Climate–Human–Land Interactions: A Review of Major Modelling Approaches
Abstract
:1. Introduction
2. Modelling Land and Its Feedbacks: Different Approaches to Dealing with the Same Problem
Strengths | Limitations | |
---|---|---|
Geographic/Land-Cover Models | Spatial dimension of land-use change; Biophysical constraints on land-use change. | No endogenous economics; No endogenous land-use change; No global analysis; No feedbacks with climate nor with the economic system. |
Economic/Land-Use Models | Endogenous land allocation mostly based on economic theory; Opportunity costs explicitly considered; Consideration of markets interactions. | No spatial assessment; No physical constraints or biophysical land characteristics; Land-use allocation entirely driven by market structure. No feedbacks with climate nor with biophysical aspects of the land system. |
Model Linkages | Economy linked with biosphere and atmosphere in a unique framework; Ability to account for feedbacks amongst human and physical systems. Synergies and trade-offs of different policy strategies; Long-time scale analysis. | High complexity and demanding for computer power; Sacrifices a detailed representation of land processes; Linking models maintain details but require much harmonization to reach convergence; Difficult to perform uncertainty analysis. |
- Standalone Models
- Geographical/land-cover models
- Statistical models
- Rule-based models
- Economic/land-use change models
- Econometric models
- Partial equilibrium models
- General equilibrium models
- Model Linkages and Integration
- C.
- Linked or integrated models
- Earth System Models
- Integrated Assessment Models
3. Geographic/Spatial Frameworks for Land-Cover Change Analysis
Model Name | Type of Model | Nature of Model | Land-Use Type | Geographic Scale | Dynamics Technique | Temporal Dimension |
---|---|---|---|---|---|---|
KLUM [32] | Optimization model/Rule-based model | Geographic model; allocation rules based on profit maximization | Agriculture | Global | Static | Base year 1997; Analysis 1997–2050 |
ACCELERATES [30] | Optimization model/Rule-based model/IAM | Geographic model: allocation rules based on profit maximization | Mainly agriculture | Macro-Regional or other local areas | Comparative static | Analysis 2000–2050 |
CLUE [24,25,33,34,35,36,37,38] | Statistical/Simulation Model | Geographic model | Multiple land-use types | Regional areas | Systems dynamics model/ statistical techniques | Several decades’ analysis-20–40 years. |
ELPEN-System ,26] | Statistical/Simulation Model | Geographic model | Agriculture-Livestock sector | Europe | multiple linear regression model | Base year: 1997 and 2000 |
SALU ,27,28] | Rule-based model | Geographic model | Agriculture | Sahel area | Dynamic simulation model | Up to some decades of analysis |
4. Economic Frameworks for Land-Use Change Analysis
4.1. Econometric Models and Ricardian Approach
4.2. Equilibrium Approaches. A General Overview
4.2.1. Partial Equilibrium Models (PEMs), Some Examples
4.2.2. General Equilibrium Models (GEMs), Some Examples
Model Name | Type of Model | Nature of Model | Land-Use Type | Geographic Scale | Dynamics | Temporal Dimension |
---|---|---|---|---|---|---|
FASOM-GHG | PEM | Economic model | Agriculture, Forestry, Grazing. Good treatment of forestry | USA in 11 regions | Dynamic-perfect foresight, nonlinear programming | Base year: 2000. 10 yr time step. 10-yr analysis |
WATSIM | PEM | Economic model | Agriculture | Global: 9 regions | Quasi-dynamic model. No price expectations | Base year: 2000 5 yr time step |
GTM | PEM | Economic model | Timber sector | Global: 12 regions | Intertemporal optimization with perfect foresight | 1 yr time step; Analysis 1990–2140 |
IMPACT-Water | PEM | Economic model | Agriculture | Global: 36 regions | Comparative static | Base year: 2000. 1 yr time step. Analysis in 2020/2025/2050 |
AgLU | PEM | Economic model with focus on land use | Agriculture, Forestry, Grazing | Global: 11 regions | Comparative static | Base year: 1990. 15 yr time step. Analysis 1990–2096 |
CAPRI and CAPRI-DynaSpat | PEM | Economic model | Agriculture | EU15-EU27 | Comparative static, solved by iterating supply and market modules | Base year: 2002. 5–10 yr analysis. Specific cases of 20 yr analysis scenario |
GLOBIOM | PEM | Economic model, good focus on land use | Agriculture, Forestry, Livestock, Bioenergy production | Global: 11 or 27 regions | Recursive Dynamic | Base year: 2000; Analysis up to 2030, 2050. 10 yr time step: |
GTAP | CGE | Economic model | Agriculture | Global: latest version (GTAP7) accounts for 113 regions | Comparative static | Max 50 yr projections |
G-cubed | CGE | Economic model | Agriculture | Global:12 regions | Dynamic | Analysis 1993–2070 in 1 yr time step |
GTAPE-L | CGE | Economic model | Competition among different land uses: agriculture, forestry and other sectors | Global: 5 regions | Comparative static | Base year:1997 |
GTAPEM | CGE | Economic model | Agriculture | Global: 7 regions | Comparative static | Base year: 2001. Analysis: 2001–2020 |
GTAP-AGR | CGE | Economic model | Agriculture + explicit substitution amongst feedstuff in livestock | Global: 23 regions | Comparative static | Base year1997 |
BLS-IIASA | CGE | Economic model | Focus on agriculture and pastureland | Global: 34 regions | Recursive dynamic | Base year2000. 1 yr time step |
GTAP-AEZ | CGE | Economic model | Agriculture, Forestry, Grazing | Global: 3 regions | Comparative static | Base year: 2001. Max 50 yr projections |
GTAP-Dyn | CGE | Economic model | Agriculture, Forestry, Grazing | Global: 11 regions | Recursive Dynamic | Base year: 1997; Analysis: 1997–2025. |
AgLU2x | CGE | Economic model + mapped watersheds | Agriculture, Forestry, Grazing | USA in 18 regions | steady-state comparisons consistent with an intertemporal model for forestry | Base year: 1990 Model in steady state |
ICES-AEZ | CGE | Economic model | Agriculture, Forestry, Grazing | Global: 13 regions | Static Comparative Exercise | Base year: 2001 Analysis: 2001–2025. |
MIT-EPPA | CGE | Economic model | Agriculture, Forestry, Grazing, Bioenergy production | Global: 16 regions | Recursive Dynamic | Base year: 1997; Analysis: 1997–2100; 5 yr time step |
BLS-IIASA | CGE | Economic model + agronomic-based datasets | Agriculture, Forestry, Grazing, Bioenergy production | Global: 34 regions grouped in 11 | Recursive Dynamic | Base year: 2000; Analysis: 1990–2080. 10 yr time step |
STRENGTHS | LIMITATIONS | Examples | ||||
---|---|---|---|---|---|---|
GEOGRAPHIC MODELS | Statistical models | Multiple land-use drivers considered; Multiple land-cover types considered. | Driving factors assumed exogenous; Not endogenous land allocation or climatic change; Very limited feedback effects, if any. | Normally short-run and non-global analysis. | CLUE and Dyna-CLUE, ELPEN | |
Rule-based models | More explicit assessment of land processes and drivers w.r.t. Statistical Models; Multiple rules considered; Multiple land-cover types considered. | Rules based on subjective judgments. | SALU, EFISCEN, ACCELETATES*, KLUM* | |||
ECONOMIC MODELS | Econometric models | Econometric | Multiple land-use drivers; Multiple land-cover types considered; Agents’ reactions under similar or different policy scenarios. | Technology and climate variability not always considered; Need to deal with problems of endogeneity and reverse causality; normally short-run, local and small sample analysis. | Stavins [39], Plantinga and Mauldin [40], Lubowski et al. [41], Pfaff et al. [42], Munroe and Muller [43] | |
Ricardian Analysis | Multiple land-use drivers; Multiple land-cover types considered; Greater focus on climate variability w.r.t. Econometric Models; Recently extended to panel-data analysis. | Ignore technology change; No global analysis; Very limited feedback effects. | Sanghi and Mendelsohn [49], Mendelsohn and Dinar [50] | |||
Optimization and Equilibrium Approaches | Partial equilibrium models | Multiple land-use drivers; Agents’ reactions under similar or different policy scenarios; Good detail in land-using markets; Land allocation endogenously derived w.r.t. Econometric and Ricardian Analysis, so that economic feedbacks are accounted for; Often global and forward-looking models. | Only a part of the economy is modelled and represented; Models not frequently validated; Agents’ preferences on land allocation assumed to be the same; Climate and biophysics have rarely a time-variant impact on land differences and productivity; | CAPRI, IMPACT-Water, WATSIM, AgLU, FASOM, GTM, GLOBIOM° | ||
General equilibrium models | Agents’ reactions under similar or different policy scenarios; Compared with Econometric and Ricardian Analysis, land allocation among land covers endogenously derived; Compared with Partial Equilibrium Models all the economy is considered; Global scale investigations. | Land exclusive input for agriculture, represented as value added to production; Normally, only currently managed land is represented: land is not allowed to expand; Less detailed production description compared with Partial Equilibrium models; Identical agents’ preferences on land allocation within regions and sectors; Climate and biophysics have normally no impact on land differences and productivity. | G-cubed, GTAPE-L, GTAPEM, GTAP-AGR, GTAP-AEZ, GTAP-Dyn, AgLU2x |
