ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Contents
1
SCIENTIFIC/TECHNICAL/MANAGEMENT .................................................................. 1-1
1.1
Objectives and Expected Significance .......................................................................... 1-4
1.1.1
Objectives .............................................................................................................. 1-4
1.1.2
Expected Significance ............................................................................................ 1-4
1.2
Technical Approach and Methodology ......................................................................... 1-5
1.3
Perceived Impact to State of Knowledge .................................................................... 1-13
1.4
Relevance to Element Programs and Objectives in the NRA ..................................... 1-13
1.5
Work Plan.................................................................................................................... 1-14
1.5.1
Key Milestones .................................................................................................... 1-15
1.5.2
Management Structure ......................................................................................... 1-15
1.5.3
Contributions of Principal Investigator ................................................................ 1-15
1.5.4
Collaborators ........................................................................................................ 1-15
2
REFERENCES AND CITATIONS ..................................................................................... 2-1
3
BIOGRAPHICAL SKETCH ............................................................................................... 3-1
3.1
4
5
Principal Investigator .................................................................................................... 3-1
CURRENT AND PENDING SUPPORT ............................................................................ 4-1
4.1
Current Awards ............................................................................................................. 4-1
4.2
Pending Awards ............................................................................................................ 4-1
BUDGET JUSTIFICATION: NARRATIVE AND DETAILS ........................................... 5-1
5.1
Budget Narrative ........................................................................................................... 5-1
5.1.1
Personnel and Work Effort .................................................................................... 5-1
5.1.2
Facilities and Equipment........................................................................................ 5-1
5.2
Budget Details ............................................................................................................... 5-1
ii
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
1 Scientific/Technical/Management
Objective Summary:
We propose to employ NASA Earth Science products to develop a framework for global
application of a recently-developed energy balance model that estimates water temperature and
height in artificial containers that serve as habitat for the immature life stages of the dengue
vector mosquito Aedes aegypti. The simulations will allow us to produce, with a physicallybased approach, global suitability maps for the development of Ae. aegypti. The energy balance
model will then be coupled with a dynamic life cycle model to best describe the seasonality and
interannual variability of Ae. aegypti population dynamics for locations in North America and
the Caribbean where we have field data at several locations for refining and validating our
simulations. Finally, the sensitivity of results to climate change scenarios will be explored.
Motivation and Background:
Dengue is the most common and important vector-borne virus in the world (WHO 2009),
with 3.5 billion people in over 100 countries living in regions of high risk (Guha-Sapir and
Schimmer 2005; Kroeger and Nathan 2006; Beatty et al. 2009). Dengue risk areas (Fig. 1-1)
extend across the tropical and sub-tropical Americas and Africa, southeast Asia, India, and
Oceana (Guzman and Kouri 2002; Renganathan et al. 2003). A recent study indicates that
dengue infections, once thought to number 50-200 million per year (Gubler 1998, 2004; Beatty
et al. 2009; WHO 2009), actually total about 390 million per year (Bhatt et al. 2013), with about
1% of cases exhibiting the severe and often deadly dengue hemorrhagic fever (Gubler 1998).
Both the geographic range and the magnitude of dengue infections have increased in the past 50
years (WHO 2009), due to population growth and urbanization in endemic areas, increases in
global mobility and trade (Westaway and Blok 1997), and the discontinuation of insecticide
spraying programs (particularly in the Americas) because of financial and environmental
concerns (Gubler 1989). Despite the almost nonexistent risk for dengue in the United States in
Fig. 1-1, dengue outbreaks have recently occurred in Key West FL (Graham et al. 2011; Radke
et al. 2012), and dengue is present in the U.S./Mexico border region (e.g., Ramos et al. 2008;
Hotez et al. 2012), with low
income, absence of air
conditioning, non-functioning
window screens, and inadequate
sanitation being strong risk
factors (Reiter et al. 2003;
Brunkard et al. 2007). An
estimated 100,000-200,000
dengue cases occur annually
among the Mexican-American
Figure 1-1. National and subnational evidence consensus
on complete absence (green) to complete presence (red)
population in the United States,
of dengue, following Bhatt et al. (2013).
presumably near the U.S./Mexico
border (Hotez 2008). Aedes aegypti populations extend well into the United States, including
southern Arizona (Engelthaler et al. 1997; Hoeck et al. 2003; Merrill et al. 2005; Hayden et al.
2010), and historically the mosquito’s range has extended up the U.S. eastern seaboard, being
responsible for the 1793 Yellow Fever outbreak in Philadelphia (Foster et al. 1998).
1-1
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
The primary dengue vector mosquito Aedes aegypti is closely associated with humans. It
lives exclusively in urban and semi-urban areas, preferentially bites humans, and spends its
developmental stages in artificial water containers (Focks and Alexander 2006; Halstead 2008;
Scott et al. 1993). Ae. aegypti population dynamics thus depend on human behavior and socioeconomic factors, including infrastructure, solid waste disposal, housing characteristics, and
transportation (Chang et al. 1997; Gubler 1997; Nagao et al. 2003; Kay and Vu 2005; Morrison
et al. 2008; Hayden et al. 2010). Another important factor to Ae. aegypti survival and
development is climate variability. The primary limiting climatic factor for survival is cold
temperature, with an approximate lower boundary of 10°C average winter temperature, below
which Ae. aegypti are not observed (Christophers 1960; Focks et al. 1993a; Hopp and Foley
2001; Farnesi et al. 2009; Yang et al. 2009; Richardson et al. 2011). Water availability is a
limiting factor in arid regions, and the seasonality of Ae. aegpyti depends on temperature in
subtropical regions and rainy seasons (water availability) in tropical regions.
Climate effects extend to development of Aedes aegypti immature mosquitoes in artificial
containers. Potential containers for Ae. aegypti immature development include, but are not
limited to, small sundry items (e.g., bottles, cans, plastic containers), buckets, tires, barrels,
tanks, and cisterns (Morrison et al. 2004; Tun-Lin et al. 2009; Bartlett-Healy et al. 2012).
Successful development of immature mosquitoes from eggs to larvae, pupae, and eventually
adults is largely dependent on the availability of water and the thermal properties of the water in
the containers. The optimal temperature for Ae. aegypti larval and pupal development, with
short development times and high survival rates, is in the range of 24°-34°C (Bar-Zeev 1958;
Rueda et al. 1990; Tun-Lin et al. 2000; Kamimura et al. 2002; Mohammed and Chadee 2011;
Padmanabha et al. 2011a, 2012; Richardson et al. 2011; Farjana et al. 2012). Larval
development can be impeded by water temperatures that are too low (8°-12°C) or high enough to
cause physical harm, through heat stress (36°-44°C) (Bar-Zeev 1958, Smith et al. 1988, Tun-Lin
et al. 2000, Kamimura et al. 2002, Chang et al. 2007, Richardson et al. 2011, Muturi et al.
2012). There also is a growing recognition that the magnitude of the daily temperature range
(i.e., fluctuations over the course of a 24-hour period) impact life history traits of Ae. aegypti,
including larval development time (Lambrechts et al. 2011, Mohammed and Chadee 2011,
Carrington et al. 2013). Other factors that can have negative effects on larval development time
or survival include poor nutrient content of the water and resource competition (Braks et al.
2004, Juliano et al. 2004, Padmanabha et al. 2011b, Walsh et al. 2011). For rain-filled
containers, there are also risks of a container drying out or of the container over-flowing and the
immatures being flushed out (Koenraadt and Harrington 2008, Bartlett-Healy et al. 2011).
Mathematical modeling of mosquito populations is done through statistical methods (i.e.,
regression or generalized models) or through dynamic life cycle models that simulate the life
cycles of cohorts of mosquitoes using a mechanistic approach. Dynamic life cycle models for
Aedes aegypti, including CIMSiM (Container Inhabiting Mosquito Simulation Model) and
Skeeter Buster, are strongly influenced by water temperature, which impacts the development
times and survival rates of eggs, larvae and pupae (Focks et al. 1993a,b; Cheng et al. 1998;
Magori et al. 2009; Ellis et al. 2011). Perhaps the greatest limitation of these complex simulation
models is the continued use of simplistic empirical relationships to predict water temperature in
and water loss from containers based on several meteorological variables. Recent work has
shown that physics-based approaches toward modeling container water properties are promising
for resolving the complexities of container water dynamics (Tarakidzwa 1997, Kearney et al.
2009). Such models solve for the energy balance of the water inside of the container, taking into
1-2
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
account shortwave (solar) and longwave (terrestrial) radiation and heat fluxes (sensible, latent,
and ground). The methodology is similar to what is done in land surface models to simulate
ground surface and soil temperature (e.g., Chen and Dudhia 2001), except modified for container
dimensions. An energy balance container model developed by the proposer, termed the Water
Height And Temperature in Container Habitats Energy Model (WHATCH’EM; Steinhoff and
Monaghan 2013), solves for water temperature and height for user-specified containers with
readily available weather data. Realistic estimation of water temperature and height from
WHATCH’EM has potential to improve output from mosquito population models.
