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Contributing factors to bloom events: chlorophyll and sea surface temperature.
Harmful Algal Blooms in the Chesapeake Bay
Grant Catlin, Johnny Roberts, and Anthony Farris Correspondence: [email protected]
INTRODUCTION
METHODS
CONCLUSIONS
Figure 3. Recorded bloom events from Summers 2014-‐16 occur primarily near agricultural faciliBes and watershed with high phosphorous and nitrogen outputs.
Figure 1. Timeline of HABs by species and region (lower Chesapeake Bay and higher) with overlay of associated MODIS AQUA chlorophyll data, monthly composite. Although different species typically occur in varying locaBons of the Bay, all bloom under condiBons of high chlorophyll values as shown above.
This project uBlized available MODIS AQUA Level 3, 1km daily chlorophyll-‐a imagery for July through September of 2015-‐16 as well as NOAA AVHRR daily 1km SST imagery. StaBsBcal analysis of these data sets were performed and compared to in situ water measurements and records of blooms in order to asses the accuracy of remotely sensing these phenomenon. • Using ENVI, a Region of Interest was created to isolate the lower Chesapeake. • Subset of ROI to extract staBsBcs. A mask was created in the staBsBcs to ignore
superfluous values. Data input range of 0.5 – 150 mg m-‐3 for chlorophyll-‐a imagery and 5 – 35° C for SST were set to obtain reasonably accurate staBsBcal outputs.
• Histogram was analyzed, and max and mean values were derived from staBsBcs for each day. These values were graphed to determine a correlaBon between dates of recorded blooms and dates with high sea-‐surface temperature and chlorophyll-‐a values.
• In situ water quality data obtained from Chesapeake Bay Water Quality Database. Chlorophyll, water temperature, total dissolved nitrogen, and total dissolved phosphorous graphed over Bme. These values were compared to values derived from remote sensing data to assess accuracy, and help see if there is a correlaBon between high data values and bloom dates.
• In ArcMap, Chesapeake Bay phosphorous and nitrogen output data, point sources of nutrient polluBon outputs and priority agriculture watersheds, as well as HAB reports from Virginia InsBtute of Marine Science, and Eyes on the Bay, were digiBzed.
• HABs were then appropriately classified based on date recorded, species, and concentraBon in cell/ml. QualitaBve symbology was applied to designate HABs by size and value. This data was overlaid on chlorophyll concentraBon rasters for each month within the studied Bme periods. This data provides us with accurate insight about the influence nutrient outputs from agriculture has on bloom events.
• Kernel density performed based off HAB event.
The United States’ largest estuary, the Chesapeake Bay, is home to a vast and complex ecosystem, which supports a diverse range of habitats and provides a crucial economic role for local communiBes. Every year, predominantly in the summer months, harmful algal blooms (HABs) occur, negaBvely impacBng economic, ecological, and human health. HABs occur when colonies of algae grow rapidly, fueled by warm water temperatures and excess nutrients such as phosphorous and nitrogen. Tracking and monitoring HABs is very challenging, as they are highly variable in nature and can pop up and disappear in a number of hours. We uBlized MODIS and AVHRR satellite data as a method to track blooms over the course of July through September 2014-‐16, and analyzed the effecBveness of using chlorophyll and sea surface temperature as a proxy for these blooms. By linking past records of HAB events with locaBons of agriculture faciliBes and their phosphorous and nitrogen watershed outputs, we were able to combine remote sensing techniques with GIS, and ulBmately provide a product that displays a Bme series trend of HAB hotspots in the Chesapeake Bay. Research quesBons: Can we remotely sense HABs using chlorophyll and SST as a proxy? Where and when are blooms most likely, and what other factors are blooms conBngent upon?
2014 2015 2016
JULY 2014 2015 2016 2014 2015 2016
AUGUST SEPTEMBER
Contributing factors to bloom events: nutrient outputs.
Can we use MODIS chlorophyll and AVHRR SST imagery to accurately track and sense
HABs?
Figure 5. Region of interest for staRsRcal study of area with the most frequent and clustered HAB events over our Rme period of interest. VIIRS 750m satellite imagery.