5. Model Linkages
5.1. Model Linkages with Focus on Biophysical and Biogeochemical Processes; Climate and Earth System Models
5.1.1. First-Generation Land Surface Models
5.1.2. Second-Generation Land Surface Models
5.1.3. Dynamical Global Vegetation Models and Third-Generation Land Surface Models
Model | Modelling Center | Atmospheric Resolution | Land-Carbon | Reference | |||||
---|---|---|---|---|---|---|---|---|---|
Model Name | Dynamic Vegetation Cover? | No. of PFTs | Inclusion of Land-use Change | Nitrogen-Cycle | Fire | ||||
BCC-CSM1.1 | BCC | ~2.8°, L26 | BCC_AVIM1.0 | N | 15 | N | N | Wu et al. [122] | |
CanESM2 | CCCma | T63, L35 | CTEM | N | 9 | Y | N | N | Arora et al. [123] |
CESM1-BGC | NSF-DOE-NCAR | FV 0.9 × 1.25 | CLM4 | N | 15 | Y | Y | Y | Long et al. [124] |
GFDL-ESM2G | NOAA GFDL | 2 × 2.5°, L24 | LM3 | LM3 | 5 | Y | N | Y | Dunne et al. [125] |
GFDL-ESM2M | NOAA GFDL | 2 × 2.5°, L25 | LM4 | LM3 | 5 | Y | N | Y | Dunne et al. [125] |
HadGEM-ES | MOHC | N96 (~1.6°), L38 | JULES | Y | 5 | Y | N | N | Collins et al. [126]; Jones et al. [127] |
IPSL-CM5A-LR | IPSL | 3.75 × 1.9, L39 | ORCHIDEE | N | 13 | Y | N | Y | Dufresne et al. [128] |
MIROC-ESM | MIROC | T42, L80 | SEIB-DGVM | Y | 13 | Y | N | N | Watanabe et al. [129] |
MPI-ESM-LR | MPI-M | T63 (~1.9°), L47 | JSBACK | Y | 12 (8 natural) | Y | N | Y | Raddatz et al. [130], Brovkin et al. [131] |
NorESM-ME | NCC | 1.9 × 2.5°, L26 | CLM4 | N | 16 | Y | Y | Y | Iversen et al. [132] |
5.1.4. Brief Discussion on Models’ Results and the Human Dimension in Land Surface Models
5.1.5. Future Advancements in Earth System Models (ESMs)
5.2. Model Linkages with Focus on Socio-Economic Systems; Integrated Assessment Models
Future Advancements in Integrated Assessment Models
Model Name | Model Linkages | Land-Use Type | Geographic Scale | Dynamics Technique | Temporal Dimension | Models Interaction and Feedbacks | Reference |
---|---|---|---|---|---|---|---|
AIM | Global Climate model + GHGs emission model including a CGE | Multiple land-use types | Focus on Asia Pacific Region | Recursive-Dynamic | Run period: 1990-2100. Time step: 5 yrs | The CGE model is applied to quantify emissions from land-use change that feed the climate model. The climate model includes a simple carbon cycle module validated by dynamic vegetation simulations. | Matsuoka et al. [175] |
ObjECTS-GCAM | Agriculture and land-use model (AgLU) + Integrated framework ObjECT (including GCAM reduced-form model for carbon cycle, atmospheric chemistry and climate change) | Multiple land-use types | Global: 14 regions | Recursive Dynamic | Base year: 1990. Run period: 1990–2095. Time step: 15 yrs | The climate model provides greenhouse gas concentrations, radiative forcing. In AgLU the link between changes in land use and land cover determine stocks and flows of terrestrial carbon. | Edmonds and Reilly [176]; Brenkert et al. [177]; Kim et al. [178] |
IGSM-MIT | Economic module (EPPA) + model of atmospheric dynamics, physics and chemistry + an ocean model including carbon cycle and sea-ice + a set of coupled land models (the Terrestrial Ecosystem Model, the Natural Emissions Model, and the Community Land Model) | Multiple land-use types | Global: 16 regions | Dynamic model | Run period: 1990 up to 2250 | The outputs of the combined anthropogenic and natural emissions models drive the coupled atmospheric chemistry and climate models. Climate model outputs, in turn, drive the outcomes of a terrestrial model on water and energy budgets, CO2, CH4, and N2O fluxes, and soil composition. These results are fed back into the coupled climate/chemistry model | Sokolov et al. [87] |
IIASA model CLUSTER | PEM (GLOBIOM) + geographically explicit agent-based model (G4M) for forestry | Multiple land use-types for GLOBIOM but focus on forestry given G4M capability | Global: 11 regions | Dynamic model | Base year: 2000; Run period: up to 2030, 2050. Time step: 10 yrs | G4M informs GLOBIOM on biophysical vegetation growth and forest management cost. GLOBIOM gives results on endogenous commodity and land prices. | General reference: Gusti et al. [179]; G4M: Benitez et al. [180]; Benitez-Obersteiner [181]; Kinderman et al. [182] |
IMAGE | Stand-alone softwares (TIMER energy model and FAIR emissions models) + IMAGE land-atmosphere model + agro-economic models (LEITAP-CGE and IMPACT) | Multiple land-use types but focus on agriculture and livestock | Global: 24 regions | Dynamic model | Run period: up to 2100; Time step: depending on sub-models but between 1 day and 5 yrs | Integration between terrestrial models (land cover and vegetation models), economic model, and a climate-ocean system. The terrestrial environment system calculates changes in land use and related emissions as a function of economic parameters. The vegetation model simulates crop productivities, distribution and natural vegetation according to climate and soil condition. Crop productivities are used in the land-cover mode to reconcile global land demand with supply. | Alcamo et al. [183]; IMAGE [184]; MNP [185] |
WITCH-GTM | Integrated/Hybrid model and Optimization model & Partial Equilibrium model for forestry (2 economic models) | Focus on forestry | Global: 12 regions | Dynamic model | Run period: up to 2100. | WITCH feeds GTM with carbon prices while GTM gives in return carbon sequestration rates. These are included into WITCH carbon emissions balance and budget constraints. | WITCH: Tavoni et al. [186]; GTM: Sohengen and Mendelsohn [67] |
WITCH | Integrated/Hybrid model and Optimization model | No explicit treatment of land use change | Global: 12 regions | Dynamic model | Run period: up to 2100. Time step: 10 yrs | The climate module feeds back into the economy via a damage function. Carbon dioxide emissions, produced by the economic activity, affect atmospheric concentration, radiative forcing, and temperature. In its turn, increases in global temperature translate into changes in regional GDPs. | Bosetti et al. [187,188] |
KLUM-GTAP | Rule based sectoral model for agriculture + CGE | Multiple land-use types but focus on cropland | Global: 16 regions | Dynamic model | Base year: 1997; Run period: up to 2050 | The economic model informs KLUM on crop prices and yields. KLUM simulates land allocation, which is fed back into the economic model together with climate and soil impacts on yields. | Ronneberg et al. [55] |
ICLIPS + AgLU | Integrated assessment (core ICLIPS) + PEM: land use model integrated into a climate-economy model and a carbon cycle module. | Multiple land-use types but focus on agriculture and livestock | Global: 11 regions | Dynamic model | Base year: 1990; Time step: 15 yrs. Run period: up to 2095 | ICLIPS provides AgLU with data on GDP growth by region and the global carbon price. In AgLU, the global carbon price influences the biomass price and production and land-use change. Emissions from land-use change are sent back to the ICLIPS model affecting the climate system. In ICLIPS the carbon price will be adjusted to meet a climate protection strategy. | ICLIPS: Toth et al. [189]; AgLU: Sands-Leimbach [60]; Sands-Edmonds [61] |
5.3. Research on Integration between Integrated Assessment Models and Earth System Models and Future Effort
6. Conclusions
- Time scales. The temporal dimension of the economic and political system is usually not consistent with the timing of natural cycles in continuous change. Recombining medium time scales (few years) of actual political processes (IAMs) with short time-scales (hours, day) of biophysical processes (ESMs) is not an easy task and generates concerning issues on the integration of spatial biophysical aspects with spatial economic information [7].
- Resolution. ESMs have reported that land-use impacts on climate are more relevant at regional rather than at global scale [3]. Consequently, further effort is needed to reconcile global with regional climate effects of land-use change making use of local assessments, observations and additional inputs and sources. As for IAMs, now that new and global databases have been made available (GTAP-AEZ, FAO-IIASA AEZ, USEPA), gridded or spatially explicit representations have increased. However, current models still operate at a rather low-resolution level (about 1–2 degrees grid intervals), in line with the aggregation of statistics on economic variables [7,193]. The spatial resolution of economic data is constrained by administrative boundaries, which is the level of detail required for economical or policy analysis, not always suitable for environmental variables [13]. Results on land allocation are shown at a coarse level (e.g., country scale) since a more detailed assessment would imply the estimation of data on input usage and output at the spatial unit [6]. This is the case of the MIT-EPPA model [86]. Exceptions for global IAMs are provided by IMAGE, and GTAP-AEZ, which produce analysis at the AEZ level. Higher ESMs resolution, probably comprised between 1 degree and a quarter of a degree (about 100 and 25 km grid intervals, respectively), can be presumably achieved for the next climate model intercomparison exercise (CMIP6) where a specific high-resolution simulations subset (HiResMIP) has been proposed.
