Some key challenges in modeling coupled human-natural systems, illustrated through agent-based land-use modeling research Dawn Cassandra Parker Associate Professor School of Planning, University of Waterloo With contributions also from many others! 26 Oct, 2010 Motivation: Concern regarding human impacts on the natural landscape
Increasing human appropriation of land Degradation of land-based natural capital Decline in land-based ecosystem services: Carbon sequestration Water purification Nutrient cycling Temperature and climate regulation (http://www.globallandproject.org/) Increased demand for coupled humannatural systems models Need to understand likely future levels of
ecosystem services and future status-quo trajectories of change Need to develop management strategies and potential policy interventions Requires an understanding of how human incentives shape land-use decisions, and how these decisions alter ecosystem services (uni-directional linkages) Also requires understanding how changes in ecosystem services alter (or fail to alter!) human incentives (A)
(B) (C Figure 2: Three approaches to linked systems: natural to social (A), natural, social, natural (B), and fully linked (C) (Parker, Hessl, Davis, 2008) Atmospheric CO2 Carbon Sequestration
Fossil Fuel Consumption Forest Productivity Forest landowner decisions Figure 1: Indirect Linkages between landowner decisions, forest productivity, C sequestration, and atmospheric C (Parker, Hessl, Davis 2008) From here forward:
What are agent-based land-use models? What are some practical, current challenges in developing models like these? As illustrated through some ongoing work in the deciduous US East Coast and Midwest: Modeling joint effects of LUCC and land manager behavior (water quality and carbon sequestration) Modeling the carbon implications of timber harvest behavior Which challenges should be highest priorities for investment and research (maybe yours!)?
H/E Interactions ABM/LUCC Land-Use Modeling Complexity Theory Agent-based models of land use consist of: An electronic representation of a landscape An agent-based simulation of decision-makers
whose choices alter the landscape (usually a computer program) (Parker et. al 2003; Parker, Berger, Manson 2002) Cellular/Spatial Landscape Model May or may not be based on real-world maps via geographic information systems layers May contain a variety of geographic and socioeconomic features such as: Slope, elevation, vegetative cover, soil types, zoning restrictions Road and rail networks, information on social
networks (who knows who) Models of spatial diffusion, such as how air pollution spreads and disperses across a region Figure 7, LUCC Report #6 Agent-Based Model Autonomous decision-making agents Interaction environment Interdependencies among agents, their environment, or both
Rules governing sequencing of actions and information flows What is an agent? Goal-oriented entity Model of cognition that links goals and behavior: Capable of autonomous action Capable of responding to changes in its environment Generally represents a land manager in landuse change models
Agent-Based Model of Decision Making Each individual decision maker is represented through a set of rules that link information about his/her environment to a decision Decisions often depend on the agents physical environment (the landscape) Decisions may also depend on what other agents do as well -- can lead to path dependence H/E Interactions
ABM/LUCC Land-Use Modeling Complexity Theory Advantages of ABM/LUCC for land-use modeling: Your model can have a realistic (and appropriate) geographic representation Potential links with geographic information systems for data input and output visualization
Modeling of structures that are nested in time and space (cross-scale) H/E Interactions ABM/LUCC Land-Use Modeling Complexity Theory Modeling human/environment interactions:
Socioeconomic and biophysical models can be linked spatially Simulation approach allows for feedbacks between dynamic social and environmental processes Applications include crop yields, hydrology, forest growth, pest species modeling, endangered species populations H/E Interactions ABM/LUCC Land-Use Modeling
Complexity Theory Human/environmental landscapes are complex: Characterized by: Interdependencies (one agents action depend on what another has done previously) Heterogeneity (diverse variation in the same type of object) nested hierarchies (overlapping structures in time and
space) Soccer example: Nested hierarchies goalkeeper defense midfield offense Complex properties of soccer Local actions of players lead to reoccurring,
recognizable patterns that are semi-stable A small change in strategic play can lead to large changes in the state of the system In general, successful strategic play is general the result of interdependent team effort (whole greater than the sum of its part). In short, the relationship between players is of key importance. Properties of complex systems: Analytical intractability Path dependency
Linkages across hierarchies Emergence: Organization into recognizable macroscopic patterns (Epstein and Axtell, 1999) Quilting examples of emergence Start with two very simple objects that are heterogeneous with respect to shape, size, and color Quilts, scaling up
