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# ECOCLIMAP: a global database of land surface parameters at 1 km resolution

J. L. Champeaux, V. Masson & F. Chauvin

Météo-France/CNRM, 42, Av Coriolis, 31057 Toulouse Cedex, France

Email: jean-louis.champeaux@meteo.fr

Soil/vegetation-atmosphere transfer schemes (SVATS) require only a few primary parameters as inputs (leaf area index, vegetation fraction and albedo). The object of this paper is to describe methods used to derive these parameters from surface cover maps, climate maps and NDVI datasets. The resolution of these new surface parameters is 1 km at the global scale. The ECOCLIMAP database is available online to the research community at http://www.cnrm.meteo.fr/gmme/PROJETS/ECOCLIMAP/page_ecoclimap.htm. An application of this strategy for urban use at a finer scale (30 m resolution) is presented here for the city of Marseille.

## 1. Introduction

As many studies show, accurate representation of land surface characteristics is often necessary if numerical models are to recreate meteorological events and climatological patterns in a reliable form. For example, particular atmospheric phenomena such as mesoscale waves, typical precipitation patterns, blocking of synoptic systems, and cyclogenesis can be generated or induced by the topography, which require that realistic and high-resolution two-dimensional fields in numerical models are used to represent this surface feature. Similarly, the land-water mask, soil-vegetation characteristics and urbanised locations need to be modelled in order to obtain realistic calculations of surface fluxes of heat, moisture and momentum over continental and oceanic surfaces. Other surface fields, like albedo and emissivity, have direct effects on the radiation balance at the surface and in the atmosphere.

Surface parameters are retrieved in different ways: some are obtained from conventional maps based on in-situ measurements, such as particular soil properties; others can be directly retrieved from remote sensing observations. In the latter case, the vegetation parameters are inferred indirectly from the land-cover map, using lookup tables (Champeaux et al. 2000).

At Météo-France, the soil/vegetation-atmosphere transfer schemes (SVATS) named ISBA (Interface Soil Biosphere Atmosphere; Noilhan & Planton 1989) is implemented in operational numerical weather prediction (NWP) models, in climate models, and in research mesoscale models. ISBA requires a few primary parameters, such as leaf area index, fraction of vegetation, and albedo, as inputs. The goal of this research is to

develop a consistent and coherent database available for the different models used by Météo-France.

## 2. Land cover map and climatic map at 1 km resolution

We created a map of important land cover parameters by combining several available land cover datasets. The coastline, lakes and river valleys, at 1 km resolution, come the Digital Chart of the World (DCW) database. The main land cover types come from the global land cover map elaborated by the University of Maryland (Hansen et al. 2000), derived from NOAA/AVHRR data. Permanent snows and wetlands come from the IGBP-DIS map (Loveland & Belward 1997). The resolution of these three datasets is 1 km. The 15 types of land cover described by the satellite data are: evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, mixed forest, woodland, wooded grassland, closed shrubland, open shrubland, grassland, cropland, bare soil, urbanised areas, permanent snow and ice, and wetlands.

The problem is that this classification takes into account only the most general pattern of the cover. For instance, open shrubland in polar regions (tundra) and in tropical areas (savanna) will have very different vegetative behaviours. The same is true for all these main types. Therefore, a map of climates of the world (Koepp & Delong 1958) was used to discriminate between these cover types. Sixteen climates were used: dry summer subtropical, tropical desert, semi-arid tropical, wet and dry tropical, wet equatorial, trade wind littoral, humid subtropical, semi-arid continental, intermediate desert,
J. L. Champeaux, V. Masson & F. Chauvin

moderate polar, cool marine, polar, cool littoral, humid continental, extreme subpolar, and marine subpolar.

The combination of the 15 cover types and the 16 climates for all the continents and the two hemispheres created a number of possible ‘ecosystems’. Some of them occupy a very small area on the planet, and are not representative. Others, even in different climates or continents, have a very similar behaviour. In order to distinguish the most representative cover-climate ecosystems, the 1-km NDVI (normalised difference vegetation index) composites from NOAA/AVHRR from April 1992 to March 1993 (Eidenshink & Faundeen 1994) were plotted for each new ecosystem. Aggregation between different ecosystems into one representative ecosystem was performed according to the following rules: an ecosystem with one cover type cannot be aggregated with an ecosystem with another cover type (e.g. closed shrubland will not be aggregated with open shrubland). This respects the main cover types above. Exceptionally, a forest could be aggregated with another forest, leading either to a forest with one type (needleleaf or broadleaf, evergreen or deciduous), or to a mixed forest.

The aggregation was performed with priority to climates on one continent. This was accomplished by comparing the NDVI annual profiles (minimum, maximum, shape of the profile). When more than one month separates the minimum (or the maximum) of two profiles, they were considered to be representative of two different vegetations, and were not aggregated. The ecosystems in the ‘wet equatorial’ climate are assumed to be the same on both sides of the equator. If two NDVI profiles for two ecosystems of the same cover type are found on several continents, they could also be aggregated. Finally, this procedure led to identification of 125 representative cover types across the world (except Europe).

