Geomorphologic approach for modelling the ... - Semantic Scholar

Geodinamica Acta 13 (2000) 245–270 © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved S0985311100010445/FLA

Geomorphologic approach for modelling the surface features of arid environments in a model of dust emissions: application to the Sahara desert Yann Callotab*, Béatrice Marticorenac, Gilles Bergamettic a

Département de géographie, faculté GHHAT, université Lumière-Lyon-2, 5, avenue Pierre-Mendès-France, 69676 Bron cedex, France b Laboratoire Prodig, UMR CNRS 8586, Institut de géographie, 191, rue Saint-Jacques, 75005 Paris, France c Laboratoire interuniversitaire des systèmes atmosphériques, UMR CNRS 7583, universités Paris-VII et XII, centre multidisciplinaire de Créteil, 61, avenue du Général-de-Gaulle, 94010 Créteil cedex, France Received 13 January 2000; accepted 10 July 2000

Abstract – Mineral dust emissions from arid regions are influenced by the surface features encountered in the source regions. These surface features control both the erosion threshold and the intensity of the dust flux. Recently, a soil-derived dust emission scheme has been designed in order to provide an explicit representation of the mineral dust accounting for the influence of the surface features on the dust emissions. This physical scheme has been validated with micro-scale field measurements. Its large scale application has required the development of additional relations to estimate the input parameters from more accessible data: the mean height and the covering rate of the roughness elements and the mineralogical soil type. The determination of these surface data has been based on a geomorphologic approach which describes the surface features of arid areas in a 1 × 1° grid. Inside each square degree, up to five different areas characterised by different surface features have been distinguished. However, these areas have not been located inside the square degree. Each area can be constituted by several combined surface features, including roughness, vegetation, granulometry. Five main types of landscapes and eight main types of surface features have been distinguished. This approach is based on the combination of various data, mainly topographical, geological maps and climatological analysis. In addition to the problem of scale transfer, the main constraints to obtain a quantitative assessment are the confidence level of the existing data and the number of parameters to document. On the opposite, with this method, the fine scale required by the dust modelling can be separated from the scale accessible by the mapping approach, of the order of the square degree. This method can also be easily improved by aggregating new data and can be extended to other deserts. An example of application is given for the north-west of the Algerian Sahara where the method has been elaborated. The data provided by the modelling of

* Correspondence and reprints. E-mail address: [email protected] (Y. Callot).

the surface have been used to simulate dust emissions for 1990, 1991 and 1992 over the central and western Sahara. Over these three years, the mean annual dust emission is about 760 Mt·year–1 Although a significant interannual variability exists (mainly due to changes in the wind pattern), the most intensive emissions remain quite constant in terms of location. The percentage of agreement with satellite observations higher than 0.7 is 74 %, but only 32 % when using a model having a single threshold function for dust emission (i.e. the same surface feature for the whole Sahara) (cf. later Marticorena et al., 1997). © 2000 Éditions scientifiques et médicales Elsevier SAS modelling / geomorphology / arid environments / dust emissions / sahara Résumé – Les émissions d’aérosols désertiques à partir des régions arides sont fortement influencées par les caractéristiques de la surface des sols en zone source, aussi bien en ce qui concerne les seuils d’érosion que l’intensité des flux de poussières émis. Récemment, un modèle physique a été développé permettant de rendre compte de l’influence des caractéristiques de surface sur les émissions de poussières. Ce modèle a été validé à partir de mesures de terrain à micro-échelle. Son application à plus grande échelle a nécessité le développement de relations supplémentaires permettant d’estimer les paramètres d’entrée du modèle à partir de données plus accessibles : la hauteur moyenne et le taux de couverture des obstacles présents à la surface et le type minéralogique de la couche superficielle du sol. Ces données de surface ont été déterminées au travers d’une modélisation, à partir d’une approche géomorphologique, de la surface des zones arides en secteurs d’un degré carré. Cinq types principaux de paysages et huit types principaux d’états de surface ont été distingués. L’approche est fondée sur l’utilisation de données variées, essentiellement les cartes topographiques, géologiques et les données climatologiques. L’utilisation de « points de calage » (observations de terrain, photographies aérien

— 245 —

Y. Callot et al. / Geodinamica Acta 13 (2000) 245–270

modélisation / géomorphologie / milieux arides / aérosols désertiques / Sahara

morphologic approach. The combination of these data with the physical dust emissions has provided simulations of the Saharan dust emissions in good agreement with the satellite observations of dust emissions over this region [1]. In this paper, we describe the geomorphologic approach that has been developed to model the surface features over arid areas and its application over the Sahara from 36° N to 66° N. Since dust erosion by wind is a phenomenon acting on the interface between soil surface and atmosphere, this work was a joint research action between geomorphologists and atmospheric scientists. In part 2, we briefly summarise the input data required by the dust emission model and the way they can be retrieved from more easily accessible data. The general principles of the geomorphologic approach developed to model the surface features and the information used for are described in part 3. An example of application over a region of 12 square degrees is given in part 4. The influence of these surface features on the potential of mineral dust emissions over the Sahara is illustrated in part 5. The advantages and limitations of this modelling of the surface features in arid regions are discussed in the conclusion.

1. Introduction

2. Definition of the data to retrieve

The simulations of the atmospheric dust cycle using the global model of the atmospheric circulation for past and present conditions help in understanding how climatic changes and dust emissions interact. Until now, most of these simulations [2–5] have failed in reproducing both the intensity of dust emissions and the atmospheric concentration fields. The reasons most frequently invoked to explain these deficiencies are the parameterisations used to represent the emission processes [2–4]. In these simulations, the spatiotemporal heterogeneity of the dust emissions is mainly due to the variability of the wind velocity. However field studies of erosion processes have shown that the surface features play an important role in both the location of the sourceregions and the intensity of dust emissions [6–9]. Indeed, the surface features control three major processes of dust production: the erosion threshold wind velocity, the wind shear-stress acting on the erodible surface, and the capability of the soil (in this paper, ‘soil’ is not used with its restricted pedological meaning, but with its general meaning) to release fine dust particles. In order to improve global simulations of the desert dust cycle, a physical dust emission scheme accounting for the influence of the surface features on the dust emissions has been developed and validated [10]. Since the input parameters required for a large scale application of this physical scheme were not directly available in the form of established data sets, additional relationships have been developed to determine these input parameters based on data describing the surface features and accessible at such scales [1]. These data characterising the surface features have been modelled over the Sahara using a method based on a geo-

Aeolian erosion is a process acting at the interface between the continental surfaces and the atmosphere. Because this interaction takes place at the extreme top of the continental surface, the relevant geomorphologic characteristics of the arid regions for wind erosion modelling are the surface features. The surface features can be defined, according to Escadafal (1989) [11] as the characteristics of the pedological cover directly in contact with the atmosphere and presenting an organisation or a composition which differ from the connected sub-layer. It includes the loose soil, the rough mineral elements present on the surface like rocks, gravel, pebbles, etc., but also the biological part of the surface, especially vegetation. These surface features influence the major physical processes involved by the wind erosion. In particular, the threshold wind velocity for aeolian erosion is a key parameter to predict the dust emissions. Indeed, the frequency of the dust events is given by the number of cases where the wind velocity exceeds this threshold, while their intensity depends on how much the threshold is exceeded. Aeolian erosion occurs only when the energy brought to the erodible surface by the wind balances the energy required to mobilise the available soil micropeds. These micropeds are defined as the primary particles that occur and persist within the soil and which correspond to the natural microaggregation of individual grains. The minimal energy required to mobilise a microped depends on its mass, i.e. mainly on its size. In fact, due to the influence of the gravity and the interparticle cohesion forces [12–15], there is an optimum size, of the order of 50–100 µm, for a microped to be

nes, images satellites) permet une approche quantitative d’autant plus fiable que l’on est proche des points de calage. À l’intérieur d’un degré carré, il a été distingué jusqu’à cinq aires différentes, sans qu’elles soient spatialement localisée dans celui-ci. Chaque aire peut elle-même être constituée de plusieurs états de surface combinés, incluant rugosité, végétation et granulométrie. En dehors du transfert d’échelle, les contraintes principales sont la fiabilité des données et le nombre de points à documenter quantitativement. Les apports sont la différence de nature entre les échelles de dimension inférieures au degré carré – du domaine de la seule modélisation – et les échelles supérieures, plus proches de la cartographie, la facilité d’amélioration et de généralisation de la méthode à d’autres déserts. Un exemple de modélisation de la région où elle a été mise au point, au NW du Sahara algérien, est décrit. Les données issues de la modélisation des états de surface sur le Sahara ont été utilisées pour simuler les émissions d’aérosols désertiques pour 1990, 1991 et 1992. En moyenne, ces émissions sont de l’ordre de 760 Mt·an–1. Bien qu’elles présentent une forte variabilité interannuelle liée à des variations des régimes de vents, les zone d’émissions les plus intenses restent relativement constantes en terme de localisation. © 2000 Éditions scientifiques et médicales Elsevier SAS

— 246 —


mobilised by the wind corresponding to the minimal threshold wind friction velocity. The energy brought to the erodible surface by the wind depends on the presence of roughness elements. On one hand, obstacles absorb a part of the wind energy, decreasing that available to raise the erodible particles. On the other hand, the surface roughness influences the wind velocity profile: for the same wind velocity at a given height (10 m in general), the wind friction is higher over a rough surface than over a smooth one. For most of the natural situations, the result of these two effects is an increase of the erosion threshold with the roughness density [7]. That is the reason why most of the strategy developed to prevent natural surfaces from aeolian erosion consists in maintaining a sufficient cover in vegetative residue or other roughness elements. Once the erosion threshold has been reached, the soil micropeds are set in motion in a saltation process: soil grains are ejected from the surface by the wind and impact downward, leading to the ejection of other soil grains. The amount of material in movement near the ground (horizontal flux) is a function of a power 3 of the wind velocity but also depends on the erosion threshold. Since the threshold for movement varies with the size of the mobilised soil micropeds, the horizontal flux depends on the size distribution of these soil micropeds. Saltation is a prerequisite step for mineral dust production: fine particles are seldomly present in the soil as loose particle, they result mainly from the breakage of the soil micropeds in the horizontal flux [16, 17]. This process is referred as ‘sandblasting’ [16] or ‘bombardment’ process [17]. The intensity of the dust flux depends on the amount of impacting soil grains (i.e. on the horizontal flux) but also on the capability of the soil to release such fine particles [17]. A physical model has been recently developed that described the different stages leading to mineral dust production and accounts for the influence of the soil surface features on the dust emissions [1,10]. It requires input parameters characterising both the soil micropeds available in the soil surface layer and the roughness elements: • the size distribution of the soil micropeds available for wind erosion; • the soil clay content which is used as an indicator of the capability of the soil to release fine particles; • the aerodynamical roughness length induced by the erodible surface and by the non-erodible obstacles. These parameters are not available in the form of global data sets. Thus, for large scale applications, we have developed additional relationships to estimate these input parameters from data characterising the surface features that could be retrieved.

