derived from the H matrix
at the analysis time. The SEKF is a point-wise method, i.e. it cannot take into account horizontal covariances between grid points.
Experimental setup
The main experiments in this study are summarized in
Table . The SIM river discharge was compared with the
observations from 546 stations over France. Firstly the baseline experiment
(NIT) was performed, which shows the impact of the biased radiative forcing
and the underestimated LAI minimum on the SIM river discharge. Thereafter,
two potential solutions to these deficiencies were investigated, as set out
in the introduction: (i) NITm, which was equivalent to NIT but
with an elevated LAI minimum of 1.2 m2 m-2 for grasslands (as
opposed to 0.3 m2 m-2 with NIT), and (ii) NITbc, which used
both the elevated LAI minimum of 1.2 m2 m-2 and the bias-corrected
radiative forcing (+5% for direct long-wave and shortwave over
France). Two DA experiments were undertaken to correct random
errors in the initial conditions: (iii) LDAS1, which used the SEKF to
assimilate only LAI with the NIT model, and (iv) LDAS2, which assimilated both
LAI and SSM observations with the NIT model. The LAI minimum parameter is
required to calculate a minimum level of photosynthesis at the start of the
growing season. The default model value is arbitrarily fixed at
0.3 m2 m-2 for grasslands, which is low enough to account for
possible fluctuations in the LAI minimum due to climatic and interannual
variability over France . However, we found that over
99% of points with a high percentage of grassland (the grassland patch
fraction exceeding 70%) had an observed average annual LAI minimum
above 1.2 m2 m-2 during the experiment period (2007–2014). But the
modelled LAI is frequently kept at the prescribed LAI minimum parameter
during winter dormancy and is therefore systematically underestimated over
most grassland regions in winter when compared to the satellite-derived
observations. Similar issues were found by
, and when comparing the model with both
MODIS and SPOT-VGT satellite-derived observations. Systematic differences
between the model and the observations can be removed by calibrating model
parameters , which was the motivation for increasing the
grassland LAI minimum parameter from 0.3 to 1.2 m2 m-2 in our
study. and demonstrated that the
direct shortwave and long-wave radiative forcing, respectively, are
underestimated by approximately 5% averaged over France. We followed
in bias-correcting the direct radiative forcing by
+5% for NITbc.
List of experiments. The bias-correct forcing option implies an
increase of the direct shortwave and long-wave radiation by 5%. The
SSM outliers removal applies to SSM observations outside the 90%
confidence interval of the model.
Three additional experiments in Table explored
whether SSM observation outliers, the underestimated LAI minimum or the
radiative forcing bias might impact the performance of the DA. The
LDAS2QC was equivalent to LDAS2 but with a strict quality control
of the SSM observations. The outliers were removed by rejecting observations
outside the 90% confidence interval of the model (as in Eqs. 1 and 2
of ) after the observations had been rescaled. The
LDAS1bc and LDAS2bc experiments were equivalent to
LDAS1 and LDAS2 respectively, except they used the NITbc model.
The SSM observations for LDAS2bc were rescaled such that the
standard deviation and mean matched those of NITbc.
The MODCOU hydrogeological model does not account for anthropogenic water
management. However, there are many parts of France where anthropogenic water
management strongly influences streamflow observations, including the
reservoir operations, for hydropower, irrigation, drinking water, flood and
low-flow alleviation, and recreational purposes. We used the reference networks
of to extract a subset of 67 river
gauges with low-anthropogenic influence from the original 546 stations, valid
for both low and high flows. We compared the results for these 67 stations
with the 546 stations in order to determine if the results were affected by
the ability of SIM (with or without DA) to simulate anthropogenically
influenced streamflow.
Performance diagnosticsSystem validation
A system validation was performed by comparing the LAI and WG1 states with
the LAI and SSM observations, respectively, for all the simulations and DA experiments. Note that this was not an independent validation of
the performance of the system, for which we would have needed independent
observations. The rationale was to check the effectiveness of the SEKF, i.e.
to examine if it improved the fit between the model simulations and the
observations. The fit to the observations was determined by the root mean
square difference (RMSD), the correlation coefficient (CC) and the bias.