While sufficient climate data is available for many tropical and sub-tropical urban areas
endemic to dengue, this is not necessarily the case in regions of Africa and southeast Asia.
Additionally, rapid urbanization, inadequate infrastructure, and poor quality housing in these
regions, along with favorable climate, result in high dengue risk. African dengue risk in
particular is often underestimated, due to symptomatically similar illnesses and underreporting
(Bhatt et al. 2013). Even in regions with high-quality climate data, variables like cloud cover
and ground temperature, important for the surface energy balance, are localized and difficult to
directly observe and apply spatially. Satellite remote sensing products and gridded numerical
weather prediction products (including atmospheric reanalyses), which utilize data from a variety
of sources, offer high quality estimate of climate data in regions where in situ climate data is
insufficient. Similarly, gridded global climate model (GCM) output provides physically-based
estimates of future climate states, and relatively simple methods exist to downscale coarse
resolution GCM output for regional applications.
Given the global importance of dengue, and the strong influence of climate and water
temperature to the dengue vector mosquito Aedes aegypti, we propose to model habitat
suitability globally by creating a framework that uses high quality gridded NASA Earth Science
products (Table 1-1), based on container water temperature and availability estimates from
WHATCH’EM. The number of mosquitoes produced by containers (e.g., productivity) of
different types and shading scenarios can be determined geographically. We will then couple the
NASA/WHATCH’EM framework with the mosquito population model Skeeter Buster to
characterize the seasonality and interannual variability of mosquito population dynamics for
several locations across North America and the Caribbean where we have detailed data on
container distributions and pupal counts. Current climate conditions will then be perturbed,
using established methodologies, based on future climate scenarios derived from a NASA GCM
(Table 1-1) to determine the sensitivity and response of habitat suitability and mosquito
population dynamics to climate change.
NASA Earth
Science Product
MERRA
GLDAS
NLDAS
TRMM
NASA Langley
Cloud Cover
GISS-E2-R
Variables Used
Grid Spacing
Time Periods Used
2 m air temperature, 2 m specific humidity, 2 m
wind speed
Ground surface temperature, Soil temperature
Ground surface temperature, Soil temperature
Precipitation
0.5° lat, 0.67° lon
1979-present
0.25° lat, 0.25° lon
0.125° lat, 0.125° lon
0.25° lat, 0.25° lon
2009-present
1979-present
1999-present
Cloud fraction
0.25° lat, 0.3125° lon
2009-present
2 m air temperature, 2 m specific humidity,
Precipitation
2.0° lat, 2.5° lon
1979-present, 2020-2035,
2060-2075
Table 1-1. NASA Earth Science products used in this study.
1-3
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
1.1 Objectives and Expected Significance
1.1.1 Objectives
The objectives of this research are the following:
1) Create a framework for global application of WHATCH’EM that employs NASA Earth
Science products
2) Refine the NASA-based WHATCH’EM system by running the model for 2013 boreal
summer and comparing direct observations of container temperatures and WHATCH’EM
simulations using in situ weather data as input. Assess model biases/errors and improve
the model as needed. Conduct additional container experiments in Boulder, Colorado as
needed to address unforeseen situations and weaknesses in WHATCH'EM.
3) Generate global Aedes aegypti habitat suitability maps for a variety of scenarios for
shading and container types and characteristics.
4) Couple WHATCH’EM with Skeeter Buster for several areas in North America and the
Caribbean with detailed container distribution data and pupal counts, to ascertain the
seasonality and interannual variability of Aedes aegypti population dynamics.
5) Assess climate change scenarios for habitat suitability and Aedes aegypti population
dynamics.
1.1.2 Expected Significance
This research has three broad areas of significance. First, the proposed work will develop
and apply a framework for assessing habitat suitability for the dengue virus vector mosquito
Aedes aegypti for present day and future scenarios. NASA products will solely be used as
weather-related input data to construct the container suitability maps – MERRA,
GLDAS/NLDAS, TRMM, and cloud cover products. Productivity of different types of
containers and shading scenarios can be assessed and be instrumental in mosquito control efforts.
Mosquito population dynamics will be studied for select areas of North America and the
Caribbean, where the coupled WHATCH’EM-Skeeter Buster model will provide estimates of
interannual variability and seasonality of mosquito populations. Changes to the range and
seasonality of the mosquito for these locations can then be examined with climate change
scenarios.
Second, this work will improve open-access community research models WHATCH’EM and
Skeeter Buster. Specific proposed improvements to WHATCH’EM include parameterizing
surface temperature and air temperature near container height based on ground temperature, 2 m
air temperature, wind speed, and the ground surface characteristics, estimating the emitting
temperature of shading surfaces, and representing thermal stratification in large containers.
Mosquito population estimates from Skeeter Buster will be improved using physically-based
water temperature and water height estimates from WHATCH’EM rather than the default
regression-based calculations.
Third, the proposed work will extend the capability of WHATCH’EM to larger-scale
applications. The model was developed to estimate container suitability for an NSF-funded
project in eastern Mexico. It is now being used in its current form for a Defense Threat
Reduction Agency (DTRA) project to develop a prototype real-time dengue surveillance system
utilizing extremely high resolution visible satellite imagery to identify containers in select
locations. The currently-proposed project represents the next step in the development of
WHATCH'EM: a framework will be developed to apply WHATCH’EM globally, using NASA
1-4
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Earth science products. WHATCH'EM will then be coupled with Skeeter Buster (to provide the
linkage between suitable container habitats and Aedes aegypti population dynamics). Finally,
simulations will be performed to explore Ae. aegypti habitat suitability and population dynamics
for present-day and future scenarios. The WHATCH’EM product run with NASA Earth Science
products could also potentially be run in near-real-time for dengue surveillance applications, and
for example would be an excellent application for NASA's SERVIR platform for decision
makers.
1.2 Technical Approach and Methodology
Overview
We propose to generate and apply a globally-applicable framework to assess habitat
suitability and population dynamics for the dengue virus vector mosquito Aedes aegypti by
adapting the container energy balance model WHATCH’EM to use gridded NASA Earth
Science datasets (MERRA, GLDAS/NLDAS, TRMM, and cloud cover), and coupling
WHATCH’EM with the mosquito population model Skeeter Buster. From these models and the
gridded NASA input products, we will create 1) global Ae. aegypti suitability maps based on
water temperature and water availability, and 2) targeted estimates of Ae. aegypti population
dynamics (seasonality and interannual variability) for select areas of North America and the
Caribbean. Suitability maps and population estimates will be created for both present day
conditions and future climate scenarios. The datasets and models to be used in this work are now
described in turn.
Description of Data Sets
A.1 MERRA
The Modern Era Retrospective-Analysis for Research and Applications (MERRA; Rienecker
et al. 2011) is produced by NASA’s Global Modeling and Assimilation Office (GMAO).
MERRA integrates conventional meteorological observation data and satellite radiances and
retrievals with a temporally and spatially consistent numerical modeling system to produce a
gridded dataset suitable for analysis of climate variability. MERRA implements a 3DVAR
assimilation system; a variational bias correction of satellite radiances; and the Incremental
Analysis Updates (IAU), a nudging technique allowing for a smooth transition from the model
states toward the observed state (Rienecker et al. 2008; Cullather and Bosilovich 2012).
MERRA is available from the start of the modern satellite era (1979) through the present, at 0.5°
latitude by 0.67° longitude grid spacing and with 72 layers in the vertical. Output is available at
hourly intervals from the Goddard Earth Sciences Data Information Services Center (GES
DISC). MERRA variables that will be used as input to the container model include temperature
at 2 m above displacement height, wind speed at 2 m above displacement height, and specific
humidity at 2 m above displacement height. Cloud cover, precipitation, ground surface
temperature, and soil temperature will be utilized from MERRA for longer simulations where
other NASA Earth science products are not available. Validation studies show that MERRA
annually overestimates (underestimates) rainfall over Africa, India, and the western tropical
Pacific (South America) compared to the Global Precipitation Climatology Project (GPCP, Adler
et al. 2003), but at levels similar to other reanalyses (Bosilovich et al. 2008; Lorenz and
Kunstmann 2012). The same validation studies show correlations between MERRA and GPCP
and other rainfall datasets at 0.7 or above for most regions for mean annual precipitation and the
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
multi-year monthly precipitation cycle. Lorenz and Kunstmann (2012) find 2 m temperature
biases of less than 4°C over most of the tropics.
