Spatial Modeling of Agricultural Land-Use Change at Global Scale

Transcription

1 NCAR IAM Group Annual Meeting, 19 Aug 013 Spatial Modeling of Agricultural Land-Use Change at Global Scale Prasanth Meiyappan PhD Candidate University of Illinois at Urbana-Champaign With contributions from Michael Dalton (NOAA), Brian O Neill (NCAR) & Atul Jain (U of I) Acknowledgement: NASA LCLUC Program

2 Why model land use at global scale Two key motivations Several key drivers of land use and its impacts have no regional boundaries and substantial feedbacks exist between them. Regions across the world are interconnected through global markets and trade that can shift the land requirements between regions.

3 Structure of IAMs Demographic, Markets, And Development Behavior Coarse resolution: world split into 9-4 regions Land-Use/Land-Cover Spatial Allocation Downscaling Biophysical Process Models Requires land information on uniform geographic grids: typically: 0.5 x 0.5 lat/lon

4 Objectives Develop a new land-use downscaling technique, with the following attributes (version 1) Address the mismatch between the scales at which landuse decisions are made and the scale at which global scale models are applied Account for variability in nature of driving factor Suitable for long-term projections Validated Can handle land-use competition Land-use representation using continuous field approach Meiyappan et al. (in prep)

5 Land-Use Allocation Framework

6 Econometric Framework for Land-Use Allocation Underlying Economic Motivation: Profit maximization of individual landowners at each grid cell thereby reflecting small scale decisions at larger scales Mathematical Formulation Component 1: Static Profit Maximization Function Maximize Maximize ( t t ) t ( t ) P W Y R Y l= 1 ( ) ( ) t t R Y S l = 1 Equation 1 Equation 'l' 't' ' g' t ' Y ' ( P t W t ) Y t Notations land-use type (1=crop, =pasture, 3= unmanaged land) time (year) grid cell area of land use net profit t Y 0 l= 1 Y t A g Grid cell level constraints ' R ( Y ' S t t ) ' ' α ' P t non-linear cost term W t '

7 Econometric Framework for Land-Use Allocation (Cont.) Component : Dynamic Adjustment Cost Model Minimize l= 1 Q ( t t 1) Y Y Equation 3 Notations ' Q ' Constant (adjustment cost per unit area) Overall Objective Function: Component 1 + Component Minimize l = 1 R ( t t ) ( t t 1) Y S + Q Y Y 3 Constraints imposed t Y 0 N t Y g 1 = l= 1 Y t A = total area demand g grid cell level constraints for land type ' l' within the aggregate region 1 regional level constraint

8 Regression Technique for Land Suitability Fractional Multinomial Logistic Regression (FMNL) Allows fractional outcomes More than two dependent variables can be modeled simultaneously 0 F 1 F = 3 e k = 1 β e 0l β + X 0k 3 l= 1 T + X F = 1 β l T k g β k 'l' 't' ' X ' S t = Notations land-use type (1=crop, =pasture, 3= unmanaged land) time (notation suppressed) Vector of driving factors A g F t World split into 35 distinct geopolitical regions; separate equations are derived for each region. Multicollinearity dealt using elastic-net regularization Spatial Autocoorelation - Autocovariate terms FMNL and Elastic-net merged using coordinate descent aorithm ' g' ' t F ' ' Ag ' grid cell Fraction of grid cell area Area of grid cell

9 Determining Local Land Suitability Broad Category Explanatory Factor Unit Climate Climate Variability Soil Characteristics Terrain Characteristics Seasonally averaged temperature Seasonally averaged precipitation Seasonally averaged Potential Evapotranspiration (PET) K mm/day mm/day Squared seasonally averaged temperature K Squared seasonally averaged precipitation mm /day Squared seasonally averaged PET mm /day Seasonal Temperature Humidity Index (THI) C Seasonal Palmer Drought Severity Index (PDSI) [-] Heat wave duration index Simple daily precipitation intensity index Rooting Conditions and Nutrient Retention Capacity Nutrient Availability Oxygen Availability Chemical Composition (indicates toxicities, salinity and sodicity) Workability (indicates texture, clay mineralogy and soil bulk-density) Elevation, Altitude and Slope Combined No of days mm/day [-] Socio-economic Spatial Autocorrelation Built-up/urban land area Urban population density Rural population density Rate of change in rural population density Rate of change in urban population density Market Influence Index Cropland Autocovariate Pastureland Autocovariate Fraction of grid area [m /m ] Inhabitants/km Inhabitants/km /yr International dollars/person Fraction of grid area [m /m ]

