Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09–0.70 to 0.82–0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.
Surface soil moisture, as well as soil properties and precipitation intensity, is involved in the partitioning of rainfall into surface runoff and infiltration (water cycle), and also in the partitioning of the incoming solar and atmospheric radiations into latent, sensible and ground heat fluxes (energy cycle). It is therefore essential to correctly represent this amount of water contained in the soil in hydrological models.
Ground measurements of soil moisture are broadly used to monitor the hydrological cycle of a specific region. Like all in situ stations, the soil moisture probes need to be maintained and are most of the time installed for a limited amount of time. Moreover, the number of in situ measurements stays scarce, especially in tropical regions where the maintenance is even more complicated. Soil moisture monitoring from space has thus been developed for a larger/wider spatial coverage and assures continuity in time as long as the space mission is still operating. These two types are very complementary with in situ stations being able to directly measure soil moisture profiles at different depths and also used for satellite soil moisture validation.
In order to take advantage of these dedicated space missions, the hydrological model simulations can be merged with available observations through data assimilation. This technique has already been widely used by weather forecast models at regional and global scales, using remote sensing observations and ground measurements to improve weather forecasting.
Numerous studies have been devoted to use soil moisture assimilation into
hydrological and land surface models for various applications. With the
availability of more than 35 years of soil moisture at the global
scale derived from a series of satellites (SMMR, SSM/I, TRMM-TMI, AMSR-E,
ASCAT, Windsat;
More recently,
Assimilation is of particular interest for regions where water management is vital, whereas in situ hydrological data are scarce. This is the case in the West African region, which faces major water-related risks (drought, floods, famine, diseases) threatening the population safety and slowing down the economical development. At the same time, the region is notoriously known to be lacking in in situ hydrological data, which limits the possibility to properly address the water management issues.
For operational applications, real-time hydrological modeling is needed and this requires one to have real-time observations and information. Various real-time observations exist but may lack accuracy with biases that will impact all the hydrologic variables, and reanalyzed versions are made available several weeks to months after the actual observations. Precipitation forcing is the main source of uncertainty in hydrological modeling.
We propose a methodology to correct for the inaccurate amount of water brought by the real-time precipitation forcing by assimilating the SMOS soil moisture products. They are available within 10 days after the observations, and could be used for hydrological applications until the reanalyzed precipitation are released. This work will focus on the Ouémé catchment located in Benin, West Africa, which is presented in the first part along with the rainfall and soil moisture satellite products. The second part describes the hydrological model and the data assimilation method. Then the impact on the simulations of the soil moisture, the water table depth and the streamflow is discussed.
The Ouémé catchment is located in Benin, West Africa. Indicated on the right panel is the location of three soil moisture stations in the northwestern part (red crosses) where water table depth is also measured, and two streamflow sensors installed in the southern part (red circles, the outlet total streamflow being the sum of the two stations measurements).
The Ouémé catchment is located in Benin, West Africa, and is part of
the AMMA-CATCH observatory (African Monsoon Multidisciplinary Analysis – Coupling
the Tropical Atmosphere and the Hydrological Cycle;
Soil moisture is measured at three locations indicated by red crosses in
Fig.
The water table, which is defined as the interface between unsaturated and
saturated soil, is measured manually every 2 days on a network of
observation wells close to the soil moisture sites
Water levels from the rivers are measured every hour at two locations
(indicated by the two red circles in Fig.
The rainfall monitoring is ensured by a dense network of rain gauges (tipping
bucket). For the study of years 2010–2012, 33 evenly distributed rain gauges
were operating. Their measurements have been treated in order to produce
1 h rainfall series that have then been spatially interpolated over a
regular 0.05
In most cases and more particularly for tropical and semi-arid regions, there are not enough rain gauges to cover the entire basin and precipitation observed by satellite can be used. Many satellite products are available and three have been used in this study.
The PERSIANN (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Networks, v. 300 and 301;
A second satellite product has been used for precipitation forcing data: the
TRMM (Tropical Rainfall Measuring Mission) Near-Real-Time 3B42RT (v7;
CMORPH (CPC MORPHing from NOAA;
Cumulative average precipitation amount over the whole basin from the in situ network (black) and the three satellite product from quasi-real-time versions (left panel) and their reanalyzed versions (right panel): PERSIANN (blue), TRMM (green) and CMORPH (red).
