The Gulf of Mexico Modeling Framework is a suite of coupled models linking the deposition and transport of sediment and nutrients to subsequent bio-geo chemical processes and the resulting effect on concentrations of dissolved oxygen in the coastal waters of Louisiana and Texas. Here, we examine the potential benefits of using multiple NASA remote sensing data products within this Modeling Framework for increasing the accuracy of the models and their utility for nutrient control decisions in the Gulf of Mexico. Our approach is divided into three components: evaluation and improvement of (a) the precipitation input data (b) atmospheric constituent concentrations in EPA's air quality/deposition model and (c) the calculation of algal biomass, organic carbon and suspended solids within the water quality/eutrophication models of the framework

Habib, Shahid

Maninio, Antonio

Pickering, Ken

Policelli, Fritz

Stehr, Jeff

Tzortziou, Maria

Crossref

Improved hypoxia modeling for nutrient control decisions in the Gulf of Mexico

NASA Technical Reports Server

IMPROVED HYPOXIA MODELING FOR NUTRIENT CONTROL DECISIONS 
IN THE GULF OF MEXICO 
Shahid Habib1, Ken Pickerring1, Maria Tzortziou2, 1, Antonio Maninio1, Fritz Policell/, JejfStehl 
INASA Goddard Space Flight Center, Greenbelt, Maryland 
2University of Maryland, College Park, Maryland 
ABSTRACT 
The Gulf of Mexico Modeling Framework is a suite of 
coupled models linking the deposition and transport of 
sediment and nutrients to subsequent bio-geo chemical 
processes and the resulting effect on concentrations of 
dissolved oxygen in the coastal waters of Louisiana and 
Texas. Here, we examine the potential benefits of using 
multiple NASA remote sensing data products within this 
Modeling Framework for increasing the accuracy of the 
models and their utility for nutrient control decisions in the 
Gulf of Mexico. Our approach is divided into three 
components: evaluation and improvement of (a) the 
precipitation input data (b) atmospheric constituent 
concentrations in EPA's air quality/deposition model and (c) 
the calculation of algal biomass, organic carbon and 
suspended solids within the water quality/eutrophication 
models of the framework. 
Index Terms- Remote -Sensing, Modeling, Nitrogen, 
Air pollution, Sea coast. 
1. INTRODUCTION 
The largest hypoxic zone currently affecting the United 
States, and the second largest hypoxic zone worldwide, 
occurs in the northern Gulf of Mexico, adjacent to the 
Mississippi River on the LouisianalTexas continental shelf. 
It forms each summer and can extend up to 80 miles 
offshore, stretching from the discharge of the Mississippi 
River westward to coastal waters of Texas. Hypoxic water 
supports fewer organisms and has been linked to massive 
fish kills in the Gulf of Mexico, reduction of biodiversity, 
disruption of ecosystems, depletion of fisheries, and loss of 
habitat for less mobile animals such as crabs and mussels. 
The size of the Gulf of Mexico hypoxic zone varies 
considerably each year with a maximum areal extent of 
22,000 km2, approximately the size of the state of 
Massachusetts, during the summer of2002. 
As required by the Harmful Algal Bloom and Hypoxia 
Research Control Act of 1998, the Mississippi River/Gulf of 
Mexico Watershed Nutrient Task Force issued the 2001 
Gulf Hypoxia Action Plan (updated in 2008) for reducing, 
mitigating, and controlling Hypoxia in the Northern Gulf of 
Mexico. According to the Gulf Hypoxia Action Plan, the 
goal is to reduce the hypoxic zone to less than 5,000 km2 by 
year 2015. While it is recognized in the Gulf of Mexico 
Hypoxia Action Plan that this is an extremely ambitious goal 
unlikely to be achieved in the prescribed time frame without 
significant additional resources, it is clear that actions taken 
in this direction can be expected to provide major benefits to 
the economic value of the Gulf of Mexico fisheries 
estimated to have been on the order of $744 million per year 
in 2001 fmdings [1]. 
In response to the Gulf Hypoxia Action Plan, the EPA 
Gulf of Mexico Hypoxia Modeling and Monitoring Project 
has established a detailed modeling framework for the 
Mississippi-Atchafalaya River Basin which provides a 
capability to forecast the multi-source nutrient loading to the 