A.2 GLDAS and NLDAS
The Global Land Data Assimilation System (GLDAS; Rodell et al. 2004) utilizes satelliteand ground-based observations and the Land Information System (LIS; Kumar et al. 2006) to run
a suite of offline land surface models for optimal estimation of ground surface and soil
conditions. GLDAS version 1 output is available at 0.25° grid spacing globally north of 60°S at
3-hourly intervals (a 15 minute model time step is used) for 2000-present near-real time, with
output utilizing the four layer National Centers for Environmental Prediction/Oregon State
University/Air Force/Hydrologic Research Lab (Noah) land surface model (Chen et al. 1996;
Koren et al. 1999). The North American Land Data Assimilation System (NLDAS; Mitchell et
al. 2004) is similar to GLDAS but specific to North America. NLDAS version 2 output is
available at 0.125° grid spacing at 1-hour intervals for 1979-present near-real time.
GLDAS and NLDAS output that will be used in this work includes ground surface (skin)
temperature, to represent terrestrial radiation emitted to the container from the ground, and
temperature of the uppermost soil layer (top 10 cm), to estimate ground heat flux to/from the
container. A global validation of GLDAS was performed by Zaitchik et al. (2010), who used a
source-to-sink (STS) river routing scheme to simulate river discharge estimates for comparison
with observations across major rivers around the world. For tropical rivers, GLDAS simulations
using Noah generally perform better than simulations with other land surface models, with
reasonable estimations of the timing of peak discharge and seasonal variability in South
America, Africa, and Asia. Discrepancies were largely tied to deficiencies in input rainfall
datasets, especially in regions of Africa with poor precipitation records. Xia et al. (2013)
validated Noah-simulated NLDAS soil temperature estimates against observations across the
United States, finding general agreement, especially for upper soil layers.
A.3 TRMM
Rainfall data will be obtained from the 0.25° Tropical Rainfall Measuring Mission Project
(TRMM) 3-hourly ‘‘TRMM and others rainfall estimate’’, product 3B42, version 7 (Huffman et
al. 2007). The TRMM product combines independent precipitation estimates from multiple
microwave and infrared instruments. Data is available equatorward of 50° latitude from 1998 to
present. Several studies have confirmed the validity of the TRMM-merged rainfall products in
relation to gauge observations and other satellite-based rainfall products in tropical and
subtropical regions, including East Africa (Dinku et al. 2007; Asadullah et al. 2008; Monaghan
et al. 2012), West Africa (Nicholson et al. 2003), Bangladesh (Islam and Uyeda 2007), tropical
Australia (Ebert et al. 2007), Brazil (Franchito et al. 2009), and the U.S. (Wolff et al. 2005).
A.4 Satellite Cloud Cover
Because in situ cloud cover observations are generally not available in many regions, and
even if available can be highly localized, satellite data is often used for cloud cover estimates.
Here we plan to use gridded 0.25° latitude x 0.3125° longitude global merged geostationary total
cloud percentage data from the Minnis research group at the NASA Langley Research Center
(http://cloudsgate2.larc.nasa.gov/index.html). Cloud products are derived using the Visible
Infrared Solar-Infrared Split Window Technique (VISST) and Solar-Infrared Infrared Split
Window Technique (SIST) (Minnis et al. 2001), and available at hourly intervals near-real time.
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Currently this product is available from present back to 2009, so any longer-term simulations can
utilize the cloud clover estimates provided by MERRA.
A.5 Container Experiment Data
To provide validation for container water temperature and height estimates from
WHATCH’EM, a series of container experiments were performed at three different locations
(Boulder CO, Orizaba MX, and Boca del Rio MX) during boreal summer 2013. At each site,
three sets of four different containers (3.8 L “small” black bucket, 19 L “medium” black and
white buckets, and 120 L “Large” gray trash can) were set up in different shading regimes (no
shade, partial shade, and full shade). Changes in water height were recorded daily and water
height re-established to 75% after measurement, with evaporation calculated after accounting for
rainfall recorded by a rain gauge. Water temperature sensors were suspended in the middle of
the water volume of each container and temperature was automatically recorded every 15
minutes. Air temperature and relative humidity measurements were also recorded at each site.
Data are available for approximately four weeks at each site during June-July 2013.
Description of Models
B.1 WHATCH’EM
Recent work has shown that physics-based approaches toward modeling container water
properties are promising for resolving the complexities of container water dynamics (Kearney et
al. 2009). WHATCH’EM - The Water Height And Temperature in Container Habitats Energy
Model – calculates the temperature and water level of a specified container, based on the energy
balance of the water volume. Required variables to be input to WHATCH’EM include air
temperature, relative humidity, and rainfall. Optional variables that improve the accuracy of
simulations include cloud fraction, near-surface wind speed, soil temperature, and surface (skin)
temperature. Container parameters that must be specified include shape (round, rectangular, or
tire), dimensions of the main container body and the top opening (diameter or length and width,
and height), thickness, reflectivity, thermal conductivity of the container material, and fraction of
the container touching the ground surface. Location parameters including latitude, longitude,
and elevation are required for solar radiation calculations. Necessary environmental parameters
include shade fraction and the amount and time interval of any manual filling of the container.
Once input data and parameters have been specified, the energy balance for water is
calculated based on the following equation:
(1)
QWS  QSW   QLW   QLW   QH   QL  QC   QC 
where QWS is heat storage in water (representing the change of temperature of the water), QSW↓
is downward shortwave radiation, QLW↓ is downward longwave radiation, QLW↑ is upwards
longwave radiation, QH↑ is upwards/downwards sensible heat transfer, QL↑ is latent heat transfer,
QC↓ is conduction between the container bottom and water, and QC→ is conduction between the
container side walls and water. The energy balance for the container is:
(2)
QCS  QSW   QLW   QLW   QH   QG  QC   QC 
where QCS is heat storage in the container, QSW→ is sideways inbound shortwave radiation, QLW→
is sideways inbound longwave radiation, QLW← is sideways outbound longwave radiation, QH← is
sideways sensible heat transfer, QG↓ is conduction between the ground and the container bottom,
QC↓ is conduction between the container bottom and the water, and QC→ is conduction between
the container side walls and water All terms are in units of power (Watts), and the sign
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
convention is that all radiation terms are positive into the water/container and all other terms are
positive out of the water/container. Figure 1-2 shows a schematic of the energy balance
described by (1) and (2). Each energy balance term is solved in turn for the container and water
volume dimensions. The heat storage terms in (1) and (2) are then converted into a temperature
change, and evaporation (from the latent heat term), precipitation, and any manual fill are used to
calculate the change in water height. Because the calculation of water and container temperature
changes involve the terms themselves (through several of the energy balance terms), and form a
series of ordinary differential equations, numerical methods are used to simultaneously solve the
equation system. In WHATCH’EM, the fourth-order Adams-Moulton (AM) predictor-corrector
method is used, which is started with a fourth-order Runge-Kutta (RK) procedure (e.g., Cheney
and Kincaid 2008). WHATCH’EM is designed to run in a Linux computing environment. It
requires a FORTRAN 90 compiler and there is a driver program written in Perl that requires the
Statistics::Descriptive module. Optionally, the NCAR
Command Language (NCL) is used for plotting
routines.
As described in section A.5, container experiments
were recently performed at three different locations.
Here we show WHATCH’EM validation results for
one of the locations (Boulder CO), for 28 days from 15
June to 12 July 2013. Figure 1-3 shows average daily
water temperature range by container and shading for
the observations and for WHATCH’EM. The
observations show a larger diurnal temperature range
for smaller containers, with shading primarily driving
differences in daily average temperature.
WHATCH’EM has a cold bias overall, especially for
no shade conditions, and has an amplified daily
temperature cycle for small and medium containers
across all shading types. Still, WHATCH’EM captures
the variability between shading types and container
sizes and colors. Figure 1-4 shows time series of water
Figure 1-2. Schematic showing terms of
temperature from observations and WHATCH’EM for
energy balance model introduced in
the two extreme conditions from the experiment –
equations (1) and (2). Terms are described
small black container in no shade (Fig. 1-4a), and large
in the text.
gray container in full shade (Fig. 1-4b).
WHATCH’EM represents the day-to-day water temperature variations for both containers,
which are largely dependent on cloud cover and wind speed. There is a consistent nighttime cold
bias for the small container, up to 5°C.
To evaluate the model simulated water height, Fig. 1-5 shows aggregated evaporation minus
precipitation (“E-P”) across the experiment time period for observations and WHATCH’EM.
Immediately clear from Fig. 1-5 is the negative bias in E-P for WHATCH’EM across all
containers. For the observed containers, the no shade E-P depends primarily on container color
(positive relationship between evaporation and water temperature through the heat transfer
resistance), however full shade E-P depends on color in an opposite sense (i.e., larger
evaporation for the translucent white container that receives more diffuse radiation than darker
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
containers). As in the observed partial shade case, WHATCH’EM E-P depends on container size
and to a lesser extent container color, for all shading scenarios.
Figure 1-3. Average daily water temperature range (°C) by container and shading for observations (cyan) and
WHATCH’EM model (magenta). Black bars represent average temperature over entire time period.
