Global Land Use Modeling for Analysis of the

Global Land Use Modeling for Analysis of the

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! Fracking! Biofuels Biofuels Power Power Generation

Generation Pumping Pumping Costs 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 Water scarcity 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 1980s 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 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? Nave, integrated world markets overstates landbased 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; Xs 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 (contd) 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) Source Data Land Cover Land Use Water Poverty Environment Sensitivity of results at final stage determines value of improving quality of source data

DSSAT Data Data Reconciliation: Data Fusion 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

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