In addition, Figures S5 and S6 show the histograms of the innovations
(difference between the model-predicted observations and the data) and
residuals (difference between the analysis and the data). The SSM innovation
PDF (probability density function) agrees very well with Kalman theory, since it closely fits the Gaussian
distribution. The LAI innovation PDF is also close to its normal fit, but
presents a left-tailed distribution. As expected, the standard deviation of
residuals is reduced compared to those of innovations. For an optimal filter
the innovation time series should be uncorrelated in time. For both SSM and
LAI the temporal evolutions of innovations are illustrated in Figs. S7 and
S8, respectively. The SSM temporal sequences of innovations are close to a
white noise time series (Fig. S7), while the LAI innovations (Fig. S8) are
quite strongly correlated over time, which is not optimal.
Validation using SIM
The SIM hydrological model was used to validate the drainage and runoff from ISBA-A-gs
by comparing the simulated streamflow from MODCOU with observations.
A complete description and validation of SIM can be found in .
The first two stages of SIM are the SAFRAN atmospheric forcing and the
ISBA-A-gs LSM, which were introduced in Sect. .
The runoff and drainage from ISBA-A-gs are fed into the MODCOU hydrogeological model
, which computes the daily evolution
of aquifer storages and 3 h river flow forecasts.
More than 900 river
gauges are simulated with areas ranging from 240 to 112 000 km2.
The temporal and spatial evolution of two aquifers in
the Rhône and Seine basins are simulated using a diffusivity equation.
The interaction between the rivers and aquifers is modelled and the soil
water is routed to the rivers using an isochronism algorithm.
The influence
of human activity, such as dams and irrigation, is not accounted for by MODCOU.
The simulated river discharge from SIM was compared with the observations from
546 river gauges that had data during the period of evaluation (2007–2014).
These observations are available from the French hydrographical database
(http://www.hydro.eaufrance.fr/, last accessed March 2016). We also analysed the results
for the subset of 67 stations with low anthropogenic influence
from the original 546 stations.
The fit of the average daily river discharge from MODCOU (measured in m3 m-3 s-1)
to the observations was measured using the Nash efficiency
score .
The Nash efficiency can
range from -∞ to 1, with 1 corresponding to a perfect match of the model
to the observed data and a negative value implying
that the model performs worse than a constant model with a value equal to the average of all the observations.
Following , we considered an efficiency
of 0.6 to be a good score and 0.5 to be a reasonable score. The median Nash scores
were calculated for all the stations.
The median is a more appropriate metric than the mean
as it is less sensitive to extreme outliers
and is a better indicator for highly skewed distributions . These
issues were present in this study due to some stations being heavily affected by
anthropogenic water management or unresolved aquifers, despite most stations being
well simulated.
The validation period extended from August 2007 to July 2014, with the hydrological
year running from August to July.
The SIM domain consists of 9892 grid points, of which 8602 are based in
France. The remaining 1290 points are based in mountainous regions bordering
the French mainland, including most of Switzerland (see Fig. 2 in
for the full domain). The LSM does not model horizontal
exchanges, but MODCOU takes into account horizontal streamflow. Therefore it
is important to include these external points in SIM because they impact the
streamflow over France, particularly in the Rhône basin in the southeast.
However, we only applied the SEKF over the 8602 points in the LDAS France
domain. Figure shows a flowchart of SIM and how LDAS
France was connected with ISBA-A-gs in SIM.
Figure shows the river network used by
MODCOU and the 546 stations used to validate the discharge. A map of the
subset of 67 stations with low anthropogenic influence can be found in
Fig. S1.
Flowchart of the SIM hydrological model and how LDAS France
is connected with SIM.
ResultsImpact of model and forcing bias-corrections on SIM
To begin with we examine the influence of the different model simulations
(NIT, NITm and NITbc) on the LAI evolution for the
four dominant vegetation patches. We can then link the hydrological
performance to each simulation. Over France, the four dominant vegetation
patches are grasslands (32%), C3 croplands (24%), deciduous
forests (20%) and coniferous forests (12%).
Figure shows the monthly averaged LAI model
simulations and observations for the grid points that contain at least
50% of the dominant vegetation types. The 50% threshold was
used because no points contain more than 70% of deciduous forests,
while over 1000 grid points contain at least 50% of any vegetation
type. Table shows the average LAI scores over France (RMSD,
CC and bias) for each of the model simulations.
Scores for LAI (prognostic variable compared with observations)
averaged over 2007–2014. The RMSD and CC stand for root mean square
difference and correlation coefficient respectively. The closest fit to the
observations is shown in bold font.
