Are you planning to attend the 2021 American Geophysical Union’s Fall meeting from December 13-17, 2021? The theme of this year’s meeting is Science is Society, representing the conference goal of providing a collaborative space to gather researchers, scientists, educators, students, policymakers, science enthusiasts, journalists, and communicators who are dedicated to understanding and preserving our planet. Registrants may attend in New Orleans, LA or virtually. Check out the following presentations from SE CASC researchers, students, and affiliates.
Phosphorus (P) is often the limiting nutrient for phytoplankton production in freshwater systems, and watershed P reduction is frequently considered an integral part of lake and reservoir management. However, there is increasing evidence that external (watershed) P loading reductions may not readily produce desired water quality outcomes due to persistent internal P loading from lake sediments. Moreover, the limited temporal scope of most lake sediment monitoring and modeling studies makes it difficult to characterize the long-term dynamics of internal P loading. In this study, a mass-balance P model was developed in a Bayesian inference framework utilizing 36 years (1983-2018) of water-column P data from a major North Carolina reservoir (Jordan Lake). Through Bayesian calibration, uncertainties in P flux parameters are constrained, and the model explains nearly 60% of the variability in historical P data. The model simulates an average sediment P release of approximately 0.3 g/m2/month with large seasonal variations (0.1 g/m2/month during Jan-Mar and 0.6 g/m2/month during Jul-Sept). Simulations indicate that internal sediment P releases are 70% of the external loading in the first decade and increase to 120% in the most recent decade. Results also confirm that changes in external loadings will have limited immediate benefits for eutrophication management, as internal loading will remain elevated for several decades into the future. At the same time, increases in lake temperature due to climate change will increase P cycling rates. Using the model, we compare several long-term scenario forecasts that explore how combined changes in external loading, internal loading, and temperature are expected to control future eutrophication trajectories.
In the Southeastern U.S., prescribed fire is used extensively to reduce wildfire risk, manage wildlife habitat, and support fire-dependent species. However, the smoke generated by this practice can be significant and must be managed acceptably to reduce impacts on visibility and air quality. The human populations experiencing wildland fire smoke in the Southeast are not well characterized, especially when considering the distinction between wild and prescribed fire. Further, it is unclear if some populations benefit from the advantages of prescribed fire while other communities experience the air pollution costs. Additionally, climate change will likely affect the timing and frequency of prescribed fire in the future, which may alter impacts and risks for nearby communities. Using spatial analysis of NOAA smoke forecasts, spatially-interpolated smoke observations, MODIS fire detections, prescribed burning records, census demographic data, and indices of existing social and environmental stress, we identify the populations in the Southeast that most frequently experience wildland fire smoke. By examining demographic variables such as socioeconomic status, race, and age, this analysis further highlights whether particularly vulnerable subgroups are disproportionately impacted by prescribed fire in the Southeast. Inclusion of U.S. EPA environmental justice indices in this analysis allows us to determine whether this environmental stressor is adding further strain to groups that are already stressed. The analysis shows that wildland fire smoke in certain regions of the country can disproportionally affect specific population groups.
Despite the growing need to model the dynamic relationships between flood risk and community displacement, our ability to anticipate when and where sea level rise (SLR) and inland flooding will ultimately threaten residents with displacement from flood-prone areas to less risky areas is limited. In response, we employ new advances in land change modeling to construct “what-if” scenarios of population redistribution and landscape change following sea level rise and increased flooding from pluvial, fluvial, and ocean inundation. Specifically, we build upon the FUTure Urban-Regional Environment Simulation (FUTURES) — a land change model that simulates the spatial patterns and drivers of development. Scientists from across disciplines have collaborated for over a decade to conceptualize, design, develop, enable, apply, and advance the FUTURES modeling framework. We developed and integrated new algorithms to simulate the dynamic interactions between flood events, community adaptive response, and land change outcomes across space and time. We demonstrate FUTURES improved functionalities by exploring the separate and joint effects of urbanization and increased flooding on land change outcomes through the middle of the 21st century. Our spatially-explicit forecasting framework accounts for multiple drivers of social-environmental change and provides stakeholders with visual analytics to weigh tradeoffs and prepare for alternative futures.
Species are being impacted by changing climate conditions, but not all species are responding in the same way. For example, while many species are shifting their ranges to higher latitudes and elevations, others are not shifting or are shifting in opposite directions. Effectively explaining observed variation in responses would help natural resource managers plan for future impacts. Differences in exposure to climate change may explain some of the observed variability, but this requires identifying regions that have experienced detectable changes in those aspects of the climate system that species are sensitive to. When paired with advances in global observational datasets, large ensemble climate model simulations can provide a powerful tool to explore a diversity of emerging climate signals that are affecting the natural world.
Here we employ a signal-to-noise signal detection method to estimate the time of emergence of 19 biologically-relevant climate variables. Local polynomial regressions with a generalized cross-validation smoothing parameter are applied to an observational dataset and initial-condition large ensemble simulations from five earth-system models to derive signal-to-noise ratios (SNR) through time for each bioclimatic variable. Time of emergence estimates are then calculated in an analogous manner to a sensitivity analysis (i.e. across multiple SNRs) to account for species-specific sensitivities to climate change rates and levels. Here we will explore similarities and differences in the signal-to-noise ratio and time of emergence between the multi-model large ensembles. Similar results between the multi-model large ensemble experiments and the observational dataset provides additional confidence that biologically-relevant climate change signals are emerging.