Figure 1-4. Time series of water temperature from observations (cyan) and WHATCH’EM model (magenta) for
(a) No shade, small, black container, and (b) Full shade, large, gray container.
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Figure 1-5. Evaporation minus precipitation (“E-P”, cm) accumulated over the time period by container and
shading for observations (cyan) and WHATCH’EM model (magenta).
The WHATCH’EM validation results, a portion of which are described above, highlight
several areas of improvement for the model that will be addressed in this work. The Boulder,
Colorado container experiments were performed on a black asphalt surface, which leads to the
cold bias for no shade conditions through an urban heat island effect. The surface skin
temperature from NLDAS does not represent paved conditions and is therefore too cold for no
shade conditions, especially at night. Longwave radiation received to the container sidewalls
from the ground, and then conducted to the water volume, is underestimated. For full shade
conditions, the cold bias results from longwave radiation emitted from a surrounding metal
tractor trailer not represented in the model. The overly active diurnal temperature cycle for small
and medium sized containers in partial and full shade results from ground surface temperature
values read in from NLDAS, which are too warm because they are not representative of shaded
conditions. The desired modification to WHATCH’EM to alleviate surface temperature
discrepancies is to explicitly model the surface temperature representative of the area around the
container. Similarly, the radiation balance of the shading surface can be modeled to improve
representation of longwave power radiated to the container (currently, WHATCH’EM assumes
the shading surface temperature is equal to the air temperature). Additionally, to improve
longwave flux directly into the container water from the air above, the air temperature at
container level will be estimated from the observed 2 m air temperature and modeled surface
temperature, fitted with an appropriate mathematical function based on the wind speed and
stability. This will also improve the underestimated evaporation from the model, as alleviation
of the model nighttime cold bias will lead to higher saturation vapor pressure.
We propose to adapt WHATCH’EM for global applications by using the NASA Earth
Science datasets described in Table 1-1 as input (MERRA, GLDAS/NLDAS, TRMM, satellite
cloud cover), interpolated to a consistent grid. The framework will be run and validated for
boreal summer of 2013 against 1) container observations at three different sites during boreal
summer 2013 (described in A.5), and 2) WHATCH’EM simulations driven with in situ
observations associated with the experiments described in A.5. Performance of the output from
the NASA-based WHATCH’EM framework can be assessed, and biases and deficiencies
addressed, with additional container experiments to be performed during boreal summer 2014 if
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necessary. We will also attempt to validate WHATCH’EM against published studies of water
temperature regimes in artificial containers, which include observations from Florida (Cheng et
al. 1998), Colombia (Padmanabha et al. 2010) and Australia (Richardson et al. 2012).
WHATCH’EM will then be run globally for a variety of scenarios (container characteristics,
shading, surface types, etc.). Even though container types, distributions, and uses are different
regionally, the estimates provided by WHATCH’EM are general enough to apply globally.
Based on data availability, three sets of simulations will be run: 1) Five years (2009-2013) using
all datasets, 2) Fifteen years (1999-2013) using MERRA and TRMM, and 3) Thirty-five years
(1979-2013) using only MERRA. What we believe to be the best set of input data sources will
be run for the time periods of availability, and sensitivity to different sources of input data (i.e.,
MERRA vs. other datasets) can be assessed for overlap periods. Habitat suitability maps will be
produced based on established water temperature ranges for immature Aedes aegypti mosquitoes.
A proxy for dengue risk is provided from habitat suitability, but also population estimates, as Ae.
aegypti is closely associated with human activity. Population data from the 1 km resolution
LandScan dataset produced by Oak Ridge National Laboratory (Dobson et al. 2003) will be used
to mask out rural areas with low dengue risk. This work will satisfy project objectives 1-3.
B.2 Skeeter Buster
Weather-driven simulation models for Aedes aegypti populations -- such as CIMSiM and
Skeeter Buster -- are strongly influenced by water temperature, which impacts the development
times and survival rates of eggs, larvae and pupae (Focks et al. 1993a,b; Cheng et al. 1998;
Magori et al. 2009; Ellis et al. 2011). CIMSiM simulates the dynamics of immature Ae. aegypti
and water dynamics within user-specified container categories, accounting for shape (circular or
rectangular), dimension, presence or absence of lid, fill method (manual or rain), fill frequency
(daily, weekly, or monthly), drawdown frequency (daily, weekly, or monthly), if it was located
under the edge of a roof or similar device to capture rain water, and if it was in shade or sun
(Focks et al. 1993a, Ellis et al. 2011). The original version of CIMSiM used default values for
key characteristics of a given container category (Focks et al. 1993a), whereas a more recent
version allows the user to input data for the container categories to represent the average
characteristics and density of that container category in the focal location (Ellis et al. 2011). The
Skeeter Buster model is based on the general characteristics of CIMSiM but operates at the level
of individual containers and also incorporates stochastic events, e.g., survival of individuals of
Ae. aegypti within a cohort (Magori et al. 2009; Xu et al. 2010). Based on the availability of
stochasticity and the ease of code modification, Skeeter Buster will be used in this study.
To test the viability of Skeeter Buster to represent differences in mosquito populations along
an elevation and climate gradient in Mexico, simulations were run for boreal summer 2011, and
pupae per house results for select locations are shown in Fig. 1-6. Simulations were run for a 25house neighborhood in each city from 1 May to 15 September, forced by local weather station
observations. A distribution of containers was placed in each city based on household sampling
results of Lozano-Fuentes et al. (2012). Results from a mosquito collection campaign (LozanoFuentes et al. 2012), performed at one time for each city during the summer, are also shown for
validation purposes. Even with the regression-based water temperature and height equations
used in Skeeter Buster, the model captures the variability along the transect, with pupae per
house largest along the eastern lower elevation portion of the transect (Veracruz, Orizaba, and
Rio Blanco), decreasing towards higher elevation locations (Acultzingo and Maltrata), and no
populations established at high elevation sites (Puebla and Atlixco). Even though there are large
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deviations in pupae counts across houses in both the observations and Skeeter Buster, the model
simulated counts fall near the average observed populations for the study period in each city.
Based on the results of Fig. 1-6, we are confident that Skeeter Buster can be a useful tool in
describing the seasonality and interannual variability of mosquito populations, which is often
missing from direct sampling and statistical models.
Figure 1-6. Time series plots of pupae per house from Skeeter Buster from 1 May to 15 September 2011. Red
line is average and red shading represents range from 25 houses. Blue box plot represents average (line),
standard deviation (box), and maximum (whisker) pupae per house from 50 sampled houses around the
corresponding date. Plot in lower right shows station locations, elevation, and July/August 2011 mean
temperature for reference.
We will couple WHATCH’EM with Skeeter Buster, so that Skeeter Buster can utilize the
physically-based water temperature and height estimates from WHATCH’EM. Mosquito
abundance estimates, output from the coupled WHATCH’EM-Skeeter Buster models, will be
validated against data from several locations: detailed container distribution and pupal surveys
from eastern Mexico (Lozano-Fuentes et al. 2012), Sonora Mexico (from a current NIHsponsored project with collaborator K. Ernst), and Key West FL (also from an NIH-sponsored
project in which K. Ernst is PI), and from published aggregated container and pupal surveys
from Chiapas Mexico (Arredondo-Jiménez and Valdez-Delgado 2006) and Puerto Rico (Barrera
et al. 2006). The coupled models will be run to provide seasonality and interannual variability of
mosquito populations. This work will satisfy project objective 4.
B.3 Climate Model Output for Future Climate Scenarios
Sensitivity to future climate change of both the habitat suitability maps described in B.1 and
the coupled WHATCH’EM-Skeeter Buster mosquito abundance estimates for selected areas
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described in B.2 can be assessed using downscaled global climate model projections. Here we
plan to use output from the GISS-E2-R global climate model (Schmidt et al. 2006), which uses
the GISS ModelE atmospheric model on a 2° x 2.5° latitude-longitude grid with 40 vertical
layers, coupled to the Russell ocean model. We will downscale future climate projections for
two time periods (2020-2035 and 2060-2075) from GISS-E2-R to MERRA using the linear
correction method of Holland et al. (2010). In this method, downscaled variables are calculated
by combining the climatological value from MERRA with an anomaly term from GISS-E2-R.
The climatological values are constructed for each three hourly period over the annual cycle.
The anomaly values represent deviations of GISS-E2-R from its own climatology (synoptic-scale
variability and long-term variability and trends). Three-hourly output is only available for one
ensemble member of RCP4.5. We will similarly downscale monthly output of multiple
ensemble members of all RCP scenarios, which neglects changes in diurnal cycle but does
provide a wider range of future scenarios.
While the simple downscaling method described above only takes into account changes in
mean climate and does not represent changes to extreme events, this method is preferable to
other statistical downscaling methods (White and Toumi 2013). This work will satisfy project
objective 5.
