The Land to Sea Network project team collaborates to expand and enhance the effectiveness of climate data, tools, and science to support community adaptation and resilience planning. This project is a collaborative knowledge sharing and creating process with seven community hubs. Focusing on a watershed-level, community hub approach of connected coastal communities to the Pacific Ocean (California, Hawai‘i), the Gulf of Mexico (Louisiana), and the Caribbean Sea (Puerto Rico), all planning for the future impacts of flooding and fire in the context of multiple stressors, including climate change.

The team brings together leading solution-builders from Indigenous and allied non-profit and community-based organizations in California, Louisiana, Hawai‘i, and Borikén/Puerto Rico, all planning for the future impacts of flooding and fire in the context of multiple stressors, including climate change. Participants include Indigenous, rural, and urban communities connected through their intimate relationships from land to sea.

The process is focused on building relationships, strengthening collaborations, and sharing a participatory process of co-learning to develop and share climate data indicators (e.g., loss of shoreline, erosion) and metrics that communities use and to enhance the usefulness of climate data for informing community adaptation responses. We ask questions about what kinds of data and knowledge are needed to address the effects of climate change on complex land-coast-sea ecosystems; what are effective ways to gather such information; and how a diversity of data and knowledges can be brought together successfully in order to solve climate-driven problems.

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Organizational collaborators are the Asian Pacific Environmental Network, El Puente Enlace Latino de Acción Climática (ELAC), First People’s Conservation Council of Louisiana, Kīpuka Kuleana, Livelihoods Knowledge Exchange Network, Lowlander Center, Para la Naturaleza, Sogorea Te Land Trust, The Sierra Fund, in collaboration with trusted partners at Lawrence Berkeley Lab, Stanford University, University of California-Berkeley, and University of California-San Diego.