10 Historical Data for Explanatory Factors: Category Climate Soil Constraints Terrain Constraints Data Variable Temperature (T a ) Daily Average Maximum Temperature (T max ) Description/ Units o C Spatial Characteris tics Period of Availability degrees (monthly) Potential Evapotranspiration Millimeters (lat/lon) Precipitation CRU TS # Wet Day Frequency Palmer Drought Severity Index (PDSI) Rooting Conditions and Nutrient Retention Capacity Nutrient Availability Oxygen Availability Chemical Composition (indicates toxicities, Salinity and Sodicity) Workability (indicates texture, clay mineralogy and soil bulk-density) Elevation, Slope and Inclination Combined Urban/built-up land Urban Population Rural Population days No units Categorical Data classified into 7 gradient classes of land suitability for agriculture Categorical Data classified into 9 gradient classes % of grid-cell area Inhabitants/k m.5 (lat/lon) 5 minutes^ (lat/lon) 5 minutes^ (lat/lon) (monthly) (monthly) Constant with time Source Climatic Research Unit (CRU) TS 3.1 (updated estimates based on Mitchell and Jones, 005) CRU TS 3.0 & Dai et al. (011a,b) FAO/IIASA, 010. Global Agro-ecological Zones (GAEZ v3.0). FAO, Rome, Italy and IIASA, Laxenburg, Austria. 10,000 BC 005 AD (decadal) % Goldewijk et al. (010) Socio- Economic Factors Gross Domestic Product (GDP) per capita Constant 1990 international (Geary- Khamis) dollars/perso n National level 1 AD-010 (annually between ) $ Bolt and Van Zanden (013) (The Maddison Project - n-project/home.htm) Market Accessibility No units 1 km^ (lat/lon) ~005 Verburg et al. (011)

11 Historical Land-Use Data: Existing data sets are based on data-model fusion Klein Goldewijk et al. (011) HYDE reconstruction Ramankutty and Foley (1999) years of cropland data set Ramankutty et al., (008) - crop and pasture, circa 000 Ramankutty (01) updated data set version II

12 Regression Technique for Land Suitability Fractional Multinomial Logistic Regression (FMNL) Allows fractional outcomes More than two dependent variables can be modeled simultaneously 0 F 1 F = 3 e k = 1 β e 0l β + X 0k 3 l= 1 T + X F = 1 β l T k g β k 'l' 't' S t = Notations land-use type (1=crop, =pasture, 3= unmanaged land) A time (notation suppressed) g F t ' g' ' t F ' Ag ' ' grid cell Fraction of grid cell area Area of grid cell World split into 35 distinct geopolitical regions; separate equations are derived for each region. Multicollinearity dealt using elastic-net regularization Spatial Autocoorelation - Autocovariate terms FMNL and Elastic-net merged using coordinate descent aorithm Regression coefficients were standardized for comparison data used for fitting the FMNL regression

13 Results: Performance of Regression Technique

14 Results: Performance of Regression Technique Units: % of grid cell area

15 Spatial Characteristics of Explanatory Variables

16 Setup for Model Validation

17 Historical Land-Use Data Aggregation Nine regions based on PET model

18 Results from Model Validation Units: % of grid cell area

19 Model Estimated Net transitions: Units: km /yr

20 Determining Land-Use Transitions Meiyappan and Jain (01)

21 Carbon Emissions from ISAM Initial testing LEGENDS: DOWNSCALED HYDE RF HOUGHTON

22 How does a proportional downscaling method perform historically Minimize l = 1 R ( t t ) ( t t 1) Y S + Q Y Y Eliminate R ( t t ) Y S Minimize l= 1 Q ( t t 1) Y Y

23 How does a proportional downscaling method perform historically

24 How does a proportional downscaling method perform historically Net transitions: Units: % of grid cell area

25 Applications from our Validation Experiment Spatial determinants of existing land-use patterns

26 Applications from our Validation Experiment How different driving factors change with the scale of analysis?

27 Applications from our Validation Experiment How different driving factors contributed to the 0 th century landuse patterns? Units: % of grid cell area

28 Explaining the causes of historical land-use change patterns (cont.) Units: % of grid cell area

29 Discussion Methodological Advantages Suited for long-term projections Continuous field approach State of the art elastic-net for multicollinearity Handles land-use competition consistently

30 Discussion Known Issues/Limitations Irrigation not included due to data limitations Crude method to calibrate the relative weighing between static profit maximization function and the dynamic cost adjustment term Further room available for methodological improvements Land-Use Intensification

31 The Big Picture: Coupled Modeling Framework

32

33 Downscaling in Current Approaches IMAGE, MagPie, MIT-EPPA follows the approach of traditional geographic models; empirical rules based on current land use are assumed to hold true for the future (up to 100) GLOBIO3, iesm-glm land use demands allocated as close as possible to existing land-use patterns In common The downscaling aorithms have not been validated in the time scales at which they are applied for [van Asselen & Verburg, 013] Land-use competition handled implicitly or not at all considered [Heistermann et al., 006]

34 Land cover/use representation in IAMs Typically 0.5 or coarser IMAGE MagPie GLOBIOM Nexus land-use model IMAGE LandSHIFT - 5 min IMAGE - 5 min van Asselen & Verburg (013); Verburg et al. (01)