View
24
Download
1
Category
Preview:
Citation preview
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
112W 108W 104W 100W
20N
24N
28N
32N
0
0.1
0.2
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Terr
ain
elev
atio
ns(k
m)
AZNM
NWMX
CNMX
Sierra Madre Occidental
●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.
-2.0
-1.0
-0.5
-0.2
-0.1
-0.01
0.01 0.1
0.2
0.5
1.0
2.0
Rainfall (mm/d)
Total precipitation Convective rainfall
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.
,,
-10
-7 -4 -2 -1 -0.5 0.5 1 2 4 7 10DMA8 [O3] differences (ppb)
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)
510
-5-10
-30
-50
30
5040
20
-20
-40
[CO
] cha
nges
(ppb
)
-1-2-4-6-8
-10
12468
10
[NO
x] c
hang
es (p
pb)
-1-2-4-6-8
-10
12468
10
[VO
C ]
chan
ges (
ppb)
[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.
0 5 10 15 20 25 30 35 40
-250
-200
-150
-100
-50
0
Cumulative ozone (ppm hr)
Acc
. Tra
nsp.
Diff
(mm
)
▬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
1 4 7 10 13 160
1
2
3Chart Title
07 08 09 10 11 12
T2 c
hang
es (o C
)
Year0 3 6 9 12 15 18 21 0
0
1
2
3NOSLOP-O40
Tem
pera
ture
cha
nges
(o C)
GMT time
-0.1-0.2-0.5-1.0-2.0-3.0 0.1 0.2 0.5 1.0 2.0 3.02-m Temp. changes (oC)
Mean Temperature changes Diurnal cycle change Interannual variations
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).
-0.1-0.2-0.5-1.0-2.0-3.0 0.1 0.2 0.5 1.0 2.0 3.0Changes (mm/d)
Mean precipitation changes Interannual variations
-3
-2
-1
0
Rai
nfal
l cha
nges
(mm
/d)
07 08 09 10 11 12
Year
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
+***
+**Educatio
n
+***+ **
Bivariate Ordered Probit
Note: ***, ** , 1%, 5% significance level.
Attitude F2:Food /
attribute+ **
Attitude FB:UF: new, fit,
frugal
+ ***
Recommended