- Uncertainty: Great uncertainties still remain relative to the effects of land-use and cover change on the regional climate [121]. This is due to the existence of multi-scale climate dynamics, to the differences in land-use effects on climate across regions, to the interpretation of the land transitions in the land surface models included in the ESMs and differences in the biogeophysical and, especially, biogeochemical processes included in the LSMs [3]. Advancing convergence on land surface and land-use, land-cover change formulations would reduce these uncertainties within ESMs. Similarly, an improvement is also required in the identification and evaluation of the most important sources of uncertainty permeating IAMs within and across integrated modules. For example, incorporated energy–economic models, not precisely developed for land-use analysis, should confine uncertainty in parameters by using available econometric estimates or by calibrating outcomes to bottom–up approaches. Some aspects of the energy related sector as hydropower can be modelled within ESMs as well [194]. In addition, uncertainty in pests’ incidence and diseases in agro-forestry sector would deserve more attention in the representation of vegetation dynamics given their impacts on production, costs, and natural sequestration capacity [166]. Plants can die due to an aggregate of processes such as wind throw, insect attack, disease, extreme temperatures or drought, age-related decline in vigor, and fires, if not considered separately. To account for all the processes mentioned above plant mortality is associated to a bulk constant rate of few percentage points per year, which is clearly an oversimplification.
- Bioenergy: bioenergy production has become an important and strategic component of the mitigation strategy [195]. Related policy, production and consumption decisions affect land-use and cover changes, and therefore the global-to-regional climate. Given its latest development, there exists a lack of historical data. As a result, current analysis fails to model biomass production competing with both agriculture (food) and forestry (timber) production. Furthermore, they poorly represent competition across different uses of wood, such as wood used for (i) traditional industrial uses; (ii) carbon sequestration; and (iii) biomass production. In addition, within the process of producing the new RCPs, the bioenergy dimension is not consistently handled across IAMs, which use different assumptions to represent its development in time. Further research would be necessary to shed new light on its effect on the economy and its effectiveness as a mitigation strategy.
- Forestry. Including forestry representation into the land-use system is one of the most challenging, though attractive, issues of this field. This explains why several studies have focused on agricultural activities rather than forestry and its mitigation potential (KLUM, ACCELERATES, ELPEN, SALU, WATSIM, IMPACT, CAPRI, GTAP, FARM, GTAPEM, etc.). A first issue is the temporal dimension. Growing new forests, increasing forest stock, or accumulating forest-carbon may require more than one decade and therefore long-run analysis [6]. Also, these processes are inherently dynamic. Unfortunately, there is still lack of a description of forest age class evolution and therefore carbon accumulation dynamics. This is true for both economically oriented models with global coverage and some IAMs as well as for ESMs, where usually each plant functional type is treated as a population of plants sharing the same properties, including the age class. More sophisticated models accounting for the age distribution might be considered for future developments of ESMs [196]. Another critical issue relates to the modelling of new land access, namely, forests that, at current conditions, are not economically accessible. Most of the existing models disregard this possibility considering land as a fixed endowment, or restraining the attention of the analysis to managed land. With this modelling structure, it is impossible to track forest-carbon resulting from deforesting new lands, or carbon sequestration coming from deforestation slowdown, resulting from the introduction of forest sequestration incentives. Similarly, the increase in timber supply derived from new lands brought into production would have no impacts on the economics of the forest sector. A final concern refers to the missing information on forests’ non-market value as well as to the “stochastic nature of the real world” [23], aspects practically not modelled in any of the aforementioned analyses.
AEZ | Agro-Ecological Zoning |
AR5 | Fifth Assessment Report |
AOGCM | Coupled Atmosphere-Ocean General Circulation model |
BLS | Basic Linked System |
CET | Constant Elasticity of Transformation |
CGE | Computable General Equilibrium Model |
CMIP5 | Climate Research Programme’s Fifth Coupled Model Intercomparison Project |
DGVM | Dynamic Global Vegetation Model |
ESM | Earth System Model |
EU | European Union |
FAO | Food and Agriculture Organization |
GDP | Gross Domestic Product |
GHG | Greenhouse Gas |
GCM | General Circulation Model |
GIS | Geographic Information System |
IAM | Integrated Assessment Model |
IIASA | International Institute for Applied Systems Analysis |
IPCC | Intergovernmental Panel on Climate Change |
LCC | Land-Cover Change |
LSM | Land Surface Model |
LULUCF | Land-Use, Land-Use Cover, and Forestry |
LUC | Land-Use Change |
PEM | Partial Equilibrium Model |
PFT | Plant Functional Type |
ppmv | Parts Per Million by Volume |
RCM | Regional Climate Model |
RCP | Representative Concentration Pathway |
USEPA | U.S. Environmental Protection Agency |
WHRC | Wood Hole Research Centre |
Acknowledgments
Author Contributions
Conflict of Interest
References
- Foley, J.A.; Defries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K.; et al. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef]
- Feddema, J.J.; Oleson, K.W.; Bonan, G.B.; Mearns, L.O.; Buja, L.E.; Meehl, G.A.; Washington, W.M. Atmospheric science: The importance of land-cover change in simulating future climates. Science 2005, 310, 1674–1678. [Google Scholar] [CrossRef]
- Deng, X.; Zhao, C.; Yan, H. Systematic modelling of impacts of land use and land cover changes on regional climate: A review. Adv. Meteorol. 2013, 317678, 1–11. [Google Scholar]
- El-Hage Scialabba, N.; Hattam, C. Organic Agriculture, Environment and Food Security; Environment and Natural Resources Series No. 4; Environment and Natural Resources Service Sustainable Development Department; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2002; p. 258. [Google Scholar]
- Avetisyan, M.; Baldos, U.; Hertel, T.W. Development of the GTAP Version 7 Land Use Data Base; GTAP Research Memorandum No. 19; Purdue University: West Lafayette, IN, USA, 2011; pp. 1–17. [Google Scholar]
- Hertel, T.W.; Rose, S.K.; Tol, R.S.J. Economic analysis of land use in global climate change policy. In Routledge Explorations in Environmental Economics; Taylor & Francis Group: New York, NY, USA, 2009; pp. 1–343. [Google Scholar]
- Van Vuuren, D.P.; Bayer, L.B.; Chuwah, C.; Ganzeveld, L.; Hazeleger, W.; van den Hurk, B.; van Noije, T.; O’Neil, B.; Strenger, B.J. A comprehensive view on climate change: Coupling of Earth system and integrated assessment models. Environ. Res. Lett. 2012, 7, 024012. [Google Scholar] [CrossRef]
- Hertel, T.W. Global Applied General Equilibrium Analysis Using the GTAP Framework; GTAP Working Paper No. 66; Purdue University: West Lafayette, IN, USA, 2012. [Google Scholar]
- Heistermann, M.; Müller, C.; Ronneberger, K. Land in sight? Achievements, deficits and potentials of continental to global scale land-use modelling. Agric. Ecosyst. Environ. 2006, 114, 141–158. [Google Scholar] [CrossRef]
- Van Ittersu, M.K.; Rabbinge, R.; van Latesteijn, H.C. Exploratory land use studies and their role in strategic policy making. Agric. Syst. 1998, 58, 309–330. [Google Scholar] [CrossRef]
- Kaimowitz, D.; Angelsen, A. Economic Models of Tropical Deforestation—A Review; Center for International Forestry Research: Bogor, Indonesia, 1998. [Google Scholar]
- Kaimowitz, D.; Angelsen, A. Rethinking the causes of deforestation: Lessons from economic models. World Bank Res. Obs. 1999, 14, 73–98. [Google Scholar] [CrossRef]
- Briassoulis, H. Analysis of land use change: Theoretical and modelling approaches. The Web Book of Regional Science; West Virginia University: Morgantown, WV, USA, 2000. Available online: Available online: http://www.rri.wvu.edu/WebBook/Briassoulis/contents.htm (accessed on 15 July 2014).