Firstly we examine the LAI performance for the NIT simulation, which
dynamically estimates the LAI evolution. Figure
shows that the NIT simulation is close to the observations for the deciduous
forests (Fig. a). However, the growth and senescence
phases are delayed for the simulated C3 crops and grasslands
(Fig. c and d) compared with the observations.
Furthermore, the grassland LAI is substantially underestimated by NIT in
winter. It is clear in Fig. that imposing this
higher minimum LAI value (NITm) increases the LAI for grasslands
in winter and improves the fit to observations. This is reflected by better
scores for NITm, reducing (increasing) the RMSD (CC) by about
4% compared with NIT. Figure shows the
average annual LAI minimum over France for the original simulation (NIT), the
new simulation (NITm) and the GEOV1 data.
Figure emphasizes that the LAI minimum is
underestimated (compared to the GEOV1 data) over much of France for NIT. By
increasing the grassland LAI minimum from 0.3 to 1.2 m2 m-2, the
model agrees much better with the data over most regions. Finally, the
benefit of the bias-correction (NITbc) on LAI is also
demonstrated in Fig. . The bias-correction has
little impact on the LAI of the deciduous and coniferous forest patch types.
However, it does reduce the phase errors for both the C3 crops and grassland
patches. This results in much better LAI scores, reducing (increasing) the
RMSD (CC) by about 10% compared with NITm.
Nash efficiency scores for each station over France for the NIT
simulation, calculated over the period 2007–2014. The river network is also
shown.
Monthly averaged LAI for the model simulations and for the
grid points with at least 50% of the four dominant vegetation types,
averaged over 2007–2014 and averaged over France.
The WG1 scores for the various simulations are given in
Table . Recall that the SSM observations are linearly
rescaled such that their mean and standard deviation match the NIT model
simulation of WG1, which removes any bias already present. Changing the model
simulation has little impact on the scores, which suggests that the LAI
evolution and the radiative forcing have a relatively small influence on the
moisture content of the surface layer.
Scores for WG1 (prognostic variable compared with observations)
averaged over 2007–2014. The RMSD and CC stand for root mean square
difference and correlation coefficient respectively. The closest fit to the
observations are shown in bold font.
Next, the Nash efficiency scores for the different model simulations are
displayed in Fig. a, showing the percentage
of gauging stations at efficiency scores between 0 and 1.0. For the NIT
simulation, about 26% of the stations have a score above 0.6 (a good
score), 42% of the stations have a score above 0.5 (a reasonable
score) and 79% of the stations have a positive Nash score. These
scores are substantially improved by increasing the LAI minimum and by
bias-correcting the radiative forcing. For the NITm
(NITbc) simulation, about 31% (42%) of stations
reach a score of at least 0.6, 48% (58%) of stations reach a
score of 0.5 or higher and 80% (83%) of the stations have a
positive score. Table shows the median Nash scores
for each simulation. The median Nash scores for NIT are increased by about
9% for NITm and further increased by 18% for
NITbc. The median discharge ratio between the simulated (Qs) and observed (Qo)
discharge is also shown for each simulation. A value that is greater
(smaller) than 1.0 indicates a positive (negative) bias in the model. NIT has
a median discharge ratio of 1.19, which indicates that the simulated
streamflow is overestimated by about 20%. This is reduced to 1.15 by
applying the LAI minimum and further reduced to 1.02 by applying the
bias-correction. Therefore it appears that the bias in the discharge ratio
has an important impact on the Nash score, with larger biases corresponding
to smaller Nash scores. This is clarified when comparing the annual median
Nash scores (Fig. a) with the annual median
discharge ratios in Fig. b. It seems that the
size of the bias in the discharge ratio is negatively correlated with the
Nash score, which would explain why NITbc performs so well.
Figure c and d show the average annual
temperature and rainfall respectively. There does not appear to be a strong
correlation between either the temperature or rainfall and the Nash score.
The Nash efficiency for NIT for each station over France is shown in
Fig. . The river discharge is well simulated
over most areas, but the southeast and northern regions have generally
negative scores (shown in black). In southeast France this is related to a
large number of dams in the Alps, which are not simulated by MODCOU. In
northern France, this is linked to a large aquifer that is also not taken
into account by MODCOU (see for details). There are a
small number of stations with negative scores elsewhere, which could also be
related to anthropogenic water management. The maps show similar patterns for
the other simulations (not shown). The vast majority of stations
(>80%) for NITbc are improved relative to NIT, including
most of the stations with negative scores. A scatter plot of the Nash
efficiency scores of NIT and NITbc for all the stations can be
found in Fig. S2a.
