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Le succès d'une gestion des écosystèmes naturels requiert une connaissance approfondiedes différents processus qui interviennent et de leurs échelles de temps et d'espaceparticulières. Pour cette raison, les décideurs ont besoin d'analyser une vaste gamme dedonnées et d'informations géographiques. Les modèles mathématiques, les systèmesd'informations géographi-ques et les systèmes experts sont capables de produire cetteanalyse, mais seule une minorité de gestionnaires les utilise actuellement. Cet articleidentifie quelques unes des raisons à l'origine de l'hésitation des gestionnaires à adopterde tels outils d'aide à la décision pour la gestion des ressources naturelles et propose unestructure qui pourrait faciliter leur utilisation pour le processus de prise de décision.Cet exercice est réalisé à l'intérieur du contexte de la gestion intégrée par bassin. Unerevue des systèmes d'aide à la décision est également présentée.Many methods of integrated or watershed management exist which account for thenecessary biophysical and socio-economic factors at the watershed level. Some of theseapproaches are ecosystem oriented while others are socio-economically oriented. Whatever thedefinition, water management at the watershed level needs to account for a plenitude ofvariables related to the air, water, soil, biology, and economy. The successful managementof natural ecosystems requires a thorough understanding of their characteristic time andspatial scales. Because of this, decision makers need to analyze a wide range of data andgeographic information. Mathematical models, geographic information systems and expertsystems are capable of performing this analysis, but only a minority of managers arecurrently using them. This paper identifies some of the reasons why ecosystem managers havebeen slow to adopt such decision support tools in natural resources management and proposesa framework to facilitate their use in the decision making process. This is done in anintegrated watershed management context. A review of related decision support systems isalso presented.Four types of decision-support tools are introduced : mathematical models,expert-systems, geographical information systems (GIS) and decision support systems (DSS).Mathematical models have long been used for simulation, prediction, and forecasting,however, they are often task specific and were rarely developed for management uses. GIS aremore and more commonly being used for decision support as they become more affordable anduser-friendly and are very well-suited for managing resources at a spatial scale. Thereexist many kinds of software ranging from a simple viewer used for cartographic purposes tocomplex GIS oriented toward spatial analysis and modelling. Expert systems are alsointeresting for decision support when specific goals are being considered. Finally, DSS areperhaps the digital tools most applicable to management purposes, often integrating one ormore models, a GIS or expert system functionalities. There are two types of DSS : 1. Environmental Information Systems (EIS), and 2. Integrated Modelling Systems (IMS) EIS can be very user- friendly, relying heavily upon GIS and statisticalfunctions.IMS also use GIS capabilities, but integrates several mathematical models as well. Thelevel of integration between models varies considerably and the complexity of IMS aregenerally high.Two questions underlie the operational use of digital technologies for decisionsupport. The first is whether or not such technology should be used at all, while the secondis why such tools take time to be adopted by government and management agencies. The use ofdigital technologies is often required when the problem is complex and where there are awide range of factors involved with different spatial and temporal scales. Three majorconstraints towards the implementation of decision support tools can bepinpointed :1. technology, 2. data, and 3. working organization. Technological constraints include cost, lack of user friendliness, and hardwareproblems, among other factors. Data constraints are mostly related to availability, cost,heterogeneity and volume. Finally, organization constraints pertain mostly to the manager'sperception of the tool and the structural integration of the tool within the decisionprocess.This paper proposes a 4-step approach to optimize the use of decision-support tools.The first step requires that managers and decision-makers clearly define their project,goals and budget, as well as, decide whether to use an integrated watershed managementapproach or a more discrete approach. This leads directly to the second step, which consistsof choosing the most appropriate digital support tool. This requires communication betweenmanagers and scientists, and at this point, data gathering and integration should begin. Thethird phase consists of the development of a new tool or adaptation of an existing onewithin the context of the agency's management structure. The final step is the operationaluse of the decision support tool by the agency, following an initial trial period. Thesuccessful use of a decision support tool for management purposes depends on proper planningthat accounts for all factors related to management needs, budget, data, ease of use, andorganization integration

Utilisation des outils numériques d'aide à la décision pour la gestion de l'eau