By defining the relationship between other similar objects at a very local level, we obtain some structure and pattern Quilts, scaling up again And by combining these elements, we create structure at a coarser spatial scale One emergent form
And another Key sources of agent heterogeneity: Pecuniary and non-pecuniary motivations: profits, preservation of family farm, environmental ethic Experience and knowledge Financial, physical, and human capital Access to credit Cultural preferences Expectation formation mechanisms
Decision strategies Types of Interactions Agent-Agent Information transfer Technology diffusion Land markets Local labor exchange Community-based resource management Agent Environment
Hydrology (ground and surface) Erosion Deforestation Transport of pollutants Species migration Soil fertility How might ABM be used to study H-E interactions? To link socioeconomic drivers of resource use to their biophysical impacts
To explore the effects of feedbacks between humans and their environment To examine whether current systems of resource use are sustainable To design policies to encourage more sustainable resource use Why the interest in ABM? Change of modelling approach? ABM seen as a tool for building: finer-scale process-based models with more flexible representation compared to
analytical or systems dynamic models with the ability to incorporate theories and drivers from many social science perspectives facilitating spatially explicit, fine scale, coupled models of human-environment interaction in the land system Can ABM address these challenges? Bridge the gap between theoretical/processbased and empirical/pattern-based modeling Model 3-way feedbacks between land-use/management, land cover, and landscape function
Provide useful information for scientific and policy analysis regarding the effects of human incentives on the natural world Some practical challenges Challenges include: Social and natural models have different key driving variables Scale issues (conceptual and empirical) Data constraints Common challenges in many coupled humannatural models
Which challenges are most important? (binding constraints) Which should be highest priorities for investment? Big issues: Desire to link socioeconomic drivers of LULCC to LULCC to effects on key ecosystem services Relevant processes, and therefore models of them, often operate on different variables of interest at different spatial and temporal scales Data often available/collected for independent
process models -> matching problem Data availability constraints Big questions: To what extent can coupled-HE models be improved by: Better process models vs... Better data? In short Are limitation practical or conceptual? What investments should we target and why?
Focusing the question: 3 research applications Residential household-level best management practices, storm water runoff, and water quality in the Potomac Gorge, USA Residential landscaping and carbon sequestration in ex-urban Michigan, USA Timber management decisions and carbon sequestration in West Virginia, USA A preview of the main issues Process-based limitation: the biophysical
dimensions of the ecosystem service of interest are not the central drivers of the land management decisions (dependent variable, not scale mismatch) Data-based limitations: collection costs confidentiality and other institutional constraints lack of field studies to link social behavior to inputs of biophysical models Exploring changes in residential land use and land management via ABM land market models
Longer-term goal is to develop agent-based models of residential suburban and ex-urban land markets that: Connect land market and land management behavior of residential agents Allow exploration of the relative contributions of land-use vs.. landmanagement change (categorical change and change in intensity) to environmental changes Preliminary work on water quality done in the Potomac Gorge watershed (DC metro area); full-fledged model under development in Southeastern Michigan; application in for development of a Waterloo Region model Explore the value added of the land market component
through comparison to comparable models without a land market component (*Can talk more about this if wanted .. ) Environmental impacts of residential development Suburban and ex-urban development bring about environmental change Evaluation of impacts requires understanding of: Location and timing of land-use change Characteristics of new or modified development Land management behavior of new land managers
Three factors jointly determined via land market interactions Residential development and water quality (Potomac Gorge) Residential land use contributes to decreased water quality through increased nutrient loadings and changes in hydrology (flow) Main research question: What linkages exist between residential land use in the Potomac Gorge watershed (DC area) and the degradation of water quality in tributary streams (a primary threat to rare and
