Methods
Ecological forecasting is a proactive tool that provides resource managers with new information that can be used to guide restoration and subsequent policy.
Microcystis Blooms - HAB
Microcystis Blooms - HABThe main factors that determine HAB occurrence and characteristics in the Potomac River are nutrient availability (primarily phosphorus), salinity, water temperature, and light availability. For blooms to occur, the water temperatures have to be above 15 °C (59 °F) and salinity below 5 ppt. More intense blooms are also likely to occur if conditions are still (little wind mixing) and cloud free (higher sunlight). An overriding influence on bloom occurrences is river flow rates, most likely due to its effect on nutrient availability. As a result, monitoring has shown HAB variability associated with annual and seasonal weather patterns affecting nutrient delivery to the estuary.
Forecast approach: This year's forecast is based on a linear relationship between total Potomac River flow and the percent of water quality samples containing bloom levels of Microcystis. Note that these methods are slightly different than those previously used.
The algal community samples for assessing cyanobacteria levels are collected from the region of the Potomac between the Washington DC/Maryland border down to the Route 301 bridge. MD DNR typically collects 8 sets of 9 samples from June to September for a total 72 samples each year. An algal sample in this case is considered at bloom levels if it contains greater than 10,000 cells per milliliter of the cyanobacteria Microcystis.
River flow data are collected at the Point of Rocks location on the Potomac River. The 2008 portion of the data set is final flow data while January through May 2009 data are provisional.
Data used to develop the relationship are from 1989-2006, however, they exclude 1997 and 2004. The bloom conditions in those two years are much lower than expected by the trajectory of the model and may be a function of conditions related to conditions associated with extremely wet conditions (2003) to flood level flow events (January 1996) impacting the region. When cumulative flow over the Point of Rocks on the Potomac River is greater than 5.9 x 1011 cubic feet over the 17 months preceding the summer as is the case with these two years, our linear model fails. Further model developments are needed to better capture the conditions involving such high flow years to more accurately model the bloom patterns.
Data are available for the earlier period 1985-1988. These data are not included in this cyanobacteria forecast model, however, as phosphate bans were going into effect among the partner States and Washington DC in the Potomac River basin over multiple years. The post-1989 P-ban implementation era represents the time beyond this important and significant nutrient control period. The forecast could benefit from the flow model being expanded to include nutrient loads or a similar nutrient index.
The worst year in our 1989-2006 time series was 1994 and it showed 25% of all summer samples containing bloom levels of Microcystis. Since that time we have experienced widespread blooms (2004) but that bloom expanded in July and died out in August limiting its duration. Recently, bloom conditions have been less than levels forecast with our basic river flow model. In 2007 MD DNR samples showed no bloom detections and in 2008, the model predicted 9% of samples would detect bloom conditions when only 1.3% of samples detected blooms. Once again this year based on the flow conditions leading up to this summer we predict 9% of samples will show Microcystis bloom conditions for summer 2009, a moderately severe cyanobacterial bloom year for the Potomac River.
Categories of bloom severity are based on the percent of maximum bloom conditions used in the regression (Between 1989 and 2006, maximum was 25% of samples in 1994). Low severity = 0-33% of maximum (0 to 8% of summer samples), moderate = 33-66% (9 to 17% of summer samples), high = 66-100% of maximum (18-25% of summer samples).
DO - anoxia
DO - anoxiaThere are many factors that determine the dissolved oxygen content of the tidal waters of Chesapeake Bay. Nutrient loading, water column stratification, wind and tidal mixing, and water temperatures are but a few of these factors. The two most important determining factors are water column stratification and nutrient loading. 
Water column stratification is caused by density differences between the surface and deeper waters of the Bay. Cooler, saltier (more dense) water from the ocean flows underneath the warmer, fresher (less dense) water from the rivers that flow into the Bay. Between the lighter surface water and heavier deeper water is a boundary called the pycnocline. Oxygen consumed beneath the pycnocline cannot be replenished from above, and this leads to lower dissolved oxygen concentrations below the pycnocline. The pycnocline is typically strongest in spring and early summer when fresh water flows are usually at their highest.