2.1. Soil characteristics 2.1.1. Definition of the required soil-size distribution As previously mentioned, the size distribution of the soil micropeds that can be mobilised by the wind is a very important factor for the description of the wind erosion. The difference between this in-situ size distribution of micropeds and the classical definition of texture used in sedimentological studies is that, in this latter, the soil texture is defined after disruption of the aggregates, especially the clayey ones. It provides a soil texture information useful for the soil capability for run-off or vegetation growth rather than information on the peds as they can be found in their natural state of aggregation. 2.1.2. Determination of the input soil parameters Such an approach based on the size distributions of the soil aggregates, determined by using ‘dry’ techniques that minimise the breakage effects, has been proposed by Chatenet et al. [18] and is briefly summarised hereafter. 2.1.2.1. Size distribution A set of 26 representative surface sediment samples (corresponding to the first five centimetres of the soil and often less) was collected in various arid and semi-arid regions (Algeria, Niger, United States (California), Spain). Because soil aggregates larger than 1–2 mm are erodible only by extremely high winds, only size fractions smaller than 2 mm were measured by dry sieving. A fitting procedure was used, based on the calibration of log-normal distributions of the measured mass size distribution. The fitting procedure minimises the difference between the simulated and observed populations for each size class [19]. For three size fractions ( to 10–2 cm), Marticorena et al. [1] have proposed an empirical relationship linking the mesoroughness length (Z0) to the height and density of the obstacles present on the surface. This relationship has been established from data obtained in wind-tunnel experiments and available in the literature: For k < 0.11

Z0 /h = 0.479 k − 0.001

For k > 0.11

Z0 /h = 0.005

(3) (4)

λ (λ = Σs / S) is the ratio of the sum of the frontal surfaces s (exposed to the wind) of the obstacles present on a given surface S, to this surface S. For smooth surfaces, (z0 of the order of 10–4 to 10–3 cm) the micro-roughness length (z0) can be estimated from the mean diameter Dp of the soil micropeds using the classical relation [22]: z0 = Dp /30

(5)

This relation also applies to the erodible parts of rough surfaces. The soils’ microped-size distribution being an input parameter of the dust emission model, the roughness length of the erodible surfaces can be estimated from the median diameter of the coarser population of the soil sizedistribution. To summarise, this set of relationships allows the retrieval of the input data required by the dust emission model based on data characterising two surface features: the soil type and the roughness characteristics: • Using the soil representation defined by Chatenet et al. [18] allows to assign to a soil type a microped sizedistribution and a clay content. The size-distribution can be used to estimate the micro-roughness length z0 of the erodible surfaces. • For rough surfaces, the meso-roughness length can be estimated from the mean height of the obstacles and the roughness density according to equation (3), (4). Assuming simple forms (ellipsoids, cubes, half-spheres, etc.) for the obstacles, the roughness density can be deduced from their covering rate. As a result, the roughness lengths Z0 may be estimated from the mean height and covering rate of the obstacles. 2.3. Synthesis of the part 2 Finally, the input parameters concerning the erodible soil (i.e. the size-distribution of the soil micropeds and soil clay content) and the surface roughness (meso- and micro- roughness length) required by the physical dust emission scheme for large scale application can be estimated providing: (1)

the soil representation and (2) the mean height and covering rate of the roughness elements (vegetation or inert obstacles). Since the dust emission model is dedicated to large-scale and then global simulations, these surface characteristics must be retrieved with resolution similar to that of the global atmospheric circulation models. At present time, the resolution of these models is of the order of 2.5 × 2.5°, but it generally tends to decrease. A 1 × 1° resolution has thus been retained as a relevant scale to determine the surface features over arid areas.

3. The geomorphologic model 3.1. General description of the method 3.1.1. General principles The direct data of surface observation cannot be collected at the micrometric to decimetric scale on the whole surface of the Sahara. Since the dust emission model requires input data at a scale close to one degree by one degree, the main methodological difficulty is therefore a transfer of scale for elements with sizes varying between 10–7 and 10–2 m, to surfaces with dimensions up to 105 m. Our methodology is based on geomorphologic connections between, on the one hand, topography and lithology, and, on the other hand, roughness and granulometry. As for the studied elements, our methodology is based on the interlocking of data at various scales. This involves various problems: as ever in case of scale transfers, we have had to generalise accurate and local data to surfaces 106 to 1011 times bigger, and to combine them with more general but less reliable information. This type of approach remains the only method we can apply to the whole surface of a desert when no other information is available. Its purpose is to determine the average surface features in the ever changing field reality. These average surface features will be more reliable when they are validated by direct ground observations. We have developed this method in an area of the northwestern Sahara desert, which will be described as an application in the fourth part of this study. 3.1.2. Elaboration of the study grid The spatial approach is quite different from those generally used in geomorphology because we do not intend to map the surface of the desert, but to model it. As previously mentioned, the dust emission will be simulated with a resolution similar to the one used in global atmospheric transport model, i.e. of the order of 1 × 1°. The surface feature grid has been adapted to this size (about 95 × 110 km at the latitude of the Central Sahara). Inside each square degree of the grid, there are almost always several surfaces with specific responses to aeolian

— 249 —


erosion. It is therefore essential to measure the percentage of each kind of area inside each grid square. We have kept the associated characteristics of each area without locating them in the square degree. Indeed, the information related to the accurate position of these areas in the square degree cannot be used, as we consider that the wind blows at the same speed in each point of the square degree. We calculate the dust emission for each surface feature, taking into account the surface percentage that it covers. We add the results to estimate the desert dust emission at a given wind speed on a square degree. Let’s take the example of a square degree including hamadas covered by a reg, sharp dunes with fine sands, coarse interdune sands and some daïas (closed depressions of metric to kilometric scale) with a clay bottom in the lowest places of the interdune corridors (a less extended example of such a place is given in part 4). We associate each part of the square degree to a surface feature, and the percentage of the square degree that it covers. Each surface feature can be explicit: a reg can be represented as the juxtaposition of reg pebbles with a median diameter of 5 cm covering one quarter of the surface, the three quarters remaining being a coarse sand with a 2 mm median diameter, the whole reg having a 20 cm mean high vegetal cover on 5 % of its surface. In the same square degree we can indicate a second percentage of serir composed of 2 cm median diameter pebbles on 10 % of the surface, the other 90 % being composed of a finer sand with a median diameter of 0.5 mm, without any vegetation on it. The number of areas inside a square is limited to five, which is an optimum between the precision needed most of the time and the complexity of data acquisition and processing. The previous examples showing that each type of surface may include several variables, our method proves to be fairly flexible. A sixth area is sometimes identified. It includes the areas which cannot provide desert dusts, like rocky outcrops, or stretches of water. In this case the computation takes only into account a part of the square degree. With this spatial approach, we can obtain a good precision in a grid of rougher size and integrate small surface features into the model. Theoretically, 1 % of the surface of a square degree can be taken into account in the model, i.e. 110 km2, which is small compared to the whole Sahara Desert. Moreover, this surface itself can be constituted by several smaller units. For instance, it is possible to take into account many small daïas (whose minimum diameter is sometimes ten to twenty metres), scattered on the square degree, if all the daïas cover at least 1 % of the square degree. So, we already observe a spatial interlocking: some small surfaces are integrated into the model when they have a special interest (for instance a salt emitting sebkha), but they are spatially located only at the scale needed for the model.

3.2. The available information One of the main obstacles we had to overcome in order to model the desert surface is the variety of reliability and of scale of the available data.

3.2.1. An essential source of information: topographical maps The limits of the available documentation relative to the whole Sahara led us to use topographical maps as a main source of information, as they are the only usable ones at a good scale for searchers working on the Sahara. The whole desert is covered by these maps, but they vary in accuracy and quality. A lot of these maps are quite old, but that does not generate real problems, the level of anthropic changes being very low on such desert areas where man is almost absent. However, at the southern edge of the desert, the data on the vegetal cover have been greatly changed by the recent climatic variations of the great Sahelian droughts. This reason among others led us to set at 16° N the southern limit of the modelled area. The topographical maps of the Sahara region have been elaborated by different organisations. The reliability of their information depends on the quality of mapping, which differs according to their data sources. Maps elaborated by photo-interpretation and photo-restitution of aerial photos are much better than those made from various data, sometimes of dubious origin. We essentially used: • the French National Geographic Institute (I.G.N.) maps at various scales (1:200 000, 1:500 000, 1:1 000 000) in the Central and Western Sahara. Made by photointerpretation, their quality is usually quite good. In the area studied in Part 4, the I.G.N. maps at the 1:200 000 scale were made at the beginning of the sixties. These maps offer a good representation of the topography and the dunes areas. But they give no detail on surface features, which are indicated only on some maps of the Southern Sahara (particularly in Mauritania); • The 1:200 000 and some 1:500 000 Russian maps were used for the Libyan and Egyptian countries. Their topography is correct, but being almost monochrome, they are difficult to read. They sometimes give data on the nature of the land, which are only indicated by texts as ‘hard rocks’, ‘sand sheet’, etc., with no precision on their limits; • The 1:250 000 American maps (J.O.G.) issued by the Army Map Service of the US Army, have also been used in regions where no other information was available (e.g. the Sudan). They vary greatly in quality. Some of them, being made without any photo-interpretation, are almost unusable. They also sometimes give indications on the nature of the land, but they are as lacking in details as the Russian maps.

