A simple model for forecasting the effects of nitrogen loads on Chesapeake Bay hypoxia (original) (raw)
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Environmental Research Letters, 2011
Increasing use of ecological models for management and policy requires robust evaluation of model precision, accuracy, and sensitivity to ecosystem change. We conducted such an evaluation of hypoxia models for the northern Gulf of Mexico and Chesapeake Bay using hindcasts of historical data, comparing several approaches to model calibration. For both systems we find that model sensitivity and precision can be optimized and model accuracy maintained within reasonable bounds by calibrating the model to relatively short, recent 3 year datasets. Model accuracy was higher for Chesapeake Bay than for the Gulf of Mexico, potentially indicating the greater importance of unmodeled processes in the latter system. Retrospective analyses demonstrate both directional and variable changes in sensitivity of hypoxia to nutrient loads.
Predicting the Hypoxic-Volume in Chesapeake Bay with the Streeter-Phelps Model: A Bayesian Approach1
JAWRA Journal of the American Water Resources Association, 2011
Hypoxia is a long-standing threat to the integrity of the Chesapeake Bay ecosystem. In this study, we introduce a Bayesian framework that aims to guide the parameter estimation of a Streeter-Phelps model when only hypoxic volume data are available. We present a modeling exercise that addresses a hypothetical scenario under which the only data available are hypoxic volume estimates. To address the identification problem of the model, we formulated informative priors based on available literature information and previous knowledge from the system. Our analysis shows that the use of hypoxic volume data results in reasonable predictive uncertainty, although the variances of the marginal posterior parameter distributions are usually greater than those obtained from fitting the model to dissolved oxygen (DO) profiles. Numerical experiments of joint parameter estimation were also used to facilitate the selection of more parsimonious models that effectively balance between complexity and performance. Parameters with relatively stable posterior means over time and narrow uncertainty bounds were considered as temporally constant, while those with time varying posterior patterns were used to accommodate the interannual variability by assigning year-specific values. Finally, our study offers prescriptive guidelines on how this model can be used to address the hypoxia forecasting in the Chesapeake Bay area.
Ecological Forecasting and the Science of Hypoxia in Chesapeake Bay
BioScience, 2017
Chronic seasonal low oxygen condition (hypoxia) occurs in the deep waters of Chesapeake Bay as a result of eutrophication-induced phytoplankton blooms and their subsequent decomposition. Summertime hypoxia has been observed in Chesapeake Bay for over 80 years, with scientific attention and understanding increasing substantially during the past several decades after rigorous and routine monitoring programs were put in place. More recently, annual forecasts of the severity of summer hypoxia and anoxia (no oxygen) from simple empirically derived nutrient load-response models have been made. A review of these models over the past decade indicates that they have been generally accurate, with the exception of a few summers when wind events or storms significantly disrupted the water column. Hypoxic and anoxic conditions, as well as their forecasts, have received increased media attention over the past 5 years, contributing to an ongoing public dialogue about Chesapeake Bay restoration progress.
Limnology and Oceanography
A numerical circulation model with a very simple representation of dissolved oxygen dynamics is used to simulate hypoxia in Chesapeake Bay for the 30-yr period 1984-2013. The model assumes that the biological utilization of dissolved oxygen is constant in both time and space in an attempt to isolate the role that physical processes play in modulating oxygen dynamics. Despite the simplicity of the model it demonstrates skill in simulating the observed inter-annual variability of hypoxic volume, capturing 50% of the observed variability in hypoxic volume (<2 mg L 21) for the month of July and 58% of the observed variability for the month of August, over the 30-yr period. Model skill increases throughout the summer suggesting that physical processes play a more important role in modulating hypoxia later in the summer. Model skill is better for hypoxic volumes than for anoxic volumes. In fact, a simple regression based on the integrated January-June Susquehanna River nitrogen load can explain more of the variability in the observed anoxic volumes than the model presented here. Model results suggest that the mean summer (June-August) wind speed is the single-most important physical variable contributing to variations in hypoxic volumes. Previous studies have failed to document the importance of summer wind speed because they have relied on winds measured at Patuxent Naval Air Station, which does not capture the observed inter-annual variations in wind speed that are observed by stations that directly measure wind over the waters of Chesapeake Bay.