Nutrient inputs to the Bay from the land are directly related to precipitation and therefore river flow. Nutrient loads from land-based sources (agriculture, urban runoff, etc.) are higher in the spring when river flows are typically at their highest. Nutrients that flow directly into the Bay from a pipe (sewage treatment plants, industry, etc.) are generally less sensitive to flow and are more consistent through the year. There is a direct relationship between the magnitude of these nutrient loads and the severity of low DO the Bay experiences. Nutrients-nitrogen and phosphorus-fuel the growth of the phytoplankton that make up the base of the Bay's food web. Unconsumed phytoplankton settle below the pycnocline and are decomposed by oxygen–consuming bacteria living in the mud on the bottom of the Bay. Since this is occurring below the pycnocline, this oxygen is not replenished from surface waters. This process occurs every year in Chesapeake Bay, fueled by spring flows that wash large amounts of nutrients into the Bay.
Recent research has shown that in many years, there is a significant difference between anoxic volume (water with DO <0.2 mg/L) in the early and later parts of the summer. This happens due to changing conditions during the summer such as large summer nutrient loads, storm events, or prevailing wind patterns that affect stratification. To improve the forecast of anoxia, this year we are providing two forecasts: early summer (June through mid-July) and late summer (mid-July through September). The early summer forecast is released in early June and the late summer forecast will be released in July.
The first step to generating the anoxic volume forecast is to calculate what the anoxic volume was in previous years. We use Chesapeake Bay Program data (http://www.chesapeakebay.net/data_waterquality.aspx) from 1985 to 2009 which consists of 1 or 2 data collection cruises every month. For each cruise, we used a statistical interpolation method (Murphy et al. 2010) to estimate DO concentrations everywhere in the Bay from the samples collected along the main channel. The anoxic volume is calculated by summing the total volume of water with DO less than 0.2 mg/L. The monthly anoxic volumes are averaged to get early summer and late summer volumes. These early and late summer anoxic volume averages are then used with nutrient load and stratification-related data to build a model that can be used to predict anoxic volume in the current year.
The model used for the early summer anoxia forecast takes into account both nutrient loads and stratification.
Nutrient Loads are represented by total nitrogen loads from both the Susquehanna and Potomac Rivers from January to April plus the below fall-line point source releases of nitrogen in the Potomac River and north of the Potomac into the Bay. Because point source loads are only available until 2007, we fit a simple linear regression model to predict point source loads for 2008-2010. Total nitrogen loads through the Susquehanna and Potomac were available for each year, including this year, from the USGS.
Stratification is strongly influenced by freshwater flow and wind, and freshwater flow is highly correlated with nitrogen loads. Since nitrogen loads are already included in the model, including freshwater flow does not improve the model fit. However, for the first time this year, we are including wind effects in the statistical model. The direction, speed, and duration of wind events over the Chesapeake Bay can impact the strength and depth of the pycnocline. To account for some of these effects, we used the fraction of hours that wind from the southeast has blown over the Bay in recent months. The time period March through May was selected because that is the most recent wind data available, and during that period the long-term wind frequency pattern is similar to the pattern in June and early July. Wind from the southeast is correlated with a smaller anoxic volume because it causes increased mixing of the water column (Scully 2010 ). Wind data was available for every year from NOAA.
Model Details: We used a generalized linear multiple regression model to predict anoxia volume. This model has two variables: total nitrogen load and SE wind frequency. In the model equation shown here, b0, b1, and b2 are fitted coefficients and log(anoxic volume) represents a method used to prevent predicting a negative anoxic volume.
log(anoxic volume) = b0 + b1(total nitrogen) - b2 (SE wind)
The model was fit using all years of data from 1985 to 2009 except for 1993 because it was an extreme outlier year for early summer anoxia.
A different model was used for the late summer anoxia forecast. Research has shown that the persistence of anoxia during the summer is correlated most strongly with late spring and early summer nutrient loads through the Susquehanna River. Stratification factors that play a role in early summer are not nearly as predictive of late summer anoxia. In general, it is fairly difficult to predict late summer anoxia due to the effects of events that sometimes occur in August and September (such as hurricanes or droughts).