Gulf and the subsequent bio-geochemical processes leading 
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Figure 1: Gulf of Mexico hypoxic zone and changes in 
its areal extent during 1985-2008 (data from Louisiana 
University Consortium). 
https://ntrs.nasa.gov/search.jsp?R=20110023407 2019-08-30T18:10:16+00:00Z
to hypoxic conditions and subsequent effects on Gulf 
habitats and fisheries. The primary purpose of the EPA 
modeling framework is to characterize the impacts of 
nutrient management actions, or proposed actions, on the 
spatial and temporal characteristics of the Gulf hypoxic 
zone. The model is expected to play a significant role in 
determining best practices and improved strategies for 
incentivizing nutrient reduction strategies, including 
installation of on-farm structures to reduce sediment and 
nutrient runoff, use of cover crops and other agricultural 
practices, restoration of wetlands and riparian buffers, 
improved waste water treatment and decreased industrial 
nitrogen emissions. These decisions are currently made in a 
fragmented way by federal, state, and local agencies, using a 
variety of small scale models and limited data. 
Our main objective in this work is to improve the 
scientific basis for management of nutrients and sediments in 
the Gulf of Mexico basin by increasing the accuracy of the 
EPA's Gulf of Mexico Modeling Framework through the 
use of NASA remote sensing Earth Observations. Use of 
NASA satellite data products in the model and for long term 
validation of the model has the potential to significantly 
increase the accuracy and therefore the utility of the model 
for decision making. The project is a joint partnership 
between NASA and EPA. 
2. OVERALL CONCEPT/ARCHITECTURE 
Multiple NASA products [5] (Fig. 2 and Table 1) are used to 
improve the EPA's Gulf of Mexico Modeling Framework 
(Gulf of Mexico Hypoxia Modeling and Monitoring, 
http://www.epa.gov/medlgrosseile_site/gom.html). Satellite 
sensors include MODIS (Moderate-Resolution Imaging 
Spectrometer) on Terra and Aqua satellites, CALIOP 
(Cloud-Aerosol Lidar with Orthogonal Polarization) on the 
Calipso satellite, Sea WIFS (Sea-viewing Wide Field-of-
view Sensor) on SeaS tar, OMI (Ozone Monitoring 
Instrument) on Aura, and TRMM (Tropical Rainfall 
Measuring Mission) Microwave Imager (TMI) and 
Precipitation Radar (PR). 
NASA Sensors In Operation 
• TRMM preCipitation 
• MODIS Aerosol (AOT) 
• CALiPSO Aerosol 
• MODIS Oce.n Color 
• SeaWIFS Ocean Color 
• OMI NO) and SO, 
NASA Sensors - Future 
• GPM Precipitation 
• NPP/VIIRS Oce.n Color and 
AOT 
• Glory Aerosol ClasSification 
Dota and 
Products 
[1 
l" 
Decision Making 
• Scenario AnalySIS 
• BeS( PractICes analys;s 
• Model Validarion 
Decision Support Systems 
• Atmospheric Model-WRF 
• Chemical Model-CMAQ 
• Hydrodynamic Model-NCOM 
• Sediment Model 
• Water Qualtty Model-QUAL-ICM 
and WASP 
• Surface Wave Model 
• Improved Dissolved Oxygen Maps 
Benefits 
• fmproveddecisklnson Nutrlentmanagement 
• Improved water quality, h.alth fish and ecosystem 
Figure 2: Enhancing EPA's decision support system using 
NASA satellite products. 
3. USE OF NASA SATELLITE DATA 
Our approach to enhance the EPA's Modeling Framework is 
divided into three components: evaluation and improvement 
of (a) the precipitation input data (b) atmospheric 
constituent concentrations in EPA's air quality/deposition 
model and (c) the calculation of algal biomass, organic 
carbon and suspended solids within the water 
quality/eutrophication models of the framework. 
3.1. Precipitation 
The Gulf of Mexico Hypoxia Modeling Framework uses 
model precipitation estimates from the regional model WRF 
(Weather and Research Forecasting model) as input to the 
atmospheric chemical transport and deposition model 
(CMAQ: Community Multiscale Air Quality model). We 
evaluate these model precipitation estimates using gridded 
precipitation observation products from NOAA and NASA. 
Over the continental United States the NOAAlNational 
Weather Service Multisensor Precipitation Estimator (MPE) 
product is used. MPE combines precipitation observations 
from over 6000 rain gauges, 160 Doppler radars, and 
multiple IR and microwave satellite sensors. Satellite 
