Earth System Models-3: Physics-Based Predictive Modeling for Integrated Agricultural and Urban Applications

Project Directors: Alex Mahalov, Arizona State University and Fei Chen, NCAR

Objectives

● Develop an integrated agricultural and urban modeling system

● Characterize decadal and regional impacts associated with agriculture/urban expansion for selected regions in the continental US

● Examine socio-economic impacts associated with agri-urban development including urban farms/community gardens

● Educate next generation of interdisciplinary scientists

Approach

● Physics based predictive modeling and data development supporting agricultural management strategies and policy decisions at multiple scales

● Advanced modeling system includes crop modeling capabilities embedded in a land surface Noah-MP/biogeochemistry/hydrology model with tiling for accommodating a mixture of crop/urban landscapes

● High resolution USDA National Agriculture Imagery Program (NAIP) datasets are integrated in data development

Impact

● Developed a new paradigm for studies of linked regional agricultural and urban systems on decadal time scales

● Assessment of agri-urban development pathways● Created advanced physical and cyberinfrastructure to

support continued integration across disciplines ● The integrated agricultural and urban modeling system

will be released for community use

USDA-NIFA awards # 2015-67003-23508and 2015-67003-23460; NSF # 1419593

NAIP Dataset

REPRESENTATIVE PUBLICATIONS in FY 2016 (from a total of 18 published papers)

Li, Mahalov and Hyde, Simulating the impacts of chronic ozone exposure on plant’s conductance and photosynthesis, and on hydroclimate in the continental U.S., Environ. Res. Lett. 11, 114017, doi:10.1088/1748-9326/11/11/114017D-15-02, 2016.

Mahalov, Li and Hyde, Regional impacts of irrigation in Mexico and southwestern U.S. on hydrometeorological fields in the North American Monsoon region, Journal of Hydrometeorology, American Meteorological Society, published DOI: http://dx.doi.org/10.1175/JHM-23.1, 2016.

Li, Mahalov and Hyde, Impact of agricultural irrigation on ozone concentrations in the Central Valley of California and in the contiguous United States based on WRF-Chem simulations. J. Agricultural and Forest Meteorology, pp. 34-49.DOI: 10.1016/j.agrformet.2016.02.004, 2016.

Shaffer, Moustaoui, Mahalov and Ruddell, A method of aggregating heterogeneous subgrid land-cover input data for multiscale urban parameterization, Journal of Applied Meteorology and Climatology, 55, 1889-1905, 2016.

Li, Middel, Harlan, Brazel, Turner, Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral demographic—Economic factors. Remote Sens. Environ. 174, 233–243, 2016.

Salamanca, Georgescu, Mahalov, Moustaoui, and Martilli, Citywide impacts of cool roof and rooftop solar photovoltaic deployment on near-surface air temperature and cooling energy demand, Boundary-Layer Meteorology, doi: 10.1007/s10546-016-0160-y, 2016.

Feedback Loops: Agricultural irrigation affects North American monsoon(NAM) rainfall

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●The irrigated lands in the NAM region comprise 22.67 million acres and consume about 70.789 million acre-feet water per year.

●First time in the scientific community to quantify how agricultural irrigation in SW US and Mexico affects North American monsoon using a modified WRF/Chem model.

Agricultural irrigation lands (Marked as Blue) and urban lands (gray)

▬Regional impacts of irrigation in Mexico and southwestern U.S. on hydrometeorological fields in the North American Monsoon region, J. Hydrometeorology., 17,2982-2995, 2016.

Agricultural irrigation affects North American monsoon rainfall

Key findings: ● Irrigation modifies rainfall which varies with location and NAM rainfall variability.

● Irrigation increases rainfall in eastern Arizona--western New Mexico and in northwestern Mexico.

● Irrigation decreases rainfall in western Arizona, along the western slope of the SMO, and in central Mexico.

● Irrigation modifies convective rainfall.