Map showing the average annual LAI minimum (2007–2014) for NIT,
NITm and the GEOV1 observations (m2 m-2) over France.
Nash efficiency scores over France for (a) the model
simulations and (b) the DA methods, calculated over the period
2007–2014.
Median annual (a) Nash efficiency scores and (b)
discharge ratio for each experiment. Average annual (c) temperature
and (d) cumulated precipitation.
Finally, we investigate the influence of the model simulations on the
evapotranspiration, drainage and runoff fluxes in order to explain the
differences in SIM discharges. Figure a–e show
the average monthly LAI, WG2, evapotranspiration, drainage and runoff
respectively, averaged over France. The NITm simulation has a
greater average LAI in winter than NIT because the NIT LAI minimum is
underestimated. The effect of a higher LAI minimum is to enhance
evapotranspiration in winter and spring, which reduces the soil moisture and
therefore diminishes the drainage and runoff. The consequence of increased
radiative forcing in NITbc is to further increase
evapotranspiration and lower WG2 during much of the year. This substantially
reduces drainage and runoff, especially from October to June. These effects
are emphasized in Fig. a, which shows the
difference between the sum of drainage and runoff for the different
simulations compared with NIT. The reduced drainage and runoff feeding into
the MODCOU hydrogeological model results in less river discharge, which
explains the reduced river discharge bias and superior Nash scores for
NITm and NITbc relative to NIT in
Table .
Impact of DA on SIM
The performance of the DA runs on the LAI and WG1 scores are shown in
Tables and respectively. LDAS1
substantially improves the fit of the simulated LAI to the LAI observations
compared to NIT. We investigate the influence of DA on the drainage and
runoff fluxes in Fig. f–j, which is equivalent
to Fig. a–e except that LDAS1 and LDAS2 are
compared with NIT. Figure g demonstrates that the
assimilation of LAI reduces the LAI phase errors in NIT, indicating that the
SEKF is working effectively during much of the year. However, the LAI
assimilation with the SEKF does not address the problem of the underestimated
LAI in winter, unlike NITm in Fig. b.
Figure b shows the differences between the combined
drainage and runoff fluxes between NIT and the DA methods. The LAI
assimilation has a relatively small influence on the drainage and runoff
fluxes in Fig. b compared to NITm in
Fig. a. The small positive correction of LAI in
spring slightly increases (reduces) evapotranspiration (drainage and runoff)
which is cancelled out by the opposite effect in autumn. Overall, LDAS1 does
not greatly modify the discharge ratio or the Nash scores.
Average monthly (a) WG2 and (b) LAI, and monthly
cumulative (c) evapotranspiration, (d) drainage and
(e) runoff for NIT and the other model simulations. Plots
(f–j) show NIT and the DA analyses for the equivalent variables as
in (a–e). Results are all averaged over the period 2007–2014 and
averaged over France.
Median Nash efficiency (NE) and discharge ratio
(Qs/Qo) scores over the 546 river gauges over France
and for the subset of 67 gauges with low anthropogenic influence, calculated
over 2007–2014. Also shown are the percentage of stations with a Nash score
above 0.6. The best scores are shown in bold
font.
The LDAS2 experiment slightly improves the WG1 scores relative to NIT
(Table ). The median Nash discharge scores are degraded by
about 7% for LDAS2 compared to NIT
(Fig. b and Table )
and the positive bias in the discharge ratio is increased by about 2%
(Table ). The reason for this is that LDAS2 has a
higher average WG2 relative to NIT (Fig. f),
which translates to increased drainage and runoff for LDAS2. This is
emphasized by comparing the combined drainage and runoff for LDAS2 relative
to NIT in Fig. b. The extra water in the rivers
exacerbates the Nash discharge bias already present in NIT, resulting in
degraded Nash efficiency scores. The LDAS2 scores are degraded for about
70% of the stations relative to the NIT simulation and a scatter plot
of the scores for all the stations can be found in Fig. S2b.
Monthly combined drainage + runoff flux differences between
(a) NIT and the other model simulations and (b) NIT and the
DA analyses averaged over the period 2007–2014 and over France.