1.3
Perceived Impact to State of Knowledge
The proposed research will improve Aedes aegypti habitat suitability estimates over current
statistically-based models because water temperature and availability, both key to development
and survival of immatures, will be estimated through physical principles by WHATCH’EM.
Similarly, mosquito population estimates from dynamic life cycle models like CIMSiM and
Skeeter Buster will be improved through use of more realistic and responsive water temperature
estimates from WHATCH’EM, compared to the current use of empirical regression equations.
This research will be the best characterization to date of habitat suitability and mosquito
population dynamics for several locations across North America and the Caribbean. Through
description of the seasonality and interannual variability of mosquito population with the coupled
WHATCH’EM-Skeeter Buster models, our knowledge of the climatic factors that modulate
Aedes aegypti populations and thereby strongly influence dengue risk will be greatly improved.
Finally, by adapting WHATCH'EM to employ NASA Earth Science datasets, a globallyapplicable framework will be in place for assessing habitat suitability and population dynamics
for the dengue virus vector mosquito Aedes aegypti. This represents an important achievement,
because dengue virus transmission disproportionately occurs in regions where poverty levels are
high and therefore in situ weather information is often non-existent or not readily available (e.g.,
Moore et al. 2012).
1.4
Relevance to Element Programs and Objectives in the NRA
The proposed research supports the Earth Science Research Program’s Applied Science area
(also part of the 2010 Science Plan for NASA’s Science Mission Directorate), which
“…develops applications knowledge and understanding of how Earth science can be applied to
serve society, increasing the benefits of the nation’s investments in NASA Earth science.” The
proposed research will apply Earth science observations (atmospheric and terrestrial) to improve
estimates of disease vector habitat suitability and mosquito population dynamics. Also, the
habitat suitability maps and mosquito population dynamics estimates produced through this
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project will be provided to SERVIR, to “…serve as a bridge between the data and knowledge
generated by NASA Earth science and the information needs and decision making of
Government agencies,…, and others.” The overarching purpose of the Applied Sciences
Program is to “discover and demonstrate innovative uses and practical benefits of NASA Earth
science research, technology, and observations”, and in the proposed research we use a variety of
NASA Earth science data products to produce information about and proxies of dengue risk.
The activities of this project also fit Objective 2.1.7 (“Enable the broad use of Earth system
science observations and results in decision-making activities for societal benefits”) in the 2011
NASA Strategic Plan.
In addition to the NASA-specific goals discussed above, the proposed research also
contributes to several objectives from the report entitled “National Global Change Research Plan
2012-2021: A Strategic Plan for the U.S. Global Change Research Program”:
Objective 1.1 – Earth system understanding: “Advance fundamental understanding of the
physical, chemical, biological, and human components of the Earth system, and the interactions
among them, to improve knowledge of the causes and consequences of global change.”
Specifically, this research explores the “interaction of climate system processes and other key
dimensions of global change, such as ecosystem dynamics” through the connection between
climate change and changes to mosquito habitat suitability. Additionally, this research addresses
the assertion that “improved integration is needed across multiple aspects and levels of the
biological sciences. Processes at the population, species, community, and ecosystem levels are
all critical for understanding the causes and consequences of global change.” through integrated
modeling of mosquito population dynamics and habitat suitability with climate parameters.
Objective 1.3 – Integrated observations: “Advance capabilities to observe the physical, chemical,
biological, and human components of the Earth system over multiple space and time scales to
gain fundamental scientific understanding and monitor important variations and trends.” This
research provides new methods to observe and predict the ecological response to climate, in both
improving estimates of container water temperature for habitat suitability, and providing new
observationally-based estimates of mosquito population dynamics.
1.5
Work Plan
The work plan for the proposed project is as follows:
Tasks Year 1
Modify WHATCH’EM for use with NASA datasets, other improvements
Obtain datasets and run WHATCH’EM for Summer 2013 test period
Compare simulation results with field data and simulations using in situ data
Manuscript: Demonstration of WHATCH’EM with gridded NASA datasets
Validation
Tasks Year 2
Construct suitability maps for present day
Construct suitability maps for future climate scenarios
Provide suitability maps to SERVIR
Manuscript: Suitability maps for present day and future climate scenarios
Steinhoff Effort
(Months)
0.75
1.25
0.5
0.5
Steinhoff Effort
(Months)
1.0
1.25
0.25
0.5
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Tasks Year 3
Couple WHATCH’EM with Skeeter Buster
Run and validate coupled models over select areas of North America and the
Caribbean
Manuscript: Modeled Aedes aegypti population dynamics
Steinhoff Effort
(Months)
1.0
1.5
0.5
1.5.1 Key Milestones
Key Milestone
WHATCH’EM Summer 2013 Test Product
Suitability Maps – Present Day
Suitability Maps – Future Climate Scenarios
WHATCH’EM – Skeeter Buster coupled models
Assessment of mosquito population dynamics for select areas of North America and the
Caribbean
Date
Sept. 2014
June 2015
Oct. 2015
June 2016
Jan. 2017
1.5.2 Management Structure
Dr. Daniel Steinhoff of NCAR is the PI and sole investigator of the proposed investigation.
He alone is responsible for the quality and direction of the proposed research and the proper use
of all awarded funds. He is solely responsible for all technical and budget issues for the project.
Contributions of Principal Investigator
Dr. Daniel Steinhoff of NCAR will conduct the acquisition of all necessary datasets,
perform all of the analysis, modify and improve the models, and disseminate results at
conference proceedings and in peer-reviewed journals.
1.5.3
1.5.4 Collaborators
Dr. Lars Eisen and Dr. Saul Lozano-Fuentes, both of Colorado State University,
Collaborators, will provide information on the bionomics of Aedes aegypti, particularly
regarding the use of artificial water containers as sites for egg-laying and development of the
immature life states and the impact of water temperature on development times and survival.
Dr. William Crosson, of Science and Technology Institute, Universities Space Research
Association, Collaborator, will advise on appropriate NASA remote sensing products to use for
this project, including estimates of cloud cover, ground surface characteristics, and any high
resolution visible imagery that may be used to characterize container distributions in select areas.
Crosson will also assist in adding displays of spatial container suitability maps to the
NASA/USAID SERVIR website, which provides environmental decision making tools to
developing countries.
Dr. Kacey Ernst, of the University of Arizona, Collaborator, will provide housing
characteristics, container distribution data, and pupal surveys for Hermosillo and Nogales
Mexico in conjunction with an NIH-funded project on dengue.
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exclusion? Oecologia, 139, 583-593.
Kamimura, K., I.T. Matsuse, H. Takahashi, J. Komukai, T. Fukuida, K. Suzuki, M. Artani, Y.
Shira, and M. Mogi, 2002: Effect of temperature on the development of Aedes aegypti and
Aedes albopictus. Med. Entomol. Zool., 53, 53–58.
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<Global Modeling of Containers for Dengue Mosquito>
Kay, B., and S.N. Vu, 2005: New strategy against Aedes aegypti in Vietnam. Lancet, 365, 613617.
Kearney, M., W.P. Porter, C. Williams, S. Ritchie, and A.A. Hoffmann, 2009: Integrating
biophysical models and evolutionary theory to predict climatic impacts on species ranges:
the dengue mosquito Aedes aegypti in Australia. Funct. Ecol., 23, 528-538.
Koenraadt, C.J.M., and L.C. Harrington, 2008: Flushing effect of rain on container-inhabiting
mosquitoes Aedes aegypti and Culex pipiens (Diptera: Culicidae). J. Med.Entomol., 45, 2835.
Koren, V., J. Schaake, K. Mitchell, Q.Y. Duan, F. Chen, and J.M. Baker, 1999: A
parameterization of snowpack and frozen ground intended for NCEP weather and climate
models. J. Geophys. Res., 104, 19 569-19 585.
Kroeger, A., and M.B. Nathan, 2006: Dengue: setting the global research agenda. Lancet, 368,
2193-2195.
Kumar, S.V., and Coauthors, 2006: Land Information System: An interoperable framework for
high-resolution land surface modeling. Environ. Model. Softw., 21, 1402-1415.
Lambrechts, L., K.P. Paaijmans, T. Fansiri, L.B. Carrington, L.D. Kramer, M.B. Thomas, and
T.W. Scott, 2011: Impact of daily temperature fluctuations on dengue virus transmission by
Aedes aegypti. Proc. Natl. Acad. Sci. U.S.A., 108, 7460-7465.
Lorenz, C., and H. Kunstmann, 2012: The hydrological cycle in three state-of-the-art reanalyses:
Intercomparison and performance analysis. J. Hydrometeor., 13, 1397-1418.
Lozano-Fuentes, S., and Coauthors, 2012: The dengue virus mosquito vector Aedes aegypti at
high elevation in México. Am. J. Trop. Med. Hyg., 87, 902-909.