Nutrient Loads are represented by total nitrogen from the Susquehanna River in January through May plus the point source loads directly into the upper Bay. Loads through the Susquehanna appear to have the longest term impact on the Bay throughout the summer, as opposed to the Potomac River loads that have a larger impact in early summer. Loads were available from USGS.
Model Details: We used a linear regression model to predict anoxia volume. This model is very similar to the early summer model, but with only total nitrogen as a variable.
Reference:
Murphy RR, Curriero FC, Ball WP (2010) Comparison of Spatial Interpolation Methods for Water Quality Evaluation in the Chesapeake Bay. ASCE Journal of Environmental Engineering 136(2):160-171
Scully ME (2010) The importance of climate variability to wind-driven modulation of hypoxia in Chesapeake Bay. Journal of Physical Oceanography 40:1435-1440
DO - hypoxia
DO - hypoxiaJuly 2010 Hypoxia Forecast
The hypoxic forecast model predicts oxygen concentration downstream from point sources of organic matter loads using two mass balance equations for oxygen-consuming organic matter, in oxygen equivalents (i.e., BOD), and dissolved oxygen deficit. This approach to modeling coastal and estuarine hypoxia has also been used successfully for Gulf of Mexico hypoxia (Scavia et al. 2003, 2004). The original model was calibrated and tested against 1950-2003 nitrogen load and hypoxic volume estimates assembled by Hagy (2002). The Chesapeake Bay Program provided load and hypoxic volume updates for 1986-2008, and even though the new estimates varied little from the original ones; the model was recalibrated for this application to the new 1986-2008 estimates. The summer hypoxic volume forecast was generated using the following relationship. For more information, visit http://www.snre.umich.edu/scavia/hypoxia-forecasts/
July and August 2010 Hypoxia Forecast
Hypoxic volume is predicted using a multiple linear regression model. A similar approach to predicting coastal hypoxia has been applied to Long Island Sound, New York (Lee and Lwiza, 2007). The summer hypoxia (HYPOXIA) forecast is based on three variables measured during the winter/spring season: river discharge (RIVER), chlorophyll a concentration (CHLA), and cross-bay (east-west) wind speed (XWIND).
Susquehanna River discharge is used to represent the freshwater input into the Chesapeake Bay. January to May river discharge contributes both nutrients from the land but also buoyancy effects on estuarine dynamics. The former enhances the production of organic matter in the water column and the latter influences the water column stratification that prevents replenishing oxygen from surface waters.
The concentration of spring chlorophyll a can be used as a proxy for spring algal biomass that is the fuel for oxygen consumption during the following summer. Therefore, it is important to include this term in the model since the amount of biomass produced in the water column can affect hypoxia through aerobic respiration in the water column and sediments. Mainstem (stations CB3.3 to CB5.3) chlorophyll a data was used.
We found that there was a strong statistical relationship between January to April cross-bay (east-west) wind speed and summer hypoxia. Although the mechanistic link between wind speed in spring and summer hypoxia is not clearly understood, the cross-bay wind significantly improves the output of the regression model. We believe that it may influence the transport and/or deposition of biomass via lateral circulation. For example, more biomass may be accumulated over shallow areas (rather than deep areas) due to stronger cross-bay wind, resulting in less hypoxia, and vice versa.
The multiple linear regression model expresses the value of a predicted (dependent) variable (HYPOXIA) as a linear function of three predictor (independent) variables (RIVER, CHLA, and XWIND) as shown the equation:
[HYPOXIA] = a + b1·[RIVER] + b2·[CHLA] + b3·[XWIND] --- (1)
where a is regression constant and b1, b2, and b3 are coefficients for the predictors. The current year hypoxic volume is predicted using all years of data from 1985 to 2007.
Reference:
Lee, Y.J. and K.M.M. Lwiza. (2008). Characteristics of bottom dissolved oxygen in Long Island Sound, New York, Estuarine, Coastal and Shelf Sciences, 76, 187-200.
Additional Info
Summer ecological forecast technical documentation
Dave Jasinski, Peter Tango, Michael Williams, Ben Longstaff
This document describes the rationale and methods for determining the summer ecological forecasts for dissolved oxygen, harmful algal blooms and submerged aquatic vegetation for 2005.