The neutral impact of LDAS1 and the detrimental influence of LDAS2 on the
soil moisture fluxes is explained in the following section by examining the
observation operator Jacobians.
Examining the SEKF Jacobians
The SEKF observation operator Jacobians are governed by the physics of the
model. Their examination is important in order to understand the SEKF
performance. The LAI increments for LDAS1 are mainly driven by the
∂LAI∂LAI Jacobian. The behaviour
of the ∂LAI∂LAI Jacobian values
for ISBA-A-gs was investigated by . Their behaviour can be
split into three distinct types, which depend on atmospheric conditions. The
type “O” Jacobian is strictly equal to zero and occurs mainly in winter
when the vegetation is dormant. In this case the LAI will be kept at its
default model minimum. The type “A” Jacobian represents a fraction between
zero and one and is correlated with the LAI value itself. It occurs during
periods of vegetation growth, i.e. predominantly in spring. The type “B”
Jacobian is equal to 1.0 and takes place during periods of low vegetation
growth or high mortality, which occur mainly in autumn. The grassland
Jacobians are plotted for LDAS1 in Fig. for a
particular point in southwest France (43.35∘ N, 1.30∘ E).
Also plotted in the same graph are the LAI values themselves, with the
minimum indicated by the red line. Indeed, the type O Jacobians tend to occur
in winter, during which time the LAI returns to its minimum value of
0.3 m2 m-2. The type A and B Jacobians tend to occur in spring and
autumn respectively. These findings are in agreement with Fig. 4 of
. The LAI performance for LDAS1 can now be explained by
these Jacobian values. Figure g shows that during
the winter the lowest LAI values are barely corrected by LDAS1 because, as
shown in Fig. , the LAI is frequently forced back to
its minimum value (type O Jacobians). During the spring there is a small
correction (type A Jacobians) and during the autumn there is a much larger
correction (type B Jacobians). Hence the LDAS1 is able to correct the LAI
phase errors to some extent, but LDAS1 is unable to correct the LAI minimum
in winter. The seasonal imbalances in the LAI Jacobian can also explain the
negative bias in Table . Since most of the drainage and
runoff is present in winter and spring, the assimilation of LAI has little
influence on SIM.
Time evolution of the LDAS1 ∂LAI∂LAI Jacobian, together with the LAI analysis and the minimum LAI
model parameter for the grassland patch at a point in southwest France
(43.35∘ N, 1.30∘ E).
Scatter plot of WG1 against the LDAS2 ∂WG1∂WG2 Jacobian for the grassland patch at the
same point as Fig. .
The ∂LAI∂WG2 Jacobian has
generally positive values, since an increase in water content in the soil
generally enhances photosynthesis and plant growth (not shown). However, this
term is close to zero from about November to March while the vegetation is
dormant. Therefore it does not substantially influence the LAI minimum in
winter. There is no evidence that it leads to long-term increases in WG2 or
results in increased drainage and/or runoff for LDAS2.
The WG2 analysis increments for LDAS2 are largely driven by the
∂WG1∂WG2 Jacobian. A scatter
plot of these Jacobian values against the WG1 variable is shown in
Fig. for the same point as Fig.
in southwest France. The density of the points is derived from the kernel
density estimation of . There are two dense regions when WG1
is equal to 0.15 and 0.30 m3 m-3, which occur because WG1 is a thin
layer, and therefore most of the time it is either dry or close to
saturation. The WG1 and ∂WG1∂WG2
values are negatively correlated, with larger values of WG1 corresponding to
smaller values of ∂WG1∂WG2. This
implies that when rain is detected in the model but not in the SSM
observations, the analysis increment will be smaller than when the rain is
missed by the model but detected by the observations. Indeed, the average WG2
analysis increment for a positive innovation is 0.7×10-3 m3 m-3, while the average increment for a negative
innovation is -0.5×10-3 m3 m-3. This imbalance in the
analysis increments leads to a net uptake of water in WG2, which induces the
positive bias in the SIM river discharge. This problem was already
highlighted by . The Jacobians exhibited similar patterns
of behaviour for other vegetation types than grasslands and across other
points in France, albeit with different magnitudes (not shown).
Additional experiments
Additional experiments were performed to examine whether the poor performance
of the SEKF was related to other factors than the Jacobians, namely the
quality control of the observations, the underestimated LAI minimum or the
bias in the atmospheric forcing. It is evident in Tables to
that applying the additional quality control of the
SSM observations (LDAS2QC) does not substantially modify the LAI,
WG1 or Nash discharge scores compared to LDAS2, despite removing about
10% of the SSM observations.
