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Chesapeake Bay Water Quality Modeling Workshop Chesapeake Bay Water Quality Modeling Workshop August 22, 2019 GODDARD APPLIED SCIENCES NASA Earth Sciences Background and Overview Stephanie Schollaert Uz National Aeronautics and Space Administration Background and Overview • What is the role of NASA Applied Sciences? • Who is the Interagency Chesapeake Bay Working Group? • How have we progressed since last year’s workshop? • What are we doing today and why? NASA uses the vantage point of space to increase our understanding of our home planet, improve lives, and safeguard our future Upcoming NASA missions relevant to water quality? • Plankton Aerosol Cloud ocean Ecosystem (PACE) in Phase C – scheduled to launch in 2022 • Surface Biology and Geology (SBG) study phase – possible launch around 2027 • Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) new venture class award – possible launch around 2025/2027 How do we go from research to operations? Credit: Schollaert Uz, S., G. Kim, G., A. Mannino, et al., In review NASA Applied Sciences bringing the benefits of space back to Earth Managed programs Multidisciplinary areas Health & Water Ecological Food Security & Air Quality Resources Forecasting Agriculture John Haynes Brad Doorn Woody Turner Disasters Capacity Building New Missions International David Green Nancy Searby Collaborations Applied Sciences at Goddard Connecting societal challenges to our basic and applied research to improve life on Earth Water Resources & Food Security Disasters • Identify problems that can be addressed with Earth observations Climate Air Quality & Sustainable Applications Public Health Development • Develop external partnerships to Chesapeake New Missions accelerate broader adoption of Bay research advances into operations Interagency Chesapeake Bay Group advancing local applications of satellite data products related to water quality and indicators of ecosystem health • Meeting monthly since April, 2018 • August, 2018 workshop • Diverse community (Federal, state, local agencies, universities, etc.) • Range of capabilities (seasoned satellite data users to non-users) • Divergent water quality needs (spatial/temporal/spectral) and scales e.g. health of entire watershed to fecal coliform <14 mpn & DO > 3 mg/L • Chemical, biological, physical parameters, economic indicators too Progress since last year’s workshop? • Reported identified needs to SBG workshops, routine meetings, AGU • Field campaign supported in theory but not looking likely • Monthly meetings have explored new technologies, new science • Interagency collaboration on field work, conferences, training • Pilot project around need for bacterial and harmful algae monitoring • Several proposals submitted Piloting proposed interagency research projects Aquaculture is a growing industry world-wide Harmful algal blooms and fecal coliform runoff cause shellfish bed closures Early warning of poor water quality could guide sampling Remotely sensed optical proxies are being explored Photo credit: John ‘Rusty’ McKay/MDE Partners: What are we doing today and why? • Introduction: stakeholder, science and modeling overviews • Water Clarity discussion • Harmful Algal Bloom discussion • Networking lunch • Break-out group discussions • Perspectives from other activities, future direction • Summary and next steps Aquaculture in the Northern Chesapeake Bay Scott Budden Orchard Point Oyster Co. To farm oysters in the Northern Bay, is to farm in a challenging environment. Photo Credit: Dr. Suzanne Bricker (NOAA) Made more challenging by recent trends in weather and overall climate change. Photo Credit: Tim Trumbauer (ShoreRivers) Source: Climate Central Approved as a BMP by EPA & The Chesapeake Bay Program, farmed oysters positively impact water quality. What positive business impacts can be generated by real-time water quality monitoring data? Chesapeake Bay Modeling Overview: How can satellite information improve these models? Marjy Friedrichs Virginia Institute of Marine Science, William & Mary Many Chesapeake Bay models exist Bottom oxygen at a mid-Bay station observations • Multiple model model mean intercomparison 8 individual models project • Models perform similarly well; mean performs best • Very little use of satellite information so far – a missed opportunity! From: Irby et al., 2016 19 Goals of Chesapeake Bay models Scenario modeling Process-based modeling Forecasting • Primary end users? • Is satellite information being used? • How could satellite information be used? 20 Chesapeake Bay Scenario Modeling Chesapeake Bay Program’s CH3D-WQSTM • EPA regulatory model, used to establish the TMDLs • Modeled scenarios determine the nutrient reductions required to attain mandated water quality levels (e.g. oxygen, chlorophyll, water