Magori, K., M. Legros, M.E. Puente, D.A. Focks, T.W. Scott, A.L. Lloyd, and F. Gould, 2009:
Skeeter Buster: a stochastic, spatially explicit modeling tool for studying Aedes aegypti
population replacement and population suppression strategies. PLoS Negl. Trop. Dis., 3,
e508.
Merrill, S.A., F.B. Ramberg, and H.H. Hagedorn, 2005: Phylogeography and population
structure of Aedes aegypti in Arizona. Am. J. Trop. Med. Hyg., 72, 304-310.
Minnis, P., W.L. Smith, Jr., D.F. Young, L. Nguyen, A.D. Rapp, P.W. Heck, S. Sun-Mack, Q.Z.
Trepte, Y. Chen, 2001: "A Near-Real Time Method for Deriving Cloud and Radiation
Properties from Satellites for Weather and Climate Studies." Proc. AMS 11th Conference on
Satellite Meteorology and Oceanography, Madison, WI, Oct. 15-18, 2001.
Mitchell, K.E., and Coauthors, 2004: The multi-institution North American Land Data
Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a
continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90,
doi:10.1029/2003JD003823.
Mohammed, A., and D.D. Chadee, 2011: Effects of different temperature regimens on the
development of Aedes aegypti (L.) (Diptera: Culicidae) mosquitoes. Acta Trop., 119, 38-43.
Monaghan, A.J., K. MacMillan, S.M. Moore, P.S. Mead, M.H. Hayden, and R.J. Eisen, 2012: A
regional climatography of West Nile, Uganda, to support human plague modeling. J. Appl.
Meteor. Climatol., 51, 1201-1221.
Moore, S.M., A.J. Monaghan, K.S. Griffith, T. Apungu, P.S. Mead, and R.J. Eisen, 2012:
Improvement of disease prediction and modeling through the use of meteorological
ensembles: Human plague in Uganda. PLoS ONE, 7, doi:10.1371/journal.pone.0044431.
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<Global Modeling of Containers for Dengue Mosquito>
Morrison, A.C., E. Zielinski-Gutierrez, T.W. Scott, and R. Rosenberg, 2008: Defining challenges
and proposing solutions for control of the virus vector Aedes aegypti. PLoS Med., 5, e68.
Morrison, A.C., K. Gray, A. Getis, H. Astete, M. Sihuincha, D. Focks, D. Watts, J. D. Stancil, J.
G. Olson, P. Blair, and T. W. Scott, 2004: Temporal and geographic patterns of Aedes
aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol., 41, 1123-1142.
Muturi, E.J., A. Nyakeriga, and M. Blackshear, 2012: Temperature-mediated differential
expression of immune and stress-related genes in Aedes aegypti larvae. J. Am. Mosq. Contr.
Assoc., 28, 79-83.
Nagao, Y., U. Thavara, P. Chitumsup, A. Tawatsin, C. Chansang, and D. Campbell-Lendrum,
2003: Climatic and social risk factors for Aedes infestation in rural Thailand. Trop. Med. Int.
Health, 8, 650-659.
Nicholson, S.E., and Coauthors, 2003: Validation of TRMM and other rainfall estimates with a
high-density gauge dataset for West Africa. Part II: Validation of TRMM rainfall products.
J. Appl. Meteor., 42, 1355-1368.
Padmanabha, H., E. Soto, M. Mosquera, C.C. Lord, and L.P. Lounibos, 2010: Ecological links
between water storage behaviors and Aedes aegypti production: Implications for dengue
vector control in variable climates. EcoHealth, 7, 78-90.
Padmanabha, H., C.C. Lord, and L.P. Lounibos, 2011a: Temperature induces trade-offs between
development and starvation resistance in Aedes aegypti (L.) larvae. Med. Vet. Entomol., 25,
445-453.
Padmanabha, H., B. Bolker, C.C. Lord, C. Rubio, and L.P. Lounibos, 2011b: Food availability
alters the effects of larval temperature on Aedes aegypti growth. J. Med. Entomol., 48, 974984.
Padmanabha, H., F. Correa, M. Legros, H.F. Nijhout, C.C. Lord, and L.P. Lounibos, 2012: An
eco-physiological model of the impact of temperature on Aedes aegypti life history traits. J.
Insect Physiol., 58, 1597-1608.
Radke, and Coauthors, 2012: Dengue outbreak in Key West, Florida, USA, 2009. Emerg. Infect.
Dis., 18, 135-137.
Ramos, M.M., and Coauthors, 2008: Epidemic dengue and dengue hemorrhagic fever at the
Texas-Mexico border: Results of a household-based seroepidemiological survey. Am. J.
Trop. Med. Hyg., 78, 364-369.
Reiter, P., and Coauthors, 2003: Texas lifestyle limits transmission of dengue virus. Emerg.
Infect. Dis., 9, 86-89.
Renganathan, E., and Coauthors, 2003: Towards sustaining behavioural impact in dengue
prevention and control. Dengue Bulletin, 27, 6–12.
Richardson, K., A.A. Hoffmann, P. Johnson, S. Ritchie, and M.R. Kearney, 2011: Thermal
sensitivity of Aedes aegypti from Australia: empirical data and prediction of effects on
distribution. J. Med. Entomol., 48, 914-923.
Richardson, K., A.A. Hoffmann, P. Johnson, S.R. Ritchie, and M.R. Kearney, 2012: A replicated
comparison of breeding-container suitability for the dengue vector Aedes aegypti in tropical
and temperate Australia. Austral Ecology, 38, 219-229.
Rienecker, M.M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis
for Research and Applications. J. Climate, 24, 3624-3648.
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Rienecker, M.M., and Coauthors, 2008: The GEOS-5 Data Assimilation System Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. Series on Global
Modeling and Data Assimilation, NASA/TM-2008-104606, Vol. 27, 92 pp.
Rodell, M., and Coauthors, 2004: The global land data assimilation system. Bull. Amer. Meteor.
Soc., 85, 381-394.
Rueda, L.M., K.J. Patel, R.C. Axtell, and R.E. Stinner, 1990: Temperature-dependent
development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera:
Culicidae). J. Med. Entomol., 27, 892-898.
Schmidt, G.A., and Coauthors, 2006: Present day atmospheric simulations using GISS ModelE:
Comparison to in-situ, satellite and reanalysis data. J. Climate, 19, 153-192.
Scott, T.W., E. Chow, D. Strickman, P. Kittayapong, R.A. Wirtz, L.H. Lorenz, J.D. Edman,
1993: Blood-feeding patterns of Aedes aegypti (Diptera: Culicidae) collected in a rural Thai
village. J Med Entomol., 30, 922–927.
Smith, G.C., D.A. Eliason, C.G. Moore, and E.N. Ihenacho, 1988: Use of elevated temperatures
to kill Aedes albopictus and Aedes aegypti. J. Am. Mosq. Contr. Assoc., 4, 557-558.
Steinhoff, D.F., and A.J. Monaghan, 2013: The Water Height and Temperature in Container
Habitats Energy Model (WHATCH’EM). doi:10.5065/D6J67DXP.
Tarakidzwa, I., 1997: Evaporation from class-A pans: measurements and modeling. Masters
Thesis. University of Zimbabwe. 109 pp.
Tun-Lin, W., and Coauthors, 2009: Reducing costs and operational constraints of dengue vector
control by targeting productive breeding places: a multi-country non-inferiority cluster
randomized trial. Trop. Med. Int. Health, 14, 1143-1153.
Tun-Lin, W., T.R. Burkot, and B.H. Kay, 2000: Effects of temperature and larval diet on
development rates and survival of the dengue vector Aedes aegypti in north Queensland,
Australia. Med. Vet. Entomol., 14, 31-37.
Walsh, R.K., L. Facchinelli, J.M. Ramsey, J.G. Bond, and F. Gould, 2011: Assessing the impact
of density dependence in field populations of Aedes aegypti. J. Vector Ecol., 36, 300-307.
Westaway, E.G., and Blok, J., 1997: Taxonomy and evolutionary relationships of flaviviruses.
Dengue and Dengue Hemorrhagic Fever, D.J. Gubler and G. Kuno, eds. London: CAB
International, 147-173.
White, R.H., and R. Toumi, 2013: The limitations of bias correcting regional climate model
inputs. Geophys. Res. Lett., 40, 2907-2912, doi:10.1002/grl.50612.
Wolff, D.B., D.A. Marks, E. Amitai, D.S. Silberstein, B.L. Fisher, A. Tokay, J. Wang, and J.L.
Pippitt, 2005: Ground validation for the Tropical Rainfall Measuring Mission (TRMM). J.
Atmos. Oceanic Technol., 22, 365-380.
World Health Organization, 2009: Dengue: guidelines for diagnosis, treatment, prevention and
control – New edition. World Health Organization and the Special Programme for Research
Training in Tropical Diseases. Geneva: WHO Press.