Figure a shows only small differences
in the Nash efficiency percentages between LDAS2 and LDAS2QC. As
expected, the LDAS1bc and LDAS2bc experiments
improved on the LAI scores of LDAS1 and LDAS2
(Tables and ), but did not improve on the WG1 scores in Table .
These changes are a similar order of magnitude to the improvement of
NITbc over NIT. In terms of discharge Nash efficiency scores,
LDAS1bc performed similarly to NITbc and LDAS2
performed substantially worse than NITbc
(Table ). The Nash efficiency percentages are shown
in Fig. (b). The comparison between
LDAS1bc and LDAS2bc with NITbc in
Fig. b is analogous to the comparison
between LDAS1 and LDAS2 with NIT in Fig. b.
The scores for the subset of 67 stations with low anthropogenic influence are
also shown in Table . The scores for this subset are
improved relative to the 546 stations in Table , as
expected. In particular, the percentage of stations with good scores (Nash
efficiency >0.6) is greatly increased. For the interested reader, scatter
plots of the Nash scores for the 67 stations are shown in Fig. S3. The
discharge bias is also slightly smaller for the stations with low
anthropogenic influence relative to the 546 stations. This suggests that a
small part of the positive bias in the discharge ratio of the NIT simulation
for the 546 stations could be attributed to abstractions not being accounted
for, such as drinking water or irrigation. However, most of the discharge
bias in the NIT simulation is still present in the 67 stations with low
anthropogenic influence. Moreover, the relative performances of the
experiments are very similar. Therefore, the conclusions of the experiments
are not affected by the ability of SIM (with or without DA) to simulate
anthropogenically influenced streamflow. These results confirm that the
inability of the SEKF to improve the soil moisture fluxes comes mostly from
the SEKF Jacobians.
Discussion
Previous work by and clearly
demonstrated that the assimilation of SSM observations with an SEKF can
improve WG2 with the three-layer ISBA-A-gs model. also
demonstrated that the assimilation of LAI reduces phase errors in the
modelled LAI evolution. However, in this work we showed that the SEKF has
little influence on the drainage and runoff fluxes when assimilating LAI
observations (LDAS1 experiment). Furthermore, the SEKF actually degrades
these fluxes when assimilating SSM and LAI observations (LDAS2 experiment).
The differences in these findings are due to the nonlinear interactions in
LSMs which can cause the assimilation of one state variable to be detrimental
to other soil moisture processes . The poor results for
LDAS1 and LDAS2 can be explained by model errors, atmospheric forcing errors
and model nonlinearities near the soil moisture wilting point and field
capacity thresholds, none of which are captured by the SEKF observation
operator Jacobians.
Could LAI assimilation be improved?
In LDAS1, the seasonal variability in the analysis LAI increments was uneven,
with large negative increments in late summer–autumn and small positive
increments in winter–spring. This occurred because the LAI Jacobian
∂LAI∂LAI was
frequently equal to zero during winter and therefore the LAI remained at its
incorrect minimum value after the analysis update. Moreover, LAI is only
assimilated every 10 days so the model LAI would drift back to its
underestimated minimum value between cycles. Consequently, the average LAI
analysis was negatively biased. These Jacobian values are physically
sensible, since the vegetation is dependent on the atmospheric conditions and
is often dormant during the winter period. The problem is related to the lack
of a model error term in the SEKF.
Average Nash efficiency scores over France for (a) the NIT,
LDAS2 and LDAS2QC experiments, and (b) the
NITbc, LDAS1bc and LDAS2bc experiments.
The lowest LAI values could be corrected with a full EKF and a model error
term, but it would be complicated to parametrize the model-error covariance
matrix because the LAI minimum is linked to several factors concerning the
atmospheric conditions and the vegetation type. A short-term solution to the
underestimated LAI minimum was demonstrated in the experiments, which was to
set a higher LAI minimum parameter in the model based on observations.
