中文摘要：

资源环境领域的研究对象常被抽象为若干子模型复合而成的复杂模型，如何有效地集成这些子模型成为资源环境模型集成的主要内容。随着各种地学相关问题的日益复杂和多样化，传统的建模语言与友好易用的图形建模等集成建模方式已无法满足大规模高复杂的模型构建需求。本文在已有的相关理论基础上，针对复合模型构建的灵活性和复杂性之间的矛盾，将模型复合模式的本质抽象化，提出资源环境“模型流”的形式化语言描述，实现了模型流建模环境。应用实例表明，基于模型流的模型构建方式采用简洁的符号表示，避免了繁琐的图形构建环节，可快速灵活地构建高复杂度模型。模型流的提出为模型复合的调度性能优化和智能自动化集成等进一步的研究奠定了基础。

英文摘要：

Resources and environmental problems directly related to the survival and development of mankind constitute hot issues of concern in the global community. Because of the complexity and diversity of concrete is-sues, research object is usually abstracted to a complicated model which consists of several sub-models in the do-main of resources and environment research. Experts and scholars divided big models into a number of subsys-tems on the base of scientific hypotheses of complex environmental and resource system generated by a specific mechanism or statistical analysis methods. How to integrate these models effectively constitutes the chief chal-lenge of the research of resource and environment model integration. With the rapid development of complexity and diversity of geo-sciences related issues, integrated modeling methods which include traditional modeling lan-guages and user-friendly graphical-modeling have been unable to meet the need of the large-scale highly compli-cated modeling. Based on the current theories, this paper presented a formal language definition termed resource and environment model-flow （REM）. REM is a useful supplement to the theory of resources and environment model integration. The build process of model-flow is described by the abstract formal symbolic language avoid-ing the concrete graphical modeling process. REM is abstracted from the nature of compound model to solve the contradiction between the flexibility and complexity of integrating model. It is suitable for building more com-plex composite models. We implemented the prototype modeling environment of REM. According to the applica-tion of the prototype system of REM, it was shown that model-flow based modeling process can build a compli-cated model by using simple symbols to express semantic meanings. The introduction of REM can provides a hard foundation for the further study of performance optimization and intelligent development about model inte-gration.