PERSIANN, TRMM and CMORPH are the quasi-real-time precipitation forcing
products used in this study (referenced as RT). They usually are available
within a few hours after the actual observations. Their post-adjusted or
reanalyzed versions (PERSIANN-CDR, TRMM-v7 and CMORPH-v1) are generated by
adding external information like in situ rain gauge measurements or soil
radar observations (referenced as RE). They are generally more accurate but
are only available 2 to 3 months after the actual observations, which
is not compatible with real-time applications most of the time.
Figure
The SMOS mission has been producing soil
moisture products for more than 5 years, observing the entire globe every
3 days at a resolution of around 40 km. Thanks to the multi-angular
observations and the sensitivity of the L-band frequency to the soil water
content, the soil moisture is retrieved with a target accuracy of
0.04 m
The SMOS level 3 soil moisture product (second reprocessing, v. 2.7, 1-day
product;
For the Ouémé catchment,
DHSVM solves the energy and water balances at each grid cell and time step
with a physically based model representing the effect of topography, soil and
vegetation. The outputs are the soil moisture, the snow quantity (not used
nor showed here), the streamflow, the evapotranspiration and the runoff. This
model has already been used in many previous studies
DHSVM has been used in this study at a resolution of 1 km with an hourly time
step and four soil layers at the following depths: 1, 5, 40 and 80 cm.
The first layer has been set for numerical reasons, the second is used
for the assimilation, and the two deeper layers are used for validation with
in situ measurements. It also needs meteorological inputs for the following
variables: relative humidity, air temperature, wind speed, pressure,
shortwave and longwave radiation. The reanalysis MERRA (Modern-Era
Retrospective analysis for Research and Applications) products from NASA have
been used in this study
The DHSVM model has many parameters, which could be measured in situ or, if no
measurement is available, can be estimated based on soil characteristics and
vegetation covers. Previous studies
DHSVM soil and vegetation parameter values (understory and
overstory) after calibration. The marker
In
As mentioned in
Open-loop (OL) water table depth simulations using the RT (top panel) and reanalyzed (bottom panel) satellite precipitation products as forcing compared to in situ measurements at Nalohou. For comparison, the water table depth simulations using in situ precipitation as forcing (not shown here) lead to a correlation of 0.76.
Open-loop (OL) streamflow simulations using the RT and reanalyzed
satellite precipitation products as forcing. Statistics are given on the
right panel. For comparison, the streamflow simulations using in situ
precipitation as forcing (not shown here) lead to a correlation of 0.92 for
a bias of 32 m
One of the five outputs of DHSVM is the water table depth. Groundwater is
an important resource, especially in West Africa where most of the drinking
water comes from the ground. Moreover, the precipitation interannual
variability can be important (1560 mm in 2010 followed by only 1100 mm in 2011
and 1450 mm in 2012 from the in situ rain gauge measurements), which has
a strong impact on groundwater recharge. The water table depth can vary
between the soil depth and the ground surface (in the latest case, an
exfiltration or a flooding can happen). Sensitivity tests have been realized
for the Ouémé catchment with many years of spin-up for various soil
depth values and the maximum water table depth was always found around 1.90 m.
After these yearly spin-ups, water was filling the soil until its natural
equilibrium. The water table depth does not depend on the soil depth but on
the ability of the model to evacuate this saturated water through the defined
hydrological network, the root density and the topography (physical processes
are explained in
Figure
Using the RT precipitation (top panel), the water table depth is correctly simulated until the first rainfalls when the soil is quickly saturated due to the inaccurately high amount of water brought by the RT products, which then percolates to the deep soil layers. The soil is completely saturated in early May with a simulated water table reaching the surface. The correlation scores are very low for PERSIANN and CMORPH (0.09 and 0.33), whereas the TRMM product gives fair simulations with a correlation of 0.70. Using the reanalyzed precipitation (bottom panel), the time evolution is improved and most of the early peaks are smoothed. The correlations are higher for PERSIANN (0.79) and CMORPH (0.84), whereas it is lower for TRMM (0.48) due to inaccurate precipitation event in spring 2011 and in winter–spring 2012.
Figure
It is not expected from the RT precipitation products to generate simulations
as good as the reanalyzed precipitation, but Figs.
SMOS soil moisture is assimilated into the DHSVM model using an optimal
interpolation method (simplification of the Kalman filter where the errors
are assumed to be known). In this study, the “3D-Cm” method proposed in
Based on the difference between the simulations and the observations, the model background predictions are updated depending on their respective error covariances. Ensemble methods can estimate these error covariances from a Monte Carlo ensemble generation but in this study, a simpler method has been applied and fixed values of the error covariances are used.