However, it would be more sensible in the long-term to resolve the underlying
issues with the model physics. A thorough comparison of the ISBA-A-gs-simulated LAI with both SPOT-VGT (used in our experiments) and MODIS data
over southwest France was performed by . They did notice
significant discrepancies between all three data sets, suggesting that there
is significant uncertainty in both the model and the observations. However,
they also noticed that the modelled LAI of the C3 natural herbaceous
(grasslands) and/or C3 crops had a delayed onset relative to both satellite products
(see Fig. 4 in ). They found that this was particularly
problematic for grasslands in mountainous regions. By comparing the data with
in situ measurements, they found that the generic temperature response of
photosynthesis used in the model is not appropriate for plants adapted to the
cold climatic conditions of the mountainous areas. This problem was also
linked to a prolonged LAI minimum in the model relative to the observations.
found similar issues when comparing the same products over
France. Indeed, Fig. in our study shows that the NIT
LAI minimum was particularly underestimated in the grassland areas of the
Massif Central mountains in central France, but not so much in lower regions
further north. Finally, these problems could explain the delayed onset and
underestimated LAI minimum for both grasslands and C3 crops in
Fig. in our study.
It should be recognized that errors in the modelled LAI are not just present
over grasslands, but also over other vegetation types.
Figure shows that there are significant discrepancies
between the model and the observations for C3 crops and deciduous forests as
well. Given that these discrepancies vary substantially between different
vegetation types, it is not optimal to assimilate a grid point averaged
observation. This issue is currently addressed by disaggregating the LAI for
each patch individually.
Finally, as already mentioned, LAI is assimilated every 10 days. LAI data
availability could be improved using higher spatial and temporal resolution
products in order to limit the impact of clouds.
Why does SSM assimilation degrade river discharges?
It is important to point out that it is physically sensible for WG1 to
decouple from WG2 during precipitation events. The precipitation forcing
leads to a saturation of the surface layer and subsequently WG1 becomes less
dependent on WG2. The degradation of drainage and runoff can be caused by
limitations in the SEKF, in the land surface model and in the data.
Firstly, as recognized by , an important problem is that
the SEKF is not designed to capture the uncertainty in the model and the
precipitation forcing, which should increase during precipitation events and
therefore compensate for the smaller Jacobians. The SAFRAN precipitation
forcing performs well for a mesoscale analysis and has a higher spatial
resolution than global satellite products such as ERA-interim
. However, by design the precipitation is
assumed to be homogeneous over 615 specified climate zones. Errors are
therefore introduced from the spatial heterogeneity of the precipitation,
particularly in mountainous regions .
Secondly, the three-layer ISBA model has strong nonlinearities near the soil
moisture thresholds, some of which lead to unrealistic behaviours of the
model Jacobians. During dry conditions in summer, the SEKF ∂WG1∂WG2 Jacobian can be excessive. This is
linked to a rapid increase in transpiration when water is added to WG2
following dry conditions . The origin of this
nonlinearity is partly related to an unrealistic feature of the surface
energy balance. One single surface temperature is used to represent the
vegetation and the surface layer, which causes the transpiration to increase
too quickly after water is added to WG2 . This
problem could be relieved to some extent by introducing the new version of
ISBA with a multiple energy balance () and by using a
multi-layer diffusion model (ISBA-DIF, ).
Lastly, regarding observations, the current ASCAT product is affected by
vegetation and a seasonal cumulative distribution function (CDF) matching is needed in
DA systems assimilating ASCAT SSM. This procedure is, however, sub-optimal. A
solution to this problem is to go towards the implementation of an
observation operator in order to assimilate the backscattering coefficients
directly. In this way, the vegetation information content in the ASCAT signal
could be used to analyse vegetation biomass and would also provide
information for the analysis of root-zone soil moisture, in addition to the
microwave soil moisture signal.
Could more sophisticated DA methods improve SSM assimilation?
The presence of the uncertainties in the model and in the forcing could more
easily be addressed with an EnKF than an SEKF because an EnKF can
stochastically represent model and precipitation errors
. found that an EnKF
with a simple stochastic rainfall error estimation demonstrated similar WG2
scores to the SEKF over 12 sites in southwest France (validated using in situ
observations). Both methods were affected by nonlinearity problems.
There are DA methods, such as particle filters, designed to handle model
nonlinearities. demonstrated that good results on a
hydrological model could be achieved with a particle filter with about 200
members. However, it is substantially more computationally expensive than an
EnKF, which typically requires about 20 members to overcome sampling error
problems for LSMs . Therefore
we intend to test an EnKF over France using the same validation framework
used in this study.