estimates are based on GOES IR and microwave products 
from the Defense Meteorological Satellite Program SSMII 
instrument and the AMSU-B on the operational NOAA 
polar orbiting platforms. All of these sources of precipitation 
data have advantages and disadvantages. The MPE 
algorithm generates a gridded precipitation field using a 
single optimal estimation technique at I-hour and 4-km 
temporal and spatial resolution. MPE does not provide 
sufficient coverage over the Gulf of Mexico for the hypoxia 
project. Therefore, we also use the NASA TMP A product. 
While the primary data are from satellite microwave and 
infrared sensors, data from the TRMM precipitation radar 
and rain gauges are used for calibration. Validation results 
have shown good performance at monthly time scales and 
for detecting large daily events. TMPA has lower skill in 
specifying light rain events over short intervals. 
The Parameter-elevation Regressions on Independent 
Slopes Model (pRISM) [4] is also used to evaluate 
meteorological outputs from WRF. PRISM interpolates 
between station-based observations of precipitation and 
other meteorological variables using a digitized elevation 
model and assuming that local differences in precipitation 
and temperature are primarily driven by differences in 
elevation, but accounting for other factors. PRISM data are 
produced on a regularly spaced grid (in this case, 4 km). 
Preliminary comparisons between WRF output and PRISM 
data are shown in Figure 3 for January and July 2006. 
Relatively good overall agreement is observed for both 
magnitude and location of precipitation over the Gulf of 
Mexico watershed area. However, the model seems to 
overestimate precipitation over Louisiana and Mississippi 
Table 1: NASA atmospheric and ocean color measurements, frequency and resolution. 
Satellite products Satellite Sensor Product Resolution FI'equency Expected Benefits 
N02 01\11 O.25°xO.25° Once per day Apply satellite data of 
S02 Ol\;ll O.25oxO.25° Once per day atmospheric S02, N02 
Aerosols Vertical CALIPSO Vertical: 30-60 m Approx. once concentrations and aerosol levels for impro,,;ng Profile per day 
estimates of both wet and 
Aerosols Optical MODIS IOxlOkm Iwice per day dry deposition 
Thickness 
Precipitation NOAA Multisensor 4kmx4km Hourly Contribute to an improved 
Precipitation Estimator estimation of the wet 
Precipitation TRMMfTMI and PR O.250 x 0.25° Every 3 hour deposition of nutrients 
multi sensor calibrated near real time within the model 
product - IMP A framework, especially for 
nitrogen compounds. 
Ocean Color MODIS-Aqua -1 kmNadir Every 1-2 days Validate the calculation of 
(e.g. Cbl-a, POC, photosynthetically active 
DOC, ISS, CDOl\;f) biomass within the 
Ocean Color SeaWIFS - I.l km Nadir Every 1-2 days modeling framework as well 
as particulate and dissolved (e.g. Cbl-a, POC, 
DOC, ISS, CDOl\;{) 
during the summer month. Further comparisons with satellite 
data may suggest improvements to the model which might 
include changing convective parameterization or 
cloud/precipitation microphysical scheme. 
3.2. Atmospheric Chemistry 
The Community Multiscale Air Quality (CMAQ) model 
simulates the linkage between emissions, meteorological 
transport, chemical concentrations and deposition over a 
continental-scale region [3]. Model inputs include chemical 
emissions and a representation of the atmosphere from a 
numerical weather simulation model. EPA is performing 
simulations over the Continental United States and the Gulf 
of Mexico region for year 2006. Emissions are generated 
using the SMOKE emissions processing system. Year 2006 
specific emissions data for motor vehicles are from the EPA 
MOBILE 6 emission factor model. Power plant emissions 
are from the EPA Continuous Emission Monitors. Fire 
emissions are derived from space-based detections of fire 
counts coincident with the time of the simulation. Biogenic 
volatile organic carbon and NOx emissions are simulated 
using BEIS v.3.13 [7] and are derived using the same 
meteorological fields as the air quality simulations. All other 
emission sources are extrapolated from the 2005 National 
Emission Inventory. 
Because N02 is the critical species in determining the 
nitrogen deposition, we evaluate the CMAQ column 
