Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.
The Sahel has been subject to significant droughts since the late sixties that emphasize the vulnerability of the hydrological cycle to climatic and environmental conditions. These droughts involve critical consequences on the water resources as well as on the Sahelian populations. Nevertheless, the hydrological response to climatic or environmental changes is complicated by the complexity of the terrestrial system: the severe climatic conditions, the large land cover heterogeneity (vegetation and soil) and the poor amount of data available for processes calibration. Reciprocally, the continental part of the hydrological cycle seems to impact on the West African Monsoon (WAM). The WAM takes place during the summer months, generally from June to September. One of the key processes is the partitioning of rainfall into runoff, infiltration, soil water storage, and evapotranspiration. Since the pioneering study of Charney [
The influence of the land surface on the WAM depends on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This fact induces the necessity to develop land surface-atmosphere coupled models for a better understanding of the role of the land surface on the WAM dynamics in order to better predict its variability. On the other hand, a realistic simulation of the hydrological impacts of seasonal climate anomalies and global warming will be critical in the near future for water resources, ecology, and human activities. In this context, many efforts are underway to improve the representation of the continental hydrological cycle in numerical land surface-atmosphere-coupled models. The use of high-resolution mesoscale models represents a significant advantage compared to coarser AGCMs. In these models, the land surface is generally represented by land surface models (LSMs) with multiple parameterizations that represent
In the present study, the Weather Research and Forecasting (WRF) model is used where the land surface is simulated via the Noah LSM [
Two experiments are performed over the Sahel in order to compare Noah-SGH with the former Noah version into WRF. The results are evaluated over Dantiandou Kori mesosite (
The Advanced Research WRF model (ARW, version 2.2) is a non-hydrostatic model using, in this study, 28 sigma vertical levels. The micro
Noah is a relatively simple LSM and the following description corresponds to the control version (
The new version of Noah (named
In addition, to account for the larger soil moisture gradient near the surface, seven soil layers are used instead of four in which the 10 cm original top layer is replaced by three fine layers (1 cm, 3 cm, and 6 cm) while the three other deeper layers are replaced by four new layers. The thicknesses of each layer depend on total soil depth computed for each vegetation type (from 2 m for grassland, cropland, shrubland, or savana to 5 m for Evergreen Forest). An exponential profile with soil depth of the saturated hydraulic conductivity,
Secondly, both Horton and Dunne runoff, as well as heterogeneities in land surface properties are taken into account. Land cover and soil depth heterogeneities are represented using a tile approach in which each grid cell is divided into a series of subgrid patches. This method has the advantage of explicitly representing very distinct surface types with specific properties. Each subgrid patch extends vertically throughout the soil-vegetation-snow column. The relative grid cell fractional coverage of each tile is used to determine the grid box average of the water and energy budgets.
The Dunne runoff is computed via a TOPMODEL approach that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas for runoff generation [
Finally, the Horton runoff is computed using a maximum infiltration capacity function,
The Noah-WRF model is implemented over a part of West-Africa and it is configured with three nested grids in a two-way mode (Figure grid 1: 45 km grid-scale resolution with a 225-second time step, grid 2: 9 km grid-scale resolution with a 45-second time step, grid 3 (evaluation domain): 3 km grid-scale resolution with a 15-second time step.
The full experimental domain over West Africa where Noah-WRF is applied in nesting mode. The two coarse grids (1 and 2) used in this study are shown as well as Dantiandou Kori (grid 3, the evaluation domain).