clarity) https://www.chesapeakebay.net/who/group/modeling_team 21 Chesapeake Bay Scenario Modeling Chesapeake Bay Program’s CH3D-WQSTM • Primary end users Chesapeake Bay managers, local governments • Is satellite information used? No (?) • How could satellite information be used? Model evaluation and reparameterization https://www.chesapeakebay.net/who/group/modeling_team 22 SeaWiFS Chl ChesROMS-ECB Chl Chesapeake Bay Process-Based Modeling • In CB, satellite information is typically used for model ubRMSD Willmott Skill evaluation (Feng et al., 2015) • Satellite information could be used for parameter optimization through data assimilation (as done in Mid- Atlantic Bight: Xiao and Friedrichs, 2014a,b) From: Feng et al., 2015 23 Chesapeake Bay Process-Based Modeling • Signorini et al. used neural network models, in situ data, satellite data & hydrodynamic ] 1 - models to study daily L estuarine export of DOC from the CB and Delaware μmol [ • Differences in DOC export between two estuaries due to April 28, 2004 geomorphologies and Dissolved Dissolved Organic Carbon (DOC) freshwater inputs From: Signorini et al., 2019 24 Chesapeake Bay Process-Based Modeling • Primary end users Mostly scientists/researchers ] 1 - • Is satellite information used? L Sometimes • How could satellite μmol information be used? Model evaluation; parameter DOC [ optimization; model-data fusion April 28, 2004 From: Signorini et al., 2019 25 Chesapeake Bay Forecasting NOAA’s operational CBOFS Hydrodynamic variables: salinity, temperature, water level Water Level Forecast Forecast Level Water 26 Chesapeake Bay Forecasting pH Ecological forecasts: hypoxia & acidification 1-day nowcast and 2-day forecast automatically produced nightly Model results automatically displayed on the VIMS website http://www.vims.edu/hypoxia Mobile Friendly! 27 Chesapeake Bay Forecasting pH • Primary end users Ship operators, fishermen, aquaculturists • Is satellite information used? Not enough! • How could satellite information be used? Data assimilation, nudging to satellite fields 28 % Likelihood of observing a P. minimum bloom Short Term Forecasting – 100% future plans for HABs 50% Stakeholder input: “oxygen and pH info is great, but we want HAB 0% forecasts!” • Based on existing habitat suitability model (Brown et al., 2013) • Probability of finding a P. minimum bloom is a function of: chlorophyll, ammonium, organic nitrogen and TSS From: Brown et al., 2013 Panel 1: Water Clarity Moderated by: Carl Friedrichs Virginia Institute of Marine Science Carl T. Friedrichs Virginia Institute of Marine Science Professor of Marine Science Associate Director, CBNERR-VA Current Scientific Interests: • Dynamics of muddy particles and interdisciplinary applications. • Especially ramifications to water quality, clarity, and living resources. • Especially by working with the NOAA National Estuarine Research Reserve Program and EPA Chesapeake Bay Program. Chesapeake Bay National Estuarine Research Reserve in Virginia Diffuse attenuation coefficient (Kd) and Secchi depth (ZSD) measure different light properties Kd measures Light Attenuation % Light = 100 exp [ (-Kd) (Z) ] (Batiuk et al. 1992) Secchi measures Transparency (More sensitive to scattering) ZSD (Courtesy C. Buchannan) CBP criteria for water clarity for SAV assumes ZSD ~ 1/Kd such that ZSD x Kd = constant. But that’s incorrect. more light scattering (Data from Chesapeake main stem, Gallegos et al. 2011) (a) Secchi (ZSD) x Kd (b) OSS / (ISS + OSS) - Scattering of light off small organic particles 4 causes ZSD to decrease relative to Kd 3 - Accounting for diffuse light reflected off disk at d x K 2 high TSS causes Z to increase relative to K SD SD d Z 1 (Data from York River and 2 other systems, 0 from Bowers, Fall, Friedrichs et al. in prep.) 10 30 100 300 TSS (mg/L) Assuming KD & TSS are only functions of reflectance using satellites is also a problem Reflectance overestimates Kd when water is relatively clear. Reflectance underestimates Kd when water is very turbid. Son & Wang (2012, 2015) Reflectance overestimates TSS when water is relatively clear. - Can we improve water clarity satellite (and model) products by better accounting for particle properties? Reflectance underestimates TSS when water is very turbid. John “Rusty” McKay Maryland Department of the Environment Water and Science Administration Field Services Program Compliance Monitoring Program Shellfish Monitoring Section [email protected] 443-996-2375 — Work Cell# BACKGROUND • UMBC Geography Major w/ Remote Sensing/ GIS Focus • Shellfish Monitor w/ 35 Years of Service with MDE- Oversee the Operations of 5-Regionally Deployed Boat Teams Baywide CURRENT PROJECTS • UM - Prevalence of Vibrios in Water, Plankton, and Oysters • MDE/MDH/NOAA UMES - Resubmergence Study • NASA/UM/MDNR/NOAA - Scoping
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