- Bockstael, N.E.; Irwin, E.G. Economics and the land use-environment link. In The International Yearbook of Environmental and Resource Economics 1999/2000; Folmer, H., Tietenberg, T., Eds.; Edward Elgar Publishing: Cheltenham, UK, 2000; pp. 1–54. [Google Scholar]
- Irwin, E.; Geoghegan, J. Theory, data, methods: Developing spatially explicit economic models of land use change. Agric. Ecosyst. Environ. 2001, 5, 7–23. [Google Scholar] [CrossRef]
- Scientific Steering Committee and International Project Office of LUCC; International Geosphere-Biosphere Programme; International Human Dimensions Programme on Global Environmental Change (IHDP). Land-Use and Land-Cover Change (LUCC) Implementation Strategy; IGBP Report 48; IHDP Report 10; Nunes, C., Auge, J.I., Eds.; International Geosphere-Biosphere Programme (IGBP) Secretariat: Stockholm, Sweden, 1999. [Google Scholar]
- Veldkamp, A.; Lambin, E.F. Predicting land-use change. Agric. Ecosyst. Environ. 2001, 85, 1–6. [Google Scholar] [CrossRef]
- Agarwal, C.; Green, G.M.; Grove, J.M.; Evans, T.P.; Schweik, C.M. A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice; U.S. Department of Agriculture, Forest Service, Northeastern Research Station: Newton Square, PA, USA, 2002; Volume NE-297. [Google Scholar]
- Parker, D.C.; Manson, S.M.; Janssen, M.A.; Hoffmann, M.J.; Deadman, P. Multi-agent systems for the simulation of land-use and land-cover change: A review. Ann. Assoc. Am. Geogr. 2003, 93, 314–337. [Google Scholar] [CrossRef]
- Verburg, P.H.; Schota, P.; Dijsta, M.; Veldkamp, A. Land use change modelling: Current practice and research priorities. Geogr. J. 2004, 61, 309–324. [Google Scholar]
- Balkhausen, O.; Banse, M.; Grethe, H. Modelling CAP decoupling in the EU: A comparison of selected simulation models and results. J. Agr. Econ. 2008, 59, 57–71. [Google Scholar]
- Palatnik, R.; Roson, R. Climate Change Assessment and Agriculture in General equilibrium Models: Alternative Modelling Strategies; Fondazione Eni Enrico Mattei Working Papers No.067; Fondazione Eni Enrico Mattei (FEEM): Milan, Italy, 2009; pp. 1–23. [Google Scholar]
- Toppinen, A.; Kuuluvainen, J. Forest sector modelling in Europe—The state of the art and future research directions. For. Policy Econ. 2010, 12, 2–8. [Google Scholar] [CrossRef]
- Veldkamp, A.; Fresco, L.O. CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecol. Model. 1996, 91, 231–248. [Google Scholar] [CrossRef]
- Verburg, P.; Overmars, K. Combining top-down and bottom-up dynamics in land use modelling: Exploring the future of abandoned farmlands in Europe with Dyna-CLUE model. Landsc. Ecol. 2009, 24, 1167–1181. [Google Scholar] [CrossRef]
- Wright, I.A.; Smeets, P.J.A.M.; Elbersen, B.S.; Roos Klein-Lankhorst, J.; Pflimlin, A.; Louloudis, L.; Vlahos, G.; Crabtree, J.R.; Williams, S.M.; Hinrichs, P.; et al. The ELPEN project. In A Protocol for Building the ELPEN Livestock Policy Decision Support System; Macaulay Land Use Research Institute (MLURI): Aberdeen, UK, 1999; p. 37. [Google Scholar]
- Stephenne, N.; Lambin, E.F. Scenarios of land-use change in Sudano-Sahelian countries of Africa to better understand driving forces. GeoJournal 2004, 61, 365–379. [Google Scholar] [CrossRef]
- Stéphenne, N.; Lambin, E.F. A dynamic simulation model of land-use changes in Sudano-Sahelian countries of Africa (SALU). Agric. Ecosyst. Environ. 2001, 85, 145–161. [Google Scholar] [CrossRef]
- Schelhaas, M.J.; Eggers, J.; Lindner, M.; Nabuurs, G.J.; Pussinen, A.; Päivinen, R.; Schuck, A.; Verkerk, P.J.; van der Werf, D.C.; Zudin, S. Model Documentation for the European Forest Information Scenario Model (EFISCEN 3.1.3); EFI Technical Report 26; Alterra: Wageningen, The Netherlands, 2007. [Google Scholar]
- Rounsevell, M.D.A.; Annetts, J.E.; Audsley, E.; Mayr, T.; Reginster, I. Modelling the spatial distribution of agricultural land use at the regional scale. Agric. Ecosyst. Environ. 2003, 95, 465–479. [Google Scholar] [CrossRef]
- Van Delden, H.; Luja, P.; Engelen, G. Integration of multi-scale dynamic spatial models of socio-economic and physical processes for river basin management. Environ. Model. Softw. 2007, 22, 223–238. [Google Scholar] [CrossRef]
- Ronneberg, K.; Tol, R.S.J.; Schneider, U.A. KLUM: A Simple Model of Global Agricultural Land Use as a Coupling Tool of Economy and Vegetation; FNU Working Paper No. 65; Hamburg University and Centre for Marine and Atmospheric Science: Hamburg, Germany, 2005. [Google Scholar]
- De Koning, G.H.J.; Verburg, P.H.; Veldkamp, A.; Fresco, L.O. Multi-scale modelling of land use change dynamics in Ecuador. Agric. Syst. 1999, 61, 77–93. [Google Scholar] [CrossRef]
- Verburg, P.H.; Veldkamp, A.; Fresco, L.O. Simulation of changes in the spatial pattern of land use in China. Appl. Geogr. 1999, 19, 211–233. [Google Scholar] [CrossRef]
- Verburg, P.H.; Veldkamp, A.; Bouma, J. Land use change under conditions of high population pressure: The case of Java. Glob. Environ. Chang. 1999, 9, 303–312. [Google Scholar] [CrossRef]
- Kok, K.; Veldkamp, A. Evaluating impact of spatial scales on land use pattern analysis in Central America. Agric. Ecosyst. Environ. 2001, 85, 205–221. [Google Scholar] [CrossRef]
- Castella, J.-C.; Kam, S.P.; Quang, D.D.; Verburg, P.H.; Hoanh, C.T. Combining top-down and bottom-up modelling approached of land use/cover change to support public policies: Application to sustainable management of natural resources in Vietnam. Land Use Policy 2006, 24, 531–545. [Google Scholar]
- Wassenaar, T.; Gerber, P.; Rosales, M.; Ibrahim, M.; Verburg, P.H.; Steinfeld, H. Projecting land use changes in the Neotropics: The geography of pasture expansion into forest. Glob. Environ. Chang. 2007, 17, 86–104. [Google Scholar] [CrossRef]
- Stavins, R. The costs of carbon sequestration: A revealed-preference approach. Am. Econ. Rev. 1999, 89, 994–1009. [Google Scholar] [CrossRef]
- Plantinga, A.; Mauldin, T. A method for estimating the cost of CO2 mitigation through afforestation. Clim. Chang. 2001, 49, 21–40. [Google Scholar] [CrossRef]
- Lubowski, R.N.; Plantinga, A.J.; Stavins, R.N. Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function. J. Environ. Econ. Manag. 2006, 51, 135–152. [Google Scholar] [CrossRef]
- Pfaff, A.S.P.; Kerr, S.; Lipper, L.; Cavatassi, R.; Davis, B.; Hendy, J.; Sanchez-Azofeifa, G.A. Will buying tropical forest-carbon benefit the poor? Evidence from Costa Rica. Land Use Policy 2007, 24, 600–610. [Google Scholar] [CrossRef]
- Munroe, D.; Muller, D. Issues in spatially explicit statistical land-use/cover change (LUCC) models: Examples from western Honduras and the Central Highlands of Vietnam. Land Use Policy 2007, 24, 521–530. [Google Scholar] [CrossRef]
- Pfaff, A.S.P. What drives deforestation in the Brazilian Amazon? Evidence from satellite and socioeconomic data. J. Environ. Econ. Manag. 1999, 37, 25–43. [Google Scholar]
- Mertens, B.; Lambin, E.F. Land-cover-change trajectories in Southern Cameroon. Ann. Assoc. Am. Geogr. 2000, 90, 467–494. [Google Scholar] [CrossRef]
- Chomitz, K.M.; Gray, D.A. Roads, land use, and deforestation: A spatial model applied to Belize. World Bank Econ. Rev. 1996, 10, 487–512. [Google Scholar] [CrossRef]
- Darwin, R. The impact of global warming on agriculture: A Ricardian analysis: comment. Am. Econ. Rev. 1999, 89, 1049–1052. [Google Scholar] [CrossRef]
- Mendelsohn, R.; Nordhaus, W.D.; Shaw, D. The impact of global warming on agriculture: A Ricardian analysis. Am. Econ. Rev. 1994, 84, 753–771. [Google Scholar]
- Sanghi, A.; Mendelsohn, R. The impact of global warming on farmers in Brazil and India. Glob. Environ. Chang. 2008, 18, 655–665. [Google Scholar] [CrossRef]
- Mendelsohn, R.; Dinar, A. Climate Change and Agriculture: An Economic Analysis of Global Impacts, Adaptation, and Distributional Effects; Edward Elgar: Cheltenham, UK, 2009. [Google Scholar]
- Van Passel, S.; Massetti, E.; Mendelsohn, R. A Ricardian Analysis of the Impact of Climate Change on European Agriculture; Fondazione Eni Enrico Mattei Working Papers No. 83; Fondazione Eni Enrico Mattei (FEEM): Milan, Italy, 2012; p. 27. [Google Scholar]
- Deschenes, O.; Greenstone, M. The economic impacts of climate change: Evidence from agricultural output and random fluctuation in weather. Am. Econ. Rev. 2007, 97, 354–385. [Google Scholar] [CrossRef]
- Massetti, E.; Mendelsohn, R. Estimating Ricardian models with panel data. Clim. Chang. Econ. 2011, 2, 301–319. [Google Scholar] [CrossRef]
- Hertel, T.W.; Tsigas, M.E. Tax policy and US agriculture: A general equilibrium analysis. Am. J. Agric. Econ. 1998, 70, 289–302. [Google Scholar] [CrossRef]
- Ronneberger, K.; Berrittella, M.; Bosello, F.; Tol, R.S.J. Chapter 12. KLUM@GTAP: Spatially-explicit, biophysical land use in a computable general equilibrium model. In Economic Analysis of Land Use in Global Climate Change Policy; Hertel, T.W., Rose, S., Tol, R.S.J., Eds.; Routledge: London, UK, 2008; pp. 304–338. [Google Scholar]
- Beckman, J.F.; Hertel, T.W.; Tyner, W. Validating energy-oriented CGE models. Energy Econ. 2011, 33, 799–806. [Google Scholar] [CrossRef]
- Kuhn, A. From world market to trade flow modelling—The re-designed WATSIM model, final report. In Market Research and Economic Sociology; WATSIM AMPS Final Report; Institute of Agricultural Policy, University of Bonn: Bonn, Germany, 2003; pp. 1–56. [Google Scholar]
- Britz, W.; Hecklei, T.; Kempen, M. Description of the CAPRI Modelling System. 2008. Available online: http://www.capri-model.org/docs/capri_documentation.pdf (accessed on 15 July 2014).