The same atmospheric and land surface
The large-scale lateral boundary conditions (pressure, wind, temperature, and humidity) are provided by the NCEP Final Analysis (FNL, ds083.2) over more than 2 years (from July 2004 to December 2006), on 6-hour time step and at 1° resolution. The two following simulations are performed starting from the same initial conditions of soil temperatures and soil moisture using NCEP FNL data at the first of July 2004 and the period from 2005 to 2006 is used at the evaluation stage:
Land surface characteristics are specified using the WRF default United States Geological Survey (USGS) data. In grids 1 and 2, these data are given at a 2 arc minute (
Dantiandou Kori (grid 3) is close to Niamey and the center of HAPEX-Sahel’s square degree (2-3°E, 13-14°N) [
In addition, simulated surface temperatures are compared to the remote sensing Meteosat Second Generation/Spinning Enhanced Visible and Infra Red Imager (MSG/SEVIRI) data where the surface temperatures are estimated using a Split-Window algorithm accounting for land surface emissivity, atmospheric water vapour, and satellite viewing angle [
Surface soil moisture estimations derived from ASAR/ENVISAT C-band radar instrument are also used to evaluate the simulation. Soil moisture data are provided at high resolution (12.5 m) and only for field with bare-soil or low-density vegetation, using low-incidence-angle radar data (ISI configuration). The comparison between
Finally, the WRF results are also compared to
Figure
Domain average comparison between simulated and observed precipitation over Dantiandou Kori: (a) the cumulated annual precipitation rate (mm) simulated by each experiment; (b) the ratio to annual precipitation of daily precipitation simulated by each experiment but not observed, expressed in %, distinctions are made for precipitations that are simulated before, during and after the observed monsoon season; (c) the times series of simulated and observed monthly and daily precipitations (mm/day). The observations are in black,
2 years cumulated precipitations.
The amount of annual precipitation simulated but not observed.
Time series of daily and monthly precipitations (mm/day).
where
Comparing the
Statistical comparison (
Figure
Comparison between the simulated and MSG/SEVIRI estimated monthly surfaces temperatures over Dantiandou Kori. The Bias,
The simulated first 10 cm soil moisture is compared to ASAR estimates acquired in 2005 over the whole Dantiandou kori. The domain average biases are shown in Figure
Comparison between surface soil moisture simulated and estimated from ASAR radar data. The domain average bias between the simulations and each ASAR image over Dantiandou Kori are shown.
Finally, the comparison of the water budget between
The 2005-2006 simulated water budgets over Dantiandou Kori: (a) each water budget component is expressed in % of total precipitations for each experiment; (b) time series of the domain average monthly total runoff and evapotranspiration.
Water budget
Time series of monthly total runoff and evapotranspiration
Figure
Simulated surface atmospheric variables versus
Variables | Exp | ||||||||
---|---|---|---|---|---|---|---|---|---|
1.73 | 6.92 | 2.37 | 15.70 | 0.64 | 16.6 | 0.08 | |||
1.96 | 11.94 | 0.23 | 12.6 | 0.18 | |||||
38.74 | 28.06 | 30.68 | 22.39 | 13.6 | 0.93 | 0.77 | |||
35.39 | 25.83 | 10.3 | 0.94 | 0.86 | |||||
302.33 | 5.23 | 304.27 | 4.90 | 1.94 | 3.2 | 0.88 | 0.64 | ||
303.49 | 4.93 | 1.16 | 2.8 | 0.88 | 0.71 | ||||
245.14 | 327.25 | 270.25 | 370.24 | 25.10 | 149.7 | 0.92 | 0.79 | ||
272.73 | 371.14 | 27.58 | 143.7 | 0.93 | 0.81 | ||||
382.32 | 40.49 | 399.64 | 39.75 | 17.32 | 25.6 | 0.89 | 0.60 | ||
398.33 | 38.64 | 16.02 | 24.4 | 0.89 | 0.64 | ||||
3.08 | 1.71 | 3.53 | 1.44 | 0.45 | 2.0 | 0.26 | |||
3.46 | 1.42 | 0.39 | 1.9 | 0.29 |
Monthly comparison between
Figure
As in Table
Variables | |||||||||
---|---|---|---|---|---|---|---|---|---|
70.12 | 182.38 | 116.87 | 233.48 | 46.75 | 98.6 | 0.94 | 0.71 | ||
126.96 | 231.88 | 56.84 | 101.2 | 0.95 | 0.69 | ||||
33.98 | 60.98 | 96.42 | 136.97 | 62.44 | 108.8 | 0.87 | |||
92.40 | 136.24 | 58.42 | 106.4 | 0.86 | |||||
56.35 | 79.17 | 36.50 | 51.34 | 55.7 | 0.75 | 0.51 | |||
50.44 | 74.26 | 54.6 | 0.76 | 0.52 | |||||