— 250 —


3.2.2. Other data 3.2.2.1. Pedological maps Many studies and maps on the texture and composition of the pedological soils (defined as the soil nature at 30 cm below the surface) of the arid regions of the Earth are available (F.A.O. world soil map, [23]). However, in arid regions, there is often a thin superficial layer above the pedological soil. The presence of this superficial layer results from: • the deposition of allochtoneous (coming from another area) material coming most frequently from the redeposition of particles risen by wind, • and/or the sorting action of the wind on local or allochtoneous material. It induces an exportation of fine material and the formation at the surface of a thin ‘deflation pavement’. Its thickness is millimetric to centimetric and its granulometry is usually very different from the granulometry of the underlying soil. The presence of these superficial layers strongly limits the capability of the pedological map to be used for wind erosion purposes since aeolian erosion is a surface process acting only on the top of the surface. But pedological maps are indirectly quite useful: the soil being the substratum of the surface feature, we can deduce a few parameters from the maps in zones where aeolian allogeneous deposits do not cover the whole surface. 3.2.2.2. Geological maps Even at a small scale, have been systematically used when available. They give essential lithological information for modelling in regions with thin sand cover or no sand cover, where rock nature has a great influence on the response of the surface to weathering. 3.2.2.3. Remote sensing data They are one of the main potential sources of information [24]. Earth Resources Satellites with a high power of resolution, such as Landsat or SPOT, can only be used on small areas, because of their cost and of the huge quantity of information they provide. The SPOT quick-looks, in spite of their very low resolution, have been used for wider zones. 3.2.2.4. Aerial photos Their scale (usually 1:40 000 to 1:96 000 in desert countries) allows a precise geomorphologic approach. Although we have studied aerial photos in few regions, they have given us essential calibration points. We call ‘calibration point’ a place where we have data whose precision permits a numerical deduction of the surface feature values and thus the extrapolation to other similar areas. 3.2.2.5. Bibliographical data When detail was needed for some regions, complementary information such as bibliographical documents, monographs, technical reports were examined. These documents

may give information on precise areas, used as calibration points, for instance pedological sections [25] or dune field studies [26, 27]. They generally have to be used indirectly for they do not give quantitative information on surface features. 3.2.2.6. Field observations They are the most reliable data. These references were provided by numerous direct observations performed in the selected regions (figure 1): • in the northern Sahara: Western and Eastern Great Sand Seas, Atlasic Hamadas, plateau of the Ghardaïa region, LowSahara [28–30]). It is partly from our previous experience in this area that we have selected it to improve our modelling method: • in the central Sahara: ergs around Tassilis N’Ajjers (Issaouane-N’Inarraren, Issaouane-N’Tifernine), large fluvial areas (Taffassasset, Sebkha Mekkherane), Hoggar, Southern Tassilis and Ténéré; • in the southern Sahara: Aïr, Erg of Bilma [27]; • in the western Sahara: area of Tarfaya-Laayoune [31], Western and Central Mauritania; • in the eastern Sahara: Northern and eastern Libya: Great Sand Sea; central hamadas; volcanic areas of Djebel Al Harudj; Oubari Edeyen; Hamadat al Hamra; coastal area. 3.2.2.7. Field photos They are an interesting source of information, also to give calibration points. They are of two types: • Photos taken especially for the concerned study (figure 2). They give essential quantitative data on field observations; • Photos available in the literature. They are more useful when their location and shooting condition are well known. 3.3. The use of data 3.3.1. Landscape and surface-feature typology In our methodological approach of the principles of representation of surface features, we have emphasised the distinction between principal landscape units and surface features as well as the relation between the first and the second. Landscape units are essentially linked to the topography in environments where the vegetation is so rare that its variations are not very important. They are not the object of this study, and their typology has been very simplified. Surface features are the target of the modelling. They have a small vertical scale and may occur in various landscapes. Their typology had to be precise so they can be physically used in the model of desert dust emissions, but it had to be simplified so it can be used with the method described here. In the Saharan desert areas, five main types of landscapes have been distinguished: mountains, ergs, hamadas, plains and depressions. They are briefly described on table II. From orographical (topographical and hydrological) consider

— 251 —


Figure 1. Location map of main sites.

ations, various general surface features can be associated to each type of landscape. These surface features are defined on table III, and table IV summarises the possible associations between landscape types and surface features. As several surface features may be found in a single type of landscape, the relevant surface features and the percentage of surface that they cover have to be determined by a geomorphologic analysis. Some surface features can cover a whole type of landscape, but others can be found only in parts of the landscape as smaller scale features. For instance, dunes can partially cover any type of landscape without being an erg. 3.3.2. Extrapolation from the geomorphologic context 3.3.2.1. Orographical approach The integration of surrounding data for the understanding of local forms is common in geomorphology, but the scale of our work led us sometimes to take into account this environment in more remote areas. The orographical approach is used at all the scales of the documents studied. Average dimensions of surface features can for instance be extrapolated from the slope and the distance between studied surfaces and higher relief with steeper slopes at the origin of

transported material. According to the scale of the document, these relationships are studied at the scale of hundreds of metres on aerial photos, or at the scale of kilometres on precise maps, or at the scale of 10, or even 100 km on less precise maps, as the world map at 1:1 000 000. This type of analysis of the orographic context can reach the level of a regional study. It is performed by examining the relations between a grid mesh of one square degree and its neighbours in relation to a larger landscape unit. This regional analysis permits the selection of the relevant surface features associated with each landscape unit and the interpretation of the spatial variations in the surface features in relation with the process(es) responsible for their formation. More generally, this method relates regional information to local information from the effects of allochtoneous processes acting on the surface features. This orographical approach is then improved by information resulting from both geological and climatological analysis of the region. 3.3.2.2. Geological data The geological analysis is essentially a lithological analysis. It provides information on the surface features due to processes acting over time scales greater than 103 years. For example, the substratum of the surface represents the ancient

— 252 —


Figure 2. Examples of desert surface features: a) serir; b) coarse sand of the huge sand-seas and sand-sheets of eastern Sahara. With measures made by hand or by image processing, calibration points for roughness and erodible soils can be obtained (Photos Y. Callot).

surface material, whose weathering has led to the present soil mineralogy and roughness characteristics. It depends on the genesis, weathering and erosion of the large topographic units. Both the mineralogy and the size of the soil grains are strongly connected to the type of the parent rocks. The litho-

logical analysis also provides information on the formation and structures of surface exhibiting large roughness since the vulnerability of rocks to be weathered and broken controls the size of the resulting pebbles. This geological analysis is sometimes less useful at the scale of the model, when

Table II. Main types of landscapes used in the model for the Sahara. Landscape type

Definition

Mountain Erg Hamada Plain Endorheic depression

High relief, where mean or steep slopes cover the main part of the surface Fields of jointed dunes or disrupted by sandy or stony places Plateau with hard summit layer Flat stretch without river incision, and usually without hard summit layer. Closed depression, usually from hydro-aeolian origin, arrival point of ancient or present endorheic flows and/or of water rising

Table III. General surface features observed in the Sahara. Surface feature

General characteristic

Rocks Reg Serir Fluviatil deposits Dune sands Sand sheets Daïa deposits Sebkha deposits

Rocky of skeletal soil, where large blocks or hard outcrops cover the whole surface or the main part of it. Surface with boulders or pebbles greater than 5 cm and of variable density. Surface with continuous or discontinuous small pebbles and/or gravel smaller than 3 to 4 cm Flat clayey silty stretch, possible gravel Fine or medium sands of dunes of variable slope Centimetric sandy sheet of coarse sand covering the whole surface Sedimentary silty clayey deposit Sedimentary salty deposit, with various surface features according to salt content and salt nature

— 253 —


Table IV. Possible associations between types of landscapes and general surface features. Mountain Rocks Reg Serir Fluviatile deposits Dune sands Sand sheets Daïa deposits Sebkha deposits

X X * *

Erg

Hamada

Plain

Endorheic depression

* X X

* X X X * X * *

X * * X X

* * * X * * *

* X *

X: General Surface Feature which can cover the whole surface. * General surface feature which can cover only a low percentage of the surface.

the surface features are entirely allochtoneous, as in continuous ergs. 3.3.2.3. Paleoclimatical and climatological data A lot of the present surface features result from processes that were efficient in the past, when the climatic conditions were quite different from the present ones [32]. As an example, the Sahara has experienced wetter periods, particularly during the recent Holocene (from 10 000 to 3 500 years B.P.). The rivers were numerous and active, allowing transportation of larger pebbles and gravel [33–35]. A lot of lakes appeared during this period, especially, but not only, in the present Saharan borders [30, 36, 37], producing specific deposits when the climate evolved to dryer conditions: some lakes previously supplied by streams loaded with detritic sediments became clayey plains with smooth surfaces; other palaeolakes, fed by superficial gypseous water, exhibited embossed topography, sensitive to weathering. The present surface features of the ancient or still active ‘sebkhas’ vary, depending on their salt content, and on the experienced periods of drying and moistening. These wet conditions have also enhanced the weathering and pedogenesis of the lithologic subtratum, producing large proportions of fine transportable material. The present climatological conditions have similar influence, the most important parameter being the precipitation rate which controls both the present weathering and the vegetation cover. The most easily observed effect of precipitation concerns the seasonal vegetation cover. To avoid any uncertainty due to the representation of the temporal variability of the vegetation cover, the selected zone excluded the Sahelian belt. For the North of the Sahara, the area having a seasonal drift of the vegetation cover is limited since the Atlas mountains induce a sudden shift between desert and wetter regions where the vegetation is mainly composed of perennials as Esparto grass (Lygeum spartum or Stipa tenacissima). Thus, only the long-term vegetation was considered.

