Global Land Use Modeling for
Analysis of the Food-EnergyWater Nexus
Thomas W. Hertel
Purdue University
Building on collaborations with
Uris Baldos, Keith Fuglie, Jing Liu, David Lobell, Farzad Taheripour and Nelson Villoria
Presentation to the NSF FEWS workshop, Iowa State University, October 12, 2015
Global scale analysis of FEWS is critical
• We grow food for people & most population growth
will be in developing countries
• Projected growth in water demand and scarcity is
also highest in developing countries
• Agricultural impacts of climate change will be most
severe in tropics
• Environmental degradation is most severe in
developing world; also lowest cost mitigation of
global environmental externalities (e.g. GHGs)
• In the past we could analyze US agriculture largely
isolation; no longer possible due to globalization
Outline
• Conceptualizing the nexus
• Modeling cross-system linkages at global scale,
challenges, opportunities:
– Water-Food linkages
– Adding energy: FEWS
– Adding Climate Change
• Knowledge gaps
• Data and cyberinfrastructure needs
Fracking!
Biofuels
Power
Generation
Pumping
Costs
Conceptualizing the FEWS nexus
Source: Liu et al. (2014)
Outline
• Conceptualizing the nexus
• Modeling cross-system linkages: challenges,
opportunities:
– Water-Food linkages
– Adding energy: FEWS
– Adding Climate Change
• Knowledge gaps
• Data and cyberinfrastructure needs
Irrigated Agriculture:
The Dominant Water Use
• Each calorie produced requires roughly 1 liter of water through crop
evapotranspiration; feeding the world each year requires enough water
to fill a canal 10m deep and 100m wide encircling the globe 193 times!
• Four-fifths is rainwater, one-fifth is irrigation water; accounts for 70% of
global freshwater withdrawals
• Irrigated area accounts for nearly 20% of cropland and 40% of production
Groundwater irrigation has become increasingly important
•
•
•
•
•
•
Accessible without large scale
government initiatives at low
capital cost (although high
operating costs)
Offers irrigation on demand
Reliability in time and space: low
transmission and storage losses
Drought resilience; surface water
not available during drought
If undertaken in areas with high
recharge rates, then it can be
sustainable
Unfortunately this is often not the
case
Most rapid growth has been in arid
areas with low recharge rates
8
Source: cited in Burke and Villholth
Water
scarcity
What happens when water
becomes scarce? The
Australian experience
•
•
•
•
Drought in 2002/3 led to a 29% drop
in water usage in the Murray-Darling
Basin
However, water used in irrigated rice
production dropped by 70%
Early analysis predicted only modest
declines in irrigation water usage;
missed the potential for:
• Shifting land to rainfed
production
• Importing rice from other
regions
Adaptation possible due to
introduction of water trading 1980’s
“Flexibility facilitated by water trading: when water is
available, produce rice. When it is scarce, sell water
rights instead of growing rice!” (Will Fargher, National
Water Commission)
Scarcity at global scale is a larger challenge
Index of irrigation water availability
Year 2000
Increased water scarcity
of water for irrigation –
particularly in South Asia
and China
Year 2030
Source: Liu et al. (2014)
Water scarcity leads to reductions in irrigated area in key
producing regions – shortfalls in supply
…. Leading to more land conversions and GHGs
Source: Liu et al. (2014)
Food trade is a key adaptation to water scarcity
Index of irrigation water availability
Year 2000
Increasing water scarcity alters the
geography of food trade
Year 2030
Source: Liu et al. (2014)
Regions facing the most severe water scarcity are
most likely to increase net food imports
Outline
• Conceptualizing the nexus
• Modeling cross-system linkages: challenges,
opportunities:
– Water-Food linkages
– Adding energy: FEWS
– Adding Climate Change
• Knowledge gaps
• Data and cyberinfrastructure needs
Biofuels
Predicting land use change and associated GHG emissions
from US ethanol
• Estimate induced land use
change owing to expansion of
US ethanol production from
1.75 bgy to 15 bgy
• Distinguish irrigated and
rainfed crop production, by
Agro-Ecological Zone
• Unconstrained case: expand
irrigation where profitable
Source: Taheripour et al. (2013)
Bulk of irrigated corn in US draws on High
Plains aquifers
Source: NRC report
15
Yet some of these aquifers are being overdrawn; others are
restricting further draw-downs
FIGURE 5-13 Usable lifetime of the High Plains Aquifer in Kansas estimated on the basis of
groundwater trends from 1996-2006 and the minimum saturated requirements to support well
yields of 400 gallons per minute. Source: NAS report, 2011, taken from DOE-EERE.
16
And Water Scarcity is a Global Problem
Source: IWMI website
17
Land use change and GHG emissions from US ethanol
with irrigation constrained in areas of physical scarcity
• When constrain irrigation
expansion in water scarce
regions:
– More expansion of cropland
area (rainfed yields are lower)
– Tend to expand more in wetter,
more carbon rich AEZs
• For case of US ethanol-driven
expansion we find that ILUC
GHG emissions rise by 25%
when accounting for future
water constraints
Source: Taheripour et al. (2013)
Geography of international trade is also important
When USA experiences a supply drop (drought/flooding)
or a demand increase (biofuels); where does production
respond in the rest of the world?