2.88 | 54.50 | 78.62 | 48.2 | 0.80 | 0.22 | ||||
72.55 | 47.1 | 0.76 | 0.25 |
As in Figure
The evaluation of Noah-WRF against Wankama
In 2005, the simulated and observed annual rates of precipitation are very close while the comparison of the simulated surface soil moisture with ASAR estimates shows that the model is relatively wet during the WAM. As it can be observed in Figure
Despite these limitations, the new land surface hydrology shows relative improvement of the model results in terms of precipitation, 2 m air temperature, relative humidity, total evapotranspiration, surface soil moisture, and surface temperature. These results confirm that the representation of the surface hydrology can impact on the surface fluxes and state variable simulated during the WAM by a coupled land-atmosphere mesoscale model. Nevertheless, this conclusion must be taken with caution. Even if the studied area is constrained by the atmospheric lateral boundary conditions simulated by the two other domains that contribute to limit its own variability, mainly ensemble experiments with different land surface initial conditions, such as soil moisture and/or different meteorological conditions (clear sky and severe weather cases), will be able to confirm the real influence of this land surface hydrology on the Sahelian WAM simulated by WRF.
The choice of some parameterizations used in
This study focuses on the evaluation of a new land surface hydrology into the coupled Noah-WRF mesocale model for hydrological applications over the Sahel. A comparison between the previous version of the Noah land surface model and the new version are presented at high resolution (3 km) over Dantiandou Kori against a dense rain gauge network, satellite estimates of surface temperature and soil moisture, and
Generally, the WAM is reasonably reproduced by the model even if some limitations appear throughout the comparison between simulations and observations. The simulated precipitation appears generally overestimated, especially concerning some extreme rainy events. However, the WAM period appears relatively well simulated by the model. The observations point out that 2006 is wetter in terms of precipitation than 2005. This finding is also well reproduced by the model but a warm bias is also found. Then, it impacts on the surface energy budget by overestimating downward shortwave radiation and consequently increasing net radiation, surface temperatures, and sensible heat fluxes, especially during the WAM. Consequently, this bias must be corrected to improve the simulated WAM over the Sahel with WRF. Further investigation should be made in the near futures using another radiative scheme that takes into account dust and aerosols and improving the representation of the soil heat flux.
The poor vegetation database in this study emphasises the need to develop alternative maps of vegetations properties using, for example, remote sensing products. This feature could be of primary importance to perform mesoscale-coupled studies in order to quantify the role of the vegetation on the Sahelian WAM dynamics. Furthermore, the land surface hydrology could be improved by adding the representation of a “soil surface crust” and accounting for soil characteristics heterogeneities which mainly control the surface runoff production over the Sahel.
Despite these limitations, the comparison between the former and the new land surface hydrology into WRF shows that Noah-SGH induces some relative improvements in terms of model performance. This new version of Noah-WRF, under condition of some additional improvements, represents an interesting tool to perform mesoscale hydrological studies within land-atmosphere-coupled experiments over the Sahel.
The authors would like to thank all their colleagues at the many French and African laboratories that have participated in the development of the AMMA-CATCH experiment and particularly the Institut pour la Recherche et le Développement (IRD). Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from France, UK, USA, and Africa. It is also the beneficiary of a major financial contribution from the European Community’s Sixth Framework Research Programme. Detailed information on scientific coordination and funding is available on the AMMA International web site