- Leip, A.; Marchi, G.; Koeble, R.; Kempen, M.; Britz, W.; Li, C. Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen and carbon losses from arable soils in Europe. Biogeosciences 2008, 5, 73–94. [Google Scholar] [CrossRef]
- Sands, R.D.; Leimbach, M. Modelling agriculture and land use in an integrated assessment framework. Clim. Change 2003, 56, 185–210. [Google Scholar] [CrossRef]
- Sands, R.D.; Edmonds, J.A. Climate change impacts for the conterminous USA: An integrated assessment. Clim. Change 2005, 69, 127–150. [Google Scholar] [CrossRef]
- Rosegrant, M.W.; The IMPACT Development Team. International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT); International Food Policy Research Institute (IFPRI): Washington, DC, USA, 2012. [Google Scholar]
- Adams, D.M.; Alig, R.J.; Callaway, J.M.; McCarl, B.A.; Winnett, S.M. The Forest and Agriculture Sector Optimization Model (FASOM): Model Structure and Policy Applications; Research Paper PNW RP-495; USDA Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 1996. [Google Scholar]
- Ohrel, S.B.; Beach, R.H.; Adams, D.; Alig, R.; Baker, J.; Latta, G.S.; McCarl, B.A.; Rose, S.K.; White, E. Model Documentation for the Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG); U.S. Environmental Protection Agency, Climate Change Division: Washington, DC, USA, 2010. [Google Scholar]
- Havlík, P.; Schneider, U.A.; Schmid, E.; Böttcher, H.; Fritz, S.; Skalský, R.; Aoki, K.; De Cara, S.; Kindermann, G.; Kraxner, F.; et al. Global land-use implications of first and second generation biofuel targets. Energ. Policy 2011, 39, 5690–5702. [Google Scholar] [CrossRef]
- Sohngen, B.; Mendelsohn, R.; Sedjo, R. Forest management, conservation, and global timber markets. Am. J. Agric. Econ. 1999, 81, 1–13. [Google Scholar] [CrossRef]
- Sohngen, B.; Mendelsohn, R. Chapter 19. A sensitivity analysis of carbon sequestration. In Human-Induced Climate Change: An Interdisciplinary Assessment; Schlesinger, M., Kheshgi, H.S., Smith, J., de la Chesnaye, F.C., Reilly, J.M., Wilson, T., Kolstad, C., Eds.; Cambridge University Press: Cambridge, UK, 2007; pp. 227–237. [Google Scholar]
- Hertel, T.W. Global Trade Analysis Modelling and Applications; Cambridge University Press: Cambridge, UK, 1997. [Google Scholar]
- Burniaux, J.M. Incorporating Carbon Sequestration into CGE Models: A Prototype GTAP Model with Land Use; Center for Global Trade Analysis Project: West Lafayette, IN, USA, 2002. [Google Scholar]
- Burniaux, J.M.; Lee, H.L. Modelling land use changes in GTAP. In Proceedings of the 6th Annual Conference on Global Economic Analysis, The Hague, The Netherlands, 12–14 June 2003.
- Hsin, H.; van Tongeren, F.; Dewbre, J.; van Meijl, H. A new representation of agricultural production technology in GTAP. In Proceedings of the 7th Annual Conference on Global Economic Analysis, Washington, DC, USA, 17–19 June 2004.
- Brooks, J.; Dewbre, J. Global trade reforms and income distribution in developing countries. J. Agric. Dev. Econ. 2006, 3, 86–111. [Google Scholar]
- Keeney, R.; Hertel, T. GTAP-AGR: A Framework for Assessing The Implications of Multilateral Changes in Agricultural Policies; GTAP Technical Paper No. 24; Center for Global Trade Analysis, Purdue University: West Lafayette, IN, USA, 2005. [Google Scholar]
- McKibbin, W.J.; Wilcoxen, P. The theoretical and empirical structure of the GCubed, model. Econ. Model. 1998, 16, 123–148. [Google Scholar] [CrossRef]
- Michetti, M.; Rosa, R. Afforestation and timber management compliance strategies in climate policy—A computable general equilibrium analysis. Ecol. Econ. 2012, 77, 139–148. [Google Scholar] [CrossRef]
- Lee, H.L.; Hertel, T.W.; Rose, S.; Avetisyan, M. An integrated global land use data base for CGE analysis of climate policy options. In Economic Analysis of Land Use in Global Climate Change Policy; Routledge Exploration in Environmental Economics, Taylor & Francis Group: New York, NY, USA, 2009; pp. 72–88. [Google Scholar]
- Lee, H.L. Incorporating Agro-ecological zoned data into the GTAP framework. In Proceedings of the 7th Annual Conference on Global Economic Analysis, Washington, DC, USA, 17–19 June 2004.
- Lee, H.L.; Hertel, T.; Sohngen, B.; Ramankutty, N. Towards An Integrated Land Use Database for Assessing the Potential for Greenhouse Gas Mitigation; GTAP Technical Papers No. 25; Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University: West Lafayette, IN, USA, 2005; Volume 1900, pp. 1–83. [Google Scholar]
- Golub, A.; Hertel, T.; Rose, S.; Sohngen, B.; Avetisyan, M. The relative role of land in climate policy. In Proceedings of the Agricultural and Applied Economics Association (AAEA) Annual Meeting, Milwaukee, WI, USA, 26–28 July 2009.
- Golub, A.; Henderson, B.; Hertel, T. Effects of GHG Mitigation Policies on Livestock Sectors; GTAP Working Paper No. 62; Purdue University: West Lafayette, IN, USA, 2010. [Google Scholar]
- Michetti, M.; Parrado, R. Improving land-use modelling within CGE to assess forest-based mitigation potential and costs. CMCC Res. Pap. 2012, 126, 1–46. [Google Scholar]
- Golub, A.; Hertel, T.; Sohngen, B. Land use modelling in recursively-dynamic GTAP framework. In Economic Analysis of Land Use in Global Climate Change Policy; Routledge Exploration in Environmental Economics, Taylor & Francis Group: New York, NY, USA, 2008; pp. 235–278. [Google Scholar]
- Pant, H.M. An analytical framework for incorporating land use change and forestry in a dynamic CGE model. In Proceedings of the 54th Conference on Australian Agricultural and Resource Economics Society, Adelaide, SA, Australia, 10–12 February 2010.
- Tubiello, F.N.; Fischer, G. Reducing climate change impacts on agriculture: Global and regional effects of mitigation, 2000–2080. Technol. Forecast. Soc. Chang. 2007, 74, 1030–1056. [Google Scholar] [CrossRef]
- Sands, R.D.; Kim, M.K. Modelling the competition for land: Methods and application to climate policy. In Economic Analysis of Land Use in Global Climate Change Policy; Routledge: London, UK, 2009; pp. 154–181. [Google Scholar]
- Paltsev, S.; Reilly, J.M.; Jacoby, H.D.; Eckaus, R.S.; McFarland, J.; Sarofim, M.; Asadoorian, M.; Babiker, M. Emissions Prediction and Policy Analysis (EPPA) Model: Version 4; Report No. 125. MIT Joint Program for the Science and Policy of Global Change: Cambridge, MA, USA, 2005; p. 72. Available online: Available online: http://web.mit.edu/globalchange/www/ MITJPSPGC_Rpt125.pdf (accessed on 15 July 2014).
- Sokolov, A.P.; Schlosser, C.A.; Dutkiewicz, S.; Paltsev, S.; Kicklighter, D.W.; Jacoby, H.D.; Prinn, R.G.; Forest, C.E.; Reilly, J.; Wang, C.; et al. The MIT Integrated Global System Model (IGSM) Version 2: Model Description and Baseline Evaluation; MIT Joint Program on the Science and Policy of Global Change: Cambridge, MA, USA, 2005. [Google Scholar]
- Pielke, R.A. Climate Vulnerability, Understanding and Addressing Threats to Essential Resources, 1st ed.; Academic Press: Amsterdam, The Netherlands/Boston, MA, USA, 2013. [Google Scholar]
- Manabe, S.; Smagorinsky, J. Simulated climatology of a general circulation model with a hydrological cycle. Monthly Weather Rev. 1965, 93, 769–798. [Google Scholar]
- Manabe, S.; Kirk, B. Climate calculations with a combined ocean-atmosphere model. J. Atmos. Sci. 1969, 26, 786–789. [Google Scholar] [CrossRef]
- Deardorff, J.W. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. J. Geophys. Res. 1978, 83, 1889–1903. [Google Scholar] [CrossRef]
- Dickinson, R.E.; Henderson-Sellers, A.; Kennedy, P.J.; Wilson, M.F. Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model; Technical Report NCAR/TN-275+STR; National Center for Atmospheric Research (NCAR): Boulder, CO, USA, 1986. [Google Scholar]
- Sellers, P.J.; Mintz, Y.; Sud, Y.C.; Dalcher, A. A Simple Biosphere Model (SIB) for use within general circulation models. J. Atmos. Sci. 1986, 43, 505–531. [Google Scholar] [CrossRef]
- Loveland, T.R.; Reed, B.C.; Brown, J.F.; Ohlen, D.O.; Zhu, J.; Yang, L.; Merchant, J.W. Development of a global land cover characteristics database and IGBP DISCover from 1-km AVHRR data. Int. J. Remote Sens. 2000, 21, 1303–1330. [Google Scholar] [CrossRef]
- Charney, J.; Stone, P.H.; Quirk, W.J. Drought in the Sahara: A biogeophysical feedback mechanism. Science 1975, 187, 434–435. [Google Scholar]
- Kabat, P.; Claussen, M.; Dirmeyer, P.A.; Gash, J.H.C.; de Guenni, L.B.; Meybeck, M.; Pielke, R.A., Sr.; Vorosmarty, C.J.; Hutjes, R.W.A.; Lutkemeier, S. (Eds.) Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System (Global Change-The IGBP Series), 1st ed.; Springer: Berlin, Germany, 2004.
