Integrating future climate projections into post-fire management: A stochastic decision-support toolbox for adaptation in arid ecosystems

Wildfire is a multi-dimensional hazard, impacting both human livelihoods and ecosystem function. Beyond wildfire prediction and containment, post-fire reconstruction is a major management challenge. With shifting and novel fire regimes, post-fire recovery represents a complex risk-management challenge where decisions made under high uncertainty have long-term implications for systemic resilience.There is an urgent need for tools which allow land managers to explore their options in an accessible, systematic and transparent way.

Here, we present a joint effort between the Schmidt Center for Data Science and Environment and the U.S. National Parks Service to design a decision-support platform, enabling park managers to create future management scenarios based on current understanding of climate futures to guide their decision making. Using the Mojave Desert ecosystem in Southern California as a case study, we discuss our collaborative co-design process, technical infrastructure and scientific reasoning in translating high-performance vegetation modeling into actionable policy insights

More specifically, we present josh, an open-source, domain-specific scripting language linked to a high-performance simulation engine. We illustrate how josh can be used to design vegetation models and management intervention for a range of ecosystems, integrate different high-resolution future climate projections and quantify risk and uncertainties through running large, stochastic ensemble simulations. The platform is freely available, open-source, and runs in any web browser, as well as on distributed computing systems; providing a transparent and accountable tool for evidence-based adaptation planning.