Xia, Y., M. Ek, J. Sheffield, B. Livneh, M. Huang, H. Wei, S. Feng, L. Luo, J. Meng, and E.
Wood, 2013: Validation of Noah-simulated soil temperature in the North American Land
Data Assimilation System Phase 2. J. Appl. Meteor. Climatol., 52, 455-471.
Xu, C., M. Legros, F. Gould, and A.L. Lloyd, 2010: Understanding uncertainties in model-based
predictions of Aedes aegypti population dynamics. PLoS Negl. Trop. Dis., 4, e830.
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<Global Modeling of Containers for Dengue Mosquito>
Yang, H.M., M.L.G. Macoris, K.C. Galvani, M.T.M. Andrighetti, and D.M.V. Wanderley, 2009:
Assessing the effects of temperature on the population of Aedes aegypti, the vector of
dengue. Epidemiol. Infect., 137, 1188-1202.
Zaitchik, B.F., M. Rodell, and F. Olivera, 2010: Evaluation of the Global Land Data
Assimilation System using global river discharge data and a source-to-sink routing scheme.
Water Resour. Res., 46, W06507, doi:10.1029/2009WR007811.
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3 Biographical Sketch
3.1
Principal Investigator
Daniel F. Steinhoff
National Center for Atmospheric Research
Research Applications Laboratory
3450 Mitchell Lane, Boulder, CO 80301 USA
Phone: 303-497-8466
Fax: 303-497-8401
Email: steinhof@ucar.edu
RELEVANT EXPERIENCE
Two years experience working with climate effects on the dengue vector mosquito Aedes
aegypti for an NSF-funded field and modeling campaign along an elevation transect in eastern
Mexico. Primary developer of the Water Height And Temperature in Container Habitats
Energy Model (WHATCH’EM). Extensive knowledge and experience with dynamic life cycle
mosquito models, atmospheric reanalyses, numerical weather prediction (including land
surface models), satellite meteorology, and global climate models.
EDUCATION
Ph.D., Atmospheric Sciences, Ohio State University (2011)
M.S., Atmospheric Sciences, Ohio State University (2008)
B.S., Atmospheric Sciences, University of Wisconsin-Madison (2003)
RESEARCH INTERESTS
Climate and human health, Mosquito population modeling (dynamic and statistical), Numerical
weather prediction, Data assimilation
PROFESSIONAL EXPERIENCE
2011 - Present: Postgraduate Scientist, Research Applications Laboratory, National Center for
Atmospheric Research.
2005 - 2011: Graduate Research Associate, Polar Meteorology Group, Byrd Polar Research
Center, The Ohio State University.
PUBLICATIONS
Steinhoff, D. F., A. J. Monaghan, and M. P. Clark, 2013: Projected impact of 21st century
ENSO changes on rainfall in Central America and northwest South America from CMIP5
AOGCMs. Int. J. Climatol., conditionally accepted.
Steinhoff, D. F., D. H. Bromwich, J. C. Speirs, H. A. McGowan, and A. J. Monaghan, 2013:
Austral summer foehn winds over the McMurdo Dry Valleys of Antarctica from Polar
WRF. Quart. J. Roy. Meteor. Soc., conditionally accepted.
Ballinger, T. J., T. W. Schmidlin, and D. F. Steinhoff, 2013: The Polar Marine climate revisited.
J. Climate, 26, 3935-3952, doi:10.1175/JCLI-D-12-00660.1.
Oleson, K. W., A. Monaghan, O. Wilhelmi, M. Barlage, N. Brunsell, J. Feddema, L. Hu, and
D. F. Steinhoff, 2013: Interactions between urbanization, heat stress, and climate change.
Climatic Change, conditionally accepted.
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Speirs, J. C., H. A. McGowan, D. F. Steinhoff, and D. H. Bromwich, 2013: Regional climate
variability driven by foehn winds in the McMurdo Dry Valleys, Antarctica. Int. J.
Climatol., 33, 945-958, doi:10.1002/joc.3481.
Zawar-Reza, P., K. Marwan, I. Soltanzadeh, T. Dallafior, S. Zhong, D. F. Steinhoff, B. Storey,
and C. Carey, 2013: Pseudo-vertical temperature profiles give insight into winter
evolution of the atmospheric boundary layer over the McMurdo Dry Valleys of
Antarctica. J. Appl. Meteor. Climatol., 52, 1664-1669.
Steinhoff, D. F., D. H. Bromwich, and A. J. Monaghan, 2012: Dynamics of the foehn
mechanism in the McMurdo Dry Valleys Antarctica from Polar WRF. Quart. J. Roy.
Meteor. Soc., doi:10.1002/qj.2038.
Lozano-Fuentes, S., M. H. Hayden, C. Welsh-Rodriguez, C. Ochoa-Martinez, B. Tapia-Santos,
K. C. Kobylinski, C. K. Uejio, E. Zielinski-Gutierrez, L. Delle Monache, A. J.
Monaghan, D. F. Steinhoff, and L. Eisen, 2012: The Dengue Virus Mosquito Vector
Aedes aegypti at High Elevation in México. American Journal of Tropical Medicine and
Hygiene, 87(5), 902-909, doi:10.4269/ajtmh.2012.12-0244.
Lozano-Fuentes, S., C. Welsh-Rodriguez, M. H. Hayden, B. Tapia-Santos, C. Ochoa-Martinez,
K. C. Kobylinski, C. K. Uejio, E. Zielinski-Gutierrez, L. Delle Monache, A. J.
Monaghan, D. F. Steinhoff, and L. Eisen, 2012: Aedes (Ochlerotatus) epactius Dyar &
Knab along an elevation and climate gradient in Veracruz and Puebla States, México. J.
Medical Entomology, 49(6), 1244-1253, doi:10.1603/ME12067.
Bromwich, D. H., D. F. Steinhoff, I. Simmonds, K. Keay, and R. L. Fogt, 2011: Climatological
aspects of cyclogenesis near Adélie Land Antarctica. Tellus, 63A, 921-938,
doi:10.1111/j.1600-0870.2011.00537.x.
Speirs, J. C., D. F. Steinhoff, H. A. McGowan, D. H. Bromwich, and A. J. Monaghan, 2010:
Foehn winds in the McMurdo Dry Valleys, Antarctica: The origin of extreme warming
events. J. Climate, 23, 3577-3598, doi:10.1175/2010JCLI3382.1.
Steinhoff, D. F., S. Chaudhuri, and D. H. Bromwich, 2009: A new perspective on the Ross Ice
Shelf Air Stream. Mon. Wea. Rev., 137, 4030-4046, doi:10.1175/2009MWR2880.1.
Steinhoff, D. F., D. H. Bromwich, M. Lambertson, S. L. Knuth, and M. A. Lazzara, 2008: A
dynamical investigation of the May 2004 McMurdo Antarctica severe wind event using
AMPS. Mon. Wea. Rev., 136, 7-26, doi:10.1175/2007MWR1999.1.
COMMUNITY ACTIVITIES
 Representative for Atmospheric Sciences to the Council of Graduate Students (CGS),
The Ohio State University, 2006 – 2007.
 43 presentations to school field trips (K-12 and college) to Byrd Polar Research Center,
January 2008 – March 2011.
 Mentor to Ruth Burrows, Upper Arlington High School, Upper Arlington OH, science
fair project, meetings every 1-2 weeks, November 2006 – February 2007.
 Mentor to Saptarshi Chaudhuri, Columbus Alternative High School, Columbus OH,
summer internship, approximately 40 hours per week, June – August 2008. This work
resulted in a manuscript in Monthly Weather Review. Saptarshi completed undergraduate
studies at Caltech and is now in graduate studies at Stanford.
 Mentor to Gabriela D. Talavera-Santiago, UCAR/NCAR Spark Pre-College Internship
Program, June – August 2013.
