Abstract: Eddy covariance (EC) estimates of carbon dioxide (CO2) fluxes and energy balance are examined to investigate the functional responses of a mature mangrove forest to a disturbance generated by Hurricane Wilma on October 24, 2005 in the Florida Everglades. At the EC site, high winds from the hurricane caused nearly 100% defoliation in the upper canopy and widespread tree mortality. Soil temperatures down to −50 cm increased, and air temperature lapse rates within the forest canopy switched from statically stable to statically unstable conditions following the disturbance. Unstable conditions allowed more efficient transport of water vapor and CO2 from the surface up to the upper canopy layer. Signifi- cant increases in latent heat fluxes (LE) and nighttime net ecosystem exchange (NEE) were also observed and sensible heat fluxes (H) as a proportion of net radiation decreased significantly in response to the disturbance. Many of these impacts persisted through much of the study period through 2009. However, local albedo and MODIS (Moderate Resolution Imaging Spectro-radiometer) data (the Enhanced Vegetation Index) indicated a substantial proportion of active leaf area recovered before the EC measurements began 1 year after the storm. Observed changes in the vertical distribution and the degree of clumping in newly emerged leaves may have affected the energy balance. Direct comparisons of daytime NEE values from before the storm and after our measurements resumed did not show substantial or consistent differences that could be attributed to the disturbance. Regression analyses on seasonal time scales were required to differentiate the storm’s impact onmonthly average daytime NEE fromthe changes caused by interannual variability in other environmental drivers. The effects of the storm were apparent on annual time scales, and CO2 uptake remained approximately 250 g Cm−2 yr−1 lower in 2009 compared to the average annual values measured in 2004–2005. Dry season CO2 uptake was relatively more affected by the disturbance than wet season values. Complex leaf regeneration dynamics on damaged trees during ecosystem recovery are hypothesized to lead to the variable dry versus wet season impacts on daytime NEE. In contrast, nighttime CO2 release (i.e., nighttime respiration) was consistently and significantly greater, possibly as a result of the enhanced decomposition of litter and coarse woody debris generated by the storm, and this effect was most apparent in the wet seasons compared to the dry seasons. The largest pre- and post-storm differences in NEE coincided roughly with the delayed peak in cumulative mortality of stems in 2007–2008. Across the hurricane-impacted region, cumulative tree mortality rates were also closely correlated with declines in peat surface elevation. Mangrove forest–atmosphere interactions are interpreted with respect to the damage and recovery of stand dynamics and soil accretion processes following the hurricane.

Abstract: The objective of this study was to evaluate relationships between local factors (beach geomorphology and management) and regional factors (infrastructure improvements and temperature changes) against levels of fecal indicator bacteria (FIB) at recreational beaches. Data were evaluated for 17 beaches located in Monroe County, Florida (Florida Keys), USA, including an assessment of sanitary infrastructure improvements using equivalent dwelling unit (EDU) connections. Results show that elevated FIB levels were associated with beach geomorphologies characterized by impeded flow and by beaches with lax management policies. The decrease in EDUs not connected coincided with a decrease in the fraction of days when bacteria levels were out of compliance. Multivariate factor analysis also identified beach management practices (presence of homeless and concession stands) as being associated with elevated FIB. Overall, results suggest that communities can utilize beach management strategies and infrastructure improvements to overcome the negative water quality impacts anticipated with climate change.

Abstract: PREMISE OF THE STUDY: Understanding the relationship between plants and changing abiotic factors is necessary to document and anticipate the impacts of climate change. METHODS: We used data from long-term research sites at Barrow and Atqasuk, Alaska, to investigate trends in abiotic factors (snow melt and freeze-up dates, air and soil temperature, thaw depth, and soil moisture) and their relationships with plant traits (inflorescence height, leaf length, reproductive effort, and reproductive phenology) over time. KEY RESULTS: Several abiotic factors, including increasing air and soil temperatures, earlier snowmelt, delayed freeze-up, drier soils, and increasing thaw depths, showed nonsignificant tendencies over time that were consistent with the regional warming pattern observed in the Barrow area. Over the same period, plants showed consistent, although typically nonsignificant tendencies toward increasing inflorescence heights and reproductive efforts. Air and soil temperatures, measured as degree days, were consistently correlated with plant growth and reproductive effort. Reproductive effort was best predicted using abiotic conditions from the previous year. We also found that varying the base temperature used to calculate degree days changed the number of significant relationships between temperature and the trait: in general, reproductive phenologies in colder sites were better predicted using lower base temperatures, but the opposite held for those in warmer sites. CONCLUSIONS: Plant response to changing abiotic factors is complex and varies by species, site, and trait; however, for six plant species, we have strong evidence that climate change will cause significant shifts in their growth and reproductive effort as the region continues to warm.

Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Abstract: Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium-Range Weather Forecasts 40-year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10-km grid resolution; stochastically generated data from weather generator; bias-corrected dynamically downscaled; and bias-corrected global reanalysis. The reanalysis products are considered as surrogates for large-scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias-corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales.

Abstract: We evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the Southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts (the Florida Climate Institute�Florida State University Seasonal Hindcast at 50 km [FISH50]) is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall-runoff models. The hydrological models are forced with rainfall from FISH50, (quantile-based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogues of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, we find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root mean square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. We contend that given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.