3.3.3. Quantification and generalisation from local data We have previously indicated that the main difficulty of this study is the huge difference in scale between the studied ‘objects’ (sands, micropeds, gravel, roughness at a millimetric to decimetric scale) and the millions of square kilometres of the modelled surfaces. It has been necessary to go from documents at a millimetric scale, like photos of soil (figure 2) or granulometric data, to documents at a kilometric scale, like small scale maps of satellites images with a small resolution power. This extrapolation of surface feature characteristics is based on the concept of calibration points (or areas) giving reliable and quantifiable data. These calibration points of the model are mainly, but not only, field data (see above 3.2.2). These calibration points have been used as ‘ground truths’: for representative areas, the surface features (mean height and covering rate of the roughness elements; soil mineralogy) were precisely and quantitatively described and then used to calibrate the whole geomorphologic information system. They give the best information on the average sizes which are then used in the neighbouring areas with an uncertainty growing with the distance from the calibration points. Especially, with photos of surface features taken vertically at a given scale (figure 2), we can obtain, after a visual or computerised analysis of the images, a quantitative determination of the size distribution of sands and/or rough elements. Especially in regions which have not been visited for the study, other data can also be used as calibration points, only if reliable information is obtained by a ‘geomorphologic reading’. Aerial photos provide a good example. Unlike topographical maps, they are not the results of previous interpretation, and with their untreated nature together with their generally quite great scale, they provide an extremely rich information. This information may be close to the ‘ground truth’ in desert environments similar in nature to environments studied elsewhere in the Sahara, even with the usu

— 254 —


Figure 3. Extract from a Panchro Spot image [52/27, 23 December 1989] of the north-eastern part of the Great Western Erg (NW of the Algerian Sahara). With the spatial resolution (pixels of 10 × 10 m), the small active sand (median 200 µm) sharp dune areas and interdune coarse sand (median 700 µm) areas with a smoother look can be distinguished.

ally 1:40 000 to 1:80 000 scale of this photos in the desert areas. An example is given in part 4. The most precise satellite data, like the SPOT Panchromatic images (pixels of 10 × 10 m) are also precise and may be read in details or even studied by automatic mapping [38], especially for the sandy areas. Ergs are actually one of the best example of forms from which surface features can be deduced from aerial images: from the morphology of dunes, seen at the scale of aerial photos or of SPOT images, especially if they are Panchromatic (figure 3), we can deduce their sand granulometry: their parts with steep slopes and sharp crests are made of active fine sands between 125 and 250 µm, and their flat and soft areas are formed with coarse sands bigger than 350 µm. So, it is possible on the same document to measure the proportions of surfaces covered by each type of forms and consequently each type of sand. 3.4. Conclusion: Quantification and data crossing With this geomorphologic approach, we can develop a semi-quantitative information system, since, inside the same

landscape unit, the surface features in a given area are known only by comparison with the neighbouring ones. To be quantitative, the method needs aggregation of quantitative information on the surface properties obtained by direct observations and/or use of high precision documents (aerial photos, satellite images). The method has been developed for modelling surface feature aggregates and crossed refined data deduced from various sources of information to obtain a larger scale and more general information. The originality of the method is the use of this type of organised information with a double aim: • the first usual aim, is to distribute data at various scales in space, but it is adapted to the needs of the model in which information is treated inside a grid of a square degree with a high level of internal resolution but are not spatially located inside the square degree units. This method can almost be called a semi-spatial model of repartition as all the information inside a square degree is kept in a kind of ‘box’ in which the location of information is unknown; • the second aim, more indirect, is to improve the reliability of the model. Actually, all necessary information cannot be

— 255 —


the large areas of serirs and Sand Seas with coarse sand in the Eastern Sahara. • Thanks to our good knowledge of the region, we have been able to avoid errors in interpreting the nature of its surfaces [28, 29, 39, 40]. Inside the studied region, we have represented two central square degrees, limited by the 1 and 2° E meridians and the 33 and 31° N parallels (figure 6), to give a better image of the landscape distribution in a smaller area. 4.1. Existing data Here we will only present the main data and the information useful for the modelling of surface features. Figure 4. Location map of the studied area.

4.1.1. Orographical data obtained from any single source. So, the method consists in gathering relevant information but more or less reliable, coming from different sources, to obtain more reliable information. In this case, for our research, we make use of a distribution of data organised in ‘strata’ with variable reliability. Each stratum, even if it is not very reliable in itself, may interact on the others and improves the reliability of the whole. For example, a very imprecise indication like ‘difficult for vehicles’ on a bad 1:200 000-map will interact with an indication of limestone on a 1:2 000 000-geological-map for the determination of a coarse reg surface where dust emissions are impossible. This reliability implies also a good knowledge of the field, either direct or indirect. This knowledge provides the calibration points necessary for the model of representation of desert surfaces. The uncertainty of estimated data grows with the distance from these calibrations points.

4. Cartographic application: an example of a regional study The general principles described above have been applied to the Sahara. There are many variables taken into account for the model. We will show the main variables together with the methods used to combine them and incorporate them inside the model, from an example: the area where the method has been devised, in the north-west of the Algerian Sahara and on its northern margin (figure 4). In this region, an area of 12 square degrees (0 to 3° E; 30 to 34° N, i.e. 125 000 km2) has been chosen (figure 5, table V) for various reasons: • In this relatively limited area, there is a great variety of topographical and climatological conditions, thus permitting the study of all types of the North-Sahara landscapes, except

The examination of topographical maps of the studied area at various scales, supplemented on the field, demonstrates the opposition between several topographical elements: • at the NW end of the area, the high (between 1 000 and 1 300 m) and flat surfaces of the Oran Southern High Plains, with their uncertain drainage. They form the northern piedmont of the northern mountain range. The mountain– piedmont contact is continuous; • from 33° N–0° E to 34° N–3° E, an open mountain range, the Saharan Atlas, formed by large elongated mountain ridges with very steep slopes, separated by large flat areas with gentle slopes: plains and wide valleys. The wadis (called ‘oueds’ in the region) have a characteristic drainage oriented towards the south: the lower altitude of the northern piedmont leads to the capture of all the local drainage; • at the south of the Saharan Atlas starts the Sahara itself. The northern piedmont of the mountain range is formed from north to south by a series of elongated zones whose general orientation is WSW–ENE: C very close to the chain, there is a region with a complex topography, although with no great differences in level, where we find wadi beds, a few foot-hills formed by the last anticlines of the range, glacis, dune fields, and 10–20 cm to one metre thick sand-sheet areas; C large hamadas with very gentle slopes towards the South (from 850–900 m in the North to 600–700 m in the South), deeply cut by the valleys of the allogeneous wadis coming from the Saharan Atlas and, more superficially by the autochtoneous wadis born on the hamadas; C the Mechfar, a region with a very complex topography, resulting from the interlocking of fragments of dismantled hamadas, closed depressions (daïas) and dune fields; C the Great Western Erg, continuous dune fields with altitudes varying between 400 and 600 m; • In the SE corner, around 30° N–3° E, the first areas without sand of the southern limit of the Great Western Erg.

— 256 —


Figure 5. Map of the studied area, on twelve square degrees (extract from Africa 1:2 000 00 – map / Army Map Service, Corps of Engineers, U.S. Army, Washington: Ed. 4-AMS, 1969. – Sheet 2 “Alger”). The map covers the areas of transition from the SouthernOran High Plains to the Sahara through the Saharan Atlas Chain. It includes a great variety of environments with most of the Saharan surface features.

— 257 —


Table V. Surface features of the studied area, by square degrees. Numbers in the first line of each box indicate the position of the northwestern corner of the box, in northern latitude degree for the first, eastern latitude for the second. Surface features are arranged by decreasing importance in the box. Details on soil types are given in table VI. 34-0

34-1

34-2

47 % Silty fine sand + steppe 20 cm mean height, covering rate 20 %



30 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 %

15 % Mountains and rocky surfaces

26 % Mountains and rocky surfaces

3 % Daïas deposits

10 % Silty fine sand + steppe 20 cm mean height, covering rate 10 % 2 % Medium sands 2 % Daïas deposits

36 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 10 % 30 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 % 17 % Silty fine sand + steppe 20 cm mean height, covering rate 10 % 10 % Mountains and rocky surfaces 4 % Daïas deposits 3 % Medium sands

33-0

33-1

33-2

35 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 10 %


30 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 % + steppe 20 cm mean height, covering rate 5 % 10 % Silty fine sand + steppe 20 cm mean height, covering rate 20 % 10 % Silty fine sand + steppe 20 cm mean height, covering rate 10 % 10 % Mountains and rocky surfaces 5 % Medium sands

30 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 % + steppe 20 cm mean height, covering rate 5% 20 % Silty fine sand + steppe 20 cm mean height, covering rate 10 % 7 % medium sands

30 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 % + steppe 20 cm mean height, covering rate 5 % 30 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 10 %

16 % fine sand (dunes)

3 % Daïa deposits (Clay)


32-0

32-1

32-2

60 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 20 % 13 % Coarse medium sand on a reg with 5 cm mean size elements, covering rate 30 % 12 % Coarse medium sand on a serir with 1 cm mean size elements, covering rate 10 % 11 % fine sand (dunes)



25 % Coarse sand (dunes)

40 % Coarse sand (dunes)


10 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 20 % 10 % Coarse medium sand on a serir with 1 cm mean size elements, covering rate 10 %


20 % coarse sand (dunes)


10 % Coarse medium sand on serir with 1 cm mean size elements, covering rate 10 %

31-0

31-1

31-2

50 % fine sand (dunes) 20 % Coarse sand (dunes) 20 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 20 % 10 % Coarse medium sand on a serir with 1 cm mean size elements, covering rate 10 %

65 % fine sand (dunes) 25 % Coarse sand (dunes) 5 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 20 %

45 % fine sand (dunes) 20 % Coarse sand (dunes) 15 % Coarse medium sand on a reg with 4 cm mean size elements, covering rate 20 % 15 % Coarse medium sand on a serir with 2 cm mean size elements, covering rate 10 % 5 % rocky surfaces

5 % Coarse medium sand on a serir with 1 cm mean size elements, covering rate 10 %

— 258 —


Figure 6. Map of the two central square degrees of the studied area (from the 1:1 000 000-I.G.N.-maps: Oran, 1974 and Ouargla, 1965). At this scale, we can only see the main wadis, the great hamadas of the southern piedmont of the Saharan Atlas (located very close to the NW corner of the map), the area of dismantled hamadas and the Great Western Erg in the south, where we can distinguish the areas of active dunes, not very high, and the great dome dunes of the central part of the Erg. The red square indicates the area covered by figure 7. Copyright © I.G.N. Paris - (1963-1974), autorisation n° 42-2000–57.