3-2
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NRA NNH13ZDA001N
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<Global Modeling of Containers for Dengue Mosquito>
4 Current and Pending Support
4.1 Current Awards
Daniel Steinhoff, PI
Name of Principal
Investigator on
Award
Andrew Monaghan
Andrew Monaghan
Award/Project Title
The Vector
Mosquito Aedes
aegypti at the
Margins: Sensitivity
of a Coupled
Natural and Human
System to Climate
Change
Disease Vector
Mapping via
Environmental/Clim
atological/Sociologic
al factors
Program Name/Sponsoring
Agency/Point of Contact
telephone and email
Period of
Performance/Total
Budget
Commitment
(PersonMonths per
Year)
12.0
NSF
Sarah Ruth
Ph: (703) 292-8521
sruth@nsf.gov
10/1/2010-9/30/2013
$1,235,153
STAR, LLC
Paul Bieringer
Ph: (303) 640-5592
paulb@starinst.org
6/17/2013-6/16/2014
$147,116
Program Name/Sponsoring
Agency/Point of Contact
telephone and email
USDA ARS CTR Medical,
Agricultural, Vet. Entomology
Kenneth Linthicum
Ph: (352) 374-5700
Kenneth.Linthicum@ars.usda.gov
Period of
Performance/Total
Budget
1/1/2014-12/31/2018
$346,000
Commitment
(Person-Months
per Year)
2.0
Climate Change Research Group,
LLC
Bill Doughtery
Ph: (508) 668-0980
billd@ccr-group.org
7/1/2013-6/30/2014
$152,995
3.0
3.0
4.2 Pending Awards
Daniel Steinhoff, PI
Name of Principal
Investigator on
Award
Andrew Monaghan
David Yates
Award/Project Title
An Operationally
relevant, DataDriven Predictive
Model That
Estimates Timing
and Spatial extent
of Exotic VectorBorne Disease
Transmission Risk
to Humans in the
United States
Regional Climate
Modeling for the
Arabian Gulf Region
– Future Scenarios
and Capacity
Building
4-1
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NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5 Budget Justification: Narrative and Details
5.1 Budget Narrative
5.1.1 Personnel and Work Effort
Title
Associate Scientist III, NCAR PI
Employee
Dr. Daniel Steinhoff
Year 1
25%
Year 2
25%
Year 3
25%
NCAR PI Daniel Steinhoff will work approximately 25% in each year of the project and will
have primary responsibility for all model development, production of water container suitability
maps, and dissemination of results in conference proceedings, journal publications, and possibly
NASA SERVIR.
5.1.2 Facilities and Equipment
NCAR investigators have access to all of the computational facilities required for this project
including: multiple laptops and desktop computers with appropriate graphical, word processing,
statistical, and data processing software; peripherals; printers; and network connections within
the Research Applications Laboratory (RAL). The National Center for Atmospheric Research
(NCAR) and RAL have site licenses to Portland Group compilers.
5.2
Budget Details
A. Senior / Key Person
An Associate Scientist III will serve as the NCAR Principal Investigator and will charge
approximately 25% in each year of the project with a salary range between $78,000.00 –
$87,994.94. This labor will include input data acquisition and processing, continued
development of the WHATCH’EM container model, coupling of WHATCH’EM with the
mosquito population model Skeeter Buster, analysis of mosquito suitability for present day and
future climate scenarios, and dissemination of final results.
A 4% annual salary increase has been included.
Fringe Benefits:
The salary budget includes a full time employee benefit rate of 53.8% for non-work time of
vacation, sick leave, holidays and other paid leave, as well as standard staff benefits. Worked
hours are based on 86% of 2080 hours in a year.
B. Other Personnel: None
C. Equipment: None
D. Travel:
5-1
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NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Domestic Travel: A total of $15,980 is budgeted for domestic travel over the life of the project.
This includes travel to two conferences: AGU in San Francisco and the International Society for
Photogrammetry and Remote Sensing (ISPRS) conference, which rotates locations but priced for
Washington DC. Each would be 5 days/nights for 1 person.
All costs (based on NCAR travel rates) include airfare, lodging, car rental, IRS-approved per
diem rates, and registration costs and are escalated by 4% per year.
Destination
Yr 1 - Travel - Trip One
Denver, CO to Washington, DC
1 Traveler
5 Days
Yr 1 - Travel - Trip Two
Denver, CO to San Francisco, CA
1 Traveler
5 Days
Total for Yr 1 Travel
Yr 2 - Travel - Trip One
Denver, CO to Washington, DC
1 Traveler
5 Days
Yr 2 - Travel - Trip Two
Denver, CO to San Francisco, CA
1 Traveler
5 Days
Airfare
Hotel
Car
Per Diem
Conf. Reg
& Misc
Total Trip
Cost
$
438.80
$ 1,368.00
$
390.50
.
$
649.95
$ 2,847.25
$
301.80
$
930.00
$
390.50
$
649.95
$ 2,272.25
$ 5,119.50
$
438.80
$ 1,368.00
$
390.50
$
649.95
$ 2,847.25
$
301.80
$
930.00
$
390.50
$
649.95
$ 2,272.25
Sub-total for Yr 2 Travel
Total with 4% Escalation on Yr 2 Travel
Yr 3 - Travel
Denver, CO to Washington, DC
1 Traveler
5 Days
Yr 3 - Travel
Denver, CO to San Francisco, CA
1 Traveler
5 Days
$ 5,119.50
5324.28
Sub-total for Yr 3 Travel
Total with 4% Escalation on Yr 3 Travel
$ 5,119.50
$ 5,537.25
$
438.80
$ 1,368.00
$
390.50
$
649.95
$ 2,847.25
$
301.80
$
$
390.50
$
649.95
$ 2,272.25
930.00
E. Participant Support Costs: None.
F. Other Direct Costs:
Materials and Supplies:
$1,000 per year has been requested for Materials and Supplies and will include purchase of
miscellaneous office supplies. The additional $3.5K in year 1 would be for a laptop computer
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ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
(Apple Macbook Pro 13 in. and accessories, $2250) and a 20TB QNAP RAID disk storage
device ($1250).
Publication Costs:
Publication costs/page charges of $3,500 per year (based on previous costs) are requested for one
journal article per year.
Consultant Services: None
Computer Services:
Scientific, computing and networking support costs have been allocated to this project through
the Computer Service Center (CSC), in accordance with OMB circulars and NCAR management
policy. The CSC rate for 2014 is $7.20 per labor hour.
Subawards: None
Other: None
F. Direct Costs: $132,055
H. Indirect Costs: $71,972 is requested for indirect costs.
Indirect Costs are applied to all modified total direct costs (MTDC). Excluded from MTDC are items
of equipment costing $5,000 or more, and individual subcontract amounts in excess of at least
$25,000 per fiscal year. The provisional FY14 rate for Indirect Costs is 58.8%. Cognizant Agency:
National Science Foundation (NSF).
I. Total Direct and Indirect Costs: $204,027 is requested for direct and indirect costs.
J. Fee: $6,121
A 3% UCAR management fee has also been included. The UCAR management fee is a fixed fee,
calculated as a percent of proposed MTDC and NCAR applied Indirect Costs.
K. Budget Total: $210,148
5-3
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
Management Fee
Total Funding To UCAR
Total MTDC + Applied Indirect Costs
UCAR Management Fee
Computing Service Center
Total Indirect Costs
MTDC Costs that Include IndirectComputing
Costs Service Center
Subtotal MTDC Costs that Include Indirect Costs
Domestic - To attend AGU Conference
Domestic - To attend ISPRS Conference
Computer / Laptop
Publication / Page Charges
General Materials
Computer Supplies & Peripherals
Regular Benefits @
ASSOC SCIENTIST III (TBD)
02-28-2017
NCAR Indirect Cost Rate (MTDC)
Modified Total Direct Costs (MTDC)
Subtotal Travel
Subtotal Materials and Supplies
Total Salaries and Benefits
Subtotal Fringe Benefits
Regular Salaries
Subtotal Salaries
2013-0060
Global modeling of the climatic suitability of artificial water containers for breeding the
dengue vector mosquito Aedes aegypti using remotely sensed data for present day and
climate change applications
NCAR
03-01-2014 DANIEL
STEINHOFF
Indirect Costs
Travel
Materials and Supplies
Fringe Benefits
Salaries
UCAR Entity:
Period of Performance:
Principle Investigator
Proposal Title:
Proposal #
3.00 %
$7.20 / hr
58.80 %
53.80 %
Unit / Rate
FTE
0.25
0.25
39,617
23,295
23,295
3,218
3,218
29,793
0
3,500
1,000
0
4,500
2,363
2,961
5,324
19,371
19,371
10,422
10,422
41,020
24,120
24,120
3,218
3,218
30,983
0
3,500
1,000
0
4,500
2,457
3,080
5,537
20,145
20,145
10,838
10,838
71,625 68,114 70,409
69,539 66,130 68,358
2,086 1,984 2,051
41,764
24,557
24,557
3,218
3,218
28,645
2,250
3,500
1,000
1,250
8,000
2,272
2,847
5,119
0.25 18,625
18,625
10,020
10,020
210,148
204,027
6,121
122,401
71,972
71,972
9,654
9,654
89,421
2,250
10,500
3,000
1,250
17,000
7,092
8,888
15,980
58,141
58,141
31,280
31,280
Effort Effort Effort Year 1 Year 2 Year 3 Cumulative Grand
Year 1 Year 2 Year 3 NASA NASA NASA
Total
UCAR Proposal Budget Detail
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
5-4
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
UCAR Standard Information
5-5
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
Rate Letter FY14
5-6
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-7
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-8
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-9
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-10
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-11
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-12
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-13
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-14
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.
ROSES 2013
NRA NNH13ZDA001N
<NIP>
<Global Modeling of Containers for Dengue Mosquito>
5-15
Use or disclosure of information contained on this sheet is subject to the restriction on the Cover Page of this proposal.