endangered species in the Gorge) Both social science (resident survey) and natural science (water quality) models were planned; social science component had to be dropped. Residential development and carbon sequestration Landscaping choices in existing and new developments may have dramatically different carbon profiles Main research question: Will ex-urban development in Southeastern Michigan produce a landscape-scale source or sink of carbon, given observed landscaping
strategies of developers and landscaping preferences of residential agents? Collaboration with UM (Dan Brown et al.) to extend Project SLUCE See recent WICI talk for many more details Sources of environmental impacts: Behavior of resident land managers Land managers are not homogeneous. Water quality and carbon profiles also depend on:
Landscaping preferences and practices Tree, turf, and horticultural choices also Fertilizer and pesticide use Management of organic matter and debris Willingness to adopt BMPs: Green roofs Rain gardens Pervious pavers
Evidence from human ecology/environmental sociology/economics that these two factors vary with agent resources, information, attitudes, beliefs, and values & neighborhood influences PoGo WQ model: Dep. Var mismatch issue What are the dependent variables? Household model: residential landscaping, building, and storm water management decisions; Water quality model: nutrient runoff Potential link between models: Decisions about best
management practices Households care about economic cost, social implications, transaction costs, and flood risk reduction when thinking about WQ BMPS They may not know about, and rarely consider, local or off-site nutrient loadings and other WQ impacts PoGo WQ model: Scale issues WQ models: coarse spatial scale (watershed); calibrated with fine temporal
scale data for few locations Social science models (adoption of rain barrels, rain gardens, pervious pavers): fine spatial scale (parcel level), coarse temporal scale (10 years for RS land cover data, annual for parcel data, often 1 point in time for a survey) Institutional/jurisdictional boundaries cross watersheds: zoning, parcel sizes, setbacks, WQ regulations, etc. PoGo WQ model: Data issues
*Coefficients to translate BMP assumptions into nutrient reductions for WQ models are sparse or missing. Most come from experimental, not field studies Lack of time series of land cover data Lack of access to/expense of obtaining parcel-level land use data Institutional constraints on data collection Concern about time and attention demands of surveys SLUCE II model: Dep. Var mismatch issue
What are the dependent variables? Household model: choice of house to buy, residential landscaping modifications and maintenance; Carbon model: carbon sequestration based on biomass growth/removal Potential link between models: Landscaping decisions Households stick with initial landscape design, care about aesthetics, imitate neighbors, economic cost, social standing, local regulations, when thinking about residential landscaping They may not know about, and rarely consider, the
carbon sequestration impacts of their landscaping SLUCE II model: Scale issues Landscaping decisions are more fine-grained in time and space (at a parcel level) Existing carbon model designed/calibrated to run on a coarser scale Problems could be solved with time, money, and computing powerbut it would require recalibrating models SLUCE II model: model: Data issues
*Coefficients to translate landscaping decisions into biomass changes for carbon model are sparse or missing; have to be estimated from field work. Other data constraints less binding due to longterm nature and resources of the project Cost limitations limit number of in-depth interviews/field surveys Complicated sample stratification due to desire to stratify by development type, LULCC history, and biophysical conditions Concern about time and attention demands of surveys
Timber management decisions and carbon sequestration in US Eastern deciduous forests Carbon dynamics in mixed-stand deciduous forests are poorly understood; stands are reaching maturity, land manager motivations and strategies are diverse Main research question: How might the ability of central hardwood forests to store C change in the future under current conditions? Under alternative policy and economic regimes? Collaboration with West Virginia University (Amy Hessl et al.) with PhD student Sean Donahoe
Experimental field work for model calibration Timber model: Dep. Var mismatch issue What are the dependent variables? Timber harvester: timber harvest (event, species type, harvest strategy) Carbon model: carbon sequestration based on biomass growth/removal Potential link between models: Timber harvested
Timber model: Dep. Var mismatch issue Harvesters make timber harvest considering only 23 high-value species, using various harvest heuristics (diameter limit cuts, clear cuts, etc.) They may or may not consider future biomass, but do not consider carbon Timber rights may be sold off; separation between land owner motivation and timber harvest; may not be forward-looking behavior Carbon model wants consistent and uniform data about species composition and corresponding canopy cover.
