A Transboundary, Socio-Environmental Data Synthesis for the Rio Grande/Río Bravo basin
The Rio Grande/Río Bravo Socio-Environmental geospatial database synthesizes a broad array of spatial data sets for a large river basin, drawing on an interdisciplinary collaboration.
Can interdisciplinary and collaborative research produce new, exciting knowledge? We say YES! And the Data descriptor that we recently published in Scientific Data is a salient proof. We invite you to read on to learn more about how our team integrated their knowledge and expertise to compile a basin-wide and cross-disciplinary spatial database for the Rio Grande/Río Bravo (RGB) - a transboundary river basin in the western part of North America.
It all began with a fortunate encounter between environmental anthropologists and modelers who took up the challenge to co-design a conceptual model and then an agent-based model for the RGB socio-environmental system. Given the theoretical and methodological divide between the disciplines, we first had to figure out: How can we effectively integrate the findings from fieldwork-based ethnographic research into a quantitative computer simulation model?
The RGB is a complex system that crosses multiple cultural, political, and ecological boundaries in the southern U.S. and northern Mexico. To understand this large, socio-environmental “mille-feuille,” our team needed to visualize the spatial heterogeneity of the whole basin, overlapping the multiple layers of human-environment interactions at the local and basin-wide levels. While the anthropologists had an in-depth knowledge of the socio-environmental dynamics on the ground, the modelers had the technical skills to manage and synthesize large sets of spatial data. All we had to do was find a way to bring it all together. And this is where the idea of the geodatabase emerged from our collaboration: a cross-cutting product to integrate complex spatial, socio-environmental dynamics in the basin.
The Rio Grande/Río Bravo in the Big Bend National Park where the river forms the border between U.S. and Mexico.
This exercise was new for all of us, with its share of questions: what kind of information should we integrate, from where (which sources), how, and for whom? For the “What,” we primarily relied on the integrated modeling expertise of the team and a typology of actors derived from the ethnographic research. From there, we collectively identified the scattered sources of information, public or private, local or global, through which we navigated for hours on end. In the end, all the collected data were spatialized and organized thematically: (i) Water & Land Governance (tied to the actors’ typology), (ii) Hydrology, (iii) Water Use & Hydraulic Infrastructures, (iv) Socio-Economics, and (v) Biophysical Environment. Given the expertise represented, this data synthesis and the massive amount of time and energy we invested, we found it important to develop an open-access product that can serve both the scientific community and local stakeholders, who have shown a unanimous interest in this type of product. The database is now accessible through the Open Science Framework : https://doi.org/10.17605/OSF.IO/79426
Dams - a dataset that we included in the geodatabase - are a key element of water management infrastructure in the RGB. Shown, Elephant Butte Dam in New Mexico (U.S.).
What do we take away from this experience? It may seem trivial but our willingness to share our knowledge and skills and work together have been the key ingredients to the overall success. Behind this paper lie hours and hours of collective meetings, discussions, data processing, and pages and pages of emails. Another big take-away from our collaboration has been that to mutually learn from each other requires patience, trust, and open-mindedness. The modelers gained a refined understanding of the local dynamics, specifically about the role of each actor in the watershed governance, while the anthropologists accessed new quantitative and spatial information.
Creating a transboundary, open-access database involved navigating a process that was definitely not a long, calm river. But the benefits that we have gained, and that hopefully others would gain, make us conclude that the effort was worth the trouble!
Post contribution by:
Sophie Plassin, Jennifer Koch, Stephanie Paladino, Jack R. Friedman, Kyndra Spencer, Kellie B. Vaché