- Davin, E.L.; de Noblet-Ducoudré, N. Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. J. Clim. 2010, 23, 97–112. [Google Scholar] [CrossRef]
- Zampieri, M.; Lionello, P. Anthropic land use causes summer cooling in central Europe. Clim. Res. 2011, 46, 255–268. [Google Scholar] [CrossRef]
- Betts, R.A. Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 2000, 408, 187–190. [Google Scholar] [CrossRef]
- Coe, M.T.; Costa, M.H.; Soares-Filho, B.S. The influence of historical and potential future deforestation on the stream flow of the Amazon River and land surface processes and atmospheric feedbacks. J. Hydrol. 2009, 369, 165–174. [Google Scholar] [CrossRef]
- Bonan, G.B.; Chapin, F.S.; Thompson, S.L. Boreal forest and tundra ecosystems as components of the climate system. Clim. Chang. 1995, 29, 145–167. [Google Scholar] [CrossRef]
- Bonan, G.B. Effects of land use on the climate of the United States. Clim. Chang. 1997, 37, 449–486. [Google Scholar] [CrossRef]
- Betts, R.A.; Fallon, P.D.; Goldewijk, K.K.; Ramankutty, N. Biogeophysical effects of land use on climate: Model simulations of radiative forcing and large-scale temperature change. Agric. For. Meteorol. 2007, 142, 216–233. [Google Scholar] [CrossRef]
- Heck, P.; Lüthi, D.; Wernli, H.; Schär, C. Climate impacts of European scale anthropogenic vegetation changes. A sensitivity study using a regional climate model. J. Geophys. Res. 2001, 106, 7817–7835. [Google Scholar] [CrossRef]
- Kalnay, E.; Cai, M. Impact of urbanization and land use on climate change. Nature 2003, 423, 528–531. [Google Scholar] [CrossRef]
- Fraedrich, K.; Kleidon, A.; Lunkeit, F. A green planet versus a desert world: Estimating the effect of vegetation extremes on the atmosphere. J. Clim. 1999, 12, 3156–3163. [Google Scholar] [CrossRef]
- Foley, J.A.; Prentice, I.C.; Ramankutty, N.; Levis, S.; Pollard, D.; Sitch, S.; Haxeltine, A. An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Glob. Biogeochem. Cycles 1996, 10, 603–628. [Google Scholar] [CrossRef]
- Sitch, S.; Smith, B.; Prentice, I.C.; Arneth, A.; Bondeau, A.; Cramer, W.; Kaplan, J.O.; Levis, S.; Lucht, W.; Sykes, M.T.; et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 2003, 9, 161–185. [Google Scholar] [CrossRef]
- Bondeau, A.; Smith, P.; Zaehle, S.; Schaphoff, S.; Lucht, W.; Cramer, W.; Gerten, D.; Lotze-Campen, H.; Müller, C.; Reichstein, M.; et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Chang. Biol. 2007, 13, 679–706. [Google Scholar] [CrossRef]
- Bonan, G.B. Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J. Geophys. Res. 1995, 100, 2817–2831. [Google Scholar] [CrossRef]
- Dai, Y.; Zeng, X.; Dickinson, R.E.; Baker, I.; Bonan, G.B.; Bosilovich, M.G.; Denning, A.S.; Dirmeyer, P.A.; Houser, P.R.; Niu, G.; et al. The common land model. Bull. Am. Meteorol. Soc. 2003, 84, 1013–1023. [Google Scholar] [CrossRef]
- Bonan, G.B.; Levis, S. Quantifying carbon-nitrogen feedbacks in the Community Land Model (CLM4). Geophys. Res. Lett. 2010, 37. [Google Scholar] [CrossRef]
- Castillo, G.; Kendra, C.; Levis, S.; Thornton, P. Evaluation of the new CNDV option of the community land model: Effects of dynamic vegetation and interactive nitrogen on CLM4 means and variability. J. Clim. 2012, 25, 3702–3714. [Google Scholar] [CrossRef]
- Von Randow, C.; Manzi, A.O.; Kruijt, B.; de Oliveira, P.; Zanchi, F.; Silva, R.; Hodnett, M.; Gash, J.; Elbers, J.; Waterloo, M.; et al. Comparative measurements and seasonal variations in energy and carbon exchange over forest and pasture in central west Amazonia. Theor. Appl. Clim. 2004, 78, 5–26. [Google Scholar]
- Jackson, R.B.; Randerson, J.T.; Canadell, J.G.; Anderson, R.G.; Avissar, R.; Baldocchi, D.D.; Bonan, G.B.; Caldeira, K.; Diffenbaugh, N.S.; Field, C.B.; et al. Protecting climate with forests. Environ. Res. Lett. 2008, 3. [Google Scholar] [CrossRef]
- Lee, X.; Goulden, M.L.; Hollinger, D.Y.; Barr, A.; Black, T.A.; Bohrer, G.; Bracho, R.; Drake, B.; Goldstei, A.; Gu, L.; et al. Observed increase in local cooling effect of deforestation at higher latitudes. Nature 2011, 479, 384–387. [Google Scholar] [CrossRef]
- Boisier, J.P.; de Noblet-Ducoudré, N.; Ciais, P. Inferring past land use-induced changes in surface albedo from satellite observations: A useful tool to evaluate model simulations. Biogeosciences 2013, 10, 1501–1516. [Google Scholar] [CrossRef]
- Pongratz, J.; Reick, C.H.; Raddatz, T.; Caldeira, K.; Claussen, M. Past land use decisions have increased mitigation potential of reforestation. Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef]
- Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef]
- Hansen, K.; Frahan, B.H.D. Evaluation of agro-environmental policy through a calibrated simulation farm model. In Proceedings of the XIIIth European Association of Agricultural Economists (EAAE) Congress—Change and Uncertainty Challenges for Agriculture, Zurich, Switzerland, 30 August–2 September 2011.
- Hurtt, G.C.; Chini, L.P.; Frolking, S.; Betts, R.A.; Feddema, J.; Fischer, G.; Fisk, J.P.; Hibbard, K.; Houghton, R.A.; Janetos, A.; et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 2011, 109, 117–161. [Google Scholar] [CrossRef]
- Wu, T.; Li, W.; Ji, J.; Xin, X.; Li, L.; Wang, Z.; Zhang, Y.; Li, J.; Zhang, F.; Wei, M.; et al. Global carbon budgets simulated by the Beijing climate center climate system model for the last century. J. Geophys. Res. Atmos. 2013, 118, 4326–4347. [Google Scholar] [CrossRef]
- Arora, V.K.; Scinocca, J.F.; Boer, G.J.; Christian, J.R.; Denman, K.L.; Flato, G.M.; Kharin, V.V.; Lee, W.G.; Merryfield, W.J. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef]
- Long, M.C.; Lindsay, K.; Peacock, S.; Keith Moore, J.; Doney, S.C. Twentieth-century oceanic carbon uptake and storage in CESM1(BGC). J. Clim. 2013, 26, 6775–6800. [Google Scholar] [CrossRef]
- Dunne, J.P.; John, J.G.; Adcroft, A.J.; Griffies, S.M.; Hallberg, R.W.; Shevliakova, E.N.; Stouffer, R.J.; Cooke, W.; Dunne, K.A.; Harrison, M.J.; et al. GFDL’s ESM2 global coupled climate-carbon Earth system models Part I: Physical formulation and baseline simulation characteristics. J. Clim. 2012, 25, 6646–6665. [Google Scholar] [CrossRef]
- Collins, W.J.; Bellouin, N.; Doutriaux-Boucher, M.; Gedney, N.; Halloran, P.; Hinton, T.; Hughes, J.; Jones, C.D.; Joshi, M.; Liddicoat, S.; et al. Development and evaluation of an Earth-system model—HadGEM2. Geosci. Model Dev. Discuss. 2011, 4, 1051–1075. [Google Scholar] [CrossRef]
- Jones, C.D.; Hughes, J.K.; Bellouin, N.; Hardiman, S.C.; Jones, G.S.; Knight, J.; Liddicoat, S.; O'Connor, F.M.; Andres, R.J.; Bell, C.; et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. Discuss. 2011, 4, 543–570. [Google Scholar] [CrossRef]
- Dufresne, J.-L.; Foujols, M.-A.; Denvil, S.; Caubel, A.; Marti, O.; Aumont, O.; Balkanski, Y.; Bekki, S.; Bellenger, H.; Benshila, R.; et al. Climate change projections using the IPSL-CM5 Earth system model: From CMIP3 to CMIP5. Clim. Dyn. 2013, 40, 2123–2165. [Google Scholar] [CrossRef]
- Watanabe, S.; Hajima, T.; Sudo, K.; Nagashima, T.; Takemura, T.; Okajima, H.; Nozawa, T.; Kawase, H.; Abe, M.; Yokohata, T.; et al. MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 2011, 4, 845–872. [Google Scholar] [CrossRef]
- Raddatz, T.J.; Reick, C.H.; Knorr, W.; Kattge, J.; Roeckner, E.; Schnur, R.; Schnitzler, K.-G.; Wetzel, P.; Jungclaus, J.; et al. Will the tropical land biosphere dominate the climate-carbon cycle feedback during the Twenty-First Century? Clim. Dyn. 2007, 29, 565–574. [Google Scholar] [CrossRef]
- Brovkin, V.; Raddaz, T.; Reick, C.H.; Claussen, M.; Gayler, V. Global biogeophysical interactions between forest and climate. Geophis. Res. Lett. 2009, 36. [Google Scholar] [CrossRef]
- Iversen, T.; Bentsen, M.; Bethke, I.; Debernard, J.B.; Kirkevåg, A.; Seland, Ø.; Drange, H.; Kristjánsson, J.E.; Medhaug, I.; Sand, M.; et al. The Norwegian Earth system model, NorESM1-M. Part 2: Climate response and scenario projections. Geosci. Model Dev. 2013, 6, 389–415. [Google Scholar] [CrossRef]
- Shevliakova, E.; Pacala, S.W.; Malyshev, S.; Hurtt, G.C.; Milly, P.C.D.; Caspersen, J.P.; Sentman, L.T.; Fisk, J.P.; Wirth, C.; Crevoisier, C. Carbon cycling under 300 years of land use change: Importance of the secondary vegetation sink. Glob. Biogeochem. Cycles 2009, 23. [Google Scholar] [CrossRef]
- Brovkin, V.; Boysen, L.; Raddaz, T.; Gayler, V.; Loew, A.; Claussen, M. Evaluation of vegetation cover and land surface albedo in MPI-ESM CMIP5 simulations. J. Adv. Model. Earth Syst. 2013, 5, 48–57. [Google Scholar] [CrossRef]
- Eastman, J.L.; Coughenour, M.B.; Pielke, R.A. The effects of CO2 and landscape change using a coupled plant and meteorological model. Glob. Chang. Biol. 2001, 7, 797–815. [Google Scholar] [CrossRef]
- Pitman, A.J.; de Noblet-Ducoudré, N.; Cruz, F.T.; Davin, E.L.; Bonan, G.B.; Brovkin, V.; Claussen, M.; Delire, C.; Ganzeveld, L.; Gayler, V.; et al. Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett. 2009, 36. [Google Scholar] [CrossRef]
