Search

Boomer, Kathleen M.B., Donald E. Weller, Thomas E. Jordan, Lewis Linker, Zhi-Jun Liu, James Reilly, Gary Shenk, and Alexey A. Voinov, 2012. Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary. Journal of the American Water Resources Association (JAWRA) 1-25. DOI: 10.1111/j.1752-1688.2012.00689.x Abstract: We analyzed an ensemble of watershed models that predict flow, nitrogen, and phosphorus discharges. The models differed in scope and complexity and used different input data, but all had been applied to evaluate human impacts on discharges to the Patuxent River or to the Chesapeake Bay. We compared predictions to observations of average annual, annual time series, and monthly discharge leaving three basins. No model consistently matched observed discharges better than the others, and predictions differed as much as 150% for every basin. Models that agreed best with the observations in one basin often were among the worst models for another material or basin. Combining model predictions into a model average improved overall reliability in matching observations, and the range of predictions helped describe uncertainty. The model average was not the closest to the observed discharge for every material, basin, and time frame, but the model average had the highest Nash-Sutcliffe performance across all combinations. Consistently poor performance in predicting phosphorus loads suggests that none of the models capture major controls. Differences among model predictions came from differences in model structures, input data, and the time period considered, and also to errors in the observed discharge. Ensemble watershed modeling helped identify research needs and quantify the uncertainties that should be considered when using the models in management decisions. [ABSTRACT FROM AUTHOR]

We present a simple modular landscape simulation model that is based on a watershed modeling framework in which different sets of processes occurring in a watershed can be simulated separately with different models. The model consists of three loosely coupled submodels: a rainfall-runoff model (TOPMODEL) for runoff generation in a subwatershed, a nutrient model for estimation of nutrients from nonpoint sources in a subwatershed, and a stream network model for integration of point and nonpoint sources in the routing process. The model performance was evaluated using monitoring data in the watershed of the Patuxent River, a tributary to the Chesapeake Bay in Maryland, from July 1997 through August 1999. Despite its simplicity, the landscape model predictions of streamflow, and sediment and nutrient loads were as good as or better than those of the Hydrological Simulation Program-Fortran model, one of the most widely used comprehensive watershed models. The landscape model was applied to predict discharges of water, sediment, silicate, organic carbon, nitrate, ammonium, organic nitrogen, total nitrogen, organic phosphorus, phosphate, and total phosphorus from the Patuxent watershed to its estuary. The predicted annual water discharge to the estuary was very close to the measured annual total in terms of percent errors for both years of the study period (≤2%). The model predictions for loads of nutrients were also good (20-30%) or very good (<20%) with exceptions of sediment (40%), phosphate (36%), and organic carbon (53%) for Year 1. [ABSTRACT FROM AUTHOR]

Watershed land use can affect submerged aquatic vegetation (SAV) by elevating nutrient and sediment loading to estuaries. We analyzed the effects of watershed use and estuarine characteristics on the spatial variation of SAV abundance among 101 shallow subestuaries of Chesapeake Bay during 1984-2003. Areas of these subestuaries range from 0.1 to 101 km², and their associated local watershed areas range from 6 to 1664 km². Watershed land cover ranges from 6% to 81% forest, 1% to 64% cropland, 2% to 38% grassland, and 0.3% to 89% developed land. Landscape analyses were applied to develop a number of subestuary metrics (such as subestuary area, mouth width, elongation ratio, fractal dimension of shoreline, and the ratio of local watershed area to subestuary area) and watershed metrics (such as watershed area). Using mapped data from aerial SAV surveys, we calculated SAV coverage for each subestuary in each year during 1984-2003 as a proportion of potential SAV habitat (the area < 2 m deep). The variation in SAV abundance among subestuaries was strongly linked with subestuary and watershed characteristics. A regression tree model indicated that 60% of the variance in SAV abundance could be explained by subestuary fractal dimension, mean tidal range, local watershed dominant land cover, watershed to subestuary area ratio, and mean wave height. Similar explanatory powers were found in wet and dry years, but different independent variables were used. Repeated measures ANOVA with multiple-mean comparison showed that SAV abundance declined with the dominant watershed land cover in the order: forested, mixed-undisturbed, or mixed-developed > mixed-agricultural > agricultural> developed. Change-point analyses indicated strong threshold responses of SAV abundance to point source total nitrogen and phosphorus inputs, the ratio of local watershed area to subestuary area, and septic system density in the local watershed. [ABSTRACT FROM AUTHOR]