Nous nous intéressons dans ce travail de recherche à la modélisation d'une série d'événements par la théorie des processus ponctuels temporels. Un processus ponctuel est défini comme étant un processus stochastique pour lequel chaque réalisation constitue une collection de points. Un grand nombre d'ouvrages traitent particulièrement de ces processus, cependant, il existe dans la littérature peu de travaux qui se préoccupent de l'analyse de séries d'événements. On identifie deux catégories de séries d'événements : une série d'un seul type d'événements et une série de plusieurs types d'événements.L'objectif de ce travail est de mettre en évidence les différents tests statistiques appliqués aux séries d'un seul ou de plusieurs types d'événements et de proposer une classification de ces tests. Nous présentons d'abord une revue de littérature des processus ponctuels temporels, accompagnée d'une classification de ces modèles. Par la suite, nous identifions les tests statistiques de séries d'un seul type d'événements et nous examinons leur applicabilité pour une série de deux ou de plusieurs types d'événements. Les tests statistiques identifiés sont répartis en quatre classes : analyse graphique, tests appliqués au processus de Poisson homogène et non homogène, tests appliqués au processus de renouvellement homogène et les tests de discrimination entre deux processus ponctuels. Ce travail est réalisé avec l'idée d'une application ultérieure dans le cadre de l'analyse du risque.Les résultats de cette recherche ont montré qu'il n'existe dans la littérature que des tests d'une série d'un seul type d'événements et ils sont, généralement, valables pour les processus ponctuels suivants : Poisson homogène et renouvellement homogène. L'application de ces tests aux séries de deux ou de plusieurs types d'événements est possible dans le cas où les événements sont définis par leurs nombres et leurs temps d'occurrence seulement, i.e. la durée de chaque événement n'est pas prise en considération., The design and management of hydraulic structures require a good knowledge of the characteristics of extreme hydrologic events such as floods and droughts, that may occur at the site of interest. Occurrences of such events may be modelled as temporal point processes. This modelling approach allows the derivation of various performance indices related to the design and operation of this infrastructure, as well as to the quantification and management of the associated risks. In this paper, we present statistical tests that may be applied for the modelling of a series of events by temporal point processes. A point process is defined as a stochastic process for which each realisation constitutes a series of points. Although a large body of literature dealt with temporal point processes, very few focused on the analysis of a series of events.In the present paper we identify two types of series of events: the first represents a series of only one type of event, and the second represents a series of several types of events. The main objective of this research is to comprehensively review the statistical tests applied to the series of one or several types of events and to propose a classification of these tests. This comprehensive review of statistical tests applied to point processes is carried out with the ultimate objective of applying these tests to real case studies within the framework of risk analysis. For example, an extended low-flow event constitutes a risk that may place a water resources system in a state of failure. Thus, it's important to identify and quantify this risk in order to ensure the optimal management of water resources. The modelling of the observed series of events by point processes can provide some statistical results, such as the distribution of number of events or the shape of the intensity function. These results are useful in a risk analysis framework, which includes two steps: risk evaluation and risk management. In the first part of the paper, a review and classification of the various temporal point processes are presented. These include the homogeneous and nonhomogeneous Poisson processes, the Negative Binomial process, the cluster point processes (such as the Neyman-Scott and the Bartlett-Lewis processes), the doubly stochastic Poisson processes, the self-exciting point processes, the homogeneous and nonhomogeneous renewal processes and the semi-markov processes. Also, we illustrate the various links and relationships that exist between these point processes. This classification is elaborated by considering the homogeneous Poisson process as the starting point. The simplicity and the wide use of this process in the statistical and hydrological literature justify this choice.In the second part of the paper, statistical tests of a series of one type of event are identified. A series of events may be characterised by the number of events, the occurrence times of the events or by the duration of each event. These characteristics are considered as random variables that must be represented by suitable statistical distributions. A series of events may also be characterised by the intensity function, which represents the instantaneous average rate of occurrence of an event. Clearly, the choice of the statistical distribution to model the number of events in a series or the intensity function depends on the nature of the observed data. For example, a stationary series of events may be represented by a constant intensity function. Thus, it is necessary to conduct an analysis of the observed series of the events, such as graphical analysis and statistical testing in order to select and validate the hypothesis underlying the point process model. The hypotheses that may be verified include trend analysis, homogeneity analysis, periodicity analysis, independence of intervals between events, and the adequacy of a given distribution for the number of events and for the time intervals separating events.In the third part, the applicability of the tests identified in the second part to the case of a series of two or more types of events is examined. In this part, our goal is to analyse the global point process (or the pooled output) obtained by the superposition of the p subsidiary point processes. The decomposition of the global process into p point processes necessitates an identification of each type of event, characterised generally by the number of occurrences and by the intervals between the successive events of the same type. We also examine the applicability of the statistical tests identified in the second part to the case where the global point process is characterised by the duration of each type of event. We investigate more specifically the case of two subsidiary point processes (p=2) where the two event types alternate in the time (an alternating point process). Finally, statistical tests identified in the second part are classified into four categories: tests based on graphical analysis; tests applied to the homogeneous and nonhomogeneous Poisson processes; tests applied to the homogeneous renewal process; and finally tests of discrimination between two specific processes. Theses tests of discrimination include the selection among the Poisson process and the renewal process, the Poisson process and the Binomial point process, and finally, the selection among these three point processes: Cox process, Neyman-Scott process and renewal process.The results of this research indicate that, in the past, mostly tests for a series of one type of event were presented in the literature. These tests are only valid for the following point processes: a homogenous Poisson process or a homogenous renewal process. The application of these tests to a series of two or several types of events is possible as long as these events are only described by their number and time of occurrence i.e. the duration of each event can not be taken into consideration. Otherwise, these tests are applicable to the alternating point process, which is characterised only by the number and the duration of the two types of events.