- De Noblet-Decoudre, N.; Boisier, J.P.; Pitman, A.; Bonan, G.B.; Brovkin, V.; Cruz, F.; Gayler, V.; van den Hurk, B.J.J.M.; Lawrence, P.J.; van der Molen, M.K.; et al. Determining robust impacts of land-use induced land-cover changes on surface climate over North America and Eurasia—Results from the first set of LUCID experiment. J. Clim. 2012, 25, 3261–3281. [Google Scholar] [CrossRef]
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis, IPCC Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK/New York, NY, USA, 2013. [Google Scholar]
- Lawrence, P.J.; Feddema, J.J.; Bonan, G.B.; Meehl, G.A.; O’Neill, B.C.; Levis, S.; Lawrence, D.M.; Oleson, K.W.; Kluzek, E.; Lindsay, K.; et al. Simulating the biogeochemical and biogeophysical impacts of transient land cover change and wood harvest in the Community Climate System Model (CCSM4) from 1850 to 2100. J. Clim. 2012, 25, 3071–3095. [Google Scholar] [CrossRef]
- Lejeune, E.L.; Guillod, D.B.P.; Seneviratne, S.I. Influence of Amazonian deforestation on the future evolution of regional surface fluxes, circulation, surface temperature and precipitation. Climate Dyn. 2014. [Google Scholar] [CrossRef]
- National Research Council. Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties; The National Academies Press: Washington, DC, USA, 2005; p. 208. [Google Scholar]
- Mahmood, R.; Pielke, R.A.; Hubbard, K.G.; Niyogi, D.; Dirmeyer, P.A.; McAlpine, C.; Carleton, A.M.; Hale, R.; Gameda, S.; Beltrán-Przekurat, A.; et al. Land cover changes and their biogeophysical effects on climate. Int. J. Climatol. 2014, 34, 929–953. [Google Scholar] [CrossRef]
- Pitman, A.J.; de Noblet-Ducoudré, N.; Avila, F.B.; Alexander, L.V.; Boisier, J.-P.; Brovkin, V.; Delire, C.; Cruz, F.; Donat, M.G.; Gayler, V.; et al. Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations. Earth Syst. Dynam. 2012, 3, 213–231. [Google Scholar] [CrossRef]
- Christidis, N.; Stott, P.A.; Hegerl, G.C.; Betts, R.A. The role of land use change in the recent warming of daily extreme temperatures. Geophys. Res. Lett. 2013, 40, 1–6. [Google Scholar] [CrossRef]
- Pielke, R.A.; Marland, G.; Betts, R.A.; Chase, T.N.; Eastman, J.L.; Niles, J.O.; Niyogi, D.; Running, S.W. The influence of land-use change and landscape dynamics on the climate system- relevance to climate change policy beyond the radiative effect of greenhouse gases. Philos. Trans. R. Soc. Lond. 2002, 360, 1705–1719. [Google Scholar] [CrossRef]
- Marland, G.; Pielke, R.A., Sr.; Apps, M.; Avissar, R.; Betts, R.A.; Davis, K.J.; Frumhoff, P.C.; Jackson, S.T.; Joyce, L.; Kauppi, P.; et al. The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy. Clim. Policy 2003, 3, 149–157. [Google Scholar] [CrossRef]
- Pielke, R.A.; Pitman, A.; Niyogi, D.; Mahmood, R.; McAlpine, C.; Hossain, F.; Goldewijk, K.; Nair, U.; Betts, R.; Fall, S.; et al. Land use/land cover changes and climate: Modelling analysis and observational evidence. WIREs Clim. Chang. 2011, 2, 828–850. [Google Scholar] [CrossRef]
- Lobell, D.B.; Bala, G.; Duffy, P.B. Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett. 2006, 33, 6. [Google Scholar] [CrossRef]
- Lo, M.H.; Famiglietti, J.S. Irrigation in California’s Central Valley strengthens the southwestern U.S. water cycle. Geophys. Res. Lett. 2013, 40, 301–306. [Google Scholar] [CrossRef]
- Koster, R.D.; Suarez, M.J.; Ducharne, A.; Stieglitz, M.; Kumar, P. A catchment-based approach to modelling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res. 2000, 105, 24809–24822. [Google Scholar] [CrossRef]
- Hostetler, S.W.; Bartlein, P.J. Simulation of lake evaporation with application to modelling lake level variations of Harney-Malheur Lake, Oregon. Water Resour. Res. 1990, 26, 2603–2612. [Google Scholar]
- Lipscomb, W.; Sacks, W. The CESM Land Ice Model Documentation and User’s Guide. 2012, p. 46. Available online: http://www.cesm.ucar.edu/models/cesm1.1/cism/ (accessed on 15 July 2014).
- Zampieri, M.; Serpetzoglou, E.; Anagnostou, E.N.; Nikolopoulos, E.I.; Papadopoulos, A. Improving the representation of river-groundwater interactions in land surface modelling at the regional scale: Observational evidence and parameterization applied in the Community Land. Model. J. Hydrol. 2012, 420–421, 72–86. [Google Scholar] [CrossRef]
- Tegen, I.; Fung, I. Modelling of mineral dust transport in the atmosphere: Sources, transport, and optical thickness. J. Geophys. Res. 1994, 99, 22897–22914. [Google Scholar] [CrossRef]
- Levis, S.; Wiedinmyer, C.; Bonan, G.B.; Guenther, A. Simulating biogenic volatile organic compound emissions in the community climate system model. J. Geophys. Res. 2003, 108. [Google Scholar] [CrossRef]
- Riley, W.J.; Subin, Z.M.; Lawrence, D.M.; Swenson, S.C.; Torn, M.S.; Meng, L.; Mahowald, N.; Hess, P. Barriers to predicting global terrestrial methane fluxes: Analyses using a methane biogeochemistry model integrated in CESM. Biogeosciences 2011, 8, 1925–1953. [Google Scholar] [CrossRef]
- Masson, V. A physically-based scheme for the urban energy budget in atmospheric models. Bound. Layer Meteorol. 2000, 94, 357–397. [Google Scholar] [CrossRef]
- Levis, S.; Bonan, G.; Kluzek, E.; Thornton, P.; Jones, A.; Sacks, W.; Kucharik, C. Interactive crop management in the community Earth system model (CESM1): Seasonal influences on land-atmosphere fluxes. J. Clim. 2012, 25, 4839–4859. [Google Scholar] [CrossRef]
- Haddeland, I.; Skaugen, T.; Lettenmaier, D.P. Anthropogenic impacts on continental surface water fluxes. Geophys. Res. Lett. 2006, 33, 8. [Google Scholar] [CrossRef]
- Ozdogan, M.; Rodell, M.; Beaudoing, H.K.; Toll, D.L. Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeorol. 2010, 11, 171–184. [Google Scholar] [CrossRef]
- Kuepper, L.M.; Snyder, M.A. Influence of irrigated agriculture on diurnal surface energy and water fluxes, surface climate, and atmospheric circulation in California. Clim. Dyn. 2012, 38, 1017–1029. [Google Scholar] [CrossRef]
- Li, F.; Zeng, X.-D.; Levis, S. A process-based fire parameterization of intermediate complexity in a dynamic global vegetation model. Biogeosciences 2012, 9, 2761–2780. [Google Scholar] [CrossRef][Green Version]
- Arora, B.; Montenegro, A. Small temperature benefits provided by realistic afforestation efforts. Nat. Geosci. 2011, 4, 514–518. [Google Scholar] [CrossRef]
- Seitzinger, S.P.; Harrison, J.A.; Dumont, E.; Beusen, A.H.W.; Bouwman, A.F. Sources and delivery of carbon, nitrogen, and phosphorus to the coastal zone: An overview of global Nutrient Export from Watersheds (NEWS) models and their application. Glob. Biogeochem. Cycles 2005, 19. [Google Scholar] [CrossRef]
- Shellito, C.J.; Sloan, L.C. Reconstructing a lost Eocene paradise, Part II: On the utility of dynamic global vegetation models in pre-Quaternary climate studies. Glob. Planet. Change 2006, 50, 18–32. [Google Scholar] [CrossRef]
- Quillet, A.; Peng, C.; Garneau, M. Toward dynamic global vegetation models for simulating vegetation-climate interactions and feedbacks: Recent developments, limitations, and future challenges. Environ. Rev. 2010, 18, 333–353. [Google Scholar] [CrossRef]
- Pielke, R.A.; Beven, K.; Brasseur, G.; Calvert, J.; Chahine, M.; Dickerson, R.; Entekhabi, D.; Foufoula-Georgiou, E.; Gupta, H.; Gupta, V.; et al. Climate change: The need to consider human forcings besides greenhouse gases. Eos Trans. Am. Geophys. Union 2009, 90. [Google Scholar] [CrossRef]
- McAlpine, C.A.; Ryan, J.G.; Seabrook, L.; Thomas, S.; Dargusch, P.J.; Syktus, J.I.; Pielke, R.A., Sr.; Etter, A.E.; Fearnside, P.M.; Laurance, W.F. More than CO2: A broader picture for managing climate change and variability to avoid ecosystem collapse. Curr. Opin. Environ. Sustain. 2010, 2, 334–336. [Google Scholar] [CrossRef]
- Menon, S.; Bawa, K.S. Applications of geographic information systems, remote-sensing, and a landscape ecology approach to biodiversity conservation in the Western Ghats. Curr. Sci. 1997, 73, 134–145. [Google Scholar]
- Bond-Lamberty, B.; Calvin, K.; Jones, A.D.; Mao, J.; Patel, P.; Shi, X.; Thomson, A.; Thornton, P.; Zhou, Y. Coupling earth system and integrated assessment models: The problem of steady state. Geosci. Model Dev. Discuss. 2014, 7, 1499–1524. [Google Scholar] [CrossRef]
- Ines, A.V.M.; Hansen, J.W. Bias correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteorol. 2006, 138, 44–53. [Google Scholar] [CrossRef]
- Hibbard, K.; Janetos, A.; van Vuuren, D.P.; Pongratz, J.; Rose, S.K.; Betts, R.; Herold, M.; Feddema, J.J. Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling. Int. J. Climatol. 2010, 30, 2118–2128. [Google Scholar] [CrossRef]
- Rose, S.; Ahammad, H.; Eickhout, B.; Fisher, B.; Kurosawa, A.; Rao, S.; Riahi, K.; van Vuuren, D. EMF 21: Land in Climate Stabilization Modelling: Initial Observations; Energy Modelling Forum, Stanford University: Stanford, CA, USA, 2008. Available online: Available online: https://emf.stanford.edu/ publications/emf-21-land-climate-stabilization-modeling-initial-observations (accessed on 15 July 2014).
