Daniel L. Tufford

Spatial and temporal hydrodynamic and water quality modeling analysis of a large reservoir on the South Carolina (USA) coastal plain

Two-dimensional, 31-segment, 61-channel hydrodynamic and water quality models of Lake Marion (surface area 330.7 km 2 ; volume 1548.310 6 m 3 ) were developed using the WASP5 modeling system. Field data from 1985 to 1990 were used to parameterize the models. Phytoplankton kinetic rates and constants were obtained from a related in situ study; others from modeling literature. The hydrodynamic model was calibrated to estimates of daily lake volume; the water quality model was calibrated for ammonia, nitrate, ortho-phosphate, dissolved oxygen, chlorophyll-a, biochemical oxygen demand, organic nitrogen, and organic phosphorus. Water quality calibration suggested the model characterized phytoplankton and nutrient dynamics quite well. The model was validated (Kolmogorov‐Smirnov two-sample goodness-of-fit test at PB0.05) by reparameterizing the nutrient loading functions using an independent set of field data. The models identified several factors that may contribute to the spatial variability previously reported from other research in the reservoir, despite the superficial absence of complex structure. Sensitivity analysis of the phytoplankton kinetic rates suggest that study site-specific estimates were important for obtaining model fit to field data. Sediment sources of ammonia (10‐60 mg m 2 day 1 ) and phosphate (1‐6 mg m 2 day 1 ) were important to achieve model calibration, especially during periods of high temperatures and low dissolved oxygen. This sediment flux accounted for 78% (nitrogen) and 50% (phosphorus) of the annual load. Spatial and temporal variability in the lake, reflected in the calibrated and validated models, suggest that ecological factors that influence phytoplankton productivity and nutrient dynamics are different in various parts of the lake. The WASP5 model as implemented here does not fully accommodate the ecological variability in Lake Marion due to model constraints on the specification of rate constants. This level of spatial detail may not be appropriate for an operational reservoir model, but as a research tool the models are both versatile and useful.

Tidal nitrogen exchanges across a freshwater wetland succession gradient in the upper Cooper River, South Carolina

Tidal freshwater sections of the Cooper River Estuary (South Carolina) include extensive wetlands, which were formerly impounded for rice culture during the 1,700s and 1,800s. Most of these former rice fields are now open to tidal exchange and have developed into productive wetlands that vary in bottom topography, tidal hydrography and vegetation dominants. The purpose of this project was to quantify nitrogen (N) transport via tidal exchange between the main estuarine channel and representative wetland types and to relate exchange patterns to the succession of vegetation dominants. We examined N concentration and mass exchange at the main tidal inlets for the three representative wetland types (submerged aquatic vegetation [SAV], floating leaf vegetation, and intertidal emergent marsh) over 18-21 tidal cycles (July 1998–August 2000). Nitrate + nitrite concentrations were significantly lower during ebb flow at all study sites, suggesting potential patterns of uptake by all wetland types. The magnitude of nitrate decline during ebb flow was negatively correlated with oxygen concentration, reflecting the potential importance of denitrification and nitrate reduction within hypoxic wetland waters and sediments. The net tidal exchange of nitrate + nitrite was particularly consistent for the intertidal emergent marsh, where flow-weighted ebb concentrations were usually 18–40% lower than during flood tides. Seasonal patterns for the emergent marsh indicated higher rates of nitrate + nitrite uptake during the spring and summer (> 400 μmol N m-2 tide-1) with an annual mean uptake of 248 ± 162 μmol m–2 tide–1. The emergent marsh also removed ammonium through most of the year (207 ± 109 μmol m–2 tide–1), and exported dissolved organic nitrogen (DON) in the fall (1,690 ± 793 μmol m–2 tide–1), suggesting an approximate annual balance between the dissolved inorganic N uptake and DON export. The other wetland types (SAV and floating leaf vegetation) were less consistent in magnitude and direction of N exchange. Since the emergent marsh site had the highest bottom elevation and the highest relative cover of intertidal habitat, these results suggest that the nature of N exchange between the estuarine waters and bordering wetlands is affected by wetland morphometry, tidal hydrography, and corresponding vegetation dominants. With the recent diversion of river discharge, water levels in the upper Cooper estuary have dropped more than 10 cm, leading to a succession of wetland communities from subtidal habitats toward more intertidal habitats. Results of this study suggest that current trends of wetland succession in the upper Cooper River may result in higher rates of system-wide inorganic N removal and DON inputs by the growing distributions of intertidal emergent marshes.

A Reservoir Model For Use in Regional Water Resources Management

ABSTRACT Lake Wateree (5,548-ha, 382.4 × 106m3), a eutrophic reservoir on the South Carolina Piedmont, is downstream from a large urban area that is rapidly expanding. A 7-segment WASP5 eutrophication model was developed to enhance our understanding of the water quality dynamics of the lake. We used monitoring data from 3 time intervals for model calibration (1993), verification (1995), and scenario analysis (1991–1996). Parameters calibrated (Kolmogorov-Smirnov 2-sample goodness-of-fit test; p<.05) were chlorophyll a, ammonia-N, nitrate-N, dissolved oxygen, and total phosphorus. Mainstem segments tested well (19 of 20). The embayment segment tests (3 of 5) suggest a different ecological subsystem that requires further study to effectively characterize. Sensitivity analysis shows the lake is hydro biochemically similar to other mainstem reservoirs, but also that phytoplankton production may be nitrogen limited. Nutrient loading analysis to predict the possible effect of increased urbanization upstream (15...

Estimating hydrologic model uncertainty in the presence of complex residual error structures

Hydrologic models provide a comprehensive tool to estimate streamflow response to environmental variables. Yet, an incomplete understanding of physical processes and challenges associated with scaling processes to a river basin, introduces model uncertainty. Here, we apply generalized additive models of location, scale and shape (GAMLSS) to characterize this uncertainty in an Atlantic coastal plain watershed system. Specifically, we describe distributions of residual errors in a two-step procedure that includes model calibration of the soil and water assessment tool (SWAT) using a sequential Bayesian uncertainty algorithm, followed by time-series modeling of residual errors of simulated daily streamflow. SWAT identified dominant hydrological processes, performed best during moderately wet years, and exhibited less skill during times of extreme flow. Application of GAMLSS to model residuals efficiently produced a description of the error distribution parameters (mean, variance, skewness, and kurtosis), differentiating between upstream and downstream outlets of the watershed. Residual error distribution is better described by a non-parametric polynomial loess curve with a smooth transition from a Box–Cox t distribution upstream to a skew t type 3 distribution downstream. Overall, the fitted models show that low flow events more strongly influence the residual probability distribution, and error variance increases with streamflow discharge, indicating correlation and heteroscedasticity of residual errors. These results provide useful insights into the complexity of error behavior and highlight the value of using GAMLSS models to conduct Bayesian inference in the context of a regression model with unknown skewness and/or kurtosis.

The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.