Before being assimilated and for an optimal analysis
At each time step
The model error covariance matrix
The SMOS observation error covariance matrix
Finally, the observation matrix
Here,
The SMOS observations are assimilated in the second soil layer of the model
(1–5 cm) since it is more representative of what is observed by the SMOS
instrument
In order to quantify the performances of the model simulations and the impact
of the SMOS soil moisture assimilation, five statistics metrics have been
chosen in this study: the temporal correlation
Weighing functions for the observation matrix
This section presents the impact of the SMOS soil moisture assimilation on different variables: soil moisture at multiple depths (control variables) at the Bira station, water table depth at the Nalohou station, and streamflow at the outlet. The simulations and performances after assimilation are compared to the open-loop simulations in the objective to reach those from the reanalyzed precipitation products.
The first variables to be impacted by the assimilation of SMOS products are
the ones directly contained in the state vector of the assimilation scheme;
i.e., the soil moisture of the four defined soil layers at 1, 5, 40 and
80 cm. Soil moisture simulations are shown in Fig.
As mentioned before, the RT satellite rainfall products bring too much water
during the winter and spring seasons. The first time period (top panel of
Fig.
Statistics of the simulated soil moisture at 3 depths (5, 40 and
80 cm) compared to the in situ measurements at the Bira station for
2010–2012. Three cases are considered: open-loop simulations using real-time
satellite precipitation (RT), assimilation of SMOS soil moisture with real-time precipitation (RT
Comparison between the simulations of soil moisture at 5 cm depth at the Bira station at two different time periods: dry season in 2011 (upper panel), and the beginning of the raining season in 2012 (lower panel). The open-loop simulations are represented on the left whereas the simulated soil moisture after assimilation are on the right. The different rainfall products are indicated with various colors. Assimilated SMOS observations are indicated by yellow triangles on the left panel.
Table
Statistics of the simulated water table depth (WTD) compared to the
in situ measurements at the Nalohou station for 2010–2012. Three cases are
considered: open-loop simulations using real-time satellite
precipitation (RT), assimilation of SMOS soil moisture with real-time
precipitation (RT
Simulations of the water table depth at the Nalohou station (in situ measurements in black) using RT precipitation for after SMOS assimilation (in colors). Correlations are also indicated in the figure.
Using the RT satellite precipitation products, the bias is always reduced
after the assimilation. At 5 cm depth, it is improved by 0 % (TRMM) to
37 % (CMORPH), at 40 cm depth by 17 % (TRMM) up to 56 % (PERSIANN), and at 80 cm,
by 12 % (TRMM) up to 47 % (PERSIANN). The biases are even lower than with
PERSIANN and TRMM reanalyzed products. This shows that the assimilation and
the model are able to propagate the information from the 5 cm layer to the
deeper layers of the soil. The largest improvements are naturally obtained
when the PERSIANN and the CMORPH products are used as precipitation forcing
since there are the ones bringing the most extra water in the model. This
proves that assimilation can correct for this additional amount of water.
Moreover, the open-loop simulations show unrealistic soil saturation at the
5 cm layer during the rain season (soil moisture value is equal to porosity,
see Fig.
Assimilation does not correct directly the precipitation: neither for the amount of water nor for the time of the event itself. So the volume of water given to the model remains the same and the peaks in the soil moisture simulations cannot be corrected until a SMOS observation becomes available, and only the drying phase can then be modified.
The impact of the assimilation on the evapotranspiration variable has also
been studied but not shown here. The changes in evapotranspiration were very
small after the assimilation using the real-time precipitation products: it
was overestimated before (
Figure
There is a clear benefit from the SMOS soil assimilation even at deeper
layers than the ones used for the assimilation directly. The peaks in the
period from April to June are strongly reduced and the temporal behavior is
in line with the in situ time evolution. The correlation scores are also a
good indicator of the improvement brought by the assimilation and it is
improved for all the precipitation products. Compared to
Fig.
Finally, Fig.
Statistics of the simulated streamflow (
Simulations of the streamflow at the outlet after SMOS soil moisture assimilation with real-time precipitation forcing (indicated in colors for PERSIANN, TRMM and CMORPH) compared to in situ measurements (black line).
Taylor diagrams of the streamflow performances for the three
rainfall products (PERSIANN on the left, TRMM in the middle, CMORPH on the
right) using their real-time version only (RT), their reanalyzed
version (RE), and the RT version after SMOS assimilation (RT
Except for the TRMM product, all the streamflow statistics are improved by the assimilation, especially the error (divided by 3), and the Nash coefficient (from negative to positive). Even if the reanalyzed precipitation produce better performances, the improvement using SMOS assimilation with RT precipitation is important. The TRMM case is different from the other two products since the RT version already gives fair performances, and the assimilation degrades these performances a little bit, whereas the reanalyzed version slightly improves them.
Another representation of these statistics is the Taylor diagram in
Fig.
The arrows on the diagram show the impact of the assimilation on the statistics using RT precipitation. For TRMM, the after-assimilation point is not much closer indicating no clear evidence of improvement from the assimilation. As mentioned before, the TRMM precipitation product already gave the proper amount of water, so SMOS assimilation cannot improve it very much. However, simulations using PERSIANN and CMORPH products are greatly improved by the assimilation attested by the long arrows ending much closer to the RE and in situ points.
Precipitation forcing is generally the main driver in hydrological models and it is generally not simple nor immediate to collect and distribute in situ measurements in sufficient number and of quality. If in situ precipitation can be used for model calibration, real-time or quasi-real-time applications require forcing and observations quickly in order to react accordingly, such as in the case of a flooding event. Accurate rainfall products from satellite observations are usually reanalyzed data sets available 2 to 3 months after. Although real-time precipitation products are expected to be biased, they are available a few hours to a couple of days after the observations. Three satellite rainfall products have been tested: PERSIANN, TRMM and CMORPH.
The study shows the benefit of the assimilation of the SMOS soil moisture products on three hydrological variables: the soil moisture, the water table depth and the streamflow, which are key variables in the hydrological processes.
By assimilating SMOS soil moisture, the first impacted variables were naturally the soil moisture of the different soil layers of the model. Here, we have showed that, even using a very simplistic methodology of assimilation, the bias in the simulated soil moisture has decreased significantly after the assimilation using the real-time precipitation product. At deeper ground, the simulations of the water table depth showed a much better correlation after the assimilation when compared to in situ measurements (from 0.09–0.70 to 0.82–0.87). These scores were either higher or equivalent to those from the reanalyzed rainfall products. This positive impact of the assimilation on these hydrological variables can lead to a better simulation and management of the actual ground water resources.
The inaccurate amount of water brought by the real-time rainfall products also has a substantial impact on the streamflow. The extra water can saturate the soil faster, thus increase the runoff and the subsurface lateral flow, and be finally intercepted by the water channel. This whole sequence of processes is also positively impacted by the soil moisture assimilation. The streamflow at the outlet of the basin has been much improved for the PERSIANN and CMORPH rainfall products with errors divided by a factor 3 and a Nash coefficient going from negative to positive (TRMM real-time product was already fairly good compared to the other real-time products). After assimilation, the performances were either slightly lower or equivalent to those using the reanalyzed products. Again, this positive impact of the assimilation can lead to a better simulation and management of extreme events such as floods during the monsoon period in this case.
This work shows the possibility to implement a near-real-time hydrologic
framework for real-time application wherever it is possible to obtain a
proper calibration of the hydrological model beforehand, which is one
limitation of this method but this can be overcome by using reanalyzed
satellite precipitation. Optionally, the real-time rainfall products could
be directly corrected using SMOS observations and following current
methodologies
The authors would like to first thank the Direction Générale de l'Eau du Bénin for the streamflow measurements at the two sub-catchments (Beterou and Cote 238), and the Centre National d'Etudes Spatiales (CNES) TOSCA program for funding this project. The authors would also like to acknowledge the Global Modeling and Assimilation Office (GMAO) and the GES DISC for the dissemination of MERRA data, NOAA and NASA for the dissemination of the precipitation products (PERSIANN, TRMM and CMORPH), the AMMA-CATCH team for providing the in situ measurements, and the ALMIP-2 team for the first DHSVM calibration set. The AMMA-CATCH regional observing system was set up thanks to an incentive funding of the French Ministry of Research that allowed pooling together various pre-existing small-scale observing setups. The continuity and long-term perennity of the measurements have been made possible by uninterrupted IRD funding since 1990 and by continuous CNRS-INSU funding since 2005. Edited by: F. Fenicia