- Schlosser, C.A.; Kicklighter, D.; Sokolov, A. A Global Land System Framework for Integrated Climate-Change Assessments; MIT Joint Program on the Science and Policy of Global Change: Cambridge, MA, USA, 2007. [Google Scholar]
- Matsuoka, Y.; Morita, T.; Kainuma, M. Integrated assessment model of climate change: The AIM approach. In Present and Future of Modelling Environmental Change: Toward Integrated Modelling; Matsuno, T., Kida, H., Eds.; TERRAPUB: Tokyo, Japan, 2001; pp. 339–361. [Google Scholar]
- Edmonds, J.; Reilly, J. A long-term, global, energy-economic model of carbon dioxide release from fossil fuel use. Energy Econ. 1983, 5, 74–88. [Google Scholar] [CrossRef]
- Brenkert, A.; Smith, S.; Kim, S.; Pitcher, H. Model Documentation for the MiniCAM; Pacific Northwest National Laboratory: Richland, WA, USA, 2003; Volume PNNL-14337. [Google Scholar]
- Kim, S.H.; Edmonds, J.A.; Lurz, J.; Smith, S.J.; Wise, M. The ObjECTS framework for integrated assessment: Hybrid modelling of transportation. Energy J. 2006, 27, 63–91. [Google Scholar]
- Gusti, M.; Havlik, P.; Obersteiner, M. Technical Description of the IIASA Model Cluster, International Institute for Applied Systems Analysis, Laxenburg, Austria; 2008. Available online: http://digital.library.unt.edu/ark:/67531/metadc13707/ (accessed on 15 July 2014).
- Benitez, P.; McCallum, I.; Obersteiner, M.; Yamagata, Y. Global Supply for Carbon Sequestration: Identifying Least-Cost Afforestation Sites under Country Risk Consideration; IAASA Interim Report IR-04-022; International Institute for Applied Systems Analysis: Laxenburg, Austria, 2004; pp. 1–27. [Google Scholar]
- Benitez, P.C.; Obersteiner, M. Site identification for carbon sequestration in Latin America: A grid-based economic approach. For. Policy Econ. 2006, 8, 636–651. [Google Scholar] [CrossRef]
- Kindermann, G.E.; McCallum, I.; Fritz, S.; Obersteiner, M. A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica 2008, 42, 387–396. [Google Scholar]
- Alcamo, J.; Kreileman, E.; Krol, M.; Leemans, R.; Bollen, J.; van Minnen, J.; Schaeffer, M.; Toet, S.; de Vries, H.J.M. Global modelling of environmental change: An overview of IMAGE 2.1. In Global Change Scenarios of the 21st Century—Results from the IMAGE 2.1 Model; Alcamo, J., Leemans, R., Kreileman, E., Eds.; Elseviers Science: Oxford, UK, 1999; pp. 3–94. [Google Scholar]
- IMAGE Team. The IMAGE 2.2 Implementation of the SRES Scenarios—A Comprehensive Analysis of Emissions, Climate Change and Impacts in the 21st Century (RIVM CD-ROM); National Institute for Public Health and the Environment: Bilthoven, The Netherlands, 2001. [Google Scholar]
- The Netherlands Environmental Assessment Agency (MNP). Integrated Modelling of Global Environmental Change—An Overview of IMAGE 2.4; MNP: Bilthoven, The Netherlands, 2006. [Google Scholar]
- Tavoni, M.; Sohngen, B.; Bosetti, V. Forestry and the carbon market response to stabilize climate. Energy Policy 2007, 35, 5346–5353. [Google Scholar] [CrossRef][Green Version]
- Bosetti, V.; Carraro, C.; Galeotti, M.; Massetti, E.; Tavoni, M. WITCH: A world induced technical change hybrid model. Energy J. 2006, 27, 13–38. [Google Scholar]
- Bosetti, V.; Tavoni, M.; De Cian, E.; Sgobbi, A. The 2008 WITCH Model: New Model Features and Baseline; Fondazione Eni Enrico Mattei Working Papers No.085; Fondazione Eni Enrico Mattei (FEEM): Milan, Italy, 2009; pp. 1–45. [Google Scholar]
- Toth, F.L.; Bruckner, T.; Füssel, H.-M.; Leimbach, M.; Petschel-Held, G. Integrated assessment of long-term climate policies: Part I—Model presentation. Clim. Change 2003, 56, 37–56. [Google Scholar] [CrossRef]
- Moss, R.; Babiker, M.; Brinkman, S.; Calvo, E.; Carter, T.; Edmonds, J.; Elgizouli, I.; Emori, S.; Erda L, Hibbard, K.; et al. Towards new scenarios for analysis of emissions, climate change, impacts, and response strategies. In Presented at IPCC Expert Meeting Report: Towards New Scenarios, Noordwijkerhout, The Netherlands, 19–21 September 2007; Volume 19–21, p. 155.
- Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; et al. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747–756. [Google Scholar] [CrossRef]
- Meinshausen, M; Smith, S.J.; Calvin, K.; Daniel, J.S.; Kainuma, M.L.T.; Lamarque, J.-F.; Matsumoto, K.; Montzka, S.A.; Raper, S.C.B.; Riahi, K.; et al. The RCP greenhouse gas concentrations and their extension from 1765 to 2300. Clim. Chang. 2011, 109, 213–241. [Google Scholar] [CrossRef]
- Monfreda, C.; Ramankutty, N.; Foley, J.A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 2008, 22. [Google Scholar] [CrossRef]
- Pokhrel, Y.; Hanasaki, N.; Koirala, S.; Cho, J.; Yeh, P.J.-F.; Kim, H.; Kanae, S.; Oki, T. Incorporating anthropogenic water regulation modules into a land surface model. J. Hydrometeorol. 2012, 13, 255–269. [Google Scholar] [CrossRef]
- Tromborg, E.; Bolkesjo, T.F.; Solberg, B. Impacts of policy means for increased use of forest based bioenergy in Norway—A spatial partial equilibrium analysis. Energy Policy 2007, 35, 5980–5990. [Google Scholar] [CrossRef]
- Collalti, A.; Perugini, L.; Santini, M.; Chiti, T.; Nolè, A.; Matteucci, G.; Valentini, R. A process-based model to simulate growth in forests with complex structure: Evaluation and use of 3D-CMCC forest ecosystem model in a deciduous forest in Central Italy. Ecol. Model. 2014, 272, 362–378. [Google Scholar] [CrossRef]
© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Share and Cite
Michetti, M.; Zampieri, M. Climate–Human–Land Interactions: A Review of Major Modelling Approaches. Land 2014, 3, 793-833. https://doi.org/10.3390/land3030793
Michetti M, Zampieri M. Climate–Human–Land Interactions: A Review of Major Modelling Approaches. Land. 2014; 3(3):793-833. https://doi.org/10.3390/land3030793
Chicago/Turabian StyleMichetti, Melania, and Matteo Zampieri. 2014. "Climate–Human–Land Interactions: A Review of Major Modelling Approaches" Land 3, no. 3: 793-833. https://doi.org/10.3390/land3030793