1. V. (n.d.). 2015 HAB AcBvity & Sampling. Retrieved June 04, 2017, from hyps://www.google.com/maps/d/viewer?mid=1PuSe4rsPyy6Onl1qhF8yAtnrxsk&ll=37.25716937140065%2C-‐76.46381380000003&z=9.
2. Harmful Algal Blooms InteracBve Data Map. (n.d.). Retrieved June 04, 2017, from hyp://eyesonthebay.dnr.maryland.gov/eyesonthebay/habs_fullsizemap.cfm
3. Wolf, J. (2009, March 9). Point Sources and Priority Agriculture Watersheds -‐ Chesapeake Bay Watershed. Retrieved June 04, 2017, from hyp://www.chesapeakebay.net/maps/map/point_sources_and_priority_agriculture_watersheds_chesapeake_bay_watershed.
4. Wolf, J. (2011, March 11). Long-‐Term Flow-‐Adjusted Trends Suspended Sediment (32 Sites in the Chesapeake Bay Watershed) 85-‐09. Retrieved June 04, 2017, fromhyp://www.chesapeakebay.net/maps/map/long_term_flow_adjusted_trends_suspended_sediment_32_sites_in_the_chesapeak.
5. Chesapeake Bay Data Hub. hyp://data.chesapeakebay.net/WaterQuality.
REFERENCES
Figure 4. Kernel density map based off clustering of bloom events. Darkest red areas show where blooms have occurred most oDen for the given month over 2014-‐16. This can be used to help predict which species will occur as well as where and when based off of past events.
StaBsBcs computed from ROI subset for Jul.-‐Sep. 2016 chlorophyll imagery and SST. We hypothesized high values of chlorophyll would match up to high values of SST, however all chlorophyll values match up to high values of SST (figure 6), showing Chlorophyll is not exclusively dependent on SST.
StaRsRcs derived from in situ water quality data of same study region. SST trends closely follow chlorophyll trends, versus the high variability in satellite data. HAB events follow high data values -‐ supporRng the hypothesis -‐ as well as portraying the increased accuracy of in situ versus satellite.
This study helps predict HAB prone areas, but also portrays the complicaBons associated with detecBng, idenBfying, and tracking blooms using remote sensing to facilitate improvements in further research. • Most blooms occur where chlorophyll concentraBons are highest. • Different species thrive in different regions of the Bay as well as Bmes of
the year. • One of the main overlooked consBtuents of HABs is locaBon of agricultural
faciliBes and their relaBve phosphorous and nitrogen outputs. All blooms observed occur within an agricultural watershed, or next to a source facility where nutrient concentraBons are highest.
• The remote sensing of blooms generally requires sensors with a 1 day temporal resoluBon, as well as a 1km – 1m spaBal resoluBon and 7-‐10 key spectral bands in the “red edge,” or the red to near-‐infrared porBon of the spectrum. The key spectral bands used to sense algal blooms are around 681 nm.
• MODIS AQUA has adequate temporal resoluBon at 1-‐2 days, but suffers in its lack of key spectral bands in the red edge, as well as its coarser 1 km spaBal resoluBon. This prevents it from disBnguishing between cyanobacteria and the other species that make up harmful algal blooms.
Moving forward: The European Space Agency has launched SenBnel 3-‐a, which has adequate spaBal and temporal resoluBon, and has began sensing our study area using a band range incorporaBng a “cyanobacteria index” that will enable more accurate remote sensing of bloom species.
Southern Chesapeake Bay
Northern Chesapeake Bay
Figure 2. NOAA AVHRR imagery showing monthly composite sea surface temperature by month and year. Most blooms typically occur when temperatures are warmest, in August, however different species thrive in different temperatures. RelaBng SST to Figure 1, we can see which species prefer warmer versus cooler temperatures.
Using HAB’s as a proxy to chlorophyll, we further hypothesized that HAB events would have occurred on days with the highest values of Chlorophyll. There are events where these condiBons hold true, however there are HAB events where the staBsBcs are very low instead. This graph shows the high variability in chlorophyll between each day by month portraying the difficulBes in sensing and tracking blooms as a proxy to chlorophyll.
35*C
15*C
0*C
Jul.- Sep. 2016 Chlorophyll Concentration
Average Chlorophyll Concentration at Surface from Jul.- Sep. 2016 Average Surface Water Temperature from Jul.- Sep. 2016
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