— 259 —


Figure 7. Extracts: a) of the I.G.N. 1:200 000 Hassi Bou Zid map (NH-31-20), in the Mechfar (see location on figure 6), b) of the aerial photo I.G.N. 1960 NH-31-XX-XXI, n° 206. This map is of the best quality and at the most detailed scale available in the Sahara. Comparison with aerial photo shows the limits of the mapping as well the interest of aerial photos. They show areas of active sand, of hamadas and the daïas. Surface features can be interpreted with a very good reliability. Copyright © I.G.N. Paris - (1961), authorisation n° 42-2000-57.

4.1.2. Geological data • The Saharan Atlas is a folded range often deeply eroded, with important inverted relief, especially upstanding synclines. A large part of the range outcrops is made of Cenozoic sandstone. The weathering and ablation of this sandstone give large quantities of sand transported by streams, then, on the southern piedmont, by wind. Triassic evaporites outcrop in large diapirs. • Piedmonts, and especially the southern piedmont are formed with detritic Neogene deposits coming from the erosion of the Saharan Atlas. These deposits, (often argillous sandstone, gritty clay) are topped by a hard carbonated, sometimes conglomeratic, hamadian crust.

4.1.4. Pedological data The climatological data indicate an aridity unfavourable to pedogenesis. The whole central and southern part of the area only contains lithosoils with almost no fine fraction (smaller than sands) at the surface. With the increase of precipitation towards the north, this fine fraction increases slowly, as well as the fragmentation of coarse elements. Mobile sands alone are accumulated and their importance increases with fixation by the vegetal cover. Thus, in the High Plains and in a few areas of the Saharan Atlas, poorly developed sandy soils appear more or less continuously that contain a small fraction of silt and clay [39].

4.1.3. Climatological data

4.1.5. Anthropic data

Available climatological data are often old [41, 42]). They indicate that the weather is often cold in winter: frost, which is rather frequent, may fragment rocks when associated with water. The most important climatic element for our study is the extent of precipitation determining the vegetal cover for a large part. Precipitation presents here a strong North-South gradient. Mean values, around 200 mm·year–1 in the HighPlains, reach 300 mm·year–1 by orographic effect in the Saharan Atlas. They decrease very rapidly at the foot of the mountain range: approximately 100 mm·year–1 at 20 km South of the range, 50 mm·year–1 at 100 km, 20 to 30 mm·year–1 at the southern limit of the studied area. Aeolian dynamics also play a role. Winds, quite strong in the classification of Fryberger [43], are predominantly oriented towards NNW–SSE [31].

‘Natural’ conditions of some areas, especially in semiarid environment, may be damaged by a factor difficult to take into account: anthropic modifications. These modifications cause a very strong increase of the denuded soil between grass clumps. This factor is important for modelling. We cannot evaluate its effects from the usual data, since it is a disruptive factor acting very rapidly inside the natural system. Topographic maps, for instance, do not give any representation of it, and in the studied area, there are no thematic maps in this field. Consequently, these anthropic influence must be studied by direct field observations, bibliography, or by remote sensing of vegetation indexes and/or soil indexes. In our example, we have integrated an area were anthropic influence is important to illustrate this factor, but at the scale of the whole Sahara, its influence is lim

— 260 —


ited to the semi-arid margins, arid deserts being already naturally degraded. 4.2. Resulting landscapes and associated geomorphologic processes The inventory of existing data is the first stage of a more general physical geographical study: by the combination of orographical, geological and climatic data, we can define landscapes giving important elements for surface feature modelling. Rapid topographical and climatological modifications create a wide range of landscapes on a north-south transect of 400 km. 4.2.1. The Oran Southern High Plains In the north of the studied area, between the ‘Chott ech Chergui’ and the Saharan Atlas, the topography is predominantly horizontal. The semi-arid climate, non Saharan, should bring about a vegetal cover with a dense esparto grass steppe, with low erosion. But this steppe is degraded by anthropic actions, consequences of an important overgrazing by sheep and goats. Hydric erosion is not very active, the hydrographic system being poorly developed. Aeolian processes, especially deflation, are favoured by vegetal cover degradation, denuding soils containing fine material (< 50 µm) [39] favourable to the production of desert dust. 4.2.2. Ksour Mountains The topographical contrast between the mountain links and the plains brings about two opposite types of landscapes: • Mountainous areas with steep slopes, are very stony and rocky. Their surface can be measured from topographical and geological maps, with which we can also estimate the proportion of sandstone outcrops, important by the sand they produce by weathering. In spite of precipitation slightly more important than on the High Plains, the topographical conditions only permit a poor bushy vegetation between the rock outcrops and a few vestigial shrubs on the higher summits, very degraded by anthropic actions. Rainwash, favoured by the lack of vegetal cover, is the dominating factor: during the strong rainfalls, it erodes the soft strata and carries the available material, sands, pebbles and dissolved salts, towards lower flat surfaces. The evaporites of Triassic diapirs, dissolved by rains, produce salt, especially halite, causing efficient haloclastic processes on sandstone. • In plains and valleys, all wadis run towards the Sahara. Intermittent, they flood one or several times during winter. Sands are abundant; they are transported by streams and deposited in the downstream parts of valleys when they leave the mountains at the decreasing drainage change of incline. Vegetation is rather abundant, but always degraded by overgrazing, a factor favouring running off, sheet erosion and deflation.

4.2.3. The Near Southern Piedmont The part of the piedmont closest to the mountain is a region where topography gives varied landscapes with no great differences in level, except rare mountain links. Secondary units have different capacities of dust emissions. Sometimes very interlocked, they can be easily distinguished on the 1:200 000 topographic maps: • great allogeneous wadi valleys are wide. Their limits are not well defined. They are flat, except for the terrace scarps, their vegetation is more abundant and they contain a few great clayey depressions (‘daïas’), especially in Wadi Seggueur. • the areas of aeolian coatings are generally associated with the sand deposits from allogeneous wadis. They are located to leeward of the streams (SSE) and may locally produce small ergs overlying previous strata. • the areas of Neogene continental deposits. The important erosion in this part of the piedmont has often dismantled the hard summit layer, outcropping the softer layers of the substratum. Precipitation, around 100 mm (up to 150 mm in the east), has allowed a beginning of pedogenesis. It has produced a fine fraction often deflated by wind after a degradation of vegetal cover due to overgrazing and a strong drought, especially between 1970 and 1986. The phenomenon can be studied by methods similar to those presented in the High Plains study. 4.2.4. The Great Hamadas The great Neogene hamadas of the piedmont are often covered by a stony carbonated layer. Precipitation decreases down to 50 mm·year–1 on their southern limits. They are notable by their extremely flat surfaces. Running off is inefficient except in allogeneous valleys and in the main autochtoneous valleys. The Great Hamadas are covered by discontinuous stony surface features in which the proportion of fine material decreases rapidly with precipitation, becoming very low in their southern margins. 4.2.5. The Mechfar The Mechfar is an area where geomorphologic processes are numerous and topography is very intricate. We can distinguish: • hamadian surfaces, similar to those of the Great Hamadas, sometimes stepped, with a poorer and poorer vegetal cover towards the south, and thin but nearly continuous sandy overlaps. • daïas, hydroaeolian closed depressions [28], sometimes large, cutting, and sometimes dismantling, the hamada, formed essentially of sandy clay. During the wetter quaternary periods, up to the Holocene, the daïas were numerous and extended palaeolakes [29, 30]). The water of these lakes altered the surrounding rocks and left lacustrine sediments, generally soft which were later quickly attacked by the aeo

— 261 —


Table VI. Size distributions of the soil types used for the model of the surface features of the Sahara. Typology

Silty fine sand Medium sand Coarse sand Coarse medium sand Fine sand Silty medium sand Moderately salty silt Highly salty silt

Code

Alumino-silicated silt

Fine Sand

Coarse sand

Salts

Dmed,=125 µm σ =1.8

Dmed,=210 µm σ =1.6

Dmed,=690 µm σ =1.6

Dmed,=520 µm σ =1.5

%

%

%

%

37.5 37.5 50 25

62.5 20 10 100 31.25 -

80 100 90 31.25 -

50 75

SFS MS CS CMS FS SMS SEM SEF

lian erosion. Aeolian deflation is the main process in these depressions. • essentially active dune fields are easily visible on aerial photos and represented on maps by specific symbols. Most of these dunes superposed on the rocky topography are located on hamadian surfaces. An example of the topographical intricacy of the Mechfar is given in figures 6 and 7.

• the nature of the soil. Surface features containing a small proportion of fine material are not favourable to the growth of vegetation. This is why, in the same square degree [table V: 33-0, for instance], we have reported a vegetal cover on sandy areas and the lack of vegetation in reg areas. • the strong anthropic degradation due to overgrazing by sheep and goats, studied by field observations, is illustrated by an important decrease of the mean height of the vegetation and of the surface proportion it covers.

4.2.6. The Great Western Erg

The above mentioned variables led us to neglect the vegetal cover in the southern half of the studied area: although it is still present, the vegetation is too rare to influence the model. In the northern half, the vegetation cover proportion increases towards the north, but remains weak (10 % to 20 % in the High Plains) because of anthropic action. In the south of the Saharan Atlas, vegetation has been incorporated in the model only in some favourable areas.