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Irrigation-induced precipitation changes (2000-2012)

Agricultural irrigation also affects atmospheric chemistry in the lower troposphere: Changes of maximum 8 hr daily (DMA8) ozone concentrations ([O3])

▬Impacts of agricultural irrigation on ozone concentrations in the Central Valley of California and in the contiguous United States based on WRF-Chem simulations, J. Agricultural and Forest Meteorology, 221, 34-49, 2016.

,,

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Ozone changes 4-km resolution Ozone changes at 12-km resolution

● Irrigation decreases surface DMA8 [O3] up to 0.5-5 ppb in the irrigated areas in California’s Central Valley.

●Irrigation increases surface DMA8 [O3] up to 0.5-7 ppb in non-irrigated areas of California’s Central Valley.

●The conclusion can be extended to the contiguous U.S.

Key findings:

Effects of agricultural irrigation on atmospheric chemistry in the lower troposphere: changes of Carbon Monoxide (CO), Nitrogen Oxide (NOx) and Volatile Organic Compounds (VOC)

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[CO] changes [NOx] changes [VOCs] changes

● Increases surface [CO] up to 40 ppb with an irrigated grid average of 16 ppb or

8.3%;

● Increase [VOC] up to 10 ppb with an irrigated grid average of 4.6 ppb or 21.4%; and

● Increase [NOx] up to 4 ppb with irrigated grid average of 0.72 ppb or 12.6%.

Agricultural irrigation:

Key findings:

Atmospheric compound (here chronic ozone) variations modify hydroclimate through non-radiative forcing effects: A new feedback loop

● Ozone can penetrate the leaves of plants through the stomata to:

▬ oxidize plant tissue, ▬ impair photosynthesis, ▬ affect the metabolic activity, and ▬ reduce stomatal conductance;

● The non-radiative effects of chronic ozone exposures on surface temperature and precipitation, both of which affect vegetation’s transpiration and photosynthesis as well as photochemical reaction rates and ultimately [O3] themselves, have first time been investigated using a modified two-way coupled model system.

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▬Simulating the impacts of chronic ozone exposure on plant conductance and photosynthesis, and on the regional hydroclimate using modified WRF/Chem, Environmental Research Letters, 11 (2016), 114017.

Chronic ozone exposures decrease transpiration

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Chronic ozone exposures

● Increase surface temperatures up to 0.5-2 oC on average;

● Increase daily temperature ranges up to 0.4-1oC; and

● Result in temperature change experiencing interannual variations.

Chronic ozone variations modify temperature through non-radiative forcing effects

Key findings:

Chronic ozone exposures

● Decrease precipitation (mainly convective rainfall) up to 0.2-1.0 mm/d on average;

● Result in precipitation change experiencing interannual variations; and

● Change precipitation features (diurnal cycle, precipitation type).

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Mean precipitation changes Interannual variations

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Chronic ozone variations modify precipitation through non-radiative forcing effects

Key findings:

Summary● A two-way, multiple-scale, and process-based multi-physics model system (including

atmospheric physics, atmospheric chemistry, biogeochemistry, land cover and land use changes, and their interactions) is developed based on Weather Research and Forecasting (WRF) model with Chemistry (WRF/Chem).

● The model’s performance is validated against observations from ground as well as from remote sensing data and its improvement is documented comparing with the model results without modifications.

● The modified model system has been applied to investigate the effects of agriculture on hydroclimate and atmospheric chemistry, and effects of atmospheric chemistry on agriculture and hydroclimate at regional to continental scale.

● Nonlinear Feedback Loops: interactions of agriculture (including productivity), hydroclimate and atmospheric processes at crop field-scale.

High Resolution National Agriculture Imagery Program (NAIP) Datasets are Integrated in Data Development. Example: recoding of the land-cover map for Baltimore County, 1m resolution

Black lines arecity boundaries

Extent of the yellow polygon60 * 73 sqkm

Study area selection of the Central California, 1-m resolution classification

Land architecture affects land surface temperature (LST) of residential parcels.

Land-cover composition has the largest effect on LST but land-cover configuration is significant.

Compact and concentrated land-covers, foremost vegetation, improves nighttime cooling.

