Funding Agency: NASA SERVIR
Award Period: 2022-2025
Hydro-ecological variability in the Amazon basin can have significant implications for both human and natural systems. These impacts include fire risk, ecosystem damage, disease outbreaks, infrastructure damage, disruption of river and road based transportation networks, food insecurity, human displacement and migration, and loss of hydropower generation. Given the disruptive nature of extreme deviations from the mean, early warning in the form of subseasonal-to-seasonal (S2S) forecasts of hydro-ecological extremes are of considerable interest. Warnings that are available on lead times from several weeks to several months can enable government organizations and non-governmental partners to take forecast-based actions, including adjusting reservoir levels, prepositioning supplies tocombat fires and floods, planning budgets and staffing, launching communications campaigns, and in some cases implementing direct land management decisions to reduce risks.
To be useful, a forecast needs to have meaningful skill. Over large portions of the Amazon, S2S hydro-ecological forecasts can deliver such skill out to several months lead. This is true both because of the timescale of teleconnections associated with meteorological variability in the region1–3, which can inform statistically-based forecasts and lend skill to dynamically-based meteorological forecasts, and from the hydrological memory of Amazonian systems: accurate initialization of hydrological states and vegetation conditions can contribute to extended skill in the forecast of hydro-ecological states and fluxes, including drought anomalies4. But predictive skill is only one element of a decision-relevant S2S-HFS forecast system. Climate services on S2S timescales need to be designed collaboratively with operational agencies and users of forecast information, to ensure that the forecast workflow is tenable, that the system is producing outputs that are relevant to user needs, and that forecast strengths and uncertainties are understood, communicated, and considered in decision making processes.
In this project, a multinational team of collaborators engaged in a range of sectors in Peru, Ecuador, Colombia, andBrazil will inform the development of a customized and regionally integrated S2S early warning system for hydro-ecological extremes (S2S-Amazonia). The S2S forecast system itself will be fully transferred to partners in these countries, along with the transdisciplinary workstream for forecast interpretation and dissemination developed over the course of the project.