amounts of N02 using tropospheric column N02 
observations from the OMI sensor on NASA's Aura 
satellite. N02 is the most robust among OMI tropospheric 
trace gas products, determined in an atmospheric 
transmission window with little interference from other 
pollutants. Total slant column N02 is accurately measured, 
and the tropospheric vertical column is computed by 
organic carbon. 
subtracting the stratospheric component, and adjusting the 
Air Mass Factor using global model prescribed vertical 
profiles. Sulfate ion is typically the largest contributor to 
acidity, and S02 gas is the precursor for sulfate. Thus. 
tropospheric column S02 amounts derived from CMAQ are 
also compared with those measured by OMI. 
Aerosol extinction optical depth (AOD) is measured 
twice daily by MODIS instruments on NASA's Terra and 
Aqua satellites. These observations are used in evaluating 
the CMAQ aerosol loading. The aerosol fields from CMAQ 
are also evaluated using vertical information on aerosol 
distributions from the CALIOP sensor. 
3.3. Water QualitylBiogeochemistry 
The extent of hypoxia in the Gulf of Mexico is a function of 
the amount of nutrients and organic matter that is delivered 
to these coastal waters and sinks to the ocean bottom layer. 
Satellite derived measurements of the amount of organic 
matter present as particulate organic carbon (POC), 
chlorophyll, dissolved organic carbon (DOC) and total 
suspended solids (TSS) in surface waters of the Gulf of 
Mexico are, thus, critical for remote sensing monitoring of 
hypoxic conditions. We compare these satellite products 
with output from the EPA's Water Quality Fate and 
Transport Model (WASP, CE-QUAL-ICM) to assess how 
well the model is performing and suggest areas of 
improvement. Levell SeaWiFS and MODIS sensor data 
have been processed to level 2 using up-to-date versions of 
SeaDAS and IDL along with all the required masking [2] 
[6]. Measurements were processed using existing algorithms 
for chlorophyll, distributed by the NASA Ocean Biology 
Processing Group (OBPG), and for POC [8]. Field 
measurements (e.g. Chl-a, DOC, POC, TSS concentrations) 
collected in the Gulf of Mexico by the EPA Gulf Breeze Lab 
Pradp-itatton: January 1006 frQm WRF 
J<JIII.I"lfl'(tI,:!f!<)if.flJnHJ_~iIjllf(. 
Mifl19R ~I)~ "'QUwt~4'i,:.!?m",)f)_:lt4 
Precipitation; July 2fH)6 from WRF 
.1111\>11'1. :ml1l>O(f:>·:m:iJ!! I.fH 
Mol f~o-, ·15.t~ •.. ~().~;." 1-l5\i. P.) ~ :n.'>'15 
Pr(~dpjblt,ion: eJun 2(Httl 
FiuaID ... t .. 
PI·ecipHatiom ~jun 200tJ 
FiI~.ruD;-,ffl 
Figure 3: Model WRF estimates (left panels) and PRISM measurements (right panels) of precipitation over the US, for 
January (upper panels) and July (lower panels) of2006. 
will be applied to the validation and refinement of MODIS 
and SeaWiFS algorithms for improved estimates of 
biogeochemical variables in the study region. 
4. REFERENCES 
[1] C.M. Adams, E. Hernandez, and lC. Kato, "The 
econowjc significance of the Gulf of Mexico related to 
population, income, employment, minerals, fisheries and 
shipping", Ocean and Coastal Management, 47, pp. 565-
580,2004. 
[2J S.W. Bailey, and PI "A multi-sensor approach 
for the on-orbit validation of ocean color satellite data 
l?er.r1ote ;.)ens, pp 2006. 
the 1Ivlodels-3 
modeling 
pp. 51··77, 2006, 
"'Review of the O"\7f'I,,',,no 
and other cornpcments 
Muhiscale Air 
system", Applied /Yiechanics 
,-""",_nL "The PRISM 
Preprints, 10th Coni. on Applied Climatology, Reno, NV, 
Amer. Meteor.Soc., 10-12, 1997. 
[5) S. Habib, "Utilizing Earth Science Observations for 
Applied Research", Asian Journal of Geoinformatics, 
Volume 9, No.1, ISSN 1513-6728, March 2009. 
[6] A Mannino, .M.E. Russ, and S.B. Hooker, "pJgorith.m 
development for satellite-derived distributions of Doe ::md 
eDOM in the U.S. Middle Atlantic Bight". 1. Geophys, 
Res., 113, e07051, doi:1O.1029!2007JC004493, 2008. 
In 4th CMAS 
Nmth 
and D" .-''''''''''''It, 
concentration in 
based on ocean color observations with :-"PP!··\,'lP\V! 
Field-of view Sensor (Sea'~ViFS). I 
ClOtHS, doi:W. 2005. 


Improved Hypoxia Modeling for Nutrient Control Decisions in the Gulf of Mexico

Improved Hypoxia Modeling for Nutrient Control Decisions in the Gulf of Mexico

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