Timber model: Scale issues Like SLUCE II, scale issues are not conceptually high hurdles--both models could run at a parcel scale In this case, the carbon model was modified and recalibrated at a parcel scale using local data inputs so there is less mismatched between the scales at which models are designed to operate. Problem seem to be solved with time, money, and computing power However, Sean tried to apply the new model to the landscape scale, using state-level data, and it didnt perform well.
Instead he developed a simpler, alternative growth and carbon model Timber II model: model: Data issues *Compromises needed to be made on dependent variable (total biomass removal vs. timber harvest strategy) due to data quality issues Design of FIA database was good (gathers data on both management type and intent and biophysical measurements of growth and harvest But, sampling strategy was too sparse and data quality too poor to meet all of our goals for parcel level analysis
(data collection was designed for aggregation). Probably influenced failure of the carbon model. Confidentiality constraint on ownership type and location; could be solved with more time and money Land market dynamics mean that highest valued timber is not available for harvestneed data to model land markets Conclusions and recommendations: Dependent/state variables More conceptual work needed on the problem of mismatch of dependent variables between models:
socioeconomic and biophysical models generally are built around different state variables This reflects missing feedbacks/incentives in the system. The practical result is that disciplinary conceptual and empirical models tend not to match. We also need to consider whether models and developed under mismatched incentives will still be valid under a regime of corrected incentives (i.e., carbon markets) Conclusions and recommendations: Scale
issues I see these as less important than I did in 2007! Many can be solved by time, money and computing power However, scale-related process and data matching issues should be considered when designing data collection protocols Socioeconomic models tend to have better spatial coverage and poor temporal coverage; biophysical models, the reverse.
Conclusions and recommendations: Data issues Improved field study and survey data to develop technical linkages between land management behavior and biophysical model inputs is the highest priority Coupled modeling needs should be considered when designing public data collection protocols Issues of privacy and time cost for survey respondents will continue to be important We face new problems in Canada due to the compromised census sampling protocol.
Final Thoughts Lack of empirical studies that link social and biophysical dependent variables not a surprise: Practically each system has different driving variables; Disciplinary studies designed in isolation focus efforts on the driving variable of interest The gap results These deficits have been discovered (and in some cases resolved through interdisciplinary research teams funded by new, targeted funding initiatives.
Hope/predict to see similar funding initiatives in Canada, too. Acknowledgements ALMA-v1.0: Tatiana Filatova and Anne Van der Veen, University of Twente; funding from NWO-ALW (LOICZ-NL) project 014.27.012 and NSF 041406
Potomac Gorge project: Ryan Albert, Robin A. Brake, Susan A. Crate, R. Christian Jones, Brandy Holstein, Atesmachew Hailegiorgis, and Charles Nguyen; Departments of Computational Social Science, Environmental Science and Policy & Sociology, George Mason University; Giselle Mora-Bourgeois, Urban Ecology Research Learning Alliance, National Park Service; Funding from Chesapeake Watershed CEUS. U. Michigan: Dan Brown, Bill Currie, Joan Nassauer, Scott Page, Rick Riolo, Derek Robinson, etc. funding for grant development from
NSF BCS-0119804 , new funding from NSF CNH-0813799 WVU: Amy Hessl, Sarah Davis, Bill Peterjohn, Richard Thomas, Maction Komwa, Sean Donahoe, NSF grant 0414565
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