Naïve, integrated
world markets
overstates land-based
GHG emissions due to
US ethanol production
by 2x by stimulating
too much output in
regions with low
emissions efficiencies
Source: Villoria and Hertel (2011)
Predictions (95% CI) of
additional harvested area
due to 1993 crop shortfall
in USA
Outline
• Conceptualizing the nexus
• Modeling cross-system linkages: challenges,
opportunities:
– Water-Food linkages
– Adding energy: FEWS
– Adding Climate Change
• Knowledge gaps
• Data and cyberinfrastructure needs
Climate change is already reducing yields of
some crops
Source: AR5, as presented by CSIRO/Mark Howden for the IPCC Food Security
Summit, Dublin, May 2015
Impacts vary greatly by crop, location and adaptation
actions, but become predominantly negative by 2100
Source: AR5, Fig. SPM.7
Tropics will be hit hardest, agronomic
adaptation could play important role
Source: AR5-WGII, Fig. 7-4. Dots show CO2 effects included; X’s ignore this effect.
Shaded area represents 95% CI from non-parametric regressions. Adaptation = solely
agronomic adaptation
Adaptation depends on R&D investments: proven means of
boosting productivity, but take time to have impact
Calibrated using
best fit model
from given U.S.
experience from
Alston et al. (2011)
From year 1 to 24, the contribution of an outlay in year 0 to stock is increasing so
incremental TFP index is rising
After the peak year, the contribution of expenditure in year 0 is falling so the growth
rate of the incremental TFP is negative, TFP index returns to zero in year 50
Costs of adaptation inherit uncertainty and
reflect magnitude of CC impacts…
in B 2005 USD$
LPJmL Crop Model
in B 2005 USD$
pDDSAT Crop Model
HADGEM
Source: Baldos et al. (2015)
IPSL
MIROC
Global Circulation Models
GFDL
NORESM
… which also influence economic returns to adaptation.
Bulk of the benefits accrue after 2050.
in B 2005 USD$
LPJmL Crop Model
in B 2005 USD$
pDDSAT Crop Model
HADGEM
Source: Baldos et al. (2015)
IPSL
MIROC
Global Circulation Models
GFDL
NORESM
Climate adaptation also provides
environmental and food security co-benefits
Market integration tends to lead to
more extreme outcomes; GHGs
actually rise in two cases
Climate adaptation generally reduces
GHG emissions from cropland conversion
Source: Baldos et al. (2015)
Climate adaptation also provides
environmental and food security co-benefits (cont’d)
CC adaptation is even more important for
food security if market barriers persists
Climate adaptation improves
food security
Source: Baldos et al. (2015)
Cost of achieving mitigation through adaptation
Which parameters drive this uncertainty?
All Regions Adapt: $16.7/tCO2e
LAmer and SS_Afr Only Adapt: $36.2/tCO2e
Top three parameters:
- TFP elasticity wrt R&D
- Terrestrial carbon factor
- Land supply elasticity
(distribution reflects parameter uncertainty)
Lobell, Baldos & Hertel, ERL (2013)
Outline
• Conceptualizing the nexus
• Modeling cross-system linkages: challenges,
opportunities:
– Water-Food linkages
– Adding energy: FEWS
– Adding Climate Change
• Knowledge gaps
• Data and cyberinfrastructure needs
FEWS knowledge gaps
• Understanding current institutions and potential for revision of
water allocation rules; crises can precipitate reform!
• Current valuation of water, by sector: prices are largely unknown
• Analytical frameworks to identify key parameters / econometric
investigations of those parameters
– Key parameters depend on the problem being addressed
– Contribution to uncertainty depends on current state of
knowledge about the parameter distribution – parameter
could be very influential, but distribution is relatively tight so
further research is a lower priority
FEWS data needs
• Consistent global, gridded data on rainfed and irrigated areas,
yields, and water use, by sector (GEOSHARE)
Estimates of
irrigated area
in India differ by
a factor of two!
IWMI (113 Mha)
(2001-2003)
Based on remote sensing
FAO (66 Mha)
(2008)
Based on census/surveys
Workflows on GEOSHARE HUB
• Gridded source data on land
cover & use, water, poverty and
environment - flows into:
• Data reconciliation ‘models’,
which produce usable data - for
use in….
• Biophysical and economic
models (e.g., DSSAT, IMPACT,
SIMPLE, GTAP)
Data
Data Reconciliation:
Data Fusion
Source Data
Land Cover
Land Use
Water
Poverty
Environment
Sensitivity of results at final stage
determines value of improving quality
of source data
DSSAT
IMPACT
FEWS data needs
Source: World Meteorological Organization and NOAA Global Summary of the Day database
1048 stations with at least
3-yrs daily weather data
706 stations with at least
15-yrs daily weather data
126 stations with 15-yrs
daily weather data, <10%
missing days, and no gaps
with >consecutive days
References