2

Its surface is approximately 80 000 km . Precipitation is less than 30 to 20 mm·year–1. It is essentially constituted by a continuous substratum of coarse sands producing: • stable dome dunes made of coarse sands (median particle diameter approximately 700 µm) overlaid by active dunes made of fine sands (median particle diameter approximately 250 µm) [29, 38]; • interdune corridors where coarse sands are attacked by aeolian deflation. Neogene substratum practically never outcrops. 4.3. Surface feature interpretation The extreme simplification that modelling always implies is amplified by the great difference of scale between surface features and studied areas. The modelled surface features are presented in table V in front of the map of the studied region (figure 5). 4.3.1. Vegetal cover The vegetal cover is indicated in the model with its mean height and its cover percentage. In the whole Central Sahara, the role played by the vegetal cover is negligible as far as roughness is concerned. It is different in its margins. The importance of the vegetal cover is linked to the level of precipitation as well as its distribution in time. We have especially taken them into account in our interpretation. But our interpretation can be modified by two variables:

4.3.2. Surface features ‘strictly speaking’ The aim of modelling is limited to roughness and the presence of erodible material. So, formations of utterly different nature and/or origin have been gathered when weathering has altered their surface and given them a close roughness and fine fraction. For instance, a Pleistocene calcareous crust and a Mesozoic outcrop can be gathered more synthetically than for the elaboration of a lithologic map. Thus, we have been able to realise a simple modelling from a few large patterns distinguished in tables II to V. We have then selected: • non emissive areas, mountains and rocks, which are simpler to model. They are located on the northern part of the studied area (Saharan Atlas and its northern piedmont). Denuded rocky outcrops corresponding essentially to the outcrops of the Mesozoic hard levels (especially sandstone, and sometimes limestone) can be observed on the whole range as well as scattered on the surface of the piedmont or of the plains of valleys located between mountainous ridges. They can be distinguished from topographical maps and from calibration points obtained by field observation and photo- inter-

— 262 —


pretation. Thus we can attribute a variable surface proportion to rocky surfaces which do not produce desert dust [table V: 34-0, 34-1, 34-2, 33-0]. This proportion is not very high for two reasons: the Saharan Atlas range is too narrow to occupy a whole square degree, and its strictly mountainous surfaces are separated by large depressed areas, plains and valleys. • daïa deposits, gathering surface features from several depressions of various sizes, with clay bottoms. (see above 3.1.2.). Topographical maps give a faithful representation of the main ones. The smallest daïas are not represented individually but with a general symbol. To model them, their surface must be estimated by field observation or by extrapolation from aerial photos or precise satellite images. • continuous sandy areas. Topographical maps (figures 4, 5 and 7a), the study of aerial photos (figure 7b) and Landsat TM and Spot Panchromatic images (figure 3), allows an easy and reliable interpretation, since there are good correlations between dune morphology and granulometry. So, we have been able to distinguish different areas, sometimes on the same large dune: C fine sands constitute the active sharp dunes of the Great Western Erg. They are more numerous in the south than in the north of the Erg [table V: 31-0, 31-1, 31-2], because these part of the Erg contains many seif dunes. This type of sand may cover as much as 65 % of the surface [table V: 31-1]. C coarse sands constitute the main mass of the Erg. We find them especially in the NE part of the Erg where they cover the largest part of interdune corridors and of spaces between active dunes, for instance in [table V: 32-2] where their surface percentage has been estimated at 40 % of the square degree. C medium sands, in rather limited areas in the southern piedmont of the Saharan Atlas. They have been distinguished from the previous types of sand, for the sands left on the near piedmont by mountain wadis have essentially a medium granulometry between active and coarse sands [39]. • complex areas, the most difficult to model, correspond to outcrops without any dune or rock. They are important for modelling, dunes covering only one fifth to one sixth of the Sahara surface [27]. A combination of roughness and fine material proportion can be observed. They cover large surfaces, especially in the northern part of the studied area where they appear in combination with vegetation cover.

becomes negligible below 70 to 50 mm isohyets. Similarly, regs become more and more coarse when precipitation decreases. • the proportion of sandy material covering the spaces between large size elements, as well as its granulometry. The research made on these three variables led us to a different interpretation in the north and in the centre of the studied area: • in the north, the dense vegetal cover has trapped – and sometimes yields through anthropic degradation – important amount of sands affected by a beginning of pedogenesis. Consequently, we observed a predominance of areas where the essential factor is the presence of a silty fine sand limited to the southern margin of the Saharan Atlas. This surface feature covers 82 % of the surface of the square degree [table V: 34-0], and corresponds to every feature which is not a rocky area or a daïa. • Already in the north, but more and more towards the south, the predominance of aeolian processes on flat surfaces maintains an often thin superficial layer made of coarse sandy material inside which are trapped medium size sands, called ‘coarse medium sand’. This layer – attacked by deflation – can be compared to the coarser part of the red film (‘pellicule rousse’) described by Coudé-Gaussen [44]. This layer has been classified as the dominant surface feature for the non stony fractions in the whole southern piedmont of the Saharan Atlas, except in dune areas. For instance, it covers 70 % of the square degree [table V: 33-1] where it appears in two types of surface features because of the difference in size of the stony elements. This illustrates one of the practical problems encountered in the classification and interpretation of surface feature areas: the integration in the surface features of elements of different sizes. In the studied area, coarse medium sands are always associated with regs and serirs. In its central and southern part, these sands have been combined with three sizes of coarse elements [table V: 32-1] because of the increase in size of reg elements together with the increase of aridity, other things being equal.

5. Results and discussion 5.1. Results

4.4. Conclusion

5.1.1. Surface feature models

Field observations on the whole of the north Saharan margin (figure 8) show that the surface features of these areas result from several interfering variables: • lithology: the outcrops of the hard hamadian summit layer and the terrace levels produce regs, while the Neogene outcrops are constituted by ‘serirs’. • climatic conditions: the proportion of fine material between reg or serir elements decreases with precipitation and

As mentioned above, each grid mesh was characterised by up to five surface features types and the surface fraction covered by each of them in the grid mesh. However, these features were not geographically localised inside the grid mesh. When applied to the dust emission flux computation, since the wind velocity is given for a square degree mesh, a unique value of the dust flux is computed for each grid mesh. This global dust flux is the sum of the flux produced by the

— 263 —


Figure 8. Main landscapes of the area studied in 4 (Photos Y. Callot): A) Central part of Saharan Atlas. In the background, mountains, and in front, an overgrazed plain; B) Northern part of Saharan Atlas. Esparto grass steppe degraded by a strong anthropic action. The sandy soil disappears, denuding the sandstone substratum; C) The Great Hamada south-west of El-Abiodh-Sidi-Cheikh; D) Daïa in the Mechfar, at Hassi Cheikh well; E) North-east of the Great Western Erg: coarse sand interdune corridor with deflation cauldron and palaeolake deposits; F) North-east of the Great Western Erg: great coarse sand dome dunes, covered by fine sand active dunes.

— 264 —


various surface features weighted by the fraction they represent in the grid mesh. By this way, the effect of the heterogeneity of the surface features on the intensity of the dust emissions is accounted for with a resolution finer than a square degree, but not explicitly and spatially represented. 5.1.1.1. Soil types Eight soil types have been used for the model. Based on the typology exposed in chapter 2.1 (Soil characteristics), a size distribution and a value of the ratio of vertical to horizontal flux, α, have been affected to each of these eight soil types. The statistical parameters of the log-normal mass size distributions and the values of the soils are listed on table VI. The dominant soil types are the coarse sand (CS: 36 %) and the coarse medium sand (CMS: 23 %). They spread homogeneously over large areas and are associated to specific landscape units such as regs or hamadas and dune fields (erg). The fine sand (FS: 8 %) is generally associated with the coarse sand on the ergs. Low surface fractions of these sands and of medium sand (MS: 10 %) are spread over the whole region, representing films of sand moving on the surface. The silty fine sand (FSS) corresponds to ancient alluvial or fluvial depressions (daïas) and thus is mainly observed in the northern margin where the precipitation rates were sufficient in the past to produce such deposits. This soil type covers 8 % of the total surface but never constitutes the dominant soil type of a grid mesh. In the same manner, the coarse silty sand (CSS: 5 %) is generally located on areas where climatic conditions allow the pedogenesis of the soil, i.e. on the Mediterranean coast and on the Sahelian border. The salty soils (SEM and SEF) corresponding to salty deposit (sebkha) constitute minor soil types in the studied zone (less than 1 %). Finally, 7 % of the total surface (mountains, urban areas, etc.) was considered be to a non-erodible surface. 5.1.1.2. Roughness modelling The roughness modelling (table VII) consists in determining for each typical surface the most frequent roughness elements, their mean height and covering rate. About forty different roughness types have been distinguished, classified in four classes: • S refers to the presence of vegetation; • R refers to inert roughness elements (pebbles, gravel); • S+R represents the association of vegetation and inert obstacles; • L refers to smooth erodible surfaces. For the vegetation roughness, the mean height of the bushes are 10, 20 or 40 cm, with covering rates of 0 %, 2 %, 5 %, 10 %, or 20 %. For the inert obstacles, the mean height varies from a few millimetres to about 10 cm, and five covering rates have been used: 0 %, 5 %, 10 %, 20 %, 30 %. The roughness length of the smooth surfaces is estimated from the median diameter of the coarser population constituting the soil. Based on the eight soil types used for the