Conclusions
This study assessed the impact on streamflow simulations of assimilating
SSM and LAI observations into the
ISBA-A-gs LSM. The drainage and runoff outputs were used
to force the MODCOU hydrogeological model and were validated by comparing the
simulated streamflow with over 500 river-gauge observations over France
over several years. To our knowledge, this is the first article to examine
the impact of LAI assimilation on streamflow simulations using a distributed
hydrological model. The validation is robust due to the large number of
river gauge observations employed and the long evaluation period
(2007–2014). The results from this study could also have ramifications for
flood warning accuracy since SIM is used operationally by Meteo-France as a
tool for flood forecasting. Furthermore, this study highlights the importance
of systematic model and/or forcing deficiencies on the streamflow simulations.
Reasons for not obtaining improvements in discharge simulations are related
to model deficiencies, model nonlinearities and the set-up of the
assimilation system.
Increasing the LAI minimum parameter resulted in greater evapotranspiration
in winter–spring, and bias-correcting the radiative forcing increased
evapotranspiration during much of the year. Both corrections effectively
reduced the positive bias in the drainage and/or runoff fluxes and substantially
improved the Nash efficiency scores. Although DA is not theoretically
designed to correct systematic model deficiencies, it was found that
assimilating only LAI observations substantially reduced the LAI phase errors
in the model. However, this induced a net negative bias in the LAI analysis
relative to the observations. Given that drainage and runoff occurs
predominantly in late winter and spring, the LAI assimilation had a negligible
impact on these fluxes.
Assimilating SSM resulted in spurious increases in drainage and runoff, which
degraded the SIM discharge Nash efficiency.
An issue in DA experiments was the underlying assumption made by the SEKF
that the model is perfect. Allowing for model and atmospheric forcing errors
could more easily be addressed with an EnKF method
than the SEKF, although both methods are affected by nonlinearity issues. In
the future we will test the EnKF using a similar validation employed in this
study. Regarding LAI assimilation, the SEKF assimilates the LAI observations
by aggregating the different vegetation patches in each grid box. This
approach is not optimal because each vegetation type exhibits unique seasonal
variability. Given the high resolution of LAI observations (1 km), work is
underway to disaggregate the observations.
While the ISBA LSM is well established and is used operationally at
Meteo-France, this study has helped us to identify some limitations that need
to be addressed. A new multi-layer diffusion model should improve
representation of the coupling between the surface and root-zone soil
moisture. Furthermore, a new multiple energy balance version should decouple
the bare soil evaporation and the transpiration processes that lead to an
unphysical link in ISBA between surface and deep soil moisture. Previous
research has demonstrated that the generic temperature response of
photosynthesis used in the model is not appropriate for plants adapted to the
cold climatic conditions of the mountainous areas. This is consistent with
the phase errors and the underestimated grassland LAI minimum in our study.
Solving this problem would presumably increase the LAI minimum in winter,
which would be more sensible than simply fitting the LAI minimum to
observations. Finally, the LDAS should benefit from further improvement of
the satellite-derived LAI and SSM. Using an observation operator for the
ASCAT backscattering coefficients would permit accounting for the vegetation
information content in the ASCAT signal.
The SAFRAN data are available to the research community
through the HYMEX database (http://mistrals.sedoo.fr/HyMeX/, Meteo-France, 2015). The
satellite-derived observations are freely accessible from the Copernicus
Global Land Service (http://land.copernicus.eu/global/, Copernicus, 2015). Streamflow
data are available from the Global Runoff Data Centre (GRDC)
(http://www.bafg.de/GRDC/EN/Home/homepage_node.html, GRDC, 2015) and identified in
the GRDC as the French contribution to the “Climate sensitive stations”.
The Supplement related to this article is available online at doi:10.5194/hess-21-2015-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work is a contribution to the IMAGINES (grant agreement 311766) project,
co-funded by the European Commission within the Copernicus initiative in FP7.
The work was also funded by the EUMETSAT H-SAF service. Discussions with
Patrick Le Moigne were useful for understanding the SIM hydrological model.
Useful feedback was also obtained through discussions with DA scientists at
the Met Office. We would like to thank the two anonymous reviewers for their
constructive comments. We would also like to thank Jean-Philippe Vidal from
IRSTEA for his useful comments and suggestions regarding anthropogenic water
management in the SIM hydrological model.
Edited by: W. Wagner Reviewed by: four anonymous referees
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