Physical controls on hypoxia in Chesapeake Bay: A numerical modeling study
Journal of Geophysical Research: Oceans, 2013
A three-dimensional circulation model with a relatively simple dissolved oxygen model is used to examine the role that physical forcing has on controlling hypoxia and anoxia in Chesapeake Bay. The model assumes that the biological utilization of dissolved oxygen is constant in both time and space, isolating the role that physical forces play in modulating oxygen dynamics. Despite the simplicity of the model, it demonstrates skill in reproducing the observed variability of dissolved oxygen in the bay, highlighting the important role that variations in physical forcing have on the seasonal cycle of hypoxia. Model runs demonstrate significant changes in the annual integrated hypoxic volume as a function of river discharge, water temperature, and wind speed and direction. Variations in wind speed and direction had the greatest impact on the observed seasonal cycle of hypoxia and large impacts on the annually integrated hypoxic volume. The seasonal cycle of hypoxia was relatively insensitive to synoptic variability in river discharge, but integrated hypoxic volumes were sensitive to the overall magnitude of river discharge at annual time scales. Increases in river discharge were shown to increase hypoxic volumes, independent from the associated biological response to higher nutrient delivery. However, increases in hypoxic volume were limited at very high river discharge because increased advective fluxes limited the overall length of the hypoxic region. Changes in water temperature and its control on dissolved oxygen saturation were important to both the seasonal cycle of hypoxia and the overall magnitude of hypoxia in a given year.
Limnology and Oceanography, 2014
We use geostatistical universal kriging and conditional realizations to provide the first quantitative estimates, with robust estimates of uncertainties, of the seasonal and interannual variability in hypoxic volume in Chesapeake Bay, covering early April to late October for 1985 to 2010, and explore factors controlling that variability. Results show that the time when the hypoxic volume reaches its maximum has moved from late to early July over the examined period, but that there is no trend in the seasonal-maximum hypoxic volume itself. No significant trend was found in the timing of onset of hypoxia, but the end of the hypoxic period has moved from October to September. Including nutrient loading from the Rappahannock River in addition to the Susquehanna and Potomac Rivers is found to be beneficial for explaining the interannual variability of hypoxia. Overall, January to May total nitrogen loads from these three rivers, April to August southwesterly and northeasterly winds, and April and May precipitation explain . 85% of the seasonally averaged interannual variability in hypoxic volumes. Southwesterly winds affect hypoxia by increasing vertical stratification, while precipitation likely acts as a surrogate for nonpoint sources of nitrogen downstream from monitoring stations. The relative contribution of nutrient loading to the overall interannual variability suggests that 28-35% reductions in monitored nutrient loads may not be sufficient to achieve a corresponding reduction in hypoxic conditions as had been suggested in previous studies, at least in the short term.
1] The overall size of the ''dead zone'' within the main stem of the Chesapeake Bay and its tidal tributaries is quantified by the hypoxic volume (HV), the volume of water with dissolved oxygen (DO) less than 2 mg/L. To improve estimates of HV, DO was subsampled from the output of 3-D model hindcasts at times/locations matching the set of 2004-2005 stations monitored by the Chesapeake Bay Program. The resulting station profiles were interpolated to produce bay-wide estimates of HV in a manner consistent with nonsynoptic, cruise-based estimates. Interpolations of the same stations sampled synoptically, as well as multiple other combinations of station profiles, were examined in order to quantify uncertainties associated with interpolating HV from observed profiles. The potential uncertainty in summer HV estimates resulting from profiles being collected over 2 weeks rather than synoptically averaged $5 km 3 . This is larger than that due to sampling at discrete stations and interpolating/extrapolating to the entire Chesapeake Bay (2.4 km 3 ). As a result, sampling fewer, selected stations over a shorter time period is likely to reduce uncertainties associated with interpolating HV from observed profiles. A function was derived that when applied to a subset of 13 stations, significantly improved estimates of HV. Finally, multiple metrics for quantifying bay-wide hypoxia were examined, and cumulative hypoxic volume was determined to be particularly useful, as a result of its insensitivity to temporal errors and climate change. A final product of this analysis is a nearly three-decade time series of improved estimates of HV for Chesapeake Bay. Citation: Bever, A. J., M. A. M. Friedrichs, C. T. Friedrichs, M. E. Scully, and L. W. J. Lanerolle (2013), Combining observations and numerical model results to improve estimates of hypoxic volume within the Chesapeake Bay, USA,