The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making data and models more accessible, but are, in most cases, limited to particular types or classes of data or models and also lack the type of collaborative and iterative functionality needed to enable shared data collection and modeling workflows. File sharing systems currently used within many scientific communities for private sharing of preliminary and intermediate data and modeling products do not support collaborative data capture, description, visualization, and annotation. In this article, we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. This article describes the generic data model and content packaging scheme for diverse hydrologic datasets and models used by a new hydrologic collaborative environment called HydroShare to enable storage, management, sharing, publication, and annotation of the diverse types of data and models used by hydrologic scientists. The flexibility of HydroShare's data model and packaging scheme is demonstrated using multiple hydrologic data and model use cases that highlight its features.

Abstract. Computational modeling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature – models as both infrastructure and hypotheses – means that modeling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language-bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed.

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A feedback simulation model based on radial basis function neural networks is newly developed in this research to analyse the interaction between urban densities and travel mode split. The changes of populations, road mileages, travel mode split, and so on of the enlarging urbanized areas of different cities in China are studied for the trainings of the radial basis function neural networks constituting the proposed feedback model. Furthermore, the effect of different development policies for Beijing on distinct indicators of the urban density and trip shares of various travel modes is also evaluated by the newly developed model. It is found that stopping the quick urban sprawl of Beijing is the most important for the sustainable development of its urban transport. It is confirmed that the newly developed model is able to rationally explain the interactive correlation between urban densities and travel mode split of a city for its different development plans.