Use of the swat model for hydro-sedimentologic simulation in a small rural watershed

Mathematical models have great potential to support land use planning, with the goal of improving water and land quality. Before using a model, however, the model must demonstrate that it can correctly simulate the hydrological and erosive processes of a given site. The SWAT model (Soil and Water Assessment Tool) was developed in the United States to evaluate the effects of conservation agriculture on hydrological processes and water quality at the watershed scale. This model was initially proposed for use without calibration, which would eliminate the need for measured hydro-sedimentologic data. In this study, the SWAT model was evaluated in a small rural watershed (1.19 km²) located on the basalt slopes of the state of Rio Grande do Sul in southern Brazil, where farmers have been using cover crops associated with minimum tillage to control soil erosion. Values simulated by the model were compared with measured hydro-sedimentological data. Results for surface and total runoff on a daily basis were considered unsatisfactory (Nash-Sutcliffe efficiency coefficient - NSE < 0.5). However simulation results on monthly and annual scales were significantly better. With regard to the erosion process, the simulated sediment yields for all years of the study were unsatisfactory in comparison with the observed values on a daily and monthly basis (NSE values < -6), and overestimated the annual sediment yield by more than 100 %.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Communication in networks with hierarchical branching

We present a simple model of communication in networks with hierarchical branching. We analyze the behavior of the model from the viewpoint of critical systems under different situations. For certain values of the parameters, a continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra. At the critical point the behavior of the model is totally independent of the number of hierarchical levels. Also scaling properties are observed when the size of the system varies. The presence of noise in the communication is shown to break the transition. The analytical results are a useful guide to forecasting the main features of real networks.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Absence of epidemic threshold in scale free networks with degree correlations

Random scale-free networks have the peculiar property of being prone to the spreading of infections. Here we provide for the susceptible-infected-susceptible model an exact result showing that a scale-free degree distribution with diverging second moment is a sufficient condition to have null epidemic threshold in unstructured networks with either assortative or disassortative mixing. Degree correlations result therefore irrelevant for the epidemic spreading picture in these scale-free networks. The present result is related to the divergence of the average nearest neighbors degree, enforced by the degree detailed balance condition.

Synchronization reveals topological scales in complex networks

We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

Affaires de patrons, usages de la frontière : géographie des réseaux marchands entre Niger, Bénin et Nigéria

Résumé L'objectif de la thèse est de comprendre le mode d'organisation économique spécifique aux petits centres urbains qui composent les espaces frontaliers sahéliens, en s'interrogeant sur leur concurrence ou leur complémentarité éventuelle à l'intérieur d'un régime de spatialité particulier. En s'appuyant sur l'exemple du carrefour économique de Gaya-Malanville-Kamba situé à la frontière entre le Niger, le Bénin et le Nigeria, il questionne le rôle de la ville-frontière ainsi que le jeu des acteurs marchands localement dominants, à partir de quatre grandes interrogations : Quelles sont les spécificités de l'Afrique sahélienne qui obligent à renouveler les approches géographiques de l'espace marchand? Quels sont les facteurs déterminants de l'activité économique frontalière? Les formes d'organisation de l'espace qui concourent à la structuration de l'économie sont-elles concurrentes ou coopératives? Les logiques économiques frontalières sont-elles compatibles avec l'orientation des programmes de développement adoptés par les pays sahéliens et leurs partenaires bi- ou multilatéraux? Dans une première partie, un modèle territorial de l'Afrique sahélienne permet de rendre compte de la prédominance des logiques circulatoires sur les logiques productives, une propriété essentielle de toute organisation économique confrontée à l'instabilité climatique. Dans une seconde partie, l'étude considère les facteurs déterminants de l'activité économique frontalière que sont le degré d'enclavement des territoires, la libre circulation des biens et des personnes, les relations concurrentielles ou coopératives qui lient les marchés ainsi que les liens clientélistes qui unissent patron et obligés. Une troisième partie est consacrée aux productions agricoles de tente organisées sous forme de coopératives paysannes ou d'initiatives privées. Une quatrième partie s'intéresse aux réseaux de l'import-export et du commerce de détail qui bénéficient de l'augmentation des besoins engendrée par l'urbanisation sahélienne. L'économie spatiale qui résulte de ces flux est organisée selon deux logiques distinctes : d'une part, les opportunités relatives à la production agricole conduisent certains investisseurs à intensifier l'irrigation pour satisfaire la demande des marchés urbains, d'autre part, les acteurs du capitalisme marchand, actifs dans l'import-export et la vente de détail, développent des réseaux informels et mobiles qui se jouent des différentiels nationaux. Les activités commerciales des villes-marchés connaissent alors des fluctuations liées aux entreprises productives et circulatoires de ces patrons, lesquelles concourent à l'organisation territoriale générale de l>Afrique sahélienne. Ces logiques évoluent dans un contexte fortement marqué par les politiques des institutions financières internationales, des agences bilatérales de coopération et des ONGs. Celles-ci se donnent pour ambition de transformer les économies, les systèmes politiques et les organisations sociales sahéliennes, en faisant la promotion du libéralisme, de la bonne gouvernance et de la société civile. Ces axes directeurs, qui constituent le champ de bataille contemporain du développement, forment un ensemble dans lequel la spécificité sahélienne notamment frontalière est rarement prise en compte. C'est pourquoi l'étude conclut en faveur d'un renouvellement des politiques de développement appliquées aux espaces frontaliers. Trois grands axes d'intervention peuvent alors être dégagés, lesquels permettent de réconcilier des acteurs et des logiques longtemps dissociés: ceux des espaces séparés par une limite administrative, ceux de la sphère urbaine et rurale et ceux du capitalisme marchand et de l'investissement agricole, en renforçant la coopération économique transfrontalière, en prenant en considération les interactions croissantes entre villes et campagnes et en appuyant les activités marchandes. Abstract: Urbanisation in West Africa is recent and fast. If only 10 % of the total population was living in urban areas in 1950, this proportion reached 40 % in 2000 and will be estimated to 60 % in 2025. Small and intermediate cities, located between the countryside and large metropolis, are particularly concerned with this process. They are nowadays considered as efficient vectors of local economic development because of fiscal or monetary disparities between states, which enable businessmen to develop particular skills based on local urban networks. The majority of theses networks are informal and extremely flexible, like in the Gaya - Malanville - Kamba region, located between Niger, Benin and Nigeria. Evidence show that this economic space is characterised by high potentialities (climatic and hydrological conditions, location on main economic West African axis) and few constraints (remoteness of some potentially high productive areas). In this context, this PhD deals with the economic relationships between the three market cities. Focusing on the links that unite the businessmen of the local markets - called patron; - it reveals the extreme flexibility of their strategies as well as the deeply informal nature of their activities. Through the analysis of examples taken from the commerce of agricultural products, import and export flows and detail activities, it studies the changes that have taken place in the city centres of Gaya, Malanville and Kamba. Meanwhile, this research shows how these cities represent a border economical area based on rival and complementary connections. In the first Part, it was necessary to reconsider the usual spatial analysis devoted to the question of economic centrality. As a matter of fact, the organisation of West African economic spaces is very flexible and mobile. Centrality is always precarious because of seasonal or temporary reasons. This is why the first chapters are devoted to the study of the specificity of the Sahelian territoriality. Two main elements are relevant: first the population diversity and second, the urban-rural linkages. In the second part, the study considers three main factors on which the cross-border economic networks are dependent: enclosure that prevents goods to reach the markets, administrative constraints that limit free trade between states and cities and the concurrent or complementary relationships between markets. A third part deals with the clientelist ties engaged between the patrons and their clients with the hypothesis that these relationships are based on reciprocity and inequality. A fourth part is devoted to' the study of the spatial organisation of commercial goods across the borders, as far as the agriculture commercial products, the import-export merchandises and the retail products are concerned. This leads to the conclusion that the economic activity is directly linked to urban growth. However, the study notices that there is a lack of efficient policies dealing with strengthening the business sector and improving the cross-border cooperation. This particularity allows us to favour new local development approaches, which would take into account the important potential of private economical actors. In the same time, the commercial flows should be regulated with the help of public policies, as long as they are specifically adapted to the problems that these areas have to deal with.

Self-similarity of complex networks and hidden metric spaces

We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.

Percolation and epidemic thresholds in clustered networks

We develop a theoretical approach to percolation in random clustered networks. We find that, although clustering in scale-free networks can strongly affect some percolation properties, such as the size and the resilience of the giant connected component, it cannot restore a finite percolation threshold. In turn, this implies the absence of an epidemic threshold in this class of networks, thus extending this result to a wide variety of real scale-free networks which shows a high level of transitivity. Our findings are in good agreement with numerical simulations.

Clustering in complex networks. I. General formalism

We develop a full theoretical approach to clustering in complex networks. A key concept is introduced, the edge multiplicity, that measures the number of triangles passing through an edge. This quantity extends the clustering coefficient in that it involves the properties of two¿and not just one¿vertices. The formalism is completed with the definition of a three-vertex correlation function, which is the fundamental quantity describing the properties of clustered networks. The formalism suggests different metrics that are able to thoroughly characterize transitive relations. A rigorous analysis of several real networks, which makes use of this formalism and the metrics, is also provided. It is also found that clustered networks can be classified into two main groups: the weak and the strong transitivity classes. In the first class, edge multiplicity is small, with triangles being disjoint. In the second class, edge multiplicity is high and so triangles share many edges. As we shall see in the following paper, the class a network belongs to has strong implications in its percolation properties.

Generalized percolation in random directed networks

We develop a general theory for percolation in directed random networks with arbitrary two-point correlations and bidirectional edgesthat is, edges pointing in both directions simultaneously. These two ingredients alter the previously known scenario and open new views and perspectives on percolation phenomena. Equations for the percolation threshold and the sizes of the giant components are derived in the most general case. We also present simulation results for a particular example of uncorrelated network with bidirectional edges confirming the theoretical predictions.

Clustering in complex networks. II. Percolation properties

The percolation properties of clustered networks are analyzed in detail. In the case of weak clustering, we present an analytical approach that allows us to find the critical threshold and the size of the giant component. Numerical simulations confirm the accuracy of our results. In more general terms, we show that weak clustering hinders the onset of the giant component whereas strong clustering favors its appearance. This is a direct consequence of the differences in the k-core structure of the networks, which are found to be totally different depending on the level of clustering. An empirical analysis of a real social network confirms our predictions.

Tuning clustering in random networks with arbitrary degree distributions

We present a generator of random networks where both the degree-dependent clustering coefficient and the degree distribution are tunable. Following the same philosophy as in the configuration model, the degree distribution and the clustering coefficient for each class of nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding topologies by applying first a closure of triangles and second the classical closure of remaining free stubs. The procedure unveils an universal relation among clustering and degree-degree correlations for all networks, where the level of assortativity establishes an upper limit to the level of clustering. Maximum assortativity ensures no restriction on the decay of the clustering coefficient whereas disassortativity sets a stronger constraint on its behavior. Correlation measures in real networks are seen to observe this structural bound.