cartography (table VI), three values of the smooth roughness length were used to characterise the smooth surfaces (0.0007 cm; 0.0017 cm; 0.0023 cm). The mean height and covering rates of the non-erodible elements and the relations (3) and (4) allow to affect to each roughness type the corresponding roughness length. 5.1.2. Threshold velocity maps Since the roughness of the surface controls the erosion threshold, the cartography of the roughness elements can be illustrated by the resulting maps of erosion thresholds. Each square degree being characterised by several surface features, it was not possible to report the corresponding threshold wind friction velocities on one map. Thus, for the sake of readability, we have computed the wind velocities at the height of 10 m for which a minimal dust flux is simulated by the model (F > 10–13 g·cm–2·s–1) (figure 9). In fact, this map illustrates the erosion threshold of the most easily mobilised fraction of the surface. Except for the areas considered as non-sources, the threshold wind velocities are spatially highly variable but relatively homogenous for a specific topographic unit. They range from 6.5 to 20 m·s–1 with most of the values lower than 14 m·s–1. This range is consistent with the threshold wind velocities reported in the literature, 4 to about 20 m·s–1 [45–47]. The map of figure 9 illustrates the problems of ‘geographical’ reading that it implies. Areas with high threshold wind velocities and with low dust emission potentiality, correspond to mountainous areas as well as large hamadas, which have consequently the same representation. Moreover, the different proportions of surface features of a square degree are integrated in the calculation of the wind threshold. That makes even more difficult the geographical reading, since it is rather rare that rocky mountainous areas cover the whole surface of a square degree and that the figure can be assimilated to a map only at dimension scales higher than a square degree. For instance, between 0 to 10° E and 16 to 26° N, the Hoggar and Aïr Mountains (see figure 1) have a very different look: the Hoggar appears as a region with a strong roughness, but only two square degrees are considered as ‘non-source’. The Aïr, much smaller in size, can hardly be discerned: a central square degree (18–19° N; 8–9° E) appears as ‘non-source’, but the environing square degrees have rather low threshold wind velocities (approx. 7.5 m·s–1), the rocky areas being surrounded by emissive areas. Some plateaus, like the Hamadat-a-Hamra, Libya (29–31° N; 10° E) appear more clearly because of the homogeneity of their slightly emissive surface over several tens of thousands of square kilometres. 5.1.3. Dust emission modelling The modelling of the surface features over the Sahara has provided the data set to determine the input parameters

— 265 —


Table VII. Classes and types of roughness used for the model. Type of roughness

Code

Type 1 h (cm)

Vegetation (S)

Mineral obstacles (peebles, gravel) (R)

Smooth (L)

T (%)

Zo (cm)

h (cm)

T (%)

Zo (cm)

S400

40

2

0.082

S203 S202 S201 S200 S101 S100

20 20 20 20 10 10

20 10 5 2 5 2

0.873 0.347 0.138 0.041 0.069 0.020

R104

10

30

0.500

10 6 5 5 5 5 4 4 4 3 3 2 2 2 1 1 1 0.75 0.5 0.2 0.2 0.2

20 30 30 20 5 2 30 10 5 30 20 20 10 2 20 10 5 20 20 30 30 10

0.437 0.300 0.250 0.218 0.035 0.010 0.200 0.069 0.028 0.150 0.131 0.100 0.087 0.010 0.050 0.044 0.017 0.038 0.025 0.01 0.01 0.009

S202.R044

20

10

0.347

4

30

0.200

S202.R012 S201.R042 S201.R022 S201.R013 S200.R042 S200.R013 S100;R051 S100.R042 S100.R033

20 20 20 20 20 20 10 10 10

10 5 5 5 2 2 2 2 2

0.347 0.138 0.138 0.138 0.041 0.041 0.020 0.200 0.200

1 4 2 1 4 1 5 4 3

10 10 10 20 10 20 5 10 20

0.044 0.069 0.087 0.050 0.690 0.050 0.035 0.069 0.131

100 100 100

0.0017 0.0023 0.0007

R103 R064 R054 R053 R051 R050 R044 R042 R041 R034 R033 R023 R022 R020 R013 R012 R011 R0.753 R0.53 R0.24 R0.24 R0.22 Vegetation and obstacles (S + R)

Type 2

LS LDU LDA

0.0051 0.0069 0.0021

required by the dust emission model over this region. The combination of the dust emission scheme with these input data allows to account for the influence of the local surface characteristics on the dust emissions. To illustrate this influence, we have computed for the years 1990, 1991 and 1992 the average annual dust emissions (fig-

ure 10). These global emissions are based on the simulation of the dust flux for each square degree for the wind velocity at 0 h, 6 h, 12 h, and 18 h derived each day from the ECMWF (European Centre for Medium Range Weather Forecast, Reading, U.K.) surface wind fields (10 m). The daily mean fluxes are computed by considering these instantaneous

— 266 —


fluxes as representative of a six hour interval. The daily fluxes are averaged over the three years 1990–1992 and weighted by the surface of the square degree to obtain mean annual dust emission amount by square degree. These dust emission amounts are then averaged over the whole area to represent the annual mean dust amount emitted by the Saharan desert. 5.2. Discussion The dust emission fluxes, computed daily for each square degree and for five months of 1991, have been compared to the dustiness index [48] derived from Infrared Meteosat observations (≈ 60 000 tested cases). Both the relative intensity and the frequency of the simulated dust events are in good agreement with these observations. An additional comparison with the estimations of the single-threshold function model of Saharan dust emissions from d’Almeida [49] has shown that the percentage of concistency indexes Ic higher than 0.7 of his model is only of 32 % when our model is of 74 %. Our model reproduces well the seasonal pattern and the intensity of the dust emissions [1]. Average annual dust emission data for the period 1990– 1992 do not give an image of the important interannual variation of the phenomenon. The comparison between the three years shows that: • the areas of dust emissions do not change much from one year to the other; • on the contrary, the quantities of emitted dust vary greatly according to the years, since the desert dust emissions have been estimated at 608 Mt·year–1 in 1990, 907 Mt·year–1 in 1991 and 756 Mt·year–1 in 1992. These two observations indicate that, on a period of three years, the areas with stronger winds remain constant, but that the average wind strength may vary markedly at the scale of the Sahara. The results and the comparison between figures 9 and 10 clearly demonstrate the relevance of dust emission simulations that account for the influence of the surface features in the source regions. Indeed, as seen in the figures, the capabilities of the various regions in the same arid desert are inhomogeneous both in space and time: on one hand, some regions with a low erosion threshold exhibit high frequencies of dust events with a low intensity of dust emissions, and on the other hand regions with a high erosion threshold can be the source of infrequent but very intense dust events. It can be observed, for instance, that the western part of the Sahara is strongly emissive due to the strength of tradewinds in this region, even though roughness is not particularly favourable to dust emissions. The same phenomenon can be observed in Cyrenaica where the thresholds are high, but surface features may produce more fine soil material, because precipitation is more abundant. Except close to the Mediterranean coast, the eastern part of the Sahara does not

produce much dust. We interpret this fact as a consequence of a lower frequency of efficient winds in this part of the desert.

6. Conclusion A soil-derived dust emission scheme has been designed in order to provide an explicit representation of the mineral dust sources for the atmospheric transport models [10]. This physical scheme allows to account for the influence of the surface features on the erosion threshold and the intensity of the dust emissions. It has been validated by comparison with relevant microscale experimental data. To extent its applicability to large scales and to test its capability to reproduce dust emissions over large arid areas, a modelling of the surface feature characteristics (dimensions of the roughness elements and soil mineralogy) has been developed. The aim of the geomorphologic approach of desert surface feature modelling is to improve the reliability of desert dust emission modelling. With this approach using scattered data of unequal reliability, we can obtain approximate results that are sufficient to improve the values given by the desert dust emission model, as it has been shown in Part 5. This modelling, during its elaboration and application, has demonstrated its limits and advantages. Among limits and problems encountered, we can distinguish the following points: • the reliability of data. In certain regions of the Sahara, they are quite uncertain and limit the validity of the reported values; • the great gap of the scale transfer imposed by this approach is one of the main methodological and practical problems. The introduction of a surface feature grid in the desert dust emission model, even imperfect, is better than a total lack of data. But we must keep in mind that the aim of the surface feature modelling is to determine average surface features to give average roughness data; • these average surface features will be more reliable when they are validated by direct field observations. We have paid careful attention to the scale transfers from calibration points but the limits linked to these transfers remain; • the number of variables used and the problems encountered to pass from qualitative data to quantitative data. We have selected the particularly complex border area shown in Part 4, to elaborate and experiment our modelling method. This example demonstrates the difficulties of the method. The very arid parts of the central Sahara are easier to model, because two variables, vegetation and anthropic degradation, are negligible. Among advantages, some points appeared during the elaboration and application of the surface feature modelling: • the difference in the scales of our data: Our study can be considered as cartographic only at a scale larger than a square

— 267 —


Figure 9. Map of the surface wind velocity (at 10 m) required to reach a mineral dust flux of 10–13 g·cm–2·s–1. The yellow color indicates the areas with a strong power of dust emissions.

Figure 10. Map of the average annual dust emission for 1990, 1991 and 1992.

— 268 —


degree. At smaller dimension scales, it is not a cartography but only modelling, since collected data are not spatially located inside the square degrees. This difference in nature according to scales has allowed us to integrate complex spatial patterns inside each square degree; • one can say that such a research organisation is a scale transfer from a model to a cartography. By this difference in nature according to scale our work differs from a G.I.S. where all the elements are spatially located at every scale; • the methods used, by combinations of multiple information inside a square degree, allow a constant improvement of the model, since all new data – for instance data obtained by interpretation of new satellite images – can be integrated in the square degree without the need of new modelling; • this method can be easily generalised to other desert regions, wherever reliable data are available. The modelling of arid areas in Arabia, Middle-East and deserts of Iran, Afghanistan, Pakistan and India is now undertaken with the same method. Since the roughness of the surface controls the erosion threshold, the modelling of the roughness elements has been illustrated by the resulting maps of erosion thresholds. Except for the areas considered as non-sources, the threshold wind velocities are spatially highly variable but relatively homogenous for a specific topographic unit. They range from 6.5 to 20 m·s–1 with most of the values lower than 14 m·s–1. This range is consistent with the threshold wind velocities reported in the literature, 4 to about 20 m·s-1. The dust emission physical scheme associated with the surface feature modelling allows to estimate the Saharan dust emissions. The simulations performed for the years 1990, 1991 and 1992 lead to annual emissions of 608, 907 and 756 Mt respectively. These results indicate also that the location of the areas contributing the most to the dust emissions do not change significantly from year to year.