Large land-cover units of irregular shape improve daytime cooling.

Parcel level land architecture can be used to mitigate the LST of residences.

Examples of the land cover of Phoenix neighborhoods (1 m) and their land surface temperatures (LST) (6.8 m). (a) Low-level (xeric) and (b) high level (mesic) vegetated neighborhoods; (c) daytime LST of xeric and (d) nighttime LST of mesic neighborhoods.

(a) (b)

(c) (d)

SUMMARYData Development: land identification from high resolution remote sensing imagery

• Identified vacant land for potential urban agriculture applications over large metropolitan areas by developing an accurate, replicable method utilizing remote sensing data and cadastral data:

• Cadastral data alone does not provide information on parcel physical conditions and are not always correct or up-to-date;

• Remote sensing alone cannot discern parcel boundaries, is time consuming, and has difficulty classifying some land-covers in urban areas.

USDA National Agriculture Imagery Program (NAIP) datasets are integrated in data development

Consumer Behavior as a Success Factor of Urban Farming

Carola Grebitus, Co-PD

Arizona State UniversityMorrison School of Agribusiness

 

Research ObjectiveLinking consumer behavior to urban farming successMotivation

• Increasing urban population leads to a need of raising overall food production

• One solution: converting available land to agricultural landscapes

To make urban farming successful, consumer demand is necessary

Creating consumer demand: Consumers have to be able to perceive urban farming as a viable source for produce

Research Questions1. How do consumers

generally perceive urban farming?

2. What is consumers subjective knowledge re: urban farming?

3. Do consumers hold positive attitudes towards urban farming?

4. How do these factors influence whether consumers are likely to buy produce from an urban farm andlikely to grow their own produce at an urban farm?

Online survey: N=325

Consumers’ perception of urban farmingFree elicitation technique

“What comes into your mind when you think of urban gardens…”

Total of 478 different concepts• Single terms (e.g., nature) • Whole phrases (e.g., “A place where people share

something…”)Grouped into 6 categories

Consumers’ perception of urban farming

Other: 8% (e.g., good idea,

not used enough)

Point of sale: 6% (e.g., CSAs,

farmers markets)

Environment: 15%

(e.g., earthfriendly, sustainable)

Society: 16% (e.g., helping &

supporting local

community)

Economy: 16%(e.g., expensive, higher cost; cheap/cost saving)

Food & Attributes: 38% (e.g., organic, healthy)

Urban farming

Consumers’ subjective knowledge on urban farming

Feeling informed about …

Scale from 1=no knowledge to 5=very knowledgeable

Attitudes towards urban farming

Factor A: Urban Farming is better for me

Factor B: Urban Farming: new, fit, frugal

• Urban farming allows me to eat more fruits and vegetables

• When going to an urban farm I spend less money on food

• Urban farming helps me to care more about the environment

• Because of urban farming I am more physically active

• Urban farming helps me to learn more about gardening

• Urban farming allows me to eat new kinds of food

• Urban farming allows me to eat more organic food

• Urban farming helps me make new friends

Reasons that prevent or encourage purchase of produce from urban farms

Factor 1: Healthy individual,

economy and environment

Factor 2: Foods and attributes

Factor 3: Cost and

inconvenience

Health Food Safety CostFreshness Variety available Convenience

Support economy TasteTime

commitment

Support environment Variety in generalDistance traveled

Too much work

Likelihood to buy produce from urban farms

60%

N=325

Likelihood to participate in growing produce at urban farms

44%

N=325

Bivariate ordered probitBehavioral success factors of buying and growing at urban farms

Attitude F1:UF healthier

Attitude F3:Cost &

Convenience

BuyingGrowing

+ ***

+ ***

Perceived knowledg

eGeneral positive attitude

(FA)

+ **

Gender (F)

Age

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+**Educatio

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Bivariate Ordered Probit

Note: ***, ** , 1%, 5% significance level.

Attitude F2:Food /

attribute+ **

Attitude FB:UF: new, fit,

frugal

+ ***