A barrage of dust storms in the 1930s devastated croplands in the United States and in Canada. Drought ravaged agriculture and inflicted suffering across the southern U.S. plains. Entire houses were buried under dirt, and families were uprooted and forced to relocate. Millions of dollars in economic damage reverberated across sectors affected by the collapse of agricultural commodities.
Far from forgotten, the “Dirty Thirties” are still discussed today in the context of the Farm Bill, which is reauthorized every 5 years by Congress. The bill includes provisions for programs that incentivize farmers to adopt certain agricultural practices. In light of the evidence of changing precipitation and temperature patterns across U.S. croplands, questions have been raised about whether farming practices shaped by policies are contributing to a possible recurrence of the Dust Bowl. Can we harness technology to share science-informed best practices to enhance resilience?
We communicated to Congress our endorsement of the Preparedness and Risk Management for Extreme Weather Patterns Assuring Resilience and Effectiveness (PREPARE) Act of 2017. The PREPARE Act, which was reported on by the U.S. House of Representatives Committee on Transportation and Infrastructure but died when the 115th session of Congress ended in January, called for “the development of innovative, actionable, and accessible Federal extreme weather resilience, preparedness, and risk identification and management-related information, data, tools, and examples of successful actions at appropriate scales for decisionmakers.”
Continuing this push for action, we call for science, technology, and policy leadership to launch the Resilience Genome Initiative (RGI). (RGI is our nod to the Human Genome Project and the Materials Genome Initiative.) The RGI comprises (1) a technology platform to accelerate the innovation of resilience plans through the evolution of an open library of “resilience genomes” and (2) capacity building to stimulate a transdisciplinary community of resilience genome engineers. Just as biological genomes may be conceptualized as a set of “assembly instructions” for living organisms, resilience genomes carry the instructions for assembling a fully fledged, data-driven, science-informed resilience plan [Wee, 2019]. Just as biologists use information in genomic libraries to edit an organism’s genes, resilience planners will utilize a Resilience Genome Library to retrieve an existing climate resilience plan, customize the plan, and contribute the evolved solution back to the library.
Connecting Data and Policy
The urgent drumbeat of large-scale socioenvironmental changes unfolding across the globe has led many scientists to ask, How can science be fully incorporated into policies and decisions?
One example is the Yakima River Basin Water Enhancement Project (YRB), a multibillion dollar, multidecade plan in Washington State. The YRB is used as a case study in the U.S. Climate Resilience Toolkit (CRT), part of the Climate Action Plan introduced by President Barack Obama in 2013. The CRT is a website “designed to help people find and use tools, information, and subject matter expertise to build climate resilience.” The CRT organizes content through a five-step resilience framework, starting with “explore hazards” and ending with “take action,” with each step encompassing a more detailed series of steps.
The RGI builds on the strategy behind the CRT. Central to the RGI is the concept of informatics as the pipeline that enables data to be transformed into policies and decisions. Figure 1, first presented to President Obama’s Council of Advisors on Science and Technology in 2013, depicts this informatics pipeline.
The Resilience Genome Initiative
The sequence of steps in Figure 1 that connects data, code, and information to decisions represents a data-to-decisions workflow. That workflow—the resilience genome—carries the instructions for assembling a fully fledged, data-driven, science-informed resilience plan. Linking data to decisions is difficult but possible, using decision analysis models and artificial intelligence methods [Wee, 2019]. An ideal genome supports traceability of decisions back to data, code, and information. Genomes can be retrieved, modified, and contributed back to a Web-accessible Resilience Genome Library.
Genomes should be structured using an appropriate climate adaptation framework like the CRT, so that they can be mapped to a common conceptual framework. Users can then search and use visualization tools to retrieve resilience solutions from the library, even if those solutions are described using slightly different vocabularies.
For example, a YRB resilience team might use the Resilience Genome Library upon noticing that hydrologic cycle changes are affecting sockeye salmon populations of commercial and cultural importance to the Yakama Nation. Searching the library, the team might find that wildlife managers in Massachusetts had contributed a genome based on their work to improve the health of shad populations and had used a decision analysis model to weigh the pros and cons of alternative management plans. The YRB team could retrieve the Massachusetts decision analysis model, substitute the shad population model with a sockeye model, input different model assumptions to reflect Washington State laws, and add a regional climate model to the analysis. Later, the YRB team could upload its modified workflow as an evolved genome into the library for use by others. The cumulative result of these contributions would be an open, freely Web accessible library of data-driven, science-informed solutions.
The nature of building, understanding, and implementing resilience genome libraries requires individuals who are capable of traversing complex socioenvironmental decision landscapes. Such landscapes are characterized by numerous connections between traditional disciplinary silos, leaders who represent deeply held community values, and policies that constrain adaptation implementation options. Acquiring the skills to navigate these complex landscapes requires a substantial investment of time and effort, often through project-based initiatives led by individuals who encourage feedback, dialog, and reflection. Alternatively, interested parties can try attending the few existing workshops designed to develop transdisciplinary thinking. The National Science Foundation funds the Employing Model-Based Reasoning in Socio-Environmental Synthesis (EMBeRS) project at the University of Texas at El Paso to test “a new model for integrating knowledge across disciplines based on cognitive science theories of model-based reasoning.” Investments in such capacity-building programs are necessary to foster professionals who credibly code-switch between different disciplines.
Human capital is as much a part of any scientific infrastructure as physical and cyber infrastructure. The Human Genome Project was the equivalent of biology’s moon shot that inspired a cadre of scientists and engineers and spawned a rich ecosystem of stakeholders across government, academic, nongovernmental organization, and for-profit entities.
We anticipate that the RGI could serve a similar role to corral a broad spectrum of stakeholders motivated in confronting emergent socioenvironmental challenges. If the prospects of a possible recurrent Dust Bowl are too amorphous to spur action, consider the contemporary costs to society caused by extreme climate and weather events that provided the impetus for the PREPARE Act of 2017. The RGI represents a sociotechnical platform to address such pressing climate and weather challenges that evolve over time. We envision using the RGI to bootstrap a group of transdisciplinary resilience genome engineers capable of leveraging data-driven, science-informed solutions that are scalable and responsive to evolving socioenvironmental threats.
We would like to thank Michael S. Henry, Kei Koizumi, Thomas Narock, William L. Teng, and Phyllis Pouyat Thibodeau for their invaluable comments on earlier drafts of this article.
Wee, B. (2019), D2dprov: Vision 2025. A transdisciplinary science, technology, and policy vision for data-driven, science-informed resilience planning for 2025 and beyond, Washington, D.C., https://doi.org/10.6084/m9.figshare.7591238.
Brian Wee (firstname.lastname@example.org), Massive Connections, Washington, D.C.; and Aaron J. Piña, Aeris, Louisville, Colo.
Wee, B.,Piña, A. J. (2019), A vision for adapting at the pace of socioenvironmental change, Eos, 100, https://doi.org/10.1029/2019EO116643. Published on 25 February 2019.
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