References [1] Marticorena B., Bergametti G., Aumont B., Callot Y., N’Doumé C., Legrand M., Modelling the atmospheric dust cycle: 2–Simulations of Saharan dust sources, J. Geophys. Res 102 (1997) 4387–4404. [2] Joussaume S., Three-dimensional simulations of the atmospheric cycle of desert dust particles using a general circulation model, J. Geophys. Res. 95 (1990) 1909–1941. [3] Joussaume S., Paleoclimatic tracers: An investigation using an atmospheric general circulation model under ice age conditions 1. Desert Dust, J. Geophys. Res. 98 (1993) 2767–2805. [4] Genthon C., Simulations of desert dust and sea-salt aerosols in Antarctica with a general circulation model of the atmosphere, Tellus 44 (B) (1992) 371–389. [5] Tegen I., Fung I., Modelling of mineral dust in the atmosphere: sources, transport, and optical thickness, J. Geophys. Res. 99 (1994) 22897–22914.

[6] Gillette D.A., Environmental factors affecting dust emission by wind erosion, in: Morales C. (Ed.), Saharan Dust, John Wiley, New York, 1979, pp. 71–94. [7] Gillette D.A., Adams J., Muhs D.R., Khil R., Threshold friction velocities and rupture moduli for crusted desert soils for the input of soil particles into the air, J. Geophys. Res. 87 (1982) 9003–9015. [8] Nickling W.G., The initiation of particle movement by wind, Sedimentology 31 (1988) 111–117. [9] Nickling W.G., Aeolian sediment transport and deposition, in: Pye K. (Ed.), Sediment Transport and Depositional Processes, Blackwell Scientific, Boston, Ma, 1994, pp. 293–350. [10] Marticorena B., Bergametti G., Modelling the atmospheric dust cycle: 1-Design of a soil-derived dust emission scheme, J. Geophys. Res. 100 (1995) 16415–16430. [11] Escadafal R., Caractérisation de la surface des sols arides par observations de terrain et télédétection, Collect. Etudes et Thèses, ORSTOM, Bondy, France, 1989, 317 p. [12] Bagnold R.A., The Physics of Blown Sand and Desert Dunes, Methuen, New York, 1941, 265 p. [13] Chepil W.S., Properties of soil which influence wind erosion, 4, State of dry aggregate structure, Soil Sci. 72 (1951) 387–401. [14] Iversen J.D., Pollack J.B., Greeley R., White B.R., Saltation threshold on Mars: The effect on interparticle force, surface roughness, and low atmospheric density, Icarus 29 (1976) 381– 393. [15] Iversen J.D., White B.R., Saltation threshold on Earth, Mars and Venus, Sedimentology 29 (1982) 111–119. [16] Gomes L., Bergametti G., Coudé-Gaussen G., Rognon P., Submicron desert dust: A sandblasting process, J. Geophys. Res. 95 (13) (1990) 927–935. [17] Shao Y., Raupach M.R., Findlater P.A., Effect of saltation bombardment on the entrainment of dust by wind, J. Geophys. Res. 98 (12) (1993) 719–726. [18] Chatenet B., Marticorena B., Gomes L., Bergametti G., Assessing the size distribution of desert soils erodible by wind, Sedimentology 43 (1996) 901–911. [19] Gomes L., Bergametti G., Dulac F., Ezat U., Assessing the actual size distribution of atmospheric aerosols collected with a cascade impactor, J. Aeros. Sci. 21 (1990) 47–59. [20] Breuninger R.H., Gillette D.A., Khil R., Formation of winderodible aggregates for salty soils and soils with less than 50 % sand composition in natural terrestrial environments, in: Leinen M., Sarnthein M. (Eds.), Paleoclimatology and Paleometeorology: Modern and Past Patterns of Global Atmospheric Transport, Kluwer Acad. Pub., Norwell, Ma., 1989, pp. 31–63. [21] Greeley R., Dobrovolskis A., Gaddis L., Iversen J.D., Lancaster N., Leach R., Rasmussen K., Saunders S., Van Zyl J., Wall S., White B., Zebker H., Radar-aeolian project, NASA Rep. CR-4378, 1991, 132 p. [22] Greeley R., Iversen J.D., Wind as a Geological Process on Earth, Mars, Venus and Titan, Cambridge Univ. Press, New York, 1985, 333 p. [23] d’Hoore J.L., Soil maps of Africa, Joint Project 11, Comm. for Tech. Coop. in Africa, Inter African Pedological Service, Inst. Geogr. Militaire, Brussels, Belgium. 1963, sheets 4, 5, 6. [24] Escadafal R., Callot Y., Monitoring saharan dust source areas with multispectral imagery, Proc. of the Eighth Thematic Conference on Geologic Remote Sensing, 1991, 1473–1483. [25] Dutil P., Contribution à l’étude des sols et des paléosols au Sahara, Thesis, univ. Strasbourg, 1971, 346 p. [26] Wilson I.G., Ergs, Sedimentary Geol. 10 (1973) 77–106.

— 269 —


[27] Mainguet M., Callot Y., L’erg de Fachi-Bilma (Tchad-Niger): Contribution à la connaissance de la dynamique des ergs et des dunes des zones arides chaudes, CNRS, 18, Paris, 1978, 184 p. [28] Callot Y., Evolution polyphasée d’un massif dunaire subtropical: le Grand Erg Occidental (Algérie), Bull. Soc. Géol. de France, Paris, 8th. Ser. 4 (6) (1988) 1073–1079. [29] Callot Y., Paléolacs holocènes du Nord du Grand Erg Occidental (NW du Sahara algérien), Bull. Soc. Géol. de France, Paris, mém. N.S. 160 (1992) 19–26. [30] Callot Y., Fontugne M., Les étagements de nappes dans les paléolacs holocènes du nord-est du Grand Erg Occidental (Algérie), C. R. Acad. Sci., Paris 315 (2) (1992) 471–477. [31] Callot Y., Oulerhi T., Géodynamique des sables éoliens dans le Nord-Ouest saharien: relations entre aérologie et géomorphologie, Geodinamica Acta 9 (1) (1996) 1–12. [32] Rognon P., Climatic Change in African deserts between 130 000 and 10 000 year BP., C. R. Acad. Sci., Paris 323 (1996) 549–561. [33] Chavaillon J., Etude stratigraphique des formations quaternaires du Sahara nord-occidental, Thesis, CNRS, Paris, 1964, 393 p. [34] Conrad J., L’évolution continentale post-hercynienne du Sahara algérien, Thesis, CNRS, Paris, 1969, 527 p. [35] Pachur H.J., Röper H.P., Kropelin S., Goschin M., Late Quaternary hydrography of the eastern Sahara, Berlin Geowiss. Abh. (Berlin) (A) 75 (2) (1987) 331–384. [36] Petit-Maire N., Riser J., Sahara ou Sahel ? Quaternaire récent du bassin de Taoudenni (Mali), CNRS and ministère des Relations extérieures, Paris, 1983, 473 p. [37] Petit-Maire N., Interglacial environments in presently hyperarid Sahara: Paleoclimatic implications, in: Leinen M., Sarnthein M. (Eds.), Paleoclimatology and Paleometeorology: Modern and Past Patterns of Global Atmospheric Transport, Kluwer Acad. Pub., Norwell, Ma., 1989, pp. 637–661. [38] Callot Y., Mering C., Simonin A., Image-analysis and cartography of sand-hill massifs on high-resolution images:

Application to the Great Western Erg (NW of Algerian Sahara), Int. J. Remote Sens 15 (18) (1994) 3799–3822. [39] Callot Y., Géomorphologie et paléoenvironnements de l’Atlas Saharien au Grand Erg Occidental : dynamique éolienne et paléo-lacs holocènes, Thesis, université P.-et-M.-Curie-Paris-6, 1987, 412 p. [40] Callot Y., Le Grand Erg occidental (Algérie). Histoire d’un massif de dunes, Science et changements planétaires – Sécheresse 2 (1) (1991) 26–39. [41] Seltzer P., Le climat de l’Algérie, Travaux de l’Institut de Météorologie et de Physique du Globe de l’Algérie (hors-série), Alger, 1946, 220 p. [42] Dubief J., Le climat du Sahara, t. 2, fasc. 1, Mém. de l’Inst. de Rech. Sahariennes, Univ. d’Alger, Alger, 1963, 275 p. [43] Fryberger S.G., Dune forms and wind regime, In McKee E.D. (Ed.) A study of Global Sand Seas, Geol. Survey Prof. Paper 1052., U.S. Govt. Printing Office, Washington, 1979, pp. 137–169. [44] Coudé-Gaussen G., Les poussières sahariennes, John Libbey Eurotext, Paris, 1991, 485 p. [45] Pye K., Aeolian dust and dust deposit, Academic Press, San Diego, Ca, 1987, 334 p. [46] Helgren D.M., Prospero J.M., Wind velocities associated with dust deflation events in the Western Sahara, J. Clim. Appl. Meteorol. 26 (1987) 1147–1151. [47] Nickling W.G., Gillies J.A., Emission of fine-grained particulates from desert soils, in: Leinen M., Sarnthein M. (Eds.), Paleoclimatology and Paleometeorology: Modern and Past Patterns of Global Atmospheric Transport, Kluwer Academic Publ., Dordrecht, 1989, pp. 133–165. [48] Legrand M., N’Doumé C., Jankowiak I., Satellite-derived climatology of the Saharan Aerosol, in: Lynch D.K. (Ed.), Passive Infrared Remote Sensing of Clouds and the Atmosphere II, proc. SPIE, Bellingham, WA, 1994, pp. 127–135. [49] d’Almeida G.A., A model for Saharan dust transport, J. Clim. Appl. Meteorol. 25 (1986) 903–916.

— 270 —