The same procedure was used over Europe, but with initial data at a better resolution. Where available, the coastline and the land-cover types are derived from the CORINE land cover dataset (CEC 1993) at 250 m resolution, averaged at 1 km. Where this was not available, the PELCOM dataset (Mucher 2000) was used. The European climate map is derived from the FIRS project (EC 1995). This leads to approximately 90 ecosystems being assigned in Europe.

### 3. Surface parameters derived from the classification

In order to obtain a complete dataset, one needs to derive all the surface parameters needed in meteorological models. Orography and soil composition data (e.g. clay and sand fraction) are independent of cover types and come from an independent source (e.g. FAO 1998, for soil) while vegetation parameters depend on vegetation

<figure><img src="image_2.png" /><figcaption>Figure 1. Leaf Area Index January (upper) and for July (lower).</figcaption></figure>

type. These are derived from the 215 ecosystems defined in section 2. Initially, values are selected for the maximum and minimum LAI for each class of vegetation using in-situ databases. Then, the temporal variations of NDVI are used to impose a seasonality on the monthly LAI values using a linear relation between NDVI and LAI (Figure 1).

Other parameters such as the fraction of vegetation or roughness length are computed from LAI and the main land-cover type using analytical formulae. Albedo is retrieved from soil albedo using the soil map and from vegetation albedo using look-up tables widely available in the literature.

These fields can be obtained for any spatial resolution (1 km or more), using aggregation rules and the fraction of each ecosystem in each desired grid square.

### 4. Application at the urban scale

The same strategy that we followed for our global maps can be extended to small-scale maps in order to provide surface parameters at a very fine resolution. This is particularly useful for urban studies. As above, the prerequisite is to have the land-cover information at the desired scale. Figure 2 presents the CORINE land cover map for the city of Marseille (France), at a resolution of about 30 m. In this case, the same land-cover classes are available as for the global database, leading to a very simple methodology.
<figure><img src="image_3.png" /></figure>

Carte d'occupation des sols sur Marseille à la résolution de 10m.
Occupation du SOL PACA, 1999-CRIGE

<caption>**Figure 2.** Land cover map for the city of Marseille (France).</caption>

The climate information, at such a small scale, is as-
sumed to be homogeneous (here it is the Mediterranean
climate). Then, combination of the land cover (say
'dense urban') and this particular climate ('Mediter-
ranean') leads to an ecosystem that is already known.
Therefore, all of its physiographic surface parameters
are known from global maps (i.e. fraction of urban
surfaces and LAI). This is valid for each land cover,
leading to the desired maps of physiographic surface
fields. Their aggregation at the desired resolution (say
100 m), according to the fraction of each ecosystem,
works exactly as for the 1 km-ECOCLIMAP database.

Two additional possibilities are available, in order to
obtain more accurate maps:

* In specific areas, the surface parameters are known with better accuracy for one land cover. This is the case in Marseille, where detailed investigations were performed (Lemonsu et al. forthcoming) in order to simulate the energy balance of the city centre (Masson 2000; Masson et al. 2002). These parameters can then replace those given per default for this ecosystem.

* Additional land cover classes are available. This is the case in Marseille, where two suburban classes have been added to the original CORINE classes. This allows a better representation of the transition between city and countryside, but the new ecosystems must be defined, as well as their physiographic quantities.

Following this methodology, we have been able to
initialise accurately the surface parameters of the vege-
tation (ISBA) and urban schemes in the French meso-
scale research model named Meso-NH at a spatial
resolution of 250 m. Thanks to this, numerical studies

of the urban boundary layer over Marseille have been
performed in a realistic way.

5. Conclusion

This complete and coherent surface dataset has been
developed to be used in meteorological and climate
models. The very fine resolution of the global data
(1 km) allows initialisation of the model surface
parameters down to the meso-gamma scale. Such a
resolution already existed for land-use maps, but with
this dataset, vegetation (and urban) parameters are also
available. The 1 km dataset is used at full resolution in
the meso-scale research model.

The vegetation parameters are deduced from satellite
data for each month, climate and continent. This implies
that you can use the atmospheric numerical model
anywhere in the world, and at any time of the year. This
was already possible before, but only at a much coarser
resolution (1 degree). This is important both for climate
and global NWP models (because the resolution of the
global models increases) and for the limited area models
(because you can use them anywhere at high resolution
with data coherent with the actual surface).

This dataset is very interesting for grid-nested models
because it provides a coherent surface between all
the models. This results from the combination of the
two above points: the larger models will present a
low resolution, but they cover a large surface, so the
climate information is important, especially if numerous
climates are present in the domain. The finer models
will cover a smaller area, so the resolution information
is important, but yet this is completely coherent with
the climatic information used in the larger models. A
complete presentation of the ECOCLIMAP dataset is
provided by Masson et al. (2003).

References

CEC (1993) CORINE Land Cover Technical Guide.
European Union, Directorate-Environment, Luxembourg.

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