ALBERT

Description: LIDAR (Light Detection and Ranging) and GEODAS (GEOphysical DAta System) are respectively taken as the land elevation data for a 3-D storm surge and inundation model to investigate the subsequent inundation differences. Hilton Head, South Carolina, and Croatan-Albemarle-Pamlico Estuary System (CAPES), North Carolina, are the two investigated regions. Significant inundation differences with LIDAR versus GEODAS are found in both regions. The modeled inundation area with GEODAS is larger than with LIDAR. For Category 2–3 hypothetical hurricanes, the maximum inundation difference in Hilton Head region is 67%, while the difference in the CAPES is 156%. Generally, vertical precision difference of the two databases is the major reason for the inundation difference. Recently constructed man-made structures, not included in the GEODAS, but included in the LIDAR data sets may be another contributing reason.

Description: As three-dimensional (3-D) aquatic ecosystem models are becoming used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, two-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient.